Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study

Altered brain dynamic functional connectivity in patients with obstructive sleep apnea and its association with cognitive performance

OBJECTIVES

Obstructive sleep apnea (OSA) is associated with potential disruptions in brain function and structure. The aim was to investigate alterations in dynamic functional connectivity (dFC) in OSA patients utilizing resting-state functional magnetic resonance imaging (rs-fMRI) and multiplication of temporal derivatives (MTD) to better understand the neurological implications of OSA.

METHODS

This cross-sectional study eventually recruited 111 patients, aged 25-65 years. We categorized participants based on the apnea-hypopnea index (AHI) assessed via polysomnography (PSG), 43 patients were groupAHI <15 and 68 patients were group AHI ≥15. Rs-fMRI and neuropsychological assessments were conducted to assess the brain function and visual-spatial memory, respectively. We evaluated the intergroup differences in dFC as well as its correlation with clinical parameters.

RESULTS

The dFC analysis identified five distinct connectivity states, comprising four hyperconnected states (State 1, 2, 3, and 5) and one hypoconnected state (State 4). Group AHI≥ 15 showed altered fraction time (FT) and mean dwell time (MDT) in States 1, 3, and 4. The partial correlation showed that the FT/MDT of State 1 negatively correlated with hypoxia parameters, while the FT/MDT of State 3 positively correlated with total sleep time in Group AHI≥ 15. Group AHI≥ 15 exhibited a negative association between FT of state 3 and Visuospatial/Executive score in MoCA (r = -0.297, p = 0.033).

CONCLUSIONS

Untreated male moderate to severe OSA patients exhibited altered in dFC, which significantly correlated with hypoxia parameters and cognitive performance, high lighting that dFC changes may be an indicator of the neurological consequence of OSA, especially moderate to severe OSA.

Altered Patterns of Dynamic Functional Connectivity Underpin Reduced Expressions of Social-Emotional Reciprocity in Autistic Adults

To identify the neurocognitive mechanisms underpinning the social difficulties that characterize autism, we performed functional magnetic resonance imaging on pairs of autistic and non-autistic adults simultaneously whilst they interacted with one another on the iterated Ultimatum Game (iUG)-an interactive task that emulates the reciprocal characteristic of naturalistic interpersonal exchanges. Two age-matched sets of male-male dyads were investigated: 16 comprised an autistic Responder and a non-autistic Proposer, and 19 comprised non-autistic pairs of Responder and Proposer. Players' round-by-round behavior on the iUG was modeled as reciprocal choices, and dynamic functional connectivity (dFC) was measured to identify the neural mechanisms underpinning reciprocal behaviors. Behavioral expressions of reciprocity were significantly reduced in autistic compared with non-autistic Responders, yet no such differences were observed between the non-autistic Proposers in either set of dyads. Furthermore, we identified latent dFC states with temporal properties associated with reciprocity. Autistic interactants spent less time in brain states characterized by dynamic inter-network integration and segregation among the Default Mode Network and cognitive control networks, suggesting that their reduced expressions of social-emotional reciprocity reflect less efficient reconfigurations among brain networks supporting flexible cognition and behavior. These findings advance our mechanistic understanding of the social difficulties characterizing autism.

Theta and alpha connectivity in children with autism spectrum disorder

Spontaneous electroencephalography (EEG) measurements have demonstrated putative variations in the neural connectivity of subjects with autism spectrum disorder, as compared to neurotypical individuals. However, the exact nature of these connectivity differences has remained unknown, a question that we now address. Resting-state, eyes-open EEG data were recorded over 20 min from a cohort of 13 males aged 3-5 years with autism spectrum disorder, and nine neurotypical individuals as a control group. We use time-localized, phase-based methods of data analysis, including wavelet phase coherence and dynamical Bayesian inference. Several 3 min signal segments were analysed to evaluate the reproducibility of the proposed measures. In the autism spectrum disorder cohort, we demonstrate a significant (P < 0.05) reduction in functional connectivity strength across all frontal probe pairs. In addition, the percentage of time during which frontal regions were coupled was significantly reduced in the autism spectrum disorder group compared to the control group. These changes remained consistent across repeated measurements. To further validate the findings, an additional resting-state EEG dataset (eyes open and closed) from 67 individuals with autism spectrum disorder and 66 control group individuals (male, 5-15 years) was assessed. The functional connectivity results demonstrated a reduction in theta and alpha connectivity on a local, but not global, level. No association was found with age. The connectivity differences observed suggest the potential of theta and alpha connectivity as biomarkers for autism spectrum disorder. Additionally, the robustness to amplitude perturbations of the methods proposed here makes them particularly suitable for the clinical assessment of autism spectrum disorder and of the efficacy of therapeutic interventions.

Prob-sparse self-attention extraction of time-aligned dynamic functional connectivity for ASD diagnosis

Dynamic functional connectivity (DFC) has shown promise in the diagnosis of Autism Spectrum Disorder (ASD). However, extracting highly discriminative information from the complex DFC matrix remains a challenging task. In this paper, we propose an ASD classification framework PSA-FCN which is based on time-aligned DFC and Prob-Sparse Self-Attention to address this problem. Specifically, we introduce Prob-Sparse Self-Attention to selectively extract global features, and use self-attention distillation as a transition at each layer to capture local patterns and reduce dimensionality. Additionally, we construct a time-aligned DFC matrix to mitigate the time sensitivity of DFC and extend the dataset, thereby alleviating model overfitting. Our model is evaluated on fMRI data from the ABIDE NYU site, and the experimental results demonstrate that the model outperforms other methods in the paper with a classification accuracy of 81.8 %. Additionally, our research findings reveal significant variability in the DFC connections of brain regions of ASD patients, including Cuneus (CUN), Lingual gyrus (LING), Superior occipital gyrus (SOG), Posterior cingulate gyrus (PCG), and Precuneus (PCUN), which is consistent with prior research. In summary, our proposed PSA framework shows potential in ASD diagnosis as well as automatic discovery of critical ASD-related biomarkers.

