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REVIEW article

Front. Med., 20 January 2026

Sec. Ophthalmology

Volume 12 - 2025 | https://doi.org/10.3389/fmed.2025.1697871

This article is part of the Research TopicBeyond the Eye: How Oculomics is Transforming Retinal Imaging Into a Gateway to Systemic BiomarkersView all articles

Retinal biomarkers in schizophrenia spectrum disorders: evidence and implications for the neurodevelopmental and neurodegenerative models

  • 1Department of Psychology, University of Rochester, Rochester, NY, United States
  • 2Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, United States
  • 3Department of Ophthalmology, University of Rochester Medical Center, Rochester, NY, United States
  • 4Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, United States

Retinal morphological and functional alterations, such as changes in the thickness and volume of the retinal neural layers, architecture of the microvasculature, and functioning of neurons, have been observed in schizophrenia and have been interpreted in terms of neurodegenerative aspects of the disorder. However, little consideration has been given to the issue of whether, and the extent to which, these retinal differences may reflect neurodevelopmental features of schizophrenia. There are also no current conceptualizations that integrate retinal alteration findings in schizophrenia across different stages of illness, thereby helping to integrate neurodevelopmental and neurodegenerative perspectives on pathophysiology. Therefore, the present review aims to organize evidence of retinal abnormalities in schizophrenia in terms of findings from clinical high-risk for psychosis (CHR), genetic risk, first-episode psychosis (FEP), and chronic schizophrenia samples, and to consider factors such as age and duration of illness. Our goal is to move toward a lifespan model that integrates and transcends prior neurodevelopmental and neurodegenerative viewpoints. Toward this end, we also review studies of retinal alterations among those with prenatal/perinatal insults, neurodevelopmental disorders, and neurological soft signs, as such data can inform what has been observed in schizophrenia. We also mention, where appropriate, relevant findings from neurodegenerative disorders. A better understanding of the trajectories of central nervous system differences throughout the lifespan in people with schizophrenia, as observed in the retina (often called “a window to the brain”), can aid in understanding brain dysfunction in the disorder, assist with characterizing heterogeneity in clinical course, and inform more targeted prevention, monitoring, and intervention efforts.

Introduction

The earliest view of schizophrenia, conceptualized by Kraepelin (1), was that it is an early-onset form of dementia (“dementia praecox”). This theory has received criticism due to significant heterogeneity in the illness's course and functional outcomes, with many patients not experiencing progressive symptomatic or cognitive impairment or functional decline, even though premorbid levels of functioning are rarely re-attained (25). However, about 30%−50% of patients with schizophrenia do experience cumulative residual symptoms, which may be indicative of a progressive trajectory, at least for a proportion of individuals (68). Moreover, many patients do experience cognitive decline and progressive gray and white matter loss that exceeds age-related norms, and that is related to poor outcomes (912), and the rates of dementia diagnoses in people with schizophrenia are in excess of age norms (13, 14). These data are consistent with both Kraepelin's view and with the recent hypothesis that schizophrenia involves a form of accelerated aging (1519). In contrast, the neurodevelopmental hypothesis posits that early-life pathological processes (e.g., prenatally) disrupt typical neural development, eventually leading to the manifestation of psychotic illness in adolescence and early adulthood (5, 2023). According to Lewis and Levitt (24), a genetic predisposition to schizophrenia causes alterations in the expression of genes whose proteins are critical for brain function. These genetic factors interact with environmental factors during ontogenesis or pregnancy (e.g., prenatal infections, exposure to toxins, or chronic stress), leading to cumulative disruptions of developmental processes. In this view, once adolescent development is complete, there are no additional neurobiological consequences beyond those of typical maturation and aging (5, 22). The neurodevelopmental hypothesis can explain features observed in children and adolescents who later go on to develop schizophrenia (25, 26), but it does not fully account for the progressive changes seen in many patients.

Many researchers agree with an integration of the neurodevelopmental and neurodegenerative theories, or the idea that schizophrenia is a “progressive neurodevelopmental disorder” (27). This view has received support from research demonstrating overlap between the genetic and proteomic bases of neurodevelopment and neurodegeneration, that certain genetic and neurodevelopmental disorders are characterized by progressive cognitive decline, and that neurodegenerative disorders share commonalities with aspects of abnormal neural development (2835).

Biomarker studies examining genetic factors, neural processes, brain structure and function, and neuroinflammatory markers have helped to clarify the etiology of schizophrenia and its neurodevelopmental/neurodegenerative features, but the exact pathophysiological mechanisms remain obscure. More recently, researchers have begun studying retinal structure and function in individuals with schizophrenia to understand these mechanisms better. The retina, which can be thought of as a window to the brain, develops from the same tissue as the brain, the neuroectoderm, and possesses neural characteristics similar to those of the cortex, including neurons, glial cells, and similar neurotransmitters and receptor types (36) (see Figures 1, 2). It also shares the same microvascular and immunological features of the brain (37), and many studies have reported associations between retinal neural/microvascular changes and cognitive decline, decreased brain volume, and brain atrophy in the general population (3844). There is also robust evidence that retinal morphological alterations are associated with cognitive decline, functional impairment, and disease progression in Alzheimer's disease, Parkinson's disease, and multiple sclerosis (MS) (4553). Another benefit of studying the retina in this context is that it is much more accessible than the brain, as it is the only part of the central nervous system (CNS) that can be viewed in vivo, non-invasively. Further, retinal imaging techniques, such as optical coherence tomography (OCT) and OCT angiography (OCTA), are highly reliable (54), quick, radiation-free, and significantly less expensive than neuroimaging (55), making them ideal methods for retinal biomarker identification that can be integrated into clinical practice to monitor disease risk, course, and progression, which is already occurring in MS (56).

Figure 1
Cross-sectional diagram of the human retina showing various cell types and layers. Photoreceptors at the bottom include rods and cones, labeled R and C. Layers such as OPL, INL, and GCL contain different neurons like bipolar cells (B), amacrine cells (Am), and ganglion cells (G). Müller cells (M) span the retina’s thickness. Labels indicate layers: ILM, NFL, GCL, IPL, INL, OPL, ONL, ELM, IS, OS, RPE, BrM, and ChC.

Figure 1. Visual depiction of retinal cell types and layers. Cells: RPE, retinal pigment epithelium (support to photoreceptors); C, cone photoreceptor; R, rod photoreceptor; H, horizontal cell; B, bipolar cell; M, Müller cell (radial glial cell); Am, amacrine cell; DA, displaced amacrine cell; G, ganglion cell (output neuron). Müller cells (M) form the ELM, and their foot processes partially form the ILM. Layers: ChC, choriocapillaris (capillary bed for RPE and photoreceptors); BrM, Bruch's membrane (vessel wall and RPE substratum); ELM, external limiting membrane (junctional complexes); ONL, outer nuclear layer; OPL, outer plexiform layer (synapses); INL, inner nuclear layer; IPL, inner plexiform layer (includes ganglion cell dendrites, bipolar cell axons, and amacrine cells); GCL, ganglion cell (body) layer; NFL, nerve fiber layer (ganglion cell axons); ILM, inner limiting membrane. Reproduced from “Human Eye C, chorioretinal cells and layers” by Wenchao Zheng, Rachel E. Reem, Saida Omarova, Suber Huang, Pier Luigi DiPatre, Casey D. Charvet, Christine A. Curcio and Irina A. Pikuleva, licensed under CC BY 4.0.

Figure 2
Cross-section diagram of the retina showing various cell layers and blood vessels. Labeled structures include retinal ganglion cells, amacrine cells, bipolar cells, Müller glia, horizontal cells, rods, and cones. Blood vessels are categorized into superficial, intermediate, and deep vascular plexuses. Additional labels include GCL, INL, ONL, IS/OS, RPE, Bruch's membrane, and choroid.

Figure 2. Retinal neural layers and microvasculature plexuses. Retinal microvasculature plexuses are displayed with associated neural layers. GCL, ganglion cell layer; INL, inner nuclear layer; ONL, outer nuclear layer; IS/OS, inner segments/outer segments; RPE, retinal pigment epithelium. Reproduced from “Schematics of retinal neuronal and vascular structure” by Zhongjie Fu, Ye Sun, Bertan Cakir, Yohei Tomita, Shuo Huang, Zhongxiao Wang, Chi-Hsiu Liu, Steve S. Cho, William Britton, Timothy S. Kern, David A. Antonetti, Ann Hellström, and Lois E.H. Smith, licensed under CC BY 4.0.

A multitude of studies have now shown that schizophrenia is associated with abnormalities in retinal neural thickness, microvasculature, and cell functioning (5769). In particular, most of the literature on the retina in schizophrenia has focused on retinal neural structure, revealing in some studies that individuals with schizophrenia have reduced thickness of the retinal nerve fiber layer (RNFL) adjacent to the optic disc, and, more consistently, reduced thickness and volume of the macula, which reflects in large part thinning of the ganglion cell layer and inner plexiform layer (GCL-IPL). Although there are inconsistencies across studies, other layers and characteristics of the retina appear to be affected as well, such as the photoreceptor layer, inner nuclear layer (INL), outer nuclear layer (ONL), outer plexiform layer (OPL), retinal pigment epithelium (RPE), optic cup, and the optic cup-to-disc ratio (7074) (see Table 1 for a glossary of retinal terms and acronyms). These retinal abnormalities are associated with poorer performance on cognitive assessments, reduced cortical thickness, white matter hypo-intensities, and smaller total brain and white matter volume (70, 7577). Further, because retinal ganglion cell axons are unmyelinated, RNFL thinning can be considered an unambiguous measure of axonal loss (68, 78). Thus, retinal biomarkers may help elucidate our understanding of the neuropathological mechanisms of schizophrenia and how both neurodevelopmental and neurodegenerative processes are operative in the disorder. Importantly, while retinal findings are not specific to schizophrenia, they are valuable indices of neurodevelopmental and neurodegenerative insults that could be used to identify important clinical outcomes, such as cognitive decline, illness progression, and treatment response.

Table 1
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Table 1. Retinal terminology and their definitions.

To date, the majority of studies examining retinal alterations in schizophrenia have included individuals with an established, chronic diagnosis. Some of these findings support a neurodegenerative disease process, as retinal layer thinning in these samples becomes more pronounced with longer illness duration beyond the effects of aging (15, 71, 72, 7883). However, one recent study of patients aged 18–65 years showed that the retinal age gap was most pronounced in the youngest patients and that it declined with advancing age (17). In addition, studies are needed that investigate retinal biomarkers during different phases of the illness, especially the prodromal and early phases of schizophrenia. For example, studying at-risk populations, such as individuals who are at genetic risk due to either a family history of schizophrenia or higher polygenic risk, and those who exhibit preclinical signs and symptoms indicative of a clinical high-risk (CHR) for psychosis syndrome, can enhance our understanding of how schizophrenia develops. Evidence of retinal alterations in these populations would suggest neurodevelopmental origins due to their existence prior to the onset of psychosis. Currently, there is a paucity of research on retinal biomarkers among these individuals; however, there is sufficient evidence to draw tentative conclusions.

Despite the rapidly growing body of evidence for retinal alterations in schizophrenia, and their links to genetic risk for, and clinical features of, schizophrenia, prior reviews of this literature have not integrated the findings into a lifespan view of how schizophrenia develops and progresses over time, even though schizophrenia is, arguably, best conceptualized as a “lifetime disorder,” concerning development, plasticity, and aging (8488). Therefore, the aim of the current review is to (1) provide an overview of the existing literature on retinal structure findings in schizophrenia1 using a life course lens that synthesizes evidence from all phases of the illness, (2) describe the extent to which retinal abnormalities in schizophrenia are consistent with neurodevelopmental and/or neurodegenerative theories, and the extent to which the findings as a whole support the progressive neurodevelopmental hypothesis; and (3) identify gaps in the literature and provide recommendations for future directions in retinal imaging research in this population. While there are also retinal functioning abnormalities in schizophrenia (62), to maintain a focused synthesis of evidence and theory, the current paper will primarily discuss retinal morphological abnormalities for which there is the most evidence.

We will first describe the evidence for the general overlap between neurodevelopmental and neurodegenerative processes to demonstrate how schizophrenia could be characterized by both (i.e., a progressive neurodevelopmental disorder). Next, we will briefly summarize the proposed mechanisms of retinal thinning in schizophrenia. We will then attempt to explain how these retinal findings align with neurodevelopmental and neurodegenerative accounts of schizophrenia using evidence from retinal imaging studies assessing genetic risk, CHR, first-episode psychosis (FEP), and chronic schizophrenia samples. Supplementary Table S1 summarizes the retinal evidence in schizophrenia, noting which findings align with which model. In addition, we will also draw on retinal imaging literature in people exposed to prenatal/perinatal adverse events, neurodevelopmental disorders, and neurodegenerative diseases to further argue these points. Note that this is not meant to be an exhaustive review of the literature, as several meta-analyses and systematic reviews on the topic already exist (5769), but rather a focused overview of key findings relevant to an integrative lifespan model.

Literature for this review was identified using the keywords schizophrenia, psychosis, clinical-high risk for psychosis, first-episode psychosis, genetic risk for psychosis, optical coherence tomography, retina, and macula on PubMed and Web of Science. Peer-reviewed empirical articles, meta-analyses, and systematic reviews published in English from inception to August 2025 were included.

Links between neurodevelopmental abnormalities and neurodegeneration

It is increasingly recognized that neurodevelopmental abnormalities increase vulnerability for neurodegeneration later in life, given that both processes share overlapping mechanisms. For example, brain structure development and aging seem to follow a “last in, first out” pattern, in which brain areas that develop later phylogenetically and ontogenetically tend to be the same areas that are most vulnerable to neurodegeneration, such as the association cortices and the neostriatum (28, 89, 90). In support of this theory, Douaud et al. (28) observed a symmetric inverted-U association between age and transmodal cortex gray matter structure variability in a large sample of healthy participants. Because the transmodal cortex develops later than other regions of the brain, and, therefore, may be a strong indicator of “last in, first out” processes, Douaud et al. argue that this finding demonstrates that neurodevelopmental and aging processes mirror each other. Moreover, these regions demonstrated an elevated vulnerability to diseases involving altered neurodevelopment (schizophrenia) and aging (Alzheimer's disease).

Shared molecular mechanisms also link neurodevelopment and neurodegeneration. Similar abnormalities in protein function and protein homeostasis systems, as well as mutations in genes that encode them, have been implicated in both neurodevelopmental and neurodegenerative disorders (29, 30). One example is the function of the amyloid-beta (Aβ) protein and its precursor, amyloid precursor protein (APP), in typical and atypical neurodevelopment (e.g., Down syndrome) and the role they play in neurodegeneration due to Alzheimer's disease. In typical CNS development, APP is involved in proliferation and differentiation of neural stem cells, neuronal migration, neuronal plasticity, and neuronal learning, among other functions, while in the neurodegenerative disease process of Alzheimer's disease, the accumulation of Aβ leads to neuronal damage and neurotransmission dysfunction (30, 91). Overexpression of APP has been found in the placenta of fetuses with Down syndrome and is associated with disruptions in placental development (92). Also, the majority of individuals with Down syndrome show an accumulation of Aβ in their brains by the time they are 40 years of age, which is believed to be related to the high incidence of early-onset dementia due to Alzheimer's disease in this population (30, 93). This suggests that neurodegenerative processes can originate in neurodevelopment, including at the prenatal stage (30).

Neurovascular impairments may be another point of convergence. Impairments in angiogenesis, cerebral blood flow, and the blood-brain barrier have been implicated in the development and progression of both neurodevelopmental disorders (e.g., autism spectrum disorder, schizophrenia, and Down syndrome) and neurodegenerative disorders (e.g., Alzheimer's disease, Huntington's disease, Parkinson's disease, and MS) (33). For example, studies have found an association between decreased cerebral blood flow with increasing age and degenerative changes in schizophrenia (33, 94, 95). In addition, widespread cerebral hypoperfusion has been found in about 75% of children with autism (96) and is associated with language difficulties, executive functioning difficulties, and altered reactivity to sensory input (33). Similarly, reduced cerebral blood flow has been found among individuals with Alzheimer's disease and has been observed before the onset of cognitive decline and plaque deposition (33). The evidence reviewed above, and similar findings, have been used to support the emerging concept that schizophrenia is a progressive neurodevelopmental condition.

Proposed mechanisms of retinal changes in schizophrenia

Although it is currently unknown which retinal indices are most related to schizophrenia, the evidence so far suggests that macular regions (e.g., GCL-IPL) may be the most sensitive (97). This is thought to be due to the high density of retinal ganglion cells in the macula, as this cell type is highly sensitive to insults (98). The RNFL comprises retinal ganglion cell axons, while the GCL-IPL consists of retinal ganglion cell bodies (GCL) and ganglion cell dendrites, bipolar cell axons, and amacrine cells (IPL). Retinal ganglion cell axons are located parallel to the surface of the retina and converge as the optic nerve, where the RNFL leaves the eye, synapsing onto the lateral geniculate nucleus (LGN) of the thalamus, which relays sensory information to the visual cortex (63, 68, 99).

A question that has yet to be answered is the nature of the relationship between retinal changes and brain changes in schizophrenia. One possibility is that brain changes may lead to retinal structure and function alterations in schizophrenia by the process of retrograde trans-synaptic degeneration (RTSD), which refers to a process involving atrophy of cells in V1 (10, 12, 100), followed by loss of neurons in the LGN that projected to the lost V1 neurons, followed by loss of retinal ganglion cell axons and cell bodies from cells that projected to the lost LGN cells (59, 68, 81, 101). RTSD has been previously demonstrated in MS and occipital lobe injury (102, 103).

On the other hand, findings of retinal changes among individuals with neurodegenerative disorders that become more pronounced with cognitive decline and disease progression (53, 104, 105) suggest that retinal changes and brain changes parallel each other. Evidence for shared genes associated with retinal characteristics, brain MRI traits, eye disorders, and brain disorders (106) also suggests this.

