Next Article in Journal
Habitual Aerobic Exercise Is Associated with Reduced Negative Emotional Elicitation: An fNIRS Study
Previous Article in Journal
Using N400 Event-Related Potential to Detect Differences in Design-Mode and Belief-Mode Scaffold Use
Previous Article in Special Issue
Driving Performance in Schizophrenia: The Role of Neurocognitive Correlates—A Systematic Review
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Driving with Motor Neuron Disease: Disease-Specific Considerations, Multi-Domain Assessments and Support Strategies

1
Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
2
Department of Neurology, St James’s Hospital, D08 NHY1 Dublin, Ireland
3
The Irish FTD/FTLD Network, Dublin, Ireland
*
Author to whom correspondence should be addressed.
Brain Sci. 2026, 16(4), 408; https://doi.org/10.3390/brainsci16040408
Submission received: 23 March 2026 / Revised: 30 March 2026 / Accepted: 9 April 2026 / Published: 10 April 2026

Abstract

Motor neuron diseases (MNDs) encompass a clinically heterogeneous group of neurodegenerative conditions with varying impact on dexterity, mobility, decision making, respiratory and bulbar dysfunction. While consensus best-practice recommendations exist for genetic screening, diagnostic work-up, pharmacological and respiratory management, disease-specific facets of driving safety, assessment approaches and intervention strategies to support patients for safe driving have not been comprehensively reviewed. MNDs have unique, phenotype-specific clinical features, which are distinct form other neuromuscular conditions which necessitate a careful and systematic approach to evaluate driving safety. While MNDs are primarily associated with progressive motor impairment, extrapyramidal, cerebellar, cognitive, behavioural, and respiratory manifestations of the disease also affect driving safety and necessitate comprehensive driving assessments and individualised strategies to enable patients to continue to drive. The majority of existing papers focus on amyotrophic lateral sclerosis, and low-incidence MND phenotypes, such as PLS, SBMA, PPS, are glaringly understudied from a driving safety perspective despite the relatively slower progression of these conditions. Beyond the review of specific aspects of driving in MNDs, the main objective of this review paper is to raise awareness of non-motor aspects of MNDs with regard to driving safety and to explore viable strategies to support patients to maintain their independence. Despite the considerable differences in driving regulations around the globe, there are core, disease-specific aspects of MND which are universal. The careful consideration of these clinical factors, comprehensive domain-by-domain assessments, and the implementation of practical, individualised adaptations may enable patients to continue driving safely, maintain their independence and enhance their quality of life.

1. Introduction

Motor neuron disease (MND) is an umbrella term encompassing diverse neurodegenerative conditions with distinctive clinical features. Amyotrophic lateral sclerosis (ALS) is the most common form of MND affecting middle-aged people and typically presenting with insidious onset motor symptoms and exhibiting a rapidly progressive clinical course. ALS however is a clinically heterogeneous syndrome with considerable variations in motor disability profiles, cognitive involvement, and progression rates. The notable symptomatic heterogeneity in ALS (Figure 1) necessitates careful and comprehensive assessments in a range of clinical domains to establish patient-specific profiles early in the course of the disease to inform individualised management strategies. There are two key attributes that distinguishes ALS from the most common neurodegenerative conditions, such as Parkinson’s disease (PD), or Alzheimer’s disease (AD). ALS typically affects younger people, who are often still working, raising families, running a business, frequently travelling domestically and internationally and may be particularly reliant on driving. The other unique aspect of ALS compared to other neurodegenerative conditions, such as AD or PD, is the notable diversity of initial presentations, the striking differences in disability profiles and variability in progression rates. Some patients walk into the clinic with marked bulbar symptoms and no limb manifestations at all, while others struggle to use their limbs, but their speech and swallowing are unaffected. Some present with marked spasticity and hyperreflexia, while motor disability in others is dominated by muscle wasting and flaccid weakness. The main dimensions of disease heterogeneity in ALS includes differences in the genetic profile, site of onset, upper versus lower motor neuron predominance, cognitive and behavioural involvement, and extrapyramidal manifestations (Figure 1). Clinical and genetic overlap with other neurodegenerative conditions such as FTD/FTLD or PLS adds to the complexity of disease heterogeneity. In light of the notable differences in clinical manifestations, ALS is often conceptualised as a “spectrum disorder”. The clinical diversity of ALS is often approached along the UMN-LMN, ALS-FTD, slow–fast progressors, bulbar–spinal disability, and “familial” (genetic)–sporadic axes. While this is simplistic, it is useful to subcategorise patients into specific phenotypes, hence the generation of various patient categorisation and staging schemes based on disability profiles. The Milano–Torino staging system (MITOS), King’s staging, the Fine’til 9 (FT9) staging method, Strong criteria, and terms such as spinal–bulbar onset, are just some of the commonly used strategies to subcategorise patients with ALS [1,2,3,4,5,6,7]. The striking clinical heterogeneity of ALS (Figure 1) precludes the adoption of a single, unified management strategy and necessitates comprehensive assessments evaluating motor, extrapyramidal, cerebellar, cognitive, behavioural and social domains. Similarly to the adoption of precision pharmacological interventions and genotype-specific therapies [8,9], supportive strategies also necessitate an individualised approach based on multi-domain assessments to best support patients in their own environment, based on their current disability and in their personal social, spiritual and psychological context.

2. Driving with MND

Driving is surprisingly understudied in MNDs despite its unquestionable relevance to independence, employment and social interactions. While driving may seem less relevant in urban environments and locations well serviced by public transport, clinical experience and interaction with patients would suggest that it is hugely important for a significant proportion of patients with considerable quality of life ramifications. Independence, autonomy and dignity are key components of quality of life in ALS and in MNDs in general, and driving is often considered as a particularly important factor in maintaining social interactions, attending family events, engaging with the local community, attending religious or spiritual services, maintaining employment, looking after personal affairs such as banking, shopping, hair-dressing, finances, post, leisure activities, attending sporting events, going out to restaurants, bars, pubs, and, as the patients often put it, remaining an “active member of the local community”. As air travel may become increasing problematic and international travel increasingly daunting, car trips are often favoured by patients with respiratory difficulties, due to the ease of stopping when needed. Taking holidays by car and organising weekends away often provide a welcome respite from hospital attendances to relieve stress. Patients with respiratory symptoms and patients with longer symptom duration often opt for car travel and taking ferries to travel abroad instead of flying, to avoid queuing at the airports, lengthy security checks, facing flight delays and long flights in a confined space. Empirical evidence would also suggest that driving is important for psychological well-being and feeling less of a burden to family members. Beyond the social and quality of life (QoL) aspects of driving, driving is often indispensable to attend hospital consultations, clinical trial sessions, and physiotherapy, speech pathology, rehabilitation and dietetics consultations. While seldom evaluated formally, the inability to drive and residing in the countryside may impact on the frequency of hospital attendances and willingness to participate in pharmacological trials and research studies and may lead to social isolation. It is also noteworthy that travelling in a car as a passenger may also be challenging even if driven by someone else, as access, seating, getting out of the car, and communication may become problematic or uncomfortable. Expert review, adjustments for individual disability profiles are therefore also paramount for non-driving car passengers. The emotional impact of driving cessation is relatively well researched [10,11] and it has been extensively studied in older individuals. It is a notoriously difficult transition as driving is often consider as an integral factor of one’s independence [12] and may have self-esteem ramifications. Family support, caregiver network and support in the community play a crucial role to make this transition less troublesome. In a condition where a substantive literature has been generated for presymptomatic screening [13], diagnostic work-up, disease staging [2], spasticity management, genetic counselling, pharmacological management, multidisciplinary interventions [14], respiratory support, feeding tube placement, palliative interventions, machine learning applications, etc. [15], it is curious how driving in MND is glaringly under-researched despite its considerable “real-life”, practical ramifications and quality of life implications. The objective of this paper is therefore the careful review of motor and non-motor aspects of MNDs and their potential impact on driving, the suggestion of a comprehensive domain-by-domain assessment scheme and the discussion of practical strategies to enhance patient independence.

3. Methods

A narrative review has been conducted to review existing studies, assessment recommendations and driving adaptations in motor neuron diseases. The search terms “MND”, “ALS”, “PLS”, “Polio”/”Poliomyelitis”, and “Kennedy’s disease”/”SBMA” were individually paired with “Driving” on PubMed. Information on study design (prospective, retrospective, multicentre, etc.), patient cohort (ALS, SBMA, PPS, etc.), number of participants, main objectives, clinical instruments and assessment batteries, interventions, and main study findings were retrieved from the identified studies. To provide a context for multi-domain assessments, motor and extra-motor manifestations of MNDs are also briefly reviewed, focusing on recently published data. Only original articles published in English were considered, and opinion pieces, editorials, reviews, and meta-analyses were excluded. In light of the paucity of relevant papers identified, a narrative review format has been adopted instead of a “systematic review” to highlight study shortcomings, knowledge gaps, and research priorities.

4. Results

4.1. Motor Manifestations of ALS

ALS presents with varying degrees of comorbid lower (LMN) and upper motor neuron (UMN) dysfunction (Figure 1). The most common clinical manifestations of LMN involvement is flaccid paresis with muscle bulk loss, fasciculations, often leading to poor grip, selective hand muscle involvement with the preferential involvement of the first dorsal interosseous (FDI) [16], wrist-drop, finder-drop, head-drop, foot drop, proximal muscle weakness and respiratory weakness [17]. Wrist-drops, foot-drops and head-drops are typically managed by the careful fitting of orthoses, chin lifts, splints, etc. High-resolution biomechanical measures in gait studies reveal considerable ankle dysfunction with reduced range of motion and plantarflexion strength [18]. This not only affects walking stability, but interferes with driving. It is important to highlight that despite their disability, patients with early stage ALS perform similarly to controls on simulated driving tasks [19]. Therapeutic exercise, particularly low-to-moderate-intensity aerobic and resistance training, may help preserve muscle strength and function in ALS and slow functional decline, potentially supporting safer driving for longer [20,21]. UMN dysfunction manifests as spasticity, cramping, and clonus, but the resulting clinical picture depends on the proportion of UMN/LMN dysfunction in a specific body region. Spasticity in ALS stems from upper motor neuron dysfunction and leads to progressive muscle stiffness, reduced mobility, difficulty with activities of daily living (ADLs), and driving [22]. Spasticity often contributes to fatigue and pain [23,24]. First-line treatments for spasticity in ALS include oral antispasmodic drugs, such as baclofen or tizanidine, with botulinum toxin type A (BTX-A) injections and physiotherapy. BTX-A combined with physiotherapy has shown short-term improvements in muscle function without significant side effects [22]. Moderate-intensity endurance exercise may also reduce spasticity, and improve motor and even pulmonary function [25]. Dexterity is often affected early in the course of the disease in limb-onset forms of ALS. A notable asymmetry is often observed in the earlier stages with one limb much more affected than the other, providing an opportunity for adaptive strategies to perform daily tasks including hand controls for driving or, rarely, left-foot accelerators. Bulbar motor manifestations of ALS include dysarthria, dysphagia and pseudobulbar affect, progressively impacting communication, swallowing and social functions [26,27]. Bulbar dysfunction also contributes to excessive drooling (sialorrhea) which may be distracting while driving. Sialorrhea often necessitates careful pharmacological interventions which in turn may impact on alertness and concentration. Lower limb dexterity and bulbar dysfunction all impact driving, as they may interfere with getting in and out of the car, steering, operating switches, gears, and pedals, keeping the head upright and moving the head left and right. Discomfort from cramping, pain from adhesive capsulitis, weight loss, and drooling are all common symptoms in ALS which may impact on concentration. Spasticity may reduce fine control of pedals and gears. Dysarthria may impact on making calls and engaging with voice commands and satellite navigation. Sudden emotional responses, especially in those experiencing pseudobulbar affect or pathological crying and laughter (PCL) may also be distracting [28]. While classically conceptualised as a “pure” UMN-LMN condition, solely driven by primary motor cortex [29] and spinal anterior horn degeneration [30], it is increasingly clear that extrapyramidal, cerebellar, and sensory components contribute to impaired fine motor control in ALS [31,32,33,34,35,36]. Cerebellar, sensory and extrapyramidal facets of ALS are notoriously overlooked [31,32,33,34,37]. The degree of cerebellar pathology is difficult to evaluate clinically due to coexisting UMN/LMN dysfunction dominating the clinical picture, but a series of recent neuroimaging papers have confirmed cerebellar cortex, deep cerebellar nuclear and cerebro-cerebellar connectivity alterations with a predilection to specific cerebellar regions in both ALS [36,37] and PLS [38,39]. Cerebellar pathology in MNDs has widespread clinical implications beyond fine motor control; due to its diverse physiological roles, it may adversely impact on judging distance, perception of speed, gaging size and dimensions, and emotional responses. It is known to contribute to pseudobulbar affect, and has numerous cognitive and behavioural correlates [40,41,42,43,44,45,46,47,48,49,50,51]. Subtle extrapyramidal manifestations have also long been noted in ALS [52]. Post mortem and neuroimaging studies have consistently highlighted basal ganglia degeneration in both ALS and PLS [53,54,55,56], and there is also evidence of both structural and functional corticobasal connectivity alterations [57]. It is therefore increasingly clear that extrapyramidal motor deficits contribute to motor impairment in MNDs [57,58,59]. While the core clinical features of ALS are widely known, there is relatively limited awareness of subtle coexisting proprioceptive, extrapyramidal and cerebellar deficits and these factors all impact on motor control, gait, dexterity and bulbar function. Accordingly, motor deficits in ALS should not be solely assessed from a UMN/LMN perspective and sensory components, proprioceptive deficits, motor integration circuits, basal ganglia degeneration and cerebellar dysfunction should also be considered [33,34]. While involuntary movements are not classically associated with ALS, they are occasionally reported and may impact on driving. These may include rest minipolymyoclonus, thumb tremors, pseudodystonic thumb posture, action minipolymyoclonus, and action tremors. Minipolymyoclonus, or polyminimyoclonus, is a low-amplitude, high-frequency, arrhythmic, jerky involuntary movement, often seen in the hands and fingers in ALS but also observed in other lower motor neuron disorders such as Spinal and Bulbar Muscular Atrophy (SBMA) or Spinal Muscular Atrophy (SMA) [60,61]. Action tremor is also observed at times and corresponds to electromyography peak frequency [62].

