Abstract
Inconsistent colloquial and professional uses of the term “brain fog” have undermined the potential for empirical study of this symptom complex, and there is no consensus about how to diagnose or treat it. Yet brain fog is frequently reported by patients with post-acute sequelae of SARS-CoV-2 (PASC) and other chronic conditions, often presenting as cognitive difficulties such as memory issues and attention deficits, accompanied by central fatigue and sometimes depression. This symptom complex is also common in Parkinson’s disease (PD). One objective of this article is to propose a theoretical model that conceptualizes brain fog in PD in terms that can guide its measurement and treatment. Neuroinflammation, dopaminergic dysfunction, and immune system interactions are examined as potential mechanisms. We present the Fatigue and Altered Cognition Scale (FACs), a patient-report tool designed to assess brain fog in clinical and research settings that has been validated in traumatic brain injury and PASC. Proper diagnosis and monitoring of brain fog are important because early research suggests that existing medications, such as methylphenidate, and natural substances, such as carnosic acid, have the potential to alleviate its symptoms. Ongoing research is crucial to establish a clear definition of brain fog and identify effective treatments in PD and other neurodegenerative disorders.
Introduction
Co-occurring complaints of fatigue and brain fog represent a debilitating symptom complex that has a strikingly similar manifestation in more than a dozen chronic conditions, such as traumatic brain injury (TBI), multiple sclerosis (MS), chronic fatigue syndrome, fibromyalgia, lupus, Sjögren’s syndrome, postural orthostatic tachycardia syndrome, hypoparathyroidism, and celiac disease (1, 2). The frequency of this symptom constellation across many chronic health conditions suggests it may be a transdiagnostic syndrome (3, 4) and a potential public health concern (5). Until recently, however, brain fog was poorly investigated, and the inconsistent, if not circular, colloquial and professional uses of the term undermine an operationalization of the construct required for empirical study. At present, there is no consensus about how to diagnose or treat brain fog.
Brain fog is reported by 25 to 90% of patients who develop post-acute sequelae of SARS-CoV-2 infection (PASC) (6). Central fatigue is also a principal symptom of PASC (1, 7). In comparison to peripheral fatigue—muscular impairment or exhaustion due to exertion—central fatigue, also called mental fatigue, is a subjective report of difficulty initiating and maintaining activity and attending to tasks that require sustained mental effort (8). The very high prevalence of brain fog in PASC has prompted a flurry of research, and there is new evidence that brain fog is a central nervous system dysfunction that has cardinal symptoms, separate from peripheral fatigue and depression, and has the potential to be measured objectively.
In this commentary, after discussing characteristics of brain fog identified in PASC, we focus on brain fog in Parkinson’s disease (PD). We propose an empirical model that may explain why brain fog develops in PD and suggest the use of a previously published scale for measuring brain fog clinically and in clinical trials.
Brain fog in PASC
To tease out more exactly what deficits constitute brain fog, a research team in Spain conducted comprehensive neuropsychological testing of 170 patients with PASC who had cognitive complaints (2). The results showed brain fog is associated with objective impairment in attention and episodic memory and is correlated with but separate from central fatigue. The effect of depression in brain fog was indirect and mediated through central fatigue.
A study in Ireland included 71 patients with PASC who self-reported brain fog (yes/no) (9). Key indicators of brain fog included word-finding difficulties, memory impairment, and reduced cognitive response times. Others were dizziness, myalgia, reduced grip strength, reduced gait speed, and poorer scores on the Chalder Fatigue Scale, which measures both peripheral and central fatigue. These neurologic and psychomotor consequences make it easy to understand why patients affected by PASC-related fog report substantially diminished quality of life (QoL) (9–11).
Intriguingly, small studies suggest brain fog may be a measurable phenomenon in PASC. Patients have exhibited lower functional connectivity in multiple brain regions on magnetic resonance imaging (12), hypometabolic regions of the cingulate cortex on cerebral fluorodeoxyglucose positron emission tomography (FDG-PET) (13), and changes in their quantitative electroencephalography profile compared with profiles recorded before they developed PASC (14).
Brain fog in PD
Although empirical studies of brain fog associated with PD are lacking, qualitative research reveals that patients living with PD and their families report problems they term “brain fog” (15, 16). The lack of consensus in defining brain fog in PD, however, is on full display in recent studies of verbatim responses to open-ended questions in a large-scale survey sponsored by the Michael J. Fox Foundation. In a study of more than 25,000 respondents, a machine learning algorithm included the term “brain fog” in a domain labeled “mental alertness/awareness” rather than domains of “memory,” “concentration/attention,” and “cognitive slowing” (17). In another study of the same survey (n > 21,000), the same algorithm excluded “brain fog” from the “mental alertness/awareness” domain and categorized it under “cognitive impairment not otherwise specified” (18). In both studies, the most bothersome problems were with memory and concentration/attention.