A potential mechanism of concurrent retinal and brain changes is neuroinflammation. It has been proposed that schizophrenia and other psychotic disorders result from an impaired ability to meet the metabolic demands of the brain following disruptions in cerebral blood flow that are caused by genetically mediated inflammatory processes that damage the interconnected system of neurons, astroglia, and microvessels (107109). At the molecular level, oxidative stress-related gene dysregulation, complement cascade activation, mitochondrial dysfunction, and chronic glial cell activation are believed to result in oxidative damage, disruptions in synaptic pruning, and decreased energy metabolism (110113). Similar processes also occur in the retina, as chronic inflammation can lead to retinal ganglion cell death through decreased efficiency of the blood-retina barriers, prolonged activation of inflammatory mediators, glutamate excitotoxicity, and oxidative stress (114). For example, oxidative stress genes (e.g., NrF2), complement cascade activation, mitochondrial dysfunction, and chronic glial cell activation have all been implicated in neurodegeneration in both the retina (e.g., age-related macular degeneration, glaucoma, diabetic retinopathy) and the brain (e.g., Alzheimer's disease, Parkinson's disease) (115121). In schizophrenia, recent studies have revealed associations between inflammatory markers and reduced retinal thickness in patients (82, 122), and a general population study (UK Biobank) indicated that the link between higher polygenic risk scores for schizophrenia and retinal thinning is mediated by genes that also code for neuroinflammatory responses (123).

Early neurodevelopmental foundations of retinal alterations

Prenatal and perinatal adverse events and retinal alterations

Prenatal and perinatal insults have long been implicated in the later development of psychosis and are consistent with the neurodevelopmental model of schizophrenia (124, 125). They are also associated with enduring cognitive impairments and neuropsychiatric conditions (126131). In neuroimaging studies, adults who had prenatal or perinatal risk factors have exhibited similar gray matter alterations and dopaminergic dysfunctions to those seen in schizophrenia, indicating that there might be a disruption to neurodevelopment in the prenatal and/or perinatal period that confers risk for developing schizophrenia (124, 132134).

Retinal neural layer and microvascular abnormalities (without signs of specific retinal disease), along with atypical brain development, have been observed in people who experienced adverse prenatal and/or perinatal events. Therefore, it is possible that retinal abnormalities in schizophrenia could reflect neurodevelopmental origins. For example, a growing body of research has revealed a range of retinal structural and microvascular abnormalities among infants, children/adolescents, and adults born prematurely (even among those without retinopathy of prematurity) compared to those born full-term (135144). In addition, evidence suggests that smaller fetal head circumference, lower birth weight, and small-for-gestational-age status are associated with retinal morphological abnormalities (139, 141, 145149). Moreover, retinal alterations, such as thinner RNFL and GCL-IPL, as well as narrower retinal arteriolar diameter, correlate with poorer cognition and motor skills, as well as smaller brain volumes and more extensive white matter injury, among children and adults with these prenatal/perinatal complications (137, 142, 150152).

It is believed that reductions in retinal layer thickness among individuals exposed to prenatal and perinatal adverse events may result from disruptions in typical retinal development and suboptimal adaptive mechanisms during gestation, driven by an adverse intrauterine environment (144, 151). Inflammation is another factor proposed to disrupt retinal morphological development concomitantly with prematurity or low birth weight (151). For example, systemic inflammation during the neonatal period can disrupt long-term neuroretinal functioning, as inflammation leads to increased microglial cell activation in the GCL and outer plexiform layer (OPL), resulting in retinal neural cell damage (153, 154).

These findings have important implications for neurodevelopment and schizophrenia. First, OCT findings in people with a history of obstetric complications, and their association with impaired cognition and brain structure, demonstrate that OCT indices are useful indicators of altered neurodevelopment, in addition to growing evidence supporting OCT indices as indicators of neurodegeneration (4552). Second, because premature birth, low birth weight, and reduced fetal head circumference are associated with abnormal brain anatomy (134, 155157), poorer cognitive functioning throughout development and into adulthood (158160), and are known risk factors for the later development of schizophrenia (124, 161), this opens the door to the possibility that retinal anomalies in schizophrenia are present perhaps as early as the prenatal period, and reflect alterations in retinal development. What is less clear is whether disruptions in retinal development confer a greater risk for accelerated retinal aging. There is evidence that premature-born adults exhibit accelerated brain aging (162), which suggests that abnormalities in retinal neural and microvascular structure could represent both disturbances in neuroretinal and retinal microvascular development and increased vulnerability to progressive retinal atrophy in schizophrenia.

Retinal findings in other neurodevelopmental disorders

There is preliminary evidence that retinal abnormalities exist in other neurodevelopmental disorders with overlapping pathophysiology with schizophrenia (163, 164), such as attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) (165167). A small body of literature has revealed reduced retinal thickness among children, adolescents, and adults with ADHD, as well as both reduced and increased retinal thickness across the lifespan in ASD. Studies have also found associations between reduced retinal thickness and lower cognitive functioning in people with ASD (168, 169).

Retinal imaging studies of individuals with ADHD and ASD suggest that abnormal retinal morphology may be reflective of atypical neurodevelopment, which suggests that similar mechanisms could occur in schizophrenia. It has been theorized that this may be due to disruptions in neurogenesis and neuronal migration (169) or neuroinflammation from microglia activation (168). Similarly, Friedel et al. (170) hypothesized that retinal volume loss observed in adults with ASD may be due to disturbances in migratory or other neurodevelopmental networks, reflecting simultaneous disturbances in neocortical networks. They also speculate that retinal volume loss may reflect an imbalance of excitatory and inhibitory signals resulting from changes in excitatory glutamatergic projection neurons and inhibitory GABAergic interneurons, which play prominent roles in retinal signal processing (170172). These changes in excitatory-inhibitory balance are found in the neocortex and hippocampus in both ASD and schizophrenia (173, 174).

Retinal findings in schizophrenia consistent with the neurodevelopmental theory

Retinal findings in familial and genetic risk samples

Retinal studies of unaffected first-degree relatives of individuals with schizophrenia have shown differences in retinal neural layer thickness and volume, retinal vessel diameter, and amplitude and latency of flash electroretinography (fERG) waveforms compared to healthy controls (175181). For example, Kurtulmus et al. (182) found reduced IPL thickness in both chronic, stable schizophrenia patients (mean illness duration = 18.31 years) and their first-degree relatives compared to controls, with no group differences in RNFL, GCL, or macular thickness. In a similar study, Kaya et al. (183) found no group differences in RNFL thickness but observed a graded pattern in GCL-IPL thickness among schizophrenia patients (mean illness duration = 11.97 years), their unaffected siblings, and controls (schizophrenia patients < siblings < controls), although the differences were significant only between the schizophrenia patients and controls. Macular volume showed a different pattern (siblings > controls > schizophrenia patients), with significant differences between the siblings and the schizophrenia patients. Another study reported thinner segments of the peripapillary RNFL (pRNFL, the portion of the RNFL adjacent to the optic disc) and GCL-IPL, as well as reduced macular volume, in schizophrenia patients compared to their unaffected siblings (184), suggesting that unaffected first-degree relatives show genetically driven retinal abnormalities, but that these are less pronounced than those in schizophrenia.

In terms of retinal microvasculature, in a sample of adolescent and young adult monozygotic and dizygotic twins with psychosis symptoms and their unaffected co-twins, Meier et al. (185) found that a higher proportion of probands and their unaffected twins had wider retinal venules relative to controls, which was independent of variables known to influence vasculature, including smoking and body mass index (BMI). Additionally, probands had the widest retinal venules, followed by their twins, and then by controls. This demonstrates that retinal vessel diameter is associated with a familial liability to psychosis symptoms and may not solely represent a consequence of the disease or lifestyle.

Consistent with OCT findings among those with familial risk for schizophrenia, studies using polygenic risk scores for schizophrenia (PRS) and Mendelian randomization have found a relationship between greater genetic risk for schizophrenia and reduced thickness of retinal neural layers (97, 123, 185188). For example, Blose et al. (97) found that, among 35,024 participants in the UK Biobank, greater polygenic risk was associated with reduced thickness of the GCL-IPL, particularly among those aged 40–59. As noted above, Rabe et al. (123) found that among the genes in the polygenic risk score for schizophrenia related to retinal thickness, there was overlap with genes involved in the neuroinflammatory response. These data indicate specific neurobiological mechanisms by which genetic differences in schizophrenia can affect CNS health (e.g., function of interneurons and neuroinflammation, respectively). They are also consistent with evidence that genetic variants associated with macular thickness overlap with those associated with schizophrenia (189) and that similar genes control retinal and brain characteristics (106, 190).

Retinal findings in CHR samples

In the first known published OCT study of clinical high risk for psychosis (CHR) individuals (191), researchers observed only an increased choroidal vascularity index (CVI) (192) in CHR and (FEP) groups compared to controls (all between the ages of 12 and 35). However, no differences were found among the three groups on several other choroidal variables measured, suggesting that at the CHR stage, an increase in vascularization may be the only abnormality present. This is consistent with the view noted above that inflammatory and vascular changes may precede neural changes in the development of schizophrenia.

Another study investigating retinal layer thickness and volumes between CHR patients, FEP patients (defined as those who had a single psychotic episode within the last 18 months), and healthy controls (between ages 14 and 31), found increased thickness and/or volume of the macula, GCL, IPL, and inner nuclear layer (INL), in CHR and FEP groups relative to controls, while macular RNFL (mRNFL) thickness and volume were reduced only in the FEP group (193). Interestingly, the CHR and FEP groups did not differ in thickness or volume across any of these indices (except mRNFL volume), suggesting that structural alterations may be present before illness onset. However, the authors hypothesized that the increased thickness and volume observed in the patient groups may be due to neuroinflammation and/or abnormal fluid accumulation resulting from a weakened retinal-blood barrier—mechanisms also proposed to explain null findings of retinal thinning in a sample of patients with a recent psychotic episode (194) and another study of FEP patients with schizophrenia or schizoaffective disorder (195). Ascaso et al. (194) postulated that inflammatory processes may increase retinal thickness, thereby masking axonal damage in schizophrenia. No direct evidence supporting these explanations in schizophrenia exists currently, although increased retinal thickness has been observed during acute episodes of MS (but not in between episodes, when neurodegeneration is visible), which has been attributed to effects of inflammation (196).

Taken together, the findings of these studies suggest that retinal morphological and choroidal vascularity abnormalities may be present before the onset of a psychotic disorder, lending support to the neurodevelopmental hypothesis of schizophrenia. The findings also indicate that in the period immediately before or after the onset of psychotic symptoms, inflammation and other processes related to preservation of neural health (e.g., reactive gliosis) may lead to thickening of neural and vascular aspects of the retina. Over time, however, and with long-term expression of these processes, neural and vascular tissue loss occurs.

Early-stage retinal indicators bridging neurodevelopmental and neurodegenerative theories

Retinal findings in FEP samples

Overall, most retinal imaging studies that included FEP samples have found significant differences in retinal parameters between FEP patients with a schizophrenia spectrum disorder and healthy controls (180, 193, 197204), although one study did not (195). Some studies have observed reduced total retinal thickness and reduction in specific segments/layers, such as the RNFL, macula, and central foveal region, as well as differences in retinal vessel density and fractal dimension among FEP patients with a schizophrenia spectrum disorder compared to controls (193, 197201), whereas other studies found increased retinal thickness and volume, particularly in the INL, GCL, and IPL (193), as well as in the pRNFL (202). A study comparing OCT indices between FEP patients with schizophrenia (with acute symptoms with a duration of less than 3 months and a total duration of illness of less than 5 years) and age- and sex-matched controls found that macular thickness and pRNFL thickness were reduced in FEP patients relative to controls (although the reduction in pRNFL thickness did not reach statistical significance) (199). Similarly, in the only longitudinal study assessing retinal parameters in schizophrenia, Zhuo et al. (200) found that in a sample of antipsychotic-naïve FEP patients with schizophrenia reporting visual disturbances, baseline retinal thickness and gray matter volume in regions of the visual cortex were reduced compared to controls. Interestingly, after 3 years of treatment with an antipsychotic medication, retinal thickness and gray matter volume in regions of the visual cortex showed significant deterioration in the FEP group, whereas no significant changes were found in the control group. Additionally, the gray matter reduction and retinal thickness reduction rates correlated with increases in self-reported visual disturbances among the FEP patients over the 3-year period. However, after an additional 6-month follow-up period, FEP patients did not exhibit any further deterioration in retinal thickness or in gray matter volume of the visual cortex. These findings suggest that retinal and brain morphological alterations were present early in the disorder and could not have been a result of long-term antipsychotic treatment, although the deterioration found over the initial 3-year period may have been exacerbated by antipsychotic treatment.

When retinal indices of FEP patients have been compared with those of chronic schizophrenia patients, several studies have found differences, including reduced macular thickness and volume, and reduced pRNFL thickness in chronic schizophrenia patients (78, 195, 198, 205). Differences in retinal microvasculature have also been observed (206, 207). In the first study to ever compare OCT parameters between schizophrenia patients in different phases of illness, Lee et al. (78) found that chronic schizophrenia patients (with a duration of illness greater than 2 years and less than 10 years) and chronic schizophrenia patients (with a duration of illness greater than 10 years) had significantly thinner pRNFL compared to acutely ill FEP patients with schizophrenia (with a duration of illness of 2 years or less). Chronic patients (with a duration of illness greater than 10 years) also had significantly reduced average macula thickness compared to the FEP patients. Further, longer duration of illness correlated with reductions in pRNFL thickness, macula thickness, and macula volume. Lee et al. stated that these findings parallel those from neuroimaging studies observing degeneration in brain volume over time among individuals with schizophrenia (208210) and, therefore, may be reflective of a progressive neurodegenerative disease process.

Some studies, however, have not observed differences in retinal microvascular parameters between FEP and chronic schizophrenia patients (195, 211, 212). For example, one study did not find differences in retinal perfusion density, vessel density, or foveal avascular zone size between a sample of FEP patients and those with later-episode schizophrenia or schizoaffective disorder (212); however, another study comparing these groups within the same sample found significantly reduced macular thickness and volume indices among later-episode, but not first-episode, patients (195). Silverstein et al. hypothesized that these results could indicate that retinal microvascular changes occur earlier than retinal neural changes, consistent with the theory that the pathophysiology of schizophrenia involves neuroinflammatory processes that lead to vascular changes and then to changes in neural structure and function (107, 213).

Overall, the evidence from studies of FEP patients indicates that retinal neural and microvascular alterations are present very early in the disorder (sometimes as reduced tissue levels and sometimes as neuroinflammation-mediated tissue swelling), which may reflect neurodevelopmental and neuroinflammatory processes. Conversely, studies showing significant retinal thickness reduction in chronic schizophrenia samples relative to FEP samples (e.g., FEP patients vs. chronic schizophrenia patients) suggest an additional, neurodegenerative process.

Retinal findings in relation to neurological soft signs

There is emerging evidence that retinal alterations in schizophrenia may be related to neurological soft signs (NSS) (214), which are indicators of neurodevelopmental abnormalities (215) characterized by subtle neurological impairments in motor coordination, sensory integration, and sequencing of complex motor tasks (216, 217). A recent study by Krukow et al. (214) found that macular thickness, macular volume, GCC, and pRNFL were significantly reduced in a group of schizophrenia inpatients relative to healthy controls, and that greater severity of various NSS was correlated with reductions in these OCT indices (except pRNFL). The researchers argued that these findings reflect a link between neurodevelopmental and neurodegenerative processes in schizophrenia due to the fact that NSS are both risk indicators for the disorder, as they are present before the development of psychosis (consistent with a neurodevelopmental process), and tend to worsen with increasing illness severity and chronicity (218) (consistent with a neurodegenerative process).

Brief overview of neuroimaging findings in schizophrenia consistent with the neurodegenerative theory

One of the most convincing pieces of evidence in favor of a neurodegenerative disease process in schizophrenia is the finding that individuals with the disorder exhibit accelerated gray matter loss, and to some extent, white matter loss, over time beyond the effects of typical aging (219), although the rate of loss varies across different windows of time (220). Studies have also found that individuals with schizophrenia have a greater brain age gap than healthy controls, which is a parameter estimated from machine learning and neuroimaging data (e.g., cortical thickness and brain volume) representing the difference between a person's model-estimated brain age and chronological age, with higher values reflecting more pronounced accelerated brain aging (221223). Moreover, brain age gaps, representing deterioration in brain volume and cortical thickness, have been found to increase with longer illness duration, suggesting progressive neurodegeneration (223, 224).

Retinal findings in schizophrenia consistent with the neurodegenerative theory

Retinal findings on duration of illness

While there is only one longitudinal retinal imaging study in schizophrenia to date (200), many existing studies have explored the effect of illness duration on retinal morphology, and these studies provide preliminary evidence of retinal degeneration. For example, several studies have found that longer disease durations are associated with reduced retinal and choroid layer thickness and volume (71, 72, 7883, 186, 205, 207, 214, 225227). Consistent with this, Celik et al. (80) found that among schizophrenia patients who were either treatment-refractory (mean illness duration = 14.55 years) or treatment-responsive (mean illness duration = 12.04 years), a longer duration of illness and a greater number of hospitalizations were correlated with reduced thickness of the GCL and choroid. Additionally, treatment-refractory patients had significantly reduced GCL volume, IPL volume, and choroid thickness compared to the treatment-responsive patients. These data are consistent with the idea of a Kraepelinian subtype of schizophrenia, one characterized by a more chronic, severe, and deteriorating course of illness with progressive CNS atrophy (228).

One issue worth mentioning is that duration of illness and age are highly correlated, which makes it difficult to disentangle the effects of aging from the relationship, so it is possible that age may be confounding the relationship between longer illness duration and retinal thinning (15). However, some studies have found significant inverse relationships between retinal thickness and duration of illness while statistically controlling for age (71, 79), suggesting that this pattern may not merely be due to the effects of typical aging on the retina. For example, one study found that both first-episode and chronic schizophrenia patients demonstrated a loss of retinal microvasculature that exceeded age-matched controls (206). Interestingly, the vasculature of the first episode group resembled that of the older control group that was matched to the chronic patient group.

