Multicenter mapping of structural network alterations in autism

Abstract

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions primarily characterized by abnormalities in social cognition. Abundant previous functional MRI studies have shown atypical activity in networks encompassing medial prefrontal cortex (mPFC) and medial parietal regions corresponding to posterior cingulate cortex and precuneus (PCC/PCU). Conversely, studies assessing structural brain anomalies in ASD have been rather inconsistent. The current work evaluated whether structural changes in ASD can be reliability detected in a large multicenter dataset. Our comprehensive structural MRI framework encompassed cortical thickness mapping and structural covariance analysis based on three independent samples comprising individuals with ASD and controls (n = 220), selected from the Autism Brain Imaging Data Exchange open-access database. Surface-based analysis revealed increased cortical thickness in ASD relative to controls in mPFC and lateral prefrontal cortex. Clusters encompassing mPFC were embedded in altered inter-regional covariance networks, showing decreased covariance in ASD relative to controls primarily to PCC/PCU and inferior parietal regions. Cortical thickness increases and covariance reductions in ASD were consistent, yet of variable effect size, across the different sites evaluated and measurable both in children and adults. Our multi-site study shows regional and network-level structural alterations in mPFC in ASD that, possibly, relate to atypical socio-cognitive functions in this condition.

Neuroimaging-Based Phenotyping of the Autism Spectrum

“…c Functional connectivity alterations showing primarily decreased connectivity between medial frontal and posterior midline regions in ASD relative to controls. Adapted from Valk et al (2015) and Di Martino et al (2014) with permission frontal cortex in 3/6 of cases (Bailey et al 1998). Another study observed laminar rearrangement together with a poorly defined gray and white matter interface in some specimens (Avino and Hutsler 2010).…”

“…2a). While the authors reported a generally consistent direction of effects across each of the analysis sites, they emphasized variability in effects sizes across the sites (that may be due to variability in scanning parameters and inclusion criteria), together with increased effects in children compared to adults (Valk et al 2015).…”

“…Recently, the Autism Brain Imaging Data Exchange (ABIDE) initiative made a multicenter repository containing neuroimaging data from 539 individuals with ASD and 573 controls, together with standardized behavioral phenotypic information, freely available (Di Martino et al 2014). Based on data from three independent ABIDE sites, Valk and colleagues performed a surface-based cortical thickness analysis and assessed overall patterns of findings as well as across-site reproducibility (Valk et al 2015). Assessing data from more than 200 male participants that passed quality control and surface correction procedures, the authors observed cortical thickness increases in ASD relative to controls, particularly in medial and lateral prefrontal cortices, when pooling data across all imaging sites (Fig.…”

Genetic risk for obesity predicts nucleus accumbens size and responsivity to real-world food cues

“…Structural images were reconstructed using FreeSurfer, an automated segmentation tool that has been demonstrated to label structures comparably to manual tracing techniques (20). Although visual inspection is commonly used to qualitatively examine the quality of structural MRI data, more recent techniques have additionally allowed for quantitative measurements of anatomical data quality (38,39). To assess data quality in an unbiased manner, Freesurfer's QA tools (https://surfer.nmr.mgh.harvard.edu/fswiki/ QATools) were used to calculate signal-to-noise ratios for each subject as well as to generate a series of screenshots taken from various steps throughout the reconstruction pipeline.…”

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

“…Indeed, given this link to connectivity, structural covariance networks are particularly appealing to examine neuropsychiatric disorders in which aberrations in structural and functional networks have been implicated. Alterations in net-20 works of structural covariance have been demonstrated in autism (Zielinski et al, 2012;Bernhardt et al, 2014;Valk et al, 2015;Bethlehem et al, 2017), schizophrenia (Shi et al, 2012;Wheeler et al, 2014;Alexander-Bloch et al, 2014), epilepsy (Bernhardt et al, 2011;Yasuda et al, 2015;Bernhardt et al, 2016), and grapheme-color synesthesia (Hänggi et al, 2011), to name a few such disorders.…”