Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis.

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Electroencephalographic ß-band oscillations in the sensorimotor network reflect motor symptom severity in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Resting-state electroencephalography (EEG) holds promise for assessing brain networks in amyotrophic lateral sclerosis (ALS). We investigated whether neural ß-band oscillations in the sensorimotor network could serve as an objective quantitative measure of progressive motor impairment and functional disability in ALS patients. METHODS: Resting-state EEG was recorded in 18 people with ALS and 38 age- and gender-matched healthy controls. We estimated source-localized ß-band spectral power in the sensorimotor cortex. Clinical evaluation included lower (LMN) and upper motor neuron scores, Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised score, fine motor function (FMF) subscore, and progression rate. Correlations between clinical scores and ß-band power were analysed and corrected using a false discovery rate of q = 0.05. RESULTS: ß-Band power was significantly lower in people with ALS than controls (p = 0.004), and correlated with LMN score (R = -0.65, p = 0.013), FMF subscore (R = -0.53, p = 0.036), and FMF progression rate (R = 0.52, p = 0.036). CONCLUSIONS: ß-Band spectral power in the sensorimotor cortex reflects clinically evaluated motor impairment in ALS. This technology merits further investigation as a biomarker of progressive functional disability.

Distinct Longitudinal Changes in EEG Measures Reflecting Functional Network Disruption in ALS Cognitive Phenotypes.

Amyotrophic lateral sclerosis (ALS) is characterised primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. We have shown previously that resting-state EEG captures dysfunction in motor and cognitive networks in ALS. However, the longitudinal development of these dysfunctional patterns, especially in networks linked with cognitive-behavioural functions, remains unclear. Longitudinal studies on non-motor changes in ALS are essential to further develop our understanding of disease progression, improve care and enhance the evaluation of new treatments. To address this gap, we examined 124 ALS individuals with 128-channel resting-state EEG recordings, categorised by cognitive impairment (ALSci, n = 25), behavioural impairment (ALSbi, n = 58), or non-impaired (ALSncbi, n = 53), with 12 participants meeting the criteria for both ALSci and ALSbi. Using linear mixed-effects models, we characterised the general and phenotype-specific longitudinal changes in brain network, and their association with cognitive performance, behaviour changes, fine motor symptoms, and survival. Our findings revealed a significant decline in [Formula: see text]-band spectral power over time in the temporal region along with increased [Formula: see text]-band power in the fronto-temporal region in the ALS group. ALSncbi participants showed widespread ß-band synchrony decrease, while ALSci participants exhibited increased co-modulation correlated with verbal fluency decline. Longitudinal network-level changes were specific of ALS subgroups and correlated with motor, cognitive, and behavioural decline, as well as with survival. Spectral EEG measures can longitudinally track abnormal network patterns, serving as a candidate stratification tool for clinical trials and personalised treatments in ALS.

Harnessing Big Data in Amyotrophic Lateral Sclerosis: Machine Learning Applications for Clinical Practice and Pharmaceutical Trials.

The arrival of genotype-specific therapies in amyotrophic lateral sclerosis (ALS) signals the dawn of precision medicine in motor neuron diseases (MNDs). After decades of academic studies in ALS, we are now witnessing tangible clinical advances. An ever increasing number of well-designed descriptive studies have been published in recent years, characterizing typical disease-burden patterns in vivo and post mortem. Phenotype- and genotype-associated traits and "typical" propagation patterns have been described based on longitudinal clinical and biomarker data. The practical caveat of these studies is that they report "group-level", stereotyped trajectories representative of ALS as a whole. In the clinical setting, however, "group-level" biomarker signatures have limited practical relevance and what matters is the meaningful interpretation of data from a single individual. The increasing availability of large normative data sets, national registries, extant academic data, consortium repositories, and emerging data platforms now permit the meaningful interpretation of individual biomarker profiles and allow the categorization of single patients into relevant diagnostic, phenotypic, and prognostic categories. A variety of machine learning (ML) strategies have been recently explored in MND to demonstrate the feasibility of interpreting data from a single patient. Despite the considerable clinical prospects of classification models, a number of pragmatic challenges need to be overcome to unleash the full potential of ML in ALS. Cohort size limitations, administrative hurdles, data harmonization challenges, regulatory differences, methodological obstacles, and financial implications and are just some of the barriers to readily implement ML in routine clinical practice. Despite these challenges, machine-learning strategies are likely to be firmly integrated in clinical decision-making and pharmacological trials in the near future.

