A Potential Source of Bias in Group-Level EEG Microstate Analysis.

Microstate analysis is a promising technique for analyzing high-density electroencephalographic data, but there are multiple questions about methodological best practices. Between and within individuals, microstates can differ both in terms of characteristic topographies and temporal dynamics, which leads to analytic challenges as the measurement of microstate dynamics is dependent on assumptions about their topographies. Here we focus on the analysis of group differences, using simulations seeded on real data from healthy control subjects to compare approaches that derive separate sets of maps within subgroups versus a single set of maps applied uniformly to the entire dataset. In the absence of true group differences in either microstate maps or temporal metrics, we found that using separate subgroup maps resulted in substantially inflated type I error rates. On the other hand, when groups truly differed in their microstate maps, analyses based on a single set of maps confounded topographic effects with differences in other derived metrics. We propose an approach to alleviate both classes of bias, based on a paired analysis of all subgroup maps. We illustrate the qualitative and quantitative impact of these issues in real data by comparing waking versus non-rapid eye movement sleep microstates. Overall, our results suggest that even subtle chance differences in microstate topography can have profound effects on derived microstate metrics and that future studies using microstate analysis should take steps to mitigate this large source of error.

Study Protocol: Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project.

BACKGROUND: Objective and quantifiable markers are crucial for developing novel therapeutics for mental disorders by 1) stratifying clinically similar patients with different underlying neurobiological deficits and 2) objectively tracking disease trajectory and treatment response. Schizophrenia is often confounded with other psychiatric disorders, especially bipolar disorder, if based on cross-sectional symptoms. Awake and sleep EEG have shown promise in identifying neurophysiological differences as biomarkers for schizophrenia. However, most previous studies, while useful, were conducted in European and American populations, had small sample sizes, and utilized varying analytic methods, limiting comprehensive analyses or generalizability to diverse human populations. Furthermore, the extent to which wake and sleep neurophysiology metrics correlate with each other and with symptom severity or cognitive impairment remains unresolved. Moreover, how these neurophysiological markers compare across psychiatric conditions is not well characterized. The utility of biomarkers in clinical trials and practice would be significantly advanced by well-powered transdiagnostic studies. The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project aims to address these questions through a large, multi-center cohort study involving East Asian populations. To promote transparency and reproducibility, we describe the protocol for the GRINS project. METHODS: The research procedure consists of an initial screening interview followed by three subsequent sessions: an introductory interview, an evaluation visit, and an overnight neurophysiological recording session. Data from multiple domains, including demographic and clinical characteristics, behavioral performance (cognitive tasks, motor sequence tasks), and neurophysiological metrics (both awake and sleep electroencephalography), are collected by research groups specialized in each domain. CONCLUSION: Pilot results from the GRINS project demonstrate the feasibility of this study protocol and highlight the importance of such research, as well as its potential to study a broader range of patients with psychiatric conditions. Through GRINS, we are generating a valuable dataset across multiple domains to identify neurophysiological markers of schizophrenia individually and in combination. By applying this protocol to related mental disorders often confounded with each other, we can gather information that offers insight into the neurophysiological characteristics and underlying mechanisms of these severe conditions, informing objective diagnosis, stratification for clinical research, and ultimately, the development of better-targeted treatment matching in the clinic.

Alterations in Local Connectivity and Their Developmental Trajectories in Autism Spectrum Disorder: Does Being Female Matter?

Autism spectrum disorder (ASD) is diagnosed more often in males with a ratio of 1:4 females/males. This bias is even stronger in neuroimaging studies. There is a growing evidence suggesting that local connectivity and its developmental trajectory is altered in ASD. Here, we aim to investigate how local connectivity and its age-related trajectories vary with ASD in both males and females. We used resting-state fMRI data from the ABIDE I and II repository: males (n = 102) and females (n = 92) with ASD, and typically developing males (n = 104) and females (n = 92) aged between 6 and 26. Local connectivity was quantified as regional homogeneity. We found increases in local connectivity in participants with ASD in the somatomotor and limbic networks and decreased local connectivity within the default mode network. These alterations were more pronounced in females with ASD. In addition, the association between local connectivity and ASD symptoms was more robust in females. Females with ASD had the most distinct developmental trajectories of local connectivity compared with other groups. Overall, our findings of more pronounced local connectivity alterations in females with ASD could indicate a greater etiological load for an ASD diagnosis in this group congruent with the female protective effect hypothesis.

Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses.

Functional brain connectivity is increasingly being seen as critical for cognition, perception and motor control. Magnetoencephalography and electroencephalography are modalities that offer noninvasive mapping of electrophysiological interactions among brain regions, yet suffer from signal leakage and signal cancellation when estimating brain activity. This leads to biased connectivity values which complicate interpretation. In this study, we test the hypothesis that a Multiple Constrained Minimum Variance beamformer (MCMV) outperforms the more traditional Linearly Constrained Minimum Variance beamformer (LCMV) for estimation of electrophysiological connectivity. To this end, MCMV and LCMV performance is compared in task related analyses with both simulated data and human MEG recordings of visual steady state signals, and in resting state analyses with simulated data and human MEG data of 89 subjects. In task related scenarios connectivity was estimated using coherence and phase locking values, whereas envelope correlations were used for the resting state data. We also introduce a novel Augmented Pairwise MCMV (APW-MCMV) approach for signal leakage suppression in resting state analyses and assess its performance against LCMV and more conventional MCMV approaches. We demonstrate that with MCMV effects of signal mixing and coherent source cancellation are greatly reduced in both task related and resting state conditions, while in contrast to other approaches 0- and short time lag interactions are preserved. In addition, we demonstrate that in resting state analyses, APW-MCMV strongly reduces spurious connections while better controlling for false negatives compared to more conservative measures such as symmetrical orthogonalization.

Neuromagnetic activation and oscillatory dynamics of stimulus-locked processing during naturalistic viewing.

Naturalistic stimuli such as watching a movie while in the scanner provide an ecologically valid paradigm that has the potential of extracting valuable information on how the brain processes complex stimuli in realistic visual and auditory contexts. Naturalistic viewing is also easier to conduct with challenging participant groups including patients and children. Given the high temporal resolution of MEG, in the present study, we demonstrate how a short movie clip can be used to map distinguishable activation and connectivity dynamics underlying the processing of specific classes of visual stimuli such as face and hand manipulations, as well as contrasting activation dynamics for auditory words and non-words. MEG data were collected from 22 healthy volunteers (6 females, 3 left handed, mean age - 27.7 â€‹± â€‹5.28 years) during the presentation of naturalistic audiovisual stimuli. The MEG data were split into trials with the onset of the stimuli belonging to classes of interest (words, non-words, faces, hand manipulations). Based on the components of the averaged sensor ERFs time-locked to the visual and auditory stimulus onset, four and three time-windows, respectively, were defined to explore brain activation dynamics. Pseudo-Z, defined as the ratio of the source-projected time-locked power to the projected noise power for each vertex, was computed and used as a proxy of time-locked brain activation. Statistical testing using the mean-centered Partial Least Squares analysis indicated periods where a given visual or auditory stimuli had higher activation. Based on peak pseudo-Z differences between the visual conditions, time-frequency resolved analyses were performed to assess beta band desynchronization in motor-related areas, and inter-trial phase synchronization between face processing areas. Our results provide the first evidence that activation and connectivity dynamics in canonical brain regions associated with the processing of particular classes of visual and auditory stimuli can be reliably mapped using MEG during presentation of naturalistic stimuli. Given the strength of MEG for brain mapping in temporal and frequency domains, the use of naturalistic stimuli may open new techniques in analyzing brain dynamics during ecologically valid sensation and perception.

Sex differences in brain connectivity and male vulnerability in very preterm children.

