Objective.Sensory processing dysfunction (SPD) not only affects most individuals with autism spectrum disorder (ASD), but at least 5% of children without ASD also experience SPD. Our understanding of the relationship between sensory dysfunction and resting state brain activity is still emerging. The objective of this study was to examine group differences and behavioral associations with resting state alpha and beta oscillatory activity in ASD, SPD, and typically developing control (TDC) groups.Approach.This study compared long-range resting state functional connectivity of neural oscillatory behavior in 60 male children aged 8-12 years with (ASD;N= 18), those with (SPD;N= 18) who did not meet ASD criteria, and typically developing control participants (TDC;N= 24) using magnetoencephalography. Functional connectivity analyses were performed in the alpha and beta frequency bands, which are known to be implicated in sensory information processing. Group differences in functional connectivity and associations between sensory abilities and functional connectivity were examined.Main results.Distinct patterns of functional connectivity differences between ASD and SPD groups were found only in the beta band, but not in the alpha band. In both alpha and beta bands, ASD and SPD cohorts differed from the TDC cohort. Distinct patterns of associations between imaginary coherence and performance-based measures of sensory processing and verbal abilities were identified across groups.Significance.These findings demonstrate distinct long-range alpha and beta band phase-lagged neural synchrony alterations in SPD and ASD that are associated with sensory processing abilities in male children. These measures could serve as potential candidate neurophysiological markers for ASD and SPD at the group level, and may provide mechanistic insights relevant to biomarker development.

Diffuse gliomas infiltrate the cerebral cortex and profoundly alter brain function, yet the multiscale consequences of this invasion on human neural circuits remain poorly understood. Here, we integrate magnetoencephalography (MEG), electrocorticography, spatial transcriptomics, single-nucleus RNA sequencing, and high density single-unit Neuropixels recordings from WHO grade 2-4 glioma patients to interrogate the structural and functional integrity of infiltrated neocortex. We identify a reproducible gain of laminar dependent synaptic gene expression in glioma-infiltrated regions (n = 10 samples; 29,011 spots), strongly correlated with local tumor proliferation (n = 6 patients) despite greater tumor burden in deeper cortical layers (n = 19 patients). Single-nucleus profiling and deconvolution revealed subtype-specific vulnerability of neuronal subclasses accompanied by transcriptomic reprogramming. Single-unit analysis across cortical depth (n = 11 patients) highlighted a profound reduction in overall unit yield and spiking activity (Controls = 169.5 units vs Glioma-infiltrated cortex = 35 units, p-value < 0.05, Mann-Whitney Test) within glioma-infiltrated cortex, with selective loss of excitatory and inhibitory neurons. This cell-type-specific vulnerability varied by glioma subtype and cortical layer, with astrocytoma showing the greatest excitatory loss in deep layers. Power spectral analysis across depth revealed altered laminar oscillatory structure in infiltrated regions—characterized by increased delta (1–4 Hz) and depth-dependent decreases in beta (12–20 Hz) power—mirroring noninvasive MEG findings (n = 144 patients) and partially restored under propofol anesthesia. Cross-modal integration uncovered a core axis linking tumor infiltration, neuron loss, and circuit dysfunction across cortical layers. These findings define laminar signatures of glioma-induced cortical dysfunction, establish a foundational framework for mapping glioma-driven cortical collapse and identify therapeutic windows for circuit-level rescue in brain cancer.

