Inter-regional Functional Connectivity Research Articles

Cortical Generators and Connections Underlying Phoneme Perception: A Mismatch Negativity and P300 Investigation.

The cortical generators of the pure tone MMN and P300 have been thoroughly studied. Their nature and interaction with respect to phoneme perception, however, is poorly understood. Accordingly, the cortical sources and functional connections that underlie the MMN and P300 in relation to passive and active speech sound perception were identified.An inattentive and attentive phonemic oddball paradigm, eliciting a MMN and P300 respectively, were administered in 60 healthy adults during simultaneous high-density EEG recording. For both the MMN and P300, eLORETA source reconstruction was performed. The maximal cross-correlation was calculated between ROI-pairs to investigate inter-regional functional connectivity specific to passive and active deviant processing.MMN activation clusters were identified in the temporal (insula, superior temporal gyrus and temporal pole), frontal (rostral middle frontal and pars opercularis) and parietal (postcentral and supramarginal gyrus) cortex. Passive discrimination of deviant phonemes was aided by a network connecting right temporoparietal cortices to left frontal areas. For the P300, clusters with significantly higher activity were found in the frontal (caudal middle frontal and precentral), parietal (precuneus) and cingulate (posterior and isthmus) cortex. Significant intra- and interhemispheric connections between parietal, cingulate and occipital regions constituted the network governing active phonemic target detection. A predominantly bilateral network was found to underly both the MMN and P300.While passive phoneme discrimination is aided by a fronto-temporo-parietal network, active categorization calls on a network entailing fronto-parieto-cingulate cortices. Neural processing of phonemic contrasts, as reflected by the MMN and P300, does not appear to show pronounced lateralization to the language-dominant hemisphere.

Intra and inter-regional functional connectivity of the human brain due to Task-Evoked fMRI Data classification through CNN & LSTM

Background and purposeOlfaction is an early marker of neurodegenerative disease. Standard olfactory function is essential due to the importance of olfaction in human life. The psychophysical evaluation assesses the olfactory function commonly. It is patient-reported, and results rely on the patient's answers and collaboration. However, methodological difficulties attributed to the psychophysical evaluation of olfactory-related cerebral areas led to limited assessment of olfactory function in the human brain. Materials and MethodsThe current study utilized clustering approaches to assess olfactory function in fMRI data and used brain activity to parcellate the brain with homogeneous properties. Deep neural network architecture based on ResNet convolutional neural networks (CNN) and Long Short-Term Model (LSTM) designed to classify healthy with olfactory disorders subjects. ResultsThe fMRI result obtained by k-means unsupervised machine learning model was within the expected outcome and similar to those found with the conn toolbox in detecting active areas. There was no significant difference between the means of subjects and every subject. Proposing a CRNN deep learning model to classify fMRI data in two different healthy and with olfactory disorders groups leads to an accuracy score of 97 %. ConclusionsThe K-means unsupervised algorithm can detect the active regions in the brain and analyze olfactory function. Classification results prove the CNN-LSTM architecture using ResNet provides the best accuracy score in olfactory fMRI data. It is the first attempt conducted on olfactory fMRI data in detail until now.

Math on cortex-enhanced delta phase synchrony in math experts during long and complex math demonstrations.

Neural oscillations are important for working memory and reasoning and they are modulated during cognitively challenging tasks, like mathematics. Previous work has examined local cortical synchrony on theta (4-8Hz) and alpha (8-13Hz) bands over frontal and parietal electrodes during short mathematical tasks when sitting. However, it is unknown whether processing of long and complex math stimuli evokes inter-regional functional connectivity. We recorded cortical activity with EEG while math experts and novices watched long (13-68seconds) and complex (bachelor-level) math demonstrations when sitting and standing. Fronto-parietal connectivity over the left hemisphere was stronger in math experts than novices reflected by enhanced delta (0.5-4Hz) phase synchrony in experts. Processing of complex math tasks when standing extended the difference to right hemisphere, suggesting that other cognitive processes, such as maintenance of body balance when standing, may interfere with novice's internal concentration required during complex math tasks more than in experts. There were no groups differences in phase synchrony over theta or alpha frequencies. These results suggest that low-frequency oscillations modulate inter-regional connectivity during long and complex mathematical cognition and demonstrate one way in which the brain functions of math experts differ from those of novices: through enhanced fronto-parietal functional connectivity.