Evidence indicates better cognitive and behavioral outcomes for females born very preterm (≤32 weeks gestation) compared to males, but the neurophysiology underlying this apparent resiliency of the female brain remains poorly understood. Here we test the hypothesis that very preterm males express more pronounced connectivity alterations as a reflection of higher male vulnerability. Resting state MEG recordings, neonatal and psychometric data were collected from 100 children at age 8 years: very preterm boys (n = 27), very preterm girls (n = 34), full-term boys (n = 15) and full-term girls (n = 24). Neuromagnetic source dynamics were reconstructed from 76 cortical brain regions. Functional connectivity was estimated using inter-regional phase-synchronization. We performed a series of multivariate analyses to test for differences across groups as well as to explore relationships between deviations in functional connectivity and psychometric scores and neonatal factors for very preterm children. Very preterm boys displayed significantly higher (p < .001) absolute deviation from average connectivity of same-sex full-term group, compared to very preterm girls versus full-term girls. In the connectivity comparison between very preterm and full-term groups separately for boys and girls, significant group differences (p < .05) were observed for boys, but not girls. Sex differences in connectivity (p < .01) were observed in very preterm children but not in full-term groups. Our findings indicate that very preterm boys have greater alterations in resting neurophysiological network communication than girls. Such uneven brain communication disruption in very preterm boys and girls suggests that stronger connectivity alterations might contribute to male vulnerability in long-term behavioral and cognitive outcome.

Extreme male developmental trajectories of homotopic brain connectivity in autism.

It has been proposed that autism spectrum disorder (ASD) may be characterized by an extreme male brain (EMB) pattern of brain development. Here, we performed the first investigation of how age-related changes in functional brain connectivity may be expressed differently in females and males with ASD. We analyzed resting-state functional magnetic resonance imaging data of 107 typically developing (TD) females, 114 TD males, 104 females, and 115 males with ASD (6-26 years) from the autism brain imaging data exchange repository. We explored how interhemispheric homotopic connectivity and its maturational curvatures change across groups. Differences between ASD and TD and between females and males with ASD were observed for the rate of changes in connectivity in the absence of overall differences in connectivity. The largest portion of variance in age-related changes in connectivity was described through similarities between TD males, ASD males, and ASD females, in contrast to TD females. We found that shape of developmental curvature is associated with symptomatology in both males and females with ASD. We demonstrated that females and males with ASD tended to follow the male pattern of developmental changes in interhemispheric connectivity, supporting the EMB theory of ASD.

Atypical resting state neuromagnetic connectivity and spectral power in very preterm children.

BACKGROUND: Children born very preterm often display selective cognitive difficulties at school age even in the absence of major brain injury. Alterations in neurophysiological activity underpinning such difficulties, as well as their relation to specific aspects of adverse neonatal experience, remain poorly understood. In the present study, we examined interregional connectivity and spectral power in very preterm children at school age, and their relationship with clinical neonatal variables and long-term outcomes (IQ, executive functions, externalizing/internalizing behavior, visual-motor integration). METHODS: We collected resting state magnetoencephalographic (MEG) and psychometric data from a cohort at the age of 8 years followed prospectively since birth, which included three groups: Extremely Low Gestational Age (ELGA, 24-28 weeks GA n = 24, age 7.7 ± 0.38, 10 girls), Very Low Gestational Age (VLGA, 29-32 weeks GA n = 37, age 7.7 ± 0.39, 24 girls), and full-term children (38-41 weeks GA n = 39, age 7.9 ± 1.02, 24 girls). Interregional phase synchrony and spectral power were tested for group differences, and associations with neonatal and outcome variables were examined using mean-centered and behavioral Partial Least Squares (PLS) analyses, respectively. RESULTS: We found greater connectivity in the theta band in the ELGA group compared to VLGA and full-term groups, primarily involving frontal connections. Spectral power analysis demonstrated overall lower power in the ELGA and VLGA compared to full-term group. PLS indicated strong associations between neurophysiological connectivity at school age, adverse neonatal experience and cognitive performance, and behavior. Resting spectral power was associated only with behavioral scores. CONCLUSIONS: Our findings indicate significant atypicalities of neuromagnetic brain activity and connectivity in very preterm children at school age, with alterations in connectivity mainly observed only in the ELGA group. We demonstrate a significant relationship between connectivity, adverse neonatal experience, and long-term outcome, indicating that the disruption of developing neurophysiological networks may mediate relationships between neonatal events and cognitive and behavioral difficulties at school age.

