Connectivity dynamics in typical development and its relationship to autistic traits and autism spectrum disorder

Atypical dynamic network reconfiguration and genetic mechanisms in patients with major depressive disorder

BACKGROUND

Brain dynamics underlie complex forms of flexible cognition or the ability to shift between different mental modes. However, the precise dynamic reconfiguration based on multi-layer network analysis and the genetic mechanisms of major depressive disorder (MDD) remains unclear.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data were acquired from the REST-meta-MDD consortium, including 555 patients with MDD and 536 healthy controls (HC). A time-varying multi-layer network was constructed, and dynamic modular characteristics were used to investigate the network reconfiguration. Additionally, partial least squares regression analysis was performed using transcriptional data provided by the Allen Human Brain Atlas (AHBA) to identify genes associated with atypical dynamic network reconfiguration in MDD.

RESULTS

In comparison to HC, patients with MDD exhibited lower global and local recruitment coefficients. The local reduction was particularly evident in the salience and subcortical networks. Spatial transcriptome correlation analysis revealed an association between gene expression profiles and atypical dynamic network reconfiguration observed in MDD. Further functional enrichment analyses indicated that positively weighted reconfiguration-related genes were primarily associated with metabolic and biosynthetic pathways. Additionally, negatively enriched genes were predominantly related to programmed cell death-related terms.

CONCLUSIONS

Our findings offer robust evidence of the atypical dynamic reconfiguration in patients with MDD from a novel perspective. These results offer valuable insights for further exploration into the mechanisms underlying MDD.

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.

Resting-state functional connectivity in children cooled for neonatal encephalopathy

Therapeutic hypothermia improves outcomes following neonatal hypoxic-ischaemic encephalopathy, reducing cases of death and severe disability such as cerebral palsy compared with normothermia management. However, when cooled children reach early school-age, they have cognitive and motor impairments which are associated with underlying alterations to brain structure and white matter connectivity. It is unknown whether these differences in structural connectivity are associated with differences in functional connectivity between cooled children and healthy controls. Resting-state functional MRI has been used to characterize static and dynamic functional connectivity in children, both with typical development and those with neurodevelopmental disorders. Previous studies of resting-state brain networks in children with hypoxic-ischaemic encephalopathy have focussed on the neonatal period. In this study, we used resting-state fMRI to investigate static and dynamic functional connectivity in children aged 6-8 years who were cooled for neonatal hypoxic-ischaemic without cerebral palsy [n = 22, median age (interquartile range) 7.08 (6.85-7.52) years] and healthy controls matched for age, sex and socioeconomic status [n = 20, median age (interquartile range) 6.75 (6.48-7.25) years]. Using group independent component analysis, we identified 31 intrinsic functional connectivity networks consistent with those previously reported in children and adults. We found no case-control differences in the spatial maps of these intrinsic connectivity networks. We constructed subject-specific static functional connectivity networks by measuring pairwise Pearson correlations between component time courses and found no case-control differences in functional connectivity after false discovery rate correction. To study the time-varying organization of resting-state networks, we used sliding window correlations and deep clustering to investigate dynamic functional connectivity characteristics. We found k = 4 repetitively occurring functional connectivity states, which exhibited no case-control differences in dwell time, fractional occupancy or state functional connectivity matrices. In this small cohort, the spatiotemporal characteristics of resting-state brain networks in cooled children without severe disability were too subtle to be differentiated from healthy controls at early school-age, despite underlying differences in brain structure and white matter connectivity, possibly reflecting a level of recovery of healthy resting-state brain function. To our knowledge, this is the first study to investigate resting-state functional connectivity in children with hypoxic-ischaemic encephalopathy beyond the neonatal period and the first to investigate dynamic functional connectivity in any children with hypoxic-ischaemic encephalopathy.

Correlation between autistic traits and brain functional connectivity in preschoolers with autism spectrum disorder: a resting state MEG study

BACKGROUND

Neurophysiological studies recognized that Autism Spectrum Disorder (ASD) is associated with altered patterns of over- and under-connectivity. However, little is known about network organization in children with ASD in the early phases of development and its correlation with the severity of core autistic features.

METHODS

The present study aimed at investigating the association between brain connectivity derived from MEG signals and severity of ASD traits measured with different diagnostic clinical scales, in a sample of 16 children with ASD aged 2 to 6 years.

RESULTS

A significant correlation emerged between connectivity strength in cortical brain areas implicated in several resting state networks (Default mode, Central executive, Salience, Visual and Sensorimotor) and the severity of communication anomalies, social interaction problems, social affect problems, and repetitive behaviors. Seed analysis revealed that this pattern of correlation was mainly caused by global rather than local effects.

CONCLUSIONS

The present evidence suggests that altered connectivity strength in several resting state networks is related to clinical features and may contribute to neurofunctional correlates of ASD. Future studies implementing the same method on a wider and stratified sample may further support functional connectivity as a possible biomarker of the condition.