Promises and pitfalls of imaging-based biomarkers in motor neuron diseases.

PURPOSE OF REVIEW: Although neuroimaging in motor neuron diseases (MNDs) continues to generate important novel academic insights, the translation of novel radiological protocols into viable biomarkers remains challenging. RECENT FINDINGS: A multitude of technological advances contribute to the success of academic imaging in MND such as the availability of high-field MRI platforms, novel imaging techniques, quantitative spinal cord protocols to whole-brain spectroscopy. International collaborations, protocol harmonization efforts, open-source image analysis suites also fuel developments in the field. Despite the success of academic neuroimaging in MND, the meaningful interpretation of radiological data from single patients and accurate classification into relevant diagnostic, phenotypic and prognostic categories remain challenging. Appraising accruing disease burden over the short follow-up intervals typically used in pharmacological trials is also notoriously difficult. SUMMARY: Although we acknowledge the academic achievements of large descriptive studies, an unmet priority of neuroimaging in MND is the development of robust diagnostic, prognostic and monitoring applications to meet the practical demands of clinical decision-making and pharmacological trials. A paradigm shift from group-level analyses to individual-level data interpretation, accurate single-subject classification and disease-burden tracking is therefore urgently needed to distil raw spatially coded imaging data into practical biomarkers.

Not a benign motor neuron disease: longitudinal imaging captures relentless motor connectome disintegration in primary lateral sclerosis.

BACKGROUND AND PURPOSE: Primary lateral sclerosis (PLS) is a progressive upper motor neuron disorder associated with considerable clinical disability. Symptoms are typically exclusively linked to primary motor cortex degeneration and the contribution of pre-motor, supplementary motor, cortico-medullary and inter-hemispheric connectivity alterations are less well characterized. METHODS: In a single-centre, prospective, longitudinal neuroimaging study 41 patients with PLS were investigated. Patients underwent standardized neuroimaging, genetic profiling with whole exome sequencing, and comprehensive clinical assessments including upper motor neuron scores, tapping rates, mirror movements, spasticity assessment, cognitive screening and evaluation for pseudobulbar affect. Longitudinal neuroimaging data from 108 healthy controls were used for image interpretation. A standardized imaging protocol was implemented including 3D T1-weighted structural, diffusion tensor imaging and resting-state functional magnetic resonance imaging. Following somatotopic segmentation, cortical thickness analyses, probabilistic tractography, blood oxygenation level dependent signal analyses and brainstem volumetry were conducted to evaluate cortical, brainstem, cortico-medullary and inter-hemispheric connectivity alterations both cross-sectionally and longitudinally. RESULTS: Our data confirm progressive primary motor cortex degeneration, considerable supplementary motor and pre-motor area involvement, progressive brainstem atrophy, cortico-medullary and inter-hemispheric disconnection, and close associations between clinical upper motor neuron scores and somatotopic connectivity indices in PLS. DISCUSSION: Primary lateral sclerosis is associated with relentlessly progressive motor connectome degeneration. Clinical disability in PLS is likely to stem from a combination of intra- and inter-hemispheric connectivity decline and primary, pre- and supplementary motor cortex degeneration. Simple 'bedside' clinical tools, such as tapping rates, are excellent proxies of the integrity of the relevant fibres of the contralateral corticospinal tract.

Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (ß-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.

