Assessing Fine-Granularity Structural and Functional Connectivity in Children With Attention Deficit Hyperactivity Disorder

Wang, Peng; Jiang, Xi; Chen, Hanbo; Zhang, Shu; Li, Xiang; Cao, Qingjiu; Sun, Li; Liu, Lu; Yang, Binrang; Wang, Yufeng

doi:10.3389/fnhum.2020.594830

Cited by 3 publications

(5 citation statements)

References 58 publications

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Section: Discussionmentioning

confidence: 94%

“…Importantly, results showed that ReHo values for the two MnlI variant genotypic groups differed in the mPFC of boys with ADHD. Previous studies revealed functional abnormalities in children with ADHD (Shanmugan et al, 2016;Wang et al, 2020). For example, Zhou et al (2018) found that these children had significantly lower ReHo values for the right middle frontal gyrus, thereby implying fewer spontaneous neuronal activities in this region.…”

Section: Discussionmentioning

confidence: 94%

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Role of SNAP‐25 MnlI variant in impaired working memory and brain functions in attention deficit/hyperactivity disorder

et al. 2022

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Introduction Attention deficit/hyperactivity disorder (ADHD) is a hereditary neurodevelopmental disorder characterized by working memory (WM) deficits. The Mnl I variant (rs3746544) of the synaptosomal‐associated protein 25 (SNAP‐25) gene is associated with ADHD. In this study, we investigated the role and underlying mechanism of SNAP‐25 Mnl I variant in cognitive impairment and brain functions in boys with ADHD. Method We performed WM capacity tests using the fourth version of the Wechsler Intelligence Scale for Children (WISC‐IV) and regional homogeneity (ReHo) analysis for the resting‐state functional magnetic resonance imaging data of 56 boys with ADHD divided into two genotypic groups (TT homozygotes and G‐allele carriers). Next, Spearman's rank correlation analysis between the obtained ReHo values and the WM index (WMI) calculated for each participant. Results Compared with G‐allele carrier group, there were higher ReHo values for the left medial prefrontal cortex (mPFC) and higher WM capacity in TT homozygote group. Contrary to TT homozygote group, the WM capacity was negatively correlated with the peak ReHo value for the left mPFC in G‐allele carrier group. Conclusion These findings suggest that SNAP‐25 Mnl I variant may underlie cognitive and brain function impairments in boys with ADHD, thus suggesting its potential as a new target for ADHD treatment.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 94%

Role of SNAP‐25 MnlI variant in impaired working memory and brain functions in attention deficit/hyperactivity disorder

et al. 2022

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“…Further, changes in GWC have been associated with cognitive performance among AD individuals with underlying β-Amyloit pathology and with increased age among individuals with dementia (Salat et al 2009;Xu et al 2024). Changes in GWC have not only been observed among older clinical populations but also among young children with attention deficit hyperactivity disorder (Wang et al 2023). Further, GWC is suggested to capture microstructural changes before the emergence of cortical atrophy and be more sensitive to capturing these microstructural changes earlier than conventional neuroimaging parameters (Xu et al 2024;Putcha et al 2023).…”

Section: Discussionmentioning

confidence: 99%

“…GWC might not be a sensitive neural marker for capturing differences in risk populations with relatively low risk. However, studies measuring GWC in different clinical populations have demonstrated that GWC is a sensitive marker for capturing illness-related changes (Putcha et al 2023;Salat et al 2009;Xu et al 2024;Wang et al 2023). Studies have found GWC to be sensitive to capturing β-amyloid pathology in earlier stages of Alzheimer's disorder (AD) (Putcha et al 2023).…”

Section: Discussionmentioning

confidence: 99%

MRI-based cortical gray/white matter contrast in young adults who endorse psychotic experiences or are at genetic risk for psychosis

Naseri,

Beck,

Ferschmann

et al. 2024

Preprint

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Group-level differences in brain macrostructure between individuals at risk for psychosis and healthy controls have been well documented. However, while differences in cortical grey/white matter contrast (GWC), likely reflecting differences in myelin content, have been reported in clinical populations with psychotic disorders, no studies have explored GWC in individuals at elevated risk for psychosis. In this study, we explored whether brain microstructure, as measured with GWC, differs between young adults who endorsed psychotic experiences or genetic high risk for psychosis and healthy controls, and whether individual differences in GWC in at-risk individuals are associated with the number and psychotic experiences. The sample included individuals from two magnetic resonance imaging (MRI) substudies of the Avon Longitudinal Study of Parents and Children (ALSPAC): the psychotic experiences study and the schizophrenia recall-by-genotype study. The final sample included four groups of young adults 19-24 years old: individuals endorsing psychotic experiences (n=119) and health controls (n=117) and individuals with high (n=95) and low genetic risk for psychosis (n=95). Statistical analyses were performed using FSLs Permutation Analysis of Linear Models (PALM), controlling for age and sex. The results showed no statistically significant differences in GWC between any of the groups and no significant associations between GWC and the number and experiences of psychotic experiences. In conclusion, the results indicate that GWC is not a sensitive neuroimaging marker for psychosis risk in young adults.

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“…More importantly, as the major modeling of SGCP is formulated in a self-supervised scheme, we are testing its capability to perform subregion parcellation, investigating the unique structural-functional characteristics of the fine-grained compositions in certain brain regions, such as the entorhinal cortex, where preliminary studies have shown the presence of subregion with distinguished connectivity patterns in different cortical pathways [ 32 , 33 ]. Finally, while in this study SGCP is used to analyze structural connectivity patterns derived from DWI images, it can be applied to functional connectivity derived from fMRI or MEG/EEG data [ 34 , 35 ], formulating a structural-functional parcellation framework [ 36 ]. Further, rich information can be encoded in the node features, including morphological features derived from T1 imaging, pathological and proteinopathies features derived from PET imaging, as well as genetic features derived from microarrays.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution

You

Zhang

et al. 2022

BME Front

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Objective. Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity. Impact Statement. The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation, an important medical image analysis and neuroscientific problem. Introduction. The concept of “connectional fingerprint” has motivated many investigations on the connectivity-based cortical parcellation, especially with the technical advancement of diffusion imaging. Previous studies on multiple brain regions have been conducted with promising results. However, performance and applicability of these models are limited by the relatively simple computational scheme and the lack of effective representation of brain imaging data. Methods. We propose the Spatial-graph Convolution Parcellation (SGCP) framework, a two-stage deep learning-based modeling for the graph representation brain imaging. In the first stage, SGCP learns an effective embedding of the input data through a self-supervised contrastive learning scheme with the backbone encoder of a spatial-graph convolution network. In the second stage, SGCP learns a supervised classifier to perform voxel-wise classification for parcellating the desired brain region. Results. SGCP is evaluated on the parcellation task for 5 brain regions in a 15-subject DWI dataset. Performance comparisons between SGCP, traditional parcellation methods, and other deep learning-based methods show that SGCP can achieve superior performance in all the cases. Conclusion. Consistent good performance of the proposed SGCP framework indicates its potential to be used as a general solution for investigating the regional/subregional composition of human brain based on one or more connectivity measurements.

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Assessing Fine-Granularity Structural and Functional Connectivity in Children With Attention Deficit Hyperactivity Disorder

Cited by 3 publications

References 58 publications

Role of SNAP‐25 MnlI variant in impaired working memory and brain functions in attention deficit/hyperactivity disorder

Role of SNAP‐25 MnlI variant in impaired working memory and brain functions in attention deficit/hyperactivity disorder

MRI-based cortical gray/white matter contrast in young adults who endorse psychotic experiences or are at genetic risk for psychosis

Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution

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