Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism

Gender and age related brain structural and functional alterations in children with autism spectrum disorder

To explore the effects of age and gender on the brain in children with autism spectrum disorder using magnetic resonance imaging. 185 patients with autism spectrum disorder and 110 typically developing children were enrolled. In terms of gender, boys with autism spectrum disorder had increased gray matter volumes in the insula and superior frontal gyrus and decreased gray matter volumes in the inferior frontal gyrus and thalamus. The brain regions with functional alterations are mainly distributed in the cerebellum, anterior cingulate gyrus, postcentral gyrus, and putamen. Girls with autism spectrum disorder only had increased gray matter volumes in the right cuneus and showed higher amplitude of low-frequency fluctuation in the paracentral lobule, higher regional homogeneity and degree centrality in the calcarine fissure, and greater right frontoparietal network-default mode network connectivity. In terms of age, preschool-aged children with autism spectrum disorder exhibited hypo-connectivity between and within auditory network, somatomotor network, and visual network. School-aged children with autism spectrum disorder showed increased gray matter volumes in the rectus gyrus, superior temporal gyrus, insula, and suboccipital gyrus, as well as increased amplitude of low-frequency fluctuation and regional homogeneity in the calcarine fissure and precentral gyrus and decreased in the cerebellum and anterior cingulate gyrus. The hyper-connectivity between somatomotor network and left frontoparietal network and within visual network was found. It is essential to consider the impact of age and gender on the neurophysiological alterations in autism spectrum disorder children when analyzing changes in brain structure and function.

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification

Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However, previous studies are largely node-centralized and ignore edge features for graph classification tasks, resulting in moderate performance of graph classification accuracy. Moreover, the generalizability of GNN model is still far from satisfactory in brain disorder [e.g., autism spectrum disorder (ASD)] identification due to considerable individual differences in symptoms among patients as well as data heterogeneity among different sites. In order to address the above limitations, this study proposes a novel adversarial learning-based node-edge graph attention network (AL-NEGAT) for ASD identification based on multimodal MRI data. First, both node and edge features are modeled based on structural and functional MRI data to leverage complementary brain information and preserved in the constructed weighted adjacent matrix for individuals through the attention mechanism in the proposed NEGAT. Second, two AL methods are employed to improve the generalizability of NEGAT. Finally, a gradient-based saliency map strategy is utilized for model interpretation to identify important brain regions and connections contributing to the classification. Experimental results based on the public Autism Brain Imaging Data Exchange I (ABIDE I) data demonstrate that the proposed framework achieves a classification accuracy of 74.7% between ASD and typical developing (TD) groups based on 1007 subjects across 17 different sites and outperforms the state-of-the-art methods, indicating satisfying classification ability and generalizability of the proposed AL-NEGAT model. Our work provides a powerful tool for brain disorder identification.

Impaired effective functional connectivity in the social preference of children with autism spectrum disorder

Background

The medial prefrontal cortex (mPFC), amygdala (Amyg), and nucleus accumbens (NAc) have been identified as critical players in the social preference of individuals with ASD. However, the specific pathophysiological mechanisms underlying this role requires further clarification. In the current study, we applied Granger Causality Analysis (GCA) to investigate the neural connectivity of these three brain regions of interest (ROIs) in patients with ASD, aiming to elucidate their associations with clinical features of the disorder.

Methods

Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from the ABIDE II database, which included 37 patients with ASD and 50 typically developing (TD) controls. The mPFC, Amyg, and NAc were defined as ROIs, and the differences in fractional amplitude of low-frequency fluctuations (fALFF) within the ROIs between the ASD and TD groups were computed. Subsequently, we employed GCA to investigate the bidirectional effective connectivity between the ROIs and the rest of the brain. Finally, we explored whether this effective connectivity was associated with the social responsiveness scale (SRS) scores of children with ASD.

Results

The fALFF values in the ROIs were reduced in children with ASD when compared to the TD group. In terms of the efferent connectivity from the ROIs to the whole brain, the ASD group exhibited increased connectivity in the right cingulate gyrus and decreased connectivity in the right superior temporal gyrus. Regarding the afferent connectivity from the whole brain to the ROIs, the ASD group displayed increased connectivity in the right globus pallidus and decreased connectivity in the right cerebellar Crus 1 area and left cingulate gyrus. Additionally, we demonstrated a positive correlation between effective connectivity derived from GCA and SRS scores.

