Brain Connectivity

Many researchers using many different approaches have attempted to find features discriminating between autism spectrum disorder (ASD) and typically control (TC) subjects. In this study, this attempt has been continued by analyzing global metrics of functional graphs and metrics of functional triadic interactions of the brain in the low, middle, and high-frequency bands (LFB, MFB, and HFB) of the structural graph. The graph signal processing (GSP) provided the combinatorial usage of the functional graph of resting-state fMRI and structural graph of DTI. In comparison to TCs, ASDs had significantly higher clustering coefficients in the MFB, higher efficiencies and strengths in the MFB and HFB, and lower small-world propensity in the HFB. These results show over-connectivity, more global integration, and probably decreased local specialization in ASDs compared to TCs. Triadic analysis showed that the numbers of unbalanced triads were significantly lower for ASDs in the MFB. This finding may show the reason for restricted and repetitive behavior in ASDs. Also, in the MFB and HFB, the numbers of balanced triads and the energies of triadic interactions were significantly higher and lower for ASDs, respectively. These findings may reflect the disruption of the optimum balance between functional integration and specialization. All of these results demonstrated that the significant differences between ASDs and TCs existed in the MFB and HFB of the structural graph when analyzing the global metrics of the functional graph and triadic interaction metrics. In conclusion, the results demonstrate the promising perspective of GSP for attaining discriminative features and new knowledge, especially in the case of ASD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/469268v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@100df67org.highwire.dtl.DTLVardef@4b2455org.highwire.dtl.DTLVardef@13e51d5org.highwire.dtl.DTLVardef@6e789c_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

IntroductionComputational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical connections in the brain. However, group averaged data is often used in this context and it remains unclear whether individual predictions of FC patterns using this approach can be made. Here, we assess the value of using individualized structural data for simulation of individual whole-brain FC. MethodsThe Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC) obtained from diffusion weighted imaging. Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using both phase-based (phase lag index (PLI), phase locking value (PLV)) and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze the goodness-of-fit of different metrics for individual predictions. Prediction of individual FC was compared against the prediction of group averaged FC. We further tested whether SC of a different participant could equally well predict a participants FC pattern. ResultsThe AEC provided a significantly better match between individually simulated and empirical FC than phase-based metrics. Simulations with individual SC provided higher correlations between simulated and empirical FC compared to using the group-averaged SC. However, using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants own SC. DiscussionThis work underlines the added value of FC simulations based on individual instead of group-averaged SC, and could aid in a better understanding of mechanisms underlying individual functional network trajectories in neurological disease. Impact statementIn this work, we investigated how well individual empirical functional connectivity can be simulated using the individuals structural connectivity matrix combined with neural mass modeling. Our research highlights the potential added value of using individual simulations of functional connectivity, and could aid in a better understanding of mechanisms underlying individual functional network trajectories in neurological disease. Moreover, individualized prediction of disease trajectories could enhance patient care and may provide better treatment options.

BackgroundDynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying of functional integration between brain networks. In a typical dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters through a conventional clustering criterion computed by running the algorithm repeatedly, over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline, called iterative sparse kmeans or iSparse kmeans, to analyze large dFNC data without having access to huge computational power. MethodIn iSparse kmeans, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of iSparse kmeans, we analyzed four dFNC datasets from the human connectome project (HCP). ResultsWe found that both conventional kmeans and iSparse kmeans generate similar brain dFNC states while iSparse kmeans is 27 times faster than the traditional method in finding the optimum number of clusters. We show that the results are replicated across four different datasets from HCP. ConclusionWe developed a new analytic pipeline which facilitates analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.

Resting state functional connectivity (rsFC) and resting state effective connectivity (rsEC) are two of the most common measures that can be extracted from resting state functional magnetic resonance imaging (rs-fMRI) data. RSFC is often used to indicate the statistical dependencies among different brain regions of interest, whereas rsEC describes the causal influences among them. Many studies have explored utilities of rsFC and rsEC measures for classifying psychiatric conditions. Several studies showed that rsEC were better than rsFC features for classifying major depression (Frassle et al., 2020; Geng et al., 2018) and schizophrenia ((Brodersen et al., 2014)). However, no study to-date has investigated whether rsEC is inherently better than rsFC for classifying psychiatric conditions or the impact of autocorrelation on classifying rsFC, even though autocorrelation is known to be present in rs-fMRI data. To fill these gaps, we performed a series of computational experiments, by varying the size of the network and the number of participants, to gain some insight into these two aspects of supervised classification with resting state connectivity. Contrary to what has been reported in the literature, the results from our study suggest that rsEC cannot be, in principle, better than rsFC features for classification. In fact, rsEC measures led to systematically worse classification results, compared to rsFC features. In terms of the impact of autocorrelation, we found that lag-one autocorrelation could lead to both false negative and false positive classification results for studies with a small sample size.

BackgroundLanguage dysfunction is increasingly recognized as a prevalent and early affected cognitive domain in individuals with MS. ObjectivesTo establish a network-level model of language dysfunction in MS. MethodsCognitive data and 3T functional and structural brain MRI were acquired from 54 MS patients and 54 matched healthy controls (HCs). Functional summary measures (anteriority, segregation, betweenness, within-ness) of the extended language network (ELN) were calculated and structural imaging metrics were derived. Group differences in ELN connectivity were evaluated. Associations between ELN connectivity and language performance were assessed; in the MS group, an unsupervised learning approach was used to assess relationships between multimodal neuroimaging features derived from language-related areas and performance on language tasks. ResultsThe MS group performed worse on semantic fluency and rapid automized naming tests (p<0.005) compared to HC. Regarding ELN measures, the MS group exhibited higher within-ELN connectivity than HCs (p<0.05). Principal component analysis (PCA) yielded a multimodal latent component that uniquely correlated with language performance (p<0.05). ConclusionWe identified network-level functional and structural measures to potentially characterize language dysfunction in MS. Further studies leveraging these features may reveal mechanisms and predictors of language dysfunction specific to MS.

This study proposes a novel metric called Homological Vertex Importance Profile (H-VIP), utilizing topological data analysis tool persistent homology, to analyze human brain structural and functional connectomes. Persistent homology is a useful tool for identifying topological features such as cycles and cavities within a network. The salience of persistent homology lies in the fact that it offers a global view of the network as a whole. However, it falls short in precisely determining the relative relevance of the vertices of the network that contribute to these topological features. Our aim is to quantify the contribution of each individual vertex in the formation of homological cycles and provide insight into local connectivity. Our proposed H-VIP metric captures, quantifies, and compresses connectivity information from vertices even at multiple degrees of separation and projects back onto each vertex. Using this metric, we analyze two independent datasets: structural connectomes from the Human Connectome Project and functional connectomes from the Alzheimers Disease Neuroimaging Initiative. Our findings indicate a positive correlation between various cognitive measures and H-VIP, in both anatomical and functional brain networks. Our study also demonstrates that the connectivity in the frontal lobe has a higher correlation with cognitive performance compared to the whole brain network. Furthermore, the H-VIP provides us with a metric to easily locate, quantify, and visualize potentially impaired connectivity for each subject and may have applications in the context of personalized medicine for neurological diseases and disorders.

IntroductionThe balance between excitatory and inhibitory (E/I) neural processes is a fundamental principle of brain function, and its disruption has been implicated in the pathophysiology of multiple sclerosis (MS). In vivo assessment of E/I balance has traditionally relied on electrophysiological measures, and despite the abundance of fMRI data on MS, no fMRI-based technique has so far been presented to measure E/I balance in MS. MethodsRecently, a novel MRI-based method has been introduced to estimate E/I balance by integrating functional MRI and diffusion weighted imaging data. We use this approach to study E/I balance in MS at a global (over the whole head) and at the local level of specific resting state networks affected by MS: the somatomotor and Default Mode network (DMN). Furthermore, we perform the analysis using three different atlases: the Schaefer atlas, which is functionally defined, and the Automatic Anatomical Labeling (AAL) and Desikan Killany (DK) atlas, which are defined based on structural features. ResultsOur findings reveal a significant alteration in E/I balance within the somatomotor and default mode networks when using the functionally defined Schaefer atlas, suggesting a network-specific dysfunction in MS. We also find that the E/I balance inferred within the somatomotor network correlates with motor fatigue. ConclusionsThis study demonstrates a promising framework for investigating E/I balance alterations in neurological disorders and paves the way for validation in larger cohorts.

Visual impairment is one of the early symptoms of Multiple sclerosis (MS) disease. The objective of this study is evaluating function of Lateral geniculate nucleus, which bridges visual information from retina to other higher order visual processing areas. We collected BOLD fMRI data from 19 MS and 19 control subjects by employing selective visual stimulation tasks to provoke the whole LGN, Magnocellular, Parvocellular, and Koniocellular pathways as part of LGN multilayer structure. Through statistical analysis, we observed a significant reduction (p<0.05) of the average BOLD signal from the whole LGN structure in MS group. Further investigations showed a significant reduction of BOLD signal (p<0.05) in response to Magno and Parvo stimuli compared to healthy controls that suggested a selective functional impairment emerging in primary visual pathways in MS. In summary, we showed functional abnormalities in LGN structure and its M and P subdivisions based on functional MRI.

Objective1) To assess fMRI-based functional connectivity (FC) anomalies in early multiple sclerosis (MS), 2) To determine the relation between FC changes and structural brain damage due to disease progression 3) To study the association between FC changes and cognitive and physical disability. MethodsStructural MRI and resting-state fMRI were acquired from 76 early relapsing-remitting MS patients at baseline (average disease duration 71.7 months {+/-} 63) and after five years. Ninety-four healthy controls (HCs) matched for age and sex were included at baseline. Independent component analysis (ICA) and network modelling were used to measure FC. FC variation was related to expanded disability status scale and neuropsychological outcomes. Brain and lesion volumes were quantified using standard methods. We used the 25 independent components obtained from ICA to estimate the longitudinal stability of the brain connectome as a proxy for functional reorganization over time. ResultsThe MS subjects were clinically and cognitively stable. Compared to HCs, FC abnormalities were detected within networks and in single connections in patients with early MS at baseline. Over time, FC was relatively invariable, but changes in FC were associated with progression of brain atrophy ({rho} = 0.39, p = .06). No significant relationship with clinical and cognitive measures or lesion load was detected. ConclusionPatients with MS showed evidence of altered FC in the early stages of the disease. Over time, changes in FC seem to be related to a progression of brain atrophy, which are known to precede changes in clinical and cognitive functioning.

BackgroundWhile functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. MethodsWe proposed an independent component analysis (ICA) - based approach to capture information at multiple model orders (component numbers) and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting fMRI (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine (SVM)-based classification. ResultsIn addition to consistent predictive patterns at both multiple-model orders and single model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model order 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared to other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. ConclusionsIn sum, multi-model order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact StatementMulti-model order ICA provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single model order analysis. This work expands upon and adds to the relatively new literature on resting fMRI-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

ObjectiveYouth male athletes show changes in resting-state causal brain connectivity following subacute concussion; however, little is known about how concussion alters causal brain connectivity in female youth. In this study, we compared resting-state causal brain connectivity in healthy and subconcussed females. Materials and methodsData from 11 concussed and 15 healthy control female athletes were included in this study. Five minutes of resting state eyes-closed EEG data were collected from all participants. SCAT5 data were also collected from all concussed participants. Causal connectivity was calculated from EEG source data. Network topology was evaluated using the degree assortativity coefficient, a summary statistic describing network structure of information flow between source locations. ResultsWe observed three main results: 1) a qualitative difference in the spatial pattern of the most active connections, marked by posterior connectivity shifting in the concussed group, 2) an increase in the magnitude of connectivity in the concussed group, and 3) no significant difference in degree assortativity between the concussed and control groups. ConclusionCausal connectivity changes following concussion in females do not follow the same trends reported in males. These findings suggest a potential sex difference in injury response and may have implications for recovery.

Familial Adenomatous Polyposis (FAP) is an autosomal dominant disorder caused by mutation of the APC gene presenting with numerous colorectal adenomatous polyps and a near 100% risk of colon cancer. Preliminary research findings from our group indicate that FAP patients experience significant deficits across many cognitive domains. In the current study, fMRI brain metrics in a FAP population and matched controls were used to further the mechanistic understanding of reported cognitive deficits. This research identified and characterized any possible differences in resting brain networks and associations between neural network changes and cognition from 34 participants (18 FAP patients, 16 healthy controls). Functional connectivity analysis was performed using FSL with independent component analysis (ICA) to identify functional networks. Significant differences between cases and controls were observed in 8 well-established resting state networks. With the addition of an aggregate cognitive measure as a covariate, these differences were virtually non-existent, indicating a strong correlation between cognition and brain activity at the network level. The data indicate robust and pervasive effects on functional neural network activity among FAP patients and these effects are likely involved in cognitive deficits associated with this disease.

Research on brain functional connectivity often relies on intra-individual moment-to-moment correlations of functional activity, typically using functional MRI (fMRI). Inter-individual correlations are also employed on data from fMRI and positron emission tomography (PET). Many studies have not specified tasks during scanning, keeping participants in an implicit "resting" condition. This lack of task specificity raises questions about how different tasks impact inter-individual correlation estimates. In our analysis of fMRI data from 100 unrelated participants, scanned during seven tasks and in a resting state, we calculated Regional Homogeneity (ReHo) for each task as a regional measure of brain functions. We found that changes in ReHo due to tasks were relatively small compared with its variations across brain regions. Cross-region variations of ReHo were highly correlated among tasks. Similarly, whole-brain inter-individual correlation patterns were remarkably consistent across the tasks, showing correlations greater than 0.78. Changes in inter-individual correlations between tasks were primarily driven by connectivity in the visual, somatomotor, default mode network, and the interactions between them. This subtle yet statistically significant differences in functional connectivity may be linked to specific brain regions associated with the studied tasks. Future studies should consider task design when exploring inter-individual connectivity in specific brain systems. Impact StatementInter-individual correlation is increasingly used to estimate brain connectivity, complementing intra-individual correlations in fMRI, particularly for measures like cerebral blood flow obtained via fMRI and PET. However, how task performance affects inter-individual correlations is largely unknown. This study used regional homogeneity as a summary measure of brain functions from task-based fMRI data across eight tasks. The inter-individual correlations were highly similar across tasks, indicating the underlying brain network structure can be inferred under various conditions. Subtle but statistically significant differences in connectivity estimates suggest the functional significance of this approach.

Writers cramp (WC) dystonia is a disabling brain disorder characterized by abnormal postures during writing tasks. Although abnormalities were identified in the sensorimotor, parietal, basal ganglia, and cerebellum, the network-level interactions between these brain regions and dystonia symptoms are not well understood. This study investigated the relationship between peak accelerations, an objective measure of writing dysfluency, and functional network (FN) activation in WC and healthy volunteers (HVs). Twenty WC and 22 HV performed a writing task using a kinematic software outside an MRI scanner and repeated it during functional MRI. Group independent component analysis identified 21 FNs, with left sensorimotor, superior parietal, cerebellum, and basal ganglia FNs selected for further analysis. These FNs were activated during writing and no group differences in FN activity were observed. Correlational analysis between FN activity and peak acceleration behavior revealed that reduced activity in left sensorimotor and superior parietal FNs correlated with greater writing dysfluency in WC, a pattern distinct from HVs. These findings suggest that enhanced activation of the left sensorimotor and superior parietal networks may mitigate writing dysfluency in WC. This study provides a mechanistic hypothesis to guide the development of network-based neuromodulation therapies for WC dystonia. Authors summaryA critical barrier to advancing clinical therapies for writers cramp (WC) dystonia is the limited understanding of how brain activation patterns associate with worsening disease severity. Our study addressed this gap by integrating an objective behavioral measure of WC dystonia symptom with changes in functional network activity, revealing the direction of brain activity associated with increased symptom severity. We showed that reduced activity in the left sensorimotor and superior parietal cortices correlated with greater writing dysfluency. These findings suggested that neuromodulation strategies aimed at increasing activity in these cortical regions may offer a promising avenue for developing network-based therapies for WC dystonia. Conflict of InterestAll authors report no financial disclosures or conflicts of interest relevant to this research. Authors rolesNBP: conceptualization, data collection, data analysis, statistical analysis, and manuscript writing. PJM: data analysis, and manuscript writing. MF: data analysis. MWL: statistical analysis and manuscript review. JV: study design. SWD: data analysis advice and manuscript critique. AMM: conceptualization, data analysis critique, manuscript writing and critique.

Individuals suffering from Obsessive Compulsive Disorder (OCD) and Schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain with an effective connectivity pattern exhibits small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. We analyzed neuroimaging data from 60 subjects (13healthy controls, 21 OCD and 26 SCZ) using functional near-infrared spectroscopy (fNIRS) during a color word matching Stroop Task and computed FCMs. Small-world features were evaluated using the Global Efficiency (GE), Clustering Coefficient (CC), Modularity (Q), and small-world parameter ({sigma}). The proposed pipeline in this study for fNIRS data processing demonstrates that patients with OCD and SCZ exhibit small-world features resembling random networks, as indicated by higher GE and lower CC values compared to healthy controls, implying a higher operational cost for these patients. AUTHOR SUMMARYIndividuals suffering from Obsessive Compulsive Disorder (OCD) and Schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain with an effective connectivity pattern exhibits small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. We analyzed neuroimaging data from 60 subjects (13healthy controls, 21 OCD and 26 SCZ) using functional near-infrared spectroscopy (fNIRS) during a color word matching Stroop Task and computed FCMs. Small-world features were evaluated using the Global Efficiency (GE), Clustering Coefficient (CC), Modularity (Q), and small-world parameter ({sigma}). The proposed pipeline in this study for fNIRS data processing demonstrates that patients with OCD and SCZ exhibit small-world features resembling random networks, as indicated by higher GE and lower CC values compared to healthy controls, implying a higher operational cost for these patients.

Connectome analysis of neuroimaging data is a rapidly expanding field to identify disease specific biomarkers. Structural diffusion MRI connectivity has been useful in individuals with radiological features of small vessel disease, such as white matter hyperintensities. Global efficiency, a network metric calculated from the structural connectome, is an excellent predictor of cognitive decline. To dissect the biological underpinning of these changes, animal models are required. We tested whether the structural connectome is altered in a mouse model of vascular cognitive impairment. White matter damage was more pronounced by 6 compared to 3 months. Global efficiency remained intact, but the visual association cortex exhibited increased structural connectivity with other brain regions. Exploratory resting state functional MRI connectivity analysis revealed diminished default mode network activity in the model compared to shams. Further perturbations were observed in a primarily cortical hub and the retrosplenial and visual cortices, and the hippocampus were the most affected nodes. Behavioural deficits were observed in the cued water maze, supporting the suggestion that the visual and spatial memory networks are affected. We demonstrate specific circuitry is rendered vulnerable to vascular stress in the mouse, and the model will be useful to examine pathophysiological mechanisms of small vessel disease. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

IntroductionThere is a lack of research in the existing literature when it comes to analyzing the dynamics of resting-state functional magnetic resonance imaging to understand the underlying mechanisms of isolated rapid eye movement sleep behavior disorder (iRBD). This study aims to contribute to our understanding of abnormalities in brain network dynamics in iRBD and their association with alpha-synucleinopathy. Additionally, I employed graph theoretical metrics to obtain a topological insight into the brain network of iRBD. MethodsResting-state fMRI data from 55 iRBD patients and 97 healthy controls (HCs) were utilized. A sliding window approach, functional connectivity analysis, and graph theory analysis were applied to the data. I calculated the mean, standard deviation, skewness, and kurtosis of the time series for both dynamic functional connectivity (dFC) and four graph metrics (clustering coefficient, global efficiency, assortativity coefficients, and eigenvector centrality). Subsequently, I compared the those metrices between iRBDs and HCs. Relationships between clinical scales and abnormal dFC were assessed using a general linear model. ResultsiRBD patients exhibited abnormal mean dFC, particularly in the default mode network, sensorimotor network, basal ganglia network, and cerebellum. Kurtosis of dFC revealed abnormalities between the middle temporal gyrus and cerebellum. Group differences were also observed in the mean eigenvector centrality of the precentral gyrus and thalamus. ConclusionThe mean of dFC identified impairments putatively in movement functions and various compensatory mechanisms. Moreover, mean eigenvector centrality revealed topological changes in motor-related network in iRBDs. The use of kurtosis as a potential index for extracting dynamic information may provide additional insights into pathophysiology in iRBDs.

Among the cognitive symptoms that are associated with Parkinsons disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 21 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the group-specific brain network states that were dominant during the task. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, HC displayed a significant functional network state that involved typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). Both group- and subject-level analyses showed that this network was less present in PD to the benefit of other networks involving lateralized temporal and insular components. The presence of this prefrontal network was associated with decreased reaction time. In the gamma band, two networks (fronto-cingulate and fronto-temporal) followed one another in HC, while 3 partially overlapping networks that included fronto-temporal, fronto-occipital and cross-hemispheric temporal connections were found in PD. At the subject-level, differences between PD and HC were less marked. Altogether, this study showed that the functional brain networks observed during CAC and their temporal changes were different in PD patients as compared to HC, and that these differences partially relate to behavioral changes. This study also highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond. HighlightsO_LICognitive action control is associated with dynamic functional networks C_LIO_LIPrefrontal and cingulate beta connectivity are prominent in healthy controls C_LIO_LIPD patients have different dynamic networks in which prefrontal nodes are absent C_LIO_LIThe occurrence of prefrontal beta networks was associated with a decreased reaction time C_LIO_LIFunctional networks in the gamma band were temporally organized in HC, but overlapping in PD patients C_LI

BackgroundAssessment of the level of awareness of people with disorders of consciousness (DOC) is clinically challenging, motivating several studies to combine brain imaging with machine learning to improve this process. While this work has shown promise, it has limited clinical utility, as misdiagnosis of DOC patients is relatively high. As machine learning algorithms rely on accurately labelled data, any error in diagnosis will be learned by the algorithm, resulting in an equally limited diagnostic tool. The goal of the present study is to overcome this problem by stratifying patients, not by diagnosis, but by their capacity to perform volitional tasks during functional magnetic resonance imaging (fMRI) scanning. MethodsA total of 71 patients were assessed for inclusion. They were excluded for the final analysis if they had large focal brain damage, excessive head motion during scanning, or suboptimal MRI preprocessing. Patients underwent both resting-state and task-based fMRI scanning. Univariate fMRI analysis was performed to determine if an individual patient had brain activity consistent with having retained volitional capacity (VC). Differences in resting brain network connectivity between patients with VC and patients without volitional capacity (non-VC) were measured. Connectivity data was then entered as input to a deep learning framework. We used a deep graph convolutional neural network (DGCNN) on connectivity data to identify a specific brain network that most significantly differentiates patients. FindingsWe included 30 patients in our final analysis. Univariate analysis revealed that 13 patients displayed signs of VC, while 17 did not. We found that resting-state connectivity between frontoparietal control and salience network was significantly different between VC and non-VC patients (T(28) = 3.347, p = 0.0023, Bonferroni corrected p = 0.042). Furthermore, we found that using frontoparietal control network connectivity as input to the DGCNN resulted in the best classification performance (test accuracy = 0.85; ROC AUC = 0.92). InterpretationWe found that the DGCNN performed best at discriminating between patients with VC when using only the frontoparietal control network as input to the model. The use of this deep learning method is a significant advance since its inherent flexibility permits the inclusion of both whole-brain and network-specific properties as input, allowing us to classify patients as either having or not having VC. This inclusion of multi-scale inputs (e.g. whole-brain and network-level) facilitates model interpretability and increases our understanding of the neurobiology of DOC. The results propose that the integrity of frontoparietal control network, a brain network well known to play a key role in executive functions and cognitive control, is essential for volitional capacity preservation in patients with DOC. The study also lays groundwork for development of a biomarker to aid in the diagnosis of DOC patients. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSDisorders of consciousness (DOC) are a group of severe brain disorders characterised by damage to the neural systems underlying wakefulness and awareness. DOC are often caused by traumatic brain injury, hypoxia, or neurodegenerative diseases. The motor and cognitive impairments in DOC patients make providing an accurate diagnosis very challenging. Diagnosis is primarily made at the bedside by assessing a patients response to motor commands.

ObjectiveParkinsons Disease (PD) is a neurological disorder characterized by impaired postural control (PC) and balance issues. To date, few studies have explored the relationship between brain activity and responses during specific tasks designed to challenge balance in individuals with PD. Our exploratory research employs an innovative paradigm to assess PC by integrating virtual reality (VR) and electroencephalography (EEG). ApproachIn the study, 20 individuals diagnosed with PD who self-reported postural instability participated in the BioVRSea paradigm. This paradigm tested their PC using visuomotor stimuli and collected EEG signals to assess brain responses throughout the experiment. The results of the Parkinsons group were compared with those of 22 age-matched healthy controls (CTR). From functional connectivity between brain regions, we employed novel techniques that use clustering algorithms to identify brain network states (BNSs). These BNSs define brain dynamics and can be compared with resting-state networks (RSNs) to further explore and identify neural alterations in individuals with PD. Main ResultsSix distinct BNSs were identified, with the dorsal attention network (DAN) dominant in five states. A significant reduction in the occurrence of BNS2 (p=0.005) was observed in PD patients during the PRE movement and visuomotor (MOV) phases compared to CTR. This reduced occurrence of BNS2 suggests impaired visuomotor integration in PD patients during PC tasks. DAN dominance highlights its crucial role in maintaining attentional control during the task. SignificanceThe findings of this study highlight the potential of using brain dynamics as a biomarker of neural dysfunction in PD, especially during specific PC tasks. Altered BNSs, particularly in networks associated with attention and sensorimotor integration, reveal key neural deficits related to PD.