Consistent with our understanding of brain fog in other chronic conditions, patients with PD are often said to experience “confusion” (19) and cognitive impairment in PD characterized by attentional disturbances (20). The most relevant research about brain fog in PD concerns subjective cognitive decline (SCD)—subjective cognitive complaints without concurrent objective cognitive deficits as measured by validated cognitive scales. When 139 patients with newly diagnosed PD underwent comprehensive neuropsychological evaluation, the prevalence of SCD was 28% (21). As in the Spanish study of brain fog in PASC (2), the most commonly affected domains were memory (28% of patients with SCD) and attention/working memory (26%). Overlapping symptoms in multivariable linear regression analysis were anxiety and depression.
Italian researchers asked 90 consecutively enrolled non-demented patients with PD to complete the Parkinson’s Disease Cognitive Functional Rating Scale (subjective measure) and the Montreal Cognitive Assessment (MoCA, objective measure) (22). Twenty-nine patients (32%) were classified as “underestimators” who had SCD but no objective cognitive impairment. Scores on the Fatigue Severity Scale and the Beck Depression Inventory distinguished underestimators from accurate estimators and overestimators (those with objective impairment but no self-reported complaints).
Using FDG-PET, a German team documented neural correlates of SCD in PD (23). Among 18 PD patients determined to have SCD, greater concern about cognition in everyday situations correlated with greater hypometabolism in middle frontal, middle temporal, and occipital areas of the brain, as well as the angular gyrus.
As it does in PASC, brain fog in PD can have profound negative effects on QoL. In an online assessment of 612 individuals with PD, confusion more than quadrupled the odds of poor QoL (McGill Quality of Life Questionnaire), and memory loss more than tripled it (24). In a similar study of 139 patients, confusion and memory problems were significantly correlated with poor QoL reported on the Parkinson’s Disease Questionnaire–39 (25).
Toward an empirical model of brain fog in PD
Recent attempts to advance our understanding of possible mechanisms that may account for the transdiagnostic nature of co-occurring brain fog and fatigue have focused on issues of energy required for cognitive processing (26) and the neural pathways involved in cognitive and motivational control (27). Ideally, clinicians assisting patients with PD will benefit from a theoretical model that conceptualizes brain fog in terms that can guide its measurement and treatment. One such model conceptualizes brain fog as a symptom of pituitary dysfunction that can occur in the wake of TBI and is usually accompanied by central fatigue (28). Reasoning from this model, research has examined the ability of growth hormone replacement therapy to alleviate these symptoms (29–31). Clinical case studies of patients with TBI (31) and PASC (32) have been promising, but randomized clinical trials are needed.
As a transdiagnostic entity, brain fog may have different causes, but in PASC and MS, neuroinflammation clearly has a key role. Fernández-Castanẽda and colleagues recently reported similarities between brain fog in PASC and “chemo fog,” or cancer therapy–related cognitive impairment (CRCI) (33, 34). During chemotherapy or radiation, elevations in neurotoxic cytokines and reactive microglia can lead to cascades of multicellular events that negatively affect gray and white matter plasticity, which is important for cognition. Reminiscent of CRCI, the researchers found elevated reactivity of microglia and macrophages in subcortical and hippocampal white matter in mice following mild respiratory COVID-19, and the elevation persisted 7 weeks post-infection. In murine cerebrospinal fluid, cytokine levels remained persistently elevated, including levels of CCL11, which has been causally linked to cognitive impairment in normal aging (35). Moreover, CCL11 was elevated in plasma from patients with PASC—but only those with cognitive symptoms.
A case–control study pointed to a different impact of neuroinflammation in PASC (36). Radiolabeled PET showed that, compared with healthy controls, patients with PASC who had depressive symptoms and/or cognitive impairment had higher levels of the translocator protein, a marker of microglial activation, in cortex, hippocampus, and other brain regions. Very similar results were reported from a study of 28 patients with MS, and the level of the translator protein in white matter and cortex correlated with decreased information processing speed and neurologic disability (37).
One intriguing question is whether the proinflammatory process launched by hyperactivated microglia is mediated by the nod-like receptor protein-3 (NLRP3) inflammasome. Activation of NLRP3 in monocytes and microglial cells by alpha-synuclein is well documented in PD, and it can be triggered by dopaminergic degeneration even in the absence of alpha-synuclein aggregates (38–40). Serum levels of NLRP3 and interleukin-1β, a potent proinflammatory cytokine, are elevated in PD patients, and there is a linear correlation of NLRP3 with α-synuclein (39).
MS is a good model for us to understand the impact of neuroinflammation and dopaminergic dysfunction on mental processes in other neurodegenerative diseases such as PD. We hypothesize that lessons learned from MS could be applicable to PD as the two diseases share some common pathophysiological aspects, such as microglial-driven neuroinflammation and dopaminergic dysfunction. Indeed, neuroimmune interaction is considered one of the most promising directions in MS research. Dopamine is a direct mediator of interactions between the immune and nervous systems and can influence the course of MS by modulating immune cell activity and cytokine production (41–43). Fatigue, the most common symptom in MS (44), has been attributed to a dopamine imbalance that disrupts communication between the striatum and prefrontal cortex (45). Many patients who receive disease-modifying treatment of MS, most of them with potent anti-inflammatory effects, report improvements in fatigue, cognition, or both (46–51). Amantadine, which is used off-label to treat fatigue in MS, has also improved cognitive function in some studies (52, 53). Amantadine has multiple mechanisms of action, but its effect on brain fog may be attributable to its interference with dopamine transmission.