Conclusion

Impairments in social preference ASD children is linked to impaired effective connectivity in brain regions associated with social cognition, emotional responses, social rewards, and social decision-making. This finding further reveals the potential neuropathological mechanisms underlying ASD.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder

BACKGROUND

Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder.

METHODS

We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.

RESULTS

Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD.

CONCLUSIONS

Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

Brain functional connectivity alterations of Wernicke's area in individuals with autism spectrum conditions in multi-frequency bands: A mega-analysis

Characterized by severe deficits in communication, most individuals with autism spectrum conditions (ASC) experience significant language dysfunctions, thereby impacting their overall quality of life. Wernicke's area, a classical and traditional brain region associated with language processing, plays a substantial role in the manifestation of language impairments. The current study carried out a mega-analysis to attain a comprehensive understanding of the neural mechanisms underpinning ASC, particularly in the context of language processing. The study employed the Autism Brain Image Data Exchange (ABIDE) dataset, which encompasses data from 443 typically developing (TD) individuals and 362 individuals with ASC. The objective was to detect abnormal functional connectivity (FC) between Wernicke's area and other language-related functional regions, and identify frequency-specific altered FC using Wernicke's area as the seed region in ASC. The findings revealed that increased FC in individuals with ASC has frequency-specific characteristics. Further, in the conventional frequency band (0.01-0.08 Hz), individuals with ASC exhibited increased FC between Wernicke's area and the right thalamus compared with TD individuals. In the slow-5 frequency band (0.01-0.027 Hz), increased FC values were observed in the left cerebellum Crus II and the right lenticular nucleus, pallidum. These results provide novel insights into the potential neural mechanisms underlying communication deficits in ASC from the perspective of language impairments.

Aberrant dynamic and static functional connectivity of the striatum across specific low-frequency bands in patients with autism spectrum disorder

BACKGROUND

Dysfunctions of the striatum have been repeatedly observed in autism spectrum disorder (ASD). However, previous studies have explored the static functional connectivity (sFC) of the striatum in a single frequency band, ignoring the dynamics and frequency specificity of brain FC. Therefore, we investigated the dynamic FC (dFC) and sFC of the striatum in the slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) frequency bands.

METHODS

Data of 47 ASD patients and 47 typically developing (TD) controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. A seed-based approach was used to compute the dFC and sFC. Then, a two-sample t-test was performed. For regions showing abnormal sFC and dFC, we performed clinical correlation analysis and constructed support vector machine (SVM) models.

RESULTS

The middle frontal gyrus (MFG), precuneus, and medial superior frontal gyrus (mPFC) showed both dynamic and static alterations. The reduced striatal dFC in the right MFG was associated with autism symptoms. The dynamic‒static FC model had a great performance in ASD classification, with 95.83 % accuracy.

CONCLUSIONS

The striatal dFC and sFC were altered in ASD, which were frequency specific. Examining brain activity using dynamic and static FC provides a comprehensive view of brain activity.

Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking

Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.

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.

Interpretation of deep non-linear factorization for autism

Autism, a neurodevelopmental disorder, presents significant challenges for diagnosis and classification. Despite the widespread use of neural networks in autism classification, the interpretability of their models remains a crucial issue. This study aims to address this concern by investigating the interpretability of neural networks in autism classification using the deep symbolic regression and brain network interpretative methods. Specifically, we analyze publicly available autism fMRI data using our previously developed Deep Factor Learning model on a Hibert Basis tensor (HB-DFL) method and extend the interpretative Deep Symbolic Regression method to identify dynamic features from factor matrices, construct brain networks from generated reference tensors, and facilitate the accurate diagnosis of abnormal brain network activity in autism patients by clinicians. Our experimental results show that our interpretative method effectively enhances the interpretability of neural networks and identifies crucial features for autism classification.

Parsing the heterogeneity of brain-symptom associations in autism spectrum disorder via random forest with homogeneous canonical correlation

BACKGROUND

Autism spectrum disorder (ASD) is a highly heterogeneous developmental disorder, but the neuroimaging substrates of its heterogeneity remain unknown. The difficulty lies mainly on the significant individual variability in the brain-symptom association.