Higher Fat-Related Body Composition Measurement and Lower Resting-State Inter-Network Functional Connectivity of APOE ε4 Carrier in Mild Cognitive Impairment Patients With Aβ Deposition.

We aimed to evaluate the impact of interaction between APOE ε4 carrier status and body composition measurements on intra- and inter-regional functional connectivity (FC) in mild cognitive impairment (MCI) patients with Aβ deposition. MCI patients with and without APOE ε4 allele (carrier, n=86; non-carrier, n=95) underwent neuropsychological battery, resting-state functional magnetic resonance imaging scans, positron emission tomography scans with [18F]flutemetamol, and bioelectrical impedance analysis for measuring body composition. We employed a priori defined regions of interest to investigate the intra- and inter-network FC profiles of default mode network (DMN), central executive network (CEN), and salience network. There was a significant interaction of APOE ε4 carrier status with body fat mass index, visceral fat area, and waist-hip circumference ratio for inter-network FC between DMN and CEN, contributing higher fat-related body composition measurements in the APOE ε4 carrier with lower DMN-CEN FC. The present results highlight the detrimental effect of APOE ε4 carrier status on the associations between the fat-related body composition measurements and FC in the MCI patients with Aβ accumulation.

Leading basic modes of spontaneous activity drive individual functional connectivity organization in the resting human brain

Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity.

Posterior parietal cortical areas and recovery after motor stroke: a scoping review.

Brain imaging and electrophysiology have significantly enhanced our current understanding of stroke-related changes in brain structure and function and their implications for recovery processes. In the motor domain, most studies have focused on key motor areas of the frontal lobe including the primary and secondary motor cortices. Time- and recovery-dependent alterations in regional anatomy, brain activity and inter-regional connectivity have been related to recovery. In contrast, the involvement of posterior parietal cortical areas in stroke recovery is poorly understood although these regions are similarly important for important aspects of motor functioning in the healthy brain. Just in recent years, the field has increasingly started to explore to what extent posterior parietal cortical areas might undergo equivalent changes in task-related activation, regional brain structure and inter-regional functional and structural connectivity after stroke. The aim of this scoping review is to give an update on available data covering these aspects and thereby providing novel insights into parieto-frontal interactions for systems neuroscience stroke recovery research in the upper limb motor domain.

Trait representation of embodied cognition in dancers pivoting on the extended mirror neuron system: a resting-state fMRI study.

Dance is an art form that integrates the body and mind through movement. Dancers develop exceptional physical and mental abilities that involve various neurocognitive processes linked to embodied cognition. We propose that dancers' primary trait representation is movement-actuated and relies on the extended mirror neuron system (eMNS). A total of 29 dancers and 28 non-dancer controls were recruited. A hierarchical approach of intra-regional and inter-regional functional connectivity (FC) analysis was adopted to probe trait-like neurodynamics within and between regions in the eMNS during rest. Correlation analyses were employed to examine the associations between dance training, creativity, and the FC within and between different brain regions. Within the eMNS, dancers exhibited increased intra-regional FC in various brain regions compared to non-dancers. These regions include the left inferior frontal gyrus, left ventral premotor cortex, left anterior insula, left posterior cerebellum (crus II), and bilateral basal ganglia (putamen and globus pallidus). Dancers also exhibited greater intrinsic inter-regional FC between the cerebellum and the core/limbic mirror areas within the eMNS. In dancers, there was a negative correlation observed between practice intensity and the intrinsic FC within the eMNS involving the cerebellum and basal ganglia. Additionally, FCs from the basal ganglia to the dorsolateral prefrontal cortex were found to be negatively correlated with originality in dancers. Our results highlight the proficient communication within the cortical-subcortical hierarchy of the eMNS in dancers, linked to the automaticity and cognitive-motor interactions acquired through training. Altered functional couplings in the eMNS can be regarded as a unique neural signature specific to virtuoso dancers, which might predispose them for skilled dancing performance, perception, and creation.

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear.In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC.Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies.We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.