4.2. Non-Motor Features of ALS/MND

Adding to the complexity of motor manifestations, a range of extra-motor manifestations may impact on activities of daily driving in ALS and other MNDs. Cognitive deficits [63], behavioural disturbances [64], fatigue [23], apathy [65], and respiratory weakness [66] are just some of the clinical facets of ALS which may impact on driving safety. Neuropsychological deficits in ALS are traditionally linked to executive dysfunction [67] and behavioural deficits [68,69], but recent studies have confirmed a wider spectrum of neuropsychological manifestations including memory [70,71,72] and language deficits [73,74,75], deficits in social cognition [76,77], and apathy [65]. Impulsivity, disinhibition [78], impaired decision making and risk ascertainment, deciphering the intentions of others [77,79,80], and poor concentration all have obvious ramifications for driving safety [81]. Data from other neurodegenerative conditions confirm that disinhibition and executive dysfunction are associated with increased driving errors [82]. The neuropathological substrate of these deficits has been extensively investigated in ALS [83,84,85], but the modifying effects of cognitive reserve, education, cognitive rehabilitation and medications on neuropsychological manifestations are also increasingly recognised [14,86,87,88]. There is a range of other ALS-associated non-motor manifestations which may impact on driving such as discomfort from weight loss, poor tolerability of longer drives, sialorrhea, somnolence, dyspnoea, morning headaches, and fatigue [23]. Understanding of the causes of both motor and non-motor manifestations is crucial so that the appropriate adaptive strategies can be implemented. Somnolence and poor concentration are also commonly reported in ALS and have an obvious impact on driving. Fatigue in ALS and other MNDs is multifactorial [23,89], arising from a combination of hypoxia, hypercapnia, sedating medications, low mood, fragmented sleep, etc. (Figure 2). Commonly used medications in ALS, such as baclofen, anticholinergics prescribed for drooling, opiate analgesia, and benzodiazepines prescribed for spasticity, are all notoriously sedating in isolation and especially in combination. A systematic approach for evaluating somnolence and fatigue allows targeted interventions such as the introduction of non-invasive ventilation (NIV), medication adjustments, etc., which may improve these symptoms. Evidence for effectively treating fatigue in ALS is very limited. Pharmacological and non-pharmacological interventions show possible, but largely unproven, benefits [23,90]. Possible pharmacological therapies include Modafinil [91] and 3-4 diaminopyridine (DAP), with only mild improvement in subjective fatigue scores [92]. Fatigue is recognised as the leading cause of fatal road traffic accidents (RTAs) worldwide and has been linked to functional connectivity alterations [93]. Sensory dysfunction is not typically associated with MNDs even though paraesthesiae are often reported by patients. Recent clinical [94,95,96,97], neuropathology [98,99,100,101], neurophysiology [102,103,104,105], spinal and brain imaging studies [103,106,107,108] have all highlighted varying degrees of primary sensory and sensory processing deficits [109]. These are thought to include subclinical proprioceptive deficits and suggest evidence of spinal posterior column degeneration [103,110]. Weight loss in ALS has been traditionally exclusively linked to bulbar dysfunction, but recent studies have identified complex neuroendocrine- and hypothalamus-mediated network alterations [111,112]. Significant weight loss and catabolic state may influence driving comfort, mood, concentration, and also contribute to a sense of generalised fatigue (Figure 2).
Respiratory weakness is a hallmark of ALS, leading to reduced ventilatory function, weak cough, ineffective secretion clearance, and eventual respiratory failure. This decline is primarily driven by diaphragmatic and intercostal muscle denervation and results in reduced maximum inspiratory/expiratory pressures [113,114]. Impaired respiratory function impacts on concentration, decision making, and cognitive function [115,116]. Data from non-ALS cohorts indicate that even mild increases in CO2 can impair decision making and problem-solving, and may cause fatigue, headaches, and dizziness [117,118]. Respiratory muscle training and expert physiotherapy may improve quality of life, but effects on overall lung function are variable [114,119]. The negative impact of hypercapnia on cognitive and psychomotor function is well established [120] and this is increasingly prevalent in ALS as the disease progresses [121]. Individuals with hypercapnia perform worse on tasks measuring vigilance and processing speed and logical memory tests compared to non-hypercapnic OSA patients [122]. The mainstay of therapy is non-invasive ventilation (NIV). Improvement in hypercapnia-associated symptoms can be typically noted soon after NIV initiation. Early introduction of NIV is associated with better outcomes [123,124,125].
Sleep disorders are also highly prevalent in ALS and are multifactorial. Respiratory muscle weakness, pain, psychological factors, existential distress, and impaired sleep regulation are all contributory factors. The most common presentations include insomnia, nocturnal hypoventilation (NH), obstructive sleep apnoea (OSA), restless legs syndrome (RLS), excessive daytime sleepiness (EDS), and, less commonly, rapid eye movement sleep behaviour disorder (REMSBD). In patients with restrictive lung disorders, sleep-related hypoventilation associated with hypercapnia may affect approximately 30% of patients and is typically linked to diaphragmatic, intercostal, and accessory respiratory muscles weakness [126,127,128,129]. Sleep disturbances worsen physical and mental health during the day, and are associated with faster progression rates and reduced survival, especially when coupled with respiratory dysfunction [130]. Untreated OSA confers a 1.5 to 4 times higher risk for RTAs, and RTA risk is thought to be commensurate with OSA severity. While OSA is typically treated by Continuous Positive Airway Pressure (CPAP) [131,132], BIPAP/NIV with pressure support is the preferred intervention in ALS [66]. Insomnia is typically driven by a combination of physical symptoms and psychological distress, and it may raise RTA risk twofold [126,128,133,134]. Sleep deprivation reduces vigilance, even without subjective sleepiness, and impairs multitasking [132]. Excessive daytime sleepiness (EDS) at the wheel is associated with an increased risk of accidents, with an odds ratio (OR) of 2.51 in some studies [135]. EDS may manifest in diminished attention, poor concentration, slower reaction times, and inappropriate line crossing. In recognition of this association, the automobile industry has recently developed a range of safety systems such as lane departure warning, eye-tracking, steering wheel grip detection, back-up cameras, 360° camera systems, park-assist technologies, traffic sign and traffic light recognition sensors, radar systems to maintain a safe distance, and various collision avoidance systems, such as automated braking and steering corrections [136]. The Intelligent Drowsiness and Fatigue Recognition (IDFR) system, for example, employs a customised convolutional neural network (CNN) algorithm for ocular tracking [137]. These systems often integrate data from a multitude of sensors and feed real-time data into complex machine learning (ML) models. Input physiological and behavioural markers typically include eye and mouth movements, brain activity markers, heart rate variability, head posture, steering behaviour, and lane deviation. Neuromorphic vision systems based on on-board camera data achieve excellent accuracy in detecting driver drowsiness [138,139].
From a driving perspective, it is very important that note that not every neuronal circuit and functional network is affected in ALS and that several functions such as vision, hearing and many parietal processes are relatively preserved. ALS is not associated with global atrophy, but it selectively affects specific brain regions, with a predilection to the primary motor cortex, corpus callosum, and descending corticospinal tracts. Depending on the phenotype, certain frontotemporal regions are also affected. Occipital and parietal brain regions however are relatively spared [140] and, while some visual processing networks may be eventually affected [141], frank visual field defects are never experienced by patients. Similarly, while some auditory regions are eventually involved [106], hearing loss and hearing impairment are not typically experienced in ALS.

4.3. Non-ALS MND Phenotypes

The vast majority of best-practice recommendations in motor neuron diseases solely focus on ALS as the most common form of MND. MNDs, however, encompass a clinically diverse spectrum of conditions, each posing unique management challenges with regard to driving. Primary Lateral Sclerosis, or PLS, is a progressive neurodegenerative condition which manifests with progressive limb spasticity and pseudobulbar manifestations and is primarily dominated by upper motor neuron degeneration. Limb stiffness, decreased rapid finger movements, difficulty getting in and out of the car, and spasticity slowing pedal operation are just some of the motor sequelae of UMN degeneration in PLS. In addition to progressive primary motor cortex and corticospinal tract degeneration in PLS [142], recent studies have demonstrated cognitive deficits [143,144] and subcortical [55,145], cerebellar [39], and frontotemporal involvement [143,146,147], contributing to the extra-motor and extrapyramidal manifestations of the disease [85,148]. These observations highlight that PLS is not a “pure” UMN disorder and clinical assessments need to explore extra-motor domains. Spinal and Bulbar Muscular Atrophy (SBMA), a.k.a. Kennedy’s disease, is another progressive multi-system neurodegenerative condition which primarily manifest as a lower motor neuron (LMN) disorder. It is associated however with a range of other neurological, cardiac, metabolic and endocrine features. Glucose intolerance, type II diabetes, hyperlipidaemia, sensory neuropathy, postural tremor, obstructive sleep apnoea, painful muscle cramps and myalgia, breathlessness, cognitive manifestations, repolarisation abnormalities and ensuing arrhythmias are just some of the disease-specific clinical factors that need to be carefully weighted up when assessing driving safety [149,150]. Post-poliomyelitis syndrome (PPS) typically manifest decades after the initial infection, and while assessments typically focus on limb control and strength, a range of extra-motor manifestations are often reported in PPS, including fatigue, somnolence, poor concentration, pain and discomfort [89]. A systematic approach (Figure 3), exploring all disease-specific clinical facets of these conditions, is therefore paramount to assess driving safety in these MND subtypes. Progressive muscular atrophy (PMA) is a low-incidence, LMN-predominant motor neuron disease phenotype, which is associated with longer survival than ALS, and typically presents with relentless muscle wasting and fasciculations. Recent studies have demonstrated some degree of extra-motor manifestations including mild cognitive impairment [151,152].

4.4. Driving Studies in MND

There is a striking paucity of studies addressing driving safety in MND; these are summarised in Table 1. The most commonly administered instruments in these studies are the Montreal cognitive assessment (MoCA); the ALS Cognitive Behavioural Scale (ALS-CBS); gait speed (m/s); ALS Functional Rating Scale—revised total score (ALSFRS-r); and simulated driving assessment with a Lane Change Task [19,153]. Studies using driving simulation tasks show that individuals with mild-to-moderate ALS generally perform similarly to healthy controls under cognitive or visual distractions. However, in motor distraction tasks, the ALS group performed significantly slower [154]. Safe driving requires complex motor skills, especially when multitasking or when distracted. The impact of cognitive impairment on driving safety is glaringly understudied and poorly characterised in ALS. A recent longitudinal study identified no direct association between baseline cognitive function and driving status 4 months later [155], suggesting that current cognitive performance is not a predictor of future driving ability. ALSFRS-r scores however have been suggested to predict driving cessation [156]. Longitudinal tracking of ALSFRS-r scores over at least 24 weeks reveals a drop in both fine and gross motor scores and exhibits a non-linear trajectory of functional decline [157]. Motor impairment and driving capacity is more apparent under distraction and may unmask motor vulnerabilities not evident during undistracted driving. Driving assessment methods in these studies included driving simulation, computer simulation and also the modified questionnaire of the Norwegian ParkWest study [158]. The Lane Change Task used in simulation studies quantifies perception, lane change quality, and lane-keeping ability, with secondary tasks designed to detect at-risk drivers. Evidence regarding how driving ability declines across the stages of disease remains glaringly limited [19,154,155]. The driving literature of non-ALS MNDs is even more limited. No prospective purpose-designed studies have specifically evaluated the driving performance of patients with PLS, despite the considerable literature on motor disability in PLS [147,159,160] and the emerging literature on the neuropsychological [85,143,144], cerebellar [38] and extrapyramidal [56] facets of the disease. Similarly, no study has been specifically dedicated to the disability profiles of patients with SBMA, and only generic recommendations have been suggested for this cohort, such as on-road assessments with standardised routes to determine driver competence [161]. There have been no studies dedicated to PMA, which classically presents with progressive LMN dysfunction and gradual respiratory involvement [162]. PPS is known to impair driving ability in up to 70% of those affected [163], but Swedish data indicate that with adequate support, up to 57% of patients can continue to drive safely. Common car adaptations in this cohort include hand controls, hand braking and accelerating systems, and steering wheel knobs [164,165]. Some studies highlight the positive impact of continued driving on self-esteem [166]. Reliance on assistive devices for mobility and activities of daily living (ADLs) is negatively associated with driving [167]. A 10-year cohort study of patients with lower limb disability, including poliomyelitis patients, revealed that only 0.6% of patients had road traffic accidents (RTAs) due to disability and confirmed that drivers with physical disabilities are not at an increased risk of RTAs [168]. Individualised interventions, such as modern ankle–foot orthoses (AFOs) in polio patients, are associated with improved automobile driving [169]. Data from questionnaires on driver disability, adaptations and involvement in RTAs showed no difference in accident rates from drivers in the general population [170].

4.5. Lessons from Other Neurological Conditions

Parkinson’s disease (PD) is another neurodegenerative condition which is associated with a range of both motor and cognitive symptoms, but it has a much higher incidence than ALS and the specific impact of motor and non-motor manifestations on driving safety is much better characterised [172,173,174]. It has been associated with indecisiveness at T-junctions and reduced rear-view and side mirror usage. Standard clinical measures of PD were not predictive of actual driving performance [175]. Contrary to the limited impact of physical disability on driving in polio, early stages of dementia are associated with a higher risk of failing performance-based road tests and impaired driving abilities [176]. Neuropsychological testing in older individuals revealed that driving performance may be linked to three key cognitive domains: speed of processing, visuospatial abilities, and memory. A comprehensive neuropsychological assessment is necessary to accurately determine the risks of unsafe driving [177,178]. There is a consensus in the literature that comprehensive neuropsychological testing is required in any neurodegenerative syndrome as part of an integrated driving assessment [178,179,180]. The Trail Making Test (TMT), Symbol Digit Modalities Test, and Purdue Pegboard Test have shown predictive value for driving performance in neurological populations. Combined neuropsychological, driving simulator and clinical assessments have shown the most accurate prediction for fitness to drive, with an overall accuracy of 92.7% [181,182,183,184]. Driving assessment strategies in the dementia literature include Lane Change Task (LCT) [154], on-road assessments [185], driving simulator evaluation [186], Standard New Zealand licencing testing and the advanced driver assessment [187]. Driving in multiple sclerosis (MS) has been linked to more accidents, slower reaction times, and lower Driving Safety Scores (DSSs). Impaired driving performance in MS has been linked to cognitive deficits such as impaired spatial short-term memory, working memory and selective attention, translating into a higher number of accidents [182]. In summary, lessons from other neurological conditions suggest that cognitive performance, rather than motor performance, predicts driving safety. Disease-specific, motor-function focused rating scales are poor predictors of driving safety.

4.6. Assessment Strategies in ALS/MND

A systematic (Figure 3) domain-by-domain approach is necessary to examine driving safety in ALS and other MNDs, as seen in Table 2. It is crucial to fist explore the social context and the importance attached to driving in individual patients. Assessments for driving safety should first consider phenotype-specific motor disability profiles, such as UMN versus LMN dysfunction predominance, disease stage, fine and rapid movements, spasticity, and the ability to control steering, pedals, and main driving controls. In light of the emerging evidence of multi-system cerebral involvement in ALS, PLS, and SBMA (see above), clinical assessment should also be expanded to uncover additional cerebellar, extrapyramidal and proprioceptive deficits. Given the considerable extra-motor features of most MNDs (see above), executive function, behavioural profiles, mood, medications, fatigue, somnolence, medications, respiratory function, and cardiac function (SBMA) should also be systematically evaluated. A number of disease-specific cognitive and behavioural instruments have been developed and validated in ALS in recent years, which can be administered and interpreted with ease by non-neuropsychologists either in person or remotely [68,188,189,190,191,192,193,194]. Other cognitive assessment schemes have been used to predict on-road driving ability in other neurological conditions: the Visual Object and Space Perception battery (VOSP) [195], the Behavioural Assessment of the Dysexecutive Syndrome (BADS) [196] and the Rookwood Driving Battery (RDB) [197]. A poor score on the VOSP is linked to worse performance in simulated driving scenarios. The BADS screens for executive impairments in problem solving, cognitive flexibility and temporal judgement. An RDB with a score over 10 has 88% positive predictive value for failing an on-road assessment. Medications, doses, interaction, synergistic effects, and time of administration need to be carefully reviewed by either an experienced neurologist or pharmacologist. There is compelling evidence that commonly prescribed drugs in ALS adversely affect driving performance, such as anti-spasticity medications, anticholinergics, and TCAs, but benzodiazepines [198], opiates [199,200], SSRIs [201,202], and cannabis [203,204], and their various combinations [202,205], pose a particular risk for driving safety and response times. Respiratory function needs to be evaluated as per relevant guidelines [66] and NIV settings adjusted if indicated. Fatigue, somnolence and sleep disorders need to be explored using the appropriate clinical questions or questionnaires used in OSA.

4.7. Interventions

Interventions should always be individualised following a comprehensive multi-domain assessment to address the identified deficits in specific domains (Figure 4). The optimisation of respiratory support may include nocturnal and/or additional daytime non-invasive ventilation, the careful adjustment of IPAP (Inspiratory Positive Airway Pressure) and EPAP (Expiratory Positive Airway Pressure) settings, airway and secretion clearance with breath-stacking and the use of cough-assist machines. Medications, salivary gland botulinum toxin injections or salivary gland radiotherapy (RT) maybe used to dry up distracting secretions and sialorrhea. Medication adjustments may improve somnolence, improve concentration and alleviate fatigue. Frequently used anti-spasticity medications, such as baclofen, tizanidine or benzodiazepines, are notorious to contribute to fatigue and drowsiness; therefore, dose reduction or evening administration should be considered. Commonly prescribed tricyclic antidepressants (TCA), such as Amitriptyline, and tetracyclic antidepressants (TECA), such as Mirtazapine, are also sedating and should be administered in the evening when no further driving is planned. Opiate analgesia, administered per os (PO), as a patch or via a syringe driver, and anticholinergic medications for sialorrhea (Scopolamine patches, Glycopyrrolate, etc.) are all sedating and may cause drowsiness. It is noteworthy that most patients with ALS are on a combination of the above medications, often with synergistic side-effect profiles contributing to drowsiness. The attentive review of medications is therefore paramount when assessing suitability for driving in MND. Practical considerations, such as only driving during the day, in familiar environments, accompanied by a friend or family member, or only driving for short distances, should be considered. Patient-tailored, disability-specific adaptations (Figure 4), such as push–pull hand controls, steering knobs, and swivel seats, may enable patients with MND to operate their vehicles independently. Personalised adaptations should always reflect a patient’s individual disability profile. In patients with lower limb disability, push/pull lever devices are often considered to allow both acceleration and braking control with just one hand. Electronic, fly-by-wire “trigger accelerators” requiring simple finger flexion and extension, and “ghost ring accelerators” requiring side-to-side finger movements, are just some of the solutions implemented. In patients with limited right leg mobility, a left-footed accelerator can be installed to the left of the brake pedal. In patients with impaired hand function, a steering wheel knob is often attached to the steering wheel and so that the car to be steered with just one hand. Additionally, 360-degree swivelling seats are often installed for easier access. Voice-activated or touch-based secondary controls are sometimes fitted for lights and wipers. While not a common practice, in some jurisdictions, vehicles can be modified to allow drivers to remain in their wheelchairs while driving [206,207,208,209]. Recent innovations in driverless car technology promise to increase levels of independence for people with disabilities [185]. Many governments offer various tax reduction schemes, and financial and grant support for adaptive car modifications. Frequent breaks and naps are commonly recommended with patients with respiratory compromise and they have been shown to significantly reduce RTAs [210]. The European Respiratory Society (ERS) formed an OSA task force and recommended an Apnoea–Hypopnea Index (AHI) threshold of >15 events/h for driving restriction [211].

