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.

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.

Cortico-muscular coherence in primary lateral sclerosis reveals abnormal cortical engagement during motor function beyond primary motor areas.

Primary lateral sclerosis (PLS) is a slowly progressing disorder, which is characterized primarily by the degeneration of upper motor neurons (UMNs) in the primary motor area (M1). It is not yet clear how the function of sensorimotor networks beyond M1 are affected by PLS. The aim of this study was to use cortico-muscular coherence (CMC) to characterize the oscillatory drives between cortical regions and muscles during a motor task in PLS and to examine the relationship between CMC and the level of clinical impairment. We recorded EEG and EMG from hand muscles in 16 participants with PLS and 18 controls during a pincer-grip task. In PLS, higher CMC was observed over contralateral-M1 (α- and γ-band) and ipsilateral-M1 (ß-band) compared with controls. Significant correlations between clinically assessed UMN scores and CMC measures showed that higher clinical impairment was associated with lower CMC over contralateral-M1/frontal areas, higher CMC over parietal area, and both higher and lower CMC (in different bands) over ipsilateral-M1. The results suggest an atypical engagement of both contralateral and ipsilateral M1 during motor activity in PLS, indicating the presence of pathogenic and/or adaptive/compensatory alterations in neural activity. The findings demonstrate the potential of CMC for identifying dysfunction within the sensorimotor networks in PLS.

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.

Cognitive network hyperactivation and motor cortex decline correlate with ALS prognosis.

We aimed to quantitatively characterize progressive brain network disruption in Amyotrophic Lateral Sclerosis (ALS) during cognition using the mismatch negativity (MMN), an electrophysiological index of attention switching. We measured the MMN using 128-channel EEG longitudinally (2-5 timepoints) in 60 ALS patients and cross-sectionally in 62 healthy controls. Using dipole fitting and linearly constrained minimum variance beamforming we investigated cortical source activity changes over time. In ALS, the inferior frontal gyri (IFG) show significantly lower baseline activity compared to controls. The right IFG and both superior temporal gyri (STG) become progressively hyperactive longitudinally. By contrast, the left motor and dorsolateral prefrontal cortices are initially hyperactive, declining progressively. Baseline motor hyperactivity correlates with cognitive disinhibition, and lower baseline IFG activities correlate with motor decline rate, while left dorsolateral prefrontal activity predicted cognitive and behavioural impairment. Shorter survival correlates with reduced baseline IFG and STG activity and later STG hyperactivation. Source-resolved EEG facilitates quantitative characterization of symptom-associated and symptom-preceding motor and cognitive-behavioral cortical network decline in ALS.

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.

Sustained attention to response task-related beta oscillations relate to performance and provide a functional biomarker in ALS.

Objective.To characterize the cortical oscillations associated with performance of the sustained attention to response task (SART) and their disruptions in the neurodegenerative condition amyotrophic lateral sclerosis (ALS).Approach.A randomised SART was undertaken by 24 ALS patients and 33 healthy controls during 128-channel electroencephalography (EEG). Complex Morlet wavelet transform was used to quantify non-phase-locked oscillatory activity in event-related spectral perturbations associated with performing the SART. We investigated the relationships between these perturbations and task performance, and associated motor and cognitive changes in ALS.Main results.SART induced theta-band event-related synchronization (ERS) and alpha- and beta-band event-related desynchronization (ERD), followed by rebound beta ERS, in both Go and NoGo trials across the frontoparietal axis, with NoGo trials eliciting greater theta ERS and lesser beta ERS. Controls with greater Go trial beta ERS performed with greater speed and less accuracy. ALS patients exhibited increased anticipation compared to controls but similar reaction times and accuracy. Prefrontal (area under the receiver operating characteristic curve (AUROC) = 0.8, Cohen'sd= 0.97) and parietal (AUROC = 0.82, Cohen'sd= 1.12) beta-band ERD was significantly reduced in ALS but did not relate to performance, while patients with higher Edinburgh Cognitive and Behavioural ALS Screen (ECAS) ALS-specific scores demonstrated greater ERS in beta (rho = 0.72) upon successful withholding.Significance.EEG measurement of task-related oscillation changes reveals variation in cortical network engagement in relation to speed versus accuracy strategies. Such measures can also capture cognitive and motor network pathophysiology in the absence of task performance decline, which may facilitate development of more sensitive early neurodegenerative disease biomarkers.

Altered supraspinal motor networks in survivors of poliomyelitis: A cortico-muscular coherence study.

OBJECTIVE: Poliomyelitis results in changes to the anterior horn cell. The full extent of cortical network changes in the motor physiology of polio survivors has not been established. Our aim was to investigate how focal degeneration of the lower motor neurons (LMN) in infancy/childhood affects motor network connectivity in adult survivors of polio. METHODS: Surface electroencephalography (EEG) and electromyography (EMG) were recorded during an isometric pincer grip task in 25 patients and 11 healthy controls. Spectral signal analysis of cortico-muscular (EEG-EMG) coherence (CMC) was used to identify the cortical regions that are functionally synchronous and connected to the periphery during the pincer grip task. RESULTS: A pattern of CMC was noted in polio survivors that was not present in healthy individuals. Significant CMC in low gamma frequency bands (30-47 Hz) was observed in frontal and parietal regions. CONCLUSION: These findings imply a differential engagement of cortical networks in polio survivors that extends beyond the motor cortex and suggest a disease-related functional reorganisation of the cortical motor network. SIGNIFICANCE: This research has implications for other similar LMN conditions, including spinal muscular atrophy (SMA). CMC has potential in future clinical trials as a biomarker of altered function in motor networks in post-polio syndrome, SMA, and other related conditions.