Executive Dysfunction, Social Cognition Impairment, and Gray Matter Pathology in Myotonic Dystrophy Type 2: A Pilot Study.

BACKGROUND: In contrast to myotonic dystrophy type 1, the cognitive and radiologic profile of myotonic dystrophy type 2 (DM2) is relatively poorly characterized. OBJECTIVE: To conduct a pilot study to systematically evaluate cognitive and radiologic features in a cohort of Greek individuals with DM2. METHOD: Eleven genetically confirmed individuals with DM2 and 26 age- and education-matched healthy controls were administered the Edinburgh Cognitive and Behavioural Amyotrophic Lateral Sclerosis Screen (ECAS) to screen for impairment in multiple cognitive domains. MRI data were evaluated by morphometric analyses to identify disease-specific gray and white matter alterations. The following statistical thresholds were used for cognitive comparisons: PFDR < 0.05 and Bayes factor (BF 10 ) >10. RESULTS: The DM2 group exhibited cognitive impairment (ECAS Total score; PFDR = 0.001; BF 10 = 108.887), which was dominated by executive impairment ( PFDR = 0.003; BF 10 = 25.330). A trend toward verbal fluency impairment was also identified. No significant impairments in memory, language, or visuospatial function were captured. The analysis of subscores revealed severe impairments in social cognition and alternation. Voxel-based morphometry identified widespread frontal, occipital, and subcortical gray matter atrophy, including the left superior medial frontal gyrus, right medial orbitofrontal gyrus, right operculum, right precuneus, bilateral fusiform gyri, and bilateral thalami. CONCLUSION: DM2 may be associated with multifocal cortical and thalamic atrophy, which is likely to underpin the range of cognitive manifestations mostly characterized by executive impairment and specifically by impaired social cognition.

Alterations in somatosensory, visual and auditory pathways in amyotrophic lateral sclerosis: an under-recognised facet of ALS.

BACKGROUND: While amyotrophic lateral sclerosis (ALS) is widely recognised as a multi-network disorder with extensive frontotemporal and cerebellar involvement, sensory dysfunction is relatively under evaluated. Subtle sensory deficits have been sporadically reported, but there is a prevailing notion that sensory pathways may be relatively spared in ALS. METHODS: In a prospective neuroimaging study we have systematically evaluated cerebral grey and white matter structures involved in the processing, relaying and mediation of sensory information. Twenty two C9orf72 positive ALS patients (C9+ ALS), 138 C9orf72 negative ALS patients (C9- ALS) and 127 healthy controls were included. RESULTS: Widespread cortical alterations were observed in C9+ ALS including both primary and secondary somatosensory regions. In C9- ALS, cortical thickness reductions were observed in the postcentral gyrus. Thalamic nuclei relaying somatosensory information as well as the medial and lateral geniculate nuclei exhibited volume reductions. Diffusivity indices revealed posterior thalamic radiation pathology and a trend of left medial lemniscus degeneration was also observed in C9- ALS (p = 0.054). Our radiology data confirm the degeneration of somatosensory, visual and auditory pathways in ALS, which is more marked in GGGGCC hexanucleotide repeat expansion carriers. CONCLUSIONS: In contrast to the overwhelming focus on motor system degeneration and frontotemporal dysfunction in recent research studies, our findings confirm that sensory circuits are also affected in ALS. The involvement of somatosensory, auditory and visual pathways in ALS may have important clinical ramifications which are easily overlooked in the context of unremitting motor decline. Subtle sensory deficits may exacerbate mobility, contribute to fall risk, impair dexterity, and worsen bulbar dysfunction, therefore comprehensive sensory testing should also be performed as part of the clinical assessments in ALS.

Cerebellar remodelling decades after spinal cord insult: neuroplasticity in poliomyelitis survivors.

