Partially impaired functional connectivity states between right anterior insula and default mode network in autism spectrum disorder

T1w/T2w ratio maps identify children with autism spectrum disorder and the relationships between myelin-related changes and symptoms

BACKGROUND

Modern neuroimaging methods have revealed that autistic symptoms are associated with abnormalities in brain morphology, connectivity, and activity patterns. However, the changes in brain microstructure underlying the neurobiological and behavioral deficits of autism remain largely unknown.

METHODS

we characterized the associated abnormalities in intracortical myelination pattern by constructing cortical T1-weighted/T2-weighted ratio maps. Voxel-wise comparisons of cortical myelination were conducted between 150 children with autism spectrum disorder (ASD) and 139 typically developing (TD) children. Group differences in cortical T1-weighted/T2-weighted ratio and gray matter volume were then examined for associations with autistic symptoms. A convolutional neural network (CNN) model was also constructed to examine the utility of these regional abnormalities in cortical myelination for ASD diagnosis.

RESULTS

Compared to TD children, the ASD group exhibited widespread reductions in cortical myelination within regions related to default mode, salience, and executive control networks such as the inferior frontal gyrus, bilateral insula, left fusiform gyrus, bilateral hippocampus, right calcarine sulcus, bilateral precentral, and left posterior cingulate gyrus. Moreover, greater myelination deficits in most of these regions were associated with more severe autistic symptoms. In addition, children with ASD exhibited reduced myelination in regions with greater gray matter volume, including left insula, left cerebellum_4_5, left posterior cingulate gyrus, and right calcarine sulcus. Notably, the CNN model based on brain regions with abnormal myelination demonstrated high diagnostic efficacy for ASD.

CONCLUSIONS

Our findings suggest that microstructural abnormalities in myelination contribute to autistic symptoms and so are potentially promising therapeutic targets as well as biomarkers for ASD diagnosis.

Functional connectivity of the posterior cingulate cortex in autism spectrum disorder

The purpose of this study was to assess the functional connectivity of the posterior cingulate cortex in autism spectrum disorder (ASD). We used resting-state functional magnetic resonance imaging (rsfMRI) brain scans of adolescents diagnosed with ASD and a neurotypical control group. The Autism Brain Imaging Data Exchange (ABIDE) consortium was utilized to acquire data from the University of Michigan (145 subjects) and data from the New York University (183 subjects). The posterior cingulate cortex showed reduced connectivity with the anterior cingulate cortex for the ASD group compared to the control group. These two brain regions have previously both been linked to ASD symptomology. Specifically, the posterior cingulate cortex has been associated with behavioral control and executive functions, which appear to be responsible for the repetitive and restricted behaviors (RRB) in ASD. Our findings support previous data indicating a neurobiological basis of the disorder, and the specific functional connectivity changes involving the posterior cingulate cortex and anterior cingulate cortex may be a potential neurobiological biomarker for the observed RRBs in ASD.

Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles

Although it is well recognized that autism spectrum disorder (ASD) is associated with atypical dynamic functional connectivity patterns, the dynamic changes in brain intrinsic activity over each time point and the potential molecular mechanisms associated with atypical dynamic temporal characteristics in ASD remain unclear. Here, we employed the Hidden Markov Model (HMM) to explore the atypical neural configuration at every scanning time point in ASD, based on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange. Subsequently, partial least squares regression and pathway enrichment analysis were employed to explore the potential molecular mechanism associated with atypical neural dynamics in ASD. 8 HMM states were inferred from rs-fMRI data. Compared to typically developing, individuals on the autism spectrum showed atypical state-specific temporal characteristics, including number of states and occurrences, mean life time and transition probability between states. Moreover, these atypical temporal characteristics could predict communication difficulties of ASD, and states assoicated with negative activation in default mode network and frontoparietal network, and positive activation in somatomotor network, ventral attention network, and limbic network, had higher predictive contribution. Furthermore, a total of 321 genes was revealed to be significantly associated with atypical dynamic brain states of ASD, and these genes are mainly enriched in neurodevelopmental pathways. Our study provides new insights into characterizing the atypical neural dynamics from a moment-to-moment perspective, and indicates a linkage between atypical neural configuration and gene expression in ASD.

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

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

Resolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder

BACKGROUND

Heterogeneity in resting-state functional connectivity (FC) are one of the characteristics of autism spectrum disorder (ASD). Traditional resting-state FC primarily focuses on linear correlations, ignoring the nonlinear properties involved in synchronization between networks or brain regions.

METHODS

In the present study, the cross-recurrence quantification analysis, a nonlinear method based on dynamical systems, was utilized to quantify the synchronization stability between brain regions within the salience network (SN) of ASD. Using the resting-state functional magnetic resonance imaging data of 207 children (ASD/typically-developing controls (TC): 105/102) in Autism Brain Imaging Data Exchange database, we analyzed the laminarity and trapping time differences of the synchronization stability between the ASD subtype derived by a K-means clustering analysis and the TC group, and examined the relationship between synchronization stability and the severity of clinical symptoms of the ASD subtypes.

RESULTS

Based on the synchronization stability within the SN of ASD, we identified two subtypes that showed opposite changes in synchronization stability relative to the TC group. In addition, the synchronization stability of ASD subtypes 1 and 2 can predict the social interaction and communication impairments, respectively.

CONCLUSIONS

These findings reveal that ASD subgroups with different patterns of synchronization stability within the SN appear distinct clinical symptoms, and highlight the importance of exploring the potential neural mechanism of ASD from a nonlinear perspective.

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

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

Associations between thalamocortical functional connectivity and sensory over-responsivity in infants at high likelihood for ASD

Despite growing evidence implicating thalamic functional connectivity atypicalities in autism spectrum disorder (ASD), it remains unclear how such alterations emerge early in human development. Because the thalamus plays a critical role in sensory processing and neocortical organization early in life, its connectivity with other cortical regions could be key for studying the early onset of core ASD symptoms. Here, we investigated emerging thalamocortical functional connectivity in infants at high (HL) and typical (TL) familial likelihood for ASD in early and late infancy. We report significant thalamo-limbic hyperconnectivity in 1.5-month-old HL infants, and thalamo-cortical hypoconnectivity in prefrontal and motor regions in 9-month-old HL infants. Importantly, early sensory over-responsivity (SOR) symptoms in HL infants predicted a direct trade-off in thalamic connectivity whereby stronger thalamic connectivity with primary sensory regions and basal ganglia was inversely related to connectivity with higher order cortices. This trade-off suggests that ASD may be characterized by early differences in thalamic gating. The patterns reported here could directly underlie atypical sensory processing and attention to social vs. nonsocial stimuli observed in ASD. These findings lend support to a theoretical framework of ASD whereby early disruptions in sensorimotor processing and attentional biases early in life may cascade into core ASD symptomatology.