4.8. Governing Concepts

The priority of any of the above assessments (Figure 3) and interventions (Figure 4) is to enhance the independence, dignity and autonomy of people with motor neuron diseases. Initial discussion around driving has to commence by exploring the patient’s own views on their driving safety and the importance of driving to them. Driving safety assessments need to be comprehensive, with the evaluation of motor (hand and lower limb function), bulbar (sialorrhea and communication), cognitive (decision making, visuospatial skills, distractibility, and concentration), and behavioural (apathy and disinhibition) function, sedative medication and drug combinations (anti-spasticity, analgesia, and anticholinergics), respiratory function, sleep, and fatigue, as shown in Table 2. By focusing on the patient’s own preferences (importance of travel and social interactions), social circumstances (residence, town/country, financial means, and family support), and clinical profiles (bulbar/spinal and UMN/LMN predominance), patient-centred, individualised interventions need to be designed and implemented by a multidisciplinary team.

4.9. Stakeholders

Most national health authorities issue expert driving guidelines for people with neurological conditions including temporary bans following neurosurgery, stroke, traumatic head injury, seizures, etc. Particularly detailed disease-specific guidelines typically exist for epilepsy, dementia, and patients with movement disorders, but most countries do not have MND-specific driving regulations or best-practice regulations. In most European countries the neurologist, physiotherapist and occupational therapist have an initial discussion with the patient about their driving preferences and their own views regarding driving safety. This is typically followed by an expert on-the-road assessment. Many insurance companies mandate the notification of new diagnoses and an opinion from the patient’s general practitioner (GP) or specialist with regard to driving safety. Based on the multidisciplinary assessment, a letter is often issued to the GP with regard to driving and a consensus opinion is then communicated to the insurance company. An on-the-road driving assessment is often requested from a licenced driving instructor or evaluator, which can be extremely helpful as constructive suggestions are provided based on observations under real-life driving conditions. National charities and patient advocacy groups are often actively involved in the assessment and support of patients in the community. UK Driver and Vehicle Licensing Agency (DVLA) guidelines provide detailed procedural guidance for either a new or a worsening notifiable medical condition [185]. The DVLA needs to be notified by the individual, and a medical and driving assessment subsequently arranged, and special restriction may then be introduced. In Ireland, the National Driver Licence Service (NDLS) and insurance company must be formally informed. Communication between regulatory bodies, insurance companies, funding agencies and health care professionals (GPs, OTs, and neurologists) is often complex, strained, fragmented and lengthy. In a rapidly progressive condition, a simple, transparent and streamlined process is desirable. Only very limited data exist on insurance costs for patients with specific conditions [212] and many would argue that such costs should be subsidised.

4.10. Knowledge Gaps and Future Directions

In addition to the strikingly low number of driving safety studies in MND, research is limited to ALS and PPS cohorts. The sample sizes of the identified studies were generally small and samples were particularly small for patients with mild-to-moderate motor disability [19], as shown in Table 3. The impacts of respiratory involvement and sleep disorders are under evaluated. Clinical data in many of the identified studies are scarce and the sedating effect of prescribed medication combinations is often overlooked. Assessments typically primarily focus on motor function. Current assessment batteries for driving fitness in motor neuron diseases are not standardised and disease-specific instruments are seldom utilised. A variety of tools have been implemented in recent studies, including the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) [68], the Montreal cognitive assessment (MOCA) [213], the ALS Cognitive Behavioural Scale (ALS-CBS) [188] and the revised ALS Functional Rating Scale [214] (ALSFRS-r). There is a notable lack of on-road driving evaluation under specific driving conditions (rain, snow, night time, and motorway) and longitudinal follow-up on performance. The limited literature and experience from other neurological conditions would suggest that driving should be systematically evaluated, simulator-based screening should be considered and on-the-road testing should be carried out under specific driving conditions. Assessments should be performed by medical practitioners, neuropsychologists, occupational therapists, and driving instructors [215]. Comprehensive domain-by-domain assessments should be performed (Table 2) and any incidents or near-misses, including minor parking accidents, getting lost, anxiety attacks, and abandoning the car and asking for assistance, should be recorded and registered so that future driving safety decisions can be informed based on real-life data and feedback. Prospective studies need to be designed to specifically explore the determinants of safe driving across cognitive, behavioural, respiratory, sleep and motor domains. The impact of continued driving and driving cessation on the social, professional, recreational, mental health, and medical aspects of an individual affected by MDN should also be systematically studied. The QoL implications for driving cessation should also be evaluated in ALS/MND.
In the absence of an international consensus regarding driving recommendations for ALS/MND, dedicated satellite meetings should be held at large international meetings to address this important practical gap in MND care. A multitude of well-attended international meetings dedicated to ALS/MND take place annually, often also attended by patients and their caregivers, which may be ideal platforms for such discussions. The annual ALS/MND symposium organised by the UK MNND association, the annual ENCALS meeting organised by the European network to cure ALS, FILSLAN, the French health network for rare diseases, focusing on ALS and motor neuron diseases, and NEALS—Network of Excellence for ALS are just some of the large organisation holding annual meetings attended by a large group of experts from around the globe. Expert committees should be formed with the representation of relevant stakeholders, patients, physiotherapies, occupational therapies, patient advocacy groups and charities to discuss the principles of multi-domain driving safety assessments and consensus strategies to enhance driving with MND. Draft guidelines could then be circulated internationally and refined based on feedback. Such international documents could then serve as the basis of national guidelines. There are also valuable learning opportunities from more common neurological conditions such as multiple sclerosis, acquired spinal injuries, stroke and dementia syndromes [216]. Large prospective studies specifically exploring driving preferences, barriers and effective interventions should be conducted and published to shape future guidelines. Research groups and clinical centres should capitalise on remote assessment tools and connected devices to measure fatigue, respiratory function, and bulbar and limb function remotely in real time. With the relevant data regulations in place, this data could be interpreted by neurologists and physiotherapists remotely and they could advise patients and families regarding driving safety. No patient should have to face increased premiums for driving with a disability. Patient advocacy groups and neurologists engaging in MND care should encourage discussions with insurance companies and government agencies to advocate for tax reliefs, insurance fee reductions and car adaptation grants to support patients with MND and their families. Machine learning (ML) models have been successfully used in nearly all aspects of ALS/MND research to accurately classify individual patients into relevant diagnostic, phenotypic and prognostic categories or disease clusters [217,218,219,220,221,222,223,224]. Based on the success of recent ML initiatives, it is conceivable that clinical data from individual patients may be used to predict driving safety if large, well-designed data repositories are generated to assess, establish and validate the determinants of driving safety in ALS/MND. Driving simulators have been successfully used for assessments in some countries, and while this does not replace on-the-road assessment, it may be an ideal initial screening tool for driving safety. The implementation of new technologies, such as voice command-based navigation, semi-autonomous driving, camera systems, fatigue monitors, and advanced collision avoidance systems may facilitate driving with a significant disability.

5. Conclusions

In light of the complex multi-domain disability profile of ALS and other MNDs, there is an urgent and unmet need to study driving safety with the involvement of relevant stakeholders and generate evidence-based, consensus, best-practice recommendations for driving assessments and adaptations. Communication between government agencies, health care professionals and insurers needs to be streamlined. The governing principle behind such initiatives is to enhance driving safety, maintain independence, support patient autonomy, and improve the quality of life of people living with ALS and various MNDs.

Author Contributions

The manuscript was drafted by J.K., J.T., J.L., W.F.S. and P.B. Study conceptualisation: J.K. and P.B. The manuscript was reviewed for intellectual content by J.K., J.T., J.L., E.L.T., A.T., W.F.S. and P.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Health Research Board (HRB JPND-Cofund-2025-3), the EU Joint Programme—Neurodegenerative Disease Research (JPND—2025 “Qual-Bulb-MND”) and Science Foundation Ireland (SFI SP20/SP/8953).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analysed in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Glossary

ABG: arterial blood gas, AD: Alzheimer’s disease, ADLs: activities of daily living, AFO: ankle–foot orthoses, AHI: Apnoea–Hypopnea Index, ALS: amyotrophic lateral sclerosis, ALSbi: ALS with behavioural impairment, ALS-CBS: ALS Cognitive Behavioural Scale, ALSci: ALS with cognitive impairment, ALSFRS-r: revised ALS Functional Rating Scale, ALS-FTD: ALS with frontotemporal dementia, ALSnci: ALS with no cognitive impairment, B-ADL: basic activities of daily living, BADS: Behavioural Assessment of the Dysexecutive Syndrome, BTX-A: botulinum toxin type A, CNN: convolutional neural network, CPAP: continuous positive airway pressure, DAP: 3-4 diaminopyridine, DSS: Driving Safety Score, DVLA: Driver and Vehicle Licencing Agency, E-ADL: extended activities of daily living, ECAS: Edinburgh Cognitive and Behavioural ALS Screen, EDS: excessive daytime sleepiness, ENCALS: the European network to cure ALS, EPAP: expiratory positive airway pressure, ERS: European Respiratory Society, FDI: first dorsal interosseous, FILSLAN: Filière de Santé Maladies Rares SLA, FT9: Fine’til 9, FTD: frontotemporal dementia, FTLD: frontotemporal lobar degeneration, GP: general practitioner, HC: Healthy controls, HD: Huntington’s disease, ICF: International Classification of Functioning, Disability, and Health, IDFR: Intelligent Drowsiness and Fatigue Recognition System, IPAP: inspiratory positive airway pressure, LCT: lane change task, LEoP: late effects of poliomyelitis, LMN: lower motor neuron, LSP: longstanding poliomyelitis, MDT: multidisciplinary, MDT: motor distraction task, MITOS: the Milano–Torino staging system, ML: machine learning, MND: motor neuron disease, MOCA: Montreal cognitive assessment, MS: multiple sclerosis, NDLS: National Driver Licence Service, NEALS: Network of Excellence for ALS, NH: nocturnal hypoventilation, NIV: non-invasive ventilation, NP: neuropsychology, OR: odds ratio, OSA: obstructive sleep apnoea, OT: occupational therapy, pALS: patients with ALS, PBA: pseudobulbar affect, PCL: pathological crying and laughter, PCV: pressure-controlled ventilation, PD: Parkinson’s disease, PLS: primary lateral sclerosis, PO: per os—by mouth, Polio: patients with prior poliomyelitis, PPS: post-poliomyelitis syndrome, PSV: pressure support, PT: physiotherapy, QoL: quality of life, RDB: Rookwood Driving Battery, REMSBD: rapid eye movement sleep behaviour disorder, RLS: restless legs syndrome, RT: Reaction time, RT: radiotherapy, RTA: road traffic accident, SALT: speech and language therapy, SBMA: spinal and bulbar muscular atrophy, SMA: spinal muscular atrophy, SP: speech pathology, TCA: prescribed tricyclic, TECA: tetracyclic antidepressants, TMT: Trail Making Test, TMTB: Trail Making Test Part B, TOSCA: transcutaneous tcpCO2 and SpO2 monitoring system, UMN: upper motor neuron, VDT: visual distraction task, VOSP: Visual Object and Space Perception battery, WHO: World Health Organisation.