The mechanism by which gliomas disrupt the brain’s intrinsic rhythms, thereby altering the delicate balance of cortical excitability and neural synchrony at both local and global scales, remains a fundamental challenge. It is still unknown whether different glioma subtypes and malignancy grades impose distinct disruptions to network dynamics or whether these patterns reflect a shared functional fingerprint across tumor biology. Unraveling these questions is essential for glioma characterization and transforming clinical decision-making. Resting-state MEG was acquired in 135 glioma patients (WHO grade 2-4) and 100 matched healthy controls. We quantified local synchrony and cortical excitability through power spectral density and the aperiodic slope, an established surrogate marker of excitation-inhibition (E-I) balance, across canonical frequency bands. Long-range network integration was assessed using imaginary coherence. Spatially resolved, subtype-, grade-, and frequency-specific patterns were evaluated with voxel-wise non-parametric permutation testing. Glioma patients exhibited spatially widespread disruptions in neural synchrony and excitability. Across tumor subtypes, delta power is globally accentuated, with alpha power paradoxically increased at distant sites but suppressed near tumor-infiltrated cortex. High-grade GBMs exhibit the most dramatic spectral shifts, with marked increases in delta and prominent theta suppression near the tumor-infiltrated region. In contrast, oligodendrogliomas displayed a unique neurophysiological profile—high delta/gamma but attenuated alpha/theta. Long-range connectivity in the alpha and theta bands showed distinct, location- and subtype-dependent patterns: GBMs featured reduced theta coherence adjacent to tumor infiltration and decreased distal alpha coherence. Strikingly, aperiodic slope flattening, a marker of hyperexcitability, was flattened most in GBM, less so in astrocytoma, and least in oligodendroglioma, directly correlating network hyperexcitability to underlying malignancy grade. The most aberrant excitatory shifts were consistently localized to the tumor-infiltrated cortex when compared with the normal cortex. MEG mapping unveils striking, frequency-dependent patterns of network disruption and hyperexcitability signatures. These functional biomarkers could transform outcome prediction, risk assessment, and design precision for network-based therapies for patients

Changes in brain network function have been clearly demonstrated in patients with Alzheimer's disease (AD). Specifically, previous electrophysiological studies have shown that delta and theta oscillatory activity increases in AD, while alpha and beta activity reduces, compared to controls. These frequency-specific abnormalities have also shown to be region dependent, where low frequency delta-theta increases are more predominant in the frontal cortices while alpha and beta reductions are more predominant in the temporal and parietal cortices, in AD. What is unknown is how consistent these frequency-specific and region-dependent abnormalities are, across different metrics of oscillatory activity ranging from local to long-range connectivity. Here, in a well characterized, AD biomarker positive cohort of 77 AD patients and age-matched controls ( n  = 90), we used magnetoencephalography (MEG) to examine the local and long-range oscillatory abnormalities. Specifically, source-space reconstructed MEG signal for 40 cortical regions of Brainnetome was used to compute three different metric: local neural synchrony estimated as regional spectral power (SP); long-range synchrony at slow time-scale estimated from amplitude-envelope correlation (AEC); and long-range synchrony at fast time-scale estimated from imaginary coherence (IMCOH). Each measure was computed for 2–7 Hz (delta-theta), 8–12 Hz (alpha), and 15–29 Hz (beta) bands. Consistent with previous results, we found that increased delta-theta and reduced alpha and beta oscillatory activity patterns in AD compared to controls (Figure 1A). A conjoint analysis, in which we examined the common spatial patterns across different metrics of connectivity demonstrated that the frequency-specific patterns have consistent regional dependencies (Fig-1B). For example, the highest activity increases in delta-theta was found in the dorsal frontal and anterior cingulate cortices, while the greatest reductions in alpha was consistently found in the inferolateral temporal cortices and posterior temporoparietal regions. Reductions in beta also showed regional consistencies like alpha. Our results show that frequency-specific, region-dependent neurophysiological manifestations in AD are conserved across different synchronization paradigms that contribute to the functional architecture of neural networks. Importantly, the current findings define unified region-of-interests within each frequency component (delta-theta, alpha and beta) that can be further interrogated in future studies to investigate other pathobiological relationships in AD.