Disrupted functional networks within white-matter served as neural features in adolescent patients with conduct disorder

BACKGROUND

Conduct disorder (CD) has been conceptualized as a psychiatric disorder associated with white-matter (WM) structural abnormalities. Although diffusion tensor imaging could identify WM structural architecture changes, it cannot characterize functional connectivity (FC) within WM. Few studies have focused on disentangling the WM dysfunctions in CD patients by using functional magnetic resonance imaging (fMRI).

METHODS

The resting-state fMRI data were first obtained from both adolescent CD and typically developing (TD) controls. A voxel-based clustering analysis was utilized to identify the large-scale WM FC networks. Then, we examined the disrupted WM network features in CD, and further investigated whether these features could predict the impulsive symptoms in CD using support vector regression prediction model.

RESULTS

We identified 11 WM functional networks. Compared with TDs, CD patients showed increased FCs between occipital network (ON) and superior temporal network (STN), between orbitofrontal network (OFN) and corona radiate network (CRN), as well as between deep network and CRN. Further, the disrupted FCs between ON and STN and between OFN and CRN were significantly negatively associated with non-planning impulsivity scores in CD. Moreover, the disrupted WM networks could be served as features to predict the motor impulsivity scores in CD.

CONCLUSIONS

Our results provided further support on the existence of WM functional networks and could extended our knowledge about the WM functional abnormalities related with emotional and perception processing in CD patients from the view of WM dysfunction.

Altered cortical gyrification, sulcal depth, and fractal dimension in the autism spectrum disorder comorbid attention-deficit/hyperactivity disorder than the autism spectrum disorder

Autism spectrum disorder (ASD) frequently occurs accompanied by attention-deficit/hyperactivity disorder (ADHD), which catches increasing attention. The comorbid diagnosis of ASD with ADHD (ASD + ADHD) is permitted in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). However, compared to autism spectrum disorder without other symptoms (ASD-only), the special neural underpinnings in ASD+ADHD remain unclear. Therefore, this study aimed to uncover the differences in cortical complexity between ASD + ADHD and ASD-only patients. A total of 114 ASD participants (i.e. containing 40 ASD + ADHD and 74 ASD-only participants) with T1-weighted magnetic resonance images were collected from the Autism Brain Imaging Data Exchange II. Afterward, a surface-based morphometry method was carried out to compare the cortical complexity (i.e. gyrification index, fractal dimension, and sulcal depth) between the ASD + ADHD and ASD-only cohorts. Results showed the increased fractal dimension in the right fusiform gyrus of the ASD + ADHD cohort in comparison to the ASD-only cohort. Moreover, the ASD + ADHD cohort exhibited increased sulcal depth in the left middle temporal gyrus/inferior temporal gyrus and right middle temporal gyrus compared to the ASD-only cohort. Last but not least, the increased gyrification index in the insula/postcentral gyrus was observed in the ASD + ADHD cohort in comparison to the ASD-only cohort. Overall, the present study contributes to the delineation of particular structural abnormalities in ASD + ADHD than ASD-only, enriching the evidence of the combined phenotype of ASD + ADHD.

Shared and distinct patterns of dynamic functional connectivity variability of thalamo-cortical circuit in bipolar depression and major depressive disorder

Evidence has indicated abnormalities of thalamo-cortical functional connectivity (FC) in bipolar disorder during a depressive episode (BDD) and major depressive disorder (MDD). However, the dynamic FC (dFC) within this system is poorly understood. We explored the thalamo-cortical dFC pattern by dividing thalamus into 16 subregions and combining with a sliding-window approach. Correlation analysis was performed between altered dFC variability and clinical data. Classification analysis with a linear support vector machine model was conducted. Compared with healthy controls (HCs), both patients revealed increased dFC variability between thalamus subregions with hippocampus (HIP), angular gyrus and caudate, and only BDD showed increased dFC variability of the thalamus with superior frontal gyrus (SFG), HIP, insula, middle cingulate gyrus, and postcentral gyrus. Compared with MDD and HCs, only BDD exhibited enhanced dFC variability of the thalamus with SFG and superior temporal gyrus. Furthermore, the number of depressive episodes in MDD was significantly positively associated with altered dFC variability. Finally, the disrupted dFC variability could distinguish BDD from MDD with 83.44% classification accuracy. BDD and MDD shared common disrupted dFC variability in the thalamo-limbic and striatal-thalamic circuitries, whereas BDD exhibited more extensive and broader aberrant dFC variability, which may facilitate distinguish between these 2 mood disorders.

Catecholaminergic and cholinergic neuromodulation in autism spectrum disorder: A comparison to attention-deficit hyperactivity disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by social impairments and restricted, repetitive behaviors. Treatment of ASD is notoriously difficult and might benefit from identification of underlying mechanisms that overlap with those disturbed in other developmental disorders, for which treatment options are more obvious. One example of the latter is attention-deficit hyperactivity disorder (ADHD), given the efficacy of especially stimulants in treatment of ADHD. Deficiencies in catecholaminergic systems [dopamine (DA), norepinephrine (NE)] in ADHD are obvious targets for stimulant treatment. Recent findings suggest that dysfunction in catecholaminergic systems may also be a factor in at least a subgroup of ASD. In this review we scrutinize the evidence for catecholaminergic mechanisms underlying ASD symptoms, and also include in this analysis a third classic ascending arousing system, the acetylcholinergic (ACh) network. We complement this with a comprehensive review of DA-, NE-, and ACh-targeted interventions in ASD, and an exploratory search for potential treatment-response predictors (biomarkers) in ASD, genetically or otherwise. Based on this review and analysis we propose that (1) stimulant treatment may be a viable option for an ASD subcategory, possibly defined by genetic subtyping; (2) cerebellar dysfunction is pronounced for a relatively small ADHD subgroup but much more common in ASD and in both cases may point toward NE- or ACh-directed intervention; (3) deficiency of the cortical salience network is sizable in subgroups of both disorders, and biomarkers such as eye blink rate and pupillometric data may predict the efficacy of targeting this underlying deficiency via DA, NE, or ACh in both ASD and ADHD.