References

  1. Fang, T.; Al Khleifat, A.; Stahl, D.R.; Lazo La Torre, C.; Murphy, C.; Young, C.; Shaw, P.J.; Leigh, P.N.; Al-Chalabi, A. Comparison of the King’s and MiToS staging systems for ALS. Amyotroph. Lateral Scler. Front. Degener. 2017, 18, 227–232. [Google Scholar] [CrossRef]
  2. Thakore, N.J.; Lapin, B.R.; Kinzy, T.G.; Pioro, E.P. Deconstructing progression of amyotrophic lateral sclerosis in stages: A Markov modeling approach. Amyotroph. Lateral Scler. Front. Degener. 2018, 19, 483–494. [Google Scholar] [CrossRef]
  3. Roche, J.C.; Rojas-Garcia, R.; Scott, K.M.; Scotton, W.; Ellis, C.E.; Burman, R.; Wijesekera, L.; Turner, M.R.; Leigh, P.N.; Shaw, C.E.; et al. A proposed staging system for amyotrophic lateral sclerosis. Brain A J. Neurol. 2012, 135, 847–852. [Google Scholar] [CrossRef] [PubMed]
  4. Tramacere, I.; Dalla Bella, E.; Chio, A.; Mora, G.; Filippini, G.; Lauria, G. The MITOS system predicts long-term survival in amyotrophic lateral sclerosis. J. Neurol. Neurosurg. Psychiatry 2015, 86, 1180–1185. [Google Scholar] [CrossRef]
  5. Strong, M.J.; Grace, G.M.; Freedman, M.; Lomen-Hoerth, C.; Woolley, S.; Goldstein, L.H.; Murphy, J.; Shoesmith, C.; Rosenfeld, J.; Leigh, P.N.; et al. Consensus criteria for the diagnosis of frontotemporal cognitive and behavioural syndromes in amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 2009, 10, 131–146. [Google Scholar] [CrossRef]
  6. Strong, M.J.; Abrahams, S.; Goldstein, L.H.; Woolley, S.; McLaughlin, P.; Snowden, J.; Mioshi, E.; Roberts-South, A.; Benatar, M.; Hortobágyi, T.; et al. Amyotrophic lateral sclerosis—Frontotemporal spectrum disorder (ALS-FTSD): Revised diagnostic criteria. Amyotroph. Lateral Scler. Front. Degener. 2017, 18, 153–174. [Google Scholar] [CrossRef] [PubMed]
  7. Al-Chalabi, A.; Chiò, A.; Merrill, C.; Oster, G.; Bornheimer, R.; Agnese, W.; Apple, S. Clinical staging in amyotrophic lateral sclerosis: Analysis of Edaravone Study 19. J. Neurol. Neurosurg. Psychiatry 2021, 92, 165–171. [Google Scholar] [CrossRef] [PubMed]
  8. Lagier-Tourenne, C.; Baughn, M.; Rigo, F.; Sun, S.; Liu, P.; Li, H.R.; Jiang, J.; Watt, A.T.; Chun, S.; Katz, M.; et al. Targeted degradation of sense and antisense C9orf72 RNA foci as therapy for ALS and frontotemporal degeneration. Proc. Natl. Acad. Sci. USA 2013, 110, E4530–E4539. [Google Scholar] [CrossRef]
  9. Tran, H.; Moazami, M.P.; Yang, H.; McKenna-Yasek, D.; Douthwright, C.L.; Pinto, C.; Metterville, J.; Shin, M.; Sanil, N.; Dooley, C.; et al. Suppression of mutant C9orf72 expression by a potent mixed backbone antisense oligonucleotide. Nat. Med. 2022, 28, 117–124. [Google Scholar] [CrossRef]
  10. Ciani, G.; Pincus, C.; Grimaldi, G. Contextual Factors & Barriers to Driving Among People With Amyotrophic Lateral Sclerosis: Research in Progress. Am. J. Occup. Ther. 2023, 77, 7711505064p1. [Google Scholar] [CrossRef]
  11. Stepney, M.; Kirkpatrick, S.; Locock, L.; Prinjha, S.; Ryan, S. A licence to drive? Neurological illness, loss and disruption. Sociol. Health Illn. 2018, 40, 1186–1199. [Google Scholar] [CrossRef] [PubMed]
  12. Savoie, C.; Voyer, P.; Lavallière, M.; Bouchard, S. Transition from driving to driving-cessation: Experience of older persons and caregivers: A descriptive qualitative design. BMC Geriatr. 2024, 24, 219. [Google Scholar] [CrossRef] [PubMed]
  13. Chipika, R.H.; Siah, W.F.; McKenna, M.C.; Li Hi Shing, S.; Hardiman, O.; Bede, P. The presymptomatic phase of amyotrophic lateral sclerosis: Are we merely scratching the surface? J. Neurol. 2021, 268, 4607–4629. [Google Scholar] [CrossRef]
  14. Bede, P.; Bogdahn, U.; Lope, J.; Chang, K.M.; Xirou, S.; Christidi, F. Degenerative and regenerative processes in amyotrophic lateral sclerosis: Motor reserve, adaptation and putative compensatory changes. Neural Regen. Res. 2021, 16, 1208–1209. [Google Scholar] [CrossRef] [PubMed]
  15. Bede, P.; Murad, A.; Lope, J.; Li Hi Shing, S.; Finegan, E.; Chipika, R.H.; Hardiman, O.; Chang, K.M. Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach. J. Neurol. Sci. 2022, 432, 120079. [Google Scholar] [CrossRef]
  16. Corcia, P.; Bede, P.; Pradat, P.F.; Couratier, P.; Vucic, S.; de Carvalho, M. Split-hand and split-limb phenomena in amyotrophic lateral sclerosis: Pathophysiology, electrophysiology and clinical manifestations. J. Neurol. Neurosurg. Psychiatry 2021, 92, 1126–1130. [Google Scholar] [CrossRef]
  17. Thakore, N.J.; Pioro, E.P.; Rucker, J.C.; Leigh, R.J. Motor neuronopathy with dropped hands and downbeat nystagmus: A distinctive disorder? A case report. BMC Neurol. 2006, 6, 3. [Google Scholar] [CrossRef]
  18. Holdom, C.J.; Williamson, J.L.; O’Reilly, G.; Henderson, R.D.; Neville, S.; Ngo, S.T.; Dick, T.J.M.; Steyn, F.J. Lower-limb biomechanics in motor neuron disease: A joint-level perspective of gait disruption. Amyotroph. Lateral Scler. Front. Degener. 2025, 27, 44–54. [Google Scholar] [CrossRef] [PubMed]
  19. Hayes, H.A.; Hu, N.; Wang, X.; Gibson, S.; Mathy, P.; Berggren, K.; Bromberg, M. Comparison of driving capacity among patients with amyotrophic lateral sclerosis and healthy controls using the lane change task. J. Neurol. Sci. 2020, 413, 116741. [Google Scholar] [CrossRef] [PubMed]
  20. Dal Bello-Haas, V.; Florence, J.M.; Krivickas, L.S. Therapeutic exercise for people with amyotrophic lateral sclerosis or motor neuron disease. Cochrane Database Syst. Rev. 2013, 2013, CD005229. [Google Scholar] [CrossRef] [PubMed]
  21. Souza, A.A.D.; da Silva, S.T.; Macedo, L.R.D.D.; Aires, D.N.; Pondofe, K.D.M.; Melo, L.P.D.; Valentim, R.A.D.M.; Ribeiro, T.S. Physical therapy for muscle strengthening in individuals with amyotrophic lateral sclerosis: A protocol for a systematic review and meta-analysis. PLoS ONE 2024, 19, e0307470. [Google Scholar] [CrossRef]
  22. Marvulli, R.; Megna, M.; Citraro, A.; Vacca, E.; Napolitano, M.; Gallo, G.; Fiore, P.; Ianieri, G. Botulinum Toxin Type A and Physiotherapy in Spasticity of the Lower Limbs Due to Amyotrophic Lateral Sclerosis. Toxins 2019, 11, 381. [Google Scholar] [CrossRef]
  23. Young, C.A.; Chaouch, A.; McDermott, C.J.; Al-Chalabi, A.; Chhetri, S.K.; Bidder, C.; Ellis, C.; Annadale, J.; Mills, R.J.; Tennant, A. Fatigue in amyotrophic lateral sclerosis/motor neuron disease: Prevalence, influences and trajectories. Amyotroph. Lateral Scler. Front. Degener. 2025, 27, 78–89. [Google Scholar] [CrossRef]
  24. Ng, L.; Khan, F. Multidisciplinary Rehabilitation in Amyotrophic Lateral Sclerosis. In Amyotrophic Lateral Sclerosis; Maurer, M.H.H., Ed.; IntechOpen: London, UK, 2012. [Google Scholar]
  25. Meng, L.; Li, X.; Li, C.; Tsang, R.C.C.; Chen, Y.; Ge, Y.; Gao, Q. Effects of Exercise in Patients With Amyotrophic Lateral Sclerosis: A Systematic Review and Meta-Analysis. Am. J. Phys. Med. Rehabil. 2020, 99, 801–810. [Google Scholar] [CrossRef] [PubMed]
  26. Finegan, E.; Chipika, R.H.; Li Hi Shing, S.; Hardiman, O.; Bede, P. Pathological Crying and Laughing in Motor Neuron Disease: Pathobiology, Screening, Intervention. Front. Neurol. 2019, 10, 260. [Google Scholar] [CrossRef]
  27. Bede, P.; Finegan, E. Revisiting the pathoanatomy of pseudobulbar affect: Mechanisms beyond corticobulbar dysfunction. Amyotroph. Lateral Scler. Front. Degener. 2018, 19, 4–6. [Google Scholar] [CrossRef]
  28. Finegan, E.; Kleinerova, J.; Hardiman, O.; Hutchinson, S.; Garcia-Gallardo, A.; Tan, E.L.; Bede, P. Pseudobulbar affect: Clinical associations, social impact and quality of life implications—Lessons from PLS. J. Neurol. 2025, 272, 266. [Google Scholar] [CrossRef]
  29. Tahedl, M.; Chipika, R.H.; Lope, J.; Li Hi Shing, S.; Hardiman, O.; Bede, P. Cortical progression patterns in individual ALS patients across multiple timepoints: A mosaic-based approach for clinical use. J. Neurol. 2021, 268, 1913–1926. [Google Scholar] [CrossRef] [PubMed]
  30. Bede, P.; Bokde, A.L.; Byrne, S.; Elamin, M.; Fagan, A.J.; Hardiman, O. Spinal cord markers in ALS: Diagnostic and biomarker considerations. Amyotroph. Lateral Scler. 2012, 13, 407–415. [Google Scholar] [CrossRef] [PubMed]
  31. Abidi, M.; de Marco, G.; Couillandre, A.; Feron, M.; Mseddi, E.; Termoz, N.; Querin, G.; Pradat, P.-F.; Bede, P. Adaptive functional reorganization in amyotrophic lateral sclerosis: Coexisting degenerative and compensatory changes. Eur. J. Neurol. 2020, 27, 121–128. [Google Scholar] [CrossRef]
  32. Abidi, M.; de Marco, G.; Grami, F.; Termoz, N.; Couillandre, A.; Querin, G.; Bede, P.; Pradat, P. Neural Correlates of Motor Imagery of Gait in Amyotrophic Lateral Sclerosis. J. Magn. Reson. Imaging 2021, 53, 223–233. [Google Scholar] [CrossRef]
  33. Abidi, M.; Pradat, P.F.; Termoz, N.; Couillandre, A.; Bede, P.; de Marco, G. Motor imagery in amyotrophic lateral Sclerosis: An fMRI study of postural control. NeuroImage Clin. 2022, 35, 103051. [Google Scholar] [CrossRef]
  34. Feron, M.; Couillandre, A.; Mseddi, E.; Termoz, N.; Abidi, M.; Bardinet, E.; Delgadillo, D.; Lenglet, T.; Querin, G.; Welter, M.-L.; et al. Extrapyramidal deficits in ALS: A combined biomechanical and neuroimaging study. J. Neurol. 2018, 265, 2125–2136. [Google Scholar] [CrossRef]
  35. Chipika, R.H.; Mulkerrin, G.; Pradat, P.F.; Murad, A.; Ango, F.; Raoul, C.; Bede, P. Cerebellar pathology in motor neuron disease: Neuroplasticity and neurodegeneration. Neural Regen. Res. 2022, 17, 2335–2341. [Google Scholar] [CrossRef]
  36. Tahedl, M.; Tan, E.L.; Kleinerova, J.; Delaney, S.; Hengeveld, J.C.; Doherty, M.A.; Mclaughlin, R.L.; Pradat, P.-F.; Raoul, C.; Ango, F.; et al. Progressive Cerebrocerebellar Uncoupling in Sporadic and Genetic Forms of Amyotrophic Lateral Sclerosis. Neurology 2024, 103, e209623. [Google Scholar] [CrossRef]
  37. Bede, P.; Chipika, R.H.; Christidi, F.; Hengeveld, J.C.; Karavasilis, E.; Argyropoulos, G.D.; Lope, J.; Shing, S.L.H.; Velonakis, G.; Dupuis, L.; et al. Genotype-associated cerebellar profiles in ALS: Focal cerebellar pathology and cerebro-cerebellar connectivity alterations. J. Neurol. Neurosurg. Psychiatry 2021, 92, 1197–1205. [Google Scholar] [CrossRef]
  38. Kleinerova, J.; Tahedl, M.; Tan, E.L.; Delaney, S.; Hengeveld, J.C.; Doherty, M.A.; McLaughlin, R.L.; Hardiman, O.; Chang, K.M.; Finegan, E.; et al. Supra- and infra-tentorial degeneration patterns in primary lateral sclerosis: A multimodal longitudinal neuroradiology study. J. Neurol. 2024, 271, 3239–3255. [Google Scholar] [CrossRef] [PubMed]
  39. Finegan, E.; Siah, W.F.; Li Hi Shing, S.; Chipika, R.H.; Hardiman, O.; Bede, P. Cerebellar degeneration in primary lateral sclerosis: An under-recognized facet of PLS. Amyotroph. Lateral Scler. Front. Degener. 2022, 23, 542–553. [Google Scholar] [CrossRef] [PubMed]
  40. Malm, J.; Kristensen, B.; Karlsson, T.; Carlberg, B.; Fagerlund, M.; Olsson, T. Cognitive impairment in young adults with infratentorial infarcts. Neurology 1998, 51, 433–440. [Google Scholar] [CrossRef] [PubMed]
  41. Stoodley, C.J.; MacMore, J.P.; Makris, N.; Sherman, J.C.; Schmahmann, J.D. Location of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke. NeuroImage Clin. 2016, 12, 765–775. [Google Scholar] [CrossRef]
  42. Keren-Happuch, E.; Chen, S.H.; Ho, M.H.; Desmond, J.E. A meta-analysis of cerebellar contributions to higher cognition from PET and fMRI studies. Hum. Brain Mapp. 2014, 35, 593–615. [Google Scholar]
  43. Stoodley, C.J.; Schmahmann, J.D. Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies. Neuroimage 2009, 44, 489–501. [Google Scholar] [CrossRef]
  44. Argyropoulos, G.P.D.; van Dun, K.; Adamaszek, M.; Leggio, M.; Manto, M.; Masciullo, M.; Molinari, M.; Stoodley, C.J.; Van Overwalle, F.; Ivry, R.B.; et al. The Cerebellar Cognitive Affective/Schmahmann Syndrome: A Task Force Paper. Cerebellum 2020, 19, 102–125. [Google Scholar] [CrossRef]
  45. Tedesco, A.M.; Chiricozzi, F.R.; Clausi, S.; Lupo, M.; Molinari, M.; Leggio, M.G. The cerebellar cognitive profile. Brain 2011, 134, 3672–3686. [Google Scholar] [CrossRef] [PubMed]
  46. Levisohn, L.; Cronin-Golomb, A.; Schmahmann, J.D. Neuropsychological consequences of cerebellar tumour resection in children: Cerebellar cognitive affective syndrome in a paediatric population. Brain 2000, 123, 1041–1050. [Google Scholar] [CrossRef] [PubMed]
  47. Tahedl, M.; Tan, E.L.; Siah, W.F.; Hengeveld, J.C.; Doherty, M.A.; McLaughlin, R.L.; Hardiman, O.; Finegan, E.; Bede, P. Radiological correlates of pseudobulbar affect: Corticobulbar and cerebellar components in primary lateral sclerosis. J. Neurol. Sci. 2023, 451, 120726. [Google Scholar] [CrossRef] [PubMed]
  48. Trojsi, F.; Di Nardo, F.; D’Alvano, G.; Caiazzo, G.; Passaniti, C.; Mangione, A.; Sharbafshaaer, M.; Russo, A.; Silvestro, M.; Siciliano, M.; et al. Resting state fMRI analysis of pseudobulbar affect in Amyotrophic Lateral Sclerosis (ALS): Motor dysfunction of emotional expression. Brain Imaging Behav. 2023, 17, 77–89. [Google Scholar] [CrossRef]
  49. Argyropoulos, G.D.; Christidi, F.; Karavasilis, E.; Velonakis, G.; Antoniou, A.; Bede, P.; Seimenis, I.; Kelekis, N.; Douzenis, A.; Papakonstantinou, O.; et al. Cerebro-cerebellar white matter connectivity in bipolar disorder and associated polarity subphenotypes. Prog. Neuropsychopharmacol. Biol. Psychiatry 2021, 104, 110034. [Google Scholar] [CrossRef]
  50. McKenna, M.C.; Chipika, R.H.; Li Hi Shing, S.; Christidi, F.; Lope, J.; Doherty, M.A.; Hengeveld, J.C.; Vajda, A.; McLaughlin, R.L.; Hardiman, O.; et al. Infratentorial pathology in frontotemporal dementia: Cerebellar grey and white matter alterations in FTD phenotypes. J. Neurol. 2021, 268, 4687–4697. [Google Scholar] [CrossRef]