While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (similar to 40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 +/- 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 +/- 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology. Prabhu et al. examined phase-amplitude coupling between gamma amplitude and phase of lower frequencies in patients with Alzheimer's disease (AD) and their associations with network hyperexcitability. Theta-gamma coupling in AD was reduced in medial temporal regions (parahippocampus), which is the earliest affected region in AD, and was associated network hyperexcitability. Graphical abstract

Sensory processing dysfunction not only affects most individuals with autism spectrum disorder (ASD), but at least 5% of children without ASD also experience dysfunctional sensory processing. Our understanding of the relationship between sensory dysfunction and resting state brain activity is still emerging. This study compared long-range resting state functional connectivity of neural oscillatory behavior in children aged 8-12 years with autism spectrum disorder (ASD; N=18), those with sensory processing dysfunction (SPD; N=18) who do not meet ASD criteria, and typically developing control participants (TDC; N=24) using magnetoencephalography (MEG). Functional connectivity analyses were performed in the alpha and beta frequency bands, which are known to be implicated in sensory information processing. Group differences in functional connectivity and associations between sensory abilities and functional connectivity were examined. Distinct patterns of functional connectivity differences between ASD and SPD groups were found only in the beta band, but not in the alpha band. In both alpha and beta bands, ASD and SPD cohorts differed from the TDC cohort. Somatosensory cortical beta-band functional connectivity was associated with tactile processing abilities, while higher-order auditory cortical alpha-band functional connectivity was associated with auditory processing abilities. These findings demonstrate distinct long-range neural synchrony alterations in SPD and ASD that are associated with sensory processing abilities. Neural synchrony measures could serve as potential sensitive biomarkers for ASD and SPD.

Alzheimer’s disease (AD) carries an increased risk of seizures and subclinical epileptiform activity (Vossel et al. 2016). Network hyperexcitability which is the underlying phenomenon of epileptic manifestations is thought to contribute to AD pathophysiological processes. Recently we demonstrated that greater degree of neural synchronization deficits within 2-8Hz range of frequency oscillations are sensitive indicators of network hyperexcitability (Ranasinghe et al. 2021). Here, we sought to examine the high frequency gamma band deficits associated with network hyperexcitability in AD patients. Specifically, we quantified Phase Amplitude Coupling (PAC) between the amplitude of gamma oscillations (30-40Hz); with the phase of 2-8Hz oscillations) (Figure 1). We used 60s resting-state magnetoencephalography (MEG) recordings from 48 AD patients (n = 22, with subclinical epileptiform activity, AD-EPI+; n = 28 without subclinical epileptiform activity, AD-EPI-), and 35 age-matched controls. We computed PAC for each of 68 cortical regions (Desikan et al. 2006) on source-space reconstructed MEG signal using the mean vector length (Canolty et al. 2006). Permutation cluster test was performed for statistical comparisons between AD patients vs. age matched controls, and AD-EPI+ vs. AD-EPI-. Patients with AD showed significantly higher theta (4-8 Hz)-gamma coupling in the left parahippocampal and right caudal-middle frontal regions. Importantly, this increased left parahippocampal theta-gamma coupling was significantly higher in AD-EPI+ patients than in AD-EPI-. AD-EPI+ also showed higher alpha (8-12Hz)-gamma coupling in the right parahippocampal region compared to AD-EPI- (Figure 2). These results not only identify gamma band coupling deficits specifically localized to medial temporal regions which are the earliest affected regions in AD pathophysiology but also delineate the associated vulnerabilities of network hyperexcitability in AD.