BACKGROUND: The cerebellum integrates a multitude of motor and cognitive processes through ample spinal and supratentorial projections. Despite emerging evidence of adaptive neuroplasticity, cerebellar reorganisation in response to severe spinal insult early in life is poorly characterised. The objective of this study is the systematic characterisation of cerebellar integrity metrics in a cohort of adult poliomyelitis survivors as a template condition for longstanding lower motor neuron injury. METHODS: A total of 143 participants, comprising 43 adult poliomyelitis survivors and 100 age- and sex-matched healthy controls were recruited in a prospective, single-centre neuroimaging study with a uniform structural and diffusion imaging protocol. First, standard voxelwise grey and white matter analyses were performed. Then, the cerebellum was anatomically segmented into lobules, and cortical thickness and grey matter volumes were evaluated in each lobule. The integrity of cerebellar peduncles was also assessed based on their diffusivity profiles. RESULTS: Compared to healthy controls, poliomyelitis survivors exhibited greater cortical thickness in lobules I, II, and III in the right hemisphere and in lobules VIIIA and VIIIB bilaterally. A trend of higher cortical thickness was also detected lobules I, II and III in the left hemisphere. Enhanced cerebellar peduncle organisation was detected, particularly within the middle cerebellar peduncles. CONCLUSIONS: Increased cerebellar integrity measures in poliomyelitis survivors are primarily identified in lobules associated with sensorimotor functions. The identified pattern of cerebellar reorganisation may represent compensatory changes in response to severe lower motor neuron injury in childhood and ensuing motor disability.

Neurometabolic Alterations in Motor Neuron Disease: Insights from Magnetic Resonance Spectroscopy.

Magnetic resonance spectroscopy (MRS) has contributed important academic insights in motor neuron diseases (MNDs), particularly in amyotrophic lateral sclerosis (ALS). Over the past three decades momentous methodological advances took place, including the emergence of high-field magnetic resonance imaging (MRI) platforms, multi-voxel techniques, whole-brain protocols, novel head-coil designs, and a multitude of open-source imaging suites. Technological advances in MRS are complemented by important conceptual developments in MND, such as the recognition of the importance of extra-motor brain regions, multi-timepoint longitudinal study designs, assessment of asymptomatic mutation carriers, description of genotype-associated signatures, and the gradual characterisation of non-ALS MND phenotypes. We have conducted a systematic review of published MRS studies in MND to identify important emerging research trends, key lessons from pioneering studies, and stereotyped shortcomings. We also sought to highlight notable gaps in the current literature so that research priorities for future studies can be outlined. While MRS remains relatively underutilised in MND compared to other structural, diffusivity and functional imaging modalities, our review suggests that MRS can not only advance our academic understanding of MND biology, but has a multitude of practical benefits for clinical and pharmaceutical trial applications.

Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis.

OBJECTIVE: Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression. METHODS: Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets. RESULTS: We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients. INTERPRETATION: Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725-738.

Split-hand and split-limb phenomena in amyotrophic lateral sclerosis: pathophysiology, electrophysiology and clinical manifestations.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder affecting the upper and lower motor neurons. A key clinical feature of ALS is the absence of accurate, early-stage diagnostic indicators. 'Split-hand syndrome' was first described in ALS at the end of the last century and a considerable body of literature suggests that the split-hand phenomenon may be an important clinical feature of ALS. Considering the published investigations, it is conceivable that the 'split-hand syndrome' results from the associated upper and lower motor neuron degeneration, whose interaction remains to be fully clarified. Additionally, other split syndromes have been described in ALS involving upper or lower limbs, with a nuanced description of clinical and neurophysiological manifestations that may further aid ALS diagnosis. In this review, we endeavour to systematically present the spectrum of the 'split syndromes' in ALS from a clinical and neurophysiology perspective and discuss their diagnostic and pathogenic utility.