Dynamic functional connectivity in the right temporoparietal junction captures variations in male autistic trait expression

Autistic individuals can experience difficulties with attention reorienting and Theory of Mind (ToM), which are closely associated with anterior and posterior subdivisions of the right temporoparietal junction. While the link between these processes remains unclear, it is likely subserved by a dynamic crosstalk between these two subdivisions. We, therefore, examined the dynamic functional connectivity (dFC) between the anterior and posterior temporoparietal junction, as a biological marker of attention and ToM, to test its contribution to the manifestation of autistic trait expression in Autism Spectrum Condition (ASC). Two studies were conducted, exploratory (14 ASC, 15 TD) and replication (29 ASC, 29 TD), using resting-state fMRI data and the Social Responsiveness Scale (SRS) from the Autism Brain Imaging Data Exchange repository. Dynamic Independent Component Analysis was performed in both datasets using the CONN toolbox. An additional sliding-window analysis was performed in the replication study to explore different connectivity states (from highly negatively to highly positively correlated). Dynamic FC was reduced in ASC compared to TD adults in both the exploratory and replication datasets and was associated with increased SRS scores (especially in ASC). Regression analyses revealed that decreased SRS autistic expression was predicted by engagement of highly negatively correlated states, while engagement of highly positively correlated states predicted increased expression. These findings provided consistent evidence that the difficulties observed in ASC are associated with altered patterns of dFC between brain regions subserving attention reorienting and ToM processes and may serve as a biomarker of autistic trait expression.

Nodal properties of the resting-state brain functional network in childhood and adolescence

BACKGROUND AND PURPOSE

Changes in the topological properties of brain functional network nodes during childhood and adolescence can provide more detailed and intuitive information on the rules of brain development. This study aims to explore the characteristics of nodal attributes in child and adolescent brain functional networks and analyze the correlation between nodal attributes in different brain regions and age.

METHODS

Forty-two healthy volunteers aged 6-18 years who were right-handed primary and middle school students were recruited, and the subgroup analysis included children (6-12 years, n = 19) and adolescents (13-18 years, n = 23). Resting-state functional magnetic resonance imaging data were collected using a 3.0 Tesla MRI scanner. The topological properties of the functional brain network were analyzed using graph theory.

RESULTS

Compared with the children group, the degree centrality and nodal efficiency of multiple brain regions in the adolescent group were significantly increased, and the nodal shortest path was reduced (q<0.05, false discovery rate corrected). These brain regions were widely distributed in the whole brain and significantly correlated with age. Compared with the children group, reduced degree centralities were observed in the left dorsolateral fusiform gyrus, left rostral cuneus gyrus, and right medial superior occipital gyrus.

CONCLUSION

The transmission efficiency of the brain's core network gradually increased, and the subnetwork function gradually improved in children and adolescents with age. The functional development of each brain area in the occipital visual cortex was uneven and there was functional differentiation within the occipital visual cortex.

Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis

BACKGROUND

The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases.

PURPOSE

To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients.

METHODS

We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis.

RESULTS

(1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum.

CONCLUSIONS

Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: Age, sex, and behavioral associations

Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5-22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.

Sex differences of the triple network model in children with autism: A resting-state fMRI investigation of effective connectivity

Autism spectrum disorder (ASD) has a pronounced male predominance, but the underlying neurobiological basis of this sex bias remains unclear. Gender incoherence (GI) theory suggests that ASD is more neurally androgynous than same-sex controls. Given its central role, altered structures and functions, and sex-dependent network differences in ASD, the triple network model, including the central executive network (CEN), default mode network (DMN), and salience network (SN), has emerged as a candidate for characterizing this sex difference. Here, we measured the sex-related effective connectivity (EC) differences within and between these three networks in 72 children with ASD (36 females, 8-14 years) and 72 typically developing controls (TCs) (36 females, 8-14 years) from 5 sites of the Autism Brain Imaging Data Exchange repositories using a 2 × 2 analysis of covariance factorial design. We also assessed brain-behavior relationships and the effects of age on EC. We found significant diagnosis-by-sex interactions on EC: females with ASD had significantly higher EC than their male counterparts within the DMN and between the SN and CEN. The interaction pattern supported the GI theory by showing that the higher EC observed in females with ASD reflected a shift towards the higher level of EC displayed in male TCs (neural masculinization), and the lower EC seen in males with ASD reflected a shift towards the lower level of EC displayed in female TCs (neural feminization). We also found significant brain-behavior correlations and significant effects of age on EC.

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: age, sex, and behavioral associations

Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5 to 22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting-state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting-state fMRI-based measures called dynamic functional network connectivity (dFNC) using state-of-the-art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the B-SNIP biomarker-based (Biotype) approach. Statistical group differences and cross-validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM-IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM-IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM-IV and biology-based categories while also emphasizing the importance of future work in this direction, including employing further data types.

Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study

The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time-resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time-resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U-shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State-based statistical summary measures presented robust and significant group differences that also showed significant age-related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time-resolved brain state trajectories.

Exploring the longitudinal associations of functional network connectivity and psychiatric symptom changes in youth

BACKGROUND

Functional connectivity has been associated with psychiatric problems, both in children and adults, but inconsistencies are present across studies. Prior research has mostly focused on small clinical samples with cross-sectional designs.

METHODS

We adopted a longitudinal design with repeated assessments to investigate associations between functional network connectivity (FNC) and psychiatric problems in youth (9- to 17-year-olds, two time points) from the general population. The largest single-site study of pediatric neurodevelopment was used: Generation R (N = 3,131 with data at either time point). Psychiatric symptoms were measured with the Child Behavioral Checklist as broadband internalizing and externalizing problems, and its eight specific syndrome scales (e.g., anxious-depressed). FNC was assessed with two complementary approaches. First, static FNC (sFNC) was measured with graph theory-based metrics. Second, dynamic FNC (dFNC), where connectivity is allowed to vary over time, was summarized into 5 states that participants spent time in. Cross-lagged panel models were used to investigate the longitudinal bidirectional relationships of sFNC with internalizing and externalizing problems. Similar cross-lagged panel models were run for dFNC.