Differences in Sleep EEG Coherence and Spindle Metrics in Toddlers With and Without Language Delay: A Prospective Observational Study.

Background: Sleep plays a crucial role in early language development, and sleep disturbances are common in children with neurodevelopmental disorders. Examining sleep microarchitecture in toddlers with and without language delays can offer key insights into neurophysiological abnormalities associated with atypical neurodevelopmental trajectories and potentially aid in early detection and intervention. Methods: Here, we investigated electroencephalogram (EEG) coherence and sleep spindles in 16 toddlers with language delay (LD) compared with a group of 39 typically developing (TD) toddlers. The sample was majority male (n = 34, 62%). Participants were aged 12-to-22 months at baseline, and 34 (LD, n=11; TD, n=23) participants were evaluated again at 36 months of age. Results: LD toddlers demonstrated increased EEG coherence compared to TD toddlers, with differences most prominent during slow-wave sleep. Within the LD group, lower expressive language skills were associated with higher coherence in REM sleep. Within the TD group, lower expressive language skills were associated with higher coherence in slow-wave sleep. Sleep spindle density, duration, and frequency changed between baseline and follow-up for both groups, with the LD group demonstrating a smaller magnitude of change than the TD group. The direction of change was frequency-dependent for both groups. Conclusions: These findings indicate that atypical sleep EEG connectivity and sleep spindle development can be detected in toddlers between 12 and 36 months and offers insights into neurophysiological mechanisms underlying the etiology of neurodevelopmental disorders. Trial registration: https://clinicaltrials.gov/study/NCT01339767; Registration date: 4/20/2011.

Spindle chirp and other sleep oscillatory features in young children with autism.

OBJECTIVES: To determine whether spindle chirp and other sleep oscillatory features differ in young children with and without autism. METHODS: Automated processing software was used to re-assess an extant set of polysomnograms representing 121 children (91 with autism [ASD], 30 typically-developing [TD]), with an age range of 1.35-8.23 years. Spindle metrics, including chirp, and slow oscillation (SO) characteristics were compared between groups. SO and fast and slow spindle (FS, SS) interactions were also investigated. Secondary analyses were performed assessing behavioural data associations, as well as exploratory cohort comparisons to children with non-autism developmental delay (DD). RESULTS: Posterior FS and SS chirp was significantly more negative in ASD than TD. Both groups had comparable intra-spindle frequency range and variance. Frontal and central SO amplitude were decreased in ASD. In contrast to previous manual findings, no differences were detected in other spindle or SO metrics. The ASD group displayed a higher parietal coupling angle. No differences were observed in phase-frequency coupling. The DD group demonstrated lower FS chirp and higher coupling angle than TD. Parietal SS chirp was positively associated with full developmental quotient. CONCLUSIONS: For the first time spindle chirp was investigated in autism and was found to be significantly more negative than in TD in this large cohort of young children. This finding strengthens previous reports of spindle and SO abnormalities in ASD. Further investigation of spindle chirp in healthy and clinical populations across development will help elucidate the significance of this difference and better understand this novel metric.

Electroencephalographic Microstates During Sleep and Wake in Schizophrenia.