Altered time-varying local spontaneous brain activity pattern in patients with high myopia: a dynamic amplitude of low-frequency fluctuations study

PURPOSE

To investigate the abnormal time-varying local spontaneous brain activity in patients with high myopia (HM) on the basis of the dynamic amplitude of low-frequency fluctuations (dALFF) approach.

METHODS

Age and gender matching were performed based on resting-state functional magnetic resonance imaging data from 86 HM patients and 87 healthy controls (HCs). Local spontaneous brain activities were evaluated using the time-varying dALFF method. Support vector machine combined with the radial basis function kernel was used for pattern classification analysis.

RESULTS

Inter-group comparison between HCs and HM patients has demonstrated that dALFF variability in the left inferior frontal gyrus (orbital part), left lingual gyrus, right anterior cingulate and paracingulate gyri, and right calcarine fissure and surrounding cortex was decreased in HM patients, while increased in the left thalamus, left paracentral lobule, and left inferior parietal (except supramarginal and angular gyri). Pattern classification between HM patients and HCs displayed a classification accuracy of 85.5%.

CONCLUSION

In this study, the findings mentioned above have suggested the association between local brain activities of HM patients and abnormal variability in brain regions performing visual sensorimotor and attentional control functions. Several useful information has been provided to elucidate the mechanism-related alterations of the myopic nervous system. In addition, the significant role of abnormal dALFF variability has been highlighted to achieve an in-depth comprehension of the pathological alterations and neuroimaging mechanisms in the field of HM.

Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression

Autism spectrum disorder (ASD) is a complex brain neurodevelopmental disorder related to brain activity and genetics. Most of the ASD diagnostic models perform feature selection at the group level without considering individualized information. Evidence has shown the unique topology of the individual brain has a fundamental impact on brain diseases. Thus, a data-constructing method fusing individual topological information and a corresponding classification model is crucial in ASD diagnosis and biomarker discovery. In this work, we trained an attention-based graph neural network (GNN) to perform the ASD diagnosis with the fusion of graph data. The results achieved an accuracy of 79.78%. Moreover, we found the model paid high attention to brain regions mainly involved in the social-brain circuit, default-mode network, and sensory perception network. Furthermore, by analyzing the covariation between functional magnetic resonance imaging data and gene expression, current studies detected several ASD-related genes (i.e. MUTYH, AADAT, and MAP2), and further revealed their links to image biomarkers. Our work demonstrated that the ASD diagnostic framework based on graph data and attention-based GNN could be an effective tool for ASD diagnosis. The identified functional features with high attention values may serve as imaging biomarkers for ASD.

Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks.

METHODS

Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model.

RESULTS

Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively.

LIMITATIONS

Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity.

CONCLUSIONS

These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations 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.

Brain Structural Covariance Network in Asperger Syndrome Differs From Those in Autism Spectrum Disorder and Healthy Controls

Introduction

Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD).

Method

We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14-50, IQ=92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144).

Result

The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD.

Conclusion

This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.

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.

Hypoconnectivity between anterior insula and amygdala associates with future vulnerabilities in social development in a neurodiverse sample of neonates

Altered resting state functional connectivity (FC) involving the anterior insula (aINS), a key node in the salience network, has been reported consistently in autism. Here we examined, for the first time, FC between the aINS and the whole brain in a sample of full-term, postmenstrual age (PMA) matched neonates (mean 44.0 weeks, SD = 1.5) who due to family history have high likelihood (HL) for developing autism (n = 12) and in controls (n = 41) without family history of autism (low likelihood, LL). Behaviors associated with autism were evaluated between 12 and 18 months (M = 17.3 months, SD = 2.5) in a subsample (25/53) of participants using the First Year Inventory (FYI). Compared to LL controls, HL neonates showed hypoconnectivity between left aINS and left amygdala. Lower connectivity between the two nodes was associated with higher FYI risk scores in the social domain (r(25) = -0.561, p = .003) and this association remained robust when maternal mental health factors were considered. Considering that a subsample of LL participants (n = 14/41) underwent brain imaging during the fetal period at PMA 31 and 34 weeks, in an exploratory analysis, we evaluated prospectively development of the LaINS-Lamy connectivity and found that the two areas strongly coactivate throughout the third trimester of pregnancy. The study identifies left lateralized anterior insula-amygdala connectivity as a potential target of further investigation into neural circuitry that enhances likelihood of future onset of social behaviors associated with autism during neonatal and potentially prenatal periods.

Mapping brain functional and structural abnormities in autism spectrum disorder: moving toward precision treatment

Autism spectrum disorder (ASD) is a formidable challenge for psychiatry and neuroscience because of its high prevalence, lifelong nature, complexity, and substantial heterogeneity. A major goal of neuroimaging studies of ASD is to understand the neurobiological underpinnings of this disorder from multi-dimensional and multi-level perspectives, by investigating how brain anatomy, function, and connectivity are altered in ASD, and how they vary across the population. However, ongoing debate exists within those studies, and neuroimaging findings in ASD are often contradictory. Over the past decade, we have dedicated to delineate a comprehensive and consistent mapping of the abnormal structure and function of the autistic brain, and this review synthesizes the findings across our studies reaching a consensus that the "social brain" are the most affected regions in the autistic brain at different levels and modalities. We suggest that the social brain network can serve as a plausible biomarker and potential target for effective intervention in individuals with ASD.

Altered resting-state functional connectivity of insula in children with primary nocturnal enuresis

Objective

Primary nocturnal enuresis (PNE) is a common developmental condition in school-aged children. The objective is to better understand the pathophysiology of PNE by using insula-centered resting-state functional connectivity (rsFC).