Localization of Brain Networks Engaged by the Sustained Attention to Response Task Provides Quantitative Markers of Executive Impairment in Amyotrophic Lateral Sclerosis.

OBJECTIVE: To identify cortical regions engaged during the sustained attention to response task (SART) and characterize changes in their activity associated with the neurodegenerative condition amyotrophic lateral sclerosis (ALS). METHODS: High-density electroencephalography (EEG) was recorded from 33 controls and 23 ALS patients during a SART paradigm. Differences in associated event-related potential peaks were measured for Go and NoGo trials. Sources active during these peaks were localized, and ALS-associated differences were quantified. RESULTS: Go and NoGo N2 and P3 peak sources were localized to the left primary motor cortex, bilateral dorsolateral prefrontal cortex (DLPFC), and lateral posterior parietal cortex (PPC). NoGo trials evoked greater bilateral medial PPC activity during N2 and lesser left insular, PPC and DLPFC activity during P3. Widespread cortical hyperactivity was identified in ALS during P3. Changes in the inferior parietal lobule and insular activity provided very good discrimination (AUROC > 0.75) between patients and controls. Activation of the right precuneus during P3 related to greater executive function in ALS, indicative of a compensatory role. INTERPRETATION: The SART engages numerous frontal and parietal cortical structures. SART-EEG measures correlate with specific cognitive impairments that can be localized to specific structures, aiding in differential diagnosis.

Patterned functional network disruption in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting-state electroencephalography recordings from 74 ALS patients and 47 age-matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co-modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ- to ß-band), lateral/orbitofrontal (δ- to θ-band) and sensorimotor (ß-band) regions of the brain in patients with ALS. Furthermore, we show increased co-modulation of neural oscillations in the central and posterior (δ-, θ- and γl -band) and frontal (δ- and γl -band) regions, as well as decreased synchrony in the temporal and frontal (δ- to ß-band) and sensorimotor (ß-band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease-associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.

Dysfunction of attention switching networks in amyotrophic lateral sclerosis.

OBJECTIVE: To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms. RATIONALE: The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigated as a quantitative biomarker of impairment in ALS or its sub-phenotypes. METHODS: MMN responses from 128-channel electroencephalography (EEG) recordings in 58 ALS patients and 39 healthy controls were localised to source by three separate localisation methods, including beamforming, dipole fitting and exact low resolution brain electromagnetic tomography. RESULTS: Compared with controls, ALS patients showed significant increase in power of the left posterior parietal, central and dorsolateral prefrontal cortices (false discovery rate = 0.1). This change correlated with impaired cognitive flexibility (rho = 0.45, 0.45, 0.47, p = .042, .055, .031 respectively). ALS patients also exhibited a decrease in the power of dipoles representing activity in the inferior frontal (left: p = 5.16 × 10-6, right: p = 1.07 × 10-5) and left superior temporal gyri (p = 9.30 × 10-6). These patterns were detected across three source localisation methods. Decrease in right inferior frontal gyrus activity was a good discriminator of ALS patients from controls (AUROC = 0.77) and an excellent discriminator of C9ORF72 expansion-positive patients from controls (AUROC = 0.95). INTERPRETATION: Source localization of evoked potentials can reliably discriminate patterns of functional network impairment in ALS and ALS subgroups during involuntary attention switching. The discriminative ability of the detected cognitive changes in specific brain regions are comparable to those of functional magnetic resonance imaging (fMRI). Source analysis of high-density EEG patterns has excellent potential to provide non-invasive, data-driven quantitative biomarkers of network disruption that could be harnessed as novel neurophysiology-based outcome measures in clinical trials.

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.

Estimation of coherence using the median is robust against EEG artefacts.

Coherence is a mathematical measure of correlation in the frequency domain, commonly used to quantify the oscillatory synchrony of bio-signals such as the electroencephalogram (EEG). In biomedical applications, such as assessment of functional connectivity, reliable estimation of coherence is of paramount importance for studying the function of complex brain networks, as well as their disruption in neurological disorders. A major challenge for robust estimation of coherence measures is the presence of artefacts. Here, we propose an alternative method for finding coherence by estimating auto- and cross-spectral densities based on the median or trimmed-mean values across trials, rather than the mean. The variance of the average cortico-cortical coherence measures, i.e., the inter-individual variability, was taken as a measure of robustness and tested on resting-state recordings from 34 healthy individuals, both without screening, as well as after screening by a statistical thresholding artefact rejection. The variability of average coherence in individual channels and frequency bands decreased by using the median-based estimation of coherence. Averaged across all channels and frequency bands, the variability of coherence estimates based on median was significantly lower than mean-based estimates for both unscreened data (F = 9.28, p = 0.003, 1-ß0.05 = 0.98) and screened data (F = 6.58, p = 0.01, 1-ß0.05 = 0.91). Moreover, the variability for median-based estimates was almost identical for unscreened and screened data (F = 0.004, p = 0.95), suggesting that coherence based on median without artefact rejection might be sufficient for robust estimation of coherence.