A Diffusion Tensor Imaging Study in Children With ADHD, Autism Spectrum Disorder, OCD, and Matched Controls: Distinct and Non-Distinct White Matter Disruption and Dimensional Brain-Behavior Relationships

Abstract

Objective: Neurodevelopmental disorders (NDDs) (attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and obsessive-compulsive disorder [OCD]) share genetic vulnerability and symptom domains. The authors present direct comparison of structural brain circuitry in children and adolescents with NDDs and control subjects and examine brain circuit-behavior relationships across NDDs using dimensional measures related to each disorder.Method: Diffusion imaging and behavioral measures were acquired in 200 children and adolescents (ADHD: N=31; OCD: N=36; ASD: N=71; controls: N=62; mean age range: 10.3-12.6 years). Following Tract-Based Spatial Statistics, multigroup comparison of white matter indices was conducted, followed by pairwise comparisons. Relationships of fractional anisotropy with dimensional measures of inattention, social deficits, obsessive-compulsive symptoms, and general adaptive functioning were conducted across the NDD sample.Results: Lower fractional anisotropy within the splenium of the corpus callosum was found in each NDD group, compared with the control group. Lower fractional anisotropy in additional white matter tracts was found in the ASD and ADHD groups, compared with the control group, but not in the OCD group. Fractional anisotropy was lower in the ASD and ADHD groups compared with the OCD group but was not different in ADHD participants compared with ASD participants. A positive relation between fractional anisotropy (across much of the brain) and general adaptive functioning across NDDs was shown.Conclusions: This study identified disruption in interhemispheric circuitry (i.e., fractional anisotropy alterations in the corpus callosum) as a shared feature of ASD, ADHD, and OCD. However, fractional anisotropy alterations may be more widespread and severe in ASD and ADHD than in OCD. Higher fractional anisotropy throughout the brain appears to be related to better adaptive function across NDDs.

Decision-Making in Adolescents According to Reaction Time, HEG and PASS

“…They are studies based on brain lesions [17][18][19], electrophysiological findings, particularly, Event-Related Potential (ERP) [20][21][22], but predominantly Magnetic Resonance Image (MRI) [23], Functional Magnetic Resonance Image (fMRI) [24][25][26][27][28][29], and Connectivity Functional Magnetic Resonance Image (cfMRI).…”

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“…These results seem to support both models, suggesting that the cerebral alterations in psychosis involve an on-going process of genetic and environmental as well as epistatic and epigenetic interactions 37,42 , but also illness-or treatment related factors. As stated before, multiple brain areas have been associated with schizophrenia, pointing to altered networks / neuronal connectivity, as demonstrated in several neurophysiological and functional imaging studies 43,44 . The dyscon-nectivity hypothesis as formulated by implies that the core pathology of schizophrenia is the result of faults in abnormal epigenetic, activity-and experience dependent N-methyl-D-aspartate receptor (NMDAR) mediated synaptic plasticity by modulatory neurotransmitters like dopamine.…”

“…the internal capsule, corona radiata, superior and ILF, in patients with adolescent-onset schizophrenia 14 . In contrast, Peters and colleagues (2009) found FA increases in the bilateral uncinate fasciculus, anterior internal capsule, and frontal WM in patients with recent onset schizophrenia who had started using cannabis before the age of 17 years, compared to a similar group with no history of cannabis use 44 . Reduced FA has also been found in the splenium of the corpus callosum in noncannabis using patients with schizophrenia compared to patients with schizophrenia and early onset cannabis use (before age 15 years) 45 .…”

“…The PTR has previously been associated with schizophrenia 15,43 , e.g. with impaired emotional self-awareness 44 .…”

“…A binary mask was created containing the significant clusters in order to extract FA values of the individual participants. For visualization purposes (see Fig.1) and to extend the TBSS analyses, from all 38 JHU labeled white matter tracts, skeleton mean FA values were extracted and exported to Stata version 12 44 . Regional group differences were tested with multilevel random regression procedures using the Stata XTREG command with adjustment for the a priori hypothesized confounding variables age, sex, handedness, and level of education.…”