We postulate that, as in MS, dopaminergic dysfunction is a mechanism of brain fog in PD. Neurons are designed to live and function in a peaceful sanctuary, thanks to the blood–brain barrier and the work of glial cells in maintaining brain homeostasis and regulating inflammatory responses. Any disturbance in the surrounding microenvironment may lead to neuronal dysfunction. PD constitutes a situation where dopaminergic neurons are not only scarce (some already died) but also under attack from inflammation, alpha-synuclein accumulation, tau phosphorylation, and so forth. This imbalance could lead to a constellation of symptoms, including objective cognitive decline, mood disorders, and brain fog.
Other neurotransmitter pathways, such as cholinergic and serotoninergic networks, do not seem to play a role in brain fog. Deterioration in cholinergic projections from the basal nucleus of Meynert to the cortex is one hallmark of cognitive dysfunction in Alzheimer’s disease (AD), and acetylcholinesterase inhibitors produce modest, observable improvement in the cognitive symptoms of AD. But the evidence for anticholinesterase inhibitors in PD is not as clear as in AD (54). Neither does brain fog seem to be associated with serotonergic dysfunction, as selective serotonin reuptake inhibitors can fail to completely relieve brain fog–like symptoms in patients with depression (55, 56).
Finally, there are confounding factors in studying the mechanism of brain fog in PD and in diagnosing and treating it. Cognitive impairment in PD is often associated with depression or adverse effects from levodopa, dopamine agonists, antipsychotics, or antidepressants (57–60). Clinicians should keep in mind that self-report measures of depression include items that overlap with symptoms of brain fog and fatigue (e.g., tiredness, concentration difficulties, psychomotor retardation): Total scores from self-report measures do not indicate a depressive disorder. Brain fog and fatigue can exist in the absence of depressed mood and anhedonia.
Future directions
Early detection of brain fog and altered cognition is critical for clinical management strategies that might delay PD dementia. To date, much research into brain fog has relied on time-consuming neuropsychological testing, which is too cumbersome and impractical for routine use in clinical encounters and monitoring symptom response to treatment, and may be compromised by patient fatigue. For assessment of fatigue, at least 14 scales are available to measure PD patients’ self-reports, but most lack a consistent definition of fatigue (61). Furthermore, many of them, such as the Parkinson Fatigue Scale (62), emphasize the physical aspects of fatigue rather than central fatigue. Indeed, Chen and colleagues determined in a systematic review that no single scale is sufficient for diagnosing central fatigue in PD (61).
To address these problems, Elliott et al. (63) developed the Fatigue and Altered Cognition Scale (FACs) to measure the presence and severity of brain fog and central fatigue over the past 2 weeks (Table 1). The format expedites patient reporting and clinician use across platforms, including mobile phones and tablet and laptop computers. The FACs two-factor structure and internal consistency (α’s ranging from 0.91 to 0.97) has been validated in TBI (64), MS (65), and PASC (6), where no FACs item significantly overlapped with the cardinal symptoms of depression (sad mood, loss of pleasure) or anxiety (excessive worry). Construct validity of the two factors has also been established with measures of physical and cognitive fatigue, loss of vitality, attention deficits, and subjective cognitive complaints (65), as well as measures of cognitive and emotional burnout (66). Other scales have been recently developed to measure brain fog alone (67–69), although only one of these was developed and validated with a clinical sample (67). Evidence about their utility in specific chronic health conditions is lacking.
Table 1
| Item no. | Item text |
|---|---|
| Fatigue scale | |
| 1 | I felt fatigued |
| 2 | I felt alert* |
| 6 | I felt worn out |
| 7 | I felt sluggish |
| 8 | I felt run down |
| 10 | I had the energy to do what I wanted to do* |
| 13 | I had to force myself to get things done |
| 15 | I felt tired |
| 17 | I had to struggle to finish what I started to do |
| 20 | I had problems feeling energetic no matter if I slept or napped |
| Altered cognition scale | |
| 3 | I lost track of what I was going to say |
| 4 | I was forgetful |
| 5 | I had trouble concentrating |
| 9 | I had trouble focusing on things I wanted to do |
| 11 | I was easily confused |
| 12 | I felt “spaced out” like I was in a fog |
| 14 | I was clear-headed* |
| 16 | I did not process things as quickly or accurately as I should have |
| 18 | I had trouble paying attention |
| 19 | It was hard for me to make up my mind and reach a decision |
The Fatigue and Altered Cognition Scale.
*Reverse coded before conducting item analyses.
Modified from this table in Elliott et al. (63) with permission under the auspices of a Creative Commons Attribution License.