Anticipating social feedback involves basal forebrain and mesolimbic functional connectivity

The mesolimbic system and basal forebrain (BF) are implicated in processing rewards and punishment, but their interplay and functional properties of subregions with respect to future social outcomes remain unclear. Therefore, this study investigated regional responses and interregional functional connectivity of the lateral (l), medial (m), and ventral (v) Substantia Nigra (SN), Nucleus Accumbens (NAcc), Nucleus basalis of Meynert (NBM), and Medial Septum/Diagonal Band (MS/DB) during reward and punishment anticipation in a social incentive delay task with neutral, positive, and negative feedback using high-resolution fMRI (1.5mm3). Neuroimaging data (n = 36 healthy humans) of the anticipation phase was analyzed using mass-univariate, functional connectivity, and multivariate-pattern analysis. As expected, participants responded faster when anticipating positive and negative compared to neutral social feedback. At the neural level, anticipating social information engaged valence-related and valence-unrelated functional connectivity patterns involving the BF and mesolimbic areas. Precisely, valence-related connectivity between the lSN and NBM was associated with anticipating neutral social feedback, while connectivity between the vSN and NBM was associated with anticipating positive social feedback. A more complex pattern was observed for anticipating negative social feedback, including connectivity between the lSN and MS/DB, lSN and NAcc, as well as mSN and NAcc. To conclude, functional connectivity patterns of the BF and mesolimbic areas signal the anticipation of social feedback depending on their emotional valence. As such, our findings give novel insights into the underlying neural processes of social information processing.

Local brain oscillations and interregional connectivity differentially serve sensory and expectation effects on pain

Pain emerges from the integration of sensory information about threats and contextual information such as an individual's expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and interregional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced interregional connectivity. Specifically, expectations altered connectivity at alpha (8 to 12 hertz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60 to 100 hertz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain.

Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity.

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet, it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here, we recorded the activity from ∼40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ∼200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.

Investigation of neurophysiologic and functional connectivity changes following glioma resection using magnetoencephalography.

In patients with glioma, clinical manifestations of neural network disruption include behavioral changes, cognitive decline, and seizures. However, the extent of network recovery following surgery remains unclear. The aim of this study was to characterize the neurophysiologic and functional connectivity changes following glioma surgery using magnetoencephalography (MEG). Ten patients with newly diagnosed intra-axial brain tumors undergoing surgical resection were enrolled in the study and completed at least two MEG recordings (pre-operative and immediate post-operative). An additional post-operative recording 6-8 weeks following surgery was obtained for six patients. Resting-state MEG recordings from 28 healthy controls were used for network-based comparisons. MEG data processing involved artifact suppression, high-pass filtering, and source localization. Functional connectivity between parcellated brain regions was estimated using coherence values from 116 virtual channels. Statistical analysis involved standard parametric tests. Distinct alterations in spectral power following tumor resection were observed, with at least three frequency bands affected across all study subjects. Tumor location-related changes were observed in specific frequency bands unique to each patient. Recovery of regional functional connectivity occurred following glioma resection, as determined by local coherence normalization. Changes in inter-regional functional connectivity were mapped across the brain, with comparable changes in low to mid gamma-associated functional connectivity noted in four patients. Our findings provide a framework for future studies to examine other network changes in glioma patients. We demonstrate an intrinsic capacity for neural network regeneration in the post-operative setting. Further work should be aimed at correlating neurophysiologic changes with individual patients' clinical outcomes.

Task-dependent plasticity in distributed neural circuits after transcranial direct current stimulation of the human motor cortex: A proof-of-concept study.

The ability of non-invasive brain stimulation to induce neuroplasticity and cause long-lasting functional changes is of considerable interest for the reversal of chronic pain and disability. Stimulation of the primary motor cortex (M1) has provided some of the most encouraging after-effects for therapeutic purposes, but little is known about its underlying mechanisms. In this study we combined transcranial Direct Current Stimulation (tDCS) and fMRI to measure changes in task-specific activity and interregional functional connectivity between M1 and the whole brain. Using a randomized counterbalanced sham-controlled design, we applied anodal and cathodal tDCS stimulation over the left M1. In agreement with previous studies, we demonstrate that tDCS applied to the target region induces task-specific facilitation of local brain activity after anodal tDCS, with the stimulation effects having a negative relationship to the resting motor threshold. Beyond the local effects, tDCS also induced changes in multiple downstream regions distinct from the motor system that may be important for therapeutic efficacy, including the operculo-insular and cingulate cortex. These results offer opportunities to improve outcomes of tDCS for the individual patient based on the degree of presumed neuroplasticity. Further research is still warranted to address the optimal stimulation targets and parameters for those with disease-specific symptoms of chronic pain.