  51. Kleinerova, J.; Tahedl, M.; McKenna, M.C.; Garcia-Gallardo, A.; Hutchinson, S.; Hardiman, O.; Raoul, C.; Ango, F.; Schneider, B.; Pradat, P.-F.; et al. Cerebellar dysfunction in frontotemporal dementia: Intra-cerebellar pathology and cerebellar network degeneration. J. Neurol. 2025, 272, 289. [Google Scholar] [CrossRef]
  52. Pradat, P.-F.; Bruneteau, G.; Munerati, E.; Salachas, F.; Le Forestier, N.; Lacomblez, L.; Lenglet, T.; Meininger, V. Extrapyramidal stiffness in patients with amyotrophic lateral sclerosis. Mov. Disord. 2009, 24, 2143–2148. [Google Scholar] [CrossRef]
  53. Geser, F.; Prvulovic, D.; O’Dwyer, L.; Hardiman, O.; Bede, P.; Bokde, A.L.; Trojanowski, J.; Hampel, H. On the development of markers for pathological TDP-43 in amyotrophic lateral sclerosis with and without dementia. Prog. Neurobiol. 2011, 95, 649–662. [Google Scholar] [CrossRef]
  54. Brettschneider, J.; Del Tredici, K.; Toledo, J.B.; Robinson, J.L.; Irwin, D.J.; Grossman, M.; Suh, E.R.; Van Deerlin, V.M.; Wood, E.M.; Baek, Y.; et al. Stages of pTDP-43 pathology in amyotrophic lateral sclerosis. Ann. Neurol. 2013, 74, 20–38. [Google Scholar] [CrossRef]
  55. Finegan, E.; Hi Shing, S.L.; Chipika, R.H.; McKenna, M.C.; Doherty, M.A.; Hengeveld, J.C.; Vajda, A.; Donaghy, C.; McLaughlin, R.L.; Hutchinson, S.; et al. Thalamic, hippocampal and basal ganglia pathology in primary lateral sclerosis and amyotrophic lateral sclerosis: Evidence from quantitative imaging data. Data Brief. 2020, 29, 105115. [Google Scholar] [CrossRef]
  56. Finegan, E.; Li Hi Shing, S.; Chipika, R.H.; Doherty, M.A.; Hengeveld, J.C.; Vajda, A.; Donaghy, C.; Pender, N.; McLaughlin, R.L.; Hardiman, O.; et al. Widespread subcortical grey matter degeneration in primary lateral sclerosis: A multimodal imaging study with genetic profiling. NeuroImage Clin. 2019, 24, 102089. [Google Scholar] [CrossRef]
  57. Tahedl, M.; Kleinerova, J.; Doherty, M.A.; Hengeveld, J.C.; McLaughlin, R.L.; Hardiman, O.; Tan, E.L.; Bede, P. Progressive thalamo-cortical disconnection in amyotrophic lateral sclerosis genotypes: Structural degeneration and network dysfunction of thalamus-relayed circuits. Eur. J. Neurol. 2025, 32, e70146. [Google Scholar] [CrossRef]
  58. Westeneng, H.J.; Verstraete, E.; Walhout, R.; Schmidt, R.; Hendrikse, J.; Veldink, J.H.; Heuvel, M.P.v.D.; Berg, L.H.v.D. Subcortical structures in amyotrophic lateral sclerosis. Neurobiol. Aging 2015, 36, 1075–1082. [Google Scholar] [CrossRef] [PubMed]
  59. Westeneng, H.J.; Walhout, R.; Straathof, M.; Schmidt, R.; Hendrikse, J.; Veldink, J.H.; Heuvel, M.P.v.D.; Berg, L.H.v.D. Widespread structural brain involvement in ALS is not limited to the C9orf72 repeat expansion. J. Neurol. Neurosurg. Psychiatry 2016, 87, 1354–1360. [Google Scholar] [CrossRef]
  60. Ganguly, J.; Chai, J.R.; Jog, M. Minipolymyoclonus: A Critical Appraisal. J. Mov. Disord. 2021, 14, 114–118. [Google Scholar] [CrossRef] [PubMed]
  61. de Carvalho, M.; Swash, M. Origin of fasciculations in amyotrophic lateral sclerosis and benign fasciculation syndrome. JAMA Neurol. 2013, 70, 1562–1565. [Google Scholar] [CrossRef] [PubMed]
  62. Vogelnik, K.; Koritnik, B.; Leonardis, L.; Dolenc Grošelj, L.; Saifee, T.A.; Zidar, J.; Kojović, M. Shaky hands are a part of motor neuron disease phenotype: Clinical and electrophysiological study of 77 patients. J. Neurol. 2022, 269, 4498–4509. [Google Scholar] [CrossRef]
  63. McKenna, M.C.; Corcia, P.; Couratier, P.; Siah, W.F.; Pradat, P.F.; Bede, P. Frontotemporal Pathology in Motor Neuron Disease Phenotypes: Insights From Neuroimaging. Front. Neurol. 2021, 12, 723450. [Google Scholar] [CrossRef]
  64. Crockford, C.; Newton, J.; Lonergan, K.; Chiwera, T.; Booth, T.; Chandran, S.; Colville, S.; Heverin, M.; Mays, I.; Pal, S.; et al. ALS-specific cognitive and behavior changes associated with advancing disease stage in ALS. Neurology 2018, 91, e1370–e1380. [Google Scholar] [CrossRef]
  65. Radakovic, R.; Stephenson, L.; Colville, S.; Swingler, R.; Chandran, S.; Abrahams, S. Multidimensional apathy in ALS: Validation of the Dimensional Apathy Scale. J. Neurol. Neurosurg. Psychiatry 2016, 87, 663–669. [Google Scholar] [CrossRef]
  66. Kleinerova, J.; Tan, E.L.; Delaney, S.; Smyth, M.; Bede, P. Advances and research priorities in the respiratory management of ALS: Historical perspectives and new technologies. Rev. Neurol. 2025, 181, 525–534. [Google Scholar] [CrossRef]
  67. Abrahams, S.; Leigh, P.N.; Harvey, A.; Vythelingum, G.N.; Grise, D.; Goldstein, L.H. Verbal fluency and executive dysfunction in amyotrophic lateral sclerosis (ALS). Neuropsychologia 2000, 38, 734–747. [Google Scholar] [CrossRef]
  68. Abrahams, S.; Newton, J.; Niven, E.; Foley, J.; Bak, T.H. Screening for cognition and behaviour changes in ALS. Amyotroph. Lateral Scler. Front. Degener. 2014, 15, 9–14. [Google Scholar] [CrossRef] [PubMed]
  69. Strong, M.J. The syndromes of frontotemporal dysfunction in amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 2008, 9, 323–338. [Google Scholar] [CrossRef] [PubMed]
  70. Christidi, F.; Karavasilis, E.; Rentzos, M.; Velonakis, G.; Zouvelou, V.; Xirou, S.; Argyropoulos, G.; Papatriantafyllou, I.; Pantolewn, V.; Ferentinos, P.; et al. Hippocampal pathology in amyotrophic lateral sclerosis: Selective vulnerability of subfields and their associated projections. Neurobiol. Aging 2019, 84, 178–188. [Google Scholar] [CrossRef] [PubMed]
  71. Christidi, F.; Karavasilis, E.; Velonakis, G.; Ferentinos, P.; Rentzos, M.; Kelekis, N.; Evdokimidis, I.; Bede, P. The Clinical and Radiological Spectrum of Hippocampal Pathology in Amyotrophic Lateral Sclerosis. Front. Neurol. 2018, 9, 523. [Google Scholar] [CrossRef] [PubMed]
  72. Christidi, F.; Karavasilis, E.; Zalonis, I.; Ferentinos, P.; Giavri, Z.; Wilde, E.A.; Xirou, S.; Rentzos, M.; Zouvelou, V.; Velonakis, G.; et al. Memory-related white matter tract integrity in amyotrophic lateral sclerosis: An advanced neuroimaging and neuropsychological study. Neurobiol. Aging 2017, 49, 69–78. [Google Scholar] [CrossRef]
  73. Tahedl, M.; Tan, E.L.; Chipika, R.H.; Lope, J.; Hengeveld, J.C.; Doherty, M.A.; McLaughlin, R.L.; Hardiman, O.; Hutchinson, S.; McKenna, M.C.; et al. The involvement of language-associated networks, tracts, and cortical regions in frontotemporal dementia and amyotrophic lateral sclerosis: Structural and functional alterations. Brain Behav. 2023, 13, e3250. [Google Scholar] [CrossRef] [PubMed]
  74. Bak, T.H.; Hodges, J.R. Cognition, Language and Behaviour in Motor Neurone Disease: Evidence of Frontotemporal Dysfunction. Dement. Geriatr. Cogn. Disord. 1999, 10, 29–32. [Google Scholar] [CrossRef]
  75. Grossman, M.; Anderson, C.; Khan, A.; Avants, B.; Elman, L.; McCluskey, L. Impaired action knowledge in amyotrophic lateral sclerosis. Neurology 2008, 71, 1396–1401. [Google Scholar] [CrossRef]
  76. Burke, T.; Elamin, M.; Bede, P.; Pinto-Grau, M.; Lonergan, K.; Hardiman, O.; Pender, N. Discordant performance on the ‘Reading the Mind in the Eyes’ Test, based on disease onset in amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. Front. Degener. 2016, 17, 467–472. [Google Scholar] [CrossRef]
  77. Michielsen, A.; van Veenhuijzen, K.; Hiemstra, F.; Jansen, I.M.; Kalkhoven, B.; Veldink, J.H.; Kruitwagen, E.T.; van Es, M.; van Zandvoort, M.J.E.; Berg, L.H.v.D.; et al. Cognitive impairment within and beyond the FTD spectrum in ALS: Development of a complementary cognitive screen. J. Neurol. 2025, 272, 268. [Google Scholar] [CrossRef]
  78. Chio, A.; Vignola, A.; Mastro, E.; Giudici, A.D.; Iazzolino, B.; Calvo, A.; Moglia, C.; Montuschi, A. Neurobehavioral symptoms in ALS are negatively related to caregivers’ burden and quality of life. Eur. J. Neurol. 2010, 17, 1298–1303. [Google Scholar] [CrossRef]
  79. Burke, T.; Pinto-Grau, M.; Lonergan, K.; Elamin, M.; Bede, P.; Costello, E.; Hardiman, O.; Pender, N. Measurement of Social Cognition in Amyotrophic Lateral Sclerosis: A Population Based Study. PLoS ONE 2016, 11, e0160850. [Google Scholar] [CrossRef] [PubMed]
  80. Castelnovo, V.; Canu, E.; Aiello, E.N.; Curti, B.; Sibilla, E.; Torre, S.; Freri, F.; Tripodi, C.; Lumaca, L.; Spinelli, E.G.; et al. How to detect affect recognition alterations in amyotrophic lateral sclerosis. J. Neurol. 2024, 271, 7208–7221. [Google Scholar] [CrossRef]
  81. Mioshi, E.; Hsieh, S.; Caga, J.; Ramsey, E.; Chen, K.; Lillo, P.; Simon, N.; Vucic, S.; Hornberger, M.; Hodges, J.R.; et al. A novel tool to detect behavioural symptoms in ALS. Amyotroph. Lateral Scler. Front. Degener. 2014, 15, 298–304. [Google Scholar] [CrossRef] [PubMed]
  82. Depestele, S.; Ross, V.; Verstraelen, S.; Brijs, K.; Brijs, T.; van Dun, K.; Meesen, R. The impact of cognitive functioning on driving performance of older persons in comparison to younger age groups: A systematic review. Transp. Res. Part. F Traffic Psychol. Behav. 2020, 73, 433–452. [Google Scholar] [CrossRef]
  83. Chipika, R.H.; Christidi, F.; Finegan, E.; Li Hi Shing, S.; McKenna, M.C.; Chang, K.M.; Karavasilis, E.; Doherty, M.A.; Hengeveld, J.C.; Vajda, A.; et al. Amygdala pathology in amyotrophic lateral sclerosis and primary lateral sclerosis. J. Neurol. Sci. 2020, 417, 117039. [Google Scholar] [CrossRef] [PubMed]
  84. Christidi, F.; Kleinerova, J.; Tan, E.L.; Delaney, S.; Tacheva, A.; Hengeveld, J.C.; Doherty, M.A.; McLaughlin, R.L.; Hardiman, O.; Siah, W.F.; et al. Limbic Network and Papez Circuit Involvement in ALS: Imaging and Clinical Profiles in GGGGCC Hexanucleotide Carriers in C9orf72 and C9orf72-Negative Patients. Biology 2024, 13, 504. [Google Scholar] [CrossRef]
  85. Finegan, E.; Shing, S.L.H.; Chipika, R.H.; Chang, K.M.; McKenna, M.C.; Doherty, M.A.; Hengeveld, J.C.; Vajda, A.; Pender, N.; Donaghy, C.; et al. Extra-motor cerebral changes and manifestations in primary lateral sclerosis. Brain Imaging Behav. 2021, 15, 2283–2296. [Google Scholar] [CrossRef] [PubMed]
  86. Costello, E.; Rooney, J.; Pinto-Grau, M.; Burke, T.; Elamin, M.; Bede, P.; McMackin, R.; Dukic, S.; Vajda, A.; Heverin, M.; et al. Cognitive reserve in amyotrophic lateral sclerosis (ALS): A population-based longitudinal study. J. Neurol. Neurosurg. Psychiatry 2021, 92, 460–465. [Google Scholar] [CrossRef] [PubMed]
  87. Montuschi, A.; Iazzolino, B.; Calvo, A.; Moglia, C.; Lopiano, L.; Restagno, G.; Brunetti, M.; Ossola, I.; Presti, A.L.; Cammarosano, S.; et al. Cognitive correlates in amyotrophic lateral sclerosis: A population-based study in Italy. J. Neurol. Neurosurg. Psychiatry 2015, 86, 168–173. [Google Scholar] [CrossRef] [PubMed]
  88. Arenaza-Urquijo, E.M.; Landeau, B.; La Joie, R.; Mevel, K.; Mezenge, F.; Perrotin, A.; Desgranges, B.; Bartrés-Faz, D.; Eustache, F.; Chételat, G. Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders. NeuroImage 2013, 83, 450–457. [Google Scholar] [CrossRef]
  89. Li Hi Shing, S.; Lope, J.; Chipika, R.H.; Hardiman, O.; Bede, P. Extra-motor manifestations in post-polio syndrome (PPS): Fatigue, cognitive symptoms and radiological features. Neurol. Sci. 2021, 42, 4569–4581. [Google Scholar] [CrossRef]
  90. Gibbons, C.; Pagnini, F.; Friede, T.; Young, C.A. Treatment of fatigue in amyotrophic lateral sclerosis/motor neuron disease. Cochrane Database Syst. Rev. 2018, 1, CD011005. [Google Scholar] [CrossRef]
  91. Rabkin, J.G.; Gordon, P.H.; McElhiney, M.C.; Rabkin, R.; Chew, S.; Mitsumoto, H. Modafinil treatment of fatigue in patients with ALS: A placebo-controlled study. Muscle Nerve 2009, 39, 297–303. [Google Scholar] [CrossRef]
  92. Bertorini, T.E.; Rashed, H.; Zeno, M.; Tolley, E.A.; Igarashi, M.; Li, Y.D. Effects of 3-4 Diaminopyridine (DAP) in Motor Neuron Diseases. J. Clin. Neuromuscul. Dis. 2011, 12, 129–137. [Google Scholar] [CrossRef]
  93. Wang, H.; Liu, X.; Hu, H.; Wan, F.; Li, T.; Gao, L.; Bezerianos, A.; Sun, Y.; Jung, T.-P. Dynamic Reorganization of Functional Connectivity Unmasks Fatigue Related Performance Declines in Simulated Driving. IEEE Trans. Neural Syst. Rehabil. Eng. 2020, 28, 1790–1799. [Google Scholar] [CrossRef]
  94. Hammad, M.; Silva, A.; Glass, J.; Sladky, J.T.; Benatar, M. Clinical, electrophysiologic, and pathologic evidence for sensory abnormalities in ALS. Neurology 2007, 69, 2236–2242. [Google Scholar] [CrossRef]
  95. Gubbay, S.S.; Kahana, E.; Zilber, N.; Cooper, G.; Pintov, S.; Leibowitz, Y. Amyotrophic lateral sclerosis. A study of its presentation and prognosis. J. Neurol. 1985, 232, 295–300. [Google Scholar] [CrossRef] [PubMed]
  96. Isaacs, J.D.; Dean, A.F.; Shaw, C.E.; Al-Chalabi, A.; Mills, K.R.; Leigh, P.N. Amyotrophic lateral sclerosis with sensory neuropathy: Part of a multisystem disorder? J. Neurol. Neurosurg. Psychiatry 2007, 78, 750–753. [Google Scholar] [CrossRef]
  97. Gregory, R.; Mills, K.; Donaghy, M. Progressive sensory nerve dysfunction in amyotrophic lateral sclerosis: A prospective clinical and neurophysiological study. J. Neurol. 1993, 240, 309–314. [Google Scholar] [CrossRef] [PubMed]
  98. Dalla Bella, E.; Lombardi, R.; Porretta-Serapiglia, C.; Ciano, C.; Gellera, C.; Pensato, V.; Cazzato, D.; Lauria, G. Amyotrophic lateral sclerosis causes small fiber pathology. Eur. J. Neurol. 2016, 23, 416–420. [Google Scholar] [CrossRef] [PubMed]
  99. Isak, B.; Pugdahl, K.; Karlsson, P.; Tankisi, H.; Finnerup, N.B.; Furtula, J.; Johnsen, B.; Sunde, N.; Jakobsen, J.; Fuglsang-Frederiksen, A. Quantitative sensory testing and structural assessment of sensory nerve fibres in amyotrophic lateral sclerosis. J. Neurol. Sci. 2017, 373, 329–334. [Google Scholar] [CrossRef] [PubMed]
  100. Nolano, M.; Provitera, V.; Manganelli, F.; Iodice, R.; Caporaso, G.; Stancanelli, A.; Marinou, K.; Lanzillo, B.; Santoro, L.; Mora, G. Non-motor involvement in amyotrophic lateral sclerosis: New insight from nerve and vessel analysis in skin biopsy. Neuropathol. Appl. Neurobiol. 2017, 43, 119–132. [Google Scholar] [CrossRef]
