Altered Cortico-Striatal–Thalamic Connectivity in Relation to Spatial Working Memory Capacity in Children with ADHD

Introduction: Attention deficit hyperactivity disorder (ADHD) captures a heterogeneous group of children, who are characterized by a range of cognitive and behavioral symptoms. Previous resting-state functional connectivity MRI (rs-fcMRI) studies have sought to understand the neural correlates of ADHD by comparing connectivity measurements between those with and without the disorder, focusing primarily on cortical–striatal circuits mediated by the thalamus. To integrate the multiple phenotypic features associated with ADHD and help resolve its heterogeneity, it is helpful to determine how specific circuits relate to unique cognitive domains of the ADHD syndrome. Spatial working memory has been proposed as a key mechanism in the pathophysiology of ADHD. Methods: We correlated the rs-fcMRI of five thalamic regions of interest (ROIs) with spatial span working memory scores in a sample of 67 children aged 7–11 years [ADHD and typically developing children (TDC)]. In an independent dataset, we then examined group differences in thalamo-striatal functional connectivity between 70 ADHD and 89 TDC (7–11 years) from the ADHD-200 dataset. Thalamic ROIs were created based on previous methods that utilize known thalamo-cortical loops and rs-fcMRI to identify functional boundaries in the thalamus. Results/Conclusion: Using these thalamic regions, we found atypical rs-fcMRI between specific thalamic groupings with the basal ganglia. To identify the thalamic connections that relate to spatial working memory in ADHD, only connections identified in both the correlational and comparative analyses were considered. Multiple connections between the thalamus and basal ganglia, particularly between medial and anterior dorsal thalamus and the putamen, were related to spatial working memory and also altered in ADHD. These thalamo-striatal disruptions may be one of multiple atypical neural and cognitive mechanisms that relate to the ADHD clinical phenotype

DNAJA1 controls the fate of misfolded mutant p53 through the mevalonate pathway

Stabilization of mutant p53 (mutp53) in tumours greatly contributes to malignant progression. However, little is known about the underlying mechanisms and therapeutic approaches to destabilize mutp53. Here, through high-throughput screening we identify statins, cholesterol-lowering drugs, as degradation inducers for conformational or misfolded p53 mutants with minimal effects on wild-type p53 (wtp53) and DNA contact mutants. Statins preferentially suppress mutp53-expressing cancer cell growth. Specific reduction of mevalonate-5-phosphate by statins or mevalonate kinase knockdown induces CHIP ubiquitin ligase-mediated nuclear export, ubiquitylation, and degradation of mutp53 by impairing interaction of mutp53 with DNAJA1, a Hsp40 family member. Knockdown of DNAJA1 also induces CHIP-mediated mutp53 degradation, while its overexpression antagonizes statin-induced mutp53 degradation. Our study reveals that DNAJA1 controls the fate of misfolded mutp53, provides insights into potential strategies to deplete mutp53 through the mevalonate pathway–DNAJA1 axis, and highlights the significance of p53 status in impacting statins’ efficacy on cancer therapy

Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks.

Clinical investigations of many neuropsychiatric disorders rely on the assumption that diagnostic categories and typical control samples each have within-group homogeneity. However, research using human neuroimaging has revealed that much heterogeneity exists across individuals in both clinical and control samples. This reality necessitates that researchers identify and organize the potentially varied patterns of brain physiology. We introduce an analytical approach for arriving at subgroups of individuals based entirely on their brain physiology. The method begins with Group Iterative Multiple Model Estimation (GIMME) to assess individual directed functional connectivity maps. GIMME is one of the only methods to date that can recover both the direction and presence of directed functional connectivity maps in heterogeneous data, making it an ideal place to start since it addresses the problem of heterogeneity. Individuals are then grouped based on similarities in their connectivity patterns using a modularity approach for community detection. Monte Carlo simulations demonstrate that using GIMME in combination with the modularity algorithm works exceptionally well--on average over 97% of simulated individuals are placed in the accurate subgroup with no prior information on functional architecture or group identity. Having demonstrated reliability, we examine resting-state data of fronto-parietal regions drawn from a sample (N = 80) of typically developing and attention-deficit/hyperactivity disorder (ADHD) -diagnosed children. Here, we find 5 subgroups. Two subgroups were predominantly comprised of ADHD, suggesting that more than one biological marker exists that can be used to identify children with ADHD based from their brain physiology. Empirical evidence presented here supports notions that heterogeneity exists in brain physiology within ADHD and control samples. This type of information gained from the approach presented here can assist in better characterizing patients in terms of outcomes, optimal treatment strategies, potential gene-environment interactions, and the use of biological phenomenon to assist with mental health

Characterizing heterogeneity in children with and without ADHD based on reward system connectivity

One potential obstacle limiting our ability to clarify ADHD etiology is the heterogeneity within the disorder, as well as in typical samples. In this study, we utilized a community detection approach on 106 children with and without ADHD (aged 7–12 years), in order to identify potential subgroups of participants based on the connectivity of the reward system. Children with ADHD were compared to typically developing children within each identified community, aiming to find the community-specific ADHD characteristics. Furthermore, to assess how the organization in subgroups relates to behavior, we evaluated delay-discounting gradient and impulsivity-related temperament traits within each community. We found that discrete subgroups were identified that characterized distinct connectivity profiles in the reward system. Importantly, which connections were atypical in ADHD relative to the control children were specific to the community membership. Our findings showed that children with ADHD and typically developing children could be classified into distinct subgroups according to brain functional connectivity. Results also suggested that the differentiation in “functional” subgroups is related to specific behavioral characteristics, in this case impulsivity. Thus, combining neuroimaging data and community detection might be a valuable approach to elucidate heterogeneity in ADHD etiology and examine ADHD neurobiology

Variation of information (VI) in simulated and empirical data across varying degrees of perturbation.

<p>Red triangles indicate VI values obtained from random perturbations; black squares correspond to VI values obtained on the original matrices.</p

Patterns of effects used to simulate data and results.

<p>Patterns of effects used to simulate data and results.</p

Group affiliation by ADHD diagnostic category.

<p>Group affiliation by ADHD diagnostic category.</p

Regions and results from empirical sample.

<p>Red lines indicate the subgroup had higher connection values than the average of the other subgroups; blue lines indicate the subgroup had lower connection values than the average of the other subgroups; gray paths indicate the connection values were similar to the average of other subgroups. Abbreviations: “dlPFC” = dorsolateral prefrontal cortex; “FC” = frontal cortex; “IPS” = intraperietal sulcus; “IPL” = inferior parietal lobule; “R” preceding these ROI names and abbreviations denotes right and “L” denotes left.</p