scholarly journals The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder: Predicting symptoms in autism with brain imaging

2018 ◽  
Author(s):  
Evelyn MR Lake ◽  
Emily S Finn ◽  
Stephanie M Noble ◽  
Tamara Vanderwal ◽  
Xilin Shen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Organization ◽  
Functional Brain ◽  
Functional Brain Organization

ABSTRACTAutism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individual’s functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

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 An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

scholarly journals Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6001
Author(s):  
Zarina Rakhimberdina ◽  
Xin Liu ◽  
Tsuyoshi Murata
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Network Architecture ◽  
Data Exchange ◽  
Imaging Techniques ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Model Ensemble

With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

scholarly journals Modulation of striatal functional connectivity differences in adults with and without autism spectrum disorder in a single-dose randomized trial of cannabidivarin

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Charlotte M. Pretzsch ◽  
Dorothea L. Floris ◽  
Bogdan Voinescu ◽  
Malka Elsahib ◽  
Maria A. Mendez ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Repeated Measures ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Controlled Study ◽  
Imaging Data ◽  
Small Pilot Study ◽  
Double Blind ◽  
Methodological Aspects

Abstract Background Autism spectrum disorder (ASD) has a high cost to affected individuals and society, but treatments for core symptoms are lacking. To expand intervention options, it is crucial to gain a better understanding of potential treatment targets, and their engagement, in the brain. For instance, the striatum (caudate, putamen, and nucleus accumbens) plays a central role during development and its (atypical) functional connectivity (FC) may contribute to multiple ASD symptoms. We have previously shown, in the adult autistic and neurotypical brain, the non-intoxicating cannabinoid cannabidivarin (CBDV) alters the balance of striatal ‘excitatory–inhibitory’ metabolites, which help regulate FC, but the effects of CBDV on (atypical) striatal FC are unknown. Methods To examine this in a small pilot study, we acquired resting state functional magnetic resonance imaging data from 28 men (15 neurotypicals, 13 ASD) on two occasions in a repeated-measures, double-blind, placebo-controlled study. We then used a seed-based approach to (1) compare striatal FC between groups and (2) examine the effect of pharmacological probing (600 mg CBDV/matched placebo) on atypical striatal FC in ASD. Visits were separated by at least 13 days to allow for drug washout. Results Compared to the neurotypicals, ASD individuals had lower FC between the ventral striatum and frontal and pericentral regions (which have been associated with emotion, motor, and vision processing). Further, they had higher intra-striatal FC and higher putamenal FC with temporal regions involved in speech and language. In ASD, CBDV reduced hyperconnectivity to the neurotypical level. Limitations Our findings should be considered in light of several methodological aspects, in particular our participant group (restricted to male adults), which limits the generalizability of our findings to the wider and heterogeneous ASD population. Conclusion In conclusion, here we show atypical striatal FC with regions commonly associated with ASD symptoms. We further provide preliminary proof of concept that, in the adult autistic brain, acute CBDV administration can modulate atypical striatal circuitry towards neurotypical function. Future studies are required to determine whether modulation of striatal FC is associated with a change in ASD symptoms. Trial registration clinicaltrials.gov, Identifier: NCT03537950. Registered May 25th, 2018—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03537950?term=NCT03537950&draw=2&rank=1.

scholarly journals A-24 Metacognition and behavioral regulation associated with distinct connectivity patterns in autism spectrum disorder

2019 ◽  
Vol 34 (6) ◽  
pp. 883-883
Author(s):  
H Bednarz ◽  
R Kana
Keyword(s):  
Autism Spectrum Disorder ◽  
Executive Function ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Parent Ratings ◽  
Behavioral Regulation ◽  
Imaging Data ◽  
Frontal Pole