Altered Temporospatial Variability of Dynamic Amplitude of Low-Frequency Fluctuation in Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with altered brain activity. However, little is known about the integrated temporospatial variation of dynamic spontaneous brain activity in ASD. In the present study, resting-state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically-matched typically developmental controls (TC) children obtained from the Autism Brain Imaging Data Exchange database. Using the sliding-window approach, temporal, spatial, and temporospatial variability of dynamic amplitude of low-frequency fluctuation (tvALFF, svALFF, and tsvALFF) were calculated for each participant. Group-comparisons were further performed at global, network, and brain region levels to quantify differences between ASD and TC groups. The relationship between temporospatial dynamic amplitude of low-frequency fluctuation variation alterations and clinical symptoms of ASD was finally explored by a support vector regression model. Relative to TC, we found enhanced tvALFF in visual network (Vis), somatomotor network (SMT), and salience/ventral attention network (SVA) of ASD, and weakened tvALFF in dorsal attention network (DAN) of ASD. Besides, ASD showed decreased svALFF in Vis, SVA, and limbic network (Limbic), and increased svALFF in DAN and default mode network (DMN). Elevated tsvALFF was found in the Vis, SMT, and DMN of ASD. More importantly, the altered tsvALFF from the DMN can predict the symptom severity of ASD. These findings demonstrate altered temporospatial dynamics of the spontaneous brain activity in ASD and provide novel insights into the neural mechanism underlying ASD.

Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder

Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

BACKGROUND

Autism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD.

METHODS

Resting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD.

RESULTS

EEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD.

CONCLUSION

The multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Heterogeneity and convergence across seven neuroimaging modalities: a review of the autism spectrum disorder literature

Background

A growing body of literature classifies autism spectrum disorder (ASD) as a heterogeneous, complex neurodevelopmental disorder that often is identified prior to three years of age. We aim to provide a narrative review of key structural and functional properties that differentiate the neuroimaging profile of autistic youth from their typically developing (TD) peers across different neuroimaging modalities.

Methods

Relevant studies were identified by searching for key terms in PubMed, with the most recent search conducted on September 1, 2023. Original research papers were included if they applied at least one of seven neuroimaging modalities (structural MRI, functional MRI, DTI, MRS, fNIRS, MEG, EEG) to compare autistic children or those with a family history of ASD to TD youth or those without ASD family history; included only participants <18 years; and were published from 2013 to 2023.

Results

In total, 172 papers were considered for qualitative synthesis. When comparing ASD to TD groups, structural MRI-based papers (n = 26) indicated larger subcortical gray matter volume in ASD groups. DTI-based papers (n = 14) reported higher mean and radial diffusivity in ASD participants. Functional MRI-based papers (n = 41) reported a substantial number of between-network functional connectivity findings in both directions. MRS-based papers (n = 19) demonstrated higher metabolite markers of excitatory neurotransmission and lower inhibitory markers in ASD groups. fNIRS-based papers (n = 20) reported lower oxygenated hemoglobin signals in ASD. Converging findings in MEG- (n = 20) and EEG-based (n = 32) papers indicated lower event-related potential and field amplitudes in ASD groups. Findings in the anterior cingulate cortex, insula, prefrontal cortex, amygdala, thalamus, cerebellum, corpus callosum, and default mode network appeared numerous times across modalities and provided opportunities for multimodal qualitative analysis.

Conclusions

Comparing across neuroimaging modalities, we found significant differences between the ASD and TD neuroimaging profile in addition to substantial heterogeneity. Inconsistent results are frequently seen within imaging modalities, comparable study populations and research designs. Still, converging patterns across imaging modalities support various existing theories on ASD.

Comparison of autism spectrum disorder subtypes based on functional and structural factors

Autism spectrum disorder (ASD) is a series of neurodevelopmental disorders that may affect a patient's social, behavioral, and communication abilities. As a typical mental illness, ASD is not a single disorder. ASD is often divided into subtypes, such as autism, Asperger's, and pervasive developmental disorder-not otherwise specified (PDD-NOS). Studying the differences among brain networks of the subtypes has great significance for the diagnosis and treatment of ASD. To date, many studies have analyzed the brain activity of ASD as a single mental disorder, whereas few have focused on its subtypes. To address this problem, we explored whether indices derived from functional and structural magnetic resonance imaging (MRI) data exhibited significant dissimilarities between subtypes. Utilizing a brain pattern feature extraction method from fMRI based on tensor decomposition, amplitude of low-frequency fluctuation and its fractional values of fMRI, and gray matter volume derived from MRI, impairments of function in the subcortical network and default mode network of autism were found to lead to major differences from the other two subtypes. Our results provide a systematic comparison of the three common ASD subtypes, which may provide evidence for the discrimination between ASD subtypes.

Sex heterogeneity of dynamic brain activity and functional connectivity in autism spectrum disorder

Sex heterogeneity has been frequently reported in autism spectrum disorders (ASD) and has been linked to static differences in brain function. However, given the complexity of ASD and diagnosis-by-sex interactions, dynamic characteristics of brain activity and functional connectivity may provide important information for distinguishing ASD phenotypes between females and males. The aim of this study was to explore sex heterogeneity of functional networks in the ASD brain from a dynamic perspective. Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were analyzed in 128 ASD subjects (64 males/64 females) and 128 typically developing control (TC) subjects (64 males/64 females). A sliding-window approach was adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dynamic functional connectivity (dFC) to characterize time-varying brain activity and functional connectivity respectively. We then examined the sex-related changes in ASD using two-way analysis of variance. Significant diagnosis-by-sex interaction effects were identified in the left anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC) and left precuneus in the dALFF analysis. Furthermore, there were significant diagnosis-by-sex interaction effects of dFC variance between the left ACC/mPFC and right ACC, left postcentral gyrus, left precuneus, right middle temporal gyrus and left inferior frontal gyrus, triangular part. These findings reveal the sex heterogeneity in brain activity and functional connectivity in ASD from a dynamic perspective, and provide new evidence for further exploring sex heterogeneity in ASD.