Retinal findings indicative of accelerated aging

There is some evidence that age-related retinal thinning is accelerated in schizophrenia relative to healthy controls (15, 17, 74, 75), which is consistent with research findings of other biomarkers indicating signs of accelerated aging in the disorder (229). For example, Blose et al. (15) found a significant negative relationship between age and retinal thickness, particularly in the GCL-IPL, among individuals with a schizophrenia spectrum disorder. This relationship was significantly more pronounced among individuals with a schizophrenia spectrum disorder than it was in healthy controls. Accelerated retinal thinning with age was also found in the RNFL and macular volume in the schizophrenia spectrum disorder group, although these relationships weakened after accounting for diabetes and hypertension. These findings are consistent with results from a large sample of individuals from the AlzEye project, which found that the rate of GCL-IPL thinning with increasing age was greater for schizophrenia patients than it was for healthy controls (74). Also corroborating these findings, Domagała et al. (75) found that in a group of schizophrenia patients, macular thickness and volume decreased at an accelerated rate relative to a group of healthy controls. They observed that this was particularly apparent among patients aged 32–45 years, relative to those aged 20–31 years and 46–65 years, indicating that accelerated retinal thinning may occur in discrete, narrow intervals, as observed in neuroimaging studies of schizophrenia (220). In partial contrast to these findings, Krukow et al. (17) demonstrated a retinal age gap in schizophrenia, with the largest gaps observed in the youngest patients. This difference may be due to differences in the methods used to calculate the age gap across studies.

In sum, these studies suggest that age-related retinal layer thinning, especially in the macular regions, occurs at a greater rate in schizophrenia compared to those without the disorder, which is consistent with a neuroprogressive disease course. Additionally, accelerated retinal aging may occur across certain windows of time, increasing from middle age to older age. However, these cross-sectional findings need to be corroborated with longitudinal research.

Discussion

In sum, research using retinal imaging in populations as diverse as schizophrenia, people at high risk for the development of schizophrenia, neurodevelopmental disorders, people with a history of prenatal and perinatal adverse events, and neurodegenerative disorders suggests that retinal imaging findings in schizophrenia are likely to reflect both neurodevelopmental and neurodegenerative aspects of the disease, supporting the progressive neurodevelopmental theory. Specifically, evidence of retinal alterations in genetic risk, familial risk, CHR, and FEP populations is in line with a neurodevelopmental disease process because it implies that retinal alterations are present before the full manifestation of psychotic illness or (in the case of FEP) very early in the illness course, perhaps due to some early life insult disrupting retinal neural development. In addition, findings of retinal thinning in other neurodevelopmental disorders that have genetic and pathophysiological overlap with schizophrenia (e.g., ASD), as well as among individuals exposed to prenatal and perinatal complications, which are known risk factors for schizophrenia (e.g., low birth weight, hypoxia), are indirect evidence consistent with the neurodevelopmental model. Conversely, findings of greater reductions in retinal thickness among chronic schizophrenia patients vs. FEP patients, greater reductions in retinal thickness with increasing illness duration in correlation analyses, and more pronounced age-related retinal thinning among schizophrenia patients relative to controls point to neurodegenerative disease processes.

The idea that retinal abnormalities in schizophrenia reflect both neurodevelopmental and neurodegenerative processes is supported by evidence of overlap in signaling pathways utilized by retinal cells for both development and degeneration (172), such as the Wnt signaling pathway (230) and the Notch signaling pathway (231). It is also consistent with growing evidence that early disruptions in neurodevelopment increase vulnerability for neurodegeneration, given that both are associated with alterations in similar brain regions and impairments in the neurovascular system. Finally, this view is consistent with the finding that alterations in protein processing, tracking, and aggregation during brain development are implicated in neurodegeneration in Alzheimer's disease and Parkinson's disease, and genes that mediate vulnerability to these disorders are associated with neurodevelopmental disorders, such as epilepsy, ASD, and schizophrenia (30).

Although more research is needed, converging evidence tentatively implies that in schizophrenia, retinal degeneration may be the product of disruptions in retinal neural development that can be observed as early as the prodromal phase, and that accelerate as the illness progresses, at least for a significant proportion of individuals with the disorder. Therefore, the extant evidence of retinal findings supports the progressive neurodevelopmental theory of schizophrenia, at least for a subtype of schizophrenia patients.

Future directions and conclusions

There are several gaps in the retinal imaging in schizophrenia literature that, if addressed, are likely to further elucidate the neurodevelopmental and neurodegenerative trajectories of the condition. First, there is an obvious need for more research examining the retina in young individuals with a CHR syndrome and those at genetic risk for schizophrenia to replicate findings of the small number of existing studies. Studies with these populations are beneficial because these individuals tend to be relatively free from many of the confounding factors typically associated with chronic psychotic disorders, such as long-term antipsychotic medication use, unhealthy lifestyle, and systemic diseases.

In addition, more studies are needed that investigate group differences between at-risk patients, early-onset schizophrenia patients, FEP patients, and chronic schizophrenia patients. Currently, only a few studies have taken this approach (180, 191, 193, 195, 198), so replication is needed to draw definitive conclusions. Doing so can further our understanding of the extent and rate of retinal thinning across different stages of illness. Relatedly, more studies that compare the relationship between age and retinal thickness and volume changes between individuals with schizophrenia and healthy controls are needed to help clarify how retinal atrophy in schizophrenia may be indicative of accelerated aging.

Recently, researchers have developed the retinal age gap index, which, similar to the brain age gap, represents the difference between estimated retinal age derived from retinal imaging data and chronological age using a machine learning model (232). So far, greater retinal age gap has predicted an increased risk for Parkinson's disease, all-cause mortality, mortality due to cardiovascular disease and cancer, more progressive diabetic retinopathy, and stroke, among several other conditions, similar to the brain age gap (233237). Thus, it will be worthwhile to determine the retinal age gap with CHR, FEP, and chronic schizophrenia patients to predict risk of disease onset (for CHR) and trajectories of progressive neurodegeneration. In addition, the retinal age gap could be used to predict and monitor systemic comorbidities that are significantly more prevalent in schizophrenia and contribute to their earlier mortality (238), which is needed because conditions such as diabetes and cardiovascular disease are not well monitored in this population (239). Further, the data collection methods on which retinal age gap models are based are non-invasive, less expensive, and more accessible than other biomarker methodologies, and well-suited for large datasets (232, 240, 241), making them easier to integrate into clinical care.

The evidence base on retinal imaging in schizophrenia would also benefit from longitudinal studies, as they can help identify the degree to which retinal changes in schizophrenia align with those that occur with typical neurodevelopment and aging, thus aiding our understanding of the neurodevelopmental and neurodegenerative characteristics of the disease. Additionally, longitudinal designs should be employed to investigate how retinal changes across development, maturation, and aging compare with those in the brain over time in schizophrenia, since it is not entirely clear whether patterns of retinal degeneration mirror those of brain degeneration over time. Relatedly, more mixed-method studies that incorporate brain imaging and retinal imaging in schizophrenia are needed to further elucidate the nature of their relationship. If retinal imaging can serve as a proxy for brain imaging, this could have important clinical implications because retinal imaging, such as OCT, is significantly less expensive, quicker, better tolerated by patients, and has fewer exclusion criteria (e.g., weight, metal in body) compared to brain imaging techniques, such as MRI (55).

Lastly, the influence of potential confounders, such as antipsychotic medication, systemic diseases (e.g., hypertension, diabetes, etc.), smoking, BMI, and ocular conditions (e.g., myopia, glaucoma, etc.) on retinal findings in schizophrenia is unclear. Findings in genetic-risk, CHR, and antipsychotic-naïve FEP groups suggest these abnormalities are not secondary to antipsychotic medication. However, as noted by Komatsu et al. (61), it is possible that antipsychotics could affect the retina, given that their primary targets (dopamine D2 receptors) are expressed in the retina (242, 243). There is no direct evidence, though, that antipsychotics cross the blood-retina barrier (61). Studies reporting possible GCL-IPL thinning with antipsychotic use are inconsistent, likely due to medication heterogeneity in terms of their receptor activities, and many studies of chronic patients fail to find a significant association with antipsychotic dose (61). Therefore, additional studies evaluating the effect of these medications on retina structure and function in schizophrenia are warranted.

Research suggests that tobacco smoking is associated with alterations in retinal layer thickness and microvasculature (244, 245), likely via endothelial dysfunction, inflammation, and oxidative stress (246248). However, its influence on retinal findings in schizophrenia remains unclear because most studies have not measured or controlled for smoking. Recent meta-analyses have generally not found a significant effect of smoking, but a number of studies were not included due to a lack of data on the issue (57, 62, 66, 67). Thus, it will be important for future retinal imaging studies to measure tobacco use in schizophrenia to determine the extent to which it accounts for variance in retinal alterations.

While the effect of systemic diseases and ocular conditions known to affect the retina on retinal imaging findings in schizophrenia remains in question, mounting evidence suggests that retinal thinning in the disorder occurs independently of these factors. Most studies have excluded participants with ocular conditions, and many have also controlled for systemic diseases. For example, a recent meta-analysis, which included only studies that excluded individuals with systemic diseases, revealed significant retinal thinning across most OCT parameters in schizophrenia (62). Similarly, large population-based data show thinner mGCL-IPL and larger cup-to-disc ratio in schizophrenia after adjusting for hypertension and diabetes (74). Microvascular findings may be at least partially attributable to cardiometabolic disease (74), though evidence remains limited due to fewer studies. The influence of BMI on retinal findings in schizophrenia also deserves attention, as obesity is more prevalent in schizophrenia (249, 250) and is generally associated with retinal alterations (251254), yet most studies have not controlled for these factors. However, those that do have still observed differences in retinal neural layers and microvasculature (122, 183, 198, 255, 256).

One important limitation is that the current paper primarily focuses on structural retinal findings in schizophrenia. Findings on retinal function and physiology using methods such as ERG and pupillometry in schizophrenia are so far consistent with structural findings. For example, there is growing evidence of atypical retinal cell functioning using ERG in schizophrenia, as well as those with familial/genetic risk for schizophrenia and CHR individuals (175, 176, 178, 257259). In addition, atypical pupil reactivity, such as reduced pupil dilation, has been observed in schizophrenia (260263), and there is some evidence that larger tonic pupil size in infancy is associated with increased genetic risk for schizophrenia (264). Therefore, future research examining the extent to which functional visual physiological measures, such as ERG and pupillometry, support retinal structure findings in schizophrenia and inform the neurodevelopmental and neurodegenerative models is warranted. Lastly, another limitation is that, because this is a narrative review, it is important to keep in mind the potential influence of selection bias and lack of reproducibility inherent to this type of approach.

In conclusion, schizophrenia is a heterogeneous syndrome possessing both neurodevelopmental and neurodegenerative characteristics, with the extent of evidence for these varying across patients. Retinal imaging in schizophrenia has helped elucidate the neurodegenerative nature of the disease thus far and is now beginning to uncover its neurodevelopmental aspects. Continued research using inexpensive, non-invasive, and rapid data acquisition methodologies, such as OCT, that employ longitudinal designs, group patients according to their illness phase (e.g., CHR vs. FEP vs. chronic), and utilize the retinal age gap index can further our understanding of the pathophysiology of schizophrenia, which can then inform more targeted prevention, treatment, and monitoring strategies.

Author contributions

BB: Conceptualization, Writing – review & editing, Writing – original draft. SS: Supervision, Writing – review & editing, Conceptualization.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2025.1697871/full#supplementary-material

Footnotes

1. ^To reduce verbiage, the current paper will refer to schizophrenia spectrum disorders (encompassing schizophrenia, schizoaffective disorder, schizophreniform disorder, and unspecified psychosis not due to a substance or known physiological condition) as ‘schizophrenia.'

References

1. Kraepelin E, editor. Dementia Praecox and Paraphrenia. 8th ed. Edinburgh: E. & S. Livingstone (1919).

Google Scholar

2. Lang DJ, Kopala LC, Vandorpe RA, Rui Q, Smith GN, Goghari VM, et al. An MRI study of basal ganglia volumes in first-episode schizophrenia patients treated with risperidone. Am J Psychiatry. (2001) 158:625–31. doi: 10.1176/appi.ajp.158.4.625

PubMed Abstract | Crossref Full Text | Google Scholar

3. Hasan A, Falkai P, Wobrock T, Lieberman J, Glenthoj B, Gattaz WF, et al. World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for biological treatment of schizophrenia, part 2: update 2012 on the long-term treatment of schizophrenia and management of antipsychotic-induced side effects. World J Biol Psychiatry. (2013) 14:2–44. doi: 10.3109/15622975.2012.739708

PubMed Abstract | Crossref Full Text | Google Scholar

4. Falkai P, Schmitt A. Failed regeneration and inflammation in schizophrenia: two sides of the same coin? J Neural Transm. (2022) 129:611–5. doi: 10.1007/s00702-022-02496-3

PubMed Abstract | Crossref Full Text | Google Scholar

5. Lieberman JA. Is schizophrenia a neurodegenerative disorder? A clinical and neurobiological perspective. Biol Psychiatry. (1999) 46:729–39. doi: 10.1016/S0006-3223(99)00147-X

PubMed Abstract | Crossref Full Text | Google Scholar

6. Muller N. Neuroprogression in schizophrenia and psychotic disorders: the possible role of inflammation. Mod Trends Pharmacopsychiatry. (2017) 31:1–9. doi: 10.1159/000470802

PubMed Abstract | Crossref Full Text | Google Scholar

7. Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. (2016) 388:86–97. doi: 10.1016/S0140-6736(15)01121-6

Crossref Full Text | Google Scholar

8. Reckziegel R, Czepielewski LS, Hasse-Sousa M, Martins DS, de Britto MJ, Lapa CO, et al. Heterogeneous trajectories in schizophrenia: insights from neurodevelopment and neuroprogression models. Braz J Psychiatry. (2022) 44:74–80. doi: 10.1590/1516-4446-2020-1670

PubMed Abstract | Crossref Full Text | Google Scholar

9. Yang Y, Clouston SAP, Reichenberg A, Callahan JL, Ruggero CJ, Carlson GA, et al. Predictors and outcomes associated with 25-year cognitive decline in psychotic disorders. Schizophr Bull. (2025) sbaf051. doi: 10.1093/schbul/sbaf051

PubMed Abstract | Crossref Full Text | Google Scholar

10. Mitelman SA, Buchsbaum MS. Very poor outcome schizophrenia: clinical and neuroimaging aspects. Int Rev Psychiatry. (2007) 19:345–57. doi: 10.1080/09540260701486563

PubMed Abstract | Crossref Full Text | Google Scholar

11. Mitelman SA, Canfield EL, Brickman AM, Shihabuddin L, Hazlett EA, Buchsbaum MS. Progressive ventricular expansion in chronic poor-outcome schizophrenia. Cogn Behav Neurol. (2010) 23:85–8. doi: 10.1097/WNN.0b013e3181cfb52a

PubMed Abstract | Crossref Full Text | Google Scholar

12. Mitelman SA, Brickman AM, Shihabuddin L, Newmark RE, Hazlett EA, Haznedar MM, et al. A comprehensive assessment of gray and white matter volumes and their relationship to outcome and severity in schizophrenia. Neuroimage. (2007) 37:449–62. doi: 10.1016/j.neuroimage.2007.04.070

PubMed Abstract | Crossref Full Text | Google Scholar

13. Ribe AR, Laursen TM, Charles M, Katon W, Fenger-Gron M, Davydow D, et al. Long-term risk of dementia in persons with schizophrenia: a danish population-based cohort study. JAMA Psychiatry. (2015) 72:1095–101. doi: 10.1001/jamapsychiatry.2015.1546

PubMed Abstract | Crossref Full Text | Google Scholar

14. Tao F, Deng S, Zhuo B, Liu J, Liang X, Shi J, et al. Causal relationship between schizophrenia and five types of dementia: a bidirectional two-sample Mendelian randomization study. PLoS ONE. (2025) 20:e0322752. doi: 10.1371/journal.pone.0322752

PubMed Abstract | Crossref Full Text | Google Scholar

15. Blose BA, Lai A, Crosta C, Thompson JL, Silverstein SM. Retinal neurodegeneration as a potential biomarker of accelerated aging in schizophrenia spectrum disorders. Schizophr Bull. (2023) 49:1316–24. doi: 10.1093/schbul/sbad102

PubMed Abstract | Crossref Full Text | Google Scholar

16. Han R, Wang W, Liao J, Peng R, Liang L, Li W, et al. Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data. Brain Res Bull. (2025) 226:111363. doi: 10.1016/j.brainresbull.2025.111363

PubMed Abstract | Crossref Full Text | Google Scholar

17. Krukow P, Domagala A, Kiersztyn A, Blose BA, Lai A, Silverstein SM. The retinal age gap as a marker of accelerated aging in the early course of schizophrenia. Schizophr Bull. (2025) sbaf038. doi: 10.1093/schbul/sbaf038

PubMed Abstract | Crossref Full Text | Google Scholar

18. Shirai T, Okazaki S, Tanifuji T, Numata S, Nakayama T, Yoshida T, et al. Meta-analyses of epigenetic age acceleration and GrimAge components of schizophrenia or first-episode psychosis. Schizophrenia. (2024) 10:108. doi: 10.1038/s41537-024-00531-8

PubMed Abstract | Crossref Full Text | Google Scholar

19. Sun Z, Guo X, Wu S, Jia T, Kou C, Bai W. Accelerated biological aging and schizophrenia risk: evidence from the UK biobank. Schizophr Bull. (2025). doi: 10.1093/schbul/sbaf210