METHODS

T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Database Exchange (ABIDE) (N_TDC = 1146) were used to generate a normative model to map brain structure deviations of cases (N_ASD = 571). Voxel-based morphometry (VBM) was used to compute gray matter volume (GMV). Singular Value Decomposition (SVD) was employed to perform dimensionality reduction. A tree-based algorithm was proposed to identify the ASD subtypes according to the pattern of brain-symptom association as assessed by a homogeneous canonical correlation.

RESULTS

We identified 4 ASD subtypes with distinct association patterns between residual volumes and a social symptom score. More severe the social symptom was associated with greater GMVs in both the frontoparietal regions for the subtype1 (r = 0.29-0.44) and the ventral visual pathway for the subtype3 (r = 0.19-0.23), but lower GMVs in both the right anterior cingulate cortex for the subtype4 (r = -0.25) and a few subcortical regions for the subtype2 (r = -0.31-0.20). The subtyping significantly improved the classification accuracy between cases and controls from 0.64 to 0.75 (p < 0.05, permutation test), which was also better than the accuracy of 0.68 achieved by the k-means-based subtyping (p < 0.01).

LIMITATIONS

Sample size limited the study due to the missing data.

CONCLUSIONS

These findings suggest that the heterogeneity of ASD might reflect changes in different subsystems of the social brain, especially including social attention, motivation, perceiving and evaluation.

Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

Previous studies have explored resting-state functional connectivity (rs-FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency-specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age-matched and sex-matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed-based FC analyses in the conventional, slow-5, and slow-4 frequency bands at each site. Image-based meta-analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta-analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow-5 frequency band instead of the conventional and slow-4 frequency bands. The deep learning results showed that, compared with the conventional and slow-4 frequency bands, the slow-5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.

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.

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.

Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics

Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

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.

The atypical functional connectivity of Broca's area at multiple frequency bands in autism spectrum disorder

As a developmental disorder, autism spectrum disorder (ASD) has drawn much attention due to its severe impacts on one's language capacity. Broca's area, an important brain region of the language network, is largely involved in language-related functions. Using the Autism Brain Image Data Exchange (ABIDE) dataset, a mega-analysis was performed involving a total of 1454 participants (including 618 individuals with ASD and 836 healthy controls (HCs). To detect the neural pathophysiological mechanism of ASD from the perspective of language, we conducted a functional connectivity (FC) analysis with Broca's area as the seed in multiple frequency bands (conventional: 0.01-0.08 Hz; slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz). We found that compared with HC, ASD patients demonstrated increased FC in the left thalamus, left precuneus, left anterior cingulate and paracingulate gyri, and left medial orbital of the superior frontal gyrus in the conventional frequency band (0.01-0.08 Hz). The results of the slow-5 frequency band (0.01-0.027 Hz) presented increased FC values of the left precuneus, left medial orbital of the superior frontal gyrus, right medial orbital of the superior frontal gyrus and right thalamus. No significant cluster was detected in the slow-4 frequency band (0.027-0.073 Hz). In conclusion, the abnormal functional connectivity in patients with ASD has frequency-specific properties. Furthermore, the slow-5 frequency band (0.01-0.027 Hz) mainly contributed to the findings of the conventional frequency band (0.01-0.08 Hz). The current study might shed new light on the neural pathophysiological mechanism of language impairments in people with ASD.

Different functional connectivity optimal frequency in autism compared with healthy controls and the relationship with social communication deficits: Evidence from gene expression and behavior symptom analyses

Studies have reported that different brain regions/connections possess distinct frequency properties, which are related to brain function. Previous studies have proposed altered brain activity frequency and frequency-specific functional connectivity (FC) patterns in autism spectrum disorder (ASD), implying the varied dominant frequency of FC in ASD. However, the difference of the dominant frequency of FC between ASD and healthy controls (HCs) remains unclear. In the present study, the dominant frequency of FC was measured by FC optimal frequency, which was defined as the intermediate of the frequency bin at which the FC strength could reach the maximum. A multivariate pattern analysis was conducted to determine whether the FC optimal frequency in ASD differs from that in HCs. Partial least squares regression (PLSR) and enrichment analyses were conducted to determine the relationship between the FC optimal frequency difference of ASD/HCs and cortical gene expression. PLSR analyses were also performed to explore the relationship between FC optimal frequency and the clinical symptoms of ASD. Results showed a significant difference of FC optimal frequency between ASD and HCs. Some genes whose cortical expression patterns are related to the FC optimal frequency difference of ASD/HCs were enriched for social communication problems. Meanwhile, the FC optimal frequency in ASD was significantly related to social communication symptoms. These results may help us understand the neuro-mechanism of the social communication deficits in ASD.

