Reproducible functional connectivity alterations are associated with autism spectrum disorder

AbstractDespite the high clinical burden little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here we address these questions in the most comprehensive, large-scale effort to date comprising evaluation of four large ASD cohorts. We followed a strict exploration and replication procedure to identify core rs-fMRI functional connectivity (degree centrality) alterations associated with ASD as compared to typically developing (TD) controls (ASD: N=841, TD: N=984). We then tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status and clinical symptom severity. We find reproducible patterns of ASD-associated functional hyper- and hypo-connectivity with hypo-connectivity being primarily restricted to sensory-motor regions and hyper-connectivity hubs being predominately located in prefrontal and parietal cortices. We establish shifts in between-network connectivity from outside to within the identified regions as a key driver of these abnormalities. The magnitude of these alterations is linked to core ASD symptoms related to communication and social interaction and is not affected by age, sex or medication status. The identified brain functional alterations provide a reproducible pathophysiological phenotype underlying the diagnosis of ASD reconciling previous divergent findings. The large effect sizes in standardized cohorts and the link to clinical symptoms emphasize the importance of the identified imaging alterations as potential treatment and stratification biomarkers for ASD.

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Patients with autism spectrum disorders display reproducible functional connectivity alterations

Science Translational Medicine ◽

10.1126/scitranslmed.aat9223 ◽

2019 ◽

Vol 11 (481) ◽

pp. eaat9223 ◽

Cited By ~ 28

Author(s):

Štefan Holiga ◽

Joerg F. Hipp ◽

Christopher H. Chatham ◽

Pilar Garces ◽

Will Spooren ◽

...

Keyword(s):

Functional Connectivity ◽

Clinical Symptoms ◽

Brain Activity ◽

Network Connectivity ◽

Autism Spectrum ◽

Key Driver ◽

Spectrum Disorders ◽

Living Skills ◽

Parietal Cortices ◽

Medication Status

Despite the high clinical burden, little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting-state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here, we addressed these questions in four large ASD cohorts. Using rs-fMRI, we identified functional connectivity alterations associated with ASD. We tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status, and clinical symptom severity. Our results showed reproducible patterns of ASD-associated functional hyper- and hypoconnectivity. Hypoconnectivity was primarily restricted to sensory-motor regions, whereas hyperconnectivity hubs were predominately located in prefrontal and parietal cortices. Shifts in cortico-cortical between-network connectivity from outside to within the identified regions were shown to be a key driver of these abnormalities. This reproducible pathophysiological phenotype was partially associated with core ASD symptoms related to communication and daily living skills and was not affected by age, sex, or medication status. Although the large effect sizes in standardized cohorts are encouraging with respect to potential application as a treatment and for patient stratification, the moderate link to clinical symptoms and the large overlap with healthy controls currently limit the usability of identified alterations as diagnostic or efficacy readout.

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mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity

Nature Communications ◽

10.1038/s41467-021-26131-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Marco Pagani ◽

Noemi Barsotti ◽

Alice Bertero ◽

Stavros Trakoshis ◽

Laura Ulysse ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Large Scale ◽

Resting State Fmri ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Spine Density ◽

Large Scale Network ◽

Synaptic Pathology ◽

Transcriptomic Signature

AbstractPostmortem studies have revealed increased density of excitatory synapses in the brains of individuals with autism spectrum disorder (ASD), with a putative link to aberrant mTOR-dependent synaptic pruning. ASD is also characterized by atypical macroscale functional connectivity as measured with resting-state fMRI (rsfMRI). These observations raise the question of whether excess of synapses causes aberrant functional connectivity in ASD. Using rsfMRI, electrophysiology and in silico modelling in Tsc2 haploinsufficient mice, we show that mTOR-dependent increased spine density is associated with ASD -like stereotypies and cortico-striatal hyperconnectivity. These deficits are completely rescued by pharmacological inhibition of mTOR. Notably, we further demonstrate that children with idiopathic ASD exhibit analogous cortical-striatal hyperconnectivity, and document that this connectivity fingerprint is enriched for ASD-dysregulated genes interacting with mTOR or Tsc2. Finally, we show that the identified transcriptomic signature is predominantly expressed in a subset of children with autism, thereby defining a segregable autism subtype. Our findings causally link mTOR-related synaptic pathology to large-scale network aberrations, revealing a unifying multi-scale framework that mechanistically reconciles developmental synaptopathy and functional hyperconnectivity in autism.