PubMed Abstract | Crossref Full Text | Google Scholar

20. Lewis DA. Development of the prefrontal cortex during adolescence: insights into vulnerable neural circuits in schizophrenia. Neuropsychopharmacology. (1997) 16:385–98. doi: 10.1016/S0893-133X(96)00277-1

PubMed Abstract | Crossref Full Text | Google Scholar

21. Murray RM, Lewis SW. Is schizophrenia a neurodevelopmental disorder? Br Med J. (1987) 295:681–2. doi: 10.1136/bmj.295.6600.681

Crossref Full Text | Google Scholar

22. Weinberger DR. Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry. (1987) 44:660–9. doi: 10.1001/archpsyc.1987.01800190080012

PubMed Abstract | Crossref Full Text | Google Scholar

23. Bloom FE. Advancing a neurodevelopmental origin for schizophrenia. Arch Gen Psychiatry. (1993) 50:224–7. doi: 10.1001/archpsyc.1993.01820150074008

PubMed Abstract | Crossref Full Text | Google Scholar

24. Lewis DA, Levitt P. Schizophrenia as a disorder of neurodevelopment. Annu Rev Neurosci. (2002) 25:409–32. doi: 10.1146/annurev.neuro.25.112701.142754

Crossref Full Text | Google Scholar

25. Murray RM. Neurodevelopmental schizophrenia: the rediscovery of dementia praecox. Br J Psychiatry Suppl. (1994) 165:6–12. doi: 10.1192/S0007125000293148

PubMed Abstract | Crossref Full Text | Google Scholar

26. Schenkel LS, Silverstein SM. Dimensions of premorbid functioning in schizophrenia: a review of neuromotor, cognitive, social, and behavioral domains. Genet Soc Gen Psychol Monogr. (2004) 130:241–70. doi: 10.3200/MONO.130.3.241-272

PubMed Abstract | Crossref Full Text | Google Scholar

27. Gupta S, Kulhara P. What is schizophrenia: a neurodevelopmental or neurodegenerative disorder or a combination of both? A critical analysis. Indian J Psychiatry. (2010) 52:21–7. doi: 10.4103/0019-5545.58891

PubMed Abstract | Crossref Full Text | Google Scholar

28. Douaud G, Groves AR, Tamnes CK, Westlye LT, Duff EP, Engvig A, et al. A common brain network links development, aging, and vulnerability to disease. Proc Natl Acad Sci USA. (2014) 111:17648–53. doi: 10.1073/pnas.1410378111

PubMed Abstract | Crossref Full Text | Google Scholar

29. Haouari S, Vourc'h P, Jeanne M, Marouillat S, Veyrat-Durebex C, Lanznaster D, et al. The roles of NEDD4 subfamily of HECT E3 ubiquitin ligases in neurodevelopment and neurodegeneration. Int J Mol Sci. (2022) 23:3882. doi: 10.3390/ijms23073882

PubMed Abstract | Crossref Full Text | Google Scholar

30. Schor NF, Bianchi DW. Neurodevelopmental clues to neurodegeneration. Pediatr Neurol. (2021) 123:67–76. doi: 10.1016/j.pediatrneurol.2021.07.012

PubMed Abstract | Crossref Full Text | Google Scholar

31. Grilli M, Ferrari Toninelli G, Uberti D, Spano P, Memo M. Alzheimer's disease linking neurodegeneration with neurodevelopment. Funct Neurol. (2003) 18:145–8.

PubMed Abstract | Google Scholar

32. Bothwell M, Giniger E. Alzheimer's disease: neurodevelopment converges with neurodegeneration. Cell. (2000) 102:271–3. doi: 10.1016/S0092-8674(00)00032-5

PubMed Abstract | Crossref Full Text | Google Scholar

33. Ouellette J, Lacoste B. From neurodevelopmental to neurodegenerative disorders: the vascular continuum. Front Aging Neurosci. (2021) 13:749026. doi: 10.3389/fnagi.2021.749026

PubMed Abstract | Crossref Full Text | Google Scholar

34. Hickman RA, O'Shea SA, Mehler MF, Chung WK. Neurogenetic disorders across the lifespan: from aberrant development to degeneration. Nat Rev Neurol. (2022) 18:117–24. doi: 10.1038/s41582-021-00595-5

PubMed Abstract | Crossref Full Text | Google Scholar

35. Ruiz-Reig N, Hakanen J, Tissir F. Connecting neurodevelopment to neurodegeneration: a spotlight on the role of kinesin superfamily protein 2A (KIF2A). Neural Regen Res. (2024) 19:375–9. doi: 10.4103/1673-5374.375298

PubMed Abstract | Crossref Full Text | Google Scholar

36. London A, Benhar I, Schwartz M. The retina as a window to the brain-from eye research to CNS disorders. Nat Rev Neurol. (2013) 9:44–53. doi: 10.1038/nrneurol.2012.227

PubMed Abstract | Crossref Full Text | Google Scholar

37. Díaz-Coránguez M, Ramos C, Antonetti DA. The inner blood-retinal barrier: cellular basis and development. Vision Res. (2017) 139:123–37. doi: 10.1016/j.visres.2017.05.009

PubMed Abstract | Crossref Full Text | Google Scholar

38. Chua SYL, Lascaratos G, Atan D, Zhang B, Reisman C, Khaw PT, et al. Relationships between retinal layer thickness and brain volumes in the UK Biobank cohort. Eur J Neurol. (2021) 28:1490–8. doi: 10.1111/ene.14706

PubMed Abstract | Crossref Full Text | Google Scholar

39. Mammadova N, Neppl TK, Denburg NL, West Greenlee MH. Reduced retinal thickness predicts age-related changes in cognitive function. Front Aging Neurosci. (2020) 12:81. doi: 10.3389/fnagi.2020.00081

PubMed Abstract | Crossref Full Text | Google Scholar

40. Mauschitz MM, Lohner V, Koch A, Stocker T, Reuter M, Holz FG, et al. Retinal layer assessments as potential biomarkers for brain atrophy in the Rhineland Study. Sci Rep. (2022) 12:2757. doi: 10.1038/s41598-022-06821-4

PubMed Abstract | Crossref Full Text | Google Scholar

41. Ong YT, Hilal S, Cheung CY, Venketasubramanian N, Niessen WJ, Vrooman H, et al. Retinal neurodegeneration on optical coherence tomography and cerebral atrophy. Neurosci Lett. (2015) 584:12–6. doi: 10.1016/j.neulet.2014.10.010

PubMed Abstract | Crossref Full Text | Google Scholar

42. Sekimitsu S, Shweikh Y, Shareef S, Zhao Y, Elze T, Segre A, et al. Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline. Br J Ophthalmol. (2024) 108:599–606. doi: 10.1136/bjo-2022-322762

PubMed Abstract | Crossref Full Text | Google Scholar

43. Uchida A, Pillai JA, Bermel R, Jones SE, Fernandez H, Leverenz JB, et al. Correlation between brain volume and retinal photoreceptor outer segment volume in normal aging and neurodegenerative diseases. PLoS ONE. (2020) 15:e0237078. doi: 10.1371/journal.pone.0237078

PubMed Abstract | Crossref Full Text | Google Scholar

44. van der Heide FCT, Steens ILM, Limmen B, Mokhtar S, van Boxtel MPJ, Schram MT, et al. Thinner inner retinal layers are associated with lower cognitive performance, lower brain volume, and altered white matter network structure -The Maastricht Study. Alzheimers Dement. (2024) 20:316–29. doi: 10.1002/alz.13442

Crossref Full Text | Google Scholar

45. Britze J, Pihl-Jensen G, Frederiksen JL. Retinal ganglion cell analysis in multiple sclerosis and optic neuritis: a systematic review and meta-analysis. J Neurol. (2017) 264:1837–53. doi: 10.1007/s00415-017-8531-y

PubMed Abstract | Crossref Full Text | Google Scholar

46. Carazo-Barrios L, Cabrera-Maestre A, Alba-Linero C, Gutierrez-Bedmar M, Garzon-Maldonado FJ, Serrano V, et al. Retinal neurodegeneration measured with optical coherence tomography and neuroimaging in Alzheimer disease: a systematic review. J Neuro-Ophthalmol. (2023) 43:116–25. doi: 10.1097/WNO.0000000000001673

PubMed Abstract | Crossref Full Text | Google Scholar

47. Ge YJ, Xu W, Ou YN, Qu Y, Ma YH, Huang YY, et al. Retinal biomarkers in Alzheimer's disease and mild cognitive impairment: a systematic review and meta-analysis. Ageing Res Rev. (2021) 69:101361. doi: 10.1016/j.arr.2021.101361

PubMed Abstract | Crossref Full Text | Google Scholar

48. Huang L, Wang C, Wang W, Wang Y, Zhang R. The specific pattern of retinal nerve fiber layer thinning in Parkinson's disease: a systematic review and meta-analysis. J Neurol. (2021) 268:4023–32. doi: 10.1007/s00415-020-10094-0

PubMed Abstract | Crossref Full Text | Google Scholar

49. Katsimpris A, Karamaounas A, Sideri AM, Katsimpris J, Georgalas I, Petrou P. Optical coherence tomography angiography in Alzheimer's disease: a systematic review and meta-analysis. Eye. (2022) 36:1419–26. doi: 10.1038/s41433-021-01648-1

PubMed Abstract | Crossref Full Text | Google Scholar

50. Katsimpris A, Papadopoulos I, Voulgari N, Kandarakis S, Petrou P, Karampitsakos T, et al. Optical coherence tomography angiography in Parkinson's disease: a systematic review and meta-analysis. Eye. (2023) 37:2847–54. doi: 10.1038/s41433-023-02438-7

PubMed Abstract | Crossref Full Text | Google Scholar

51. Mohammadi S, Gouravani M, Salehi MA, Arevalo JF, Galetta SL, Harandi H, et al. Optical coherence tomography angiography measurements in multiple sclerosis: a systematic review and meta-analysis. J Neuroinflammation. (2023) 20:85. doi: 10.1186/s12974-023-02763-4

PubMed Abstract | Crossref Full Text | Google Scholar

52. Petzold A, Balcer LJ, Calabresi PA, Costello F, Frohman TC, Frohman EM, et al. Retinal layer segmentation in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol. (2017) 16:797–812. doi: 10.1016/S1474-4422(17)30278-8

PubMed Abstract | Crossref Full Text | Google Scholar

53. Mirmosayyeb O, Yazdan Panah M, Mokary Y, Ghaffary EM, Ghoshouni H, Zivadinov R, et al. Optical coherence tomography (OCT) measurements and disability in multiple sclerosis (MS): a systematic review and meta-analysis. J Neurol Sci. (2023) 454:120847. doi: 10.1016/j.jns.2023.120847

PubMed Abstract | Crossref Full Text | Google Scholar

54. Wadhwani M, Bali SJ, Satyapal R, Angmo D, Sharma R, Pandey V, et al. Test-retest variability of retinal nerve fiber layer thickness and macular ganglion cell-inner plexiform layer thickness measurements using spectral-domain optical coherence tomography. J Glaucoma. (2015) 24:e109–15. doi: 10.1097/IJG.0000000000000203

PubMed Abstract | Crossref Full Text | Google Scholar

55. Green KM, Choi JJ, Ramchandran RS, Silverstein SM. OCT and OCT angiography offer new insights and opportunities in schizophrenia research and treatment. Front Digit Health. (2022) 4:836851. doi: 10.3389/fdgth.2022.836851

PubMed Abstract | Crossref Full Text | Google Scholar

56. Mehmood A, Ali W, Song S, Din ZU, Guo RY, Shah W, et al. Optical coherence tomography monitoring and diagnosing retinal changes in multiple sclerosis. Brain Behav. (2021) 11:e2302. doi: 10.1002/brb3.2302

PubMed Abstract | Crossref Full Text | Google Scholar

57. Gonzalez-Diaz JM, Radua J, Sanchez-Dalmau B, Camos-Carreras A, Zamora DC, Bernardo M. Mapping retinal abnormalities in psychosis: meta-analytical evidence for focal peripapillary and macular reductions. Schizophr Bull. (2022) 48:1194–205. doi: 10.1093/schbul/sbac085

PubMed Abstract | Crossref Full Text | Google Scholar

58. Janti SS, Tikka SK. Retinal microvasculature in schizophrenia: a meta-analysis with trial sequential analysis of studies assessing vessel density using optical coherence tomography angiography. Asian J Psychiatr. (2023) 84:103570. doi: 10.1016/j.ajp.2023.103570

PubMed Abstract | Crossref Full Text | Google Scholar

59. Kazakos CT, Karageorgiou V. Retinal changes in schizophrenia: a systematic review and meta-analysis based on individual participant data. Schizophr Bull. (2020) 46:27–42. doi: 10.1093/schbul/sbz106

PubMed Abstract | Crossref Full Text | Google Scholar

60. Kennedy KG, Mio M, Goldstein BI, Brambilla P, Delvecchio G. Systematic review and meta-analysis of retinal microvascular caliber in bipolar disorder, major depressive disorder, and schizophrenia. J Affect Disord. (2023) 331:342–51. doi: 10.1016/j.jad.2023.03.040

PubMed Abstract | Crossref Full Text | Google Scholar

61. Komatsu H, Onoguchi G, Jerotic S, Kanahara N, Kakuto Y, Ono T, et al. Retinal layers and associated clinical factors in schizophrenia spectrum disorders: a systematic review and meta-analysis. Mol Psychiatry. (2022) 27:3592–616. doi: 10.1038/s41380-022-01591-x

PubMed Abstract | Crossref Full Text | Google Scholar

62. Komatsu H, Onoguchi G, Silverstein SM, Jerotic S, Sakuma A, Kanahara N, et al. Retina as a potential biomarker in schizophrenia spectrum disorders: a systematic review and meta-analysis of optical coherence tomography and electroretinography. Mol Psychiatry. (2024) 29:464–82. doi: 10.1038/s41380-023-02340-4

PubMed Abstract | Crossref Full Text | Google Scholar

63. Lizano P, Bannai D, Lutz O, Kim LA, Miller J, Keshavan M, et al. A meta-analysis of retinal cytoarchitectural abnormalities in schizophrenia and bipolar disorder. Schizophr Bull. (2020) 46:43–53. doi: 10.1093/schbul/sbz029

PubMed Abstract | Crossref Full Text | Google Scholar

64. Pan J, Zhou Y, Xiang Y, Yu J. Retinal nerve fiber layer thickness changes in Schizophrenia: a meta-analysis of case-control studies. Psychiatry Res. (2018) 270:786–91. doi: 10.1016/j.psychres.2018.10.075

PubMed Abstract | Crossref Full Text | Google Scholar

65. Prasannakumar A, Kumar V, Mailankody P, Appaji A, Battu R, Berendschot T, et al. A systematic review and meta-analysis of optical coherence tomography studies in schizophrenia, bipolar disorder and major depressive disorder. World J Biol Psychiatry. (2023) 24:707–20. doi: 10.1080/15622975.2023.2203231

PubMed Abstract | Crossref Full Text | Google Scholar

66. Sheehan N. Retinal Abnormalities Associated with Schizophrenia and Bipolar Disorder: A Meta-Analysis. Boston University (2022).

Google Scholar

67. Shew W, Zhang DJ, Menkes DB, Danesh-Meyer HV. Optical coherence tomography in schizophrenia spectrum disorders: a systematic review and meta-analysis. Biol Psychiatry Glob Open Sci. (2024) 4:19–30. doi: 10.1016/j.bpsgos.2023.08.013

PubMed Abstract | Crossref Full Text | Google Scholar

68. Silverstein SM, Rosen R. Schizophrenia and the eye. Schizophr Res Cogn. (2015) 2:46–55. doi: 10.1016/j.scog.2015.03.004

Crossref Full Text | Google Scholar

69. Silverstein SM, Fradkin SI, Demmin DL. Schizophrenia and the retina: towards a 2020 perspective. Schizophr Res. (2020) 219:84–94. doi: 10.1016/j.schres.2019.09.016

PubMed Abstract | Crossref Full Text | Google Scholar

70. Bannai D, Lizano P, Kasetty M, Lutz O, Zeng V, Sarvode S, et al. Retinal layer abnormalities and their association with clinical and brain measures in psychotic disorders: a preliminary study. Psychiatry Res Neuroimaging. (2020) 299:111061. doi: 10.1016/j.pscychresns.2020.111061

PubMed Abstract | Crossref Full Text | Google Scholar

71. Samani NN, Proudlock FA, Siram V, Suraweera C, Hutchinson C, Nelson CP, et al. Retinal layer abnormalities as biomarkers of schizophrenia. Schizophr Bull. (2018) 44:876–85. doi: 10.1093/schbul/sbx130

PubMed Abstract | Crossref Full Text | Google Scholar

72. Schönfeldt-Lecuona C, Kregel T, Schmidt A, Kassubek J, Dreyhaupt J, Freudenmann RW, et al. Retinal single-layer analysis with optical coherence tomography (OCT) in schizophrenia spectrum disorder. Schizophr Res. (2020) 219:5–12. doi: 10.1016/j.schres.2019.03.022

PubMed Abstract | Crossref Full Text | Google Scholar

73. Silverstein SM, Paterno D, Cherneski L, Green S. Optical coherence tomography indices of structural retinal pathology in schizophrenia. Psychol Med. (2018) 48:2023–33. doi: 10.1017/S0033291717003555

PubMed Abstract | Crossref Full Text | Google Scholar

74. Wagner SK, Cortina-Borja M, Silverstein SM, Zhou Y, Romero-Bascones D, Struyven RR, et al. Association between retinal features from multimodal imaging and schizophrenia. JAMA Psychiatry. (2023) 80:478–87. doi: 10.1001/jamapsychiatry.2023.0171

PubMed Abstract | Crossref Full Text | Google Scholar

75. Domagała A, Domagala L, Kopis-Posiej N, Harciarek M, Krukow P. Differentiation of the retinal morphology aging trajectories in schizophrenia and their associations with cognitive dysfunctions. Front Psychiatry. (2023) 14:1207608. doi: 10.3389/fpsyt.2023.1207608

