Elena Scaffei
Chiara Bosetti
Roberta Battini
Michela Fagiolini
Laura Baroncelli- 1Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
- 2Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
- 3National Research Council (CNR), Institute of Neuroscience, Pisa, Italy
- 4Department of Neurology, Boston Children's Hospital, F. M. Kirby Neurobiology Center, Harvard Medical School, Boston, MA, United States
Neurodevelopmental disorders (NDDs) cause profound intellectual and physical impairment, yet therapeutic progress remains hindered by the lack of quantitative, unbiased, and non-invasive biomarkers to monitor disease onset and progression. Visual evoked potentials (VEPs) have emerged as promising functional biomarkers for X-linked NDDs, with reduced VEP amplitudes correlating with disease severity. Complementary approaches, like functional Near Infrared Spectroscopy (fNIRS), offer a non-invasive additional tool to assess brain metabolism, monitor disease progression, and evaluate therapeutic responses. This perspective explores the potential of fNIRS in studying visually evoked hemodynamic responses (vHDR) across different age groups, demonstrating its reliability in capturing task-specific cortical activation and tracking brain maturation even in challenging populations. Notably, fNIRS identifies unique vHDR patterns in conditions like optic neuritis, myopia, glaucoma, and migraines, validating its role as a biomarker for disease severity and treatment efficacy. Moreover, fNIRS has proven effective in detecting early neural deficits in high-risk infants, including pre-term newborns. Preclinical studies support that visually induced hemodynamic changes can differentiate healthy from pathological conditions in X-linked NDDs. However, direct evidence from human cohorts with X-linked NDDs remains limited, highlighting the urgent need for further research to validate the potential of visual fNIRS as a reliable functional biomarker in clinical settings. To enhance clinical relevance, the development of standardized protocols, engaging stimuli, and age-stratified analyses is also crucial for improving diagnostic accuracy, tracking neurodevelopmental trajectories, and evaluating therapeutic interventions.
1 Introduction
Neurodevelopmental disorders (NDDs) pose a significant global public health challenge, impacting millions of children and their families across the world. These chronic and heterogeneous conditions are often underdiagnosed, with an estimated prevalence exceeding 1.5% of the global population (Chung et al., 2022). Over 90 genes on the X chromosome have been linked to NDDs, which encompass rare disorders characterized by severe intellectual and physical disabilities (Lumsden and Urv, 2023). Despite the urgent need for effective treatments, progress in therapeutic development remains constrained by the lack of robust translational endpoints and reliable, non-invasive biomarkers essential for accelerating preclinical and clinical testing (Kearns and Artman, 2015; Budimirovic et al., 2017; Krainc et al., 2023).
Recent animal model studies have demonstrated that the visual system serves as a powerful proxy for assessing overall brain function (Durand et al., 2012; Della Sala and Pizzorusso, 2014; Mazziotti et al., 2020). Visual evoked potential (VEP) electrophysiological recordings have revealed significantly reduced VEP amplitudes and diminished visual acuity in Mecp2 mutant mice compared to their wild-type (WT) littermates. Notably, the reduction in VEP amplitude is most pronounced in animals with greater phenotypic severity and more advanced RTT-like symptoms, suggesting that VEP amplitude reflects overall neurological health and disorder progression (LeBlanc et al., 2015). Importantly, similar findings have been reported in RTT patients, who exhibit significantly diminished P1 amplitudes, which worsen as the disorder advances (Stauder et al., 2006; LeBlanc et al., 2015; Saby et al., 2021). These data underscore the potential clinical utility of P1 amplitude as a translatable biomarker. Furthermore, recent analyses have extended these observations to other X-linked disorders, with similar results replicated in both animal models and patients with CDKL5 Deficiency (Mazziotti et al., 2017; Saby et al., 2022), and MECP2 duplication syndrome (Saby et al., 2023).
These findings highlight neurophysiological measures, such as VEPs, as valuable, quantitative, and unbiased biomarkers for assessing disease severity and progression in X-linked NDDs. Their potential for repeated, non-invasive longitudinal assessments also makes them highly suitable for clinical trial readiness (LeBlanc et al., 2015; McPartland, 2016; Sidorov et al., 2017). However, the development of additional biomarkers is critical for advancing our understanding of these disorders and refine therapeutic strategies. Given that disruptions in energy metabolism are a key pathological mechanism in many X-linked disorders (Ghirardini et al., 2021; Oyarzábal et al., 2021; van Bergen et al., 2021), assessing cerebral blood flow and oxygen consumption offers a complementary and highly sensitive approach for quantifying functional changes in neural circuits. This strategy provides crucial insights into neurovascular coupling, enhancing our ability to monitor disease progression and therapeutic efficacy.