RESULTS

Small longitudinal relationships between dFNC and certain syndrome scales were observed, especially for baseline syndrome scales (i.e., rule-breaking, somatic complaints, thought problems, and attention problems) predicting connectivity changes. However, no association between any of the psychiatric problems (broadband and syndrome scales) with either measure of FNC survived correction for multiple testing.

CONCLUSION

We found no or very modest evidence for longitudinal associations between psychiatric problems with dynamic and static FNC in this population-based sample. Differences in findings may stem from the population drawn, study design, developmental timing, and sample sizes.

Altered temporal connectivity and reduced meta-state dynamism in adolescents born very preterm

Adolescents born very preterm have an increased risk for anxiety, social difficulties and inattentiveness, i.e. the 'preterm behavioural phenotype'. The extreme end of these traits comprises the core diagnostic features of attention and hyperactivity disorders and autism spectrum disorder, which have been reported to show aberrant dynamic resting-state functional network connectivity. This study aimed to compare this dynamism between adolescents born very preterm and controls. A resting-state functional magnetic resonance imaging was performed on 24 adolescents born very preterm (gestational age <32 weeks and/or birth weight ≤1500 g) and 32 controls born full term (≥37 weeks of gestation) at 13 years of age. Group-wise comparisons of dynamic connectivity between the resting-state networks were performed using both hard clustering and meta-state analysis of functional network connectivity. The very preterm group yielded a higher fraction of time spent in the least active connectivity state in hard clustering state functional network connectivity, even though no group differences in pairwise connectivity patterns were discovered. The meta-state analysis showed a decreased fluidity and dynamic range in the very preterm group compared with controls. Our results suggest that the 13-year-old adolescents born very preterm differ from controls in the temporal characteristics of functional connectivity. The findings may reflect the long-lasting effects of prematurity and the clinically acknowledged 'preterm behavioural phenotype'.

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.

Excessive Functional Coupling With Less Variability Between Salience and Default Mode Networks in Autism Spectrum Disorder

BACKGROUND

Atypical activity in the salience network (SN) and default mode network (DMN) has been previously reported in individuals with autism spectrum disorder (ASD). However, no study to date has investigated the nature and dynamics of the interaction between these two networks in ASD.

METHODS

Here, we aimed to characterize the functional connectivity between the SN and the DMN by using resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange and comparing individuals with ASD (n = 325) to a typically developing group (n = 356). We examined static and dynamic levels of functional connectivity using the medial prefrontal cortex (mPFC) seed as a core region of the DMN.

RESULTS

We found that individuals with ASD have higher mPFC connectivity with the insula, a core region of the SN, when compared with the typical development group. Moreover, the mPFC-insula coupling showed less variability in ASD compared with the typical development group. A novel semblance-based network dynamic analysis further confirmed that the strong mPFC-insula coupling in the ASD group reduced spontaneous attentional shift for possible external elements of the environment. Indeed, we found that excessive mPFC-insula coupling was significantly associated with a tendency for reduced social responsiveness.

CONCLUSIONS

These findings suggest that the internally oriented cognition in individuals with ASD may be due to excessive coupling between the DMN and the SN.

Atypical Dynamic Functional Network Connectivity State Engagement during Social-Emotional Processing in Schizophrenia and Autism

Autism spectrum disorder (ASD) and schizophrenia (SZ) are separate clinical entities but share deficits in social-emotional processing and static neural functional connectivity patterns. We compared patients' dynamic functional network connectivity (dFNC) state engagement with typically developed (TD) individuals during social-emotional processing after initially characterizing such dynamics in TD. Young adults diagnosed with ASD (n = 42), SZ (n = 41), or TD (n = 55) completed three functional MRI runs, viewing social-emotional videos with happy, sad, or neutral content. We examined dFNC of 53 spatially independent networks extracted using independent component analysis and applied k-means clustering to windowed dFNC matrices, identifying four unique whole-brain dFNC states. TD showed differential engagement (fractional time, mean dwell time) in three states as a function of emotion. During Happy videos, patients spent less time than TD in a happy-associated state and instead spent more time in the most weakly connected state. During Sad videos, only ASD spent more time than TD in a sad-associated state. Additionally, only ASD showed a significant relationship between dFNC measures and alexithymia and social-emotional recognition task scores, potentially indicating different neural processing of emotions in ASD and SZ. Our results highlight the importance of examining temporal whole-brain reconfiguration of FNC, indicating engagement in unique emotion-specific dFNC states.

Triple Network Model-Based Analysis on Abnormal Core Brain Functional Network Dynamics in Different Stage of Amnestic Mild Cognitive Impairment

Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage of Alzheimer’s disease (AD) because it has the same clinical symptoms as AD but with lower severity. Studies have confirmed that patients with aMCI are more likely to develop to AD. Although studies on resting state functional connectivity have revealed the abnormal organization of brain networks, the dynamic changes of the functional connectivity across the scans have been ignored. Objective: Dynamic functional connectivity is a novel method to reveal the temporal variation of brain networks. This paper aimed to investigate the dynamic characteristics of brain functional connectivity in the early and late phases of aMCI. Methods: Based on the “triple network” model, we used the sliding time window approach to construct dynamical functional networks and then analyzed the dynamic characteristics of the functional connectivity across the entire scan. Results: The results showed that patients with aMCI had longer dwell times in weaker network connection than in the strong network. The transitions between different states become more frequent, and the stability of the patient’s brain core network deteriorates. This study also found the correlation between the altered dynamic properties of the core functional networks and the patient’s clinical Mini-Mental State Examination assessment scale sores. Conclusion: This study revealed that the characteristics of dynamic functional networks constructed by the core cognitive networks varied in distinct ways at different stages of aMCI, which could provide a new idea for exploring the neuro-mechanisms of neurological disorders.

Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics

Autism spectrum disorder (ASD) is associated with the impaired integrating and segregating of related information that is expanded within the large-scale brain network. The varying ASD symptom severities have been explored, relying on their behaviors and related brain activity, but how to effectively predict ASD symptom severity needs further exploration. In this study, we aim to investigate whether the ASD symptom severity could be predicted with electroencephalography (EEG) metrics. Based on a publicly available dataset, the EEG brain networks were constructed, and four types of EEG metrics were calculated. Then, we statistically compared the brain network differences among ASD children with varying severities, i.e., high/low autism diagnostic observation schedule (ADOS) scores, as well as with the typically developing (TD) children. Thereafter, the EEG metrics were utilized to validate whether they could facilitate the prediction of the ASD symptom severity. The results demonstrated that both high- and low-scoring ASD children showed the decreased long-range frontal-occipital connectivity, increased anterior frontal connectivity and altered network properties. Furthermore, we found that the four types of EEG metrics are significantly correlated with the ADOS scores, and their combination can serve as the features to effectively predict the ASD symptom severity. The current findings will expand our knowledge of network dysfunction in ASD children and provide new EEG metrics for predicting the symptom severity.

Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease

Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.

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.

Abnormal dynamic functional connectivity in first-episode, drug-naïve adolescents with major depressive disorder

Previous neuroimaging studies have identified disrupted large-scale functional brain networks in major depressive disorder (MDD); however, most of them focused on adult patients and were based on static functional connectivity (FC). Thus, we aimed to investigate the patterns of change in dynamic FC in depressed adolescents. Resting-state functional magnetic resonance imaging data were acquired from 60 first-episode, drug-naïve adolescents with MDD and 60 matched healthy controls (HCs). Then, the dynamic FC properties were analyzed using a sliding windows approach, k-means clustering, and graph theory methods. The intrinsic brain FC were clustered into two configuration states-a more frequent and relatively sparsely connected State 1 and a less frequent and more strongly interconnected State 2. Compared with HCs, depressed adolescents had higher reoccurrence fraction and dwell time in State 1, and lower reoccurrence fraction and dwell time in State 2, and higher total number of transitions between the two states. Depressed adolescents showed decreased FC within the default mode network (DMN) and between the DMN and other networks in State 1. Additionally, the MDD group showed higher variances in the global and local efficiency. Furthermore, the duration of illness was positively correlated with the number of state transitions, and the 17-item Hamilton Depression Rating Scale score was positively correlated with the mean dwell time in State 1. This study demonstrated abnormal dynamic FC in depressed adolescents, which provided new insights into the pathophysiological mechanisms of adolescent-onset depression.

A Novel Constrained Non-negative Matrix Factorization Method for Group Functional Magnetic Resonance Imaging Data Analysis of Adult Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood psychiatric disorder that often persists into adulthood. Extracting brain networks from functional magnetic resonance imaging (fMRI) data can help explore neurocognitive disorders in adult ADHD. However, there is still a lack of effective methods to extract large-scale brain networks to identify disease-related brain network changes. Hence, this study proposed a spatial constrained non-negative matrix factorization (SCNMF) method based on the fMRI real reference signal. First, non-negative matrix factorization analysis was carried out on each subject to select the brain network components of interest. Subsequently, the available spatial prior information was mined by integrating the interested components of all subjects. This prior constraint was then incorporated into the NMF objective function to improve its efficiency. For the sake of verifying the effectiveness and feasibility of the proposed method, we quantitatively compared the SCNMF method with other classical algorithms and applied it to the dynamic functional connectivity analysis framework. The algorithm successfully extracted ten resting-state brain functional networks from fMRI data of adult ADHD and healthy controls and found large-scale brain network changes in adult ADHD patients, such as enhanced connectivity between executive control network and right frontoparietal network. In addition, we found that older ADHD spent more time in the pattern of relatively weak connectivity. These findings indicate that the method can effectively extract large-scale functional networks and provide new insights into understanding the neurobiological mechanisms of adult ADHD from the perspective of brain networks.

A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD

The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.

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.

Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence

The longitudinal study of typical neurodevelopment is key for understanding deviations due to specific factors, such as psychopathology. However, research utilizing repeated measurements remains scarce. Resting-state functional magnetic resonance imaging (MRI) studies have traditionally examined connectivity as 'static' during the measurement period. In contrast, dynamic approaches offer a more comprehensive representation of functional connectivity by allowing for different connectivity configurations (time varying connectivity) throughout the scanning session. Our objective was to characterize the longitudinal developmental changes in dynamic functional connectivity in a population-based pediatric sample. Resting-state MRI data were acquired at the ages of 10 (range 8-to-12, n = 3,327) and 14 (range 13-to-15, n = 2,404) years old using a single, study-dedicated 3 Tesla scanner. A fully-automated spatially constrained group-independent component analysis (ICA) was applied to decompose multi-subject resting-state data into functionally homogeneous regions. Dynamic functional network connectivity (FNC) between all ICA time courses were computed using a tapered sliding window approach. We used a k-means algorithm to cluster the resulting dynamic FNC windows from each scan session into five dynamic states. We examined age and sex associations using linear mixed-effects models. First, independent from the dynamic states, we found a general increase in the temporal variability of the connections between intrinsic connectivity networks with increasing age. Second, when examining the clusters of dynamic FNC windows, we observed that the time spent in less modularized states, with low intra- and inter-network connectivity, decreased with age. Third, the number of transitions between states also decreased with age. Finally, compared to boys, girls showed a more mature pattern of dynamic brain connectivity, indicated by more time spent in a highly modularized state, less time spent in specific states that are frequently observed at a younger age, and a lower number of transitions between states. This longitudinal population-based study demonstrates age-related maturation in dynamic intrinsic neural activity from childhood into adolescence and offers a meaningful baseline for comparison with deviations from typical development. Given that several behavioral and cognitive processes also show marked changes through childhood and adolescence, dynamic functional connectivity should also be explored as a potential neurobiological determinant of such changes.

Dynamic functional connectivity changes in the triple networks and its association with cognitive impairment in hemodialysis patients

INTRODUCTION

Cognitive impairment is common in hemodialysis (HD) patients; however, the underlying mechanisms have not been fully understood. The "triple-network model" that consists of the salience network (SN), central executive network (CEN), and default mode network (DMN) has been suggested to play an important role in various cognitive functions. However, dynamic functional connectivity (FC) alterations within the triple networks have not been investigated in HD patients.

METHODS

Sixty-six HD patients and 66 healthy controls (HCs) were included in this study. The triple networks were identified using a group spatial independent component analysis, and dynamic FC was analyzed using a sliding window approach and k-means clustering algorithm. Furthermore, we analyzed the relationships between altered dynamic FC parameters and clinical variables in HD patients.

RESULTS

The intrinsic brain FC within the triple networks was clustered into four configuration states. Compared with HCs, HD patients spent more time in State 1, which was characterized by weak connections between the DMN and CEN and SN. HD patients showed lower number of transitions across different states than HCs. Moreover, the number of transitions and mean dwell time in State 1 were associated with cognitive performance in HD patients.

CONCLUSION

Our findings suggest abnormal dynamic FC properties within the triple networks in HD patients, which may provide new insights into the pathophysiological mechanisms of their cognitive deficits from the perspective of dynamic FC.

Lateralization of Resting-State Networks in Children: Association with Age, Sex, Handedness, Intelligence Quotient, and Behavior

Introduction: Lateralization in brain function has been associated with age and sex in previous work; however, there has been less focus on lateralization of functional networks during development. Aim: We aim to examine laterality in typical development; a clearer understanding of how and to what extent functional brain networks are lateralized in typical development may eventually prove to hold predictive information in psychopathology. Material and Methods: In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years. This is an extension of our previous work on normal aging in adults, where we now assess whether there are similar patterns in children. Results: Unlike the results from our study of healthy aging in adults, which showed a decrease in laterality with increasing age, in this study we found both decreases and increases in lateralization in multiple networks with development. For example, auditory and sensorimotor regions had greater bilateral connectivity with development, whereas regions including the dorsolateral frontal cortex (Brodmann area left 9 and left 46) showed an increase in left lateralization with development. Conclusion: Our findings support a complex, nonlinear association between laterality and age in school-age children, a time when brain function and structure are developing rapidly. We also found brain networks in which laterality was significantly associated with sex, handedness, and intelligence quotient, but we did not find any significant association with behavioral scores. Impact statement Lateralization in brain function has been associated with age and sex in several previous studies; however, there has been less focus on lateralization of functional networks during development. A clearer understanding of how and to what extent functional brain networks are lateralized in typical development may eventually prove to hold predictive information in psychopathology. In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years.

The Prediction Analysis of Autistic and Schizotypal Traits in Attentional Networks

OBJECTIVE

Empirical findings confirmed that autistic and schizotypal traits are associated with attentional function as well as include various dimensions. So far, no study has reported which dimension of these traits relates to attentional networks. This study aimed to find out whether there are associations between attentional networks and autistic traits; and between attentional networks and schizotypal traits.

METHODS

A total of 449 volunteers was included in this study, and autism-spectrum quotient (AQ), schizotypal personality questionnaire (SPQ), and attention network test (ANT) were used to measure autistic traits and schizotypal traits. The three independent attentional networks, including alerting network, orienting network, and executive control network, were also measured.

RESULTS

Autistic traits were associated with the orienting network, whereas schizotypal traits were associated with the orienting network and executive control network. Furthermore, attentional networks could be predicted by specific dimensions of autistic and schizotypal traits. AQ-attention switching [0.104 (-1.175- -0.025), p=0.041] and AQ-attention to detail [-0.097 (-0.798- -0.001), p=0.049] were significant predictors of orienting network and gender were significant predictor of executive network (Beta=0.107; 95% CI=-0.476-10.139; p=0.031). Whereas, schizotypal dimension "interpersonal" was a significant predictor of all three attentional networks [Alerting: 0.147 (-0.010-0.861), p=0.045; Orienting: 0.147 (0.018-0.733), p=0.040; Executive: 0.198 (0.215-1.309), p=0.006].

CONCLUSION

This study demonstrated that autistic and schizotypal traits were associated with attentional networks. The specific dimensions of autistic and schizotypal traits could predict attentional networks. Nevertheless, the attentional networks predicted with these two traits were different.