PubMed Abstract | Crossref Full Text | Google Scholar

76. Korann V, Appaji A, Jacob A, Devi P, Nagendra B, Chako DM, et al. Association between retinal vascular caliber and brain structure in schizophrenia. Asian J Psychiatr. (2021) 61:102707. doi: 10.1016/j.ajp.2021.102707

PubMed Abstract | Crossref Full Text | Google Scholar

77. Korann V, Suhas S, Appaji A, Nagendra B, Padmanabha A, Jacob A, et al. Association between retinal vascular measures and brain white matter lesions in schizophrenia. Asian J Psychiatr. (2022) 70:103042. doi: 10.1016/j.ajp.2022.103042

PubMed Abstract | Crossref Full Text | Google Scholar

78. Lee WW, Tajunisah I, Sharmilla K, Peyman M, Subrayan V. Retinal nerve fiber layer structure abnormalities in schizophrenia and its relationship to disease state: evidence from optical coherence tomography. Invest Ophthalmol Vis Sci. (2013) 54:7785–92. doi: 10.1167/iovs.13-12534

PubMed Abstract | Crossref Full Text | Google Scholar

79. Boudriot E, Schworm B, Slapakova L, Hanken K, Jager I, Stephan M, et al. Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. Eur Arch Psychiatry Clin Neurosci. (2023) 273:575–88. doi: 10.1007/s00406-022-01455-z

PubMed Abstract | Crossref Full Text | Google Scholar

80. Celik M, Kalenderoglu A, Sevgi Karadag A, Bekir Egilmez O, Han-Almis B, Simsek A. Decreases in ganglion cell layer and inner plexiform layer volumes correlate better with disease severity in schizophrenia patients than retinal nerve fiber layer thickness: findings from spectral optic coherence tomography. Eur Psychiatry. (2016) 32:9–15. doi: 10.1016/j.eurpsy.2015.10.006

PubMed Abstract | Crossref Full Text | Google Scholar

81. Jerotic S, Ristic I, Pejovic S, Mihaljevic M, Pavlovic Z, Britvic D, et al. Retinal structural abnormalities in young adults with psychosis spectrum disorders. Prog Neuropsychopharmacol Biol Psychiatry. (2020) 98:109825. doi: 10.1016/j.pnpbp.2019.109825

PubMed Abstract | Crossref Full Text | Google Scholar

82. Liu Y, Chen J, Huang L, Yan S, Bian Q, Yang F. Relationships among retinal nerve fiber layer thickness, vascular endothelial growth factor, and cognitive impairment in patients with schizophrenia. Neuropsychiatr Dis Treat. (2021) 17:3597–606. doi: 10.2147/NDT.S336077

PubMed Abstract | Crossref Full Text | Google Scholar

83. Topcu-Yilmaz P, Aydin M, Ilhan BC. Evaluation of retinal nerve fiber layer, macular, and choroidal thickness in schizophrenia: spectral optic coherence tomography findings. Psychiatry Clin Psychopharmacol. (2019) 29:28–33. doi: 10.1080/24750573.2018.1426693

Crossref Full Text | Google Scholar

84. DeLisi LE. Is schizophrenia a lifetime disorder of brain plasticity, growth and aging? Schizophr Res. (1997) 23:119–29. doi: 10.1016/S0920-9964(96)00079-5

PubMed Abstract | Crossref Full Text | Google Scholar

85. Lieberman JA, Sheitman BB, Kinon BJ. Neurochemical sensitization in the pathophysiology of schizophrenia: deficits and dysfunction in neuronal regulation and plasticity. Neuropsychopharmacology. (1997) 17:205–29. doi: 10.1016/S0893-133X(97)00045-6

PubMed Abstract | Crossref Full Text | Google Scholar

86. Olney JW, Farber NB. Glutamate receptor dysfunction and schizophrenia. Arch Gen Psychiatry. (1995) 52:998–1007. doi: 10.1001/archpsyc.1995.03950240016004

PubMed Abstract | Crossref Full Text | Google Scholar

87. Keshavan MS. Development, disease and degeneration in schizophrenia: a unitary pathophysiological model. J Psychiatr Res. (1999) 33:513–21. doi: 10.1016/S0022-3956(99)00033-3

PubMed Abstract | Crossref Full Text | Google Scholar

88. DeLisi LE. Brain plasticity, language anomalies, genetic risk and the patient with schizophrenia: trajectory of change over a lifetime. A commentary. Psychiatry Res. (2023) 320:115034. doi: 10.1016/j.psychres.2022.115034

PubMed Abstract | Crossref Full Text | Google Scholar

89. Raz N. Aging of the brain and its impact on cognitive performance: Integration of structural and functional findings. In:Craik FIM, Salthouse TA, , editors. The Handbook of Aging and Cognition. 2nd ed. Mahwah, NJ: Lawrence Erlbaum Associates Publishers (2000). p. 1–90.

Google Scholar

90. Raz N. Ageing and the Brain. Encyclopedia of Life Sciences, Vol. 1. Chichester, UK: John Wiley & Sons, Ltd (2005). p. 235–40. doi: 10.1038/npg.els.0004063

Crossref Full Text | Google Scholar

91. Rajmohan R, Reddy PH. Amyloid-beta and phosphorylated tau accumulations cause abnormalities at synapses of Alzheimer's disease neurons. J Alzheimers Diseas. (2017) 57:975–99. doi: 10.3233/JAD-160612

Crossref Full Text | Google Scholar

92. Wong OGW, Cheung CLY, Ip PPC, Ngan HYS, Cheung ANY. Amyloid precursor protein overexpression in down syndrome trophoblast reduces cell invasiveness and interferes with syncytialization. Am J Pathol. (2018) 188:2307–17. doi: 10.1016/j.ajpath.2018.07.004

PubMed Abstract | Crossref Full Text | Google Scholar

93. Lee CW, Stankowski JN, Chew J, Cook CN, Lam YW, Almeida S, et al. The lysosomal protein cathepsin L is a progranulin protease. Mol Neurodegener. (2017) 12:55. doi: 10.1186/s13024-017-0196-6

PubMed Abstract | Crossref Full Text | Google Scholar

94. Kawakami K, Wake R, Miyaoka T, Furuya M, Liaury K, Horiguchi J. The effects of aging on changes in regional cerebral blood flow in schizophrenia. Neuropsychobiology. (2014) 69:202–9. doi: 10.1159/000358840

PubMed Abstract | Crossref Full Text | Google Scholar

95. Schultz SK, O'Leary DS, Boles Ponto LL, Arndt S, Magnotta V, Watkins GL, et al. Age and regional cerebral blood flow in schizophrenia: age effects in anterior cingulate, frontal, and parietal cortex. J Neuropsychiatry Clin Neurosci. (2002) 14:19–24. doi: 10.1176/jnp.14.1.19

PubMed Abstract | Crossref Full Text | Google Scholar

96. Zilbovicius M, Boddaert N, Belin P, Poline JB, Remy P, Mangin JF, et al. Temporal lobe dysfunction in childhood autism: a PET study. Positron emission tomography. Am J Psychiatry. (2000) 157:1988–93. doi: 10.1176/appi.ajp.157.12.1988

PubMed Abstract | Crossref Full Text | Google Scholar

97. Blose BA, Silverstein SM, Stuart KV, Keane PA, Khawaja AP, Wagner SK. Association between polygenic risk for schizophrenia and retinal morphology: a cross-sectional analysis of the United Kingdom Biobank. Psychiatry Res. (2024) 339:116106. doi: 10.1016/j.psychres.2024.116106

PubMed Abstract | Crossref Full Text | Google Scholar

98. Goldberg JL, Corredor RG. Retinal ganglion cell life and death - Mechanisms and implications. Eur Ophthalmic Rev. (2011) 3:109–12. doi: 10.17925/eor.2009.03.02.109

Crossref Full Text | Google Scholar

99. Petzold A, Wong S, Plant GT. Autoimmunity in visual loss. Handb Clin Neurol. (2016) 133:353–76. doi: 10.1016/B978-0-444-63432-0.00020-7

PubMed Abstract | Crossref Full Text | Google Scholar

100. Mitelman SA, Shihabuddin L, Brickman AM, Hazlett EA, Buchsbaum MS, MRI. assessment of gray and white matter distribution in Brodmann's areas of the cortex in patients with schizophrenia with good and poor outcomes. Am J Psychiatry. (2003) 160:2154–68. doi: 10.1176/appi.ajp.160.12.2154

Crossref Full Text | Google Scholar

101. Dinkin M. Trans-synaptic retrograde degeneration in the human visual system: slow, silent, and real. Curr Neurol Neurosci Rep. (2017) 17:16. doi: 10.1007/s11910-017-0725-2

PubMed Abstract | Crossref Full Text | Google Scholar

102. Donaldson L, Chen M, Margolin E. Transsynaptic ganglion cell degeneration in adult patients after occipital lobe stroke. J Neuro-Ophthalmol. (2023) 43:243–7. doi: 10.1097/WNO.0000000000001657

PubMed Abstract | Crossref Full Text | Google Scholar

103. Petzold A, de Boer JF, Schippling S, Vermersch P, Kardon R, Green A, et al. Optical coherence tomography in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol. (2010) 9:921–32. doi: 10.1016/S1474-4422(10)70168-X

PubMed Abstract | Crossref Full Text | Google Scholar

104. Kim HM, Han JW, Park YJ, Bae JB, Woo SJ, Kim KW. Association between retinal layer thickness and cognitive decline in older adults. JAMA Ophthalmol. (2022) 140:683–90. doi: 10.1001/jamaophthalmol.2022.1563

PubMed Abstract | Crossref Full Text | Google Scholar

105. Murueta-Goyena A, Del Pino R, Galdos M, Arana B, Acera M, Carmona-Abellan M, et al. Retinal thickness predicts the risk of cognitive decline in Parkinson disease. Ann Neurol. (2021) 89:165–76. doi: 10.1002/ana.25944

PubMed Abstract | Crossref Full Text | Google Scholar

106. Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, et al. Eye-brain connections revealed by multimodal retinal and brain imaging genetics. Nat Commun. (2024) 15:6064. doi: 10.1038/s41467-024-50309-w

PubMed Abstract | Crossref Full Text | Google Scholar

107. Hanson DR, Gottesman II. Theories of schizophrenia: a genetic-inflammatory-vascular synthesis. BMC Med Genomics. (2005) 6:7. doi: 10.1186/1471-2350-6-7

PubMed Abstract | Crossref Full Text | Google Scholar

108. Pong S, Karmacharya R, Sofman M, Bishop JR, Lizano P. The role of brain microvascular endothelial cell and blood-brain barrier dysfunction in schizophrenia. Complex Psychiatry. (2020) 6:30–46. doi: 10.1159/000511552

PubMed Abstract | Crossref Full Text | Google Scholar

109. Stanca S, Rossetti M, Bokulic Panichi L, Bongioanni P. The cellular dysfunction of the brain-blood barrier from endothelial cells to astrocytes: the pathway towards neurotransmitter impairment in schizophrenia. Int J Mol Sci. (2024) 25:1250. doi: 10.3390/ijms25021250

PubMed Abstract | Crossref Full Text | Google Scholar

110. Sekar A, Bialas AR, de Rivera H, Davis A, Hammond TR, Kamitaki N, et al. Schizophrenia risk from complex variation of complement component 4. Nature. (2016) 530:177–83. doi: 10.1038/nature16549

PubMed Abstract | Crossref Full Text | Google Scholar

111. Murray AJ, Rogers JC, Katshu M, Liddle PF, Upthegrove R. oxidative stress and the pathophysiology and symptom profile of schizophrenia spectrum disorders. Front Psychiatry. (2021) 12:703452. doi: 10.3389/fpsyt.2021.703452

PubMed Abstract | Crossref Full Text | Google Scholar

112. Roberts RC. Mitochondrial dysfunction in schizophrenia: with a focus on postmortem studies. Mitochondrion. (2021) 56:91–101. doi: 10.1016/j.mito.2020.11.009

PubMed Abstract | Crossref Full Text | Google Scholar

113. Vellucci L, Mazza B, Barone A, Nasti A, De Simone G, Iasevoli F, et al. The role of astrocytes in the molecular pathophysiology of schizophrenia: between neurodevelopment and neurodegeneration. Biomolecules. (2025) 15:615. doi: 10.3390/biom15050615

PubMed Abstract | Crossref Full Text | Google Scholar

114. Kaur G, Singh NK. Inflammation and retinal degenerative diseases. Neural Regen Res. (2023) 18:513–8. doi: 10.4103/1673-5374.350192

PubMed Abstract | Crossref Full Text | Google Scholar

115. Ferrington DA, Fisher CR, Kowluru RA. Mitochondrial defects drive degenerative retinal diseases. Trends Mol Med. (2020) 26:105–18. doi: 10.1016/j.molmed.2019.10.008

PubMed Abstract | Crossref Full Text | Google Scholar

116. Wang M, Wong WT. Microglia-Muller cell interactions in the retina. Adv Exp Med Biol. (2014) 801:333–8. doi: 10.1007/978-1-4614-3209-8_42

Crossref Full Text | Google Scholar

117. Yednock T, Fong DS, Lad EM. C1q and the classical complement cascade in geographic atrophy secondary to age-related macular degeneration. Int J Retina Vitreous. (2022) 8:79. doi: 10.1186/s40942-022-00431-y

PubMed Abstract | Crossref Full Text | Google Scholar

118. Zhang J, Zhang T, Zeng S, Zhang X, Zhou F, Gillies MC, et al. The role of Nrf2/sMAF signalling in retina ageing and retinal diseases. Biomedicines. (2023) 11:512. doi: 10.3390/biomedicines11061512

PubMed Abstract | Crossref Full Text | Google Scholar

119. Fatoba O, Itokazu T, Yamashita T. Complement cascade functions during brain development and neurodegeneration. FEBS J. (2022) 289:2085–109. doi: 10.1111/febs.15772

PubMed Abstract | Crossref Full Text | Google Scholar

120. Klemmensen MM, Borrowman SH, Pearce C, Pyles B, Chandra B. Mitochondrial dysfunction in neurodegenerative disorders. Neurotherapeutics. (2024) 21:e00292. doi: 10.1016/j.neurot.2023.10.002

PubMed Abstract | Crossref Full Text | Google Scholar

121. Lull ME, Block ML. Microglial activation and chronic neurodegeneration. Neurotherapeutics. (2010) 7:354–65. doi: 10.1016/j.nurt.2010.05.014

PubMed Abstract | Crossref Full Text | Google Scholar

122. Kurtulmus A, Sahbaz C, Elbay A, Guler EM, Sonmez Avaroglu G, Kocyigit A, et al. Clinical and biological correlates of optical coherence tomography findings in schizophrenia. Eur Arch Psychiatry Clin Neurosci. (2023) 273:1837–50. doi: 10.1007/s00406-023-01587-w

PubMed Abstract | Crossref Full Text | Google Scholar

123. Rabe F, Smigielski L, Georgiadis F, Kallen N, Omlor W, Kirschner M, et al. Genetic susceptibility to schizophrenia through neuroinflammatory pathways is associated with retinal thinning: findings from the UK-Biobank. medRxiv. [preprint]. (2024). medRxiv:2024.04.05.24305387. doi: 10.1101/2024.04.05.24305387

PubMed Abstract | Crossref Full Text | Google Scholar

124. Davies C, Segre G, Estrade A, Radua J, De Micheli A, Provenzani U, et al. Prenatal and perinatal risk and protective factors for psychosis: a systematic review and meta-analysis. Lancet Psychiatry. (2020) 7:399–410. doi: 10.1016/S2215-0366(20)30057-2

PubMed Abstract | Crossref Full Text | Google Scholar

125. Millan MJ, Andrieux A, Bartzokis G, Cadenhead K, Dazzan P, Fusar-Poli P, et al. Altering the course of schizophrenia: progress and perspectives. Nat Rev Drug Discov. (2016) 15:485–515. doi: 10.1038/nrd.2016.28

PubMed Abstract | Crossref Full Text | Google Scholar

126. Fullana MA, Tortella-Feliu M, Fernandez de. la Cruz L, Chamorro J, Perez-Vigil A, Ioannidis JPA, et al. Risk and protective factors for anxiety and obsessive-compulsive disorders: An umbrella review of systematic reviews and meta-analyses. Psychol Med. (2020) 50:1300–15. doi: 10.1017/S0033291719001247

Crossref Full Text | Google Scholar

127. Fusar-Poli P, Tantardini M, De Simone S, Ramella-Cravaro V, Oliver D, Kingdon J, et al. Deconstructing vulnerability for psychosis: meta-analysis of environmental risk factors for psychosis in subjects at ultra high-risk. Eur Psychiatry. (2017) 40:65–75. doi: 10.1016/j.eurpsy.2016.09.003

PubMed Abstract | Crossref Full Text | Google Scholar

128. Kim JY, Son MJ, Son CY, Radua J, Eisenhut M, Gressier F, et al. Environmental risk factors and biomarkers for autism spectrum disorder: an umbrella review of the evidence. Lancet Psychiatry. (2019) 6:590–600. doi: 10.1016/S2215-0366(19)30181-6

PubMed Abstract | Crossref Full Text | Google Scholar

129. Kohler CA, Evangelou E, Stubbs B, Solmi M, Veronese N, Belbasis L, et al. Mapping risk factors for depression across the lifespan: an umbrella review of evidence from meta-analyses and Mendelian randomization studies. J Psychiatr Res. (2018) 103:189–207. doi: 10.1016/j.jpsychires.2018.05.020

PubMed Abstract | Crossref Full Text | Google Scholar

130. Oliver D, Reilly TJ, Baccaredda Boy O, Petros N, Davies C, Borgwardt S, et al. What causes the onset of psychosis in individuals at clinical high risk? A meta-analysis of risk and protective factors. Schizophr Bull. (2020) 46:110–20. doi: 10.1093/schbul/sbz039