The functional Near Infrared Spectroscopy (fNIRS) is a non-invasive optical imaging technique that has gained increasing recognition for analyzing energy metabolism in the brain (Strangman et al., 2002; Cui et al., 2011; Chen et al., 2020). By detecting hemodynamic responses (HDR) associated with cortical activation, fNIRS measures increase in total hemoglobin (THb) and oxygenated hemoglobin (OHb) levels while recording decreases in deoxygenated hemoglobin (DHb) concentrations in regions of interest (ROIs) (van de Rijt et al., 2016). Although fNIRS was introduced into clinical care nearly 40 years ago (Villringer et al., 1993; Chen et al., 2020), its application in studying brain development and NDDs has only recently gained attention (Vanderwert and Nelson, 2014). To date, fNIRS has primarily been used to investigate typical developmental processes such as speech perception and language, sensory and motor functions, social communication, and object and action processing in toddlers and children (Lloyd-Fox et al., 2010; Gervain et al., 2011). Its ability to capture brain activation from the earliest stages of development (Cabrera and Gervain, 2020) makes it particularly well-suited for studying NDDs.
This perspective paper explores the potential of fNIRS for analyzing visually-evoked hemodynamic responses (vHDR), assessing its viability as biomarker for brain function across both pediatric and adult populations.
2 The application of fNIRS in the study of the visual system
This section reviews key findings from studies employing fNIRS to analyze vHDR. The discussion is divided into two subsections: studies in adults and those in children. We summarize major findings, methodological approaches, and clinical applications while addressing the limitations of current research. Detailed technical information—including equipment, participant characteristics, and experimental paradigms—is systematically presented in Table 1 (adults) and Table 2 (children).
Table 1. The table summarizes fNIRS studies investigating visual processing in adults, presented chronologically to illustrate the evolving complexity of the visual paradigms utilized over time.
Table 2. The table summarizes fNIRS studies investigating visual processing in children.
2.1 Visual cortical processing through fNIRS: methodological insights and clinical applications in adults
Early studies in the 1990s used the visual system as a model to investigate cortical HDR during task-related experiments in healthy adults. Villringer’s and Zeki’s group employed various visual stimuli- including flashes, pictures, and moving colored shapes- to examine occipital cortex activation. These studies revealed a distinct HDR pattern, with increased OHb levels and a smaller-magnitude decrease in DHb, peaking approximately 5 s after stimulus onset. This pattern underscored fNIRS’s potential as a bedside tool for monitoring brain activity with minimal interference from superficial blood flow (Villringer et al., 1993; Meek et al., 1995). Subsequent research explored the temporal dynamics of vHDR during sustained stimulation, showing that OHb levels rise and remain elevated, while DHb initially decreases before returning to baseline and exhibiting a post-stimulus overshoot (Heekeren et al., 1997). Multi-channel fNIRS studies confirmed the regional specificity of these responses, with activation localized to the contralateral occipital cortex reinforcing the stimulus-specific nature of vHDR (Colier et al., 2001; Plichta et al., 2006a; Wijeakumar et al., 2012).
Over the years, experimental paradigms have refined visual stimulation parameters- such as shape, contrast, chromaticity, and frequency- to optimize vHDR reliability (Gratton et al., 2001; Rovati et al., 2007; Wijeakumar et al., 2012; Thang et al., 2014; Haigh et al., 2015). High-contrast black-and-white checkerboard and pattern-reversal gratings have proven the most effective and are now widely used in fNIRS studies of visual processing (Heekeren et al., 1999; Obrig et al., 2000; Takahashi et al., 2000; Wijeakumar et al., 2012; Haigh et al., 2015). Higher stimulus frequencies produce linear increases in vHDR amplitude, whereas greater contrast elicits logarithmic changes in OHb and DHb levels (Gratton et al., 2001; Rovati et al., 2007). These findings highlight how optimizing visual stimulation parameters can enhance the reliability and robustness of fNIRS measurements in capturing brain activity.