Aberrant Flexibility of Dynamic Brain Network in Patients with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders whose pathobiology remains elusive. This study aimed to investigate the possible neural mechanisms underlying ASD using a dynamic brain network model and a relatively large-sample, multi-site dataset. Resting-state functional magnetic resonance imaging data were acquired from 208 ASD patients and 227 typical development (TD) controls, who were drawn from the multi-site Autism Brain Imaging Data Exchange (ABIDE) database. Brain network flexibilities were estimated and compared between the ASD and TD groups at both global and local levels, after adjusting for sex, age, head motion, and site effects. The results revealed significantly increased brain network flexibilities (indicating a decreased stability) at the global level, as well as at the local level within the default mode and sensorimotor areas in ASD patients than TD participants. Additionally, significant ASD-related decreases in flexibilities were also observed in several occipital regions at the nodal level. Most of these changes were significantly correlated with the Autism Diagnostic Observation Schedule (ADOS) total score in the entire sample. These results suggested that ASD is characterized by significant changes in temporal stabilities of the functional brain network, which can further strengthen our understanding of the pathobiology of ASD.

Dynamic brain functional states associated with inhibition control under different altitudes

Chronic exposure to the hypobaric hypoxia environment of plateau could influence human cognitive behaviours which are supported by dynamic brain connectivity states. Until now, how functional connectivity (FC) of the brain network changes with altitudes is still unclear. In this article, we used EEG data of the Go/NoGo paradigm from Weinan (347 m) and Nyingchi (2950 m). A combination of dynamic FC (dFC) and the K-means cluster was employed to extract dynamic FC states which were later distinguished by graph metrics. Besides, temporal properties of networks such as fractional windows (FW), transition numbers (TN) and mean dwell time (MDT) were calculated. Finally, we successfully extracted two different states from dFC matrices where State 1 was verified to have higher functional integration and segregation. The dFC states dynamically switched during the Go/NoGo tasks and the FW of State 1 showed a rise in the high-altitude participants. Also, in the regional analysis, we found higher state deviation in the fronto-parietal cortices and enhanced FC strength in the occipital lobe. These results demonstrated that long-term exposure to the high-altitude environment could lead brain networks to reorganize as networks with higher inter- and intra-networks information transfer efficiency, which could be attributed to a compensatory mechanism to the compromised brain function due to the plateau environment. This study provides a new perspective in considering how the plateau impacted cognitive impairment.

Transcranial photobiomodulation for neurodevelopmental disorders: a narrative review

BACKGROUND

Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Down syndrome (DS) significantly impact social, communicative, and behavioral functioning. Transcranial photobiomodulation (t-PBM) with near-infrared light is a promising non-invasive neurostimulation technique for neuropsychiatric disorders, including NDDs. This narrative review aimed to examine the preclinical and clinical evidence of photobiomodulation (PBM) in treating NDDs.

METHODS

A comprehensive search across six databases was conducted, using a combination of MeSH terms and title/abstract keywords: "photobiomodulation", "PBM", "neurodevelopmental disorders", "NDD", and others. Studies applying PBM to diagnosed NDD cases or animal models replicating NDDs were included. Protocols, reviews, studies published in languages other than English, and studies not evaluating clinical or cognitive outcomes were excluded.

RESULTS

Nine studies were identified, including one preclinical and eight clinical studies (five on ASD, two on ADHD, and one on DS). The reviewed studies encompassed various t-PBM parameters (wavelengths: 635-905 nm) and targeted primarily frontal cortex areas. t-PBM showed efficacy in improving disruptive behavior, social communication, cognitive rigidity, sleep quality, and attention in ASD; in enhancing attention in ADHD; and in improving motor skills and verbal fluency in DS. Minimal adverse effects were reported. Proposed mechanisms involve enhanced mitochondrial function, modulated oxidative stress, and reduced neuroinflammation.

CONCLUSIONS

t-PBM emerges as a promising intervention for NDDs, with potential therapeutic effects across ASD, ADHD, and DS. These findings underscore the need for further research, including larger-scale, randomized sham-controlled clinical trials with comprehensive biomarker analyses, to optimize treatment parameters and understand the underlying mechanisms associated with the effects of t-PBM.

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder

Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are two commonly used imaging techniques to visualize brain function. The use of inter-network covariation (a functional connectome) is a widely used approach to infer links among different brain networks. While whole brain resting fMRI connectomes are widely used, PET data has mostly been analyzed using a few regions of interest. There has been much less work estimating PET spatial networks and almost no work on their connectivity (covariation) in the context of a whole brain data-driven connectome, nor have there been direct comparisons between whole brain PET and fMRI connectomes. Here we present an approach to leverage spatially constrained ICA to compute an estimate of the PET connectome. Results reveal highly modularized connectome patterns that are complementary to that identified from resting fMRI. Similarly, we were able to identify comparable resting networks from a PiB PET scan that can be directly compared to networks in rest fMRI data and results reveal similar, but not identical, network spatial patterns, with the PET networks being slightly smoother and, in some cases, showing variations in subnodes. The resulting networks, decomposed into spatial maps and subject expressions (loading parameters) linked to resting fMRI provide a new way to evaluate the complementary information in PET and fMRI and open up new possibilities for biomarker development.Clinical Relevance-This study analyzes the whole-brain PET and fMRI connectomes, capturing the complementary information from both imaging modalities, thereby introducing a new scope for biomarker development.