Gliomas synaptically integrate into neural circuits 1 , 2 . Previous research has demonstrated bidirectional interactions between neurons and glioma cells, with neuronal activity driving glioma growth 1 – 4 and gliomas increasing neuronal excitability 2 , 5 – 8 . Here we sought to determine how glioma-induced neuronal changes influence neural circuits underlying cognition and whether these interactions influence patient survival. Using intracranial brain recordings during lexical retrieval language tasks in awake humans together with site-specific tumour tissue biopsies and cell biology experiments, we find that gliomas remodel functional neural circuitry such that task-relevant neural responses activate tumour-infiltrated cortex well beyond the cortical regions that are normally recruited in the healthy brain. Site-directed biopsies from regions within the tumour that exhibit high functional connectivity between the tumour and the rest of the brain are enriched for a glioblastoma subpopulation that exhibits a distinct synaptogenic and neuronotrophic phenotype. Tumour cells from functionally connected regions secrete the synaptogenic factor thrombospondin-1, which contributes to the differential neuron–glioma interactions observed in functionally connected tumour regions compared with tumour regions with less functional connectivity. Pharmacological inhibition of thrombospondin-1 using the FDA-approved drug gabapentin decreases glioblastoma proliferation. The degree of functional connectivity between glioblastoma and the normal brain negatively affects both patient survival and performance in language tasks. These data demonstrate that high-grade gliomas functionally remodel neural circuits in the human brain, which both promotes tumour progression and impairs cognition.

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition. SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

The amplitude envelope of speech is crucial for accurate comprehension. Considered a key stage in speech processing, the phase of neural activity in the theta-delta bands (1-10 Hz) tracks the phase of the speech amplitude envelope during listening. However, the mechanisms underlying this envelope representation have been heavily debated. A dominant model posits that envelope tracking reflects entrainment of endogenous low-frequency oscillations to the speech envelope. Alternatively, envelope tracking reflects a series of evoked responses to acoustic landmarks within the envelope. It has proven challenging to distinguish these two mechanisms. To address this, we recorded MEG while participants (n = 12, 6 female) listened to natural speech, and compared the neural phase patterns to the predictions of two computational models: an oscillatory entrainment model and a model of evoked responses to peaks in the rate of envelope change. Critically, we also presented speech at slowed rates, where the spectro-temporal predictions of the two models diverge. Our analyses revealed transient theta phase-locking in regular speech, as predicted by both models. However, for slow speech, we found transient theta and delta phase-locking, a pattern that was fully compatible with the evoked response model but could not be explained by the oscillatory entrainment model. Furthermore, encoding of acoustic edge magnitudes was invariant to contextual speech rate, demonstrating speech rate normalization of acoustic edge representations. Together, our results suggest that neural phase-locking to the speech envelope is more likely to reflect discrete representation of transient information rather than oscillatory entrainment.SIGNIFICANCE STATEMENT This study probes a highly debated topic in speech perception: the neural mechanisms underlying the cortical representation of the temporal envelope of speech. It is well established that the slow intensity profile of the speech signal, its envelope, elicits a robust brain response that "tracks" these envelope fluctuations. The oscillatory entrainment model posits that envelope tracking reflects phase alignment of endogenous neural oscillations. Here the authors provide evidence for a distinct mechanism. They show that neural speech envelope tracking arises from transient evoked neural responses to rapid increases in the speech envelope. Explicit computational modeling provides direct and compelling evidence that evoked responses are the primary mechanism underlying cortical speech envelope representations, with no evidence for oscillatory entrainment.

INTRODUCTION: Recent evidence indicates that diffuse gliomas engage with neurons at the single-unit and circuit level through differing mechanisms. Certain malignant gliomas form glioma-neuron excitatory glutamatergic synapses and modulate neuron-neuron synapses through activity-dependent paracrine signaling, while others establish glioma-glioma connections via tumor microtubes. It is therefore possible that diffuse gliomas remodel neuronal circuits in a defined and predictable manner and demonstrate distinct electrophysiological profiles with prognostic and therapeutic significance. METHODS: Following spatial-temporal registration, an elastic net logistic regression classifier was used to distinguish between power spectra arising from glioma-remodeled cortex and within-subject control conditions (N = 140). Model significance was determined non-parametrically by re-training each model 1,000 times with randomly permuted class labels and testing the true phi coefficient against the null distribution. RESULTS: In the discovery dataset, glioma infiltration was accurately classified based on tumor intrinsic neuronal activity (p < 0.05) in 127 patients (90.7%). Thirty electrophysiological features were identified, which revealed increased power in the delta range (1-4 Hz) and decreased power in the beta range (12-20 Hz) as a unique signature of glioma remodeling (p < 0.05) which was preserved in the validation dataset as well as across WHO 2021 diffuse glioma subtypes. To identify gene expression programs and signaling mechanisms that may contribute to glioma-induced remodeling but are potentially not identified in the current clinical classification scheme, targeted, next generation sequencing was performed and used as covariates, which again demonstrated the significance of the delta-beta spectral features. CONCLUSIONS: These data support converging mechanisms of glioma-induced neuronal network remodeling across tumor subtypes, setting the stage for novel therapies such as neuromodulation.