Methods

We recruited 66 right-handed participants in our analysis, 33 with PNE and 33 healthy control (HC) children without enuresis matched for gender and age. Functional and structural MRI data were obtained from all the children. Seed-based rsFC was used to examine differences in insular functional connectivity between the PNE and HC groups. Correlation analyses were carried out to explore the relationship between abnormal insula-centered functional connectivity and clinical characteristics in the PNE group.

Results

Compared with HC children, the children with PNE demonstrated decreased left and right insular rsFC with the right medial superior frontal gyrus (SFG). In addition, the bilateral dorsal anterior insula (dAI) seeds also indicated the reduced rsFC with right medial SFG. Furthermore, the right posterior insula (PI) seed showed the weaker rsFC with the right medial SFG, while the left PI seed displayed the weaker rsFC with the right SFG. No statistically significant correlations were detected between aberrant insular rsFC and clinical variables (e.g., micturition desire awakening, bed-wetting frequency, and bladder volume) in results without global signal regression (GSR) in the PNE group. However, before and after setting age as a covariate, significant and positive correlations between bladder volume and the rsFC of the left dAI with right medial SFG and the rsFC of the right PI with right medial SFG were found in results with GSR in the PNE group.

Conclusion

To the best of our knowledge, this study explored the rsFC patterns of the insula in children with PNE for the first time. These results uncovered the abnormal rsFC of the insula with the medial prefrontal cortex without and with GSR in the PNE group, suggesting that dysconnectivity of the salience network (SN)-default mode network (DMN) may involve in the underlying pathophysiology of children with PNE. However, the inconsistent associations between bladder volume and dysconnectivity of the SN-DMN in results without and with GSR need further studies.

Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by substantial clinical and biological heterogeneity. Quantitative and individualized metrics for delineating the heterogeneity of brain structure in ASD are still lacking. Likewise, the extent to which brain structural metrics of ASD deviate from typical development (TD) and whether deviations can be used for parsing brain structural phenotypes of ASD is unclear.

METHODS

T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Data Exchange (ABIDE) II (n_TD = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (n_TD = 560, n_ASD = 496). Voxel-based morphometry was used to compute gray matter volume. Non-negative matrix factorization was employed to decompose the gray matter matrix into 6 factors and weights. These weights were used for normative modeling to estimate the factor deviations. Then, clustering analysis was used to identify ASD subtypes.

RESULTS

Compared with TD, ASD showed increased weights and deviations in 5 factors. Three subtypes with distinct neuroanatomical deviation patterns were identified. ASD subtype 1 and subtype 3 showed positive deviations, whereas ASD subtype 2 showed negative deviations. Distinct clinical manifestations in social communication deficits were identified among the three subtypes.

CONCLUSIONS

Our findings suggest that individuals with ASD have heterogeneous deviation patterns in brain structure. The results highlight the need to test for subtypes in neuroimaging studies of ASD. This study also presents a framework for understanding neuroanatomical heterogeneity in this increasingly prevalent neurodevelopmental disorder.

Dynamic connectivity alterations in anterior cingulate cortex associated with suicide attempts in bipolar disorders with a current major depressive episode

OBJECTIVE

Considering that the physiological mechanism of the anterior cingulate cortex (ACC) in suicide brain remains elusive for bipolar disorder (BD) patients. The study aims to investigate the intrinsic relevance between ACC and suicide attempts (SA) through transient functional connectivity (FC).

METHODS

We enrolled 50 un-medicated BD patients with at least one SA, 67 none-suicide attempt patients (NSA) and 75 healthy controls (HCs). The sliding window approach was utilized to study the dynamic FC of ACC via resting-state functional MRI data. Subsequently, we probed into the temporal properties of dynamic FC and then estimated the relationship between dynamic characteristics and clinical variables using the Pearson correlation.

RESULTS

We found six distinct FC states in all populations, with one of them being more associated with SA. Compared with NSA and HCs, the suicide-related functional state showed significantly reduced dwell time in SA patients, accompanied by a significantly increased FC strength between the right ACC and the regions within the subcortical (SubC) network. In addition, the number of transitions was significantly increased in SA patients relative to other groups. All these altered indicators were significantly correlated with the suicide risk.

CONCLUSIONS

The results suggested that the dysfunction of ACC was relevant to SA from a dynamic FC perspective in BD patients. It highlights the temporal properties in dynamic FC of ACC that could be used as a putative target of suicide risk assessment for BD patients.

Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study

The resting‐state human brain is a dynamic system that shows frequency‐dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub‐bands from slow‐5 (0.01–0.027 Hz), slow‐4 (0.027–0.073 Hz), slow‐3 (0.073–0.198 Hz), to slow‐2 (0.198–0.25 Hz), in addition to the typical low‐frequency range (0.01–0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow‐5, 4, and 3, but not in slow‐2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between‐state transitions. Specifically, from slow‐5 to slow‐4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow‐5. Using leave‐one‐pair‐out, hold‐out and resampling validations, the highest classification accuracy (84%) was achieved by slow‐4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.

Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model

Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model (HMM) analysis for resting-state functional MRI (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange (ABIDE). Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls (HCs). Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks [including visual network, auditory network, and sensory and motor network (SMN)]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD.

Shared and distinct changes in local dynamic functional connectivity patterns in major depressive and bipolar depressive disorders

BACKGROUND

Distinguishing bipolar depressive disorder (BDD) from major depressive disorder (MDD) solely relying on clinical clues is a challenge. Evidence in neuroimaging have revealed potential neurological markers for the differential diagnosis.

METHODS

We aimed to characterize common and specific alterations in the dynamic local functional connectivity pattern in BDD and MDD by using the dynamic regional phase synchrony (DRePS), a newly developed method for assessing intrinsic dynamic local functional connectivity. A total of 98 patients with MDD and 56 patients with BDD patients, and 97 age-, gender-, and education-matched healthy controls (HC) were included and underwent the resting-state functional magnetic resonance imaging.