Changes in white matter functional networks during wakefulness and sleep

Blood oxygenation level‐dependent (BOLD) signals in the white matter (WM) have been demonstrated to encode neural activities by showing structure‐specific temporal correlations during resting‐state and task‐specific imaging of fiber pathways with various degrees of correlations in strength and time delay. Previous neuroimaging studies have shown state‐dependent functional connectivity and regional amplitude of signal fluctuations in brain gray matter across wakefulness and nonrapid eye movement (NREM) sleep cycles. However, the functional characteristics of WM during sleep remain unknown. Using simultaneous electroencephalography and functional magnetic resonance imaging data during wakefulness and NREM sleep collected from 66 healthy participants, we constructed 10 stable WM functional networks using clustering analysis. Functional connectivity between these WM functional networks and regional amplitude of WM signal fluctuations across multiple low‐frequency bands were evaluated. In general, decreased WM functional connectivity between superficial and middle layer WM functional networks was observed from wakefulness to sleep. In addition, functional connectivity between the deep and cerebellar networks was higher during light sleep and lower during both wakefulness and deep sleep. The regional fluctuation amplitude was always higher during light sleep and lower during deep sleep. Importantly, slow‐wave activity during deep sleep negatively correlated with functional connectivity between WM functional networks but positively correlated with fluctuation strength in the WM. These observations provide direct physiological evidence that neural activities in the WM are modulated by the sleep–wake cycle. This study provided the initial mapping of functional changes in WM during sleep.

Frequency-specific age-related changes in the amplitude of spontaneous fluctuations in autism

BACKGROUND

Autism spectrum disorder is characterized by atypical developmental changes during brain maturation, but regional brain functional changes that occur with age and across different frequency bands are unknown. Therefore, the current study aimed to explore potential age and frequency band-related changes in the regional brain activities in autism.

METHODS

A total of 65 participants who met the DSM-IV criteria for autistic disorder and 55 typically developed (TD) participants (both age 6-30 years) were recruited in the current study. The two groups were matched in age (t=-1.314, P=0.191) and gender (χ²=2.760, P=0.097). The amplitude of low-frequency fluctuations (ALFF) was employed to explore the effect of development on spontaneous brain activity in individuals with autism and in TD participants across slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), and slow-3 (0.073-0.1 Hz) frequency bands. The diagnosis-by-age interaction effect in the whole brain voxels in autism and TD groups was investigated.

RESULTS

Autism individuals showed significantly higher ALFF in the dorsal striatum in childhood (Caudate cluster: t=3.626, P=0.001; Putamen cluster: t=2.839, P=0.007) and remarkably lower ALFF in the dorsal striatum in adulthood (Caudate cluster: t=-2.198, P=0.038; Putamen cluster: t=-2.314, P=0.030) relative to TD, while no significant differences were observed in adolescence (all P>0.05). In addition, abnormal ALFF amplitudes were specific to the slow-4 (0.027-0.073 Hz) frequency band in the clusters above.

CONCLUSIONS

The current study indicated abnormal development patterns in the spontaneous activity of the dorsal striatum in autism and highlighted the potential role of the slow-4 frequency band in the pathology of autism. Also, the potential brain mechanism of autism was revealed, suggesting that autism-related variations should be investigated in a specific frequency.

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.

Desynchronized white matter function and structure in drug-naive first-episode major depressive disorder patients

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent mental disease. Using magnetic resonance imaging (MRI), although numerous studies have revealed the alterations in structure and function of grey matter (GM), few studies focused on the synchronization of white matter (WM) structure and function in MDD. The aim of this study was to investigate whether functional and structural abnormalities of WM play an essential role in the neurobiological mechanisms of MDD.