Background: Aberrant functional connectivity is a hallmark of schizophrenia. The precise nature and mechanism of dysconnectivity in schizophrenia remains unclear, but evidence suggests that dysconnectivity is different in wake versus sleep. Microstate analysis uses electroencephalography (EEG) to investigate large-scale patterns of coordinated brain activity by clustering EEG data into a small set of recurring spatial patterns, or microstates. We hypothesized that this technique would allow us to probe connectivity between brain networks at a fine temporal resolution and uncover previously unknown sleep-specific dysconnectivity. Methods: We studied microstates during sleep in patients with schizophrenia by analyzing high-density EEG sleep data from 114 patients with schizophrenia and 79 control participants. We used a polarity-insensitive k-means analysis to extract a set of 6 microstate topographies. Results: These 6 states included 4 widely reported canonical microstates. In patients and control participants, falling asleep was characterized by a shift from microstates A, B, and C to microstates D, E, and F. Microstate F was decreased in patients during wake, and microstate E was decreased in patients during sleep. The complexity of microstate transitions was greater in patients than control participants during wake, but this reversed during sleep. Conclusions: Our findings reveal behavioral state-dependent patterns of cortical dysconnectivity in schizophrenia. Furthermore, these findings are largely unrelated to previous sleep-related EEG markers of schizophrenia such as decreased sleep spindles. Therefore, these findings are driven by previously undescribed sleep-related pathology in schizophrenia.

EEG microstates are stereotyped patterns of scalp voltage topography that provide information about brain activity at millisecond-level temporal resolution. We used this method to study brain activity in schizophrenia during sleep and wake. We found state-dependent case-control differences in EEG microstates that were unrelated to the results of classic EEG analyses. These differences reflect aberrant neural functioning during sleep in patients with schizophrenia.

Amyloid beta-independent sleep markers associated with early regional tau burden and cortical thinning.

INTRODUCTION: Sleep is crucial for memory consolidation and the clearance of toxic proteins associated with Alzheimer's disease (AD). We examined the association between sleep characteristics and imaging biomarkers of early amyloid beta (Aß) and tau pathology as well as neurodegeneration in brain regions known to be affected in the incipient stages of AD. METHODS: Thirty-nine cognitively unimpaired (CU) participants of the Harvard Aging Brain Study underwent at-home polysomnography as well as tau positron emission tomography (flortaucipir-PET), amyloid PET (Pittsburgh compound B [PiB]-PET), and magnetic resonance imaging-derived assessment of cortical thickness (CT). RESULTS: Increased N1 sleep was associated with a higher tau PET signal (ß = 0.009, p = 0.001) and lower CT in the temporal composite region of interest (ß = -0.017, p = 0.007). Decreased slow-wave sleep (SWS) was associated with higher tau burden in the temporal composite (ß = -0.008, p = 0.005) and lower CT (ß = 0.008, p = 0.002), even after controlling for global PiB-PET. DISCUSSION: In CU older adults, lower SWS and higher N1 sleep were associated with higher tau burden and lower CT in brain regions associated with early tau deposition and vulnerable to AD-related neurodegeneration through mechanisms dissociable from amyloid deposition. Highlights: We report the results of an observational study, which leveraged -a well-characterized cohort of healthy aging (Harvard Aging Brain Study) by adding in-home full polysomnograms.By adding at-home polysomnograms to this unique and deeply phenotyped cohort, we examined variations in sleep architecture that are associated with Alzheimer's disease (AD) pathologic changes.Our results confirmed the association of sleep changes with early tau and cortical neurodegenerative changes that were independent of amyloid.The results will be of importance in monitoring sleep-related variations in relation to the natural history of AD pathology and in designing sleep-focused clinical trials.

A spectrum of altered non-rapid eye movement sleep in schizophrenia.

Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations, are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, ageing, cognitive profiles and medication regimens. Using newly collected high-density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported group-level differences between patients and controls (original N=130) during N2 stage. Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability. We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (AUC=0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics. Although multiple clinical and cognitive factors were associated with NREM metrics, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater contributor to variability. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on the sleep EEG; further, among patients, certain medications exacerbated these effects, in particular olanzapine. Collectively, our results point to a spectrum of N2 sleep deficits among SCZ patients that can be measured objectively and at scale, with relevance to both the etiological heterogeneity of SCZ as well as potential iatrogenic effects of antipsychotic medication.

A spectrum of altered non-rapid eye movement sleep in schizophrenia.