  101. Weis, J.; Katona, I.; Muller-Newen, G.; Sommer, C.; Necula, G.; Hendrich, C.; Ludolph, A.; Sperfeld, A.-D. Small-fiber neuropathy in patients with ALS. Neurology 2011, 76, 2024–2029. [Google Scholar] [CrossRef]
  102. Radtke, R.A.; Erwin, A.; Erwin, C.W. Abnormal sensory evoked potentials in amyotrophic lateral sclerosis. Neurology 1986, 36, 796–801. [Google Scholar] [CrossRef]
  103. Iglesias, C.; Sangari, S.; El Mendili, M.M.; Benali, H.; Marchand-Pauvert, V.; Pradat, P.F. Electrophysiological and spinal imaging evidences for sensory dysfunction in amyotrophic lateral sclerosis. BMJ Open 2015, 5, e007659. [Google Scholar] [CrossRef]
  104. Pugdahl, K.; Fuglsang-Frederiksen, A.; de Carvalho, M.; Johnsen, B.; Fawcett, P.R.; Labarre-Vila, A.; Liguori, R.; Nix, W.A.; Schofield, I.S. Generalised sensory system abnormalities in amyotrophic lateral sclerosis: A European multicentre study. J. Neurol. Neurosurg. Psychiatry 2007, 78, 746–749. [Google Scholar] [CrossRef] [PubMed]
  105. Pugdahl, K.; Fuglsang-Frederiksen, A.; Johnsen, B.; de Carvalho, M.; Fawcett, P.R.; Labarre-Vila, A.; Liguori, R.; Nix, W.A.; Schofield, I.S. A prospective multicentre study on sural nerve action potentials in ALS. Clin. Neurophysiol. 2008, 119, 1106–1110. [Google Scholar] [CrossRef] [PubMed]
  106. Chipika, R.H.; Mulkerrin, G.; Murad, A.; Lope, J.; Hardiman, O.; Bede, P. Alterations in somatosensory, visual and auditory pathways in amyotrophic lateral sclerosis: An under-recognised facet of ALS. J. Integr. Neurosci. 2022, 21, 88. [Google Scholar] [CrossRef]
  107. Zhou, C.; Hu, X.; Hu, J.; Liang, M.; Yin, X.; Chen, L.; Zhang, J.; Wang, J. Altered Brain Network in Amyotrophic Lateral Sclerosis: A Resting Graph Theory-Based Network Study at Voxel-Wise Level. Front. Neurosci. 2016, 10, 204. [Google Scholar] [CrossRef] [PubMed]
  108. Devine, M.S.; Pannek, K.; Coulthard, A.; McCombe, P.A.; Rose, S.E.; Henderson, R.D. Exposing asymmetric gray matter vulnerability in amyotrophic lateral sclerosis. NeuroImage Clin. 2015, 7, 782–787. [Google Scholar] [CrossRef]
  109. Kleinerova, J.; Chipika, R.H.; Tan, E.L.; Yunusova, Y.; Marchand-Pauvert, V.; Kassubek, J.; Pradat, P.-F.; Bede, P. Sensory Dysfunction in ALS and Other Motor Neuron Diseases: Clinical Relevance, Histopathology, Neurophysiology, and Insights from Neuroimaging. Biomedicines 2025, 13, 559. [Google Scholar] [CrossRef]
  110. Simmatis, L.; Atallah, G.; Scott, S.H.; Taylor, S. The feasibility of using robotic technology to quantify sensory, motor, and cognitive impairments associated with ALS. Amyotroph. Lateral Scler. Front. Degener. 2019, 20, 43–52. [Google Scholar] [CrossRef]
  111. Chang, J.; Shaw, T.B.; Holdom, C.J.; McCombe, P.A.; Henderson, R.D.; Fripp, J.; Barth, M.; Guo, C.C.; Ngo, S.T.; Steyn, F.J.; et al. Lower hypothalamic volume with lower body mass index is associated with shorter survival in patients with amyotrophic lateral sclerosis. Eur. J. Neurol. 2023, 30, 57–68. [Google Scholar] [CrossRef]
  112. Chang, J.; Shaw, T.B.; McCombe, P.A.; Henderson, R.D.; Lucia, D.; Guo, C.C.; Lv, J.; Garner, K.; Bollmann, S.; Ngo, S.T.; et al. Appetite loss in patients with motor neuron disease: Impact on weight loss and neural correlates of visual food cues. Brain Commun. 2025, 7, fcaf111. [Google Scholar] [CrossRef]
  113. Niedermeyer, S.; Murn, M.; Choi, P.J. Respiratory Failure in Amyotrophic Lateral Sclerosis. Chest 2019, 155, 401–408. [Google Scholar] [CrossRef]
  114. Pondofe, K.; Marcelino, A.A.; Ribeiro, T.S.; Torres-Castro, R.; Vera-Uribe, R.; Fregonezi, G.A.F.; Resqueti, V.R. Effects of respiratory physiotherapy in patients with amyotrophic lateral sclerosis: Protocol for a systematic review of randomised controlled trials. BMJ Open 2022, 12, e061624. [Google Scholar] [CrossRef]
  115. de Bernardo, N.; de la Rubia Ortí, J.E.; Villarón-Casales, C.; Privado, J.; Maset-Roig, R.; Cañabate, M.; Sancho-Cantus, D.; Orrit Sanz, I.; Fernández, R.F.; Proaño, B.; et al. Autonomic nervous system and mediating role of respiratory function in patients with ALS. Sci. Rep. 2025, 15, 10513. [Google Scholar] [CrossRef]
  116. Huynh, W.; Sharplin, L.E.; Caga, J.; Highton-Williamson, E.; Kiernan, M.C. Respiratory function and cognitive profile in amyotrophic lateral sclerosis. Eur. J. Neurol. 2020, 27, 685–691. [Google Scholar] [CrossRef]
  117. Azuma, K.; Kagi, N.; Yanagi, U.; Osawa, H. Effects of low-level inhalation exposure to carbon dioxide in indoor environments: A short review on human health and psychomotor performance. Environ. Int. 2018, 121, 51–56. [Google Scholar] [CrossRef]
  118. Lowther, S.D.; Dimitroulopoulou, S.; Foxall, K.; Shrubsole, C.; Cheek, E.; Gadeberg, B.; Sepai, O. Low Level Carbon Dioxide Indoors—A Pollution Indicator or a Pollutant? A Health-Based Perspective. Environments 2021, 8, 125. [Google Scholar] [CrossRef]
  119. Benzo-Iglesias, M.J.; Rocamora-Pérez, P.; Valverde-Martínez, M.d.l.Á.; García-Luengo, A.V.; Benzo-Iglesias, P.M.; López-Liria, R. Efficacy of respiratory muscle training in improving pulmonary function and survival in patients with amyotrophic lateral sclerosis: A systematic review and meta-analysis. Ther. Adv. Respir. Dis. 2025, 19, 17534666251346095. [Google Scholar] [CrossRef] [PubMed]
  120. Beaudin, A.E.; Raneri, J.K.; Ayas, N.T.; Skomro, R.P.; Smith, E.E.; Hanly, P.J. Contribution of hypercapnia to cognitive impairment in severe sleep-disordered breathing. J. Clin. Sleep. Med. 2022, 18, 245–254. [Google Scholar] [CrossRef]
  121. Morrison, A.H.; Jimenez, J.V.; Hsu, J.Y.; Elman, L.; Choi, P.J.; Ackrivo, J. Identifying Daytime Hypercapnia Using Transcutaneous Carbon Dioxide Monitoring in Patients with Amyotrophic Lateral Sclerosis. Muscle Nerve 2025, 71, 611–619. [Google Scholar] [CrossRef] [PubMed]
  122. Kung, S.-C.; Shen, Y.-C.; Chang, E.-T.; Hong, Y.-L.; Wang, L.-Y. Hypercapnia impaired cognitive and memory functions in obese patients with obstructive sleep apnoea. Sci. Rep. 2018, 8, 17551. [Google Scholar] [CrossRef]
  123. Ackrivo, J.; Geronimo, A. Transcutaneous carbon dioxide monitoring in ALS: Assessment of hypoventilation heats up. Muscle Nerve 2022, 65, 371–373. [Google Scholar] [CrossRef] [PubMed]
  124. Dorst, J.; Behrendt, G.; Ludolph, A.C. Non-invasive ventilation and hypercapnia-associated symptoms in amyotrophic lateral sclerosis. Acta Neurol. Scand. 2019, 139, 128–134. [Google Scholar] [CrossRef]
  125. Boentert, M.; Brenscheidt, I.; Glatz, C.; Young, P. Effects of non-invasive ventilation on objective sleep and nocturnal respiration in patients with amyotrophic lateral sclerosis. J. Neurol. 2015, 262, 2073–2082. [Google Scholar] [CrossRef]
  126. Zhang, Y.; Ren, R.; Yang, L.; Nie, Y.; Zhang, H.; Shi, Y.; Sanford, L.D.; Vitiello, M.V.; Tang, X. Sleep in amyotrophic lateral sclerosis: A systematic review and meta-analysis of polysomnographic findings. Sleep. Med. 2023, 107, 116–125. [Google Scholar] [CrossRef]
  127. Boentert, M. Sleep and Sleep Disruption in Amyotrophic Lateral Sclerosis. Curr. Neurol. Neurosci. Rep. 2020, 20, 25. [Google Scholar] [CrossRef]
  128. Boentert, M. Sleep disturbances in patients with amyotrophic lateral sclerosis: Current perspectives. Nat. Sci. Sleep. 2019, 11, 97–111. [Google Scholar] [CrossRef]
  129. Hermann, D.M.; Bassetti, C.L. Role of sleep-disordered breathing and sleep-wake disturbances for stroke and stroke recovery. Neurology 2016, 87, 1407–1416. [Google Scholar] [CrossRef] [PubMed]
  130. Silva, F.; Silva, J.; Salgueira, S.; Mendes, A.; Matos, E.; Conde, B. Sleep Disturbances in Amyotrophic Lateral Sclerosis and Prognostic Impact—A Retrospective Study. Life 2024, 14, 1284. [Google Scholar] [CrossRef]
  131. Charlton, J.L.; Di Stefano, M.; Dimech-Betancourt, B.; Aburumman, M.; Osborne, R.; Peiris, S.; Cross, S.L.; Williams, G.; Stephens, A.; McInnes, A.; et al. What is the motor vehicle crash risk for drivers with a sleep disorder? Transp. Res. Part F Traffic Psychol. Behav. 2022, 90, 229–242. [Google Scholar] [CrossRef]
  132. Gottlieb, D.J.; Ellenbogen, J.M.; Bianchi, M.T.; Czeisler, C.A. Sleep deficiency and motor vehicle crash risk in the general population: A prospective cohort study. BMC Med. 2018, 16, 44. [Google Scholar] [CrossRef]
  133. Garbarino, S.; Magnavita, N.; Guglielmi, O.; Maestri, M.; Dini, G.; Bersi, F.M.; Toletone, A.; Chiorri, C.; Durando, P. Insomnia is associated with road accidents. Further evidence from a study on truck drivers. PLoS ONE 2017, 12, e0187256. [Google Scholar] [CrossRef]
  134. Bharadwaj, N.; Edara, P.; Sun, C. Sleep disorders and risk of traffic crashes: A naturalistic driving study analysis. Saf. Sci. 2021, 140, 105295. [Google Scholar] [CrossRef]
  135. Bioulac, S.; Micoulaud-Franchi, J.A.; Arnaud, M.; Sagaspe, P.; Moore, N.; Salvo, F.; Philip, P. Risk of Motor Vehicle Accidents Related to Sleepiness at the Wheel: A Systematic Review and Meta-Analysis. Sleep 2017, 40, zsx134. [Google Scholar] [CrossRef]
  136. Philip, P. Excessive daytime sleepiness versus sleepiness at the wheel, the need to differentiate global from situational sleepiness to better predict sleep-related accidents. Sleep 2023, 46, zsad231. [Google Scholar] [CrossRef]
  137. El-Nabi, S.A.; Ramadan, K.F.; El-Rabaie, E.-S.M.; Emam, A.; El-Shafai, W. A real-time design and implementation of intelligent drowsiness and fatigue recognition system for enhancing driver safety. Eng. Appl. Artif. Intell. 2025, 162, 112665. [Google Scholar] [CrossRef]
  138. Al-Quraishi, M.S.; Azhar Ali, S.S.; Al-Qurishi, M.; Tang, T.B.; Elferik, S. Technologies for detecting and monitoring drivers’ states: A systematic review. Heliyon 2024, 10, e39592. [Google Scholar] [CrossRef] [PubMed]
  139. Kielty, P.; Dilmaghani, M.S.; Shariff, W.; Ryan, C.; Lemley, J.; Corcoran, P. Neuromorphic Driver Monitoring Systems: A Proof-of-Concept for Yawn Detection and Seatbelt State Detection Using an Event Camera. IEEE Access 2023, 11, 96363–96373. [Google Scholar] [CrossRef]
  140. Bede, P.; Iyer, P.M.; Schuster, C.; Elamin, M.; McLaughlin, R.L.; Kenna, K.; Hardiman, O. The selective anatomical vulnerability of ALS: ‘disease-defining’ and ‘disease-defying’ brain regions. Amyotroph. Lateral Scler. Front. Degener. 2016, 17, 561–570. [Google Scholar] [CrossRef]
  141. Burke, T.; Lonergan, K.; Pinto-Grau, M.; Elamin, M.; Bede, P.; Madden, C.; Hardiman, O.; Pender, N. Visual encoding, consolidation, and retrieval in amyotrophic lateral sclerosis: Executive function as a mediator, and predictor of performance. Amyotroph. Lateral Scler. Front. Degener. 2017, 18, 193–201. [Google Scholar] [CrossRef]
  142. Tahedl, M.; Tan, E.L.; Shing, S.L.H.; Chipika, R.H.; Siah, W.F.; Hengeveld, J.C.; Doherty, M.A.; McLaughlin, R.L.; Hardiman, O.; Finegan, E.; et al. Not a benign motor neuron disease: Longitudinal imaging captures relentless motor connectome disintegration in primary lateral sclerosis. Eur. J. Neurol. 2023, 30, 1232–1245. [Google Scholar] [CrossRef]
  143. de Vries, B.S.; Rustemeijer, L.M.M.; van der Kooi, A.J.; Raaphorst, J.; Schröder, C.D.; Nijboer, T.C.W.; Hendrikse, J.; Veldink, J.H.; Berg, L.H.v.D.; van Es, M.A. A case series of PLS patients with frontotemporal dementia and overview of the literature. Amyotroph. Lateral Scler. Front. Degener. 2017, 18, 534–548. [Google Scholar] [CrossRef] [PubMed]
  144. de Vries, B.S.; Spreij, L.A.; Rustemeijer, L.M.M.; Bakker, L.A.; Veldink, J.H.; van den Berg, L.H.; Nijboer, T.C.; van Es, M.A. A neuropsychological and behavioral study of PLS. Amyotroph. Lateral Scler. Front. Degener. 2019, 20, 376–384. [Google Scholar] [CrossRef] [PubMed]
  145. Chipika, R.H.; Finegan, E.; Li Hi Shing, S.; McKenna, M.C.; Christidi, F.; Chang, K.M.; Doherty, M.A.; Hengeveld, J.C.; Vajda, A.; Pender, N.; et al. “Switchboard” malfunction in motor neuron diseases: Selective pathology of thalamic nuclei in amyotrophic lateral sclerosis and primary lateral sclerosis. NeuroImage Clin. 2020, 27, 102300. [Google Scholar] [CrossRef]
  146. de Vries, B.S.; Rustemeijer, L.M.M.; Bakker, L.A.; Schröder, C.D.; Veldink, J.H.; van den Berg, L.H.; Nijboer, T.C.W.; van Es, M.A. Cognitive and behavioural changes in PLS and PMA:challenging the concept of restricted phenotypes. J. Neurol. Neurosurg. Psychiatry 2019, 90, 141–147. [Google Scholar] [CrossRef]
  147. Bede, P.; Pradat, P.F.; Lope, J.; Vourc’h, P.; Blasco, H.; Corcia, P. Primary Lateral Sclerosis: Clinical, radiological and molecular features. Rev. Neurol. 2022, 178, 196–205. [Google Scholar] [CrossRef]
  148. Pioro, E.P.; Turner, M.R.; Bede, P. Neuroimaging in primary lateral sclerosis. Amyotroph. Lateral Scler. Front. Degener. 2020, 21, 18–27. [Google Scholar] [CrossRef]
  149. Manzano, R.; Sorarú, G.; Grunseich, C.; Fratta, P.; Zuccaro, E.; Pennuto, M.; Rinaldi, C. Beyond motor neurons: Expanding the clinical spectrum in Kennedy’s disease. J. Neurol. Neurosurg. Psychiatry 2018, 89, 808–812. [Google Scholar] [CrossRef] [PubMed]
  150. Querin, G.; Bede, P.; Marchand-Pauvert, V.; Pradat, P.F. Biomarkers of Spinal and Bulbar Muscle Atrophy (SBMA): A Comprehensive Review. Front. Neurol. 2018, 9, 844. [Google Scholar] [CrossRef] [PubMed]
  151. Raaphorst, J.; de Visser, M.; van Tol, M.J.; Linssen, W.H.; van der Kooi, A.J.; de Haan, R.J.; van den Berg, L.H.; Schmand, B. Cognitive dysfunction in lower motor neuron disease: Executive and memory deficits in progressive muscular atrophy. J. Neurol. Neurosurg. Psychiatry 2011, 82, 170–175. [Google Scholar] [CrossRef]
  152. Raaphorst, J.; van Tol, M.J.; Groot, P.F.; Altena, E.; van der Werf, Y.D.; Majoie, C.B.; van der Kooi, A.J.; Berg, L.H.v.D.; Schmand, B.; de Visser, M.; et al. Prefrontal involvement related to cognitive impairment in progressive muscular atrophy. Neurology 2014, 83, 818–825. [Google Scholar] [CrossRef]
