Abnormal Dynamic Community Structure of Patients with Attention-Deficit/Hyperactivity Disorder in the Resting State

Ding, Congli; Xiang, Jie; Cui, Xiaohong; Wang, Xiaoyue; Li, Dandan; Chen, Cheng; Wang, Bin

doi:10.1177/1087054720959712

Cited by 20 publications

(13 citation statements)

References 59 publications

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“…Meanwhile, hyperconnectivity patterns were also widely reported in ADHD subjects [30,58,59], which would potentially "lock" regions together, constrain regional module transition frequency, and reduce neural flexibility. Nevertheless, recent adult studies using the multilayer framework reported that adults with ADHD had higher flexibility and lower integration coefficient than of control subjects [60,61], opposite to our findings. While several potential factors may explain the observed discrepancies, one of the most plausible reasons may be the difference in age across the studies (current cohort: mean age 11.65 years; adult cohorts: mean age 32 years).…”

Section: Discussioncontrasting

confidence: 99%

Altered neural flexibility in children with attention-deficit/hyperactivity disorder

Yin

Mucha

et al. 2022

Mol Psychiatry

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Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood, and is often characterized by altered executive functioning. Executive function has been found to be supported by flexibility in dynamic brain reconfiguration. Thus, we applied multilayer community detection to resting-state fMRI data in 180 children with ADHD and 180 typically developing children (TDC) to identify alterations in dynamic brain reconfiguration in children with ADHD. We specifically evaluated MR derived neural flexibility, which is thought to underlie cognitive flexibility, or the ability to selectively switch between mental processes. Significantly decreased neural flexibility was observed in the ADHD group at both the whole brain (raw p = 0.0005) and sub-network levels (p < 0.05, FDR corrected), particularly for the default mode network, attention-related networks, executive function-related networks, and primary networks. Furthermore, the subjects with ADHD who received medication exhibited significantly increased neural flexibility (p = 0.025, FDR corrected) when compared to subjects with ADHD who were medication naïve, and their neural flexibility was not statistically different from the TDC group (p = 0.74, FDR corrected). Finally, regional neural flexibility was capable of differentiating ADHD from TDC (Accuracy: 77% for tenfold cross-validation, 74.46% for independent test) and of predicting ADHD severity using clinical measures of symptom severity (R2: 0.2794 for tenfold cross-validation, 0.156 for independent test). In conclusion, the present study found that neural flexibility is altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity.

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Section: Discussioncontrasting

confidence: 99%

Altered neural flexibility in children with attention-deficit/hyperactivity disorder

Yin

Mucha

et al. 2022

Mol Psychiatry

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“…The element

gives the connection strength from node

of layer

to node

of layer

, which is called “inter-layer coupling parameter”. Here, we set the structure resolution parameter and the inter-layer coupling parameter to the default value of 1 [ 8 , 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia

et al. 2022

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The analysis of resting-state fMRI signals usually focuses on the low-frequency range/band (0.01–0.1 Hz), which does not cover all aspects of brain activity. Studies have shown that distinct frequency bands can capture unique fluctuations in brain activity, with high-frequency signals (>0.1 Hz) providing valuable information for the diagnosis of schizophrenia. We hypothesized that it is meaningful to study the dynamic reconfiguration of schizophrenia through different frequencies. Therefore, this study used resting-state functional magnetic resonance (RS-fMRI) data from 42 schizophrenia and 40 normal controls to investigate dynamic network reconfiguration in multiple frequency bands (0.01–0.25 Hz, 0.01–0.027 Hz, 0.027–0.073 Hz, 0.073–0.198 Hz, 0.198–0.25 Hz). Based on the time-varying dynamic network constructed for each frequency band, we compared the dynamic reconfiguration of schizophrenia and normal controls by calculating the recruitment and integration. The experimental results showed that the differences between schizophrenia and normal controls are observed in the full frequency, which is more significant in slow3. In addition, as visual network, attention network, and default mode network differ a lot from each other, they can show a high degree of connectivity, which indicates that the functional network of schizophrenia is affected by the abnormal brain state in these areas. These shreds of evidence provide a new perspective and promote the current understanding of the characteristics of dynamic brain networks in schizophrenia.

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“…Intuitively, structural boundary controllability measures the necessary input required by a node to drive the overall system along a desired trajectory (Gu, Pasqualetti, et al, 2015). The role of controllability in regulating dynamic brain state transitions as well as predicting the maturity of adolescent brain systems during development has been corroborated by several recent studies (Cornblath et al, 2020;Cui et al, 2020;Tang et al, 2017).…”

Section: Introductionmentioning

confidence: 79%

Network controllability mediates the relationship between rigid structure and flexible dynamics

Fotiadis

Parkes

et al. 2022

Network Neuroscience

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Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid inter-areal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain’s structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region’s functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes’ boundary controllability, suggesting that a region’s strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.

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Abnormal Dynamic Community Structure of Patients with Attention-Deficit/Hyperactivity Disorder in the Resting State

Cited by 20 publications

References 59 publications

Altered neural flexibility in children with attention-deficit/hyperactivity disorder

Altered neural flexibility in children with attention-deficit/hyperactivity disorder

Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia

Network controllability mediates the relationship between rigid structure and flexible dynamics

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