Background: Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens. Using newly collected high density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology. Results: We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater (albeit still partial) contributor to variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG. Conclusion: We demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. We further documented and controlled for a major effect of medication use, and pointed to greater age-related change in NREM sleep in patients. That increased NREM heterogeneity was not explained by standard clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ patients that can be measured objectively and at scale, and that may offer a unique window on the etiological and genetic diversity that underlies SCZ risk, treatment response and prognosis.

Sources of Variation in the Spectral Slope of the Sleep EEG.

The 1/f spectral slope of the electroencephalogram (EEG) estimated in the γ frequency range has been proposed as an arousal marker that differentiates wake, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Here, we sought to replicate and extend these findings in a large sample, providing a comprehensive characterization of how slope changes with age, sex, and its test-retest reliability as well as potential confounds that could affect the slope estimation. We used 10,255 whole-night polysomnograms (PSGs) from the National Sleep Research Resource (NSRR). All preprocessing steps were performed using an open-source Luna package and the spectral slope was estimated by fitting log-log linear regression models on the absolute power from 30 to 45 Hz separately for wake, NREM, and REM stages. We confirmed that the mean spectral slope grows steeper going from wake to NREM to REM sleep. We found that the choice of mastoid referencing scheme modulated the extent to which electromyogenic, or electrocardiographic artifacts were likely to bias 30- to 45-Hz slope estimates, as well as other sources of technical, device-specific bias. Nonetheless, within individuals, slope estimates were relatively stable over time. Both cross-sectionally and longitudinal, slopes tended to become shallower with increasing age, particularly for REM sleep; males tended to show flatter slopes than females across all states. Our findings support that spectral slope can be a valuable arousal marker for both clinical and research endeavors but also underscore the importance of considering interindividual variation and multiple methodological aspects related to its estimation.

Developmental Differences in Neuromagnetic Cortical Activation and Phase Synchrony Elicited by Scenes with Faces during Movie Watching.

The neural underpinnings of humans' ability to process faces and how it changes over typical development have been extensively studied using paradigms where face stimuli are oversimplified, isolated, and decontextualized. The prevalence of this approach, however, has resulted in limited knowledge of face processing in ecologically valid situations, in which faces are accompanied by contextual information at multiple time scales. In the present study, we use a naturalistic movie paradigm to investigate how neuromagnetic activation and phase synchronization elicited by faces from movie scenes in humans differ between children and adults. We used MEG data from 22 adults (6 females, 3 left handed; mean age, 27.7 ± 5.28 years) and 20 children (7 females, 1 left handed; mean age, 9.5 ± 1.52 years) collected during movie viewing. We investigated neuromagnetic time-locked activation and phase synchronization elicited by movie scenes containing faces in contrast to other movie scenes. Statistical differences between groups were tested using a multivariate data-driven approach. Our results revealed lower face-elicited activation and theta/alpha phase synchrony between 120 and 330 ms in children compared with adults. Reduced connectivity in children was observed between the primary visual areas as well as their connections with higher-order frontal and parietal cortical areas. This is the first study to map neuromagnetic developmental changes in face processing in a time-locked manner using a naturalistic movie paradigm. It supports and extends the existing evidence of core face-processing network maturation accompanied by the development of an extended system of higher-order cortical areas engaged in face processing.

Children with autism spectrum disorder show atypical electroencephalographic response to processing contextual incongruencies.

Children with autism spectrum disorder (ASD) experience difficulties with social communication, making it challenging to interpret contextual information that aids in accurately interpreting language. To investigate how the brain processes the contextual information and how this is different in ASD, we compared event-related potentials (ERPs) in response to processing visual and auditory congruent and incongruent information. Two groups of children participated in the study: 37 typically developing children and 15 children with ASD (age range = 6 to 12). We applied a language task involving auditory sentences describing congruent or incongruent images. We investigated two ERP components associated with language processing: the N400 and P600. Our results showed how children with ASD present significant differences in their neural responses in comparison with the TD group, even when their reaction times and correct trials are not significantly different from the TD group.

