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 the dysfunction in motor and cognitive networks in ALS. However, the longitudinal development of the network-level dysfunction patterns remains unclear, particularly in the networks related to cognitive-behavioural functions. We examined 124 ALS individuals with 128-channel resting-state EEG recordings, categorised by cognitive impairment (ALSci, n = 27), behavioural impairment (ALSbi, n = 58), or non-impaired (ALSncbi, n = 53). Using linear mixed-effects models, we characterised the general and phenotype-specific longitudinal changes in brain network, and their association with cognitive performance, fine motor symptoms, and survival. We observed significant decline in \(\theta\)-band spectral power over time in the temporal region along with increased \({\gamma }_{l}\)-band power in the fronto-temporal region in the ALS group. ALSncbi participants showed widespread β-band synchrony decrease, while ALSci participants exhibited increased co-modulation correlated with verbal fluency decline. Longitudinal network-level changes varied between ALS subgroups, correlating with motor, cognitive, and behavioural decline. Spectral EEG measures can longitudinally track abnormal network patterns, serving as a candidate stratification tool for clinical trials and personalized treatments in ALS.

Speech comprehension relies on the ability to understand words within a coherent context. Recent studies have attempted to obtain electrophysiological indices of this process by modelling how brain activity is affected by a word's semantic dissimilarity to preceding words. Although the resulting indices appear robust and are strongly modulated by attention, it remains possible that, rather than capturing the contextual understanding of words, they may actually reflect word-to-word changes in semantic content without the need for a narrative-level understanding on the part of the listener. To test this, we recorded electroencephalography from subjects who listened to speech presented in either its original, narrative form, or after scrambling the word order by varying amounts. This manipulation affected the ability of subjects to comprehend the speech narrative but not the ability to recognise individual words. Neural indices of semantic understanding and low-level acoustic processing were derived for each scrambling condition using the temporal response function. Signatures of semantic processing were observed when speech was unscrambled or minimally scrambled and subjects understood the speech. The same markers were absent for higher scrambling levels as speech comprehension dropped. In contrast, word recognition remained high and neural measures related to envelope tracking did not vary significantly across scrambling conditions. This supports the previous claim that electrophysiological indices based on the semantic dissimilarity of words to their context reflect a listener's understanding of those words relative to that context. It also highlights the relative insensitivity of neural measures of low-level speech processing to speech comprehension.

Abstract 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 (β-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.

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.

Healthy ageing leads to changes in the brain that impact upon sensory and cognitive processing. It is not fully clear how these changes affect the processing of everyday spoken language. Prediction is thought to play an important role in language comprehension, where information about upcoming words is pre-activated across multiple representational levels. However, evidence from electrophysiology suggests differences in how older and younger adults use context-based predictions, particularly at the level of semantic representation. We investigate these differences during natural speech comprehension by presenting older and younger subjects with continuous, narrative speech while recording their electroencephalogram. We use time-lagged linear regression to test how distinct computational measures of (1) semantic dissimilarity and (2) lexical surprisal are processed in the brains of both groups. Our results reveal dissociable neural correlates of these two measures that suggest differences in how younger and older adults successfully comprehend speech. Specifically, our results suggest that, while younger and older subjects both employ context-based lexical predictions, older subjects are significantly less likely to pre-activate the semantic features relating to upcoming words. Furthermore, across our group of older adults, we show that the weaker the neural signature of this semantic pre-activation mechanism, the lower a subject's semantic verbal fluency score. We interpret these findings as prediction playing a generally reduced role at a semantic level in the brains of older listeners during speech comprehension and that these changes may be part of an overall strategy to successfully comprehend speech with reduced cognitive resources.

Prior knowledge facilitates perception and allows us to interpret our sensory environment. However, the neural mechanisms underlying this process remain unclear. Theories of predictive coding propose that feedback connections between cortical levels carry predictions about upcoming sensory events whereas feedforward connections carry the error between the prediction and the sensory input. Although predictive coding has gained much ground as a viable mechanism for perception, in the context spoken language comprehension it lacks empirical support using more naturalistic stimuli. In this study, we investigated theories of predictive coding using continuous, everyday speech. EEG recordings from human participants listening to an audiobook were analysed using a 2-stage regression framework. This tested the effect of top-down linguistic information, estimated using computational language models, on the bottom-up encoding of acoustic and phonetic speech features. Our results show enhanced encoding of both semantic predictions and surprising words, based on preceding context. This suggests that signals pertaining to prediction and error units can be observed in the same electrophysiological responses to natural speech. In addition, temporal analysis of these signals reveals support for theories of predictive coding that propose that perception is first biased towards what is expected followed by what is informative. Significance Statement Over the past two decades, predictive coding has grown in popularity as an explanatory mechanism for perception. However, there has been lack of empirical support for this theory in research studying natural speech comprehension. We address this issue by developing an analysis framework that tests the effects of top-down linguistic information on the auditory encoding of continuous speech. Our results provide evidence for the co-existence of prediction and error signals and support theories of predictive coding using more naturalistic stimuli.