Recent studies have expanded beyond primary visual processing to examine associative pathways in extrastriate cortices. Tasks involving depth perception, selective attention, and shape recognition have provided insights into the broader visual networks and their roles in higher-order cognitive functions (Ward et al., 2016; Cai et al., 2017; Maehara et al., 2007; Zhao et al., 2019). Advanced analytical methods, including general linear models and spatially resolved spectral techniques, have further enhanced fNIRS capability to detect cortical activity (Schroeter et al., 2004; Plichta et al., 2007). Additionally, multi-modal approaches integrating fNIRS with EEG or fMRI have validated fNIRS’s reliability by demonstrating strong signal consistency across modalities (Rovati et al., 2007; Näsi et al., 2010; Maggioni et al., 2015; Chen et al., 2017; Zhao et al., 2019; Pinti et al., 2021).
Building from the data collected in young adults, studies on healthy aging have revealed increased variability in vHDR, with OHb and DHb responses becoming less distinct between stimulation and rest in older adults. This pattern likely reflects age-related declines in neural efficiency and vascular responsiveness (Ward et al., 2015).
In clinical settings, fNIRS has been applied to ophthalmological conditions such as optic neuritis, myopia, and glaucoma, identifying distinct HDR alterations as potential biomarkers of visual impairment (Miki et al., 2005; Zhang Y. et al., 2022; Re et al., 2021). For example, optic neuritis patients exhibit reduced occipital HDR during affected-eye stimulation, despite normal visual acuity recovery, suggesting persistent neural deficits (Miki et al., 2005). Additionally, variations in vHDR patterns have been associated with disease severity in glaucoma and myopia, underscoring the diagnostic potential of fNIRS (Zhang Y. et al., 2022; Re et al., 2021).
The fNIRS has also offered valuable insights into neurological disorders. Studies in migraine patients have shown increased OHb levels during visual stimulation, which normalized after treatment with Galcanezumab, correlating with symptom relief (Coutts et al., 2012; de Tommaso et al., 2022). These findings highlight the potential of fNIRS in monitoring treatment efficacy and investigating changes in visual processing across a range of neurological conditions.
2.2 fNIRS for assessing visual processing and cortical development in infants and children
The functional Near Infrared Spectroscopy has become a key tool for investigating brain activity and development in infants and children, offering insights into functional specialization, cortical maturation, and sensory processing (Lloyd-Fox et al., 2010; Gervain et al., 2011).
Hemodynamic responses to visual stimuli have been observed from the earliest stages of life. Studies using checkerboard patterns in awake infants aged 3 days to 16 weeks have demonstrated regionally specific OHb and DHb changes (Meek et al., 1998; Taga et al., 2003a; Liao et al., 2010). These findings highlight fNIRS’s high spatial specificity, even in very young subjects, and suggest the presence of early cerebrovascular coupling that closely resembles adult patterns.
However, interpreting infant visual fNIRS data requires careful consideration. Unlike adults, neonates often exhibit increased DHb levels during stimulation likely due to immature neurovascular coupling, which typically stabilizes by 3–4 years of age (Meek et al., 1998; Kusaka et al., 2004). Additionally, factors such as consciousness state and stimulus characteristics influence HDR reliability. Checkerboard patterns consistently evoke robust responses, whereas less structured stimuli yield weaker activations (Watanabe et al., 2012). Furthermore, early sensory processing in neonates involves prefrontal cortical contributions, with neural circuits progressively maturing into an adult-like configuration over time (Taga et al., 2003a,b; Karen et al., 2008).
Building on these foundational studies, research has explored functional differentiation within visual cortical circuits. Studies in 3-month-old infants revealed that primary visual regions respond similarly to both object (e.g., moving objects) and non-object (e.g., checkerboard) stimuli, whereas the lateral occipital cortex selectively activates in response to object stimuli. The same pattern is replicated in preschool children (Watanabe et al., 2008; Remijn et al., 2011; Hirshkowitz et al., 2018). By 5–6 months of age, the inferior temporal and the lateral occipital cortices support feature-based object recognition, with infants distinguishing objects by shape and size before incorporating color clues (Wilcox, 1999; Wilcox et al., 2005; Wilcox et al., 2008; Emberson et al., 2017). These findings further indicate that stable functional segregation within the visual cortex is present from an early age. Longitudinal studies further indicate that functional specialization follows a developmental trajectory, shifting from broad, undifferentiated cortical activation at 2 months to region-specific processing by 3 months of age (Watanabe et al., 2010).
Recent research has also explored the emergence of multisensory integration in infants. Audiovisual stimuli enhance activation in both sensory-specific areas and strengthen functional connectivity between the occipital and temporal cortices, emphasizing the role of early multisensory experience in shaping neuronal networks (Watanabe et al., 2013; Emberson et al., 2015; Werchan et al., 2018 but see also Bortfeld et al., 2007).