Cognitive reserve in amyotrophic lateral sclerosis (ALS): a population-based longitudinal study.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is often associated with cognitive and/or behavioural impairment. Cognitive reserve (CR) may play a protective role in offsetting cognitive impairment. This study examined the relationship between CR and longitudinal change in cognition in an Irish ALS cohort. METHODS: Longitudinal neuropsychological assessment was carried out on 189 patients over 16 months using the Edinburgh cognitive and behavioural ALS screen (ECAS) and an additional battery of neuropsychological tests. CR was measured by combining education, occupation and physical activity data. Joint longitudinal and time-to-event models were fitted to investigate the associations between CR, performance at baseline and decline over time while controlling for non-random drop-out. RESULTS: CR was a significant predictor of baseline neuropsychological performance, with high CR patients performing better than those with medium or low CR. Better cognitive performance in high CR individuals was maintained longitudinally for ECAS, social cognition, executive functioning and confrontational naming. Patients displayed little cognitive decline over the course of the study, despite controlling for non-random drop-out. CONCLUSIONS: These findings suggest that CR plays a role in the presentation of cognitive impairment at diagnosis but is not protective against cognitive decline. However, further research is needed to examine the interaction between CR and other objective correlates of cognitive impairment in ALS.

Genotype-associated cerebellar profiles in ALS: focal cerebellar pathology and cerebro-cerebellar connectivity alterations.

OBJECTIVE: Cerebellar disease burden and cerebro-cerebellar connectivity alterations are poorly characterised in amyotrophic lateral sclerosis (ALS) despite the likely contribution of cerebellar pathology to the clinical heterogeneity of the condition. METHODS: A prospective imaging study has been undertaken with 271 participants to systematically evaluate cerebellar grey and white matter alterations, cerebellar peduncle integrity and cerebro-cerebellar connectivity in ALS. Participants were stratified into four groups: (1) patients testing positive for GGGGCC repeat expansions in C9orf72, (2) patients carrying an intermediate-length repeat expansion in ATXN2, (3) patients without established ALS-associated mutations and (4) healthy controls. Additionally, the cerebellar profile of a single patient with ALS who had an ATXN2 allele length of 62 was evaluated. Cortical thickness, grey matter and white matter volumes were calculated in each cerebellar lobule complemented by morphometric analyses to characterise genotype-associated atrophy patterns. A Bayesian segmentation algorithm was used for superior cerebellar peduncle volumetry. White matter diffusivity parameters were appraised both within the cerebellum and in the cerebellar peduncles. Cerebro-cerebellar connectivity was assessed using deterministic tractography. RESULTS: Cerebellar pathology was confined to lobules I-V of the anterior lobe in patients with sporadic ALS in contrast to the considerable posterior lobe and vermis disease burden identified in C9orf72 mutation carriers. Patients with intermediate ATXN2 expansions did not exhibit significant cerebellar pathology. CONCLUSIONS: Focal rather than global cerebellar degeneration characterises ALS. Pathognomonic ALS symptoms which are typically attributed to other anatomical regions, such as dysarthria, dysphagia, pseudobulbar affect, eye movement abnormalities and cognitive deficits, may be modulated, exacerbated or partially driven by cerebellar changes in ALS.

Neural Correlates of Motor Imagery of Gait in Amyotrophic Lateral Sclerosis.