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Electrophysiological Studies of Reception of Facial Communication in Autism Spectrum Disorder and Schizophrenia

10.1101/19013003 ◽

2019 ◽

Author(s):

Emily J. Levy ◽

Jennifer Foss-Feig ◽

Emily L. Isenstein ◽

Vinod Srihari ◽

Alan Anticevic ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Clinical Symptoms ◽

Brain Activity ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Schizophrenia Spectrum Disorders ◽

Facial Stimuli ◽

Brain Responses ◽

Electrophysiological Studies ◽

Research Findings

ABSTRACTAutism spectrum disorder (ASD) and schizophrenia spectrum disorders (SZ) are both characterized by difficulty with social cognition. Likewise, social brain activity is atypical in both disorders and indicates atypical reception of facial communication – a key area in the Research Domain Criteria framework for identifying common biological underpinnings of psychiatric disorders. To identify areas of overlap and dissociation between ASD and SZ, this paper reviews studies of electrophysiological (EEG) response to facial stimuli across ASD and SZ populations. We focus on findings regarding amplitude and latency of four brain responses implicated in social perception: P100, N170, N250, and P300. There were many inconsistent findings in both the ASD and SZ literatures; however, replication across studies was strongest for delayed N170 latency in ASD and attenuated N170 amplitude in SZ. EEG responses corresponded with clinical symptoms in multiple samples. These results highlight the challenges associated with replicating research findings in heterogeneous clinical populations, as well as the need for transdiagnostic research and for designing studies to examine relationships among continuous quantifications of behavior and neural activity across neurodevelopmental disorders.

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Resting state arousal and functional connectivity in autism spectrum disorder

Journal of Neurophysiology ◽

10.1152/jn.00292.2014 ◽

2015 ◽

Vol 113 (9) ◽

pp. 3035-3037

Author(s):

Johan Lundin Kleberg

Keyword(s):

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Resting State ◽

Electrodermal Activity ◽

Brain Activity ◽

Autism Spectrum ◽

Spectrum Disorder

A recent study (Eilam-Stock T, Xu P, Cao M, Gu X, Van Dam NT, Anagnostou E, Kolevzon A, Soorya L, Park Y, Siller M, He Y, Hof PR, Fan J. Brain 137: 153–171, 2014) demonstrated that resting state electrodermal activity is correlated with different patterns of brain activity in subjects with autism spectrum disorder (ASD) than in typical controls. These results are considered in light of theories of atypical arousal in ASD.

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Supplemental Material for Salience Network Connectivity and Social Processing in Children With Nonverbal Learning Disability or Autism Spectrum Disorder

Neuropsychology ◽

10.1037/neu0000494.supp ◽

2018 ◽

Keyword(s):

Autism Spectrum Disorder ◽

Learning Disability ◽

Network Connectivity ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Salience Network ◽

Nonverbal Learning Disability ◽

Social Processing ◽

Nonverbal Learning

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A novel automated autism spectrum disorder detection system

Complex & Intelligent Systems ◽

10.1007/s40747-021-00408-8 ◽

2021 ◽

Author(s):

Shu Lih Oh ◽

V. Jahmunah ◽

N. Arunkumar ◽

Enas W. Abdulhay ◽

Raj Gururajan ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Developmental Disorder ◽

Detection System ◽

Brain Activity ◽

Limited Range ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Nonlinear Features ◽

Genetic And Environmental Factors ◽

Future Work

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

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