METHODS

Gradient-echo imaging sequences at 3.0T were used to gather resting state functional MRI (rsfMRI) data, which were performed on 33 drug-naive first-episode MDD patients and 34 healthy controls (HCs). After data preprocessed, amplitude of low frequency fluctuation (ALFF) of WM was calculated. ALFF values in different frequency bands were analyzed, including typical (0.01-0.15 Hz) band, slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) bands. In addition, the fractional anisotropy (FA) values in WM in 23 patients and 26 HCs were examined using tract-based spatial statistics (TBSS) and tractography based on diffusion tensor imaging (DTI). Pearson correlation analysis was applied to analyze the relationships between ALFF values and Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA).

RESULTS

Compared with the HCs, MDD patients showed decreased ALFF values in posterior thalamic radiation (PTR) and superior longitudinal fasciculus (SLF) in slow-5 frequency band, no significant differences of ALFF values were found in typical and slow-4 frequency bands. In addition, there were no significant differences in FA values with TBSS analysis as well as the number of fibers in PTR and SLF with tractography analysis between two groups. Further correlation analysis showed that the ALFF value in SLF was negatively correlated with HAMA-2 score (r = -0.548, p _FDR = 0.037) in patients.

CONCLUSION

Our results indicated that WM dysfunction may be associated with the pathophysiological mechanism of depression. Our study also suggested that the functional damage of the WM may precedes the structural damage in first-episode MDD patients. Furthermore, for mental disorders, slow-5 frequency band may be a more sensitive functional indicator for early detection of abnormal spontaneous brain activity in WM.

Multi-Class ASD Classification via Label Distribution Learning with Class-Shared and Class-Specific Decomposition

The behavioral and cognitive deficits in autism spectrum disorder (ASD) patients are associated with abnormal brain function. The resting-state functional magnetic resonance imaging (rs-fMRI) is an effective non-invasive tool for revealing the brain dysfunction for ASD patients. However, most rs-fMRI based ASD diagnosis methods are developed for simple binary classification, instead of classification of multiple sub-types in ASD. Besides, they assume that the class boundary in ASD classification is crisp, whereas the symptoms of ASD sub-types are a continuum from mild to severe impairments in both social communication and restrictive repetitive behaviors/interests, and do not have crisp boundary between each other. To this end, we introduce label distribution learning (LDL) into multi-class ASD classification and propose LDL-CSCS under the LDL framework. Specifically, the label distribution is introduced to describe how individual disease labels correlate with the subject. In the learning crierion of LDL-CSCS, the label distribution is decomposed into the class-shared and class-specific components, in which the class-shared component records the common knowledge across all persons and the class-specific component records the specific information in each ASD sub-type. Low-rank constraint is imposed on the class-shared component whereas the group sparse constraint is imposed on the class-specific component, respectively. An Augmented Lagrange Method (ALM) is developed to find the optimal solution. The experimental results show that the proposed method for ASD diagnosis has superior classification performance, compared with some existing algorithms.

Altered Thalamocortical Connectivity in 6-Week-Old Infants at High Familial Risk for Autism Spectrum Disorder

Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here, we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and low-risk controls (LR). Resting-state functional connectivity and diffusion tensor imaging data were acquired in 52 6-week-old infants. Functional connectivity was examined between 6 cortical seeds-prefrontal, motor, somatosensory, temporal, parietal, and occipital regions-and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared with LR infants. Notably, aberrant connectivity indices at 6 weeks predicted atypical social development between 9 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as 6 weeks of age, providing a possible early marker of risk for ASD.

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.