  153. Hayes, H.A.; Whiting, N.; Andersen, D.M.; Berggren, K.N.; Mathy, P.; Gibson, S.; Bromberg, M. Driving capacity in drivers with Amyotrophic Lateral Sclerosis compared to healthy controls. F1000Research 2016, 5. [Google Scholar]
  154. Hayes, H.; Dorius, N.; Gibson, S.; Mathy, P.; Berggren, K.; Bromberg, M. Comparison of Driving Capacity with Distraction Using the Lane Change Task in Drivers with Amyotrophic Lateral Sclerosis Compared with Healthy Controls (P3.291). Neurology 2016, 86, P3-291. [Google Scholar] [CrossRef]
  155. Taule, T.; Tysnes, O.B.; Aßmus, J.; Morland, A.S.; Renså, M.A.; Revheim, T.; Glesnes, S.; Rekand, T. Early cognitive decline in amyotrophic lateral sclerosis and its relation to driving: An observational study. J. Rehabil. Med. 2025, 57, jrm43483. [Google Scholar] [CrossRef] [PubMed]
  156. Hayes, H.A.; Hu, N.; Wang, X.; Leatham, J.; Gibson, S.; Bromberg, M. Cessation of driving in individuals with Amyotrophic Lateral Sclerosis. F1000Research 2019, 8. [Google Scholar]
  157. Ellis, R.; Nowell, W.B.; Patel, N.; Wipperman, M.F.; Lyu, J.; Mishra, S.; Scotina, A.; Tu, D.; Wagner, J.A.; Levy, O.; et al. Driving novel endpoints and study designs in amyotrophic lateral sclerosis: Closer examination of the ALSFRS-R subdomains and a new definition of fast and slow progressors. medRxiv 2025. [Google Scholar] [CrossRef]
  158. Aarsland, D.; Brønnick, K.; Larsen, J.P.; Tysnes, O.B.; Alves, G. Cognitive impairment in incident, untreated Parkinson disease: The Norwegian ParkWest study. Neurology 2009, 72, 1121–1126. [Google Scholar] [CrossRef]
  159. Mitsumoto, H.; Chiuzan, C.; Gilmore, M.; Zhang, Y.; Simmons, Z.; Paganoni, S.; Kisanuki, Y.Y.; Zinman, L.; Jawdat, O.; Sorenson, E.; et al. Primary lateral sclerosis (PLS) functional rating scale: PLS-specific clinimetric scale. Muscle Nerve 2020, 61, 163–172. [Google Scholar] [CrossRef]
  160. Mitsumoto, H.; Jang, G.; Lee, I.; Simmons, Z.; Sherman, A.V.; Heitzman, D.; Sorenson, E.; Cheung, K.; Andrews, J.; Harms, M.; et al. Primary lateral sclerosis natural history study—Planning, designing, and early enrollment. Amyotroph. Lateral Scler. Front. Degener. 2023, 24, 394–404. [Google Scholar] [CrossRef]
  161. Fox, G.K.; Bowden, S.C.; Smith, D.S. On-road assessment of driving competence after brain impairment: Review of current practice and recommendations for a standardized examination. Arch. Phys. Med. Rehabil. 1998, 79, 1288–1296. [Google Scholar] [CrossRef] [PubMed]
  162. Van den Berg-Vos, R.M.; Visser, J.; Kalmijn, S.; Fischer, K.; de Visser, M.; de Jong, V.; de Haan, R.J.; Franssen, H.; Wokke, J.H.J.; Berg, L.H.V.D. A long-term prospective study of the natural course of sporadic adult-onset lower motor neuron syndromes. Arch. Neurol. 2009, 66, 751–757. [Google Scholar] [CrossRef]
  163. Khan, M.K.; Khan, A. Challenges Linked to Post-Polio-Paralysis in Khyber Pakhtunkhwa Region. J. Prosthet. Orthot. Sci. Technol. 2024, 3, 48–52. [Google Scholar] [CrossRef]
  164. Selander, H.; Kjellgren, F.; Sunnerhagen, K.S. Self-perceived mobility in immigrants in Sweden living with the late effects of polio. Disabil. Rehabil. 2020, 42, 3203–3208. [Google Scholar] [CrossRef] [PubMed]
  165. Naveh, Y.; Shapira, A.; Ratzon, N.Z. Using a driving simulator during vehicle adaptation. Br. J. Occup. Ther. 2015, 78, 377–382. [Google Scholar] [CrossRef]
  166. Dada, O.O.; Ogundapo, F.A.; Adejare, O.A.; Mbada, C.E.; Ekechukwu, E.N.D. (Eds.) Independent Driving Improved the Self-esteem and Health Related Quality of Life of a Polio Survivor. In Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021); Springer International Publishing: Cham, Switzerland, 2022. [Google Scholar]
  167. Zeilig, G.; Weingarden, H.; Shemesh, Y.; Herman, A.; Heim, M.; Zeweker, M.; Dudkiewicz, I. Functional and environmental factors affecting work status in individuals with longstanding poliomyelitis. J. Spinal Cord. Med. 2012, 35, 22–27. [Google Scholar] [CrossRef] [PubMed]
  168. Ysander, L. The safety of physically disabled drivers. Br. J. Ind. Med. 1966, 23, 173–180. [Google Scholar] [CrossRef]
  169. Steinfeldt, F.; Seifert, W.; Günther, K.P. Modern carbon fibre orthoses in the management of polio patients--a critical evaluation of the functional aspects. Z. Orthop. Ihre Grenzgeb. 2003, 141, 357–361. [Google Scholar] [CrossRef] [PubMed]
  170. Henriksson, P.; Peters, B. Safety and mobility of people with disabilities driving adapted cars. Scand. J. Occup. Ther. 2004, 11, 54–61. [Google Scholar] [CrossRef]
  171. Lings, S. Assessing driving capability: A method for individual testing: The significance of paraparesis inferior studied in a controlled experiment. Appl. Erg. 1991, 22, 75–84. [Google Scholar] [CrossRef]
  172. Meinders, M.J.; Maas, B.R.; Bloem, B.R.; van Geluk, H.; Darweesh, S.K.L. Exploring the Impact of Parkinson’s Disease on Driving: A Population-Based Survey. Mov. Disord. Clin. Pract. 2025, 12, 177–184. [Google Scholar] [CrossRef]
  173. Sportelli, C.; Poplawska-Domaszewicz, K.; Borley, C.; Metta, V.; Leta, V.; Wu, K.; Sauerbier, A.; Santoro, C.; Landolfo, S.; Urso, D.; et al. “Dozing off” in the car and excessive daytime sleepiness (EDS) in Parkinson’s disease: A survey of 125 patients. J. Neural Transm. 2025. [Google Scholar] [CrossRef]
  174. Fründt, O.; Fadhel, M.; Heesen, C.; Seddiq Zai, S.; Gerloff, C.; Vettorazzi, E.; Pöttgen, J.; Buhmann, C. Do Impulse Control Disorders Impair Car Driving Performance in Patients with Parkinson’s Disease? J. Park. Dis. 2022, 12, 2261–2275. [Google Scholar] [CrossRef] [PubMed]
  175. Cordell, R.; Lee, H.C.; Granger, A.; Vieira, B.; Lee, A.H. Driving assessment in Parkinson’s disease—A novel predictor of performance? Mov. Disord. 2008, 23, 1217–1222. [Google Scholar] [CrossRef]
  176. Chee, J.N.; Rapoport, M.J.; Molnar, F.; Herrmann, N.; O’Neill, D.; Marottoli, R.; Mitchell, S.; Tant, M.; Dow, J.; Ayotte, D.; et al. Update on the Risk of Motor Vehicle Collision or Driving Impairment with Dementia: A Collaborative International Systematic Review and Meta-Analysis. Am. J. Geriatr. Psychiatry 2017, 25, 1376–1390. [Google Scholar] [CrossRef] [PubMed]
  177. Anderson, S.W.; Aksan, N.; Dawson, J.D.; Uc, E.Y.; Johnson, A.M.; Rizzo, M. Neuropsychological assessment of driving safety risk in older adults with and without neurologic disease. J. Clin. Exp. Neuropsychol. 2012, 34, 895–905. [Google Scholar] [CrossRef] [PubMed]
  178. Lazeras, C.; Cartier, M.; Bonnet, M.; Laurens, B.; Meissner, W.G.; Planche, V. Why and how to evaluate driving abilities in patients with neurodegenerative diseases? Gériatrie Psychol. Neuropsychiatr. Vieil. 2021, 19. [Google Scholar]
  179. Stamatelos, P.; Economou, A.; Stefanis, L.; Yannis, G.; Papageorgiou, S.G. Driving and Alzheimer’s dementia or mild cognitive impairment: A systematic review of the existing guidelines emphasizing on the neurologist’s role. Neurol. Sci. 2021, 42, 4953–4963. [Google Scholar] [CrossRef]
  180. Drazkowski, J.F.; Sirven, J.I. Driving and Neurologic Disorders. Neurology 2011, 76, S44–S49. [Google Scholar] [CrossRef]
  181. Worringham, C.J.; Wood, J.M.; Kerr, G.K.; Silburn, P.A. Predictors of driving assessment outcome in Parkinson’s disease. Mov. Disord. 2006, 21, 230–235. [Google Scholar] [CrossRef]
  182. Seddiq Zai, S.; das Nair, R.; Heesen, C.; Buhmann, C.; Pedersen, A.; Pöttgen, J. Factors affecting driving performance in patients with Multiple Sclerosis—Still an open question. Front. Neurol. 2024, 15, 1369143. [Google Scholar] [CrossRef] [PubMed]
  183. Holowaychuk, A.; Parrott, Y.; Leung, A.W.S. Exploring the Predictive Ability of the Motor-Free Visual Perception Test (MVPT) and Trail Making Test (TMT) for On-Road Driving Performance. Am. J. Occup. Ther. 2020, 74, 7405205070p1–7405205070p8. [Google Scholar] [CrossRef] [PubMed]
  184. Piersma, D.; Fuermaier, A.B.M.; de Waard, D.; Davidse, R.J.; de Groot, J.; Doumen, M.J.A.; Bredewoud, R.A.; Claesen, R.; Lemstra, A.W.; Vermeeren, A.; et al. Prediction of Fitness to Drive in Patients with Alzheimer’s Dementia. PLoS ONE 2016, 11, e0149566. [Google Scholar] [CrossRef] [PubMed]
  185. Handley, J.D.; Thomas, R.H.; McKenna, P.; Hughes, T.A.T. On the road again: Assessing driving ability in patients with neurological conditions. Pract. Neurol. 2017, 17, 203–206. [Google Scholar] [CrossRef] [PubMed]
  186. Motnikar, L.; Stojmenova, K.; Štaba, U.Č.; Klun, T.; Robida, K.R.; Sodnik, J. Exploring driving characteristics of fit- and unfit-to-drive neurological patients: A driving simulator study. Traffic Inj. Prev. 2020, 21, 359–364. [Google Scholar] [CrossRef] [PubMed]
  187. Wood, K.J. Driving Reassessment Following Neurological Damage: An Integrated Approach. Ph.D. Dissertation, Massey University, Auckland, New Zealand, 1996. [Google Scholar]
  188. Woolley, S.C.; York, M.K.; Moore, D.H.; Strutt, A.M.; Murphy, J.; Schulz, P.E.; Katz, J.S. Detecting frontotemporal dysfunction in ALS: Utility of the ALS Cognitive Behavioral Screen (ALS-CBS). Amyotroph. Lateral Scler. 2010, 11, 303–311. [Google Scholar] [CrossRef]
  189. Murphy, J.; Ahmed, F.; Lomen-Hoerth, C. The UCSF screening exam effectively screens cognitive and behavioral impairment in patients with ALS. Amyotroph. Lateral Scler. Front. Degener. 2015, 16, 24–30. [Google Scholar] [CrossRef]
  190. Tremolizzo, L.; Lizio, A.; Santangelo, G.; Diamanti, S.; Lunetta, C.; Gerardi, F.; Messina, S.; La Foresta, S.; Riva, N.; Falzone, Y.; et al. ALS Cognitive Behavioral Screen (ALS-CBS): Normative values for the Italian population and clinical usability. Neurol. Sci. 2020, 41, 835–841. [Google Scholar] [CrossRef]
  191. Iazzolino, B.; Pain, D.; Laura, P.; Aiello, E.N.; Gallucci, M.; Radici, A.; Palumbo, F.; Canosa, A.; Moglia, C.; Calvo, A.; et al. Italian adaptation of the Beaumont Behavioral Inventory (BBI): Psychometric properties and clinical usability. Amyotroph. Lateral Scler. Front. Degener. 2022, 23, 81–86. [Google Scholar] [CrossRef]
  192. Gosselt, I.K.; Nijboer, T.C.W.; Van Es, M.A. An overview of screening instruments for cognition and behavior in patients with ALS: Selecting the appropriate tool for clinical practice. Amyotroph. Lateral Scler. Front. Degener. 2020, 21, 324–336. [Google Scholar] [CrossRef]
  193. Didcote, L.; Vitoratou, S.; Al-Chalabi, A.; Goldstein, L.H. What is the extent of reliability and validity evidence for screening tools for cognitive and behavioral change in people with ALS? A systematic review. Amyotroph. Lateral Scler. Front. Degener. 2024, 25, 437–451. [Google Scholar] [CrossRef]
  194. Didcote, L.; Vitoratou, S.; Al-Chalabi, A.; Goldstein, L.H. The reliability and validity of in-person and remote behavioural screening tools for people with amyotrophic lateral sclerosis. J. Neurol. Sci. 2024, 466, 123282. [Google Scholar] [CrossRef]
  195. Warrington, E.K.; James, M.; Thames Valley Test, C. The Visual Object and Space Perception Battery; Thames Valley Test Company: Bury St. Edmunds, UK, 1991. [Google Scholar]
  196. Wilson, B.A.; Alderman, N.; Burgess, P.W.; Emslie, H.; Evans, J.J.; Krabbendam, L.; Kalff, A.C. BADS: Behavioural Assessment of the Dysexecutive Syndrome; Pearson: London, UK, 1996. [Google Scholar]
  197. McKenna, P.; Bell, V. Fitness to drive following cerebral pathology: The Rookwood Driving Battery as a tool for predicting on-road driving performance. J. Neuropsychol. 2007, 1, 85–100. [Google Scholar] [CrossRef]
  198. Hemmelgarn, B.; Suissa, S.; Huang, A.; Boivin, J.F.; Pinard, G. Benzodiazepine use and the risk of motor vehicle crash in the elderly. JAMA 1997, 278, 27–31. [Google Scholar] [CrossRef] [PubMed]
  199. Meuleners, L.B.; Duke, J.; Lee, A.H.; Palamara, P.; Hildebrand, J.; Ng, J.Q. Psychoactive medications and crash involvement requiring hospitalization for older drivers: A population-based study. J. Am. Geriatr. Soc. 2011, 59, 1575–1580. [Google Scholar] [CrossRef] [PubMed]
  200. Betz, M.E.; Hyde, H.; DiGuiseppi, C.; Platts-Mills, T.F.; Hoppe, J.; Strogatz, D.; Andrews, H.F.; Mielenz, T.J.; Hill, L.L.; Jones, V.; et al. Self-Reported Opioid Use and Driving Outcomes among Older Adults: The AAA LongROAD Study. J. Am. Board. Fam. Med. JABFM 2020, 33, 521–528. [Google Scholar] [CrossRef]
  201. Carr, D.B.; Beyene, K.; Doherty, J.; Murphy, S.A.; Johnson, A.M.; Domash, H.; Riley, N.; Walker, A.; Sabapathy, A.; Morris, J.C.; et al. Medication and Road Test Performance Among Cognitively Healthy Older Adults. JAMA Netw. Open 2023, 6, e2335651. [Google Scholar] [CrossRef] [PubMed]
  202. Rapoport, M.J.; Zagorski, B.; Seitz, D.; Herrmann, N.; Molnar, F.; Redelmeier, D.A. At-fault motor vehicle crash risk in elderly patients treated with antidepressants. Am. J. Geriatr. Psychiatry 2011, 19, 998–1006. [Google Scholar] [CrossRef] [PubMed]
  203. Simmons, S.M.; Caird, J.K.; Sterzer, F.; Asbridge, M. The effects of cannabis and alcohol on driving performance and driver behaviour: A systematic review and meta-analysis. Addiction 2022, 117, 1843–1856. [Google Scholar] [CrossRef]
  204. Marcotte, T.D.; Umlauf, A.; Grelotti, D.J.; Sones, E.G.; Sobolesky, P.M.; Smith, B.E.; Hoffman, M.A.; Hubbard, J.A.; Severson, J.; Huestis, M.A.; et al. Driving Performance and Cannabis Users’ Perception of Safety: A Randomized Clinical Trial. JAMA Psychiatry 2022, 79, 201–209. [Google Scholar] [CrossRef]
  205. Liang, Z.; Chihuri, S.; Andrews, H.F.; Betz, M.E.; DiGuiseppi, C.; Eby, D.W.; Hill, L.L.; Jones, V.; Mielenz, T.J.; Molnar, L.J.; et al. Interaction between benzodiazepines and prescription opioids on incidence of hard braking events in older drivers. J. Am. Geriatr. Soc. 2023, 71, 3744–3754. [Google Scholar] [CrossRef]