PubMed Abstract | Crossref Full Text | Google Scholar

131. Radua J, Ramella-Cravaro V, Ioannidis JPA, Reichenberg A, Phiphopthatsanee N, Amir T, et al. What causes psychosis? An umbrella review of risk and protective factors. World Psychiatry. (2018) 17:49–66. doi: 10.1002/wps.20490

PubMed Abstract | Crossref Full Text | Google Scholar

132. Froudist-Walsh S, Bloomfield MA, Veronese M, Kroll J, Karolis VR, Jauhar S, et al. The effect of perinatal brain injury on dopaminergic function and hippocampal volume in adult life. eLife. (2017) 6:e29088. doi: 10.7554/eLife.29088

PubMed Abstract | Crossref Full Text | Google Scholar

133. Karolis VR, Froudist-Walsh S, Kroll J, Brittain PJ, Tseng CJ, Nam KW, et al. Volumetric grey matter alterations in adolescents and adults born very preterm suggest accelerated brain maturation. Neuroimage. (2017) 163:379–89. doi: 10.1016/j.neuroimage.2017.09.039

PubMed Abstract | Crossref Full Text | Google Scholar

134. Kelly CE, Shaul M, Thompson DK, Mainzer RM, Yang JY, Dhollander T, et al. Long-lasting effects of very preterm birth on brain structure in adulthood: a systematic review and meta-analysis. Neurosci Biobehav Rev. (2023) 147:105082. doi: 10.1016/j.neubiorev.2023.105082

PubMed Abstract | Crossref Full Text | Google Scholar

135. Fieß A, Christian L, Janz J, Kolb-Keerl R, Knuf M, Kirchhof B, et al. Functional analysis and associated factors of the peripapillary retinal nerve fibre layer in former preterm and full-term infants. Br J Ophthalmol. (2017) 101:1405–11. doi: 10.1136/bjophthalmol-2016-309622

PubMed Abstract | Crossref Full Text | Google Scholar

136. Fieß A, Janz J, Schuster AK, Kolb-Keerl R, Knuf M, Kirchhof B, et al. Macular morphology in former preterm and full-term infants aged 4 to 10 years. Graefes Arch Clin Exp Ophthalmol. (2017) 255:1433–42. doi: 10.1007/s00417-017-3662-5

PubMed Abstract | Crossref Full Text | Google Scholar

137. Rothman AL, Sevilla MB, Mangalesh S, Gustafson KE, Edwards L, Cotten CM, et al. Thinner retinal nerve fiber layer in very preterm versus term infants and relationship to brain anatomy and neurodevelopment. Am J Ophthalmol. (2015) 160:1296–308 e2. doi: 10.1016/j.ajo.2015.09.015

PubMed Abstract | Crossref Full Text | Google Scholar

138. Ruberto G, Angeli R, Tinelli C, Bianchi PE, Milano G. Morphologic and functional analysis of the optic nerve in premature and term children with OCT, HRT, and pVEP: a 10-year resurvey. Invest Ophthalmol Vis Sci. (2014) 55:2367–75. doi: 10.1167/iovs.13-13647

PubMed Abstract | Crossref Full Text | Google Scholar

139. Tariq YM, Pai A, Li H, Afsari S, Gole GA, Burlutsky G, et al. Association of birth parameters with OCT measured macular and retinal nerve fiber layer thickness. Invest Ophthalmol Vis Sci. (2011) 52:1709–15. doi: 10.1167/iovs.10-6365

PubMed Abstract | Crossref Full Text | Google Scholar

140. Tong AY, El-Dairi M, Maldonado RS, Rothman AL, Yuan EL, Stinnett SS, et al. Evaluation of optic nerve development in preterm and term infants using handheld spectral-domain optical coherence tomography. Ophthalmology. (2014) 121:1818–26. doi: 10.1016/j.ophtha.2014.03.020

PubMed Abstract | Crossref Full Text | Google Scholar

141. Hussain SM, Kahonen M, Raitakari OT, Skilton MR, Witt N, Chaturvedi N, et al. Impact of fetal growth and preterm birth on the retinal microvasculature in mid-adulthood. Microcirculation. (2015) 22:285–93. doi: 10.1111/micc.12197

PubMed Abstract | Crossref Full Text | Google Scholar

142. Wei FF, Raaijmakers A, Zhang ZY, van Tienoven TP, Huang QF, Yang WY, et al. Association between cognition and the retinal microvasculature in 11-year old children born preterm or at term. Early Hum Dev. (2018) 118:1–7. doi: 10.1016/j.earlhumdev.2018.01.018

PubMed Abstract | Crossref Full Text | Google Scholar

143. Kumarakulasinghe ALB, Md Din N, Mohd Noh UK, Syed Zakaria SZ, Aung T, Mohd Khialdin S. Evaluation of ocular biometric and optical coherence tomography parameters in preterm children without retinopathy of prematurity. Transl Vis Sci Technol. (2022) 11:8. doi: 10.1167/tvst.11.3.8

PubMed Abstract | Crossref Full Text | Google Scholar

144. Maleita D, Serras-Pereira R, Passos I, Elisa-Luis M, Alves M, Papoila AL, et al. Retinal structural changes in preterm children without retinopathy of prematurity. Graefes Arch Clin Exp Ophthalmol. (2021) 259:1025–33. doi: 10.1007/s00417-020-04986-0

PubMed Abstract | Crossref Full Text | Google Scholar

145. Åkerblom H, Holmstrom G, Eriksson U, Larsson E. Retinal nerve fibre layer thickness in school-aged prematurely-born children compared to children born at term. Br J Ophthalmol. (2012) 96:956–60. doi: 10.1136/bjophthalmol-2011-301010

PubMed Abstract | Crossref Full Text | Google Scholar

146. Dyer KIC, Sanfilippo PG, Yazar S, Craig JE, Hewitt AW, Newnham JP, et al. The relationship between fetal growth and retinal nerve fiber layer thickness in a cohort of young adults. Transl Vis Sci Technol. (2022) 11:8. doi: 10.1167/tvst.11.7.8

PubMed Abstract | Crossref Full Text | Google Scholar

147. Fieß A, Nickels S, Urschitz MS, Munzel T, Wild PS, Beutel ME, et al. Association of birth weight with peripapillary retinal nerve fiber layer thickness in adulthood-results from a population-based study. Invest Ophthalmol Vis Sci. (2020) 61:4. doi: 10.1167/iovs.61.8.4

PubMed Abstract | Crossref Full Text | Google Scholar

148. Samarawickrama C, Huynh SC, Liew G, Burlutsky G, Mitchell P. Birth weight and optic nerve head parameters. Ophthalmology. (2009) 116:1112–8. doi: 10.1016/j.ophtha.2008.12.061

PubMed Abstract | Crossref Full Text | Google Scholar

149. Tapp RJ, Williams C, Witt N, Chaturvedi N, Evans R, Thom SA, et al. Impact of size at birth on the microvasculature: the avon longitudinal study of parents and children. Pediatrics. (2007) 120:e1225–8. doi: 10.1542/peds.2006-2951

PubMed Abstract | Crossref Full Text | Google Scholar

150. Lehtonen T, Vesti E, Haataja L, Nyman A, Uusitalo K, Leinonen MT, et al. Peripapillary retinal nerve fibre layer thickness and macular ganglion cell layer volume in association with motor and cognitive outcomes in 11-year-old children born very preterm. Acta Ophthalmol. (2023) 101:342–8. doi: 10.1111/aos.15266

PubMed Abstract | Crossref Full Text | Google Scholar

151. Pueyo V, Perez T, Gonzalez I, Altemir I, Gimenez G, Prieto E, et al. Retinal structure assessed by OCT as a biomarker of brain development in children born small for gestational age. Br J Ophthalmol. (2017) 101:1168–73. doi: 10.1136/bjophthalmol-2016-309790

PubMed Abstract | Crossref Full Text | Google Scholar

152. Lennartsson F, Nilsson M, Flodmark O, Jacobson L. Damage to the immature optic radiation causes severe reduction of the retinal nerve fiber layer, resulting in predictable visual field defects. Invest Ophthalmol Vis Sci. (2014) 55:8278–88. doi: 10.1167/iovs.14-14913

PubMed Abstract | Crossref Full Text | Google Scholar

153. Tremblay S, Miloudi K, Chaychi S, Favret S, Binet F, Polosa A, et al. Systemic inflammation perturbs developmental retinal angiogenesis and neuroretinal function. Invest Ophthalmol Vis Sci. (2013) 54:8125–39. doi: 10.1167/iovs.13-12496

PubMed Abstract | Crossref Full Text | Google Scholar

154. Pueyo V, Gonzalez I, Altemir I, Perez T, Gomez G, Prieto E, et al. Microstructural changes in the retina related to prematurity. Am J Ophthalmol. (2015) 159:797–802. doi: 10.1016/j.ajo.2014.12.015

PubMed Abstract | Crossref Full Text | Google Scholar

155. De Bie HM, Oostrom KJ, Boersma M, Veltman DJ, Barkhof F. Delemarre-van de Waal HA, et al. Global and regional differences in brain anatomy of young children born small for gestational age. PLoS ONE. (2011) 6:e24116. doi: 10.1371/journal.pone.0024116

Crossref Full Text | Google Scholar

156. Farajdokht F, Sadigh-Eteghad S, Dehghani R, Mohaddes G, Abedi L, Bughchechi R, et al. Very low birth weight is associated with brain structure abnormalities and cognitive function impairments: a systematic review. Brain Cogn. (2017) 118:80–9. doi: 10.1016/j.bandc.2017.07.006

PubMed Abstract | Crossref Full Text | Google Scholar

157. Lodygensky GA, Seghier ML, Warfield SK, Tolsa CB, Sizonenko S, Lazeyras F, et al. Intrauterine growth restriction affects the preterm infant's hippocampus. Pediatr Res. (2008) 63:438–43. doi: 10.1203/PDR.0b013e318165c005

PubMed Abstract | Crossref Full Text | Google Scholar

158. Brydges CR, Landes JK, Reid CL, Campbell C, French N, Anderson M. Cognitive outcomes in children and adolescents born very preterm: a meta-analysis. Dev Med Child Neurol. (2018) 60:452–68. doi: 10.1111/dmcn.13685

PubMed Abstract | Crossref Full Text | Google Scholar

159. Chan E, Leong P, Malouf R, Quigley MA. Long-term cognitive and school outcomes of late-preterm and early-term births: a systematic review. Child Care Health Dev. (2016) 42:297–312. doi: 10.1111/cch.12320

PubMed Abstract | Crossref Full Text | Google Scholar

160. Oudgenoeg-Paz O, Mulder H, Jongmans MJ, van der Ham IJM, Van der Stigchel S. The link between motor and cognitive development in children born preterm and/or with low birth weight: a review of current evidence. Neurosci Biobehav Rev. (2017) 80:382–93. doi: 10.1016/j.neubiorev.2017.06.009

PubMed Abstract | Crossref Full Text | Google Scholar

161. Cannon M, Jones PB, Murray RM. Obstetric complications and schizophrenia: historical and meta-analytic review. Am J Psychiatry. (2002) 159:1080–92. doi: 10.1176/appi.ajp.159.7.1080

PubMed Abstract | Crossref Full Text | Google Scholar

162. Hedderich DM, Menegaux A, Schmitz-Koep B, Nuttall R, Zimmermann J, Schneider SC, et al. Increased brain age gap estimate (BrainAGE) in young adults after premature birth. Front Aging Neurosci. (2021) 13:653365. doi: 10.3389/fnagi.2021.653365

PubMed Abstract | Crossref Full Text | Google Scholar

163. Jutla A, Foss-Feig J, Veenstra-VanderWeele J. Autism spectrum disorder and schizophrenia: an updated conceptual review. Autism Research. (2022) 15:384–412. doi: 10.1002/aur.2659

PubMed Abstract | Crossref Full Text | Google Scholar

164. Peyre H, Iftimovici A, Ellul P, Krebs MO, Delorme R, Baghdadli A, et al. Investigating the increased risk of schizophrenia and bipolar disorders in relatives of ADHD probands using colocalization analysis of common genetic variants. Eur Child Adolesc Psychiatry. (2024). doi: 10.1007/s00787-024-02479-7

PubMed Abstract | Crossref Full Text | Google Scholar

165. Dastamooz S, Tham CCY, Yam JCS Li M, Wong SHS, Sit CHP. A systematic review and meta-analysis on the ocular characteristics in children and adolescents with neurodevelopmental disorders. Sci Rep. (2023) 13:19397. doi: 10.1038/s41598-023-46206-9

PubMed Abstract | Crossref Full Text | Google Scholar

166. Li SL, Kam KW, Chee ASH, Zhang XJ, Chen LJ, Yip WWK, et al. The association between attention-deficit/hyperactivity disorder and retinal nerve fiber/ganglion cell layer thickness measured by optical coherence tomography: a systematic review and meta-analysis. Int Ophthalmol. (2021) 41:3211–21. doi: 10.1007/s10792-021-01852-8

PubMed Abstract | Crossref Full Text | Google Scholar

167. Perna J, Bellato A, Ganapathy PS, Solmi M, Zampieri A, Faraone SV, et al. Association between Autism Spectrum Disorder (ASD) and vision problems. A systematic review and meta-analysis. Mol Psychiatry. (2023) 28:5011–23. doi: 10.1038/s41380-023-02143-7

PubMed Abstract | Crossref Full Text | Google Scholar

168. Garcia-Medina JJ, Garcia-Pinero M, Del-Rio-Vellosillo M, Fares-Valdivia J, Ragel-Hernandez AB, Martinez-Saura S, et al. Comparison of foveal, macular, and peripapillary intraretinal thicknesses between autism spectrum disorder and neurotypical subjects. Invest Ophthalmol Vis Sci. (2017) 58:5819–26. doi: 10.1167/iovs.17-22238

PubMed Abstract | Crossref Full Text | Google Scholar

169. Emberti Gialloreti L, Pardini M, Benassi F, Marciano S, Amore M, Mutolo MG, et al. Reduction in retinal nerve fiber layer thickness in young adults with autism spectrum disorders. J Autism Dev Disord. (2014) 44:873–82. doi: 10.1007/s10803-013-1939-z

PubMed Abstract | Crossref Full Text | Google Scholar

170. Friedel EBN, Tebartz van Elst L, Schafer M, Maier S, Runge K, Kuchlin S, et al. Retinal thinning in adults with autism spectrum disorder. J Autism Dev Disord. (2024) 54:1143–56. doi: 10.1007/s10803-022-05882-8

PubMed Abstract | Crossref Full Text | Google Scholar

171. Sharma RK. Molecular neurobiology of retinal degeneration. In:Lajtha A, , editor Handbook of Neurochemistry and Molecular Neurobiology: Sensory Neurochemistry. 3rd ed. New York, NY: Springer (2007). p. 47–92. doi: 10.1007/978-0-387-30374-1_3

Crossref Full Text | Google Scholar

172. Sharma RK, Johnson DA. Determinants of molecular mechanisms in neuroretinal development: sensory neurochemistry. In:Lajtha A, , editor. Handbook of Neurochemistry and Molecular Neurobiology. 3rd ed. New York, NY: Springer (2007). p. 8–46. doi: 10.1007/978-0-387-30374-1_2

Crossref Full Text | Google Scholar

173. Foss-Feig JH, Adkinson BD Ji JL, Yang G, Srihari VH, McPartland JC, et al. Searching for cross-diagnostic convergence: neural mechanisms governing excitation and inhibition balance in schizophrenia and autism spectrum disorders. Biol Psychiatry. (2017) 81:848–61. doi: 10.1016/j.biopsych.2017.03.005

PubMed Abstract | Crossref Full Text | Google Scholar

174. Gao R, Penzes P. Common mechanisms of excitatory and inhibitory imbalance in schizophrenia and autism spectrum disorders. Curr Mol Med. (2015) 15:146–67. doi: 10.2174/1566524015666150303003028

PubMed Abstract | Crossref Full Text | Google Scholar

175. Gagné AM, Moreau I, St-Amour I, Marquet P, Maziade M. Retinal function anomalies in young offspring at genetic risk of schizophrenia and mood disorder: the meaning for the illness pathophysiology. Schizophr Res. (2020) 219:19–24. doi: 10.1016/j.schres.2019.06.021

PubMed Abstract | Crossref Full Text | Google Scholar

176. Hébert M, Gagne AM, Paradis ME, Jomphe V, Roy MA, Merette C, et al. Retinal response to light in young nonaffected offspring at high genetic risk of neuropsychiatric brain disorders. Biol Psychiatry. (2010) 67:270–4. doi: 10.1016/j.biopsych.2009.08.016

PubMed Abstract | Crossref Full Text | Google Scholar

177. Maziade M, Bureau A, Jomphe V, Gagne AM. Retinal function and preclinical risk traits in children and adolescents at genetic risk of schizophrenia and bipolar disorder. Prog Neuro Psychopharmacol Biol Psychiatry. (2022) 112:110432. doi: 10.1016/j.pnpbp.2021.110432

PubMed Abstract | Crossref Full Text | Google Scholar

178. Peredo R, Gagne AM, Gilbert E, Hebert M, Maziade M, Merette C. Electroretinography may reveal cognitive impairment among a cohort of subjects at risk of a major psychiatric disorder. Psychiatry Res. (2020) 291:113227. doi: 10.1016/j.psychres.2020.113227

PubMed Abstract | Crossref Full Text | Google Scholar

179. Akin F, Danaci AE, Kayikcioglu RO, Tasci MY. Retinal abnormalities and their relationship with social cognition in patients with schizophrenia and their healthy siblings. Dusunen Adam J Psychiatry Neurol Sci. (2024) 37:179–88. doi: 10.14744/DAJPNS.2024.00259