Altered Time-Frequency Feature in Default Mode Network of Autism Based on Improved Hilbert-Huang Transform

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterized by restricted interests and repetitive behaviors. Non-invasive measurements of brain activity with functional magnetic resonance imaging (fMRI) have demonstrated that the abnormality in the default mode network (DMN) is a crucial neural basis of ASD, but the time-frequency feature of the DMN has not yet been revealed. Hilbert-Huang transform (HHT) is conducive to feature extraction of biomedical signals and has recently been suggested as an effective way to explore the time-frequency feature of the brain mechanism. In this study, the resting-state fMRI dataset of 105 subjects including 59 ASD participants and 46 healthy control (HC) participants were involved in the time-frequency clustering analysis based on improved HHT and modified k-means clustering with label-replacement. Compared with HC, ASD selectively showed enhanced Hilbert weight frequency (HWF) in high frequency bands in crucial regions of the DMN, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC). Time-frequency clustering analysis revealed altered DMN organization in ASD. In the posterior DMN, the PCC and bilateral precuneus were separated for HC but clustered for ASD; in the anterior DMN, the clusters of ACC, dorsal MPFC, and ventral MPFC were relatively scattered for ASD. This study paves a promising way to uncover the alteration in the DMN and identifies a potential neuroimaging biomarker of diagnostic reference for ASD.

Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns

Altered functional connectivity patterns play an important role in explaining autism spectrum disorder related impairments. In order to examine such connectivity, resting state functional MRI is the most commonly used technique. To date, the majority of works in this area examine a whole time series of brain activation as a discrete stationary process. This study proposes a more detailed analysis of how functional connectivity fluctuates over time and how it is used to quantify instances demonstrating overconnectivity or underconnectivity. Non-parametric surrogates test identifies the areas where underconnectivity or overconnectivity correlate with the Autism Diagnosis Observation Schedule. In addition, this study shows how the areas identified affect the subjects behaviors. Our ultimate goal is a personalized autism diagnosis and treatment CAD system, where each subject impairments are distinctly mapped so they can be addressed with targeted treatments.

Functional connectivity of specific brain networks related to social and communication dysfunction in adolescents with attention-deficit hyperactivity disorder

BACKGROUND

Adolescents with attention-deficit hyperactivity disorder (ADHD) may have impaired social cognition and communication. However, the functioning of the brain networks involved in the social cognition and communication impairment in ADHD patients remains unclear.

METHODS

In total, 18 adolescents with ADHD and 16 age- and sex-matched typically developing adolescents (controls)-all of whom underwent a brain magnetic resonance imaging examination-were enrolled. Their parents filled out Swanson, Nolan, and Pelham IV (SNAP-IV) and Social Responsiveness Scale (SRS) questionnaires. Functional connectivity analyses based on the default mode network, frontoparietal network, and cinguloopercular network were performed.

RESULTS

Compared with controls, adolescents with ADHD exhibited higher total and subscale scores on SNAP-IV and SRS. Higher SNAP-IV and SRS scores were associated with higher functional connectivity between the default mode network (ventromedial prefrontal cortex) and cinguloopercular network (anterior insula) and between the FPN (dorsolateral and prefrontal cortex) and cinguloopercular network, but with lower functional connectivity between the default mode network (posterior cingulate cortex) and frontoparietal network (inferior parietal lobule) and between the default mode network (precuneus) and cinguloopercular network (temporoparietal junction).

DISCUSSION

Social cognition and communication impairment and ADHD may commonly share the aberrant functional connectivity in the default mode network, frontoparietal network, and cinguloopercular network.

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.

Enhancement of indirect functional connections with shortest path length in the adult autistic brain

Autism is a neurodevelopmental condition characterized by atypical brain functional organization. Here we investigated the intrinsic indirect (semi-metric) connectivity of the functional connectome associated with autism. Resting-state functional magnetic resonance imaging scans were acquired from 65 neurotypical adults (33 males/32 females) and 61 autistic adults (30 males/31 females). From functional connectivity networks, semi-metric percentages (SMPs) were calculated to assess the proportion of indirect shortest functional pathways at global, hemisphere, network, and node levels. Group comparisons were then conducted to ascertain differences between autism and neurotypical control groups. Finally, the strength and length of edges were examined to explore the patterns of semi-metric connections associated with autism. Compared with neurotypical controls, autistic adults displayed significantly higher SMP at all spatial scales, similar to prior observations in adolescents. Differences were primarily in weaker, longer-distance edges in the majority between networks. However, no significant diagnosis-by-sex interaction effects were observed on global SMP. These findings suggest increased indirect functional connectivity in the autistic brain is persistent from adolescence to adulthood and is indicative of reduced functional network integration.