Background/Introduction: Widespread network disruption has been hypothesized to be an important predictor of outcomes in patients with refractory temporal lobe epilepsy (TLE). Most studies examining functional network disruption in epilepsy have largely focused on the symmetric bidirectional metrics of the strength of network connections. However, a more complete description of network dysfunction impacts in epilepsy requires an investigation of the potentially more sensitive directional metrics of information flow. Methods: This study describes a whole-brain magnetoencephalography-imaging approach to examine resting-state directional information flow networks, quantified by phase-transfer entropy (PTE), in patients with TLE compared with healthy controls (HCs). Associations between PTE and clinical characteristics of epilepsy syndrome are also investigated. Results: Deficits of information flow were specific to alpha-band frequencies. In alpha band, while HCs exhibit a clear posterior-to-anterior directionality of information flow, in patients with TLE, this pattern of regional information outflow and inflow was significantly altered in the frontal and occipital regions. The changes in information flow within the alpha band in selected brain regions were correlated with interictal spike frequency and duration of epilepsy. Conclusions: Impaired information flow is an important dimension of network dysfunction associated with the pathophysiological mechanisms of TLE.

Since the first demonstrations of network hyperexcitability in scientific models of Alzheimer's disease, a growing body of clinical studies have identified subclinical epileptiform activity and associated cognitive decline in patients with Alzheimer's disease. An obvious problem presented in these studies is lack of sensitive measures to detect and quantify network hyperexcitability in human subjects. In this study we examined whether altered neuronal synchrony can be a surrogate marker to quantify network hyperexcitability in patients with Alzheimer's disease. Using magnetoencephalography (MEG) at rest, we studied 30 Alzheimer's disease patients without subclinical epileptiform activity, 20 Alzheimer's disease patients with subclinical epileptiform activity and 35 age-matched controls. Presence of subclinical epileptiform activity was assessed in patients with Alzheimer's disease by long-term video-EEG and a 1-h resting MEG with simultaneous EEG. Using the resting-state source-space reconstructed MEG signal, in patients and controls we computed the global imaginary coherence in alpha (8-12 Hz) and delta-theta (2-8 Hz) oscillatory frequencies. We found that Alzheimer's disease patients with subclinical epileptiform activity have greater reductions in alpha imaginary coherence and greater enhancements in delta-theta imaginary coherence than Alzheimer's disease patients without subclinical epileptiform activity, and that these changes can distinguish between Alzheimer's disease patients with subclinical epileptiform activity and Alzheimer's disease patients without subclinical epileptiform activity with high accuracy. Finally, a principal component regression analysis showed that the variance of frequency-specific neuronal synchrony predicts longitudinal changes in Mini-Mental State Examination in patients and controls. Our results demonstrate that quantitative neurophysiological measures are sensitive biomarkers of network hyperexcitability and can be used to improve diagnosis and to select appropriate patients for the right therapy in the next-generation clinical trials. The current results provide an integrative framework for investigating network hyperexcitability and network dysfunction together with cognitive and clinical correlates in patients with Alzheimer's disease.