Crossref Full Text | Google Scholar

180. Bagci KA, Memis PN, Akhoroz M, Karakilinc BNT, Cop E. Exploring retinal thickness variations in adolescents with first episode psychosis and schizophrenia: a comparative study with healthy siblings and controls. Psychiatry Res Neuroimaging. (2025) 352:112020. doi: 10.1016/j.pscychresns.2025.112020

PubMed Abstract | Crossref Full Text | Google Scholar

181. Hosak L, Zeman T, Studnicka J, Stepanov A, Ustohal L, Michalec M, et al. Retinal arteriolar and venular diameters are widened in patients with schizophrenia. Psychiatry Clin Neurosci. (2020) 74:619–21. doi: 10.1111/pcn.13123

PubMed Abstract | Crossref Full Text | Google Scholar

182. Kurtulmus A, Elbay A, Parlakkaya FB, Kilicarslan T, Ozdemir MH, Kirpinar I. An investigation of retinal layer thicknesses in unaffected first-degree relatives of schizophrenia patients. Schizophr Res. (2020) 218:255–61. doi: 10.1016/j.schres.2019.12.034

PubMed Abstract | Crossref Full Text | Google Scholar

183. Kaya H, Ayik B, Tasdelen R, Sevimli N, Ertekin E. Comparing retinal changes measured by optical coherence tomography in patients with schizophrenia and their siblings with healthy controls: are retinal findings potential endophenotype candidates? Asian J Psychiatr. (2022) 72:103089. doi: 10.1016/j.ajp.2022.103089

PubMed Abstract | Crossref Full Text | Google Scholar

184. Tasdelen R, Ayik B, Kaya H, Sevimli N. Investigation of the relationship between cognitive functions and retinal findings from spectral optical coherence tomography in patients with schizophrenia and their healthy siblings. Psychiatry Investig. (2023) 20:236–44. doi: 10.30773/pi.2022.0268

PubMed Abstract | Crossref Full Text | Google Scholar

185. Meier MH, Gillespie NA, Hansell NK, Hewitt AW, Hickie IB, Lu Y, et al. Retinal microvessels reflect familial vulnerability to psychotic symptoms: a comparison of twins discordant for psychotic symptoms and controls. Schizophr Res. (2015) 164:47–52. doi: 10.1016/j.schres.2015.01.045

PubMed Abstract | Crossref Full Text | Google Scholar

186. Boudriot E, Gabriel V, Popovic D, Pingen P, Yakimov V, Papiol S, et al. Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk. Biol Psychiatry. (2024) 96:792–803. doi: 10.1016/j.biopsych.2024.04.014

PubMed Abstract | Crossref Full Text | Google Scholar

187. Fuyi Q, Xiang C, Xinling Z, Zeyi G, Liu Y, Jia W, et al. Association between retinal nerve fiber layer thickness and psychiatric disorders: a Mendelian randomization study. BMC Psychiatry. (2024) 24:640. doi: 10.1186/s12888-024-06100-8

PubMed Abstract | Crossref Full Text | Google Scholar

188. Boudriot E, Stephan M, Rabe F, Smigielski L, Schmitt A, Falkai P, et al. Genetic analysis of retinal cell types in neuropsychiatric disorders. JAMA Psychiatry. (2025) 82:285–95. doi: 10.1001/jamapsychiatry.2024.4230

PubMed Abstract | Crossref Full Text | Google Scholar

189. Gao XR, Huang H, Kim H. Genome-wide association analyses identify 139 loci associated with macular thickness in the UK Biobank cohort. Hum Mol Genet. (2019) 28:1162–72. doi: 10.1093/hmg/ddy422

PubMed Abstract | Crossref Full Text | Google Scholar

190. Ma Y, Jiang D, Li J, Zheng G, Deng Y, Gou X, et al. Systematic dissection of pleiotropic loci and critical regulons in excitatory neurons and microglia relevant to neuropsychiatric and ocular diseases. Transl Psychiatry. (2025) 15:24. doi: 10.1038/s41398-025-03243-4

PubMed Abstract | Crossref Full Text | Google Scholar

191. Demirlek C, Atas F, Yalincetin B, Gurbuz MS, Cesim E, Demir M, et al. Choroidal structural analysis in ultra-high risk and first-episode psychosis. Eur Neuropsychopharmacol. (2023) 70:72–80. doi: 10.1016/j.euroneuro.2023.02.016

PubMed Abstract | Crossref Full Text | Google Scholar

192. Agrawal R, Gupta P, Tan KA, Cheung CM, Wong TY, Cheng CY. Choroidal vascularity index as a measure of vascular status of the choroid: measurements in healthy eyes from a population-based study. Sci Rep. (2016) 6:21090. doi: 10.1038/srep21090

PubMed Abstract | Crossref Full Text | Google Scholar

193. Demirlek C, Arslan B, Eyuboglu MS, Yalincetin B, Atas F, Cesim E, et al. Retina in clinical high-risk and first-episode psychosis. Schizophr Bull. (2024). doi: 10.1093/schbul/sbae189

PubMed Abstract | Crossref Full Text | Google Scholar

194. Ascaso FJ, Rodriguez-Jimenez R, Cabezon L, Lopez-Anton R, Santabarbara J. De la Camara C, et al. Retinal nerve fiber layer and macular thickness in patients with schizophrenia: influence of recent illness episodes. Psychiatry Res. (2015) 229:230–6. doi: 10.1016/j.psychres.2015.07.028

Crossref Full Text | Google Scholar

195. Lai A, Crosta C, Loftin M, Silverstein SM. Retinal structural alterations in chronic versus first episode schizophrenia spectrum disorders. Biomark Neuropsychiatry. (2020) 2:100013. doi: 10.1016/j.bionps.2020.100013

Crossref Full Text | Google Scholar

196. Balk LJ, Petzold A. Current and future potential of retinal optical coherence tomography in multiple sclerosis with and without optic neuritis. Neurodegener Dis Manag. (2014) 4:165–76. doi: 10.2217/nmt.14.10

PubMed Abstract | Crossref Full Text | Google Scholar

197. Bannai D, Adhan I, Katz R, Kim LA, Keshavan M, Miller JB, et al. Quantifying retinal microvascular morphology in schizophrenia using swept-source optical coherence tomography angiography. Schizophr Bull. (2022) 48:80–9. doi: 10.1093/schbul/sbab111

PubMed Abstract | Crossref Full Text | Google Scholar

198. Kango A, Grover S, Gupta V, Sahoo S, Nehra R. A comparative study of retinal layer changes among patients with schizophrenia and healthy controls. Acta Neuropsychiatr. (2023) 35:165–76. doi: 10.1017/neu.2022.35

PubMed Abstract | Crossref Full Text | Google Scholar

199. Sarkar S, Rajalakshmi AR, Avudaiappan S, Eswaran S. Exploring the role of macular thickness as a potential early biomarker of neurodegeneration in acute schizophrenia. Int Ophthalmol. (2021) 41:2737–46. doi: 10.1007/s10792-021-01831-z

PubMed Abstract | Crossref Full Text | Google Scholar

200. Zhuo C, Ji F, Xiao B, Lin X, Chen C, Jiang D, et al. Antipsychotic agent-induced deterioration of the visual system in first-episode untreated patients with schizophrenia maybe self-limited: findings from a secondary small sample follow-up study based on a pilot follow-up study. Psychiatry Res. (2020) 286:112906. doi: 10.1016/j.psychres.2020.112906

PubMed Abstract | Crossref Full Text | Google Scholar

201. Huang J, Song X, Xu Y, Wang L, Li Y, Tian H, et al. Reliability and diagnostic validity of a novel visual disturbance subjective experience scale in Chinese patients with schizophrenia. Psychiatry Clin Psychopharmacol. (2020) 30:307–12. doi: 10.5455/PCP.20200302022126

Crossref Full Text | Google Scholar

202. Gonzalez-Diaz JM, Sanchez Dalmau B, Camos-Carreras A, Alba-Arbalat S, Amoretti S, Forte MF, et al. Retinal structure and its relationship with premorbid, clinical, and cognitive variables in young Spanish patients with early course schizophrenia spectrum disorders. Eur Neuropsychopharmacol. (2025) 92:38–47. doi: 10.1016/j.euroneuro.2024.12.006

PubMed Abstract | Crossref Full Text | Google Scholar

203. Liu Y, Huang L, Chen J, Tan S, Zhao K, Yan S, et al. Retinal venule correlation with schizophrenia. Int J Clin Exp Med. (2020) 13:6927–35.

Google Scholar

204. Zhuo C, Xiao B, Chen C, Jiang D, Li G, Ma X, et al. Antipsychotic agents deteriorate brain and retinal function in schizophrenia patients with combined auditory and visual hallucinations: a pilot study and secondary follow-up study. Brain Behav. (2020) 10:e01611. doi: 10.1002/brb3.1611

PubMed Abstract | Crossref Full Text | Google Scholar

205. Mota M, Pêgo P, Klut C, Coutinho I, Santos C, Pires G, et al. Evaluation of structural changes in the retina of patients with schizophrenia. Ophthalmol Res. (2015) 4:45–52. doi: 10.9734/OR/2015/17953

Crossref Full Text | Google Scholar

206. Fradkin SI, Bannai D, Lizano P, Lai A, Crosta C, Thompson JL, et al. Deep retinal layer microvasculature alterations in schizophrenia. Biomark Neuropsychiatry. (2024) 10:100084. doi: 10.1016/j.bionps.2024.100084

Crossref Full Text | Google Scholar

207. Hanifi Kokaçya M, Idil Çakmak A. Optical coherence tomography angiography in schizophrenia. Alpha Psychiatry. (2022) 23:253–61. doi: 10.5152/alphapsychiatry.2022.21629

PubMed Abstract | Crossref Full Text | Google Scholar

208. Cahn W, Hulshoff Pol HE, Lems EB, van Haren NE, Schnack HG, van der Linden JA, et al. Brain volume changes in first-episode schizophrenia: a 1-year follow-up study. Arch Gen Psychiatry. (2002) 59:1002–10. doi: 10.1001/archpsyc.59.11.1002

PubMed Abstract | Crossref Full Text | Google Scholar

209. Lieberman J, Chakos M, Wu H, Alvir J, Hoffman E, Robinson D, et al. Longitudinal study of brain morphology in first episode schizophrenia. Biol Psychiatry. (2001) 49:487–99. doi: 10.1016/S0006-3223(01)01067-8

PubMed Abstract | Crossref Full Text | Google Scholar

210. Woods BT, Ward KE, Johnson EH. Meta-analysis of the time-course of brain volume reduction in schizophrenia: implications for pathogenesis and early treatment. Schizophr Res. (2005) 73:221–8. doi: 10.1016/j.schres.2004.05.014

PubMed Abstract | Crossref Full Text | Google Scholar

211. Daneshvar R, Naghib M, Fayyazi Bordbar MR, Faridhosseini F, Fotouhi M, Motamed Shariati M. Optic nerve head neurovascular assessments in patients with schizophrenia: a cross-sectional study. Health Sci Rep. (2024) 7:e2100. doi: 10.1002/hsr2.2100

PubMed Abstract | Crossref Full Text | Google Scholar

212. Silverstein SM, Lai A, Green KM, Crosta C, Fradkin SI, Ramchandran RS. Retinal microvasculature in schizophrenia. Eye Brain. (2021) 13:205–17. doi: 10.2147/EB.S317186

PubMed Abstract | Crossref Full Text | Google Scholar

213. Moises HW, Wollschlager D, Binder H. Functional genomics indicate that schizophrenia may be an adult vascular-ischemic disorder. Transl Psychiatry. (2015) 5:e616. doi: 10.1038/tp.2015.103

PubMed Abstract | Crossref Full Text | Google Scholar

214. Krukow P, Domagala A, Silverstein SM. Specific association between retinal neural layer thinning and neurological soft signs in schizophrenia. Eur Arch Psychiatry Clin Neurosci. (2024) 274:1237–40. doi: 10.1007/s00406-023-01742-3

PubMed Abstract | Crossref Full Text | Google Scholar

215. Biswas P, Malhotra S, Malhotra A, Gupta N. Comparative study of neurological soft signs in schizophrenia with onset in childhood, adolescence and adulthood. Acta Psychiatr Scand. (2007) 115:295–303. doi: 10.1111/j.1600-0447.2006.00901.x

PubMed Abstract | Crossref Full Text | Google Scholar

216. Rathod B, Kaur A, Basavanagowda DM, Mohan D, Mishra N, Fuad S, et al. Neurological soft signs and brain abnormalities in schizophrenia: a literature review. Cureus. (2020) 12:e11050. doi: 10.7759/cureus.11050

PubMed Abstract | Crossref Full Text | Google Scholar

217. Schröder J, Niethammer R, Geider FJ, Reitz C, Binkert M, Jauss M, et al. Neurological soft signs in schizophrenia. Schizophr Res. (1991) 6:25–30. doi: 10.1016/0920-9964(91)90017-L

Crossref Full Text | Google Scholar

218. Peralta V, Cuesta MJ. Motor abnormalities: from neurodevelopmental to neurodegenerative through “functional“ (neuro) psychiatric disorders. Schizophr Bull. (2017) 43:956–71. doi: 10.1093/schbul/sbx089

Crossref Full Text | Google Scholar

219. Dietsche B, Kircher T, Falkenberg I. Structural brain changes in schizophrenia at different stages of the illness: a selective review of longitudinal magnetic resonance imaging studies. Aust N Z J Psychiatry. (2017) 51:500–8. doi: 10.1177/0004867417699473

PubMed Abstract | Crossref Full Text | Google Scholar

220. Cropley VL, Klauser P, Lenroot RK, Bruggemann J, Sundram S, Bousman C, et al. Accelerated gray and white matter deterioration with age in schizophrenia. Am J Psychiatry. (2017) 174:286–95. doi: 10.1176/appi.ajp.2016.16050610

PubMed Abstract | Crossref Full Text | Google Scholar

221. Demro C, Shen C, Hendrickson TJ, Arend JL, Disner SG, Sponheim SR. Advanced brain-age in psychotic psychopathology: evidence for transdiagnostic neurodevelopmental origins. Front Aging Neurosci. (2022) 14:872867. doi: 10.3389/fnagi.2022.872867

PubMed Abstract | Crossref Full Text | Google Scholar

222. Schnack HG, van Haren NE, Nieuwenhuis M, Hulshoff Pol HE, Cahn W, Kahn RS. Accelerated brain aging in schizophrenia: a longitudinal pattern recognition study. Am J Psychiatry. (2016) 173:607–16. doi: 10.1176/appi.ajp.2015.15070922

PubMed Abstract | Crossref Full Text | Google Scholar

223. Zhu JD, Tsai SJ, Lin CP, Lee YJ, Yang AC. Predicting aging trajectories of decline in brain volume, cortical thickness and fractional anisotropy in schizophrenia. Schizophrenia. (2023) 9:1. doi: 10.1038/s41537-022-00325-w

PubMed Abstract | Crossref Full Text | Google Scholar

224. Koutsouleris N, Davatzikos C, Borgwardt S, Gaser C, Bottlender R, Frodl T, et al. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr Bull. (2014) 40:1140–53. doi: 10.1093/schbul/sbt142

PubMed Abstract | Crossref Full Text | Google Scholar

225. Khalil DH, Aziz K, Khalil M, Khowyled A. Optical coherence tomography in Egyptian schizophrenics and its correlation to disease parameters. Delta J Ophthalmol. (2022) 23:198–205. doi: 10.4103/djo.djo_74_21

Crossref Full Text | Google Scholar

226. Kurt A, Ramazan Zor K, Kucuk E, Yildirim G, Erdal Ersan E. An optical coherence tomography study that supports the neurovascular basis of schizophrenia disease. Alpha Psychiatry. (2022) 23:12–7. doi: 10.5152/alphapsychiatry.2021.21207

PubMed Abstract | Crossref Full Text | Google Scholar

227. Liu Y, Huang L, Tong Y, Chen J, Gao D, Yang F. Association of retinal nerve fiber abnormalities with serum CNTF and cognitive functions in schizophrenia patients. PeerJ. (2020) 8:e9279. doi: 10.7717/peerj.9279

PubMed Abstract | Crossref Full Text | Google Scholar

228. Keefe RS, Mohs RC, Losonczy MF, Davidson M, Silverman JM, Kendler KS, et al. Characteristics of very poor outcome schizophrenia. Am J Psychiatry. (1987) 144:889–95. doi: 10.1176/ajp.144.7.889

PubMed Abstract | Crossref Full Text | Google Scholar

229. Nguyen TT, Eyler LT, Jeste DV. Systemic biomarkers of accelerated aging in schizophrenia: A critical review and future directions. Schizophr Bull. (2018) 44:398–408. doi: 10.1093/schbul/sbx069

PubMed Abstract | Crossref Full Text | Google Scholar

230. Hackam AS. The Wnt signaling pathway in retinal degenerations. IUBMB Life. (2005) 57:381–8. doi: 10.1080/15216540500137586

PubMed Abstract | Crossref Full Text | Google Scholar

231. Mills EA, Goldman D. The regulation of notch signaling in retinal development and regeneration. Curr Pathobiol Rep. (2017) 5:323–31. doi: 10.1007/s40139-017-0153-7

PubMed Abstract | Crossref Full Text | Google Scholar

232. Grimbly MJ, Koopowitz SM, Chen R, Sun Z, Foster PJ, He M, et al. Estimating biological age from retinal imaging: a scoping review. BMJ Open Ophthalmol. (2024) 9:e001794. doi: 10.1101/2024.02.13.24302673

PubMed Abstract | Crossref Full Text | Google Scholar

233. Abreu-Gonzalez R, Rodriguez-Martin JN, Quezada-Peralta G, Rodrigo-Bello JJ, Gil-Hernandez MA, Bermudez-Perez C, et al. Retinal age as a predictive biomarker of the diabetic retinopathy grade. Arch Soc Esp Oftalmol. (2023) 98:265–9. doi: 10.1016/j.oftale.2023.04.008