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 β-band), lateral/orbitofrontal (δ- to θ-band) and sensorimotor (β-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 β-band) and sensorimotor (β-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.

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.

Speech perception involves the integration of sensory input with expectations based on the context of that speech. Much debate surrounds the issue of whether or not prior knowledge feeds back to affect early auditory encoding in the lower levels of the speech processing hierarchy, or whether perception can be best explained as a purely feedforward process. Although there has been compelling evidence on both sides of this debate, experiments involving naturalistic speech stimuli to address these questions have been lacking. Here, we use a recently introduced method for quantifying the semantic context of speech and relate it to a commonly used method for indexing low-level auditory encoding of speech. The relationship between these measures is taken to be an indication of how semantic context leading up to a word influences how its low-level acoustic and phonetic features are processed. We record EEG from human participants (both male and female) listening to continuous natural speech and find that the early cortical tracking of a word's speech envelope is enhanced by its semantic similarity to its sentential context. Using a forward modeling approach, we find that prediction accuracy of the EEG signal also shows the same effect. Furthermore, this effect shows distinct temporal patterns of correlation depending on the type of speech input representation (acoustic or phonological) used for the model, implicating a top-down propagation of information through the processing hierarchy. These results suggest a mechanism that links top-down prior information with the early cortical entrainment of words in natural, continuous speech.SIGNIFICANCE STATEMENT During natural speech comprehension, we use semantic context when processing information about new incoming words. However, precisely how the neural processing of bottom-up sensory information is affected by top-down context-based predictions remains controversial. We address this discussion using a novel approach that indexes a word's similarity to context and how well a word's acoustic and phonetic features are processed by the brain at the time of its utterance. We relate these two measures and show that lower-level auditory tracking of speech improves for words that are more related to their preceding context. These results suggest a mechanism that links top-down prior information with bottom-up sensory processing in the context of natural, narrative speech listening.

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.

How heard speech is transformed into words in the brain remains poorly understood. New research reveals signals in auditory cortex that reflect predictions the brain makes in transforming phonetic information into words.

Objective: To identify the changes in the neural activity and cortico-cortical connectivity in amyotrophic lateral sclerosis using high-density electroencephalography and its correlates with structural MRI. Background: Amyotrophic lateral sclerosis is a progressive neurodegenerative disease of upper and/or lower motor neurons with white matter changes in the corticospinal, corticobulbar, commissural and frontotemporal regions. Evaluation of functional cortical connectivity can shed light on the underlying affected brain regions and networks, and hence the disease pathophysiology. Design/Methods: Resting-state electroencephalography (EEG) was recorded from 128 channels in 3 sets (2 minutes each) from 100 patients with amyotrophic lateral sclerosis (78 spinal onset, 15 bulbar onset, 7 ALS-FTD, age: 60.2 ± 11.1), 8 patients with frontotemporal dementia (separately recruited and analysed), and 34 age-matched healthy individuals. The ALS-specific functional connectivity was quantified using frequency-domain cortico-cortical coherence and spectral power of the EEG. Results: The patient groups showed: lower spectral power in θ band but higher coherence in θ and γ bands. This decrease in the spectral power was mapped to specific brain sources in each frequency band. These findings were statistically significant as assessed by adaptive false discovery rate, fdr, (q = 0.05) and were confirmed by empirical Bayesian inference for the changes in spectral power and cortico-cortical coherence. Structural MRI scores were correlated with EEG changes in a subset of 51 spinal-onset patients. Degeneration in motor cortical regions and corticospinal tracts were primarily correlated with changes in spectral power, whereas increased coherences on EEG correlated with extra-motor regions on MRI Conclusions: The decreased spectral power in motor areas are a direct consequence of the structural degeneration and the increased coherences reflect increased functional connectivity in the brain networks which may be a compensatory neural activity. Spectral EEG is a promising biomarker of neurodegeneration, and provides additional information that complements structural changes detected by MRI. Study Supported by: This work was supported by the Health Research Board of Ireland (award HRA-POR-2013-246); and Irish Research Council (Government of Ireland Postdoctoral Research Fellowship GOIPD/2015/213 to BN). The neuroimaging (magnetic resonance imaging) aspects of the study were supported by the Health Research Board (HRB-Ireland: Emerging Investigator Award HRB-EIA-2017-019), the Irish Institute of Clinical Neuroscience (IICN) - Novartis Ireland Research Grant, The Iris O’Brien Foundation, and The Perrigo Clinician-Scientist Research Fellowship. Disclosure: Dr. Nasseroleslami has nothing to disclose. Dr. Dukic has nothing to disclose. Dr. Broderick has nothing to disclose. Dr. Mohr has nothing to disclose. Dr. Schuster has nothing to disclose. Dr. Gavin has nothing to disclose. Dr. McLaughlin has nothing to disclose. Dr. Heverin has nothing to disclose. Dr. Vajda has nothing to disclose. Dr. Iyer has nothing to disclose. Dr. Pender has nothing to disclose. Dr. Bede has nothing to disclose. Dr. Lalor has nothing to disclose. Dr. Hardiman has nothing to disclose.

