scholarly journals Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder

2018 ◽  
Author(s):  
Vatika Harlalka ◽  
Raju S. Bapi ◽  
P.K. Vinod ◽  
Dipanjan Roy
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Symptom Severity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Modular Organization ◽  
Strong Positive Correlation ◽  
Connection Strength ◽  
Whole Brain

AbstractResting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the age, disease phenotype and their interaction in the whole brain transient connectivity patterns. Contrasting with most extant literature in the present study, we investigated precisely how age and disease phenotypes factors into dynamic changes in functional connectivity of TD and ASD using resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7–11 years) and adolescents (12–17 years), and adults (18+) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures: flexibility, cohesion strength and disjointness were explored for each subject to characterize the differences between ASD and TD.In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hypervariability relates to the symptom severity in ASD. We found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with the flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing the high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Jia Wang ◽  
Xiaomin Wang ◽  
Runshi Wang ◽  
Xujun Duan ◽  
Heng Chen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Symptom Severity ◽  
Interaction Strength ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Resting State Functional Connectivity ◽  
Connectivity Patterns ◽  
Visual Network

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.

scholarly journals Atypical Resting-State Functional Connectivity of Intra/Inter-Sensory Networks is Related to Symptom Severity in Young Boys with Autism Spectrum Disorder

2020 ◽  
Author(s):  
Jia Wang ◽  
Xiaomin Wang ◽  
Runshi Wang ◽  
Xujun Duan ◽  
Heng Chen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Symptom Severity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Social Impairment ◽  
Connectivity Pattern ◽  
Children With Asd ◽  
Visual Network

Abstract Background Autism spectrum disorder (ASD), a neurodevelopmental disorder, has been reported with an altered brain connectivity pattern in the sensory network using resting-state functional magnetic imaging (rs-fMRI) compared to typical developing participants (TDs). However, there is still no consistent conclusion. In the current study, we investigated the alterations of the intra-network and inter-network connectivity pattern relating to sensory in children with ASD compared with TDs, and further assessed whether these alterations are associated with autistic behavioral symptoms. Methods rs-fMRI was used to assess young boys with ASD (N=29) and TD (N=29), aged 3-7-years. Groups were matched for age and handiness. Spatial patterns of the sensory rest state networks (RSNs) were obtained using group Independent component analysis (ICA) method, and between-groups differences were evaluated within each sensory network. Then, the time series of each RSN were extracted from each participant preprocessed data. Correlation analysis was assessed among intra- and inter-network functional connectivity (FC) and symptom severity in children with ASD. Results Four sensory components were identified, including auditory network (AN), higher visual network (HVN), primary visual network (PVN) and sensorimotor network (SMN). Functional images revealed two sensory networks exhibiting significant increased FCs in ASD group, located within AN and SMN. Higher positive connectivity between PVN and HVN in ASD group is associated with symptom severity. Conclusion Current study might shed light that the abnormal connectivity patterns of sensory network regions may underlie impaired higher-order multisensory integration in ASD children, and social impairment of ASD are caused probably by aberrant FCs involving inter/intra- sensory network.

scholarly journals Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

2015 ◽  
Vol 72 (8) ◽  
pp. 767 ◽  
Author(s):  
Leonardo Cerliani ◽  
Maarten Mennes ◽  
Rajat M. Thomas ◽  
Adriana Di Martino ◽  
Marc Thioux ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Resting State Networks

scholarly journals Decreased homotopic interhemispheric functional connectivity in children with autism spectrum disorder

2020 ◽  
Author(s):  
Shuxia Yao ◽  
Menghan Zhou ◽  
Yuan Zhang ◽  
Feng Zhou ◽  
Qianqian Zhang ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Visual Processing ◽  
Brain Networks ◽  
Cingulate Cortex ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Volume Changes ◽  
Processing Network