The autism spectrum among transgender youth: default mode functional connectivity

The common intersection of autism and transgender identities has been described in clinical and community contexts. This study investigates autism-related neurophenotypes among transgender youth. Forty-five transgender youth, evenly balanced across non-autistic, slightly subclinically autistic, and full-criteria autistic subgroupings, completed resting-state functional magnetic resonance imaging to examine functional connectivity. Results confirmed hypothesized default mode network (DMN) hub hyperconnectivity with visual and motor networks in autism, partially replicating previous studies comparing cisgender autistic and non-autistic adolescents. The slightly subclinically autistic group differed from both non-autistic and full-criteria autistic groups in DMN hub connectivity to ventral attention and sensorimotor networks, falling between non-autistic and full-criteria autistic groups. Autism traits showed a similar pattern to autism-related group analytics, and also related to hyperconnectivity between DMN hub and dorsal attention network. Internalizing, gender dysphoria, and gender minority-related stigma did not show connectivity differences. Connectivity differences within DMN followed previously reported patterns by designated sex at birth (i.e. female birth designation showing greater within-DMN connectivity). Overall, findings suggest behavioral diagnostics and autism traits in transgender youth correspond to observable differences in DMN hub connectivity. Further, this study reveals novel neurophenotypic characteristics associated with slightly subthreshold autism, highlighting the importance of research attention to this group.

A revisit of the amygdala theory of autism: Twenty years after

The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.

Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.

Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis

Background

Neurobiological models to explain the vulnerability of autism spectrum disorders (ASDs) are scarce, and previous resting-state functional magnetic resonance imaging (rs-fMRI) studies mostly examined static functional connectivity (FC). Given that FC constantly evolves, it is critical to probe FC dynamic differences in ASD patients.

Methods

We characterized recurring phase-locking (PL) states during rest in 45 ASD patients and 47 age- and sex-matched healthy controls (HCs) using Leading Eigenvector Dynamics Analysis (LEiDA) and probed the organization of PL states across different fine grain sizes.

Results

Our results identified five different groups of discrete resting-state functional networks, which can be defined as recurrent PL state overtimes. Specifically, ASD patients showed an increased probability of three PL states, consisting of the visual network (VIS), frontoparietal control network (FPN), default mode network (DMN), and ventral attention network (VAN). Correspondingly, ASD patients also showed a decreased probability of two PL states, consisting of the subcortical network (SUB), somatomotor network (SMN), FPN, and VAN.

Conclusion

Our findings suggested that the temporal reorganization of brain discrete networks was closely linked to sensory to cognitive systems of the brain. Our study provides new insights into the dynamics of brain networks and contributes to a deeper understanding of the neurological mechanisms of ASD.

Structural filtering of functional data offered discriminative features for autism spectrum disorder

This study attempted to answer the question, "Can filtering the functional data through the frequency bands of the structural graph provide data with valuable features which are not valuable in unfiltered data"?. The valuable features discriminate between autism spectrum disorder (ASD) and typically control (TC) groups. The resting-state fMRI data was passed through the structural graph's low, middle, and high-frequency band (LFB, MFB, and HFB) filters to answer the posed question. The structural graph was computed using the diffusion tensor imaging data. Then, the global metrics of functional graphs and metrics of functional triadic interactions were computed for filtered and unfiltered rfMRI data. Compared 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 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 indicate 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. There was no significant difference between ASDs and TCs when using the unfiltered data. All of these results demonstrated that 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. Also, these results demonstrated that frequency bands of the structural graph could offer significant findings which were not found in the unfiltered data. In conclusion, the results demonstrated the promising perspective of using structural graph frequency bands for attaining discriminative features and new knowledge, especially in the case of ASD.

Dysregulated dynamic time-varying triple-network segregation in children with autism spectrum disorder

One of the remarkable characteristics of autism spectrum disorder (ASD) is the dysregulation of functional connectivity of the triple-network, which includes the salience network (SN), default mode network (DMN), and central executive network (CEN). However, there is little known about the segregation of the triple-network dynamics in ASD. This study used resting-state functional magnetic resonance imaging data including 105 ASD and 102 demographically-matched typical developing control (TC) children. We compared the dynamic time-varying triple-network segregation and triple-network functional connectivity states between ASD and TC groups, and examined the relationship between dynamic triple-network segregation alterations and clinical symptoms of ASD. The average dynamic network segregation value of the DMN with SN and the DMN with CEN in ASD was lower but the coefficient of variation (CV) of dynamic network segregation of the DMN with CEN was higher in ASD. Furthermore, partially reduced triple-network segregation associated with the DMN was found in connectivity states analysis of ASD. These abnormal average values and CV of dynamic network segregation predicted social communication deficits and restricted and repetitive behaviors in ASD. Our findings indicate abnormal dynamic time-varying triple-network segregation of ASD and highlight the crucial role of the triple-network in the neural mechanisms underlying ASD.

Topological analysis of brain dynamics in autism based on graph and persistent homology

Autism spectrum disorder (ASD) is a heterogeneous disorder with a rapidly growing prevalence. In recent years, the dynamic functional connectivity (DFC) technique has been used to reveal the transient connectivity behavior of ASDs' brains by clustering connectivity matrices in different states. However, the states of DFC have not been yet studied from a topological point of view. In this paper, this study was performed using global metrics of the graph and persistent homology (PH) and resting-state functional magnetic resonance imaging (fMRI) data. The PH has been recently developed in topological data analysis and deals with persistent structures of data. The structural connectivity (SC) and static FC (SFC) were also studied to know which one of the SC, SFC, and DFC could provide more discriminative topological features when comparing ASDs with typical controls (TCs). Significant discriminative features were only found in states of DFC. Moreover, the best classification performance was offered by persistent homology-based metrics and in two out of four states. In these two states, some networks of ASDs compared to TCs were more segregated and isolated (showing the disruption of network integration in ASDs). The results of this study demonstrated that topological analysis of DFC states could offer discriminative features which were not discriminative in SFC and SC. Also, PH metrics can provide a promising perspective for studying ASD and finding candidate biomarkers.