Prior work demonstrated synaptic integration of malignant gliomas into neural circuits induces local hyperexcitability and tumor proliferation. However, prognostication and therapeutic vulnerabilities are lacking from preclinical models. Here, we integrate in vivo and in vitro neurophysiology spatially matched with gene expression programs and protein signaling mechanisms across 66 IDH WT glioblastoma patients to identify thrombospondin-1 (TSP-1) as a molecular driver of glioma-induced network remodeling. Bulk and single cell RNA-sequencing of 11 intratumoral regions maintaining functional connectivity (13,730 cells analyzed) revealed a distinct neurogenic signature enriched for the synaptogenic factor TSP-1. Mechanistic and functional studies validating therapeutic vulnerabilities to TSP-1 silencing by shRNA knockdown and FDA-approved inhibitors (Gabapentin and LSKL) of excitatory synapse formation through the gabapentin a2d-1 receptor was performed in vitro and in vivo. Glioma-neuron co-culture of TSP-1 overexpressing cells demonstrated increased Ki67 proliferation and tumor microtube (TMT) formation when cultured in the presence of neurons. Pharmacological inhibition of TSP-1 using gabapentin or TSP-1 shRNA inhibited the proliferation and TMT-mediated expansion. Hippocampal xenografted mice with TSP-1 over expressing primary patient cultures demonstrated shorter survival and gabapentin treatment of xenografted mice significantly reduced the proliferation of TSP-1 overexpressing cells in vivo. Electrophysiological properties of glioma-neuron co-cultures analyzed using multi-electrode array (MEA) demonstrated increased neuronal spiking activity and network burst synchrony in the presence of TSP-1 over expressing cells. Strikingly, these increases were eliminated in the presence of gabapentin. We modeled survival risk in patients incorporating the effects of glioma intrinsic neuronal activity, molecular, therapeutic, and clinical factors on overall survival by recursive partitioning. Three risk groups were identified based on tumor intrinsic neuronal activity, pre- and post-operative tumor volume with shortest overall survival in patients with glioma intrinsic neuronal activity. These data identify glioma-induced secretion of TSP-1 as a key contributor of tumor proliferation shedding light on new therapies.

Recent evidence indicates that diffuse gliomas engage with neurons at the single-unit and circuit level through differing mechanisms. Certain malignant gliomas form glioma-neuron excitatory glutamatergic synapses and modulate neuron-neuron synapses through activity-dependent paracrine signaling, while others establish glioma-glioma connections via tumor microtubes. It is therefore possible that diffuse gliomas remodel neuronal circuits in a defined and predictable manner and demonstrate distinct electrophysiological profiles with prognostic and therapeutic significance. Here we apply machine learning principles in 140 patients across glioma subtypes to uncover unique electrophysiological features non-invasively via magnetoencephalography (discovery dataset) followed by feature validation using subdural electrocorticography (validation dataset). Following spatial-temporal registration, we fit an elastic net logistic regression classifier to distinguish between power spectra arising from glioma-remodeled cortex and within-subject control conditions. Model significance was determined non-parametrically by re-training each model 1,000 times with randomly permuted class labels and testing the true phi coefficient against the null distribution. In the discovery dataset, we were able to classify glioma infiltration based on tumor intrinsic neuronal activity (p < 0.05) in 127 patients (90.7%). We identified 30 electrophysiological features which revealed increased power in the delta range (1-4 Hz) and decreased power in the beta range (12-20 Hz) as a unique signature of glioma remodeling (p < 0.05) which was preserved in the validation dataset as well as across WHO 2021 diffuse glioma subtypes. In order to identify gene expression programs and signaling mechanisms that may contribute to glioma-induced remodeling but are potentially not identified in the current clinical classification scheme, we assessed targeted, next generation sequencing and DNA mutations as covariates, which again demonstrated the significance of the delta-beta spectral features. These data support converging mechanisms of glioma-induced neuronal network remodeling across tumor subtypes, setting the stage for novel therapies such as neuromodulation.