Atypical core-periphery brain dynamics in autism

The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles.

Pre-eclampsia and long-term health outcomes for mother and infant: an umbrella review

BACKGROUND

Pre-eclampsia is a pregnancy-associated condition with complex disease mechanisms and a risk factor for various long-term health outcomes for the mother and infant.

OBJECTIVE

To summarise evidence on the association of pre-eclampsia with long-term health outcomes arising in women and/or infants.

SEARCH STRATEGY

PubMed, EMBASE, Scopus and ISI Web of Science were searched from inception to July 2020.

SELECTION CRITERIA

Systematic reviews and meta-analyses examining associations between pre-eclampsia and long-term health outcomes in women and their infants.

DATA COLLECTION AND ANALYSIS

Data were extracted by two independent reviewers. We re-estimated the summary effect size by random-effects and fixed-effects models, the 95% confidence interval, the 95% prediction interval, the between-study heterogeneity, any evidence of small-study effects and excess significance bias.

RESULTS

Twenty-one articles were included (90 associations). Seventy-nine associations had nominally statistically significant findings (P < 0.05). Sixty-five associations had large or very large heterogeneity. Evidence for small-study effects and excess significance bias was found in seven and two associations, respectively. Nine associations: cerebrovascular disease (cohort studies), cerebrovascular disease (overall), cardiac disease (cohort studies), dyslipidaemia (all studies), risk of death (late-onset pre-eclampsia), fatal and non-fatal ischaemic heart disease, cardiovascular mortality (cohort studies), any diabetes or use of diabetic medication (unadjusted), and attention deficit/hyperactivity disorder (ADHD) (adjusted) were supported with robust evidence.

CONCLUSION

Many of the meta-analyses in this research field have caveats casting doubts on their validity. Current evidence suggests an increased risk for women to develop cardiovascular-related diseases, diabetes and dyslipidaemia after pre-eclampsia, while offspring exposed to pre-eclampsia are at higher risk for ADHD.

TWEETABLE ABSTRACT

Cardiovascular and cerebrovascular diseases were supported with convincing evidence for long-term health outcomes after pre-eclampsia.

Altered patterns of brain dynamics linked with body mass index in youth with autism

Children with autism spectrum disorder (ASD) have higher rates of overweight and obesity (OWOB) compared with typically developing (TD) children. Brain functional connectivity differences have been shown in both ASD and OWOB. However, only one study to date has examined ASD and OWOB concurrently, so little is known regarding the neural mechanisms associated with the higher prevalence of OWOB and its behavioral impacts in ASD. We investigated co-activation patterns (CAPs) of brain regions identified by independent component analysis in 129 children and adolescents between 6 and 18 years of age (n = 68 ASD). We examined the interaction between body mass index (BMI) and diagnosis in predicting dynamic brain metrics (dwell time, DT; frequency of occurrence, and transitions between states) as well as dimensional brain-behavior relationships. The relationship between BMI and brain dynamics was moderated by diagnosis (ASD, TD), particularly among the frequency of CAP 4, characterized by co-activation of lateral frontoparietal, temporal, and frontal networks. This pattern was negatively associated with parent-reported inhibition skills. Children with ASD had shorter CAP 1, characterized by co-activation of the subcortical, temporal, sensorimotor, and frontal networks, and CAP 4 DTs compared with TD children. CAP 1 DT was negatively associated with cognitive flexibility, inhibition, social functioning, and BMI. Cognitive flexibility moderated the relationship between BMI and brain dynamics in the visual network. Our findings provide novel evidence of neural mechanisms associated with OWOB in children with ASD. Further, poorer cognitive flexibility may result in increased vulnerability for children with ASD and co-occurring OWOB. LAY SUMMARY: Obesity is a societal epidemic and is common in autism, however, little is known about the neural mechanisms associated with the higher rates of obesity in autism. Here, we find unique patterns of brain dynamics associated with obesity in autism that were not observed in typically developing children. Further, the relationship between body mass index and brain dynamics depended on cognitive flexibility. These findings suggest that individuals with autism may be more vulnerable to the effects of obesity on brain function. Autism Res 2021, 14: 873-886. © 2021 International Society for Autism Research, Wiley Periodicals LLC.

The Development of Brain Network in Males with Autism Spectrum Disorders from Childhood to Adolescence: Evidence from fNIRS Study

In the current study, functional near-infrared spectroscopy (fNIRS) was used to collect resting-state signals from 77 males with autism spectrum disorders (ASD, age: 6~16.25) and 40 typically developing (TD) males (age: 6~16.58) in the theory-of-mind (ToM) network. The graph theory analysis was used to obtain the brain network properties in ToM network, and the multiple regression analysis demonstrated that males with ASD showed a comparable global network topology, and a similar age-related decrease in the medial prefrontal cortex area (mPFC) compared to TD individuals. Nevertheless, participants with ASD showed U-shaped trajectories of nodal metrics of right temporo-parietal junction (TPJ), and an age-related decrease in the left middle frontal gyrus (MFG), while trajectories of TD participants were opposite. The nodal metrics of the right TPJ was negatively associated with the social deficits of ASD, while the nodal metrics of the left MFG was negatively associated with the communication deficits of ASD. Current findings suggested a distinct developmental trajectory of the ToM network in males with ASD from childhood to adolescence.

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.