Recent-onset and persistent tinnitus: Uncovering the differences in brain activities using resting-state functional magnetic resonance imaging technologies.

This study aimed to investigate the differences in intra-regional brain activity and inter-regional functional connectivity between patients with recent-onset tinnitus (ROT) and persistent tinnitus (PT) using resting-state functional magnetic resonance imaging (rs-fMRI), including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and voxel-wise functional connectivity (FC). We acquired rs-fMRI scans from 82 patients (25 without recent-onset tinnitus, 28 with persistent tinnitus, and 29 healthy controls). Age, sex, and years of education were matched across the three groups. We performed ALFF, ReHo, and voxel-wise FC analyses for all patients. Compared with the control group, participants with ROT and PT manifested significantly reduced ALFF and ReHo activity within the left and right dorsolateral superior frontal gyrus (SFG) and gyrus rectus (GR). Additional voxel-wise FC revealed decreased connectivity between the dorsolateral SFG (left and right) and the right superior parietal gyrus (SPG), right middle frontal gyrus (MFG), and left medial superior frontal gyrus (mSFG) within these two groups. Significant differences were observed between the ROT and PT groups, with the ROT group demonstrating reduced FC. Our data suggest that patients with PT have more difficulty monitoring external stimuli and reorienting attention than patients with ROT. In addition, patients who perceive higher levels of disruption from tinnitus are more likely to develop persistent and debilitating tinnitus once the tinnitus lasts longer than six months. Therefore, we strongly recommend that clinicians implement effective tinnitus management strategies in patients with ROT as soon as possible.

Importin α3 (KPNA3) Deficiency Augments Effortful Reward-Seeking Behavior in Mice.

Importin α3 (Gene: Kpna3, the ortholog of human Importin α4) is a member of the importin α family and participates in nucleocytoplasmic transport by forming trimeric complexes between cargo proteins and importin β1. Evidence from human studies has indicated that single nucleotide polymorphisms (SNP) in the KPNA3 gene are associated with the occurrence of several psychiatric disorders accompanied by abnormal reward-related behavior, including schizophrenia, major depression, and substance addiction. However, the precise roles of importin α3 in controlling reward processing and motivation are still unclear. In this study, we evaluated the behavioral effects of Kpna3 knockout (KO) in mice on performance in touchscreen operant chamber-based tasks evaluating simple (fixed-ratio) and effortful (progressive-ratio) reward-seeking behaviors. While Kpna3 KO mice showed no significant differences in operant reward learning on a fixed-ratio schedule, they demonstrated significantly increased motivation (increased break point) to instrumentally respond for sucrose on a progressive-ratio schedule. We additionally measured the number of c-Fos-positive cells, a marker of neural activity, in 20 regions of the brain and identified a network of brain regions based on their interregional correlation coefficients. Network and graph-theoretic analyses suggested that Kpna3 deficiency enhanced overall interregional functional connectivity. These findings suggest the importance of Kpna3 in motivational control and indicate that Kpna3 KO mice may be an attractive line for modeling motivational abnormalities associated with several psychiatric disorders.

Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.

Evaluating the Effect of Increasing Working Memory Load on EEG-Based Functional Brain Networks