Abstract Objective Executive function (EF) deficits are well documented in children with autism spectrum disorder (ASD)1-2 and are commonly measured clinically using parent ratings3. No studies have investigated whether parent ratings of EF predict brain connectivity in ASD. Aim Examine whether the Behavior Rating Inventory of Executive Function (BRIEF) predicts functional brain connectivity in ASD. Method Resting-state fMRI and behavioral data were obtained from the Autism Brain Imaging Data Exchange (ABIDE-II) database6 (n = 106 ASD, ages 5-13). ROI-to-ROI (Region of Interest) connectivity was computed for 132 ROIs spanning the whole brain, defined using Conn Toolbox. Multiple regression analyses examined the effect of BRIEF metacognition on connectivity while controlling for BRIEF behavioral regulation, and vice versa. Age, sex, and full-scale IQ were included as covariates. FDR correction was used (p < 0.05). Results More severe deficits in metacognition were associated with stronger connectivity between the left hippocampus and several ROIs, including the cerebellum and planum temporale. More severe deficits in metacognition were associated with weaker right hippocampus – right frontal pole connectivity. More severe deficits in behavioral regulation were associated with stronger connectivity between subcortical regions (i.e., thalamus, putamen, and caudate) and regions involved in motor (superior frontal gyrus) and limbic systems (cingulate gyrus). More severe behavioral regulation deficits were associated with weaker cerebellar-cerebellar connectivity. Conclusions Findings suggest that parent-ratings of metacognitive abilities in children with ASD are associated with hippocampal connectivity, while behavioral regulation abilities are associated with thalamic and striatum connections. These results build upon previous studies of metacognition and behavioral regulation 5,6.

scholarly journals Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 32
Author(s):  
Lluis Borràs-Ferrís ◽  
Úrsula Pérez-Ramírez ◽  
David Moratal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Statistical Dependence ◽  
Middle Temporal Gyrus ◽  
Middle Temporal ◽  
Brain Functions

Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image Data Exchange (ABIDE) database. Thus, we estimated brain functional connectivity (FC) between pairs of regions as the statistical dependence between their neural-related blood-oxygen-level-dependent (BOLD) signals. We compared FC of individuals with ASD and healthy controls, matched by age and intelligence quotient (IQ), and split into three age groups (50 children, 98 adolescents, and 32 adults), from a developmental perspective. After estimating the correlation, we observed hypoconnectivities in children and adolescents with ASD between regions belonging to the default mode network (DMN). Concretely, in children, FC decreased between the left middle temporal gyrus and right frontal pole (p = 0.0080), and between the left orbitofrontal cortex and right superior frontal gyrus (p = 0.0144). In adolescents, this decrease was observed between bilateral postcentral gyri (p = 0.0012), and between the right precuneus and right middle temporal gyrus (p = 0.0236). These results help to gain a better understanding of the involved regions on autism and its connection with the affected superior cognitive brain functions.

scholarly journals The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8171
Author(s):  
Yaser ElNakieb ◽  
Mohamed T. Ali ◽  
Ahmed Elnakib ◽  
Ahmed Shalaby ◽  
Ahmed Soliman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Gold Standard ◽  
Data Exchange ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Feature Engineering

Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabilities. The onset of symptoms can start in early childhood, yet repeated visits to a pediatric specialist are needed before reaching a diagnosis. Still, this diagnosis is usually subjective, and scores can vary from one specialist to another. Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder. Currently, the gold standard diagnosis of ASD is a set of diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview–Revised (ADI-R) report. These gold standard diagnostic instruments are an intensive, lengthy, and subjective process that involves a set of behavioral and communications tests and clinical history information conducted by a team of qualified clinicians. Emerging advancements in neuroimaging and machine learning techniques can provide a fast and objective alternative to conventional repetitive observational assessments. This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an accurate classification of autistic individuals. This work highlights important findings of impacted brain areas that contribute to an autism diagnosis and presents promising accuracy results. We verified our proposed framework on a large publicly available DTI dataset of 225 subjects from the Autism Brain Imaging Data Exchange-II (ABIDE-II) initiative, achieving a high global balanced accuracy over the 5 sites of up to 99% with 5-fold cross validation. The data used was slightly unbalanced, including 125 autistic subjects and 100 typically developed (TD) ones. The achieved balanced accuracy of the proposed technique is the highest in the literature, which elucidates the importance of feature engineering steps involved in extracting useful knowledge and the promising potentials of adopting neuroimaging for the diagnosis of autism.