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Resting-state functional connectivity (rsFC) approaches provide informative estimates of the functional architecture of the brain, and recently-proposed cofluctuation analysis temporally unwraps FC at every moment in time, providing refined information for quantifying brain dynamics. As a brain network disorder, autism spectrum disorder (ASD) was characterized by substantial alteration in FC, but the contribution of moment-to-moment-activity cofluctuations to the overall dysfunctional connectivity pattern in ASD remains poorly understood. Here, we used the cofluctuation approach to explore the underlying dynamic properties of FC in ASD, using a large multisite resting-state functional magnetic resonance imaging (rs-fMRI) dataset (ASD = 354, typically developing controls [TD] = 446). Our results verified that the networks estimated using high-amplitude frames were highly correlated with the traditional rsFC. Moreover, these frames showed higher average amplitudes in participants with ASD than those in the TD group. Principal component analysis was performed on the activity patterns in these frames and aggregated over all subjects. The first principal component (PC1) corresponds to the default mode network (DMN), and the PC1 coefficients were greater in participants with ASD than those in the TD group. Additionally, increased ASD symptom severity was associated with the increased coefficients, which may result in excessive internally oriented cognition and social cognition deficits in individuals with ASD. Our finding highlights the utility of cofluctuation approaches in prevalent neurodevelopmental disorders and verifies that the aberrant contribution of DMN to rsFC may underline the symptomatology in adolescents and youths with ASD.

Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

Altered Periodic Dynamics in the Default Mode Network in Autism and Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Altered resting-state functional connectivity in the default mode network (DMN) is characteristic of both autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Standard analytical pipelines for resting-state functional connectivity focus on linear correlations in activation time courses between neural networks or regions of interest. These features may be insensitive to temporally lagged or nonlinear relationships.

METHODS

In a twin cohort study comprising 292 children, including 52 with a diagnosis of ASD and 70 with a diagnosis of ADHD, we applied nonlinear analytical methods to characterize periodic dynamics in the DMN. Using recurrence quantification analysis and related methods, we measured the prevalence, duration, and complexity of periodic processes within and between DMN regions of interest. We constructed generalized estimating equations to compare these features between neurotypical children and children with ASD and/or ADHD while controlling for familial relationships, and we leveraged machine learning algorithms to construct models predictive of ASD or ADHD diagnosis.

RESULTS

In within-pair analyses of twins with discordant ASD diagnoses, we found that DMN signal dynamics were significantly different in dizygotic twins but not in monozygotic twins. Considering our full sample, we found that these patterns allowed a robust predictive classification of both ASD (81.0% accuracy; area under the curve = 0.85) and ADHD (82% accuracy; area under the curve = 0.87) cases.

CONCLUSIONS

These findings indicate that synchronized periodicity among regions comprising the DMN relates both to neurotypical function and to ASD and/or ADHD, and they suggest generally that a dynamical analysis of network interconnectivity may be a useful methodology for future neuroimaging studies.

Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs

The study of brain network connectivity as a time-varying property began relatively recently and, to date, has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group-level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the stable identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a continuity-preserving planar embedding of high-dimensional time-varying measurements of whole-brain functional network connectivity. Planar linear exemplars summarizing dominant dynamic trends across the population are computed from local linear approximations to the two-dimensional 2D embedded trajectories. A high-dimensional representation of each 2D exemplar segment is obtained by averaging the dFNC observations corresponding to the n planar nearest neighbors of τ evenly spaced points along the 2D line segment representation (where n is the UMAP number-of-neighbors parameter and τ is the temporal duration of trajectory segments being approximated). Each of the 2D exemplars thus “lifts” to a multiframe high-dimensional dFNC trajectory of length τ. The collection of high-dimensional temporally evolving dFNC representations (EVOdFNCs) derived in this manner are employed as dynamic basis objects with which to characterize observed high-dimensional dFNC trajectories, which are then expressed as weighted combination of these basis objects. Our approach yields new insights into anomalous patterns of fluidly varying whole-brain connectivity that are significantly associated with schizophrenia as a broad diagnosis as well as with certain symptoms of this serious disorder. Importantly, we show that relative to conventional hidden Markov modeling with single-frame unvarying dFNC summary states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia including hallucinations and delusions, suggesting that a more dynamic characterization is needed to help illuminate such a complex brain disorder.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Atypically high influence of subcortical activity on primary sensory regions in autism

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The age-dependent decrease of subcortico-cortical connectivity is attenuated in ASD.

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Primary sensory regions remain less segregated from subcortical activity in ASD.

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This could underlie an excessive amount of sensory input relayed to the cortex.

Background

Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from the persistent overconnectivity between primary sensory regions and deep brain nuclei in both children and adults with ASD – as reported by several previous studies – which could reflect a decreased or arrested maturation of brain connectivity. However, it has not yet been investigated whether this overconnectivity can be modelled as an excessive directional influence of subcortical brain activity on primary sensory cortical regions in ASD, with respect to age-matched typically developing (TD) individuals.

Methods

To this aim, we used dynamic causal modelling to estimate (1) the directional influence of subcortical activity on cortical processing and (2) the functional segregation of primary sensory cortical regions from subcortical activity in 166 participants with ASD and 193 TD participants from the Autism Brain Imaging Data Exchange (ABIDE). We then specifically tested the hypothesis that the age-related changes of these indicators of brain connectivity would differ between the two groups.