Purpose: Working Memory (WM) plays a crucial role in many cognitive functions of the human brain. Examining how the inter-regional connectivity and characteristics of functional brain networks modulate with increasing WM load could lead to a more in-depth understanding of the WM system.
 Materials and Methods: To investigate the effect of WM load alterations on the inter-regional synchronization and functional network characteristics, we used Electroencephalogram (EEG) data recorded from 21 healthy participants during an n-back task with three load levels (0-back, 2-back, and 3-back). The networks were constructed based on the weighted Phase Lag Index (wPLI) in the theta, alpha, beta, low-gamma, and high-gamma frequency bands. After constructing the fully connected, weighted, and undirected networks, the node-to-node connections, graph-theory metrics consisting of mean Clustering coefficient (C), characteristic path Length (L), and node strength were analyzed by statistical tests.
 Results: It was revealed that in the presence of WM load (2- and 3-back tasks) compared with the WM-free condition (0-back task) within the alpha range, the Inter-Regional Functional Connectivity (IRFC), functional integration, functional segregation, and node strength in channels located at the frontal, parietal and occipital regions were significantly reduced. In the high-gamma band, IRFC was significantly higher in the difficult task (3-back) compared to the easy and moderate tasks (0- and 2-back). Besides, locally clustered connections were significantly increased in 3-back relative to the 2-back task.
 Conclusion: Inter-regional alpha synchronization and alpha-band network metrics can distinguish between the WM and WM-free tasks. In contrast, phase synchronization of high-gamma oscillations can differentiate between the levels of WM load, which demonstrates the potential of the phase-based functional connectivity and brain network metrics for predicting the WM load level.

Intra- and inter-regional dynamics in cortical-striatal-tegmental networks.

It is increasingly recognized that networks of brain areas work together to accomplish computational goals. However, functional connectivity networks are not often compared between different behavioral states and across different frequencies of electrical oscillatory signals. In addition, connectivity is always defined as the strength of signal relatedness between two atlas-based anatomical locations. Here, we performed an exploratory analysis using data collected from high-density arrays in the prefrontal cortex (PFC), striatum (STR), and ventral tegmental area (VTA) of male rats. These areas have all been implicated in a wide range of different tasks and computations including various types of memory as well as reward valuation, habit formation and execution, and skill learning. Novel intraregional clustering analyses identified patterns of spatially restricted, temporally coherent, and frequency-specific signals that were reproducible across days and were modulated by behavioral states. Multiple clusters were identified within each anatomical region, indicating a mesoscopic scale of organization. Generalized eigendecomposition (GED) was used to dimension-reduce each cluster to a single component time series. Dense intercluster connectivity was modulated by behavioral state, with connectivity becoming reduced when the animals were exposed to a novel object, compared with a baseline condition. Behavior-modulated connectivity changes were seen across the spectrum, with δ, θ, and γ all being modulated. These results demonstrate the brain's ability to reorganize functionally at both the intra- and inter-regional levels during different behavioral states.NEW & NOTEWORTHY We applied novel clustering techniques to discover functional subregional anatomical patches that changed with behavioral conditions but were frequency specific and stable across days. By taking into account these changes in intraregional signal generator location and extent, we were able to reveal a richer picture of inter-regional functional connectivity than would otherwise have been possible. These findings reveal that the brain's functional organization changes with state at multiple levels of scale.

Normative intracranial EEG maps epileptogenic tissues in focal epilepsy.

Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

Aberrant brain network and eye gaze patterns during natural social interaction predict multi-domain social-cognitive behaviors in girls with fragile X syndrome.

Girls with fragile X syndrome (FXS) often manifest significant symptoms of avoidance, anxiety, and arousal, particularly in the context of social interaction. However, little is currently known about the associations among neurobiological, biobehavioral such as eye gaze pattern, and social-cognitive dysfunction in real-world settings. In this study, we sought to characterize brain network properties and eye gaze patterns in girls with FXS during natural social interaction. Participants included 42 girls with FXS and 31 age- and verbal IQ-matched girls (control). Portable functional near-infrared spectroscopy (fNIRS) and an eye gaze tracker were used to investigate brain network alterations and eye gaze patterns associated with social-cognitive dysfunction in girls with FXS during a structured face-to-face conversation. Compared to controls, girls with FXS showed significantly increased inter-regional functional connectivity and greater excitability within the prefrontal cortex (PFC), frontal eye field (FEF) and superior temporal gyrus (STG) during the conversation. Girls with FXS showed significantly less eye contact with their conversational partner and more unregulated eye gaze behavior compared to the control group. We also demonstrated that a machine learning approach based on multimodal data, including brain network properties and eye gaze patterns, was predictive of multiple domains of social-cognitive behaviors in girls with FXS. Our findings expand current knowledge of neural mechanisms and eye gaze behaviors underlying naturalistic social interaction in girls with FXS. These results could be further evaluated and developed as intermediate phenotypic endpoints for treatment trial evaluation in girls with FXS.