RESULTS

Compared with HC, patients with two disorders shared decreased DRePS value in the bilateral orbitofrontal cortex (OFC) extends to insula, the right insula extends to hippocampus, the left hippocampus, the right inferior frontal gyrus (IFG), the left thalamus extends to caudate, the right caudate, the bilateral superior frontal gyrus (SFG), and the right medial frontal gyrus (MFG). Furthermore, patients with MDD exhibited specific decreased DRePS value in the left caudate. Moreover, voxel signals in these regions during the support vector machine analysis contributed to the classification of the two diagnoses.

CONCLUSIONS

Our findings provided new insight into the neural mechanism of patients with MDD and BDD and could potentially inform the diagnosis and the treatment of this disease.

Effects of the SNAP25 on Integration Ability of Brain Functions in Children With ADHD

OBJECTIVE

The present study aimed to examine the effects of SNAP25 on the integration ability of intrinsic brain functions in children with ADHD, and whether the integration ability was associated with working memory (WM).

METHODS

A sliding time window method was used to calculate the spatial and temporal concordance among five rs-fMRI regional indices in 55 children with ADHD and 20 healthy controls.

RESULTS

The SNAP25 exhibited significant interaction effects with ADHD diagnosis on the voxel-wise concordance in the right posterior central gyrus, fusiform gyrus and lingual gyrus. Specifically, for children with ADHD, G-carriers showed increased voxel-wise concordance in comparison to TT homozygotes in the right precentral gyrus, superior frontal gyrus, postcentral gyrus, and middle frontal gyrus. The voxel-wise concordance was also found to be related to WM.

CONCLUSION

Our findings provided a new insight into the neural mechanisms of the brain function of ADHD children.

Multi-dynamic Modelling Reveals Strongly Time-varying Resting fMRI Correlations

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MAGE is multi-dynamic in that it models temporal fluctuations in FC independently from fluctuations in the mean of the activity.

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MAGE reveals stronger changes in FC over time than single-dynamic approaches, such as sliding window correlations.

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Multi-dynamic modelling provides an explanation and a solution as to why resting fMRI FC has previously looked so stable.

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MAGE models fMRI data as a set of reoccurring brain states, and importantly, these states do not have to be binary and mutually exclusive (e.g., multiple states can be active at one time-point).

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MAGE estimated time-varying FC is a better predictor of behavioural variability in the resting-state fMRI data than established methods.

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability.

Altered Default Mode Network Is Associated With Cognitive Impairment in CADASIL as Revealed by Multimodal Neuoimaging

Background and Purpose: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy caused by mutations in the NOTCH3 gene is a hereditary cerebral small vessel disease, manifesting with stroke, cognitive impairment, and mood disturbances. Functional or structural changes in the default mode network (DMN), which plays important role in cognitive and mental maintenance, have been found in several neurological and mental diseases. However, it remains unclear whether DMN is altered in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Methods: Multimodal imaging methods, including MRI and positron emission tomography (PET), were applied to evaluate the functional, structural, and metabolic characteristics of DMN in 25 patients with CADASIL and 42 healthy controls. Results: Compared with controls, patients with CADASIL had decreased nodal efficiency and degree centrality of the dorsal medial pre-frontal cortex and hippocampal formation within DMN. Structural MRI and diffusion tensor imaging (DTI) showed decreased gray matter volume and fiber tracks presented in the bilateral hippocampal formation. Meanwhile, PET imaging showed decreased metabolism within the whole DMN in CADASIL. Furthermore, correlation analyses showed that these nodal characteristics, gray matter volume, and metabolic signals of DMN were related to cognitive scores in CADASIL. Conclusions: Our results suggested that altered network characteristics of DMN might play important roles in cognitive deficits of CADASIL.

Early alterations of large-scale brain networks temporal dynamics in young children with autism

Autism spectrum disorders (ASD) are associated with disruption of large-scale brain network. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A-E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain-behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.

Association of adverse childhood experiences and precuneus volume with intrusive reexperiencing in autism spectrum disorder

Compared to typically developing (TD) children, people with autism spectrum disorder (ASD) have an increased risk of adverse childhood experiences (ACEs). Exposure to ACEs is associated with adult ASD psychological comorbidities, such as posttraumatic stress disorder (PTSD). Occurrence of intrusive event reexperiencing, characteristic of PTSD, often causes social dysfunction in adults with ASD, but its pathological basis is unclear. This study examined brain regions related to the severity of intrusive reexperiencing and explored whether ACE severity was associated with that of intrusive reexperiencing and/or extracted regional gray matter volume. Forty-six individuals with ASD and 41 TD subjects underwent T1-weighted magnetic resonance imaging and evaluation of ACEs and intrusive reexperiencing. Brain regions related to the severity of intrusive reexperiencing in both groups were identified by voxel-based whole brain analyses. Associations among the severity of intrusive reexperiencing, that of ACEs, and gray matter volume were examined in both groups. The severities of intrusive reexperiencing and ACEs were significantly associated with reduced gray matter volume in the right precuneus in individuals with ASD but not in TD subjects. Although the right precuneus gray matter volume was smaller in individuals with ASD and severe ACEs than in those with mild ACEs or TD subjects, it was similar in the latter two groups. However, ACE-dependent gray matter volume reduction in the right precuneus led to intrusive reexperiencing in individuals with ASD. This suggests that exposure to ACEs is associated with right precuneus gray matter reduction, which is critical for intrusive reexperiencing in adults with ASD. LAY SUMMARY: Individuals with autism spectrum disorder (ASD) are at increased risk of adverse childhood experiences (ACEs) and of subsequent manifestation of intrusive reexperiencing of stressful life events. The present study found that reduced gray matter volume in the right precuneus of the brain was associated with more severe intrusive reexperiencing of ACEs by individuals with ASD. These results suggest that ACEs affect neural development in the precuneus, which is the pathological basis of intrusive event reexperiencing in ASD.

Superficial white-matter functional networks changes in bipolar disorder patients during depressive episodes

BACKGROUND

Bipolar disorder is a common psychiatric disorder characterized by insufficient or ineffective connections associated with white-matter (WM) abnormalities. Previous studies have detected the structural attributes of WM using magnetic resonance imaging (MRI) or diffusion tensor imaging, however, they failed to disentangle the dysfunctional organization within the WM.