Responsive neurostimulation is a promising treatment for drug-resistant focal epilepsy; however, clinical outcomes are highly variable across individuals. The therapeutic mechanism of responsive neurostimulation likely involves modulatory effects on brain networks; however, with no known biomarkers that predict clinical response, patient selection remains empiric. This study aimed to determine whether functional brain connectivity measured non-invasively prior to device implantation predicts clinical response to responsive neurostimulation therapy. Resting-state magnetoencephalography was obtained in 31 participants with subsequent responsive neurostimulation device implantation between 15 August 2014 and 1 October 2020. Functional connectivity was computed across multiple spatial scales (global, hemispheric, and lobar) using pre-implantation magnetoencephalography and normalized to maps of healthy controls. Normalized functional connectivity was investigated as a predictor of clinical response, defined as percent change in self-reported seizure frequency in the most recent year of clinic visits relative to pre-responsive neurostimulation baseline. Area under the receiver operating characteristic curve quantified the performance of functional connectivity in predicting responders (>= 50% reduction in seizure frequency) and non-responders (<50%). Leave-one-out cross-validation was furthermore performed to characterize model performance. The relationship between seizure frequency reduction and frequency-specific functional connectivity was further assessed as a continuous measure. Across participants, stimulation was enabled for a median duration of 52.2 (interquartile range, 27.0-62.3) months. Demographics, seizure characteristics, and responsive neurostimulation lead configurations were matched across 22 responders and 9 non-responders. Global functional connectivity in the alpha and beta bands were lower in non-responders as compared with responders (alpha, p(fdr) < 0.001; beta, p(fdr) < 0.001). The classification of responsive neurostimulation outcome was improved by combining feature inputs; the best model incorporated four features (i.e. mean and dispersion of alpha and beta bands) and yielded an area under the receiver operating characteristic curve of 0.970 (0.919-1.00). The leave-one-out cross-validation analysis of this four-feature model yielded a sensitivity of 86.3%, specificity of 77.8%, positive predictive value of 90.5%, and negative predictive value of 70%. Global functional connectivity in alpha band correlated with seizure frequency reduction (alpha, P = 0.010). Global functional connectivity predicted responder status more strongly, as compared with hemispheric predictors. Lobar functional connectivity was not a predictor. These findings suggest that non-invasive functional connectivity may be a candidate personalized biomarker that has the potential to predict responsive neurostimulation effectiveness and to identify patients most likely to benefit from responsive neurostimulation therapy. Follow-up large-cohort, prospective studies are required to validate this biomarker. These findings furthermore support an emerging view that the therapeutic mechanism of responsive neurostimulation involves network-level effects in the brain. To prognosticate outcomes with neurostimulation for epilepsy, Fan et al. investigate functional network connectivity measured non-invasively with magnetoencephalography as a novel biomarker for effectiveness of responsive neurostimulation (RNS) therapy. Resting-state functional connectivity in alpha and beta frequency bands predicted response to subsequent RNS therapy and correlated with seizure frequency reduction.