Altered inter- and intrahemispheric functional connectivity dynamics in autistic children

Emerging evidence has associated autism spectrum disorder (ASD) with static functional connectivity abnormalities between multiple brain regions. However, the temporal dynamics of intra‐ and interhemispheric functional connectivity patterns remain unknown in ASD. Resting‐state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically matched typically developing control (TC) children (age range: 7–12 years) available from the Autism Brain Imaging Data Exchange database. Whole‐brain functional connectivity was decomposed into ipsilateral and contralateral functional connectivity, and sliding‐window analysis was utilized to capture the intra‐ and interhemispheric dynamic functional connectivity density (dFCD) patterns. The temporal variability of the functional connectivity dynamics was further quantified using the standard deviation (SD) of intra‐ and interhemispheric dFCD across time. Finally, a support vector regression model was constructed to assess the relationship between abnormal dFCD variance and autism symptom severity. Both intra‐ and interhemispheric comparisons showed increased dFCD variability in the anterior cingulate cortex/medial prefrontal cortex and decreased variability in the fusiform gyrus/inferior temporal gyrus in autistic children compared with TC children. Autistic children additionally showed lower intrahemispheric dFCD variability in sensorimotor regions including the precentral/postcentral gyrus. Moreover, aberrant temporal variability of the contralateral dFCD predicted the severity of social communication impairments in autistic children. These findings demonstrate altered temporal dynamics of the intra‐ and interhemispheric functional connectivity in brain regions incorporating social brain network of ASD, and highlight the potential role of abnormal interhemispheric communication dynamics in neural substrates underlying impaired social processing in ASD.

Dysfunctional white-matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white-matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white-matter structural architecture, it cannot detect neural activity or white-matter functions. Recent studies demonstrated the functional organization of white-matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white-matter dysfunctions in BECT. Resting-state fMRI data were collected from 24 new-onset drug-naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white-matter functional networks were obtained using a clustering analysis on voxel-by-voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub-bands (Slow-5:0.01-0.027, Slow-4:0.027-0.073, Slow-3:0.073-0.198, and Slow-2:0.198-0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white-matter networks based on their correlations with multiple gray-matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency-specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow-3 and Slow-4. This study provided further support on the existence of white-matter functional networks which exhibited frequency-specific properties, and extended abnormalities of rolandic area from the perspective of white-matter dysfunction in BECT.

Increased scale-free dynamics in salience network in adult high-functioning autism

Autism spectrum disorder (ASD) is clinically characterized by extremely slow and inflexible behavior. The neuronal mechanisms of these symptoms remain unclear though. Using fMRI, we investigate the resting state's temporal structure in the frequency domain (scale-free activity as measured with Power-Law Exponent, PLE, and Spectral Entropy, SE) and temporal variance (neural variability) in high-functioning, adult ASD comparing them with schizophrenic and neurotypical subjects. We show that ASD is characterized by high PLE in salience network, especially in dorsal anterior cingulate. This increase in PLE was 1) specific for salience network; 2) independent of other measures such as neuronal variability/SD and functional connectivity, which did not show any significant difference; 3) detected in two independent samples of ASD but not in the schizophrenia sample. Among salience network subregions, dorsal anterior cingulate cortex exhibited PLE differences between ASD and neurotypicals in both samples, showing high robustness in ROC curves values. Salience network abnormal temporal structure was confirmed by SE, which was strongly anticorrelated with PLE and thus decreased in ASD. Taken together, our findings show abnormal temporal structure (but normal temporal variance) in resting state salience network in adult high-functioning ASD. The abnormally high PLE indicates a relative predominance of slower over faster frequencies, which may underlie the slow adaptation to unexpected changes and the inflexible behavior observed in autistic individuals. The specificity of abnormal PLE in salience network suggests its potential utility as biomarker in ASD.