The FACs was designed to expedite clinical assessment and treatment of co-occurring brain fog and central fatigue. Its use should complement current practice. The Montreal Cognitive Assessment (MoCA) is internationally recommended to screen for mild cognitive impairment among PD patients (70, 71). It is an objective measure that informs case conceptualization and initial treatment plans including the probable need for a full neuropsychological battery to determine specific deficits (70, 72). The FACs can be used in case conceptualization, but it is purpose-built for serial administrations in the clinic to assess a patient’s subjective experience of symptoms over time in response to treatment, guiding clinical decisions to alleviate patient suffering and complaints. The FACs has features that meet several criteria for “pragmatic validity” (73). It is brief and it can be self-administered with minimal respondent burden, sensitive to change, and it is designed to provide information that guides therapeutic decisions and facilitates patient-clinician communication about issues important to the patient.
Now that an efficient measurement tool is available, brain fog should be an endpoint in neurologic clinical trials. In addition, the FACs could be used to plan and monitor treatment of brain fog. For example, preliminary research shows that methylphenidate, a dopaminergic psychostimulant, is associated with subjective improvement in memory and concentration in PASC (74) and alleviates fatigue in individuals with MS (44) and PD (75). The FACs could help document objective findings in further research. The validity and reliability of the FACs in PD warrants future study.
As another example, carnosic acid, a derivative of the herb rosemary, has been shown to exert neuroprotective effects in multiple in vivo models of PD and AD (76). Its mechanism in this regard is phase 2 enzyme induction initiated by activation of the KEAP1/NRF2 transcriptional pathway, which in turn attenuates NLRP3-driven inflammation, including in human macrophages (76, 77). Carnosic acid is hypothesized to have application as a therapy for brain fog in PASC, AD, and PD (77), but at this writing no clinical study has been published.
This progress is encouraging, but the research into brain fog has been inconsistent, which obstructs a clear understanding of the phenomenon. The field will benefit from continued work to develop a working definition of brain fog, establish the mechanisms that drive it, and determine therapeutic targets. Further, it is apparent that cultural and language differences in symptom reports merit empirical scrutiny. A recent translation study of the FACs into European Portuguese found 12 items had terms or phrases that required cultural substitutions to ensure the items were understandable and meaningful (66). Programmatic and systematic research should empirically advance our understanding and help us rule out faulty and ineffective approaches.
Statements
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
TE: Conceptualization, Investigation, Writing – original draft, Writing – review & editing. XV: Funding acquisition, Writing – review & editing. SC: Project administration, Writing – review & editing. AM: Conceptualization, Investigation, 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. The authors declare that this paper received funding from Ventus Therapeutics U. S., Inc. The funder had the following involvement with the paper: conceptualization, the writing of this article, the decision to submit it for publication, and payment of open access publication fees.
Acknowledgments
The authors thank Faith Reidenbach of Caley-Reidenbach Consulting, LLC for medical writing and editorial assistance.
Conflict of interest
XV, SC, and AM were employed by Ventus Therapeutics U. S., Inc.
The remaining author declares that the research 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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References
1.
Aghajani Mir M . Brain fog: a narrative review of the most common mysterious cognitive disorder in COVID-19. Mol Neurobiol. (2024) 61:9915–26. doi: 10.1007/s12035-023-03715-y,
2.
Delgado-Alonso C Diez-Cirarda M Pagan J Perez-Izquierdo C Oliver-Mas S Fernandez-Romero L et al . Unraveling brain fog in post-COVID syndrome: relationship between subjective cognitive complaints and cognitive function, fatigue, and neuropsychiatric symptoms. Eur J Neurol. (2025) 32:e16084. doi: 10.1111/ene.16084,
3.
Denno P Zhao S Husain M Hampshire A . Defining brain fog across medical conditions. Trends Neurosci. (2025) 48:330–48. doi: 10.1016/j.tins.2025.01.003,
4.
McWhirter L . Brain fog. Pract Neurol. (2025) 25:137–42. doi: 10.1136/pn-2024-004112,
5.
Haywood D Rossell SL Hart NH . Cutting through the fog: recognising brain fog as a significant public health concern. BMC Public Health. (2025) 25:1230. doi: 10.1186/s12889-025-22525-6,
6.
Hsiao YY Elliott TR Jaramillo J Douglas ME Powers MB Warren AM . The fatigue and altered cognition scale among SARS-CoV-2 survivors: psychometric properties and item correlations with depression and anxiety symptoms. J Clin Med. (2024) 13:2186. doi: 10.3390/jcm13082186,
7.
Thaweethai T Jolley SE Karlson EW Levitan EB Levy B McComsey GA et al . Development of a definition of postacute sequelae of SARS-CoV-2 infection. JAMA. (2023) 329:1934–46. doi: 10.1001/jama.2023.8823,
8.
Zgaljardic DJ Durham WJ Mossberg KA Foreman J Joshipura K Masel BE et al . Neuropsychological and physiological correlates of fatigue following traumatic brain injury. Brain Inj. (2014) 28:389–97. doi: 10.3109/02699052.2014.884242,
9.