Results

We found that in TD participants age was significantly associated with decreased influence of subcortical activity on cortical processing, paralleled by an increased functional segregation of cortical sensory processing from subcortical activity. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD.

Conclusion

This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory input relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.

EEG microstates suggest atypical resting-state network activity in high-functioning children and adolescents with Autism Spectrum Development

EEG microstates represent transient electrocortical events that reflect synchronized activities of large-scale networks, which allows investigations of brain dynamics with sub-second resolution. We recorded resting EEG from 38 children and adolescents with Autism Spectrum Development (ASD) and 48 age, IQ, sex, and handedness-matched typically developing (TD) participants. The EEG was segmented into a time series of microstates using modified k-means clustering of scalp voltage topographies. The frequency and global explained variance (GEV) of a specific microstate (type C) were significantly lower in the ASD group compared to the TD group while the duration of the same microstate was correlated with the presence of ASD-related behaviors. The duration of this microstate was also positively correlated with participant age in the TD group, but not in the ASD group. Further, the frequency and duration of the microstate were significantly correlated with the overall alpha power only in the TD group. The signal strength and GEV for another microstate (type G) was greater in the ASD group than the TD group, and the associated topographical pattern differed between groups with greater variations in the ASD group. While more work is needed to clarify the underlying neural sources, the existing literature supports associations between the two microstates and the default mode and salience networks. The current study suggests specific alterations of temporal dynamics of the resting cortical network activities as well as their developmental trajectories and relationships to alpha power, which has been proposed to reflect reduced neural inhibition in ASD. This article is protected by copyright. All rights reserved.

Abnormal Degree Centrality in Children with Low-Function Autism Spectrum Disorders: A Sleeping-State Functional Magnetic Resonance Imaging Study

PURPOSE

This study used the graph-theory approach, degree centrality (DC) to analyze whole-brain functional networks at the voxel level in children with ASD, and investigated whether DC changes were correlated with any clinical variables in ASD children.

METHODS

The current study included 86 children with ASD and 54 matched healthy subjects Aged 2-5.5 years. Next, chloral hydrate induced sleeping-state functional magnetic resonance imaging (ss-fMRI) datasets were acquired from these ASD and healthy subjects. For a given voxel, the DC was calculated by calculating the number of functional connections with significantly positive correlations at the individual level. Group differences were tested using two-sample t-tests (p < 0.01, AlphaSim corrected). Finally, relationships between abnormal DCs and clinical variables were investigated via Pearson's correlation analysis.

RESULTS

Children with ASD exhibited low DC values in the right middle frontal gyrus (MFG) (p < 0.01, AlphaSim corrected). Furthermore, significantly negative correlations were established between the decreased average DC values within the right MFG in ASD children and the total ABC scores, as well as with two ABC subscales measuring highly relevant impairments in ASD (ie, stereotypes and object-use behaviors and difficulties in language).

CONCLUSION

Taken together, the results of our ss-fMRI study suggest that abnormal DC may represent an important contribution to elucidation of the neuropathophysiological mechanisms of preschoolers with ASD.

Abnormal dynamic brain activity and functional connectivity of primary motor cortex in blepharospasm

BACKGROUND

Accumulating evidence indicates that dynamic amplitude of low-frequency fluctuations (dALFF) or functional connectivity (dFC) can provide complementary information, distinct from static ALFF (sALFF) or FC (sFC), in detecting brain functional abnormalities in brain diseases. We aimed to examine whether dALFF and dFC can offer valuable information for the detection of functional brain abnormalities in patients with blepharospasm.

METHODS

We collected resting-state functional magnetic resonance imaging data from 46 patients each of blepharospasm, hemifacial spasm (HFS), and healthy controls (HCs). We examined inter-group differences in sALFF and dALFF to investigate abnormal regional brain activity in patients with blepharospasm. Based on the dALFF results, we conducted seed-based sFC and dFC analyses to identify static and dynamic connectivity changes in brain networks centered on areas showing abnormal temporal variability of local brain activity in patients with blepharospasm.

RESULTS

Compared with HCs, patients with blepharospasm displayed different brain functional change patterns characterized by increased sALFF in the left primary motor cortex (PMC) but increased dALFF variance in the right PMC. However, differences were not found between patients with HFS and HCs. Additionally, patients with blepharospasm exhibited decreased dFC strength, but no change in sFC, between right PMC and ipsilateral cerebellum compared with HCs; these findings were replicated when patients with blepharospasm were compared to those with HFS.

CONCLUSIONS

Our findings highlight that dALFF and dFC are complementary to sALFF and sFC and can provide valuable information for detecting brain functional abnormalities in blepharospasm. Blepharospasm may be a network disorder involving the cortico-ponto-cerebello-thalamo-cortical circuit.

Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review

Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.

Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability

Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

Convolutional Neural Network with Sparse Strategies to Classify Dynamic Functional Connectivity

Classification of dynamic functional connectivity (DFC) is becoming a promising approach for diagnosing various neurodegenerative diseases. However, the existing methods generally face the problem of overfitting. To solve it, this paper proposes a convolutional neural network with three sparse strategies named SCNN to classify DFC. Firstly, an element-wise filter is designed to impose sparse constraints on the DFC matrix by replacing the redundant elements with zeroes, where the DFC matrix is specially constructed to quantify the spatial and temporal variation of DFC. Secondly, a 11 convolutional filter is adopted to reduce the dimensionality of the sparse DFC matrix, and remove meaningless features resulted from zero elements in the subsequent convolution process. Finally, an extra sparse optimization classifier is employed to optimize the parameters of the above two filters, which can effectively improve the ability of SCNN to extract discriminative features. Experimental results on multiple resting-state fMRI datasets demonstrate that the proposed model provides a better classification performance of DFC compared with several state-of-the-art methods, and can identify the abnormal brain functional connectivity.