AbstractWhile a number of functional and structural changes occur in large-scale brain networks in autism spectrum disorder (ASD), reduced interhemispheric resting state functional connectivity (rsFC) between homotopic regions may be of particular importance as a biomarker. ASD is an early-onset developmental disorder and neural alterations are often age-dependent, reflecting dysregulated developmental trajectories, although no studies have investigated whether homotopic interhemispheric rsFC alterations occur in ASD children. The present study conducted a voxel-based homotopic interhemispheric rsFC analysis in 146 SD and 175 typically developing children under age 10 and examined associations with symptom severity in the Autism Brain Imaging Data Exchange datasets. Given the role of corpus callosum (CC) in interhemispheric connectivity and reported CC volume changes in ASD we additionally examined whether there were parallel volumetric changes in ASD children. Results demonstrated decreased homotopic rsFC in ASD children in the medial prefrontal cortex, precuneus and posterior cingulate cortex of the default mode network (DMN), the dorsal anterior cingulate cortex of the salience network, the precentral gyrus and inferior parietal lobule of the mirror neuron system, the lingual, fusiform and inferior occipital gyri of the visual processing network and thalamus. Symptom severity was associated with homotopic rsFC in regions in the DMN and visual processing network. There were no significant CC volume changes in ASD children. The present study shows that reduced homotopic interhemispheric rsFC in brain networks in ASD adults/adolescents is already present in children of 5-10 years old and further supports their potential use as a general ASD biomarker.

scholarly journals Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinlong Hu ◽  
Lijie Cao ◽  
Tenghui Li ◽  
Bin Liao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Piecewise Linear ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Support Vector ◽  
Resting State Functional Connectivity

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

scholarly journals Sex-dependent and sex-independent brain resting-state functional connectivity in children with autism spectrum disorder

2016 ◽  
Author(s):  
Xin Di ◽  
Bharat B Biswal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Main Effects

Background: Males are more likely to suffer from autism spectrum disorder (ASD) than females. As to whether females with ASD have similar brain alterations remain an open question. The current study aimed to examine sex-dependent as well as sex-independent alterations in resting-state functional connectivity in individuals with ASD compared with typically developing (TD) individuals. Method: Resting-state functional MRI data were acquired from the Autism Brain Imaging Data Exchange (ABIDE). Subjects between 6 to 20 years of age were included for analysis. After matching the intelligence quotient between groups for each dataset, and removing subjects due to excessive head motion, the resulting effective sample contained 28 females with ASD, 49 TD females, 129 males with ASD, and 141 TD males, with a two (diagnosis) by two (sex) design. Functional connectivity among 153 regions of interest (ROIs) comprising the whole brain was computed. Two by two analysis of variance was used to identify connectivity that showed diagnosis by sex interaction or main effects of diagnosis. Results: The main effects of diagnosis were found mainly between visual cortex and other brain regions, indicating sex-independent connectivity alterations. We also observed two connections whose connectivity showed diagnosis by sex interaction between the precuneus and medial cerebellum as well as the precunes and dorsal frontal cortex. While males with ASD showed higher connectivity in these connections compared with TD males, females with ASD had lower connectivity than their counterparts. Conclusions: Both sex-dependent and sex-independent functional connectivity alterations are present in ASD.

scholarly journals Hypo- and hyper-connectivity in default mode network related to social impairment in tweens with autism spectrum disorder

2016 ◽  
Author(s):  
Wilma Matthysen ◽  
Daniele Marinazzo ◽  
Roma Siugzdaite
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Social Communication ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Connectivity Pattern ◽  
Resting State Networks ◽  
Default Mode

Background. Autism spectrum disorder is a neurodevelopmental disorder, marked by impairment in social communication and restricted, repetitive patterns of behavior, interests, or activities. Accumulating data suggests that alterations in functional connectivity might contribute to these deficits. Whereas functional connectivity in resting state fMRI is expressed by several resting-state networks, for this study we examined several of them, but our particular interest was in the default mode network (DMN), given its age dependent alteration of functional connectivity and its relation to social communication. Methods. Since the studies investigating young children (6-8 years) with autism have found hypo-connectivity in DMN and studies on adolescents (12-16 years old) with autism have found hyper-connectivity in the DMN, we were interested in connectivity pattern during the age of 8 to 12, so we investigated the role of altered intrinsic connectivity in 16 children (mean age 9.75 ±1.6 years) with autism spectrum disorder compared to 16 typically developing controls in the DMN and other resting-state networks. Results. Results show that, compared to controls, the group with autism spectrum disorder showed signs of both hypo- and hyper-connectivity in different regions of the resting-state networks related to social communication. Conclusion. That suggests that transition period from childhood to adolescence carries the complexity of functional connectivity from both age groups. Regions that showed differences in functional connectivity were discussed in relation to social communication difficulties.

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