NeuroTrax Science Team and Glen M. Doniger, PhD

More than half of people with multiple sclerosis (PwMS) experience accidental falls. However, standard neurological evaluations such as the Expanded Disability Status Scale (EDSS) focus primarily on physical and ambulatory motor function and may not explain why activity-limiting fear of falling (FoF) can arise independently of physical impairment.

A 2026 peer-reviewed study published in Multiple Sclerosis and Related Disorders demonstrates why cognitive assessment should be part of the clinical conversation about fall risk. The findings suggest that FoF is not merely a psychological byproduct of physical weakness. It is a metacognitive phenomenon influenced by executive control, response inhibition, attention, and motor timing, with the most clinically relevant functions changing as physical disability progresses. The study evaluated 188 PwMS using the NeuroTrax computerized cognitive assessment battery alongside the Modified Falls Efficacy Scale. Across the full cohort, poorer executive function (β = 0.32, p = 0.023) and motor skills (β = 0.24, p = 0.009) significantly predicted greater FoF. Patients with three or more impaired cognitive domains also experienced significantly greater FoF than those with fewer or no impaired domains (1).

For clinicians, the disability-stratified findings are particularly important. Among patients with mild disability, defined as EDSS ≤ 3.5, executive function was the only significant predictor of FoF. Inhibitory control, as measured with the NeuroTrax Go No-Go test, may support the ability to monitor environmental hazards, plan movements, and inhibit unsafe actions. In moderate-to-severe disability, motor skills and response-execution timing became the primary predictors, suggesting that perceived risk increasingly reflects physical limitations as disability advances. NeuroTrax also helped differentiate patients with high and low FoF before severe motor disability developed. In mildly disabled patients, executive function and attention each had an area under the curve of 0.74, while the global cognitive score had an AUC of 0.71. These findings indicate that digital neurometrics may help identify patients whose fall-related concerns are not evident from physical disability scores alone.

Related NeuroTrax research supports a stage-dependent relationship between cognition, mobility, and fall perception. Balance control has been associated with executive function and motor skills in mild MS, while other cognitive domains become more relevant as disability progresses (2). Studies in aging and Parkinson’s disease have also linked executive function and Go No-Go response inhibition performance with future falls and dual-task gait instability (3,4). Together, these findings suggest that the cognitive targets assessed during fall-risk evaluation should change as EDSS progresses.

This cognitive-motor relationship may be most clinically actionable before recurrent falls or advanced disability occur. Among non-fallers with MS, gait variability is associated with global cognition, executive function, and motor skills, but this relationship is not evident among fallers. Additionally, attention shows a strong relationship with FoF, and information processing speed is valuable for classifying overall fall risk when perceived and physiological risk are not aligned (5–7).

FoF should therefore be evaluated as an independent clinical metric rather than treated solely as a consequence of previous falls. Non-fallers reporting FoF have been shown to walk more slowly and perform more poorly in the NeuroTrax motor skills domain than fallers without FoF. Research into motoric cognitive risk syndrome similarly shows that the convergence of slow gait and global cognitive impairment is associated with significantly greater FoF and fatigue. These findings highlight a multi-domain threshold effect whereby combined cognitive and physical deficits increase activity restriction (8,9).

Brief screens like the MMSE are not designed to capture the domain-specific deficits that may influence fall confidence. NeuroTrax provides multi-domain cognitive profiling and precise millisecond-level timing, helping clinicians look beyond a single physical disability score.

For patients with mild MS, results may support cognitive rehabilitation focused on executive function, response inhibition, hazard monitoring, and future planning. This may help clinicians address disproportionate FoF before it leads to avoidable activity restriction. In advanced MS, physical rehabilitation targeting motor execution speed and response timing may be more appropriate when combined with cognitive support.

Integrating digital neurometrics can also help clinicians discern mismatches between perceived and physiological fall risk and facilitate timely selection of more individualized fall-prevention, rehabilitation, and behavioral strategies. By revealing the cognitive functions contributing to FoF at each stage of disability, NeuroTrax provides clinically relevant information that physical assessment alone does not capture.