The divided brain: Functional brain asymmetry underlying self-construal

Self-construal (orientations of independence and interdependence) is a fundamental concept that guides human behaviour, and it is linked to a large number of brain regions. However, understanding the connectivity of these regions and the critical principles underlying these self-functions are lacking. Because brain activity linked to self-related processes are intrinsic, the resting-state method has received substantial attention. Here, we focused on resting-state functional connectivity matrices based on brain asymmetry as indexed by the differential partition of the connectivity located in mirrored positions of the two hemispheres, hemispheric specialization measured using the intra-hemispheric (left or right) connectivity, brain communication via inter-hemispheric interactions, and global connectivity as the sum of the two intra-hemispheric connectivity. Combining machine learning techniques with hypothesis-driven network mapping approaches, we demonstrated that orientations of independence and interdependence were best predicted by the asymmetric matrix compared to brain communication, hemispheric specialization, and global connectivity matrices. The network results revealed that there were distinct asymmetric connections between the default mode network, the salience network and the executive control network which characterise independence and interdependence. These analyses shed light on the importance of brain asymmetry in understanding how complex self-functions are optimally represented in the brain networks.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

Altered Cerebro-Cerebellar Dynamic Functional Connectivity in Alcohol Use Disorder: a Resting-State fMRI Study

Alcohol use disorder (AUD) is widely associated with cerebellar dysfunction and altered cerebro-cerebellar functional connectivity (FC) that lead to cognitive impairments. Evidence for this association comes from resting-state functional magnetic resonance imaging (rsfMRI) studies that assess time-averaged measures of FC across the duration of a typical scan. This approach, however, precludes the assessment of potentially FC dynamics happening at faster timescales. In this study, using rsfMRI data, we aim at exploring cerebro-cerebellar FC dynamics in AUD patients (N = 18) and age- and sex-matched controls (N = 18). In particular, we quantified group-level differences in the temporal variability of FC between the posterior cerebellum and large-scale cognitive systems, and we investigated the role of the cerebellum in large-scale brain dynamics in terms of the temporal flexibility and integration of its regions. We found that, relative to controls, the AUD group exhibited significantly greater FC variability between the cerebellum and both the frontoparietal executive control (F_1,31 = 7.01, p(FDR) = 0.028) and ventral attention (F_1,31 = 7.35, p(FDR) = 0.028) networks. Moreover, the AUD group exhibited significantly less flexibility (F_1,31 = 8.61, p(FDR) = 0.028) and greater integration (F_1,31 = 9.11, p(FDR) = 0.028) in the cerebellum. Finally, in an exploratory analysis, we found distributed changes in the dynamics of canonical large-scale networks in AUD. Overall, this study brings evidence of AUD-related alterations in dynamic FC within major cerebro-cerebellar networks. This pattern has implications for explaining the development and maintenance of this disorder and improving our understating of the cerebellum's involvement in addiction.

Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Background

Alzheimer's disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.

Method

We used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.

Results

All states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.

Conclusion

Our results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.

Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder

Cognitive flexibility enables appropriate responses to a changing environment and is associated with positive life outcomes. Adolescence, with its increased focus on transitioning to independent living, presents particular challenges for youths with autism spectrum disorder (ASD) who often struggle to behave in a flexible way when faced with challenges. This review focuses on brain mechanisms underlying the development of flexible cognition during adolescence and how these neural systems are affected in ASD. Neuroimaging studies of task switching and set-shifting provide evidence for atypical lateral frontoparietal and midcingulo-insular network activation during cognitive flexibility task performance in individuals with ASD. Recent work also examines how intrinsic brain network dynamics support flexible cognition. These dynamic functional connectivity studies provide evidence for alterations in the number of transitions between brain states, as well as hypervariability of functional connections in adolescents with ASD. Future directions for the field include addressing issues related to measurement of cognitive flexibility using a combination of metrics with ecological and construct validity. Heterogeneity of executive function ability in ASD must also be parsed to determine which individuals will benefit most from targeted training to improve flexibility. The influence of pubertal hormones on brain network development and cognitive maturation in adolescents with ASD is another area requiring further exploration. Finally, the intriguing possibility that bilingualism might be associated with preserved cognitive flexibility in ASD should be further examined. Addressing these open questions will be critical for future translational neuroscience investigations of cognitive and behavioral flexibility in adolescents with ASD.

Atypical Resting-State Functional Connectivity Dynamics Correlate With Early Cognitive Dysfunction in HIV Infection

Purpose: Previous studies have shown that HIV affects striato-cortical regions, leading to persisting cognitive impairment in 30-70% of the infected individuals despite combination antiretroviral therapy. This study aimed to investigate brain functional dynamics whose deficits might link to early cognitive decline or immunologic deterioration. Methods: We applied sliding windows and K-means clustering to fMRI data (HIV patients with asymptomatic neurocognitive impairment and controls) to construct dynamic resting-state functional connectivity (RSFC) maps and identify states of their reoccurrences. The average and variability of dynamic RSFC, and the dwelling time and state transitioning of each state were evaluated. Results: HIV patients demonstrated greater variability in RSFC between the left pallidum and regions of right pre-central and post-central gyri, and between the right supramarginal gyrus and regions of the right putamen and left pallidum. Greater variability was also found in the frontal RSFC of pars orbitalis of the left inferior frontal gyrus and right superior frontal gyrus (medial). While deficits in learning and memory recall of HIV patients related to greater striato-sensorimotor variability, deficits in attention and working memory were associated with greater frontal variability. Greater striato-parietal variability presented a strong link with immunologic function (CD4+/CD8+ ratio). Furthermore, HIV-infected patients exhibited longer time and reduced transitioning in states typified by weaker connectivity in specific networks. CD4+T-cell counts of the HIV-patients were related to reduced state transitioning. Conclusion: Our findings suggest that HIV alters brain functional connectivity dynamics, which may underlie early cognitive impairment. These findings provide novel insights into our understanding of HIV pathology, complementing the existing knowledge.

Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia

The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as "brain states", have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the "strong" states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.

Alpha connectivity and inhibitory control in adults with autism spectrum disorder

BACKGROUND

Individuals with autism spectrum disorder (ASD) often report difficulties with inhibition in everyday life. During inhibition tasks, adults with ASD show reduced activation of and connectivity between brain areas implicated in inhibition, suggesting impairments in inhibitory control at the neural level. Our study further investigated these differences by using magnetoencephalography (MEG) to examine the frequency band(s) in which functional connectivity underlying response inhibition occurs, as brain functions are frequency specific, and whether connectivity in certain frequency bands differs between adults with and without ASD.

METHODS

We analysed MEG data from 40 adults with ASD (27 males; 26.94 ± 6.08 years old) and 39 control adults (27 males; 27.29 ± 5.94 years old) who performed a Go/No-go task. The task involved two blocks with different proportions of No-go trials: Inhibition (25% No-go) and Vigilance (75% No-go). We compared whole-brain connectivity in the two groups during correct No-go trials in the Inhibition vs. Vigilance blocks between 0 and 400 ms.

RESULTS

Despite comparable performance on the Go/No-go task, adults with ASD showed reduced connectivity compared to controls in the alpha band (8-14 Hz) in a network with a main hub in the right inferior frontal gyrus. Decreased connectivity in this network predicted more self-reported difficulties on a measure of inhibition in everyday life.

LIMITATIONS

Measures of everyday inhibitory control were not available for all participants, so this relationship between reduced network connectivity and inhibitory control abilities may not be necessarily representative of all adults with ASD or the larger ASD population. Further research with independent samples of adults with ASD, including those with a wider range of cognitive abilities, would be valuable.

CONCLUSIONS

Our findings demonstrate reduced functional brain connectivity during response inhibition in adults with ASD. As alpha-band synchrony has been linked to top-down control mechanisms, we propose that the lack of alpha synchrony observed in our ASD group may reflect difficulties in suppressing task-irrelevant information, interfering with inhibition in real-life situations.

Atypical Inter-Network Deactivation Associated With the Posterior Default-Mode Network in Autism Spectrum Disorder

Previous studies have suggested that atypical deactivation of functional brain networks contributes to the complex cognitive and behavioral profile associated with autism spectrum disorder (ASD). However, these studies have not considered the temporal dynamics of deactivation mechanisms between the networks. In this study, we examined (a) mutual deactivation and (b) mutual activation-deactivation (i.e., anticorrelated) time-lag patterns between resting-state networks (RSNs) in young adults with ASD (n = 20) and controls (n = 20) by applying the recently defined dynamic lag analysis (DLA) method, which measures time-lag variations peak-by-peak between the networks. In order to achieve temporally accurate lag patterns, the brain imaging data was acquired with a fast functional magnetic resonance imaging (fMRI) sequence (TR = 100 ms). Group-level independent component analysis was used to identify 16 RSNs for the DLA. We found altered mutual deactivation timings in ASD in (a) three of the deactivated and (b) two of the transiently anticorrelated (activated-deactivated) RSN pairs, which survived the strict threshold for significance of surrogate data. Of the significant RSN pairs, 80% included the posterior default-mode network (DMN). We propose that temporally altered deactivation mechanisms, including timings and directionality, between the posterior DMN and RSNs mediating processing of socially relevant information may contribute to the ASD phenotype. LAY SUMMARY: To understand autistic traits on a neural level, we examined temporal fluctuations in information flow between brain regions in young adults with autism spectrum disorder (ASD) and controls. We used a fast neuroimaging procedure to investigate deactivation mechanisms between brain regions. We found that timings and directionality of communication between certain brain regions were temporally altered in ASD, suggesting atypical deactivation mechanisms associated with the posterior default-mode network.

Evoked and intrinsic brain network dynamics in children with autism spectrum disorder

OBJECTIVE

Brain dynamics underlie flexible cognition and behavior, yet little is known regarding this relationship in autism spectrum disorder (ASD). We examined time-varying changes in functional co-activation patterns (CAPs) across rest and task-evoked brain states to characterize differences between children with ASD and typically developing (TD) children and identify relationships with severity of social behaviors and restricted and repetitive behaviors.

METHOD

17 children with ASD and 27 TD children ages 7-12 completed a resting-state fMRI scan and four runs of a non-cued attention switching task. Metrics indexing brain dynamics were generated from dynamic CAPs computed across three major large-scale brain networks: midcingulo-insular (M-CIN), medial frontoparietal (M-FPN), and lateral frontoparietal (L-FPN).

RESULTS

Five time-varying CAPs representing dynamic co-activations among network nodes were identified across rest and task fMRI datasets. Significant Diagnosis × Condition interactions were observed for the dwell time of CAP 3, representing co-activation between nodes of the M-CIN and L-FPN, and the frequency of CAP 1, representing co-activation between nodes of the L-FPN. A significant brain-behavior association between dwell time of CAP 5, representing co-activation between nodes of the M-FPN, and social abilities was also observed across both groups of children.

CONCLUSION

Analysis of brain co-activation patterns reveals altered dynamics among three core networks in children with ASD, particularly evident during later stages of an attention task. Dimensional analyses demonstrating relationships between M-FPN dwell time and social abilities suggest that metrics of brain dynamics may index individual differences in social cognition and behavior.