Aberrant functional connectivity of neural circuits associated with social and sensorimotor deficits in young children with autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical functional integration of brain regions. The vast majority of neuroimaging studies of ASD have focused on older children, adolescents, and adults with the disorder. Very little work has explored whole-brain functional connectivity of young children with ASD. Here, we collected resting-state functional magnetic resonance imaging data from 58 young children (mean age 4.98 years; 29 with ASD; 29 matched healthy controls [HC]). All children were under sedation during scanning. A functional "connectedness" method was first used to seek for brain regions showing atypical functional connectivity (FC) in children with ASD. Then, a recurrent-seek strategy was applied to reveal atypical FC circuits in ASD children. FC matrices between regions-of-interest (ROIs) were compared between ASD and HC. Finally, a support vector regression (SVR) method was used to assess the relationship between the FC circuits and ASD symptom severity. Two atypical FC circuits comprising 23 ROIs in ASD were revealed: one predominantly comprised brain regions involved with social cognition showing under-connectivity in ASD; the other predominantly comprised sensory-motor and visual brain regions showing over-connectivity in ASD. The SVR analysis showed that the two FC circuits were separately related to social deficits and restricted behavior scores. These findings indicate disrupted FC of neural circuits involved in the social and sensorimotor processes in young children with ASD. The finding of the atypical FC patterns in young children with ASD underscores the utility of studying younger children with the disorder, and highlights nuanced patterns of brain connectivity underlying behavior closer to disorder onset. Autism Research 2018, 11: 1643-1652. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism spectrum disorder (ASD) is an early-onset neurodevelopmental disorder. Understanding brain functional alterations at early ages is important for understanding biological mechanisms of ASD. Here, we found two atypical brain functional circuits in young children with ASD that were related to social and sensorimotor function. These results show how atypical patterns of brain functional connectivity in young children with of ASD may underlie core symptoms of the disorder.

Dynamic functional connectivity analysis reveals decreased variability of the default-mode network in developing autistic brain

Accumulating neuroimaging evidence suggests that abnormal functional connectivity of the default mode network (DMN) contributes to the social-cognitive deficits of autism spectrum disorder (ASD). Although most previous studies relied on conventional functional connectivity methods, which assume that connectivity patterns remain constant over time, understanding the temporal dynamics of functional connectivity during rest may provide new insights into the dysfunction of the DMN in ASD. In this work, dynamic functional connectivity analysis based on sliding time window correlation was applied to the resting-state functional magnetic resonance imaging data of 28 young children with ASD (age range: 3-7 years) and 29 matched typically developing controls (TD group). In addition, k-means cluster analysis was performed to identify distinct temporal states based on the spatial similarity of each functional connectivity pattern. Compared with the TD group, young children with ASD showed decreased dynamic functional connectivity variance between the posterior cingulate cortex (PCC) and the right precentral gyrus, which is negatively correlated with social motivation and social relating. Cluster analysis revealed significant differences in functional connectivity patterns between the ASD and TD groups in discrete temporal states. Our findings reveal that atypical dynamic interactions between the PCC and sensorimotor cortex are associated with social deficits in ASD. Results also highlight the critical role of PCC in the social-cognitive deficits of ASD and support the concept that understanding the dynamic neural interactions among brain regions can provide insights into functional abnormalities in ASD. Autism Research 2018, 11: 1479-1493. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Social cognitive dysfunction in autism spectrum disorder (ASD) is associated with dysfunction of the default mode network (DMN), a set of brain areas involved in various domains of social processing. We found that decreases in the dynamic functional connectivity variance between the posterior cingulate cortex and the sensorimotor cortex are associated with deficits in social motivation and social relating in young children with ASD. This result suggests that aberrations in the DMN and its dynamic interactions with other networks contribute to atypical integration of information with respect to self and others.

Specificity of spontaneous EEG associated with different levels of cognitive and communicative dysfunctions in children

This study aimed to reveal electrophysiological markers of communicative and cognitive dysfunctions of different severity in children with autism spectrum disorder (ASD). Eyes-opened electroencephalograms (EEGs) of 42 children with ASD, divided into two groups according to the severity of their communicative and cognitive dysfunctions (24 with severe and 18 children with less severe ASD), and 70 age-matched controls aged 4-9 years were examined by means of spectral and group independent component (gIC) analyses. A predominance of theta and beta EEG activity in both groups of children with ASD compared to the activity in the control group was found in the global gIC together with a predominance of beta EEG activity in the right occipital region. The quantity of local gICs with enhanced slow and high-frequency EEG activity (within the frontal, temporal, and parietal cortex areas) in children 4-9 years of age might be considered a marker of cognitive and communicative dysfunction severity.