Increased interhemispheric somatomotor functional connectivity and mirror overflow in ADHD.

Mirror overflow is a developmental phenomenon defined as unintentional movements that mimic the execution of intentional movements in homologous muscles on the opposite side of the body. In children with attention-deficit/hyperactivity disorder (ADHD), mirror overflow is commonly excessive, abnormally persistent, and correlated with ADHD symptom severity. As such, it represents a promising clinical biomarker for disinhibited behavior associated with ADHD. Yet, the neural underpinnings of mirror overflow in ADHD remain unclear. Our objective was to test whether intrinsic interhemispheric functional connectivity between homologous regions of the somatomotor network (SMN) is associated with mirror overflow in school age children with and without ADHD using resting state functional magnetic resonance imaging. To this end, we quantified mirror overflow in 119 children (8-12 years old, 62 ADHD) during a finger sequencing task using finger twitch transducers affixed to the index and ring fingers. Group ICA was used to identify right- and left-lateralized SMNs and subject-specific back reconstructed timecourses were correlated to obtain a measure of SMN interhemispheric connectivity. We found that children with ADHD showed increased mirror overflow (p < 0.001; d = 0.671) and interhemispheric SMN functional connectivity (p = 0.023; d = 0.521) as compared to typically developing children. In children with ADHD, but not the typically developing children, there was a significant relationship between interhemispheric SMN functional connectivity and mirror overflow (t = 2.116; p = 0.039). Our findings of stronger interhemispheric functional connectivity between homologous somatomotor regions in children with ADHD is consistent with previous transcranial magnetic stimulation and diffusion-tractography imaging studies suggesting that interhemispheric cortical inhibitory mechanisms may be compromised in children with ADHD. The observed brain-behavior correlation further suggests that abnormally strong interhemispheric SMN connectivity in children with ADHD may diminish their ability to suppress overflow movements.

Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations.

We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.

A joint network optimization framework to predict clinical severity from resting state functional MRI data.

We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks that define a network manifold. These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients. The second term is a linear regression model that uses the patient-specific coefficients to predict a measure of clinical severity. We validate our framework on two separate datasets in a ten fold cross validation setting. The first is a cohort of fifty-eight patients diagnosed with Autism Spectrum Disorder (ASD). The second dataset consists of sixty three patients from a publicly available ASD database. Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior. In contrast, our joint network optimization framework exploits the structure of the rs-fMRI correlation matrices to simultaneously capture group level effects and patient heterogeneity. Finally, we demonstrate that our proposed framework robustly identifies clinically relevant networks characteristic of ASD.

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.