Jennings G Monaghan A Xue F Duggan E Romero-Ortuno R . Comprehensive clinical characterisation of brain fog in adults reporting long COVID symptoms. J Clin Med. (2022) 11:3440. doi: 10.3390/jcm11123440,
10.
National Academies of Sciences, Engineering, and Medicine . Long-Term Health Effects of COVID-19: Disability and Function Following SARS-CoV-2 Infection. Washington, DC: The National Academies Press (2024). doi: 10.17226/27756
11.
Callan C Ladds E Husain L Pattinson K Greenhalgh T . I can't cope with multiple inputs': a qualitative study of the lived experience of 'brain fog' after COVID-19. BMJ Open. (2022) 12:e056366. doi: 10.1136/bmjopen-2021-056366,
12.
Kesler SR Franco-Rocha OY De La Torre Schutz A Lewis KA Aziz RM Henneghan AM et al . Altered functional brain connectivity, efficiency, and information flow associated with brain fog after mild to moderate COVID-19 infection. Sci Rep. (2024) 14:22094. doi: 10.1038/s41598-024-73311-0,
13.
Hugon J Msika EF Queneau M Farid K Paquet C . Long COVID: cognitive complaints (brain fog) and dysfunction of the cingulate cortex. J Neurol. (2022) 269:44–6. doi: 10.1007/s00415-021-10655-x,
14.
Kopańska M Ochojska D Muchacka R Dejnowicz-Velitchkov A Banaś-Ząbczyk AS . Comparison of QEEG findings before and after onset of post-COVID-19 brain fog symptoms. Sensors (Basel). (2022) 22:6606. doi: 10.3390/s22176606,
15.
Armstrong MJ Rastgardani T Gagliardi AR Marras C . Impact of off periods on persons with Parkinson disease and care partners: a qualitative study. Neurol Clin Pract. (2021) 11:e232–8. doi: 10.1212/CPJ.0000000000000921,
16.
Levit AV Zebendon C Walter L O'Donnell P Marras C . Communication gaps about OFF periods between physicians and patients with Parkinson’s disease: a patient–physician dialogue analysis. Res Rev Parkinsonism. (2019) 9:3–8. doi: 10.2147/JPRLS.S188923
17.
Purks JL Arbatti L Hosamath A Amara AW Anderson KE Chahine L et al . Cognitive symptoms in cross-sectional Parkinson disease cohort evaluated by human-in-the-loop machine learning and natural language processing. Neurol Clin Pract. (2024) 14:e200334. doi: 10.1212/CPJ.0000000000200334,
18.
Weintraub D Marras C Amara A Anderson KE Chahine LM Eberly S et al . Association between subjective cognitive complaints and incident functional impairment in Parkinson's disease. Mov Disord. (2024) 39:706–14. doi: 10.1002/mds.29725,
19.
Bayulkem K Lopez G . Nonmotor fluctuations in Parkinson's disease: clinical spectrum and classification. J Neurol Sci. (2010) 289:89–92. doi: 10.1016/j.jns.2009.08.022,
20.
Hirano S . Clinical implications for dopaminergic and functional neuroimage research in cognitive symptoms of Parkinson's disease. Mol Med. (2021) 27:40. doi: 10.1186/s10020-021-00301-7,
21.
Yang N Ju Y Ren J Wang H Li P Ning H et al . Prevalence and affective correlates of subjective cognitive decline in patients with de novo Parkinson's disease. Acta Neurol Scand. (2022) 146:276–82. doi: 10.1111/ane.13662,
22.
Siciliano M Trojano L De Micco R Sant'Elia V Giordano A Russo A et al . Correlates of the discrepancy between objective and subjective cognitive functioning in non-demented patients with Parkinson's disease. J Neurol. (2021) 268:3444–55. doi: 10.1007/s00415-021-10519-4,
23.
Ophey A Krohm F Kalbe E Greuel A Drzezga A Tittgemeyer M et al . Neural correlates and predictors of subjective cognitive decline in patients with Parkinson's disease. Neurol Sci. (2022) 43:3153–63. doi: 10.1007/s10072-021-05734-w,
24.
Tarolli CG Zimmerman GA Auinger P McIntosh S Horowitz RK Kluger BM et al . Symptom burden among individuals with Parkinson disease: a national survey. Neurol Clin Pract. (2020) 10:65–72. doi: 10.1212/CPJ.0000000000000746,
25.
Rahman S Griffin HJ Quinn NP Jahanshahi M . Quality of life in Parkinson's disease: the relative importance of the symptoms. Mov Disord. (2008) 23:1428–34. doi: 10.1002/mds.21667,
26.
Gaber TAZK . Mind in the mist: the interplay between fatigue, information processing and brain fog [editorial]. Fatigue. (2025) 13:257–61. doi: 10.1080/21641846.2025.2513194
27.