Beyond typical development, fNIRS has been valuable for studying high-risk populations, such as preterm infants. Although research on pathological visual processing using fNIRS are limited, recent studies suggest that prematurity may disrupt functional connectivity and top-down modulation, leading to inefficient information transfer and potential learning difficulties (Boldin et al., 2018). These findings highlight fNIRS potential for early cerebral dysfunction detection, enabling timely interventions to mitigate developmental challenges.
3 Discussion
This perspective paper provides a comprehensive review of visual paradigms employed in fNIRS studies across adult and pediatric populations, emphasizing its potential as a biomarker for monitoring brain function. By capturing vHDR, fNIRS emerges as a promising tool in diagnosing and tracking NDDs and assessing treatment responses, advancing the field of personalized medicine.
Seminal studies have established that fNIRS reliably detects task-specific cortical activation across ages, with checkerboard stimuli consistently eliciting robust and reproducible vHDR in both adults and children (Villringer et al., 1993; Meek et al., 1995; Meek et al., 1998; Taga et al., 2003a). These foundational findings set the stage for its use in mapping developmental trajectories and revealing neural alterations in both research and clinical settings. More recent studies have applied fNIRS to pathological conditions such as migraines, visual impairments, and prematurity (Miki et al., 2005; Boldin et al., 2018; Re et al., 2021; Zhang Y. et al., 2022a; de Tommaso et al., 2022), demonstrating its ability to distinguish pathological states, predict disease severity, and monitor treatment efficacy. For example, studies in glaucoma and migraine patients have shown that fNIRS can detect disease-specific hemodynamic alterations and track therapeutic responses (Re et al., 2021; de Tommaso et al., 2022). Additionally, research on prematurity has highlighted how early disruptions in neural connectivity affect sensory processing and cognitive development, reinforcing fNIRS’s potential in detecting and addressing developmental vulnerabilities (Boldin et al., 2018).
In the study of NDDs, fNIRS has gained traction as a tool for identifying divergent hemodynamic patterns in conditions such as Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and Developmental Coordination Disorder (DCD) (Conti et al., 2022; Scaffei et al., 2023; Su et al., 2023; Huang et al., 2024; Kurane et al., 2024). Resting-state and longitudinal connectivity studies have revealed abnormalities in the temporal dynamics of brain activity, shedding insights on developmental deviations in children with NDDs (e.g., Keehn et al., 2013; Cao et al., 2021; Zhang F. et al., 2022). Event-related studies have further identified distinct alterations in HDR, such as atypical prefrontal cortex activation in ADHD (Ishii et al., 2017; Yasumura et al., 2019) and reduced vHDR amplitude in preschool females with high-functioning ASD (Scaffei et al., 2023), reinforcing the potential of task-related fNIRS as a non-invasive and accessible diagnostic and monitoring tool. However, the methodological variability in data collection and analysis presents significant challenges, limiting the comparability of results across studies and hindering the broader clinical application of fNIRS. To overcome these limitations, there is an urgent need for standardized protocols that encompass every stage of the experimental process. These protocols should provide clear guidelines for stimulation paradigms, as well as establish specific requirements for fNIRS equipment, including wavelength, source-detector distance, and channel configuration. In addition, robust signal processing procedures must be defined, with a particular focus on motion artifact correction techniques, such as short-channel regression or independent component analysis, along with appropriate filtering and data analysis methods. Finally, implementing rigorous reporting standards is essential to guarantee transparency, reproducibility, and the ability to effectively compare findings across studies.
For X-linked NDDs, preclinical research has demonstrated that visually evoked hemodynamic changes can distinguish between healthy and pathological conditions (Mazziotti et al., 2017; Mazziotti et al., 2020). These findings suggest that vHDR could serve as a non-invasive tool for early diagnosis and disease monitoring in clinical populations. However, it is important to note that the current evidence is primarily derived from animal models. Therefore, future research should prioritize examining vHDR in human cohorts with X-linked NDD to validate the translational potential of fNIRS. Additionally, to fully realize the potential of visual fNIRS as a functional biomarker, it is essential to broaden its application across a wide range of NDDs. Investigating fNIRS biomarkers in various NDDs—encompassing genetic, idiopathic, and environmentally influenced disorders—will deepen our understanding of both shared and disorder-specific mechanisms. This expanded focus will ultimately enhance diagnostic precision and support the development of more personalized intervention strategies.