BACKGROUND: Gait impairment is poorly characterized in amyotrophic lateral sclerosis (ALS), despite increasing evidence of extrapyramidal and cerebellar dysfunction. Gait impairment adds to the considerable motor disability of ALS patients and requires targeted multidisciplinary interventions. PURPOSE: To assess gait imagery-specific networks and functional adaptation in ALS. STUDY TYPE: Prospective. POPULATION: Seventeen ALS patients with lower motor neuron predominant (LMNp) disability, 14 patients with upper motor neurons predominant (UMNp) disease, and 14 healthy controls were included. FIELD STRENGTH/SEQUENCES: 3T / gradient echo echo planar (GE-EPI). ASSESSMENT: Subjects performed a dual motor imagery task: normal and precision gait. The Movement Imagery Questionnaire - Revised Second Version (MIQ-rs) was used to appraise movement imagery in each participant. Study group-specific activation patterns were evaluated during motor imagery of gait. Additional generalized psychophysiological interaction analyses were carried out using the supplementary motor area, caudate, cerebellum, and superior parietal lobule as seed regions. STATISTICAL TESTS: Repeated-measures analysis of variance (ANOVA) was used to compare time imagery and MIQ-rs scores between groups. Size effects were also reported as partial eta squared (η2). One-way ANOVA was performed to explore differences in terms of connexions during motor imagery tasks. RESULTS: A significant increase in imagery time in UMNp patients compared to controls (P < 0.05) and LMNp (P < 0.05) during imagined gait was demonstrated. UMNp patients exhibited altered supplementary motor area, precentral gyrus, superior parietal lobule, and dorsolateral prefrontal cortex activation and increased orbitofrontal (pFDR(False Discovery Rate) < 0.05), posterior parietal (pFDR < 0.05) caudate (pFDR < 0.05), and cerebellar (pFDR < 0.05) signal during imagined locomotion. Increased effective connectivity of the striato-cerebellar and parieto-cerebellar circuits was also demonstrated. Additional activation was detected in the insula and cingulate cortex. DATA CONCLUSION: Enhanced striato- and parieto-cerebellar networks in UMNp ALS patients are likely to represent a compensatory response to impaired postural control. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 5.

Extra-motor manifestations in post-polio syndrome (PPS): fatigue, cognitive symptoms and radiological features.

BACKGROUND: There is a paucity of cerebral neuroimaging studies in post-polio syndrome (PPS), despite the severity of neurological and neuropsychological sequelae associated with the condition. Fatigue, poor concentration, limited exercise tolerance, paraesthesia and progressive weakness are frequently reported, but the radiological underpinnings of these symptoms are poorly characterised. OBJECTIVE: The aim of this study is to evaluate cortical and subcortical alterations in a cohort of adult polio survivors to explore the anatomical substrate of extra-motor manifestations. METHODS: Thirty-six patients with post-polio syndrome, a disease-control group with amyotrophic lateral sclerosis patients and a cohort of healthy individuals were included in a prospective neuroimaging study with a standardised clinical and radiological protocol. Validated clinical instruments were utilised to assess mood, cognitive and behavioural domains and specific aspects of fatigue. Cortical thickness analyses, subcortical volumetry, brainstem segmentation and region-of-interest (ROI) white matter analyses were undertaken to assess regional grey and white matter alterations. RESULTS: A high proportion of PPS patients exhibited apathy, verbal fluency deficits and reported self-perceived fatigue. On ROI analyses, cortical atrophy was limited to the cingulate gyrus, and the temporal pole and subcortical atrophy were only detected in the left nucleus accumbens. No FA reductions were noted to indicate white matter degeneration in any of the lobes. CONCLUSIONS: Despite the high incidence of extra-motor manifestations in PPS, only limited cortical, subcortical and white matter degeneration was identified. Our findings suggest that non-structural causes, such as polypharmacy and poor sleep, may contribute to the complex symptomatology of post-polio syndrome.

Neuropsychological Assessment Should Always be Considered in Myotonic Dystrophy Type 2.