  206. Jones, C.; Abbassian, A.; Trompeter, A.; Solan, M. Driving a modified car: A simple but unexploited adjunct in the management of patients with chronic right sided foot and ankle pain. Foot Ankle Surg. 2010, 16, 170–173. [Google Scholar] [CrossRef]
  207. Burke, K.M.; Arulanandam, V.; Scirocco, E.; Royse, T.; Hall, S.; Weber, H.; Arnold, J.; Pathak, P.; Walsh, C.; Paganoni, S. Assistive Technology in ALS: A Scoping Review of Devices for Limb, Trunk, and Neck Weakness. Am. J. Phys. Med. Rehabil. 2025, 104, e115–e124. [Google Scholar] [CrossRef]
  208. Hegberg, A. 42—Driving and Related Assistive Devices. In Atlas of Orthoses and Assistive Devices, 5th ed.; Webster, J.B., Murphy, D.P., Eds.; Elsevier: Philadelphia, PA, USA, 2019; pp. 425–431.e1. [Google Scholar]
  209. Tachakra, S.S. Driving for the disabled. Br. Med. J. (Clin. Res. Ed) 1981, 283, 589–591. [Google Scholar] [CrossRef] [PubMed][Green Version]
  210. Garbarino, S.; Durando, P.; Guglielmi, O.; Dini, G.; Bersi, F.; Fornarino, S.; Toletone, A.; Chiorri, C.; Magnavita, N. Sleep Apnea, Sleep Debt and Daytime Sleepiness Are Independently Associated with Road Accidents. A Cross-Sectional Study on Truck Drivers. PLoS ONE 2016, 11, e0166262. [Google Scholar] [CrossRef] [PubMed]
  211. Bonsignore, M.R.; Randerath, W.; Schiza, S.; Verbraecken, J.; Elliott, M.W.; Riha, R.; Barbe, F.; Bouloukaki, I.; Castrogiovanni, A.; Deleanu, O.; et al. European Respiratory Society statement on sleep apnoea, sleepiness and driving risk. Eur. Respir. J. 2021, 57, 2001272. [Google Scholar] [CrossRef]
  212. Mittelmann, M.; Greenfield, W.H., Jr. The handicapped driver: An insurer’s point of view. Arch. Phys. Med. Rehabil. 1977, 58, 365–368. [Google Scholar]
  213. Nasreddine, Z.S.; Phillips, N.A.; Bedirian, V.; Charbonneau, S.; Whitehead, V.; Collin, I.; Cummings, J.L.; Chertkow, H. The montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 2005, 53, 695–699. [Google Scholar] [CrossRef] [PubMed]
  214. Cedarbaum, J.M.; Stambler, N.; Malta, E.; Fuller, C.; Hilt, D.; Thurmond, B.; Nakanishi, A. The ALSFRS-R: A revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J. Neurol. Sci. 1999, 169, 13–21. [Google Scholar] [CrossRef]
  215. Fox, G.K.; Bashford, G.M.; Caust, S.L. Identifying safe versus unsafe drivers following brain impairment: The Coorabel Programme. Disabil. Rehabil. 1992, 14, 140–145. [Google Scholar] [CrossRef]
  216. Nikolakakis, I.; Grigoriadis, P.; Dimitriou, N.; Parisis, D.; Nasios, G.; Messinis, L.; Bakirtzis, C. Navigating a Misty Road: Novel Ways to Study the Impact of Cognition on Driving Performance in Multiple Sclerosis. Brain Sci. 2025, 15, 1017. [Google Scholar] [CrossRef]
  217. Bede, P.; Murad, A.; Hardiman, O. Pathological neural networks and artificial neural networks in ALS: Diagnostic classification based on pathognomonic neuroimaging features. J. Neurol. 2022, 269, 2440–2452. [Google Scholar] [CrossRef] [PubMed]
  218. Bede, P.; Murad, A.; Lope, J.; Hardiman, O.; Chang, K.M. Clusters of anatomical disease-burden patterns in ALS: A data-driven approach confirms radiological subtypes. J. Neurol. 2022, 269, 4404–4413. [Google Scholar] [CrossRef] [PubMed]
  219. Grollemund, V.; Le Chat, G.; Secchi-Buhour, M.S.; Delbot, F.; Pradat-Peyre, J.F.; Bede, P.; Pradat, P.-F. Manifold learning for amyotrophic lateral sclerosis functional loss assessment: Development and validation of a prognosis model. J. Neurol. 2021, 268, 825–850. [Google Scholar] [CrossRef]
  220. Westeneng, H.J.; Debray, T.P.A.; Visser, A.E.; van Eijk, R.P.A.; Rooney, J.P.K.; Calvo, A.; Martin, S.; McDermott, C.J.; Thompson, A.G.; Pinto, S.; et al. Prognosis for patients with amyotrophic lateral sclerosis: Development and validation of a personalised prediction model. Lancet Neurol. 2018, 17, 423–433. [Google Scholar] [CrossRef] [PubMed]
  221. Grollemund, V.; Chat, G.L.; Secchi-Buhour, M.S.; Delbot, F.; Pradat-Peyre, J.F.; Bede, P.; Pradat, P.-F. Development and validation of a 1-year survival prognosis estimation model for Amyotrophic Lateral Sclerosis using manifold learning algorithm UMAP. Sci. Rep. 2020, 10, 13378. [Google Scholar] [CrossRef]
  222. Tan, H.H.G.; Westeneng, H.J.; Nitert, A.D.; van Veenhuijzen, K.; Meier, J.M.; van der Burgh, H.K.; van Zandvoort, M.J.E.; van Es, M.A.; Veldink, J.H.; Berg, L.H.v.D. MRI Clustering Reveals Three ALS Subtypes with Unique Neurodegeneration Patterns. Ann. Neurol. 2022, 92, 1030–1045. [Google Scholar] [CrossRef] [PubMed]
  223. van Veenhuijzen, K.; Tan, H.H.G.; Nitert, A.D.; van Es, M.A.; Veldink, J.H.; van den Berg, L.H.; Westeneng, H. Longitudinal Magnetic Resonance Imaging in Asymptomatic C9orf72 Mutation Carriers Distinguishes Phenoconverters to Amyotrophic Lateral Sclerosis or Amyotrophic Lateral Sclerosis with Frontotemporal Dementia. Ann. Neurol. 2025, 97, 281–295. [Google Scholar] [CrossRef]
  224. Lajoie, I.; Kalra, S.; Dadar, M. Regional Cerebral Atrophy Contributes to Personalized Survival Prediction in Amyotrophic Lateral Sclerosis: A Multicentre, Machine Learning, Deformation-Based Morphometry Study. Ann. Neurol. 2025, 97, 1144–1157. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Axes of clinical heterogeneity in ALS.
Figure 1. Axes of clinical heterogeneity in ALS.
Brainsci 16 00408 g001
Figure 2. Components of fatigue in MNDs and opportunities for intervention.
Figure 2. Components of fatigue in MNDs and opportunities for intervention.
Brainsci 16 00408 g002
Figure 3. Assessment and intervention strategies for safe driving in MNDs.
Figure 3. Assessment and intervention strategies for safe driving in MNDs.
Brainsci 16 00408 g003
Figure 4. Safety optimisation strategies for driving with ALS/MND.
Figure 4. Safety optimisation strategies for driving with ALS/MND.
Brainsci 16 00408 g004
Table 1. Overview of research studies on driving in MND.
Table 1. Overview of research studies on driving in MND.
Authors & YearCohortStudy DesignNumber of ParticipantsMain Focus & ObjectivesClinical Data and InstrumentsMain Study Findings and Conclusions
Hayes et al., 2020 [19]ALSProspective28 ALS
20 Controls
Driving capacityMOCA, ALS-CBS, gait speed, ALSFRS-r, LCTLCT scores between pALS and HC are not different under motor, cognitive, or visual distraction. Driving assessment needs to be expanded longitudinally.
Hayes et al., 2016 [153]ALSProspective30 ALS
20 Controls
Driving simulation tasks & driving skills LCT, MOCA, ALS-CBS, gait speed, ALSFRS-rpALS with mild cognitive and motor deficits perform similarly to HC. Individuals typically cease driving within 2 years but objective indicators are lacking.
Hayes et al., 2016 [154]ALSProspective20 ALS
9 Controls
Driving capacity while distracted using computer simulation Gait speed, MOCA, TMTB, LCT, MDT, VDTpALS perform poorly under motor distraction.
Taule et al., 2025 [155]ALSObservational study31 ALSImpact of cognitive change on driving cessationECAS, ALSFRS-rCognitive function is not a predictor of driving cessation.
Hayes et al., 2019 [156]ALSProspective27 ALS
20 Controls
Clinical correlates of driving capacity ALSFRS-R, LCT, MDT, VDTDistraction variables and ALSFRS-r predict driving cessation.
Lings, 1991 [171]HSP Prospective52 Paraparesis
109 Controls
The impact of paresis & spasticity on driving Grip strength, RTParesis affects reaction times more than spasticity.
Khan et al., 2024 [163]PPSCross-sectional 200 PPSChallenges faced in PPSPost-Polio Clinic QuestionnairePain, fatigue, and muscular weakness reported by 91.5%; driving deemed impossible by 70%.
Selander et al., 2020 [164]PolioRetrospective145 PolioOutdoor mobility with polioMobility, independence, pain, depression, mobility, transport questionnaireIn total, 57% independent and active drivers.
Dependence for outdoor mobility linked to depression.
Zeilig et al., 2012 [167]PolioRetrospective 123 PolioSocial and functional barriers in poliomyelitisDemographics, B-ADL, E-ADL, mobilityLSP impacts on employment as per ICF.
Ysander, 1966 [168]PolioCohort study494 PolioRTAs in patients with poliomyelitisDisability profilesSuccessful vehicle modifications for LEoP, low % (0.6) of RTAs due to disability.
Steinfeld et al., 2003 [169]PolioRetrospective55 PolioBenefits of modern AFOs in polioAFO acceptance, functional capacity, comfortBenefits of carbon fibre orthoses: improved ADLS, ambulation and driving.
Henriksson et al., 2004 [170]PolioCross-sectional793Safety of drivers with disabilitiesDriving questionnaire, adaptations, safety, involvement in RTAsBenefits of vehicle adaptation, 1 out of 10 drivers involved in RTAs over 3.5 years.
Table 2. A domain-based systematic assessment strategy for MND/ALS.
Table 2. A domain-based systematic assessment strategy for MND/ALS.
Assessment DomainSpecific Factors to Consider
Social contextIndividual driving preferences, employment, habitation (town/country), relevance to QoL, frequency of hospital attendances, clinical trial participation, community support, isolation, etc.
CognitionExecutive function, visuospatial skills, spatial memory, attention, concentration
BehaviourDisinhibition, apathy, social cognition
MoodAnxiety, depression, outlook, motivation
MedicationsAnti-spasticity meds, anticholinergics, opiates, benzodiazepine, SSRI, SNRI, TCA, antihistamines, cannabis, syringe drivers, patches
PainSpasticity, adhesive capsulitis, cramps, pressure sores, odynophagia, oral candidiasis
Extra-motor manifestationsProprioceptive, extrapyramidal, cerebellar manifestations, paraesthesia, sialorrhea, pseudobulbar affect
Involuntary movements Polyminimyoclonus, thumb tremor, involuntary crying and laughter
ToneSpasticity, cramps
FatigueSomnolence, concentration, attention
SleepOSA, Hypoxic events, REM sleep behaviour disorder, restless legs syndrome
Respiratory functionMorning headaches, orthopnoea, hypercapnia, NIV-dependence
Fine motor controlDexterity, ankle–foot control
Gross motor control Pedal and steering operation, ability to get in and out of the vehicle, wheelchair use
Sensory examination Proprioceptive deficits, sensory ataxia, pseudoathetosis, paraesthesia, vibrotactile deficits
Financial and regulatory contextsInsurance premium, availability of car modification grants, charity support, government support, free travel on public transport, car tax waiver
Table 3. Knowledge gaps and research priorities for safe driving in MND.
Table 3. Knowledge gaps and research priorities for safe driving in MND.
Knowledge and Research GapsPriorities and Future Directions
Absence of disease-specific guidelines & best-practice recommendations Prospective studies & accident rate registries
Small, poorly designed, retrospective studies Predictors and prognostic indicators for driving cessation need to be studied
Limited clinical instruments implemented Outcome assessment of driving restrictions (local, daytime, morning only, etc.)
Focus on motor function primarilyInternational expert committees for driving with MND
Overlooking cognitive and behavioural aspects of the diseaseSatellite meetings at large international conferences
Generic disability-based regulations and guidelinesRaising awareness of MND-associated challenges with decision makers, insurance companies, local driving authorities
Limited access to simulators and on-road assessmentsCampaigning at local health authorities, governments, insurance industry for subsidies and grants
Long waiting times for assessmentsPrompt access to simulators and road tests
Unclear coordination of careAccess to timely car adaptations
Slow approval of car modification grants in many jurisdictionsRenting schemes of modified vehicles
Clinicians have a low threshold of advising driving cessationFinancial grants for renting and adaptations
Social context, QoL implications often overlookedVolunteer driver network to access hospital appointments
The impact of commonly administered medications in ALS seldom consideredInvolvement of relevant stakeholders: patients, caregivers, families, charities, patient advocacy groups
Poor access to neuropsychologyImplementation of new technologies, drive-by-wire, touch screens, voice command, collision avoidance systems, back-up & 360° camera systems, park-assist technology, semi-autonomous driving, etc.
Blanket driving cessation recommendations instead of restriction such as daytime, local, morning driving Consideration of experience from other neurological conditions MS, PF, AD, MCI, etc. Establishment of MND-specific assessment and car adaptation schemes
Limited ongoing research despite huge practical relevanceCollection of patient perspectives and caregiver perspectives regarding driving experience
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kleinerova, J.; Tully, J.; Lope, J.; Tan, E.L.; Toomey, A.; Siah, W.F.; Bede, P. Driving with Motor Neuron Disease: Disease-Specific Considerations, Multi-Domain Assessments and Support Strategies. Brain Sci. 2026, 16, 408. https://doi.org/10.3390/brainsci16040408

AMA Style

Kleinerova J, Tully J, Lope J, Tan EL, Toomey A, Siah WF, Bede P. Driving with Motor Neuron Disease: Disease-Specific Considerations, Multi-Domain Assessments and Support Strategies. Brain Sciences. 2026; 16(4):408. https://doi.org/10.3390/brainsci16040408

Chicago/Turabian Style

Kleinerova, Jana, Jane Tully, Jasmin Lope, Ee Ling Tan, Alison Toomey, We Fong Siah, and Peter Bede. 2026. "Driving with Motor Neuron Disease: Disease-Specific Considerations, Multi-Domain Assessments and Support Strategies" Brain Sciences 16, no. 4: 408. https://doi.org/10.3390/brainsci16040408

APA Style

Kleinerova, J., Tully, J., Lope, J., Tan, E. L., Toomey, A., Siah, W. F., & Bede, P. (2026). Driving with Motor Neuron Disease: Disease-Specific Considerations, Multi-Domain Assessments and Support Strategies. Brain Sciences, 16(4), 408. https://doi.org/10.3390/brainsci16040408

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop