Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

Cortisol awakening response prompts dynamic reconfiguration of brain networks in emotional and executive functioning

Emotion and cognition involve an intricate crosstalk of neural and endocrine systems that support dynamic reallocation of neural resources and optimal adaptation for upcoming challenges, an active process analogous to allostasis. As a hallmark of human endocrine activity, the cortisol awakening response (CAR) is recognized to play a critical role in proactively modulating emotional and executive functions. Yet, the underlying mechanisms of such proactive effects remain elusive. By leveraging pharmacological neuroimaging and hidden Markov modeling of brain state dynamics, we show that the CAR proactively modulates rapid spatiotemporal reconfigurations (state) of large-scale brain networks involved in emotional and executive functions. Behaviorally, suppression of CAR proactively impaired performance of emotional discrimination but not working memory (WM), while individuals with higher CAR exhibited better performance for both emotional and WM tasks. Neuronally, suppression of CAR led to a decrease in fractional occupancy and mean lifetime of task-related brain states dominant to emotional and WM processing. Further information-theoretic analyses on sequence complexity of state transitions revealed that a suppressed or lower CAR led to higher transition complexity among states primarily anchored in visual-sensory and salience networks during emotional task. Conversely, an opposite pattern of transition complexity was observed among states anchored in executive control and visuospatial networks during WM, indicating that CAR distinctly modulates neural resources allocated to emotional and WM processing. Our findings establish a causal link of CAR with brain network dynamics across emotional and executive functions, suggesting a neuroendocrine account for CAR proactive effects on human emotion and cognition.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

Catecholaminergic Modulation of Large-Scale Network Dynamics Is Tied to the Reconfiguration of Corticostriatal Connectivity

Large-scale brain network function is critical for healthy cognition, yet links between such network function, neurochemistry, and smaller-scale neurocircuitry are unclear. Here, we evaluated 59 healthy individuals using resting-state fMRI to determine how network-level temporal dynamics were impacted by two well-characterized pharmacotherapies targeting catecholamines: methylphenidate (20 mg) and haloperidol (2 mg)-administered via randomized, double-blind, placebo-controlled design. Network temporal dynamic changes were tested for links with drug-induced alterations in complex corticostriatal connections as this circuit is a primary site of action for both drugs. Methylphenidate increased time in the default mode network state (DMN p < 0.001) and dorsal attention network state (DAN p < 0.001) and reduced time in the frontoparietal network state (p < 0.01). Haloperidol increased time in a sensory motor-DMN state (p < 0.01). The magnitude of change in network dynamics induced by methylphenidate vs. placebo correlated with the magnitude of methylphenidate-induced rearrangement of complex corticostriatal connectivity (R = 0.32, p = 0.014). Haloperidol did not alter complex corticostriatal connectivity. Methylphenidate enhanced time in network states involved in internal and external attention (DMN and DAN, respectively), aligning with methylphenidate's established role in attention. Methylphenidate also significantly changed complex corticostriatal connectivity by altering the relative strength between multiple corticostriatal connections, indicating that methylphenidate may shift which corticostriatal connections are prioritized relative to others. Findings show that these corticostriatal circuit changes are linked with large-scale network temporal dynamics. Collectively, these findings provide a deeper understanding of large-scale network function, set a stage for mechanistic understanding of network engagement, and provide useful information to guide medication use based on network-level effects. Trial Registration: Registry name: ClinicalTrials.gov; URL: Brain Networks and Addiction Susceptibility-Full Text View-ClinicalTrials.gov; URL Plain text: https://classic.clinicaltrials.gov/ct2/show/NCT01924468; Identifier: NCT01924468.

Impaired brain ability of older adults to transit and persist to latent states with well-organized structures at wakeful rest

The intrinsic brain functional network organization continuously changes with aging. By integrating spatial and temporal information, the process of how brain networks temporally reconfigure and remain well-organized spatial structure largely reflects the brain function, thereby holds the potential to capture its age-related declines. In this study, we examined the spatiotemporal brain dynamics from resting-state functional Magnetic Resonance Imaging (fMRI) data of healthy young and older adults using a Hidden Markov Model (HMM). Six brain states were generated by HMM, with the young group showing higher fractional occupancy and mean dwell time in states 1, 3, and 4 (SY1, SY2 and SY3), and the older group in states 2, 5, and 6 (SO1, SO2 and SO3). Importantly, comparisons of transition probabilities revealed that the older group showed a reduced brain ability to transition into states dominated by the younger group, as well as a diminished capacity to persist in them. Moreover, graph analysis revealed that these young-specific states exhibited higher modularity and k-coreness. Collectively, these findings suggested that the older group showed impaired brain ability of both transition into and sustain well spatially organized states. This emphasized that the temporal changes in brain state organization, rather than its static mode, could be a key biomarker for detecting age-related functional decline. These insights may pave the way for targeted interventions aimed at mitigating cognitive decline in the aging population.

Schizophrenia, a disease of impaired dynamic metabolic flexibility: A new mechanistic framework

Schizophrenia is a chronic, neurodevelopmental disorder with unknown aetiology and pathophysiology that emphasises the role of neurotransmitter imbalance and abnormalities in synaptic plasticity. The currently used pharmacological approach, the antipsychotic drugs, which have limited efficacy and an array of side-effects, have been developed based on the neurotransmitter hypothesis. Recent research has uncovered systemic and brain abnormalities in glucose and energy metabolism, focusing on altered glycolysis and mitochondrial oxidative phosphorylation. These findings call for a re-conceptualisation of schizophrenia pathophysiology as a progressing bioenergetics failure. In this review, we provide an overview of the fundamentals of brain bioenergetics and the changes identified in schizophrenia. We then propose a new explanatory framework positing that schizophrenia is a disease of impaired dynamic metabolic flexibility, which also reconciles findings of abnormal glucose and energy metabolism in the periphery and in the brain along the course of the disease. This evidence-based framework and testable hypothesis has the potential to transform the way we conceptualise this debilitating condition and to develop novel treatment approaches.

EEG resting state alpha dynamics predict an individual's vulnerability to auditory hallucinations

Task-free brain activity exhibits spontaneous fluctuations between functional states, characterized by synchronized activation patterns in distributed resting-state (RS) brain networks. The temporal dynamics of the networks' electrophysiological signatures reflect individual variations in brain activity and connectivity linked to mental states and cognitive functions and can predict or monitor vulnerability to develop psychiatric or neurological disorders. In particular, RS alpha fluctuations modulate perceptual sensitivity, attentional shifts, and cognitive control, and could therefore reflect a neural correlate of increased vulnerability to sensory distortions, including the proneness to hallucinatory experiences. We recorded 5 min of RS EEG from 33 non-clinical individuals varying in hallucination proneness (HP) to investigate links between task-free alpha dynamics and vulnerability to hallucinations. To this end, we used a dynamic brain state allocation method to identify five recurrent alpha states together with their spatiotemporal dynamics and most active brain areas through source reconstruction. The dynamical features of a state marked by activation in somatosensory, auditory, and posterior default-mode network areas predicted auditory and auditory-verbal HP, but not general HP, such that individuals with higher vulnerability to auditory hallucinations spent more time in this state. The temporal dynamics of spontaneous alpha activity might reflect individual differences in attention to internally generated sensory events and altered auditory perceptual sensitivity. Altered RS alpha dynamics could therefore instantiate a neural marker of increased vulnerability to auditory hallucinations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10093-1.

Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models

BACKGROUD

Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database.

RESULTS

Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function.

CONCLUSIONS

This study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade

BACKGROUND AND HYPOTHESIS

Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years.

STUDY DESIGN

The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade.

STUDY RESULTS

Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders.

CONCLUSIONS

We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

Revisiting alpha resting state dynamics underlying hallucinatory vulnerability: Insights from Hidden semi-Markov Modeling

BACKGROUND

Resting state (RS) brain activity is inherently non-stationary. Hidden semi-Markov Models (HsMM) can characterize continuous RS data as a sequence of recurring and distinct brain states along with their spatio-temporal dynamics.

NEW METHOD

Recent explorations suggest that HsMM state dynamics in the alpha frequency band link to auditory hallucination proneness (HP) in non-clinical individuals. The present study aimed to replicate these findings to elucidate robust neural correlates of hallucinatory vulnerability. Specifically, we aimed to investigate the reproducibility of HsMM states across different data sets and within-data set variants as well as the replicability of the association between alpha brain state dynamics and HP.

RESULTS

We found that most brain states are reproducible in different data sets, confirming that the HsMM characterized robust and generalizable EEG RS dynamics on a sub-second timescale. Brain state topographies and temporal dynamics of different within-data set variants showed substantial similarities and were robust against reduced data length and number of electrodes. However, the association with HP was not directly reproducible across data sets.

COMPARISON WITH EXISTING METHODS

The HsMM optimally leverages the high temporal resolution of EEG data and overcomes time-domain restrictions of other state allocation methods.

CONCLUSION

The results indicate that the sensitivity of brain state dynamics to capture individual variability in HP may depend on the data recording characteristics and individual variability in RS cognition, such as mind wandering. Future studies should consider that the order in which eyes-open and eyes-closed RS data are acquired directly influences an individual's attentional state and generation of spontaneous thoughts, and thereby might mediate the link to hallucinatory vulnerability.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

Abnormal dynamic functional connectivity and topological properties of cerebellar network in male obstructive sleep apnea

PURPOSE

To investigate dynamic functional connectivity (dFC) within the cerebellar-whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients.

METHODS

Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum-whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed.

RESULTS

Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep.

CONCLUSION

Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high-order and low-level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA.

Multivariate brain-behaviour associations in psychiatric disorders

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

Neurophysiological avenues to better conceptualizing adaptive cognition

We delve into the human brain's remarkable capacity for adaptability and sustained cognitive functioning, phenomena traditionally encompassed as executive functions or cognitive control. The neural underpinnings that enable the seamless navigation between transient thoughts without detracting from overarching goals form the core of our article. We discuss the concept of "metacontrol," which builds upon conventional cognitive control theories by proposing a dynamic balancing of processes depending on situational demands. We critically discuss the role of oscillatory processes in electrophysiological activity at different scales and the importance of desynchronization and partial phase synchronization in supporting adaptive behavior including neural noise accounts, transient dynamics, phase-based measures (coordination dynamics) and neural mass modelling. The cognitive processes focused and neurophysiological avenues outlined are integral to understanding diverse psychiatric disorders thereby contributing to a more nuanced comprehension of cognitive control and its neural bases in both health and disease.

Low-frequency rTMS induces modifications in cortical structural connectivity - functional connectivity coupling in schizophrenia patients with auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are distinctive clinical manifestations of schizophrenia. While low-frequency repetitive transcranial magnetic stimulation (rTMS) has demonstrated potential in mitigating AVH, the precise mechanisms by which it operates remain obscure. This study aimed to investigate alternations in structural connectivity and functional connectivity (SC-FC) coupling among schizophrenia patients with AVH prior to and following treatment with 1 Hz rTMS that specifically targets the left temporoparietal junction. Initially, patients exhibited significantly reduced macroscopic whole brain level SC-FC coupling compared to healthy controls. Notably, SC-FC coupling increased significantly across multiple networks, including the somatomotor, dorsal attention, ventral attention, frontoparietal control, and default mode networks, following rTMS treatment. Significant alternations in SC-FC coupling were noted in critical nodes comprising the somatomotor network and the default mode network, such as the precentral gyrus and the ventromedial prefrontal cortex, respectively. The alternations in SC-FC coupling exhibited a correlation with the amelioration of clinical symptom. The results of our study illuminate the intricate relationship between white matter structures and neuronal activity in patients who are receiving low-frequency rTMS. This advances our understanding of the foundational mechanisms underlying rTMS treatment for AVH.

Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia

Introduction

Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features.

Methods

We apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity.

Results

Importantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states.

Conclusion

We present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.

On the variability of dynamic functional connectivity assessment methods

BACKGROUND

Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods.

METHODS

We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity.

RESULTS

Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages.

CONCLUSIONS

Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.

Functional Connectivity Biomarkers in Schizophrenia

Schizophrenia is a debilitating neuropsychiatric disorder that affects approximately 1% of the population and poses a major public health problem. Despite over 100 years of study, the treatment for schizophrenia remains limited, partially due to the lack of knowledge about the neural mechanisms of the illness and how they relate to symptoms. The US Food and Drug Administration (FDA) and the National Institute of Health (NIH) have provided seven biomarker categories that indicate causes, risks, and treatment responses. However, no FDA-approved biomarkers exist for psychiatric conditions, including schizophrenia, highlighting the need for biomarker development. Over three decades, magnetic resonance imaging (MRI)-based studies have identified patterns of abnormal brain function in schizophrenia. By using functional connectivity (FC) data, which gauges how brain regions interact over time, these studies have differentiated patient subgroups, predicted responses to antipsychotic medication, and correlated neural changes with symptoms. This suggests FC metrics could serve as promising biomarkers. Here, we present a selective review of studies leveraging MRI-derived FC to study neural alterations in schizophrenia, discuss how they align with FDA-NIH biomarkers, and outline the challenges and goals for developing FC biomarkers in schizophrenia.

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

Brain signatures of chronic gut inflammation

Gut inflammation is thought to modify brain activity and behaviour via modulation of the gut-brain axis. However, how relapsing and remitting exposure to peripheral inflammation over the natural history of inflammatory bowel disease (IBD) contributes to altered brain dynamics is poorly understood. Here, we used electroencephalography (EEG) to characterise changes in spontaneous spatiotemporal brain states in Crohn's Disease (CD) (n = 40) and Ulcerative Colitis (UC) (n = 30), compared to healthy individuals (n = 28). We first provide evidence of a significantly perturbed and heterogeneous microbial profile in CD, consistent with previous work showing enduring and long-standing dysbiosis in clinical remission. Results from our brain state assessment show that CD and UC exhibit alterations in the temporal properties of states implicating default-mode network, parietal, and visual regions, reflecting a shift in the predominance from externally to internally-oriented attentional modes. We investigated these dynamics at a finer sub-network resolution, showing a CD-specific and highly selective enhancement of connectivity between the insula and medial prefrontal cortex (mPFC), regions implicated in cognitive-interoceptive appraisal mechanisms. Alongside overall higher anxiety scores in CD, we also provide preliminary support to suggest that the strength of chronic interoceptive hyper-signalling in the brain co-occurs with disease duration. Together, our results demonstrate that a long-standing diagnosis of CD is, in itself, a key factor in determining the risk of developing altered brain network signatures.

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

Network representation has been an incredibly useful concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introduces causal centrality for DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion of intervention in graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against several graph-theoretical centralities. The results showed that the subcortical structures of the eDMN were more causally central than the cortical regions, even though the graph-theoretical centralities unanimously favored the latter. Importantly, model comparison revealed that only the pattern of causal centrality was causally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality-to study causal models of other neurotypical and pathological functional networks-are discussed, and some future lines of research are outlined.

Altered brain dynamic in major depressive disorder: state and trait features

Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity.

The dynamic functional connectivity fingerprint of high-grade gliomas

Resting state fMRI has been used in many studies to investigate the impact of brain tumours on functional connectivity (FC). However, these studies have so far assumed that FC is stationary, disregarding the fact that the brain fluctuates over dynamic states. Here we utilised resting state fMRI data from 33 patients with high-grade gliomas and 33 healthy controls to examine the dynamic interplay between resting-state networks and to gain insights into the impact of brain tumours on functional dynamics. By employing Hidden Markov Models, we demonstrated that functional dynamics persist even in the presence of a high-grade glioma, and that patients exhibited a global decrease of connections strength, as well as of network segregation. Furthermore, through a multivariate analysis, we demonstrated that patients' cognitive scores are highly predictive of pathological dynamics, thus supporting our hypothesis that functional dynamics could serve as valuable biomarkers for better understanding the traits of high-grade gliomas.

Aberrant global and local dynamic properties in schizophrenia with instantaneous phase method based on Hilbert transform

BACKGROUND

Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties.

METHODS

We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features.

RESULTS

We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features.

CONCLUSIONS

SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.

Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology

The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.

Reconfiguration of brain network dynamics underlying spatial deficits in subjective cognitive decline

Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.

Explainable Fuzzy Clustering Framework Reveals Divergent Default Mode Network Connectivity Dynamics in Schizophrenia

Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics. We apply our framework for schizophrenia (SZ) default mode network analysis, identifying 5 states and characterizing those states with a new explainability approach. While also showing that features typically used in hard clustering can be extracted in our framework, we present a variety of unique features to quantify state dynamics and identify effects of SZ upon network dynamics. We further uncover relationships between symptom severity and interactions of the precuneus with the anterior and posterior cingulate cortex. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.

A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia

Schizophrenia is a severe brain disorder with serious symptoms including delusions, disorganized speech, and hallucinations that can have a long-term detrimental impact on different aspects of a patient's life. It is still unclear what the main cause of schizophrenia is, but a combination of altered brain connectivity and structure may play a role. Neuroimaging data has been useful in characterizing schizophrenia, but there has been very little work focused on voxel-wise changes in multiple brain networks over time, despite evidence that functional networks exhibit complex spatiotemporal changes over time within individual subjects. Recent studies have primarily focused on static (average) features of functional data or on temporal variations between fixed networks; however, such approaches are not able to capture multiple overlapping networks which change at the voxel level. In this work, we employ a deep residual convolutional neural network (CNN) model to extract 53 different spatiotemporal networks each of which captures dynamism within various domains including subcortical, cerebellar, visual, sensori-motor, auditory, cognitive control, and default mode. We apply this approach to study spatiotemporal brain dynamism at the voxel level within multiple functional networks extracted from a large functional magnetic resonance imaging (fMRI) dataset of individuals with schizophrenia (N = 708) and controls (N = 510). Our analysis reveals widespread group level differences across multiple networks and spatiotemporal features including voxel-wise variability, magnitude, and temporal functional network connectivity in widespread regions expected to be impacted by the disorder. We compare with typical average spatial amplitude and show highly structured and neuroanatomically relevant results are missed if one does not consider the voxel-wise spatial dynamics. Importantly, our approach can summarize static, temporal dynamic, spatial dynamic, and spatiotemporal dynamics features, thus proving a powerful approach to unify and compare these various perspectives. In sum, we show the proposed approach highlights the importance of accounting for both temporal and spatial dynamism in whole brain neuroimaging data generally, shows a high-level of sensitivity to schizophrenia highlighting global but spatially unique dynamics showing group differences, and may be especially important in studies focused on the development of brain-based biomarkers.

Increased structural connectivity in high schizotypy

The link between brain structural connectivity and schizotypy was explored in two healthy participant cohorts, collected at two different neuroimaging centres, comprising 140 and 115 participants, respectively. The participants completed the Schizotypal Personality Questionnaire (SPQ), through which their schizotypy scores were calculated. Diffusion-MRI data were used to perform tractography and to generate the structural brain networks of the participants. The edges of the networks were weighted with the inverse radial diffusivity. Graph theoretical metrics of the default mode, sensorimotor, visual, and auditory subnetworks were derived and their correlation coefficients with the schizotypy scores were calculated. To the best of our knowledge, this is the first time that graph theoretical measures of structural brain networks are investigated in relation to schizotypy. A positive correlation was found between the schizotypy score and the mean node degree and mean clustering coefficient of the sensorimotor and the default mode subnetworks. The nodes driving these correlations were the right postcentral gyrus, the left paracentral lobule, the right superior frontal gyrus, the left parahippocampal gyrus, and the bilateral precuneus, that is, nodes that exhibit compromised functional connectivity in schizophrenia. Implications for schizophrenia and schizotypy are discussed.

Metastability, fractal scaling, and synergistic information processing: What phase relationships reveal about intrinsic brain activity

Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.

Quantifying dynamic facial expressions under naturalistic conditions

Facial affect is expressed dynamically - a giggle, grimace, or an agitated frown. However, the characterisation of human affect has relied almost exclusively on static images. This approach cannot capture the nuances of human communication or support the naturalistic assessment of affective disorders. Using the latest in machine vision and systems modelling, we studied dynamic facial expressions of people viewing emotionally salient film clips. We found that the apparent complexity of dynamic facial expressions can be captured by a small number of simple spatiotemporal states - composites of distinct facial actions, each expressed with a unique spectral fingerprint. Sequential expression of these states is common across individuals viewing the same film stimuli but varies in those with the melancholic subtype of major depressive disorder. This approach provides a platform for translational research, capturing dynamic facial expressions under naturalistic conditions and enabling new quantitative tools for the study of affective disorders and related mental illnesses.

Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures

Objective

A number of studies in adults and children with generalized tonic-clonic seizure (GTCS) have reported the alterations in morphometry, functional activity, and functional connectivity (FC) in the thalamus. However, the neural mechanisms underlying the alterations in the thalamus of patients with GTCS are not well understood, particularly in children. The aim of the current study was to explore the temporal properties of functional pathways connecting thalamus in children with GTCS.

Methods

Here, we recruited 24 children with GTCS and 36 age-matched healthy controls. Static and dynamic FC approaches were used to evaluate alterations in the temporal variability of thalamo-cortical networks in children with GTCS. The dynamic effective connectivity (dEC) method was also used to evaluate the directions of the fluctuations in effective connectivity. In addition, the relationships between the dynamic properties and clinical features were assessed.

Results

The static FC analysis presented significantly decreased connectivity patterns between the bilateral thalamus and between the thalamus and right inferior temporal gyrus. The dynamic connectivity analysis found decreased FC variability in the thalamo-cortical network of children with epilepsy. Dynamic EC analyses identified increased connectivity variability from the frontal gyrus to the bilateral thalamus, and decreased connectivity variability from the right thalamus to the left thalamus and from the right thalamus to the right superior parietal lobe. In addition, correlation analysis revealed that both static FC and connectivity temporal variability in the thalamo-cortical network related to the clinical features (epilepsy duration and epilepsy onset time).

Significance

Our findings of both increased and decreased connectivity variability in the thalamo-cortical network imply a dynamic restructuring of the functional pathways connecting the thalamus in children with GTCS. These alterations in static and temporal dynamic pathways connecting the bilateral thalamus may extend our understanding of the neural mechanisms underlying the GTCS in children.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Uncovering hidden resting state dynamics: A new perspective on auditory verbal hallucinations

In the absence of sensory stimulation, the brain transits between distinct functional networks. Network dynamics such as transition patterns and the time the brain stays in each network link to cognition and behavior and are subject to much investigation. Auditory verbal hallucinations (AVH), the temporally fluctuating unprovoked experience of hearing voices, are associated with aberrant resting state network activity. However, we lack a clear understanding of how different networks contribute to aberrant activity over time. An accurate characterization of latent network dynamics and their relation to neurocognitive changes necessitates methods that capture the sub-second temporal fluctuations of the networks' functional connectivity signatures. Here, we critically evaluate the assumptions and sensitivity of several approaches commonly used to assess temporal dynamics of brain connectivity states in M/EEG and fMRI research, highlighting methodological constraints and their clinical relevance to AVH. Identifying altered brain connectivity states linked to AVH can facilitate the detection of predictive disease markers and ultimately be valuable for generating individual risk profiles, differential diagnosis, targeted intervention, and treatment strategies.

Development of a novel task-based functional magnetic resonance imaging phantom based on a bubble-compression approach

PURPOSE

Phantoms used in previous functional magnetic resonance imaging (fMRI) studies have drawbacks, such as a complicated circuit and equipment use, a single signal-change rate, and T₂ * values that do not correspond to those of living human brains. We aimed to develop a phantom for use in task-based fMRI studies (gradient-echo echo-planar imaging; GRE-EPI) with bioequivalent T₁ and T₂ * values, using an innovative method to control the rate of signal change.

METHODS

A gel phantom with T₁ and T₂ * values equivalent to that of the living brain gray matter was fixed in a 150 mm diameter container, with five holes, each of which could hold a 30-mL syringe. The gel phantom contained microscopic air bubbles; this made it possible to control the percent signal change by injector-induced water pressure changes. Using this phantom, we investigated the percent signal change, derived an equation that can approximately reproduce an arbitrary percent signal change, compared different gel phantom samples, investigated the change in relaxation time and bubble size during signal change, and assessed the change in values in each sample over time.

RESULTS

The relaxation time of the gel phantom was similar to the literature values for gray matter. The percent signal change achieved was approximately 0-13.51% and was dependent on the water pressure change. The derived equation was y = 0.000008x3 - 0.000771x2 + 0.034222x - 0.026054, with y being the percent signal change and x being the pressure in kPa; the reproducibility was high. No significant difference was detected among samples of gray matter gel phantoms (P > 0.05). The change in the rate of signal change with the change in water pressure was due to the change in T₂ * value with the change in bubble size. With pressure increasing from 0 to 151.7 kPa, the T₂ * value increased from 52 ms to 85 ms. The newly developed gel phantom was stable for 60 days, but its bubble size changed after 21 days.

CONCLUSION

We developed a novel phantom for use in fMRI, which could reproduce minute signal changes similar to the blood-oxygen-level-dependent effect and with bioequivalent T₁ and T₂ * values, and used an innovative method to control the percent signal change by compressing the air contained in the phantom for validation of fMRI using GRE-EPI. This phantom reproduced the percent signal change due to changes in T₂ * values, which is very similar to scanning a human body. This phantom is expected to be a powerful tool for advancing the study of task-based fMRI. This article is protected by copyright. All rights reserved.

Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task

OBJECTIVE

Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information.

APPROACH

A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography (EEG) data from patients with schizophrenia and cognitively healthy controls.

MAIN RESULTS

The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations.

SIGNIFICANCE

Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.

Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects, and we acquired resting state fMRI and diffusion tensor imaging. We used the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal component analysis. Of the 28 possible region pairs, 9 showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes, with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS correlated with several aspects of the Positive and Negative Syndrome Scale, with higher multimodal connectivity associated with outward-directed (externalizing) behavior and lower multimodal connectivity related to psychosis per se. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.

Age-associated network controllability changes in first episode drug-naïve schizophrenia

BACKGROUND

Recent neuroimaging studies revealed dysregulated neurodevelopmental, or/and neurodegenerative trajectories of both structural and functional connections in schizophrenia. However, how the alterations in the brain's structural connectivity lead to dynamic function changes in schizophrenia with age remains poorly understood.

METHODS

Combining structural magnetic resonance imaging and a network control theory approach, the white matter network controllability metric (average controllability) was mapped from age 16 to 60 years in 175 drug-naïve schizophrenia patients and 155 matched healthy controls.

RESULTS

Compared with controls, the schizophrenia patients demonstrated the lack of age-related decrease on average controllability of default mode network (DMN), as well as the right precuneus (a hub region of DMN), suggesting abnormal maturational development process in schizophrenia. Interestingly, the schizophrenia patients demonstrated an accelerated age-related decline of average controllability in the subcortical network, supporting the neurodegenerative model. In addition, compared with controls, the lack of age-related increase on average controllability of the left inferior parietal gyrus in schizophrenia patients also suggested a different pathway of brain development.

CONCLUSIONS

By applying the control theory approach, the present study revealed age-related changes in the ability of white matter pathways to control functional activity states in schizophrenia. The findings supported both the developmental and degenerative hypotheses of schizophrenia, and suggested a particularly high vulnerability of the DMN and subcortical network possibly reflecting an illness-related early marker for the disorder.

Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder

Major depressive disorder (MDD) is associated with increased suicidality, and it's still challenging to identify suicide in clinical practice. Although suicide attempt (SA) is the most relevant precursor with multiple functional abnormalities reported from neuroimaging studies, little is known about how the spontaneous transient activated patterns organize and coordinate brain networks underlying SA. Thus, we obtained resting-state magnetoencephalography data for two MDD subgroups of 44 non-suicide patients and 34 suicide-attempted patients, together with 49 matched health-controls. For the source-space signals, Hidden Markov Model (HMM) helped to capture the sub-second dynamic activity via a hidden sequence of finite number of states. Temporal parameters and spectral activation were acquired for each state and then compared between groups. Here, HMM states characterized the spatiotemporal signatures of eight networks. The activity of suicide attempters switches more frequently into the fronto-temporal network, as the time spent occupancy of fronto-temporal state is increased and interval time is decreased compared with the non-suicide patients. Moreover, these changes are significantly correlated with Nurses' Global Assessment of Suicide Risk scores. Suicide attempters also exhibit increased state-wise activations in the theta band (4-8 Hz) in the posterior default mode network centered on posterior cingulate cortex, which can't be detected in the static spectral analysis. These alternations may disturb the time allocations of cognitive control regulations and cause inflexible decision making to SA. As the better sensitivity of dynamic study in reflecting SA diathesis than the static is validated, dynamic stability could serve as a potential neuronal marker for SA.

Unveiling whole-brain dynamics in normal aging through Hidden Markov Models

During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs‐fMRI) data showing a link between aging and the increase of between‐networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data‐driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs‐fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph‐based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process.

Entrainment of Network Activity by Closed-Loop Microstimulation in Healthy Ambulatory Rats

As our understanding of volitional motor function increases, it is clear that complex movements are the result of the interactions of multiple cortical regions rather than just the output properties of primary motor cortex. However, our understanding of the interactions among these regions is limited. In this study, we used the activity-dependent stimulation (ADS) technique to determine the short/long-term effects on network activity and neuroplasticity of intracortical connections. ADS uses the intrinsic neural activity of one region to trigger stimulations in a separate region of the brain and can manipulate neuronal connectivity in vivo. Our aim was to compare single-unit neuronal activity within premotor cortex (rostral forelimb area, [RFA] in rats) in response to ADS (triggered from RFA) and randomly-generated stimulation in the somatosensory area (S1) within single sessions and across 21 consecutive days of stimulation. We examined firing rate and correlation between spikes and stimuli in chronically-implanted healthy ambulatory rats during spontaneous and evoked activity. At the end of the treatment, we evaluated changes of synaptophysin expression. Our results demonstrated the ability of ADS to modulate RFA firing properties and to promote synaptogenesis in S1, strengthening the idea that this Hebbian-inspired protocol can be used to modulate cortical connectivity.

Sub-second transient activated patterns to sad expressions in major depressive disorders discovered via hidden Markov model

The pathological mechanisms of major depressive disorders (MDDs) is associated with the overexpression of negative emotions, and the fast transient-activated patterns underlying overrepresentation in depression still remain to be revealed to date. We hypothesized that the aberrant spatiotemporal attributes of the process of sad expressions are related to the neuropathology of MDD and help to detect the depression severity. We enrolled a total of 96 subjects including 47 patients with MDD and 49 healthy controls (HCs), and recorded their magnetoencephalography data under a sad expression recognition task. A hidden Markov model (HMM) was applied to separate the whole neural activity into several brain states, then to characterize the dynamics. To find the disrupted temporal-spatial characteristics, power estimations and fractional occupancy (FO) of each state were estimated and contrasted between MDDs and HCs. Three states were found over the period of emotional stimuli processing procedure. The early visual stage (0-270 ms) was mainly manifested by state 1, and the emotional information processing stage (270-600 ms) was manifested by state 2, while the state 3 remained a steady proportion across the whole period. MDDs activated statistically more in limbic system during state 2 (p = 0.0045) and less in frontoparietal control network during state 3 (p = 5.38 × 10^-5 ) relative to HCs. Hamilton Depression Rating Scale scores were significantly correlated with the predicted disorder severity using FO values (p = 0.0062, r = 0.3933). Relative to HCs, MDDs perceived the sad contents quickly and spent more time overexpressing the negative emotions. These phenomena indicated MDD patients might easily indulge in negative emotion and neglect other things. Furthermore, temporal descriptors built by HMM could be potential biomarkers for identifying the severity of depression disorders.

Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting‐state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data are relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden‐state dynamics under this condition.

Hidden Markov Modeling Reveals Prolonged "Baseline" State and Shortened Antagonistic State across the Adult Lifespan

The brain networks undergo functional reorganization across the whole lifespan, but the dynamic patterns behind the reorganization remain largely unclear. This study models the dynamics of spontaneous activity of large-scale networks using hidden Markov model (HMM), and investigates how it changes with age on two adult lifespan datasets of 176/157 subjects (aged 20-80 years). Results for both datasets showed that 1) older adults tended to spend less time on a state where default mode network (DMN) and attentional networks show antagonistic activity, 2) older adults spent more time on a "baseline" state with moderate-level activation of all networks, accompanied with lower transition probabilities from this state to the others and higher transition probabilities from the others to this state, and 3) HMM exhibited higher sensitivity in uncovering the age effects compared with temporal clustering method. Our results suggest that the aging brain is characterized by the shortening of the antagonistic instances between DMN and attention systems, as well as the prolongation of the inactive period of all networks, which might reflect the shift of the dynamical working point near criticality in older adults.

Social brain network correlates with real-life social network in individuals with schizophrenia and social anhedonia

Social behaviour requires the brain to efficiently integrate multiple social processes, but it is not clear what neural substrates underlie general social behaviour. While psychosis patients and individuals with subclinical symptoms are characterized by social dysfunction, the neural mechanisms underlying social dysfunctions in schizophrenia spectrum disorders remains unclear. We first constructed a general social brain network (SBN) using resting-state functional connectivity (FC) with regions of interest based on the automatic meta-analysis results from NeuroSynth. We then examined the general SBN and its relationship with social network (SN) characteristics in 30 individuals with schizophrenia (SCZ) and 33 individuals with social anhedonia (SA). We found that patients with SCZ exhibited deficits in their SN, while SA individuals did not. SCZ patients showed decreased segregation and functional connectivity in their SBN, while SA individuals showed a reversed pattern with increased segregation and functional connectivity of their SBN. Sparse canonical correlation analysis showed that both SCZ patients and SA individuals exhibited reduced correlation between SBN and SN characteristics compared with their corresponding healthy control groups. These preliminary findings suggest that both SCZ and SA participants exhibit abnormality in segregation and functional connectivity within the general SBN and reduced correlation with SN characteristics. These findings could guide the development of non-pharmacological interventions for social dysfunction in SCZ spectrum disorders.

The Embodied-Enactive-Interactive Brain: Bridging Neuroscience and Creative Arts Therapies

The recognition and incorporation of evidence-based neuroscientific concepts into creative arts therapeutic knowledge and practice seem valuable and advantageous for the purpose of integration and professional development. Moreover, exhilarating insights from the field of neuroscience coincide with the nature, conceptualization, goals, and methods of Creative Arts Therapies (CATs), enabling comprehensive understandings of the clinical landscape, from a translational perspective. This paper contextualizes and discusses dynamic brain functions that have been suggested to lie at the heart of intra- and inter-personal processes. Touching upon fundamental aspects of the self and self-other interaction, the state-of-the-art neuroscientific-informed views will shed light on mechanisms of the embodied, predictive and relational brain. The conceptual analysis introduces and interweaves the following contemporary perspectives of brain function: firstly, the grounding of mental activity in the lived, bodily experience will be delineated; secondly, the enactive account of internal models, or generative predictive representations, shaped by experience, will be defined and extensively deliberated; and thirdly, the interpersonal simulation and synchronization mechanisms that support empathy and mentalization will be thoroughly considered. Throughout the paper, the cross-talks between the brain and the body, within the brain through functionally connected neural networks and in the context of agent-environment dynamics, will be addressed. These communicative patterns will be elaborated on to unfold psychophysiological linkage, as well as psychopathological shifts, concluding with the neuroplastic change associated with the formulation of CATs. The manuscript suggests an integrative view of the brain-body-mind in contexts relevant to the therapeutic potential of the expressive creative arts and the main avenues by which neuroscience may ground, enlighten and enrich the clinical psychotherapeutic practice.

Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics

BACKGROUND

A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.

METHODS

We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.

RESULTS

We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.

CONCLUSION

Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.

Disrupted Salience and Cingulo-Opercular Network Connectivity During Impaired Rapid Instructed Task Learning in Schizophrenia

Rapid instructed task learning (RITL) is the uniquely human ability to transform task information into goal-directed behavior without relying on trial-and-error learning. RITL is a core cognitive process supported by functional brain networks. In patients with schizophrenia, RITL ability is impaired, but the role of functional network connectivity in these RITL deficits is unknown. We investigated task-based connectivity of eight a priori network pairs in participants with schizophrenia (n = 29) and control participants (n = 31) during the performance of an RITL task. Multivariate pattern analysis was used to determine which network connectivity patterns predicted diagnostic group. Of all network pairs, only the connectivity between the cingulo-opercular network (CON) and salience network (SAN) during learning classified patients and control participants with significant accuracy (80%). CON-SAN connectivity during learning was significantly associated with task performance in participants with schizophrenia. These findings suggest that impaired interactions between identification of salient stimuli and maintenance of task goals contributes to RITL deficits in participants with schizophrenia.

Stability test of canonical correlation analysis for studying brain-behavior relationships: The effects of subject-to-variable ratios and correlation strengths

Canonical correlation analysis (CCA), a multivariate approach to identifying correlations between two sets of variables, is becoming increasingly popular in neuroimaging studies on brain‐behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Although it is known that the number of subjects should be greater than the number of variables due to the curse of dimensionality, it is unclear at what subject‐to‐variable ratios (SVR) and at what correlation strengths the CCA stability can be maintained. Here, we systematically assessed the CCA stability, in the context of investigating the relationship between the brain structural/functional imaging measures and the behavioral measures, by measuring the similarity of the first‐mode canonical variables across randomly sampled subgroups of subjects from a large set of 936 healthy subjects. Specifically, we tested how the CCA stability changes with SVR under two different brain‐behavior correlation strengths. The same tests were repeated using an independent data set (n = 700) for validation. The results confirmed that both SVR and correlation strength affect greatly the CCA stability—the CCA stability cannot be guaranteed if the SVR is not sufficiently high or the brain‐behavior relationship is not sufficiently strong. Based on our quantitative characterization of CCA stability, we provided a practical guideline to help correct interpretation of CCA results and proper applications of CCA in neuroimaging studies on brain‐behavior relationships.

Using EEG Alpha States to Understand Learning During Alpha Neurofeedback Training for Chronic Pain

Objective

Alpha-neurofeedback (α-NFB) is a novel therapy which trains individuals to volitionally increase their alpha power to improve pain. Learning during NFB is commonly measured using static parameters such as mean alpha power. Considering the biphasic nature of alpha rhythm (high and low alpha), dynamic parameters describing the time spent by individuals in high alpha state and the pattern of transitioning between states might be more useful. Here, we quantify the changes during α-NFB for chronic pain in terms of dynamic changes in alpha states.

Methods

Four chronic pain and four healthy participants received five NFB sessions designed to increase frontal alpha power. Changes in pain resilience were measured using visual analogue scale (VAS) during repeated cold-pressor tests (CPT). Changes in alpha state static and dynamic parameters such as fractional occupancy (time in high alpha state), dwell time (length of high alpha state) and transition probability (probability of moving from low to high alpha state) were analyzed using Friedman’s Test and correlated with changes in pain scores using Pearson’s correlation.

Results

There was no significant change in mean frontal alpha power during NFB. There was a trend of an increase in fractional occupancy, mean dwell duration and transition probability of high alpha state over the five sessions in chronic pain patients only. Significant correlations were observed between change in pain scores and fractional occupancy (r = −0.45, p = 0.03), mean dwell time (r = -0.48, p = 0.04) and transition probability from a low to high state (r = -0.47, p = 0.03) in chronic pain patients but not in healthy participants.

Conclusion

There is a differential effect between patients and healthy participants in terms of correlation between change in pain scores and alpha state parameters. Parameters providing a more precise description of the alpha power dynamics than the mean may help understand the therapeutic effect of neurofeedback on chronic pain.

Movie viewing elicits rich and reliable brain state dynamics

Adaptive brain function requires that sensory impressions of the social and natural milieu are dynamically incorporated into intrinsic brain activity. While dynamic switches between brain states have been well characterised in resting state acquisitions, the remodelling of these state transitions by engagement in naturalistic stimuli remains poorly understood. Here, we show that the temporal dynamics of brain states, as measured in fMRI, are reshaped from predominantly bistable transitions between two relatively indistinct states at rest, toward a sequence of well-defined functional states during movie viewing whose transitions are temporally aligned to specific features of the movie. The expression of these brain states covaries with different physiological states and reflects subjectively rated engagement in the movie. In sum, a data-driven decoding of brain states reveals the distinct reshaping of functional network expression and reliable state transitions that accompany the switch from resting state to perceptual immersion in an ecologically valid sensory experience.