Comprehensive Integrative Analyses Identify TIGD5 rs75547282 as a Risk Variant for Autism Spectrum Disorder

Although recent genome-wide association studies have identified risk loci that strongly associates with autism spectrum disorder (ASD), how to pinpoint the causal genes remains a challenge. We aimed to pinpoint the potential causal genes and explore the possible susceptibility and mechanism. A convergent functional genomics (CFG) method was used to prioritize the candidate genes by combining lines of evidence, including Sherlock analysis, spatio-temporal expression patterns, expression analysis, protein-protein interactions, co-expression and association with brain structure. A higher score in the CFG approach suggested that more evidence supported this gene as an ASD risk gene. We screened genes with higher CFG scores for candidate functional single nucleotide polymorphisms (SNPs). A genotyping experiment (602 ASD children and 604 healthy sex-matched children) and the dual-luciferase reporter gene assay were followed to validate the effects of SNPs. We identified three genes (MAPT, ZNF285, and TIGD5) as candidate causal genes using the CFG approach. The genotyping experiment showed that TIGD5 rs75547282 was associated with an increased risk of ASD under the dominant model (OR = 1.37, 95% CI = 1.09-1.72, P = 0.006) though the statistical power was limited (5.2%). The T allele of rs75547282 activated the expression of TIGD5 compared with the C allele in the dual-luciferase reporter assay. Our study indicates that such comprehensive integrative analyses may be an effective way to explore promising ASD susceptibility variants and needs to be further investigated in future research. Genotyping experiments should, however, be based on a larger population sample to increase statistical power. LAY SUMMARY: We set out to pinpoint the potential causal genes of ASD and explore the possible susceptibility and mechanism by combining lines of evidence from different analyses. Our results show that TIGD5 rs75547282 is associated with the risk of ASD in the Han Chinese population. In addition, a similar framework to seek promising ASD risk variants could be further investigated in future research Autism Res 2021, 14: 631-644. © 2021 International Society for Autism Research and Wiley Periodicals LLC.

Role of Oligodendrocytes and Myelin in the Pathophysiology of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is an early neurodevelopmental disorder that involves deficits in interpersonal communication, social interaction, and repetitive behaviors. Although ASD pathophysiology is still uncertain, alterations in the abnormal development of the frontal lobe, limbic areas, and putamen generate an imbalance between inhibition and excitation of neuronal activity. Interestingly, recent findings suggest that a disruption in neuronal connectivity is associated with neural alterations in white matter production and myelination in diverse brain regions of patients with ASD. This review is aimed to summarize the most recent evidence that supports the notion that abnormalities in the oligodendrocyte generation and axonal myelination in specific brain regions are involved in the pathophysiology of ASD. Fundamental molecular mediators of these pathological processes are also examined. Determining the role of alterations in oligodendrogenesis and myelination is a fundamental step to understand the pathophysiology of ASD and identify possible therapeutic targets.

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.

Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder

Brain connectivity studies of autism spectrum disorder (ASD) have historically relied on static measures of functional connectivity. Recent work has focused on identifying transient configurations of brain activity, yet several open questions remain regarding the nature of specific brain network dynamics in ASD. We used a dynamic coactivation pattern (CAP) approach to investigate the salience/midcingulo-insular (M-CIN) network, a locus of dysfunction in ASD, in a large multisite resting-state fMRI dataset collected from 172 children (ages 6–13 years; n = 75 ASD; n = 138 male). Following brain parcellation by using independent component analysis, dynamic CAP analyses were conducted and k-means clustering was used to determine transient activation patterns of the M-CIN. The frequency of occurrence of different dynamic CAP brain states was then compared between children with ASD and typically developing (TD) children. Dynamic brain configurations characterized by coactivation of the M-CIN with central executive/lateral fronto-parietal and default mode/medial fronto-parietal networks appeared less frequently in children with ASD compared with TD children. This study highlights the utility of time-varying approaches for studying altered M-CIN function in prevalent neurodevelopmental disorders. We speculate that altered M-CIN dynamics in ASD may underlie the inflexible behaviors commonly observed in children with the disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with altered patterns of functional brain connectivity. Little is currently known about how moment-to-moment brain dynamics differ in children with ASD and typically developing (TD) children. Altered functional integrity of the midcingulo-insular network (M-CIN) has been implicated in the neurobiology of ASD. Here we use a novel coactivation analysis approach applied to a large sample of resting-state fMRI data collected from children with ASD and TD children to demonstrate altered patterns of M-CIN dynamics in children with the disorder. We speculate that these atypical patterns of brain dynamics may underlie behavioral inflexibility in ASD.

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.

Children With Autism Produce a Unique Pattern of EEG Microstates During an Eyes Closed Resting-State Condition

Although fMRI studies have produced considerable evidence for differences in the spatial connectivity of resting-state brain networks in persons with autism spectrum disorder (ASD) relative to typically developing (TD) peers, little is known about the temporal dynamics of these brain networks in ASD. The aim of this study was to examine the EEG microstate architecture in children with ASD as compared to TD at rest in two separate conditions – eyes-closed (EC) and eyes-open (EO). EEG microstate analysis was performed on resting-state data of 13 ASD and 13 TD children matched on age, gender, and IQ. We found that children with ASD and TD peers produced topographically similar canonical microstates at rest. Group differences in the duration and frequency of these microstates were found primarily in the EC resting-state condition. In line with previous fMRI findings that have reported differences in spatial connectivity within the salience network (previously correlated with the activity of microstate C) in ASD, we found that the duration of activation of microstate C was increased, and the frequency of microstate C was decreased in ASD as compared to TD in EC resting-state. Functionally, these results may be reflective of alterations in interoceptive processes in ASD. These results suggest a unique pattern of EEG microstate architecture in ASD relative to TD during resting-states and also that EEG microstate parameters in ASD are susceptible to differences in resting-state conditions.