METHODS

This study aimed to uncover the WM functional connectivity (FC) in 45 bipolar disorder patients during depressive episodes (BDD) and 45 healthy controls based on resting-state functional MRI. Eight WM functional networks were identified by using a clustering analysis of voxel-based correlation profiles, which were further classified into superficial, middle and deep layers of networks.

RESULTS

Group comparisons on the FCs among 8 WM networks showed that the superficial tempofrontal network (TFN) in BDD patients had increased FC with the superficial cerebellar network (CN) and with the superficial pre/post-central network (PCN). Further, support vector regression prediction analysis results revealed that the increased FCs of CN-TFN and PCN-TFN could be served as features to predict the numbers of depressive episode in BDD patients.

CONCLUSIONS

The current study extended our knowledge about the impaired WM functional connections associated with emotional and sensory-motor perception processing in BDD, which may facilitate the interpretation of the pathophysiology mechanisms underlying BDD.

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.

Atypical Resting-State Functional Connectivity of Intra/Inter-Sensory Networks Is Related to Symptom Severity in Young Boys With Autism Spectrum Disorder

Autism spectrum disorder (ASD) has been reported to have altered brain connectivity patterns in sensory networks, assessed using resting-state functional magnetic imaging (rs-fMRI). However, the results have been inconsistent. Herein, we aimed to systematically explore the interaction between brain sensory networks in 3–7-year-old boys with ASD (N = 29) using independent component analysis (ICA). Participants were matched for age, head motion, and handedness in the MRI scanner. We estimated the between-group differences in spatial patterns of the sensory resting-state networks (RSNs). Subsequently, the time series of each RSN were extracted from each participant’s preprocessed data and associated estimates of interaction strength between intra- and internetwork functional connectivity (FC) and symptom severity in children with ASD. The auditory network (AN), higher visual network (HVN), primary visual network (PVN), and sensorimotor network (SMN) were identified. Relative to TDs, individuals with ASD showed increased FC in the AN and SMN, respectively. Higher positive connectivity between the PVN and HVN in the ASD group was shown. The strength of such connections was associated with symptom severity. The current study might suggest that the abnormal connectivity patterns of the sensory network regions may underlie impaired higher-order multisensory integration in ASD children, and be associated with social impairments.

Failure of resting-state frontal-occipital connectivity in linking visual perception with reading fluency in Chinese children with developmental dyslexia

It is widely accepted that impairment in visual perception impedes children's reading development, and further studies have demonstrated significant enhancement in reading fluency after visual perceptual training. However, the mechanism of the neural linkage between visual perception and reading is unclear. The purpose of this study was to examine the intrinsic functional relationship between visual perception (indexed by the texture discrimination task,TDT) and reading ability (character reading and reading fluency) in Chinese children with developmental dyslexia (DD) and those with typical development (TD). The resting-state functional connectivity (RSFC) between the primary visual cortex (V1, BA17) and the entire brain was analyzed. In addition, how RSFC maps are associated with TDT performance and reading ability in the DD and TD groups was examined. The results demonstrated that the strength of the RSFC between V1 and the left middle frontal gyrus (LMFG, BA9/BA46) was significantly correlated with both the threshold (SOA) of the TDT and reading fluency in TD children but not in DD children. Moreover, LMFG-V1 resting-state connectivity played a mediating role in the association of visual texture discrimination and reading fluency, but not in character reading, in TD children. In contrast, this mediation was absent in DD children, albeit their strengths of RSFC between V1 and the left middle frontal gyrus (LMFG) were comparable to those for the TD group. These findings indicate that typically developing children use the linkage of the RSFC between the V1 and LMFG for visual perception skills, which in turn promote fluent reading; in contrast, children with dyslexia, who had higher TDT thresholds than TD children, could not take advantage of their frontal-occipital connectivity to improve reading fluency abilities. These findings suggest that visual perception plays an important role in reading skills and that children with developmental dyslexia lack the ability to use their frontal-occipital connectivity to link visual perception with reading fluency.

Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly-available nodes approach

INTRODUCTION

Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance.

METHODS

We propose a highly-available nodes approach for constructing brain network of patients with MCI and AD. With resting-state functional magnetic resonance imaging (rs-fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier.

RESULTS

Accuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes.

CONCLUSIONS

The findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs-fMRI data for construction and topology analysis brain network.

Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.

Functional connectivity of the default mode, dorsal attention and fronto-parietal executive control networks in glial tumor patients

PURPOSE

Resting state functional magnetic resonance imaging (rsfMRI) is an emerging tool to explore the functional connectivity of different brain regions. We aimed to assess the disruption of functional connectivity of the Default Mode Network (DMN), Dorsal Attention Network(DAN) and Fronto-Parietal Network (FPN) in patients with glial tumors.

METHODS

rsfMRI data acquired on 3T-MR of treatment-naive glioma patients prospectively recruited (2015-2019) and matched controls from the 1000 functional-connectomes-project were analyzed using the CONN functional toolbox. Seed-Based Connectivity Analysis (SBCA) and Independent Component Analysis (ICA, with 10 to 100 components) were performed to study reliably the three networks of interest.

RESULTS

35 patients with gliomas (17 WHO grade I-II, 18 grade III-IV) and 70 controls were included. Global increased DMN connectivity was consistently found with SBCA and ICA in patients compared to controls (Cluster1: Precuneus, height: p < 10^-6; Cluster2: subcallosum; height: p < 10^-5). However, an area of decreased connectivity was found in the posterior corpus callosum, particularly in high-grade gliomas (height: p < 10^-5). The DAN demonstrated small areas of increased connectivity in frontal and occipital regions (height: p < 10^-6). For the FPN, increased connectivity was noted in the precuneus, posterior cingulate gyrus, and frontal cortex. No difference in the connectivity of the networks of interest was demonstrated between low- and high-grade gliomas, as well as when stratified by their IDH1-R132H (isocitrate dehydrogenase) mutation status.

CONCLUSION

Altered functional connectivity is reliably found with SBCA and ICA in the DMN, DAN, and FPN in glioma patients, possibly explained by decreased connectivity between the cerebral hemispheres across the corpus callosum due to disruption of the connections.

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.