Non-rapid eye movement sleep and wake neurophysiology in schizophrenia.

Motivated by the potential of objective neurophysiological markers to index thalamocortical function in patients with severe psychiatric illnesses, we comprehensively characterized key non-rapid eye movement (NREM) sleep parameters across multiple domains, their interdependencies, and their relationship to waking event-related potentials and symptom severity. In 72 schizophrenia (SCZ) patients and 58 controls, we confirmed a marked reduction in sleep spindle density in SCZ and extended these findings to show that fast and slow spindle properties were largely uncorrelated. We also describe a novel measure of slow oscillation and spindle interaction that was attenuated in SCZ. The main sleep findings were replicated in a demographically distinct sample, and a joint model, based on multiple NREM components, statistically predicted disease status in the replication cohort. Although also altered in patients, auditory event-related potentials elicited during wake were unrelated to NREM metrics. Consistent with a growing literature implicating thalamocortical dysfunction in SCZ, our characterization identifies independent NREM and wake EEG biomarkers that may index distinct aspects of SCZ pathophysiology and point to multiple neural mechanisms underlying disease heterogeneity. This study lays the groundwork for evaluating these neurophysiological markers, individually or in combination, to guide efforts at treatment and prevention as well as identifying individuals most likely to benefit from specific interventions.

Automatic Classification and Severity Estimation of Ataxia From Finger Tapping Videos.

Digital assessments enable objective measurements of ataxia severity and provide informative features that expand upon the information obtained during a clinical examination. In this study, we demonstrate the feasibility of using finger tapping videos to distinguish participants with Ataxia (N = 169) from participants with parkinsonism (N = 78) and from controls (N = 58), and predict their upper extremity and overall disease severity. Features were extracted from the time series representing the distance between the index and thumb and its derivatives. Classification models in ataxia archived areas under the receiver-operating curve of around 0.91, and regression models estimating disease severity obtained correlation coefficients around r = 0.64. Classification and prediction model coefficients were examined and they not only were in accordance, but were in line with clinical observations of ataxia phenotypes where rate and rhythm are altered during upper extremity motor movement.

Children with autism spectrum disorder show altered functional connectivity and abnormal maturation trajectories in response to inverted faces.

The processing of information conveyed by faces is a critical component of social communication. While the neurophysiology of processing upright faces has been studied extensively in autism spectrum disorder (ASD), less is known about the neurophysiological abnormalities associated with processing inverted faces in ASD. We used magnetoencephalography (MEG) to study both long-range and local functional connectivity, with the latter assessed using local cross-frequency coupling, in response to inverted faces stimuli, in 7-18 years old individuals with ASD and age and IQ matched typically developing (TD) individuals. We found abnormally reduced coupling between the phase of the alpha rhythm and the amplitude of the gamma rhythm in the fusiform face area (FFA) in response to inverted faces, as well as reduced long-range functional connectivity between the FFA and the inferior frontal gyrus (IFG) in response to inverted faces in the ASD group. These group differences were absent in response to upright faces. The magnitude of functional connectivity between the FFA and the IFG was significantly correlated with the severity of ASD, and FFA-IFG long-range functional connectivity increased with age in TD group, but not in the ASD group. Our findings suggest that both local and long-range functional connectivity are abnormally reduced in children with ASD when processing inverted faces, and that the pattern of abnormalities associated with the processing of inverted faces differs from the pattern of upright faces in ASD, likely due to the presumed greater reliance on top-down regulations necessary for efficient processing of inverted faces. LAY SUMMARY: We found alterations in the neurophysiological responses to inverted faces in children with ASD, that were not reflected in the evoked responses, and were not observed in the responses to upright faces. These alterations included reduced local functional connectivity in the fusiform face area (FFA), and decreased long-range alpha-band modulated functional connectivity between the FFA and the left IFG. The magnitude of long-range functional connectivity between the FFA and the inferior frontal gyrus was correlated with the severity of ASD.