PubMed Abstract | Crossref Full Text | Google Scholar

234. Hu W, Wang W, Wang Y, Chen Y, Shang X, Liao H, et al. Retinal age gap as a predictive biomarker of future risk of Parkinson's disease. Age Ageing. (2022) 51:afac062. doi: 10.1093/ageing/afac062

PubMed Abstract | Crossref Full Text | Google Scholar

235. Zhu Z, Chen Y, Wang W, Wang Y, Hu W, Shang X, et al. Association of retinal age gap with arterial stiffness and incident cardiovascular disease. Stroke. (2022) 53:3320–8. doi: 10.1161/STROKEAHA.122.038809

PubMed Abstract | Crossref Full Text | Google Scholar

236. Chen R, Xu J, Shang X, Bulloch G, He M, Wang W, et al. Association between cardiovascular health metrics and retinal ageing. GeroScience. (2023) 45:1511–21. doi: 10.1007/s11357-023-00743-3

PubMed Abstract | Crossref Full Text | Google Scholar

237. Chen R, Chen Y, Zhang J, Wang W, Hu W, He M, et al. Retinal age gap as a predictive biomarker for future risk of clinically significant diabetic retinopathy. Acta Diabetol. (2024) 61:373–80. doi: 10.1007/s00592-023-02199-5

PubMed Abstract | Crossref Full Text | Google Scholar

238. Correll CU, Solmi M, Croatto G, Schneider LK, Rohani-Montez SC, Fairley L, et al. Mortality in people with schizophrenia: a systematic review and meta-analysis of relative risk and aggravating or attenuating factors. World Psychiatry. (2022) 21:248–71. doi: 10.1002/wps.20994

PubMed Abstract | Crossref Full Text | Google Scholar

239. Mangurian C, Newcomer JW, Modlin C, Schillinger D. Diabetes and cardiovascular care among people with severe mental illness: a literature review. J Gen Intern Med. (2016) 31:1083–91. doi: 10.1007/s11606-016-3712-4

PubMed Abstract | Crossref Full Text | Google Scholar

240. Butler RN, Sprott R, Warner H, Bland J, Feuers R, Forster M, et al. Biomarkers of aging: From primitive organisms to humans. J Gerontol A Biol Sci Med Sci. (2004) 59:B560–7. doi: 10.1093/gerona/59.6.B560

PubMed Abstract | Crossref Full Text | Google Scholar

241. Chen R, Wang Y, Zhang S, Bulloch G, Zhang J, Liao H, et al. Biomarkers of ageing: current state-of-art, challenges, and opportunities. MedComm - Future Med. (2023) 2:e50. doi: 10.1002/mef2.50

Crossref Full Text | Google Scholar

242. Denis P, Elena PP, Nordmann JP, Saraux H, Lapalus P. Autoradiographic localization of D1 and D2 dopamine binding sites in the human retina. Neurosci Lett. (1990) 116:81–6. doi: 10.1016/0304-3940(90)90390-U

PubMed Abstract | Crossref Full Text | Google Scholar

243. Witkovsky P. Dopamine and retinal function. Doc Ophthalmol. (2004) 108:17–40. doi: 10.1023/B:DOOP.0000019487.88486.0a

PubMed Abstract | Crossref Full Text | Google Scholar

244. Yuen VL, Zhang XJ, Ling X, Zhang Y, Kam KW, Chen LJ, et al. Effects of firsthand tobacco smoking on retinal vessel caliber: a systematic review and meta-analysis. Graefes Arch Clin Exp Ophthalmol. (2024) 262:1397–407. doi: 10.1007/s00417-023-06223-w

PubMed Abstract | Crossref Full Text | Google Scholar

245. Yang TK, Huang XG, Yao JY. Effects of cigarette smoking on retinal and choroidal thickness: a systematic review and meta-analysis. J Ophthalmol. (2019) 2019:8079127. doi: 10.1155/2019/8079127

Crossref Full Text | Google Scholar

246. Quiroz-Reyes MA, Quiroz-Gonzalez EA, Quiroz-Gonzalez MA, Lima-Gomez V. Effects of cigarette smoking on retinal thickness and choroidal vascularity index: a systematic review and meta-analysis. Int J Retina Vitreous. (2025) 11:21. doi: 10.1186/s40942-025-00646-9

PubMed Abstract | Crossref Full Text | Google Scholar

247. Xu H, Zong Y, Yu J, Jiang C, Zhu H, Sun X. Retinal microvascular reactivity in chronic cigarette smokers and non-smokers: an observational cross-sectional study. Front Med. (2021) 8:782010. doi: 10.3389/fmed.2021.782010

PubMed Abstract | Crossref Full Text | Google Scholar

248. Dom AM, Buckley AW, Brown KC, Egleton RD, Marcelo AJ, Proper NA, et al. The alpha7-nicotinic acetylcholine receptor and MMP-2/-9 pathway mediate the proangiogenic effect of nicotine in human retinal endothelial cells. Invest Ophthalmol Vis Sci. (2011) 52:4428–38. doi: 10.1167/iovs.10-5461

Crossref Full Text | Google Scholar

249. Mitchell AJ, Vancampfort D, De Herdt A, Yu W, De Hert M. Is the prevalence of metabolic syndrome and metabolic abnormalities increased in early schizophrenia? A comparative meta-analysis of first episode, untreated and treated patients. Schizophr Bull. (2013) 39:295–305. doi: 10.1093/schbul/sbs082

PubMed Abstract | Crossref Full Text | Google Scholar

250. Mitchell AJ, Vancampfort D, Sweers K, van Winkel R, Yu W, De Hert M. Prevalence of metabolic syndrome and metabolic abnormalities in schizophrenia and related disorders–a systematic review and meta-analysis. Schizophr Bull. (2013) 39:306–18. doi: 10.1093/schbul/sbr148

PubMed Abstract | Crossref Full Text | Google Scholar

251. Salehi MA, Karimi A, Mohammadi S, Arevalo JF. Spectral-domain OCT measurements in obesity: a systematic review and meta-analysis. PLoS ONE. (2022) 17:e0267495. doi: 10.1371/journal.pone.0267495

PubMed Abstract | Crossref Full Text | Google Scholar

252. Ozgur G, Gokmen O. Associations between body mass index and choroidal thickness, superficial and deep retinal vascular indices, and foveal avascular zone measured by OCTA. Photodiagnosis Photodyn Ther. (2023) 42:103515. doi: 10.1016/j.pdpdt.2023.103515

PubMed Abstract | Crossref Full Text | Google Scholar

253. Dogan B, Dogan U, Gedik B, Turkmen B, Cakir RC, Demirer ME, et al. Optical coherence tomography angiography evaluation of optic disc and retinal vascular densities in obese patients. Photodiagnosis Photodyn Ther. (2023) 44:103826. doi: 10.1016/j.pdpdt.2023.103826

PubMed Abstract | Crossref Full Text | Google Scholar

254. Zeng X, Chen R, Bulloch G, Peng Q, Cheng CY, He M, et al. Associations of metabolically healthy obesity and retinal age gap. Transl Vis Sci Technol. (2024) 13:26. doi: 10.1167/tvst.13.11.26

PubMed Abstract | Crossref Full Text | Google Scholar

255. Alizadeh M, Delborde Y, Ahmadpanah M, Seifrabiee MA, Jahangard L, Bazzazi N, et al. Non-linear associations between retinal nerve fibre layer (RNFL) and positive and negative symptoms among men with acute and chronic schizophrenia spectrum disorder. J Psychiatr Res. (2021) 141:81–91. doi: 10.1016/j.jpsychires.2021.06.007

PubMed Abstract | Crossref Full Text | Google Scholar

256. Carriello MA, Costa DFB, Alvim PHP, Pestana MC, Bicudo DDS, Gomes EMP, et al. Retinal layers and symptoms and inflammation in schizophrenia. Eur Arch Psychiatry Clin Neurosci. (2024) 274:1115–24. doi: 10.1007/s00406-023-01583-0

PubMed Abstract | Crossref Full Text | Google Scholar

257. Bernardin F, Schwitzer T, Angioi-Duprez K, Giersch A, Ligier F, Bourion-Bedes S, et al. Retinal dysfunctions in a patient with a clinical high risk for psychosis and severe visual disturbances: a single case report. Early Interv Psychiatry. (2021) 15:1784–8. doi: 10.1111/eip.13103

PubMed Abstract | Crossref Full Text | Google Scholar

258. Maziade M. At risk for serious mental illness - Screening children of patients with mood disorders or schizophrenia. N Engl J Med. (2017) 376:910–2. doi: 10.1056/NEJMp1612520

PubMed Abstract | Crossref Full Text | Google Scholar

259. Ricard J, Berthelot N, Fortin-Fabbro É, Boisvert M-C, Garon-Bissonnette J, Arsenault E, et al. Childhood trauma and altered response of retinal neurons as an early risk endophenotype of schizophrenia and mood disorder. Biomark Neuropsychiatry. (2024) 10:100095. doi: 10.1016/j.bionps.2024.100095

Crossref Full Text | Google Scholar

260. Granholm E, Chock D, Morris S. Pupillary responses evoked during verbal fluency tasks indicate semantic network dysfunction in schizophrenia. J Clin Exp Neuropsychol. (1998) 20:856–72. doi: 10.1076/jcen.20.6.856.1107

PubMed Abstract | Crossref Full Text | Google Scholar

261. Granholm E, Verney SP. Pupillary responses and attentional allocation problems on the backward masking task in schizophrenia. Int J Psychophysiol. (2004) 52:37–51. doi: 10.1016/j.ijpsycho.2003.12.004

PubMed Abstract | Crossref Full Text | Google Scholar

262. Minassian A, Granholm E, Verney S, Perry W. Pupillary dilation to simple vs. complex tasks and its relationship to thought disturbance in schizophrenia patients. Int J Psychophysiol. (2004) 52:53–62. doi: 10.1016/j.ijpsycho.2003.12.008

PubMed Abstract | Crossref Full Text | Google Scholar

263. Silverstein SM, Choi JJ, Green KM, Bowles-Johnson KE, Ramchandran RS. Schizophrenia in translation: why the eye? Schizophr Bull. (2022) 48:728–37. doi: 10.1093/schbul/sbac050

PubMed Abstract | Crossref Full Text | Google Scholar

264. Portugal AM, Taylor MJ, Viktorsson C, Nystrom P, Li D, Tammimies K, et al. Pupil size and pupillary light reflex in early infancy: heritability and link to genetic liability to schizophrenia. J Child Psychol Psychiatry. (2022) 63:1068–77. doi: 10.1111/jcpp.13564

PubMed Abstract | Crossref Full Text | Google Scholar

265. Bloomfield SA. Retinal amacrine cells. In:Squire LR, , editor. Encyclopedia of Neuroscience. Amsterdam: Elsevier Inc. (2009). p. 171–9. doi: 10.1016/B978-008045046-9.00891-3

Crossref Full Text | Google Scholar

266. Euler T, Haverkamp S, Schubert T, Baden T. Retinal bipolar cells: elementary building blocks of vision. Nat Rev Neurosci. (2014) 15:507–19. doi: 10.1038/nrn3783

PubMed Abstract | Crossref Full Text | Google Scholar

267. Mahabadi N, Al Khalili Y. Neuroanatomy, Retina. Treasure Island, FL: StatPearls. (2025).

PubMed Abstract | Google Scholar

268. Agrawal R, Ding J, Sen P, Rousselot A, Chan A, Nivison-Smith L, et al. Exploring choroidal angioarchitecture in health and disease using choroidal vascularity index. Prog Retin Eye Res. (2020) 77:100829. doi: 10.1016/j.preteyeres.2020.100829

PubMed Abstract | Crossref Full Text | Google Scholar

269. Chalakkal RJ, Abdulla WH, Hong SC. 3 - Fundus retinal image analyses for screening and diagnosing diabetic retinopathy, macular edema, and glaucoma disorders. In:El-Baz AS, Suri JS, , editors. Diabetes and Fundus OCT. Amsterdam: Elsevier. (2020). p. 59–111. doi: 10.1016/B978-0-12-817440-1.00003-6

Crossref Full Text | Google Scholar

270. Adams NA. Atlas of OCT: Retinal Anatomy in Health & Pathology. Heidelberg: Heidelberg Engineering (2013).

Google Scholar

271. Wells-Gray EM, Choi SS, Bries A, Doble N. Variation in rod and cone density from the fovea to the mid-periphery in healthy human retinas using adaptive optics scanning laser ophthalmoscopy. Eye. (2016) 30:1135–43. doi: 10.1038/eye.2016.107

PubMed Abstract | Crossref Full Text | Google Scholar

272. Shiihara H, Terasaki H, Sonoda S, Kakiuchi N, Shinohara Y, Tomita M, et al. Objective evaluation of size and shape of superficial foveal avascular zone in normal subjects by optical coherence tomography angiography. Sci Rep. (2018) 8:10143. doi: 10.1038/s41598-018-28530-7

PubMed Abstract | Crossref Full Text | Google Scholar

273. Lemmens S, Devulder A, Van Keer K, Bierkens J, De Boever P, Stalmans I. Systematic review on fractal dimension of the retinal vasculature in neurodegeneration and stroke: assessment of a potential biomarker. Front Neurosci. (2020) 14:16. doi: 10.3389/fnins.2020.00016

PubMed Abstract | Crossref Full Text | Google Scholar

274. Remington LA, editor. Chapter 4 - Retina. In: Clinical Anatomy and Physiology of the Visual System. 3rd, ed. Amsterdam: Elsevier Inc. (2012). p. 61–92. doi: 10.1016/B978-1-4377-1926-0.10004-9

Crossref Full Text | Google Scholar

275. Hood DC, Raza AS, de Moraes CG, Liebmann JM, Ritch R. Glaucomatous damage of the macula. Prog Retin Eye Res. (2013) 32:1–21. doi: 10.1016/j.preteyeres.2012.08.003

PubMed Abstract | Crossref Full Text | Google Scholar

276. Kobat SG, Turgut B. Importance of müller cells. Beyoglu Eye J. (2020) 5:59–63.

Google Scholar

277. Bian X, Luo X, Wang C, Liu W, Lin X. Optic disc and optic cup segmentation based on anatomy guided cascade network. Comput Methods Programs Biomed. (2020) 197:105717. doi: 10.1016/j.cmpb.2020.105717

PubMed Abstract | Crossref Full Text | Google Scholar

278. Paula JS, O'Brien C, Stamer WD. Life under pressure: the role of ocular cribriform cells in preventing glaucoma. Exp Eye Res. (2016) 151:150–9. doi: 10.1016/j.exer.2016.08.014

PubMed Abstract | Crossref Full Text | Google Scholar

279. Fisher SK, Lewis GP. Chapter 115 - Cellular effects of detachment and reattachment on the neural retina and the retinal pigment epithelium. In:Ryan SJ, Hinton DR, Schachat AP, Wilkinson CP, , editors. Retina. 4th ed. Amsterdam: Elsevier Inc. (2006). p. 1991–2012. doi: 10.1016/B978-0-323-02598-0.50121-X

Crossref Full Text | Google Scholar

280. Ramos MF, Attar M, Stern ME, Brassard JA, Kim AS, Matsumoto S, et al. Chapter 29 - Safety evaluation of ocular drugs. In:Faqi AS, , editor. A comprehensive guide to toxicology in nonclinical drug development. 2nd ed. Amsterdam: Elsevier Inc. (2017). p. 757–811. doi: 10.1016/B978-0-12-803620-4.00029-3

Crossref Full Text | Google Scholar

281. Carelli V, La Morgia C, Valentino ML, Barboni P, Ross-Cisneros FN, Sadun AA. Retinal ganglion cell neurodegeneration in mitochondrial inherited disorders. Biochim Biophys Acta Bioenerg. (2009) 1787:518–28. doi: 10.1016/j.bbabio.2009.02.024

PubMed Abstract | Crossref Full Text | Google Scholar

282. Munk MR, Kashani AH, Tadayoni R, Korobelnik JF, Wolf S, Pichi F, et al. Standardization of OCT angiography nomenclature in retinal vascular diseases: first survey results. Ophthalmol Retina. (2021) 5:981–90. doi: 10.1016/j.oret.2020.12.022

PubMed Abstract | Crossref Full Text | Google Scholar

283. Narayan DS, Chidlow G, Wood JP, Casson RJ. Glucose metabolism in mammalian photoreceptor inner and outer segments. Clin Experiment Ophthalmol. (2017) 45:730–41. doi: 10.1111/ceo.12952

PubMed Abstract | Crossref Full Text | Google Scholar

284. Boulton M, Dayhaw-Barker P. The role of the retinal pigment epithelium: topographical variation and ageing changes. Eye. (2001) 15(Pt 3):384–9. doi: 10.1038/eye.2001.141

PubMed Abstract | Crossref Full Text | Google Scholar

285. Ramos L, Novo J, Rouco J, Romeo S, Alvarez MD, Ortega M. Computational assessment of the retinal vascular tortuosity integrating domain-related information. Sci Rep. (2019) 9:19940. doi: 10.1038/s41598-019-56507-7

PubMed Abstract | Crossref Full Text | Google Scholar

Keywords: neurodegeneration, neurodevelopment, optical coherence tomography (OCT), retina, schizophrenia

Citation: Blose BA and Silverstein SM (2026) Retinal biomarkers in schizophrenia spectrum disorders: evidence and implications for the neurodevelopmental and neurodegenerative models. Front. Med. 12:1697871. doi: 10.3389/fmed.2025.1697871

Received: 02 September 2025; Revised: 06 December 2025;
Accepted: 23 December 2025; Published: 20 January 2026.

Edited by:

Clara Rizzo, University of Florence, Italy

Reviewed by:

Jairo M. Gonzalez-Diaz, Rosario University, Colombia
Sieun Lee, University of Nottingham, United Kingdom

Copyright © 2026 Blose and Silverstein. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Brittany A. Blose, YmJsb3NlQFVSLlJvY2hlc3Rlci5lZHU=

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