Hypofunctional connectivity between the posterior cingulate cortex and ventromedial prefrontal cortex in autism: Evidence from coordinate-based imaging meta-analysis

BACKGROUND

Underconnectivity in the posterior cingulate cortex (PCC) may be associated with a weakened ability to interpret social signals in autism spectrum disorder (ASD) and result in cognitive inflexibility - a hallmark feature of ASD. However, previous neuroimaging studies using resting-state functional magnetic resonance imaging in ASD reported inconsistent findings on functional connectivity of the PCC. This study investigated the aberrant resting-state functional connectivity of the PCC in ASD using multilevel kernel density analysis.

METHODS

Online databases (MEDLINE/PubMed) were searched for PCC-based functional connectivity in ASD. Ten studies (501 subjects; 161 reported foci) met the inclusion criteria of this meta-analysis.

RESULTS

We found one consistent and strong abnormal functional connectivity of ASD during the resting state, which was the hypoconnectivity between the PCC and ventromedial prefrontal cortex (VMPFC). Importantly, the Jackknife sensitivity analysis revealed that the VMPFC cluster was stably hypoconnected with the PCC in ASD (maximum spatial overlap rate: 100%).

CONCLUSIONS

The reduced PCC-VMPFC functional coupling may provide an early insight into the effects of ASD on multiple dimensions of functioning, including higher-order cognitive and complex social functions.

Individual-specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation

Even with an overarching functional dysconnectivity model of adolescent-onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group-level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual-specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual-level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension-specific psychotic symptoms in 30 antipsychotic-naïve first-episode AOS patients (recruited sample of 39). Age-related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic-naïve first-episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual-specific FC biomarkers could significantly and better predict AOS positive-dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN-SN connection exhibited aberrant age-associated alteration in AOS. The individual-level findings reveal reproducible FPN-based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent-onset disorders.

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.

More than just statics: Abnormal dynamic amplitude of low-frequency fluctuation in adolescent patients with pure conduct disorder

BACKGROUND

The human brain activity is inherently dynamic over time. Conventional neuroimaging studies have reported abnormalities of static intrinsic brain activity or connectivity in adolescent patients with conduct disorder (CD). Little is known, however, regarding the temporal dynamics alterations of brain activity in CD.

METHODS

In this study, resting-state functional magnetic resonance imaging examinations were performed on adolescent patients with pure CD and age-matched typically developing (TD) controls. The dynamic amplitude of low-frequency fluctuation (dALFF) was first measured using a sliding-window method. The temporal variability (TV) was then quantified as the variance of dALFF over time and compared between the two groups. Further, the relationships between aberrant TV of dALFF and clinical features were evaluated.

RESULTS

CD patients showed reduced brain dynamics (less temporal variability) in the default-mode network, frontal-limbic cortices, sensorimotor areas, and visual regions which are involved in cognitive, emotional and perceptional processes. Importantly, receiver operating characteristic curve analysis revealed that regions with altered TV of dALFF exhibited a better ability to distinguish CD patients than the results from static ALFF in the current data set.

CONCLUSIONS

Our findings extended previous work by providing a novel perspective on the neural mechanisms underlying adolescent patients with CD and demonstrated that the altered dynamic local brain activity may be a potential biomarker for CD diagnosis.

Progressive brain structural alterations assessed via causal analysis in patients with generalized anxiety disorder

Accumulating neuroimaging studies implicate widespread brain structural alterations in patients with generalized anxiety disorder (GAD), but little is known regarding the temporal information of these changes and their causal relationships. In this study, a morphometric analysis was performed on T1-weighted structural images, and the progressive changes in the gray matter volume (GMV) in GAD were simulated by dividing the patients into different groups from low illness duration to high illness duration. The duration was defined as the interval between the onset of GAD and the time for magnetic resonance imaging collection. Then, a causal structural covariance network analysis was conducted to describe the causal relationships of the brain structural alterations in GAD. With increased illness duration, the GMV reduction in GAD originated from the subgenual anterior cingulate cortex (sgACC) and propagated to the bilateral ventromedial prefrontal cortex, right dorsomedial prefrontal cortex, left inferior temporal gyrus, and right insula. Intriguingly, the sgACC and the right insula had positive causal effects on each other. Moreover, both sgACC and right insula exhibited positive causal effects on the parietal cortex and negative effects on the posterior cingulate cortex, dorsolateral prefrontal cortex, visual cortex, and temporal lobe. The opposite causal effects were noted on the somatosensory and the ventrolateral prefrontal cortices. In conclusion, patients with GAD show gradual GMV reduction with increasing ilness duration. Furthermore, the causal effects of the sgACC and the right insula GMV reduction with shifts of duration may provide an important new avenue for understanding the pathological anomalies in GAD.

Abnormal functional connectivity as neural biological substrate of trait and state characteristics in major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is a neuropsychiatric disorder associated with functional dysconnectivity in emotion regulation system. State characteristics which measure the current presence of depressive symptoms, and trait characteristics which indicate the long-term vulnerability to depression are two important features of MDD. However, the relationships between trait and state characteristics of MDD and functional connectivity (FC) within the emotion regulation system still remain unclear.

METHODS

This study aims to examine the neural biological mechanisms of trait characteristics measured by the Affective Neuroscience Personality Scale (ANPS) and state anhedonia measured by the Snaith-Hamilton Pleasure Scale (SHAPS) in MDD. Sixty-three patients with MDD and 63 well-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. A spatial pairwise clustering and the network-based analysis approaches were adopted to identify the abnormal FC networks. Support vector regression was utilized to predict the trait and state characteristics based on abnormal FCs.

RESULTS

Four disrupted subnetworks mainly involving the prefrontal-limbic-striatum system were observed in MDD. Importantly, the abnormal FC between the left amygdala (AMYG)/hippocampus (HIP) and right AMYG/HIP could predict the SADNESS scores of ANPS (trait characteristics) in MDD. While the aberrant FC between the medial prefrontal cortex (mPFC)/anterior cingulate gyrus (ACC) and AMYG/parahippocampal gyrus could predict the state anhedonia scores (state characteristics).

CONCLUSIONS

The present findings give first insights into the neural biological basis underlying the trait and state characteristics associated with functional dysconnectivity within the emotion regulation system in MDD.

Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder

The clinical misdiagnosis ratio of bipolar disorder (BD) patients to major depressive disorder (MDD) patients is high. Recent findings hypothesize that the ability to flexibly recruit functional neural networks is differently altered in BD and MDD patients. This study aimed to explore distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. Resting state functional magnetic resonance imaging of 40 BD patients, 61 MDD patients, and 61 matched healthy controls were recruited. Dynamic functional connectivity matrices for each subject were constructed with a sliding window method. Then, network switching rate of each node was calculated and compared among the three groups. BD and MDD patients shared decreased network switching rate of regions including left precuneus, bilateral parahippocampal gyrus, and bilateral dorsal medial prefrontal cortex. Apart from these regions, MDD patients presented specially decreased network switching rate in the bilateral anterior insula, left amygdala, and left striatum. Taken together, BD and MDD patients shared decreased network switching rate of key hubs in default mode network and MDD patients presented specially decreased switching rate in salience network and striatum. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.

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.

A temporal chronnectomic framework: Cigarette smoking preserved the prefrontal dysfunction in schizophrenia

The widespread cigarette smoking behavior in schizophrenia is generally attributed to its alleviation of patients' symptomatology by the self-medication hypothesis. The prefrontal cortex (PFC), which predominantly supports orchestrating thoughts and actions, might underlie the biological underpinnings of smoking behavior in schizophrenia. However, few studies have focused on the impact of smoking on the prefrontal function in schizophrenia. This study assumed that smoking-related alterations on the prefrontal dynamics of information integration (chronnectome) were different between healthy control (HC) and schizophrenia patient (SP). We recruited SP smokers (N = 22)/nonsmokers (N = 27) and HC smokers (N = 22)/nonsmokers (N = 21) who underwent resting-state functional magnetic resonance imaging (rsfMRI) with a total of 240 volumes (lasting for 480 s). We employed a chronnectomic density analysis on the rsfMRI signal by using a sliding-window method. We examined the interaction effect between smoking status and diagnosis utilizing two-way analysis of covariance under permutation test. Whereas disease-related reduced effects were found on the bilateral dorsolateral PFC chronnectomic density, no smoking effect was observed. As regards interaction effect, a smoking-related reduced effect was found on the right dorsolateral PFC chronnectomic density in HC, while a smoking-related increased effect was observed in SP. Nevertheless, post-hoc analysis revealed significant group difference between SP smokers and HC nonsmokers. Therefore, these results indicated a smoking-related preservation effect on disrupted prefrontal dynamics in schizophrenia that cannot restore it to normal levels. The novel findings yield a prefrontal-based chronnectome framework to elaborate upon the self-medication hypothesis in schizophrenia.

Abnormal dynamic functional connectivity density in patients with generalized anxiety disorder

BACKGROUND

Numerous studies have revealed the abnormal static functional connectivity (FC) among different brain regions in patients with generalized anxiety disorder (GAD). However, little is known about the dynamic changes of FC in patients with GAD.

METHODS

This study investigated the whole-brain dynamic changes of FC in patients with GAD by combining global FC density (FCD) and sliding window correlation analyses. The standard deviation of dynamic FCD (dFCD) was calculated to evaluate its temporal variability along time. Support vector regression was then employed to predict the symptom severity of patients based on abnormal dynamic connectivity patterns.

RESULTS

The abnormal dFCD variability between 81 GAD patients and 80 healthy controls showed that the patients had higher dFCD variability in the bilateral dorsomedial prefrontal cortex (dmPFC) and left hippocampus while lower dFCD variability in the right postcentral gyrus. The abnormal dFCD variability of the left dmPFC is an important feature for anxiety prediction.

LIMITATIONS

The selection of sliding window length remains controversial, and most of our patients have been treated with medications. Future studies are expected to rule out the potential confounding effects from applying different parameters of the sliding window and recruiting large samples of medication-free patients.

CONCLUSION

The altered patterns of time-varying brain connectivity in the frontolimbic and sensorimotor areas may reflect abnormal dynamic neural communication between these regions and other regions of the brain, which may deepen our understanding of the disease.

A Review of Default Mode Network Connectivity and Its Association With Social Cognition in Adolescents With Autism Spectrum Disorder and Early-Onset Psychosis

Recent studies have demonstrated substantial phenotypic overlap, notably social impairment, between autism spectrum disorder (ASD) and schizophrenia. However, the neural mechanisms underlying the pathogenesis of social impairments across these distinct neuropsychiatric disorders has not yet been fully examined. Most neuroimaging studies to date have focused on adults with these disorders, with little known about the neural underpinnings of social impairments in younger populations. Here, we present a narrative review of the literature available through April 2020 on imaging studies of adolescents with either ASD or early-onset psychosis (EOP), to better understand the shared and unique neural mechanisms of social difficulties across diagnosis from a developmental framework. We specifically focus on functional connectivity studies of the default mode network (DMN), as the most extensively studied brain network relevant to social cognition across both groups. Our review included 29 studies of DMN connectivity in adolescents with ASD (Mean age range = 11.2-21.6 years), and 14 studies in adolescents with EOP (Mean age range = 14.2-24.3 years). Of these, 15 of 29 studies in ASD adolescents found predominant underconnectivity when examining DMN connectivity. In contrast, findings were mixed in adolescents with EOP, with five of 14 studies reporting DMN underconnectivity, and an additional six of 14 studies reporting both under- and over-connectivity of the DMN. Specifically, intra-DMN networks were more frequently underconnected in ASD, but overconnected in EOP. On the other hand, inter-DMN connectivity patterns were mixed (both under- and over-connected) for each group, especially DMN connectivity with frontal, sensorimotor, and temporoparietal regions in ASD, and with frontal, temporal, subcortical, and cerebellar regions in EOP. Finally, disrupted DMN connectivity appeared to be associated with social impairments in both groups, less so with other features distinct to each condition, such as repetitive behaviors/restricted interests in ASD and hallucinations/delusions in EOP. Further studies on demographically well-matched groups of adolescents with each of these conditions are needed to systematically explore additional contributing factors in DMN connectivity patterns such as clinical heterogeneity, pubertal development, and medication effects that would better inform treatment targets and facilitate prediction of outcomes in the context of these developmental neuropsychiatric conditions.