Myotonic dystrophies (DMs) are hereditary, multisystem, slowly progressive myopathies. One of the systems they affect is the CNS. In contrast to the well-established cognitive profile of myotonic dystrophy type 1 (DM1), only a few studies have investigated cognitive dysfunction in individuals with myotonic dystrophy type 2 (DM2), and their findings have been inconsistent. To identify the most commonly affected cognitive domains in individuals with DM2, we performed a formal comprehensive review of published DM2 studies. Using the terms "myotonic dystrophy type 2" AND "cognitive deficits," "cognitive," "cognition," "neuropsychological," "neurocognitive," and "neurobehavioral" in all fields, we conducted an advanced search on PubMed. We read and evaluated all of the available original research articles (13) and one case study, 14 in total, and included them in our review. Most of the research studies of DM2 reported primary cognitive deficits in executive functions (dysexecutive syndrome), memory (short-term nonverbal, verbal episodic memory), visuospatial/constructive-motor functions, and attention and processing speed; language was rarely reported to be affected. Based on the few neuroimaging and/or multimodal DM2 studies we could find, the cognitive profile of DM2 is associated with brain abnormalities in several secondary and high-order cortical and subcortical regions and associative white matter tracts. The limited sample size of individuals with DM2 was the most prominent limitation of these studies. The multifaceted profile of cognitive deficits found in individuals with DM2 highlights the need for routine neuropsychological assessment at both baseline and follow-up, which could unveil these individuals' cognitive strengths and deficits.

Presymptomatic spinal cord pathology in c9orf72 mutation carriers: A longitudinal neuroimaging study.

OBJECTIVE: C9orf72 hexanucleotide repeats expansions account for almost half of familial amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) cases. Recent imaging studies in asymptomatic C9orf72 carriers have demonstrated cerebral white (WM) and gray matter (GM) degeneration before the age of 40 years. The objective of this study was to characterize cervical spinal cord (SC) changes in asymptomatic C9orf72 hexanucleotide carriers. METHODS: Seventy-two asymptomatic individuals were enrolled in a prospective study of first-degree relatives of ALS and FTD patients carrying the c9orf72 hexanucleotide expansion. Forty of them carried the pathogenic mutation (C9+ ). Each subject underwent quantitative cervical cord imaging. Structural GM and WM metrics and diffusivity parameters were evaluated at baseline and 18 months later. Data were analyzed in C9+ and C9- subgroups, and C9+ subjects were further stratified by age. RESULTS: At baseline, significant WM atrophy was detected at each cervical vertebral level in C9+ subjects older than 40 years without associated changes in GM and diffusion tensor imaging parameters. At 18-month follow-up, WM atrophy was accompanied by significant corticospinal tract (CST) fractional anisotropy (FA) reductions. Intriguingly, asymptomatic C9+ subjects older than 40 years with family history of ALS (as opposed to FTD) also exhibited significant CST FA reduction at baseline. INTERPRETATION: Cervical SC imaging detects WM atrophy exclusively in C9+ subjects older than 40 years, and progressive CST FA reductions can be identified on 18-month follow-up. Cervical SC magnetic resonance imaging readily captures presymptomatic pathological changes and disease propagation in c9orf72-associated conditions. ANN NEUROL 2019;86:158-167.

Characteristic Increases in EEG Connectivity Correlate With Changes of Structural MRI in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a terminal progressive adult-onset neurodegeneration of the motor system. Although originally considered a pure motor degeneration, there is increasing evidence of disease heterogeneity with varying degrees of extra-motor involvement. How the combined motor and nonmotor degeneration occurs in the context of broader disruption in neural communication across brain networks has not been well characterized. Here, we have performed high-density crossectional and longitudinal resting-state electroencephalography (EEG) recordings on 100 ALS patients and 34 matched controls, and have identified characteristic patterns of altered EEG connectivity that have persisted in longitudinal analyses. These include strongly increased EEG coherence between parietal-frontal scalp regions (in γ-band) and between bilateral regions over motor areas (in θ-band). Correlation with structural MRI from the same patients shows that disease-specific structural degeneration in motor areas and corticospinal tracts parallels a decrease in neural activity over scalp motor areas, while the EEG over the scalp regions associated with less extensively involved extra-motor regions on MRI exhibit significantly increased neural communication. Our findings demonstrate that EEG-based connectivity mapping can provide novel insights into progressive network decline in ALS. These data pave the way for development of validated cost-effective spectral EEG-based biomarkers that parallel changes in structural imaging.