References

[1] Dhakal, B., Covey, T.J., Peterson, D.S., Zanotto, T., Weinstock-Guttman, B., Barrera, M., Ofori, E., Wilken, J., Bergmann, C.S., Jackson, D.A., Morrow, S.A., Plummer, P., Bumstead, B., Buhse, M., Doniger, G.M., Penner, I.K., Golan, D., Weller, J., and Gudesblatt, M. (2026). Multiple sclerosis and fear of falling: A complex interaction between cognitive network function and EDSS. Multiple Sclerosis and Related Disorders, 115:107880. DOI: 10.1016/j.msard.2026.107880

[2] Kalron, A. (2016). The relationship between static posturography measures and specific cognitive domains in individuals with multiple sclerosis. International Journal of Rehabilitation Research, 39(3), 249–254. DOI: 10.1097/MRR.0000000000000177

[3] Herman, T., Mirelman, A., Giladi, N., Schweiger, A., and Hausdorff, J.M. (2010). Executive control deficits as a prodrome to falls in healthy older adults: A prospective study linking thinking, walking, and falling. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 65A(10), 1086–1092. DOI: 10.1093/gerona/glq077

[4] Hausdorff, J.M., Doniger, G.M., Springer, S., Yogev, G., Giladi, N., and Simon, E.S. (2006). A common cognitive profile in elderly fallers and in patients with Parkinson’s disease: The prominence of impaired executive function and attention. Experimental Aging Research, 32(4), 411–429. DOI: 10.1080/03610730600875817

[5] Kalron, A., Aloni, R., Dolev, M., Frid, L., Givon, U., and Menascu, S. (2018). The relationship between gait variability and cognitive functions differs between fallers and non-fallers in MS. Journal of Neural Transmission, 125(6), 945–952. DOI: 10.1007/s00702-018-1843-y

[6] Kalron, A. (2014). The relationship between specific cognitive domains, fear of falling, and falls in people with multiple sclerosis. BioMed Research International, 2014:281760. DOI: 10.1155/2014/281760

[7] Zanotto, T., Kumar, D.P., Golan, D., Wilken, J., Doniger, G.M., Zarif, M., Bumstead, B., Buhse, M., Weller, J., Morrow, S.A., Penner, I.K., Hancock, L., Covey, T.J., Ofori, E., Peterson, D.S., Motl, R.W., Bogaardt, H., Barrera, M., Bove, R., Karpatkin, H., Sosnoff, J.J., and Gudesblatt, M. (2025). Does cognitive performance explain the gap between physiological and perceived fall-risk in people with multiple sclerosis? Multiple Sclerosis and Related Disorders, 95:106322. DOI: 10.1016/j.msard.2025.106322

[8] Kalron, A., and Allali, G. (2017). Gait and cognitive impairments in multiple sclerosis: The specific contribution of falls and fear of falling. Journal of Neural Transmission, 124(11), 1407–1416. DOI: 10.1007/s00702-017-1765-0

[9] Dreyer-Alster, S., Menascu, S., Aloni, R., Givon, U., Dolev, M., Achiron, A., and Kalron, A. (2022). Motoric cognitive risk syndrome in people with multiple sclerosis: Prevalence and correlations with disease-related factors. Therapeutic Advances in Neurological Disorders,15:17562864221109744. DOI: 10.1177/17562864221109744

NeuroTrax Science Team and Glen M. Doniger, PhD

It is challenging for neuropsychologists to keep up with rising demand for comprehensive neuropsychological evaluations, especially with the unprecedented growth in the aging population. Indeed, traditional testing remains highly resource-intensive, creating long waitlists for patients. Paper-and-pencil batteries require hours of direct clinician administration and extensive manual scoring, which may delay report turnaround and subsequent treatment planning. Integrating validated digital neuropsychological assessments like NeuroTrax offers a path to improve clinical workflows by providing rapid standardized point-of-care digital neurometrics (1,2).

NeuroTrax does not require direct clinician administration. It is designed for ease of use and may be supervised by office staff, nurses, or technicians. In a large usability study of more than 2,800 patients, 83% rated NeuroTrax “easy to use,” including older adults over age 75 with no prior computer experience and those with significant cognitive impairment, confirming its usability and acceptability in routine clinical care (2).

Test administration by a trained technician frees the neuropsychologist to focus precious time on clinical interviews, complex case formulation, diagnostic reasoning, and treatment recommendations. This allows practices to increase assessment capacity and leverage the neuropsychologist’s expertise for activities that provide the greatest clinical value.

Notably, digital multi-domain batteries are designed to serve as precision tools for objective screening and longitudinal monitoring rather than diagnosis. The digital output does not replace clinical judgment. Objective neurometrics are most beneficial when integrated by a neuropsychologist with information on comorbidities, effort, functional status, behavioral observations, and patient history to render a clinical interpretation (3).

In highly complex or ambiguous cases, the neuropsychologist may use supplementary neuropsychological testing for deep-dive or targeted follow-up evaluations. Indeed, primary care providers and neurologists may use NeuroTrax to identify cognitive changes early and refer complex or ambiguous cases to neuropsychologists for a more extensive workup. This focuses the referral pipeline so that specialist resources are available for patients who need them most (2–4).

Unlike hours-long traditional batteries that contribute to patient fatigue, the full NeuroTrax battery takes 45-60 minutes and delivers a detailed profile across seven core domains: memory, executive function, attention, processing speed, visual spatial ability, verbal function, and motor skills. The secure, web-enabled platform automatically and immediately calculates scores by comparison with a large, co-normed database, adjusting for age and education; manual lookup tables are not required. A comprehensive, color-coded clinical report with longitudinal graphs is generated seconds after battery completion, facilitating prompt review and treatment planning (1–3).

Digital delivery mitigates examiner subjectivity and standardization drift, including subtle variations in vocal tone, pacing, instructions, and stimulus presentation. NeuroTrax also captures response times on a millisecond scale, revealing subtle processing delays or performance lapses that untimed, accuracy-focused traditional tests may miss. Many NeuroTrax tests are adaptive, adjusting task difficulty for patient performance level, thus keeping the test challenging and minimizing ceiling effects (1,5,6).

Scientific support for NeuroTrax comes from more than two decades of peer-reviewed research demonstrating its psychometric foundation. Alternate test forms reduce practice effects with repeated testing. In US Navy divers, alternate-form reliability correlations for the NeuroTrax global score were 0.89–0.92, demonstrating stability (7,8).

Studies comparing NeuroTrax digital neurometrics with traditional gold-standard tests show good correlations for tests of corresponding cognitive domains, for example: NeuroTrax Non-Verbal Memory with the Brief Visuospatial Memory Test-Revised (r = 0.84), NeuroTrax Go-NoGo response time variability with the Trail Making Test Part B (r = 0.74), and NeuroTrax Verbal Memory with the Hopkins Verbal Learning Test (r = 0.72) (9).

By incorporating digital neurometrics into their assessment pathway, neuropsychologists can serve more patients, accelerate the assessment cycle, optimize referral pathways, and devote more of their time to complex interpretation and individualized care.

References

[1] Dwolatzky, T., Whitehead, V., Doniger, G.M., Simon, E.S., Schweiger, A., Jaffe, D., and Chertkow, H. (2003). Validity of a novel computerized cognitive battery for mild cognitive impairment. BMC Geriatrics, 3:4. DOI: 10.1186/1471-2318-3-4

[2] Fillit, H.M., Simon, E.S., Doniger, G.M., and Cummings, J.L. (2008). Practicality of a computerized system for cognitive assessment in the elderly. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 4(1), 14–21. DOI: 10.1016/j.jalz.2007.09.008

[3] Golan, D., Wilken, J., Doniger, G.M., Fratto, T., Kane, R., Srinivasan, J., Zarif, M., Bumstead, B., Buhse, M., Fafard, L., Topalli, I., and Gudesblatt, M. (2019). Validity of a multi-domain computerized cognitive assessment battery for patients with multiple sclerosis. Multiple Sclerosis and Related Disorders, 30, 154–162. DOI: 10.1016/j.msard.2019.01.051

[4] Rao SM. (2018). Role of computerized screening in healthcare teams: Why computerized testing is not the death of neuropsychology. Archives of Clinical Neuropsychology, 33(3), 375–378. DOI: 10.1093/arclin/acx137

[5] Abramovitch, A., Dar, R., Schweiger, A., and Hermesh, H. (2011). Neuropsychological impairments and their association with obsessive-compulsive symptom severity in obsessive-compulsive disorder. Archives of Clinical Neuropsychology, 26(4), 364–376. DOI: 10.1093/arclin/acr022

[6] Achiron, A., Doniger, G.M., Harel, Y., Appleboim-Gavish, N., Lavie, M., and Simon, E.S. (2007). Prolonged response times characterize cognitive performance in multiple sclerosis. European Journal of Neurology, 14(10), 1102–1108. DOI: 10.1111/j.1468-1331.2007.01909.x

[7] Schweiger, A., Doniger, G.M., Dwolatzky, T., Jaffe, D., and Simon, E.S. (2003). Reliability of a novel computerized neuropsychological battery for mild cognitive impairment. Acta Neuropsychologica, 1(4), 407–413. GICID: 01.3001.0001.0603

[8] Melton, J.L. (2005). NEDU Technical Report 06-10, Navy Experimental Diving Unit, Panama City, FL.

[9] Doniger, G.M., Okun, M.S., Simon, E.S.,Rodriguez, R.L., Jacobson, C.E., Weiss, D., Rosado, C., and Fernandez, H.H. (2006). Construct validity of a computerized neuropsychological assessment in patients with movement disorders: Interim analysis. Movement Disorders, 21(9), 1557.