People routinely hear and understand speech at rates of 120-200 words per minute [1, 2]. Thus, speech comprehension must involve rapid, online neural mechanisms that process words' meanings in an approximately time-locked fashion. However, electrophysiological evidence for such time-locked processing has been lacking for continuous speech. Although valuable insights into semantic processing have been provided by the "N400 component'' of the event-related potential [3-6], this literature has been dominated by paradigms using incongruous words within specially constructed sentences, with less emphasis on natural, narrative speech comprehension. Building on the discovery that cortical activity "tracks'' the dynamics of running speech [7-9] and psycholinguistic work demonstrating [10-12] and modeling [13-15] how context impacts on word processing, we describe a new approach for deriving an electrophysiological correlate of natural speech comprehension. We used a computational model [16] to quantify the meaning carried by words based on how semantically dissimilar they were to their preceding context and then regressed this measure against electroencephalographic (EEG) data recorded from subjects as they listened to narrative speech. This produced a prominent negativity at a time lag of 200-600 ms on centro-parietal EEG channels, characteristics common to the N400. Applying this approach to EEG datasets involving time-reversed speech, cocktail party attention, and audiovisual speech-in-noise demonstrated that this response was very sensitive to whether or not subjects understood the speech they heard. These findings demonstrate that, when successfully comprehending natural speech, the human brain responds to the contextual semantic content of each word in a relatively time-locked fashion.

To identify the altered cortical connectivity in amyotrophic lateral sclerosis using high-density electroencephalography and its correlates with structural MRI.

Objective: To evaluate the utility of mismatch negativity (MMN), a neurophysiologic marker of non-motor cognitive processing, in amyotrophic lateral sclerosis (ALS).Methods: 89 patients, stratified into 4 different phenotypic presentations of ALS (67 spinal-onset, 15 bulbar-onset, 7 ALS-FTD, 7 C9ORF72 gene careers), and 19 matched controls underwent 128-channel EEG data recording. Subjects were presented with standard auditory tones interleaved with pitch-deviant tones in three recording blocks. The MMN response was quantified by peak amplitude, peak delay, average amplitude, and average delay, 100-300 ms after stimuli. 64 patients underwent cognitive screening using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS), and 38 participants underwent contemporaneous cognitive assessment using the Stroop Color-Word Interference test (CWIT), which measures attention shift, inhibitory control, and error monitoring.Results: The MMN response was observed in frontal and frontocentral regions of patient and control groups. Compared to controls, waveforms were attenuated in early onset, and the average delay was significantly increased in all of the ALS subgroups, with no significant difference between subgroups. Comparing with the control response, the ALS MMN response clustered into four new subgroups characterized by differences in response latency. The increased average delay correlated with changes in the Stroop CWIT; however, it did not show a direct relationship with age, gender, traditional phenotypes, revised ALS Functional Rating Scale, or ECAS scores.Conclusion and significance: The MMN response in ALS patients reflects the cognitive dysfunction in specific sub-domains, as the new patient subgroups, identified by cluster analysis, do not segregate with existing clinical or cognitive classifications. Event-related potentials can provide additional quantitative neurophysiologic measures of impairment in specific cognitive sub-domains from which it may be possible to generate novel biologically relevant subgroups of ALS.

Understanding natural speech requires that the human brain convert complex spectrotemporal patterns of acoustic input into meaning in a rapid manner that is reasonably tightly time-locked to the incoming speech signal. However, neural evidence for such a time-locked process has been lacking. Here, we sought such evidence by using a computational model to quantify the meaning carried by each word based on how semantically dissimilar it was to its preceding context and then regressing this quantity against electroencephalographic (EEG) data recorded from subjects as they listened to narrative speech. This produced a prominent negativity at a time-lag of 200– 600 ms on centro-parietal EEG electrodes. Subsequent EEG experiments involving time-reversed speech, cocktail party attention and audiovisual speech-in-noise demonstrated that this response was exquisitely sensitive to whether or not subjects were understanding the speech they heard. These findings demonstrate that, when successfully comprehending natural speech, the human brain encodes meaning as a function of the amount of new information carried by each word in a relatively time-locked fashion.