Kok A . Cognitive control, motivation and fatigue: a cognitive neuroscience perspective. Brain Cogn. (2022) 160:105880. doi: 10.1016/j.bandc.2022.105880,
28.
Urban RJ . A treatable syndrome in patients with traumatic brain injury. J Neurotrauma. (2020) 37:1124–5. doi: 10.1089/neu.2019.6689,
29.
Wright T Urban R Durham W Dillon EL Randolph KM Danesi C et al . Growth hormone alters brain morphometry, connectivity, and behavior in subjects with fatigue after mild traumatic brain injury. J Neurotrauma. (2020) 37:1052–66. doi: 10.1089/neu.2019.6690,
30.
Yuen KCJ Masel BE Reifschneider KL Sheffield-Moore M Urban RJ Pyles RB . Alterations of the GH/IGF-I axis and gut microbiome after traumatic brain injury: a new clinical syndrome?J Clin Endocrinol Metab. (2020) 105:dgaa398. doi: 10.1210/clinem/dgaa398,
31.
High WM Jr Briones-Galang M Clark JA Gilkison C Mossberg KA Zgaljardic DJ et al . Effect of growth hormone replacement therapy on cognition after traumatic brain injury. J Neurotrauma. (2010) 27:1565–75. doi: 10.1089/neu.2009.1253,
32.
Wright TJ Sheffield-Moore M Pyles RB Randolph KM McGovern KA Danesi CP et al . Growth hormone treatment for neurologic symptoms of post-acute sequelae of COVID-19. Clin Transl Sci. (2024) 17:e13826. doi: 10.1111/cts.13826,
33.
Fernández-Castanẽda A Lu P Geraghty AC Song E Lee MH Wood J et al . Mild respiratory COVID can cause multi-lineage neural cell and myelin dysregulation. Cell. (2022) 185:2452–68. doi: 10.1016/j.cell.2022.06.008,
34.
Kao J Frankland PW . COVID fog demystified [editorial]. Cell. (2022) 185:2391–3. doi: 10.1016/j.cell.2022.06.020
35.
Villeda SA Luo J Mosher KI Zou B Britschgi M Bieri G et al . The ageing systemic milieu negatively regulates neurogenesis and cognitive function. Nature. (2011) 477:90–4. doi: 10.1038/nature10357,
36.
Braga J Lepra M Kish SJ Rusjan PM Nasser Z Verhoeff N et al . Neuroinflammation after COVID-19 with persistent depressive and cognitive symptoms. JAMA Psychiatry. (2023) 80:787–95. doi: 10.1001/jamapsychiatry.2023.1321,
37.
Barletta VT Herranz E Treaba CA Ouellette R Mehndiratta A Loggia ML et al . Evidence of diffuse cerebellar neuroinflammation in multiple sclerosis by (11)C-PBR28 MR-PET. Mult Scler. (2020) 26:668–78. doi: 10.1177/1352458519843048,
38.
Panicker N Kam TI Wang H Neifert S Chou SC Kumar M et al . Neuronal NLRP3 is a parkin substrate that drives neurodegeneration in Parkinson's disease. Neuron. (2022) 110:2422–2437.e9. doi: 10.1016/j.neuron.2022.05.009,
39.
Chatterjee K Roy A Banerjee R Choudhury S Mondal B Halder S et al . Inflammasome and alpha-synuclein in Parkinson's disease: a cross-sectional study. J Neuroimmunol. (2020) 338:577089. doi: 10.1016/j.jneuroim.2019.577089
40.
Gordon R Albornoz EA Christie DC Langley MR Kumar V Mantovani S et al . Inflammasome inhibition prevents alpha-synuclein pathology and dopaminergic neurodegeneration in mice. Sci Transl Med. (2018) 10:eaah4066. doi: 10.1126/scitranslmed.aah4066
41.
Belousova O Lopatina A Melnikov M . The role of dopamine in the modulation of monocyte-induced Th17- and Th1-immune response in multiple sclerosis. Int Immunopharmacol. (2024) 137:112540. doi: 10.1016/j.intimp.2024.112540,
42.
Melnikov M Rogovskii V Boyksmall o CA Pashenkov M . Dopaminergic therapeutics in multiple sclerosis: focus on Th17-cell functions. J Neuroimmune Pharmacol. (2020) 15:37–47. doi: 10.1007/s11481-019-09852-3
43.
Melnikov M Sviridova A Rogovskii V Kudrin V Murugin V Boyko A et al . The role of D(2)-like dopaminergic receptor in dopamine-mediated modulation of Th17-cells in multiple sclerosis. Curr Neuropharmacol. (2022) 20:1632–9. doi: 10.2174/1570159X19666210823103859,
44.
Natsheh JY DeLuca J Costa SL Chiaravalloti ND Dobryakova E . Methylphenidate may improve mental fatigue in individuals with multiple sclerosis: a pilot clinical trial. Mult Scler Relat Disord. (2021) 56:103273. doi: 10.1016/j.msard.2021.103273,
45.