Dynamic Resting-State Connectivity Differences in Eyes Open Versus Eyes Closed Conditions

Introduction: Previous studies have shown significant conditional differences between eyes open, fixated at an image (EO) and eyes closed (EC) in the acquired resting-state functional magnetic resonance imaging (rs-fMRI) data. Aim: We recently showed significant functional network connectivity (FNC) differences between EO and EC across a variety of networks. In this study, we aim at further evaluating differences in dynamic FNC (dFNC) between EO and EC. Materials and Methods: Rs-fMRI were collected from adolescents aged 9-15 years old during both EO and EC conditions, and dFNC was calculated by using the independent component analysis framework. Results: We found that out of five states (clusters), state 1 was observed to be more dominant in the EO condition, whereas state 2 was observed to be more dominant in the EC condition. States 1 and 2 showed significant differences in the mean dwell time based on false discovery rate, and states 1, 2, 3, and 4 differed in the frequency of occurrences. These results are consistent with our previous study of static connectivity in suggesting that EO and EC differences not only are relatively strong but also importantly reveal that these differences vary over time, especially in one particularly transient connectivity pattern. Conclusion: Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. Overall, our findings indicate that both static and dynamic rs-fMRI connectivity patterns are strongly impacted by basic conditional differences such as EO and EC. Impact statement Our findings not only suggest that eyes open, fixated at an image (EO) and eyes closed (EC) condition-related resting state functional magnetic resonance imaging differences are relatively strong, but they also reveal an important attribute of these conditions that these differences vary over time, especially in one particularly transient connectivity pattern. Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. We believe there is benefit in having the EO/EC as a contrast of interest in future studies, if time allows.

Beyond diagnosis: Cross-diagnostic features in canonical resting-state networks in children with neurodevelopmental disorders

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Resting-state connectivity did not differ across neurodevelopmental disorders.

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General adaptive function across all participants related to subcortical connectivity.

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Participants in the same data-driven clusters were highly heterogeneous in diagnosis.

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Neurobiological similarity and dissimilarity may be seen in beyond-diagnosis categories.

Children with neurodevelopmental disorders (NDDs) share common behavioural manifestations despite distinct categorical diagnostic criteria. Here, we examined canonical resting-state network connectivity in three diagnostic groups (autism spectrum disorder, attention-deficit/hyperactivity disorder and paediatric obsessive–compulsive disorder) and typically developing controls (TD) in a large single-site sample (N = 407), applying diagnosis-based and dimensional approaches to understand underlying neurobiology across NDDs. Each participant’s functional network graphs were computed using five graph metrics. In diagnosis-based comparisons, an analysis of covariance was performed to compare all NDDs to TD, followed by pairwise comparisons between NDDs. In the dimensional approach, participants’ functional network graphs were correlated with continuous behavioural measures, and a data-driven k-means clustering analysis was applied to determine if subgroups of participants were seen, without diagnostic information having been included. In the diagnosis-based comparisons, children with NDDs did not differ significantly from the TD group and the NDD categorical groups also did not differ significantly from each other, across all graph metrics. In the dimensional, diagnostic-independent approach, however, subcortical functional connectivity was significantly correlated with participants’ general adaptive functioning across all participants. The clustering analysis identified an optimal solution of two clusters, and participants assigned in the same data-driven cluster were highly heterogeneous in diagnosis. Neither cluster exclusively contained a specific diagnostic group, nor did NDDs separate cleanly from TDs. Each participant’s distance ratio between the two clusters was significantly correlated with general adaptive functioning, social deficits and attentional problems. Our results suggest the neurobiological similarity and dissimilarity between NDDs need to be investigated beyond DSM/ICD-based, behaviourally-defined diagnostic categories.

Children With Autism Produce a Unique Pattern of EEG Microstates During an Eyes Closed Resting-State Condition

Although fMRI studies have produced considerable evidence for differences in the spatial connectivity of resting-state brain networks in persons with autism spectrum disorder (ASD) relative to typically developing (TD) peers, little is known about the temporal dynamics of these brain networks in ASD. The aim of this study was to examine the EEG microstate architecture in children with ASD as compared to TD at rest in two separate conditions - eyes-closed (EC) and eyes-open (EO). EEG microstate analysis was performed on resting-state data of 13 ASD and 13 TD children matched on age, gender, and IQ. We found that children with ASD and TD peers produced topographically similar canonical microstates at rest. Group differences in the duration and frequency of these microstates were found primarily in the EC resting-state condition. In line with previous fMRI findings that have reported differences in spatial connectivity within the salience network (previously correlated with the activity of microstate C) in ASD, we found that the duration of activation of microstate C was increased, and the frequency of microstate C was decreased in ASD as compared to TD in EC resting-state. Functionally, these results may be reflective of alterations in interoceptive processes in ASD. These results suggest a unique pattern of EEG microstate architecture in ASD relative to TD during resting-states and also that EEG microstate parameters in ASD are susceptible to differences in resting-state conditions.

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.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Tools of the trade: estimating time-varying connectivity patterns from fMRI data

Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.