Different aberrant mentalizing networks in males and females with autism spectrum disorders: Evidence from resting-state functional magnetic resonance imaging

Autism ◽  
2016 ◽  
Vol 22 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Jie Yang ◽  
Jonathan Lee
Keyword(s):  
Autism Spectrum Disorder ◽  
Sex Differences ◽  
Functional Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Typically Developing ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Males And Females ◽  
Spectrum Disorders

Previous studies have found that individuals with autism spectrum disorders show impairments in mentalizing processes and aberrant brain activity compared with typically developing participants. However, the findings are mainly from male participants and the aberrant effects in autism spectrum disorder females and sex differences are still unclear. To address these issues, this study analyzed intrinsic functional connectivity of mentalizing regions using resting-state functional magnetic resonance imaging data of 48 autism spectrum disorder males and females and 48 typically developing participants in autism brain imaging data exchange. Whole-brain analyses showed that autism spectrum disorder males had hyperconnectivity in functional connectivity of the bilateral temporal-parietal junction, whereas autism spectrum disorder females showed hypoconnectivity in functional connectivity of the medial prefrontal cortex, precuneus, and right temporal-parietal junction. Interaction between sex and autism was found in both short- and long-distance functional connectivity effects, confirming that autism spectrum disorder males showed overconnectivity, while autism spectrum disorder females showed underconnectivity. Furthermore, a regression analysis revealed that in autism spectrum disorder, males and females demonstrated different relations between the functional connectivity effects of the mentalizing regions and the core autism spectrum disorder deficits. These results suggest sex differences in the mentalizing network in autism spectrum disorder individuals. Future work is needed to examine how sex interacts with other factors such as age and the sex differences during mentalizing task performance.

scholarly journals Core-Symptom-Defined Cortical Gyrification Differences in Autism Spectrum Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingmin Ning ◽  
Cuicui Li ◽  
Lei Gao ◽  
Jingyi Fan
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Data Exchange ◽  
Developmental Trajectories ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Cortical Folding ◽  
The Core ◽  
Core Symptom

Autism spectrum disorder (ASD) is a heterogeneous disease that is characterized by abnormalities in social communication and interaction as well as repetitive behaviors and restricted interests. Structural brain imaging has identified significant cortical folding alterations in ASD; however, relatively less known is whether the core symptoms are related to neuroanatomical differences. In this study, we aimed to explore core-symptom-anchored gyrification alterations and their developmental trajectories in ASD. We measured the cortical vertex-wise gyrification index (GI) in 321 patients with ASD (aged 7–39 years) and 350 typically developing (TD) subjects (aged 6–33 years) across 8 sites from the Autism Brain Imaging Data Exchange I (ABIDE I) repository and a longitudinal sample (14 ASD and 7 TD, aged 9–14 years in baseline and 12–18 years in follow-up) from ABIDE II. Compared with TD, the general ASD patients exhibited a mixed pattern of both hypo- and hyper- and different developmental trajectories of gyrification. By parsing the ASD patients into three subgroups based on the subscores of the Autism Diagnostic Interview—Revised (ADI-R) scale, we identified core-symptom-specific alterations in the reciprocal social interaction (RSI), communication abnormalities (CA), and restricted, repetitive, and stereotyped patterns of behavior (RRSB) subgroups. We also showed atypical gyrification patterns and developmental trajectories in the subgroups. Furthermore, we conducted a meta-analysis to locate the core-symptom-anchored brain regions (circuits). In summary, the current study shows that ASD is associated with abnormal cortical folding patterns. Core-symptom-based classification can find more subtle changes in gyrification. These results suggest that cortical folding pattern encodes changes in symptom dimensions, which promotes the understanding of neuroanatomical basis, and clinical utility in ASD.

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