Dobryakova E Genova HM DeLuca J Wylie GR . The dopamine imbalance hypothesis of fatigue in multiple sclerosis and other neurological disorders. Front Neurol. (2015) 6:52. doi: 10.3389/fneur.2015.00052,
46.
Zanghì A Di Filippo PS Avolio C D'Amico E . Evaluating cognitive outcomes in multiple sclerosis: real-world impact of ozanimod on processing speed using BICAMS. Neurol Ther. (2025) 14:1345–53. doi: 10.1007/s40120-025-00736-8,
47.
Leodori G Mancuso M Maccarrone D Tartaglia M Ianniello A Baione V et al . Improvement of fatigue, depression, and processing speed two weeks post natalizumab infusion in multiple sclerosis: no difference between standard and extended interval dosing schedules. Mult Scler Relat Disord. (2024) 92:106146. doi: 10.1016/j.msard.2024.106146,
48.
Cruz Rivera S Aiyegbusi OL Piani Meier D Dunne A Harlow DE Henke C et al . The effect of disease modifying therapies on fatigue in multiple sclerosis. Mult Scler Relat Disord. (2023) 79:105065. doi: 10.1016/j.msard.2023.105065,
49.
Manouchehrinia A Larsson H Karim ME Lycke J Olsson T Kockum I . Comparative effectiveness of natalizumab on cognition in multiple sclerosis: a cohort study. Mult Scler. (2023) 29:628–36. doi: 10.1177/13524585231153992,
50.
Landmeyer NC Bürkner PC Wiendl H Ruck T Hartung HP Holling H et al . Disease-modifying treatments and cognition in relapsing-remitting multiple sclerosis: a meta-analysis. Neurology. (2020) 94:e2373–83. doi: 10.1212/WNL.0000000000009522,
51.
Wilken J Kane RL Sullivan CL Gudesblatt M Lucas S Fallis R et al . Changes in fatigue and cognition in patients with relapsing forms of multiple sclerosis treated with natalizumab: the ENER-G study. Int J MS Care. (2013) 15:120–8. doi: 10.7224/1537-2073.2012-043,
52.
Generali JA Cada DJ . Amantadine: multiple sclerosis–related fatigue. Hosp Pharm. (2014) 49:710–2. doi: 10.1310/hpj4908-710,
53.
Henze T Rieckmann P Toyka KV . Symptomatic treatment of multiple sclerosis: multiple sclerosis therapy consensus group (MSTCG) of the German Multiple Sclerosis Society. Eur Neurol. (2006) 56:78–105. doi: 10.1159/000095699,
54.
Baik K Kim SM Jung JH Lee YH Chung SJ Yoo HS et al . Donepezil for mild cognitive impairment in Parkinson's disease. Sci Rep. (2021) 11:4734. doi: 10.1038/s41598-021-84243-4,
55.
Papalexi E Galanopoulos A Roukas D Argyropoulos I Michopoulos I Douzenis A et al . Residual cognitive and psychosocial functional impairment in outpatients in Greece who responded to conventional antidepressant monotherapy treatments for major depressive disorder (MDD). J Affect Disord. (2022) 314:185–92. doi: 10.1016/j.jad.2022.07.009,
56.
Liu L Lv X Zhou S Liu Q Wang J Tian H et al . The effect of selective serotonin reuptake inhibitors on cognitive impairment in patients with depression: a prospective, multicenter, observational study. J Psychiatr Res. (2021) 141:26–33. doi: 10.1016/j.jpsychires.2021.06.020,
57.
Kwon KY Park S Kim RO Lee EJ Lee M . Associations of cognitive dysfunction with motor and non-motor symptoms in patients with de novo Parkinson's disease. Sci Rep. (2022) 12:11461. doi: 10.1038/s41598-022-15630-8,
58.
Prange S Klinger H Laurencin C Danaila T Thobois S . Depression in patients with Parkinson's disease: current understanding of its neurobiology and implications for treatment. Drugs Aging. (2022) 39:417–39. doi: 10.1007/s40266-022-00942-1,
59.
Baiano C Barone P Trojano L Santangelo G . Prevalence and clinical aspects of mild cognitive impairment in Parkinson's disease: a meta-analysis. Mov Disord. (2020) 35:45–54. doi: 10.1002/mds.27902,
60.
Petkus AJ Filoteo JV Schiehser DM Gomez ME Hui JS Jarrahi B et al . Mild cognitive impairment, psychiatric symptoms, and executive functioning in patients with Parkinson's disease. Int J Geriatr Psychiatry. (2020) 35:396–404. doi: 10.1002/gps.5255,
61.
Chen J Zhou Y Rao H Liu J . Mental fatigue in Parkinson's disease: systematic review and evaluation of self-reported fatigue scales. Parkinsons Dis. (2024) 2024:9614163. doi: 10.1155/2024/9614163,
62.
Brown RG Dittner A Findley L Wessely SC . The Parkinson fatigue scale. Parkinsonism Relat Disord. (2005) 11:49–55. doi: 10.1016/j.parkreldis.2004.07.007,
63.