Sleep is a highly stereotyped phenomenon that is ubiquitous across species. Although behaviorally appearing as a homogeneous process, sleep has been recognized as cortically heterogenous and locally dynamic. PET/fMRI studies have provided key insights into regional activation and deactivation with sleep onset, but they lack the high temporal resolution and electrophysiology for understanding neural interactions. Using simultaneous electrocorticography (EEG) and magnetoencephalography (MEG) imaging, we systematically characterize whole-brain neural oscillations and identify frequency specific, cortically-based patterns associated with sleep onset. In this study, 14 healthy subjects underwent simultaneous EEG and MEG imaging. Sleep states were determined by scalp EEG. Eight 15s artifact-free epochs, e.g. 120s sensor time series, were selected to represent each behavioral state: N1, N2 and wake. Atlas-based source reconstruction was performed using adaptive beamforming methods. Functional connectivity measures were computed using imaginary coherence and across regions of interests (ROIs, segmentation of 210 cortical regions with Brainnetome Atlas) in multiple frequency bands, including delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz), beta (15-30Hz), and gamma (30-50Hz). Directional phase transfer entropy (PTE) was also evaluated to determine the direction of information flow with transition to sleep. We show that the transition to sleep is encoded in a spatially and temporally specific dynamic pattern of whole-brain functional connectivity. With sleep onset, there is increased functional connectivity diffusely within the delta frequency, while spatially specific profiles in other frequency bands, e.g. increased fronto-temporal connectivity in the alpha frequency band and fronto-occipital connectivity in the theta band. In addition, rather than a decoupling of anterior-posterior regions with transition to sleep, there is a spectral shift to delta frequencies observed in the synchrony and information flow of neural activity. Sleep onset is cortically heterogeneous, composed of spatially and temporally specific patterns of whole-brain functional connectivity, which may play an essential role in the transition to sleep. Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the NIH under Award Number (5TL1TR001871-05 to JMF). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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OBJECTIVES:Auditory cortical activation of the two hemispheres to monaurally presented tonal stimuli has been shown to be asynchronous in normal hearing (NH) but synchronous in the extreme case of adult-onset asymmetric hearing loss (AHL) with single-sided deafness. We addressed the wide knowledge gap between these two anchoring states of interhemispheric temporal organization. The objectives of this study were as follows: (1) to map the trajectory of interhemispheric temporal reorganization from asynchrony to synchrony using magnitude of interaural threshold difference as the independent variable in a cross-sectional study and (2) to evaluate reversibility of interhemispheric synchrony in association with hearing in noise performance by amplifying the aidable poorer ear in a repeated measures, longitudinal study.DESIGN:The cross-sectional and longitudinal cohorts were comprised of 49 subjects (AHL; N = 21; 11 male, 10 female; mean age = 48 years) and NH (N = 28; 16 male, 12 female; mean age = 45 years). The maximum interaural threshold difference of the two cohorts spanned from 0 to 65 dB. Magnetoencephalography analyses focused on latency of the M100 peak response from auditory cortex in both hemispheres between 50 msec and 150 msec following monaural tonal stimulation at the frequency (0.5, 1, 2, 3, or 4 kHz) corresponding to the maximum and minimum interaural threshold difference for better and poorer ears separately. The longitudinal AHL cohort was drawn from three subjects in the cross-sectional AHL cohort (all male; ages 49 to 60 years; varied AHL etiologies; no amplification for at least 2 years). All longitudinal study subjects were treated by monaural amplification of the poorer ear and underwent repeated measures examination of the M100 response latency and quick speech in noise hearing in noise performance at baseline, and postamplification months 3, 6, and 12.RESULTS:The M100 response peak latency values in the ipsilateral hemisphere lagged those in the contralateral hemisphere for all stimulation conditions. The mean (SD) interhemispheric latency difference values (ipsilateral less contralateral) to better ear stimulation for three categories of maximum interaural threshold difference were as follows: NH (≤ 10 dB)-8.6 (3.0) msec; AHL (15 to 40 dB)-3.0 (1.2) msec; AHL (≥ 45 dB)-1.4 (1.3) msec. In turn, the magnitude of difference values were used to define interhemispheric temporal organization states of asynchrony, mixed asynchrony and synchrony, and synchrony, respectively. Amplification of the poorer ear in longitudinal subjects drove interhemispheric organization change from baseline synchrony to postamplification asynchrony and hearing in noise performance improvement in those with baseline impairment over a 12-month period.CONCLUSIONS:Interhemispheric temporal organization in AHL was anchored between states of asynchrony in NH and synchrony in single-sided deafness. For asymmetry magnitudes between 15 and 40 dB, the intermediate mixed state of asynchrony and synchrony was continuous and reversible. Amplification of the poorer ear in AHL improved hearing in noise performance and restored normal temporal organization of auditory cortices in the two hemispheres. The return to normal interhemispheric asynchrony from baseline synchrony and improvement in hearing following monoaural amplification of the poorer ear evolved progressively over a 12-month period.