Despite its promises, several critical factors influence the reliability and clinical utility of fNIRS as a biomarker, including its restricted brain penetration, feasibility, test–retest reliability, age-related variations in response amplitudes, and engagement-dependent fluctuations in hemodynamic responses. fNIRS has proven to be highly feasible even in challenging populations, including infants and children with NDDs (Vanderwert and Nelson, 2014). However, participant engagement significantly impacts the quality of vHDR recordings, as active tasks tend to produce stronger responses compared to passive viewing (Kojima and Suzuki, 2010). To improve compliance, particularly in young children, innovative protocols combining animated stimuli with traditional checkerboard paradigms have been developed, enhancing both engagement and data quality (Mazziotti et al., 2022).
Age-related anatomical differences, such as thinner skulls and scalps in children, influence light propagation during fNIRS measurements, highlighting the need for age-stratified interpretations of biomarkers (Remijn et al., 2011; Mazziotti et al., 2022). Additionally, while short-term reproducibility of visual event-related hemodynamic responses has been demonstrated in adults (Plichta et al., 2006b), further research is required to assess the long-term stability and circadian effects on fNIRS measurements. Moreover, controlling for systemic physiological factors, such as blood pressure and heart rate fluctuations, is essential to improve the precision of fNIRS-based biomarkers (Minati et al., 2011).
In conclusion, fNIRS, particularly in visual paradigms, is a powerful, non-invasive tool for assessing brain function, complementing existing technologies such as fMRI and EEG, in both typical and atypical development. Expanding standardized protocols and refining vHDR analysis will strengthen its clinical applications, enabling earlier and more accurate diagnoses, tracking neurodevelopmental trajectories, and optimizing therapeutic strategies. Given the urgent need for reliable functional biomarkers in X-linked NDDs, expanding fNIRS studies across a broader spectrum of conditions will be essential. Furthermore, integrating fNIRS into a multimodal framework, such as combining it with EEG, could provide transformative insights into brain function. EEG offers high temporal resolution complementing fNIRS’s spatial information, thus enabling a more comprehensive assessment of neural activity. The potential of multimodal imaging is also evident in recent studies that simultaneously collect fNIRS and fMRI data to investigate the relationship between cortical hemodynamics and deeper brain activity. For example, the feasibility of using fMRI-measured cortical activity has been demonstrated to predict deep-brain activity, showing a strong correlation with fNIRS measurements. Since many deep brain regions are anatomically and functionally connected with cortical surface regions (Bullmore and Sporns, 2009), this suggests that fNIRS, when combined with fMRI-informed models, can extend its utility beyond its typical limitation of superficial cortical sensitivity (Liu et al., 2015; Balters et al., 2023).
Future research should also focus on validating fNIRS measures, such as vHDR, against established clinical endpoints, including cognitive assessments (e.g., Bayley Scales of Infant Development, Wechsler Intelligence Scale for Children, Vineland Adaptive Behavior Scale, etc.) and motor function scales (e.g., Gross Motor Function Measure). This validation is essential for confirming the clinical relevance of fNIRS biomarkers and for establishing their ability to predict disease severity and treatment outcomes.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
ES: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing. CB: Writing – original draft, Writing – review & editing. RB: Funding acquisition, Supervision, Writing – original draft, Writing – review & editing. MF: Funding acquisition, Supervision, Writing – original draft, Writing – review & editing. LB: Conceptualization, Data curation, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by EJP RD JTC 2022 “Development of new analytic tools and pathways to accelerate diagnosis and facilitate diagnostic monitoring of rare diseases” under the EJP-Cofund Action on PM - N° 825575 and Italian Ministry of Health.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
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Keywords: fNIRS, visual stimulation, brain biomarker, neurodevelopmental disorders, X-linked rare disease
Citation: Scaffei E, Bosetti C, Battini R, Fagiolini M and Baroncelli L (2025) fNIRS as a biomarker for X-linked neurodevelopmental disorders: leveraging visual processing to assess brain function? Front. Neurosci. 19:1560786. doi: 10.3389/fnins.2025.1560786
Edited by:
Yusuke Takatsuru, Toyo University, JapanReviewed by:
Dejan Budimirovic, Johns Hopkins University, United StatesXubin Hou, Niigata University, Japan
Copyright © 2025 Scaffei, Bosetti, Battini, Fagiolini and Baroncelli. 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: Elena Scaffei, ZWxlbmEuc2NhZmZlaUBmc20udW5pcGkuaXQ=