Elliott TR Hsiao YY Randolph K Urban RJ Sheffield-Moore M Pyles RB et al . Efficient assessment of brain fog and fatigue: development of the fatigue and altered cognition scale (FACs). PLoS One. (2023) 18:e0295593. doi: 10.1371/journal.pone.0295593,
64.
Wright TJ Elliott TR Randolph KM Pyles RB Masel BE Urban RJ et al . Prevalence of fatigue and cognitive impairment after traumatic brain injury. PLoS One. (2024) 19:e0300910. doi: 10.1371/journal.pone.0300910,
65.
Dias KS . Factor structure and validity of the fatigue and altered cognition scale among persons with multiple sclerosis [doctoral dissertation]. College Station (TX): Texas A&M University (2025).
66.
Fernandes C Baylina P Barbosa F Barros C . Psychometric validation of the European Portuguese version of the fatigue and altered cognition scale (FACs). Appl Neuropsychol Adult. (2025) 15:1–11. doi: 10.1080/23279095.2025.2560536
67.
Leavitt F Katz RS . Development of the mental clutter scale. Psychol Rep. (2011) 109:445–52. doi: 10.2466/02.07.14.22.PR0.109.5.445-452,
68.
Atik D Inel Manav A . A scale development study: brain fog scale. Psychiatr Danub. (2023) 35:73–9. doi: 10.24869/psyd.2023.73
69.
Debowska A Boduszek D Ochman M Hrapkowicz T Gaweda M Pondel A et al . Brain fog scale (BFS): scale development and validation. Pers Individ Differ. (2024) 216:112427. doi: 10.1016/j.paid.2023.112427
70.
Kalbe E Folkerts AK Witt K Buhmann C Liepelt-Scarfone I German Parkinson’s Guidelines Group . German Society of Neurology guidelines for the diagnosis and treatment of cognitive impairment and affective disorders in people with Parkinson's disease: new spotlights on diagnostic procedures and non-pharmacological interventions. J Neurol. (2024) 271:7330–57. doi: 10.1007/s00415-024-12503-0,
71.
Skorvanek M Goldman JG Jahanshahi M Marras C Rektorova I Schmand B et al . Global scales for cognitive screening in Parkinson's disease: critique and recommendations. Mov Disord. (2018) 33:208–18. doi: 10.1002/mds.27233,
72.
Boel JA de Bie RMA Schmand BA Dalrymple-Alford JC Marras C Adler CH et al . Level I PD-MCI using global cognitive tests and the risk for Parkinson's disease dementia. Mov Disord Clin Pract. (2022) 9:479–83. doi: 10.1002/mdc3.13451,
73.
Stanick CF Halko HM Dorsey CN Weiner BJ Powell BJ Palinkas LA et al . Operationalizing the 'pragmatic' measures construct using a stakeholder feedback and a multi-method approach. BMC Health Serv Res. (2018) 18:882. doi: 10.1186/s12913-018-3709-2,
74.
Clark P Rosenberg P Oh ES Parker A Vannorsdall T Azola A et al . Methylphenidate for the treatment of post-COVID cognitive dysfunction (brain fog). J Med Cases. (2024) 15:195–200. doi: 10.14740/jmc4254,
75.
Mendonça DA Menezes K Jog MS . Methylphenidate improves fatigue scores in Parkinson disease: a randomized controlled trial. Mov Disord. (2007) 22:2070–6. doi: 10.1002/mds.21656,
76.
Lin G Li N Li D Chen L Deng H Wang S et al . Carnosic acid inhibits NLRP3 inflammasome activation by targeting both priming and assembly steps. Int Immunopharmacol. (2023) 116:109819. doi: 10.1016/j.intimp.2023.109819,
77.
Satoh T Trudler D Oh CK Lipton SA . Potential therapeutic use of the rosemary diterpene carnosic acid for Alzheimer's disease, Parkinson's disease, and long-COVID through NRF2 activation to counteract the NLRP3 inflammasome. Antioxidants (Basel). (2022) 11:124. doi: 10.3390/antiox11010124,
Summary
Keywords
brain fog, Parkinson’s disease, neuroinflammation, dopamine, neuroimmunomodulation, fatigue, cognitive dysfunction, Fatigue and Altered Cognition Scale
Citation
Elliott TR, Valencia X, Chen S and Matta A (2026) Brain fog in Parkinson’s disease: unraveling mechanisms and measuring impact. Front. Neurol. 16:1571079. doi: 10.3389/fneur.2025.1571079
Received
04 February 2025
Revised
04 February 2025
Accepted
17 November 2025
Published
07 January 2026
Volume
16 - 2025
Edited by
Mya C. Schiess, University of Texas Health Science Center at Houston, United States
Reviewed by
Jinru Zhang, Second Affiliated Hospital of Soochow University, China
Sichun Gu, Longhua Hospital, China
Updates
Copyright
© 2026 Elliott, Valencia, Chen and Matta.
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: Andre Matta, amatta@ventustx.com
†These authors have contributed equally to this work
Disclaimer
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