Lifespan associations of resting-state brain functional networks with ADHD symptoms

Wang, Rong; Fan, Yongchen; Wu, Ying; Zang, Yu‐Feng; Zhou, Changsong

doi:10.1016/j.isci.2022.104673

Cited by 9 publications

(12 citation statements)

References 79 publications

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“…The motor system is responsible for brain activation during task performance, and abnormal communication with other systems was suggested to underlie the depressive and manic phases of BD (Martino et al, 2016 ), as well as the hyperactivity of ADHD (Gilbert et al, 2019 ). Furthermore, less segregation in the limbic and salient attention systems contributed to higher ADHD scores, which is consistent with a previous study that higher integration in the limbic system better predicts severe hyperactivity of ADHD, and a higher integration in salient attention system better predicts higher inattention (Wang, Fan, et al, 2022 ). However, the best prediction models were not shared for ADHD and BD scores, which may be crucially important for distinguishing ADHD from BD, or vice versa.…”

Section: Discussionsupporting

confidence: 91%

“…In general, the common and distinct alterations in brain networks in ADHD and BD patients, relative to HCs, have been widely documented (Passarotti et al, 2010 ). For example, compared to HCs, BD is related to higher segregation, and ADHD is associated with higher integration (Wang, Fan, et al, 2022 ). In the comorbid state of ADHD plus BD, Biederman et al suggested that ADHD and BD independently contribute to volumetric alterations in the brain (Biederman et al, 2008 ), and the impulsivity that is common to ADHD and BD may have different neural mechanisms mediated by reward systems (Passarotti & Pavuluri, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

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Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Chang

Wang

et al. 2022

Human Brain Mapping

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Bipolar disorder (BD) is a serious mental disorder involving widespread abnormal interactions between brain regions, and it is believed to be associated with imbalanced functions in the brain. However, how this brain imbalance underlies distinct BD symptoms remains poorly understood. Here, we used a nested‐spectral partition (NSP) method to study the segregation, integration, and balance in resting‐state brain functional networks in BD patients and healthy controls (HCs). We first confirmed that there was a high deviation in the brain functional network toward more segregation in BD patients than in HCs and that the limbic system had the largest alteration. Second, we demonstrated a network balance of segregation and integration that corresponded to lower anxiety in BD patients but was not related to other symptoms. Subsequently, based on a machine‐learning approach, we identified different system‐level mechanisms underlying distinct BD symptoms and found that the features related to the brain network balance could predict BD symptoms better than graph theory analyses. Finally, we studied attention‐deficit/hyperactivity disorder (ADHD) symptoms in BD patients and identified specific patterns that distinctly predicted ADHD and BD scores, as well as their shared common domains. Our findings supported an association of brain imbalance with anxiety symptom in BD patients and provided a potential network signature for diagnosing BD. These results contribute to further understanding the neuropathology of BD and to screening ADHD in BD patients.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Chang

Wang

et al. 2022

Human Brain Mapping

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“…Compared to classical graph theory, the NSP-based method had superior performance in predicting SANS and SAPS scores, reflecting the advantages of our method based on hierarchical modules in brain FC networks. This result is highly consistent with a series of our works wherein the NSP-based method is more powerful in linking the brain to diverse cognitive abilities 32 , task performance 31 , stress conditions 78 , ADHD symptoms 33 and bipolar disorder symptoms 79 . All these findings demonstrated that the NSP-based features detected across multiple levels are promising biomarkers for schizophrenia and other brain disorders.…”

Section: Biomarkers For Positive and Negative Symptomssupporting

confidence: 90%

“…The development of the limbic system reduces impulsive choices from early adolescence to mid-adulthood 60 , and abnormal immaturity of this system confidently predicts hyperactivity 61 . In particular, the limbic system best predicts the increase in hyperactivity in ADHD patients across the lifespan 33 . Here, the SANSх SAPS interaction has the largest negative effect on the limbic system, and its GO-based human phonotype is related to motor dysfunction that has been widely observed in schizophrenia 46,47,48,49,62,63 .…”

Section: Loss Of Global Integration In Schizophrenia Patientsmentioning

confidence: 99%

“…Recently, a nested-spectral partition (NSP) method based on eigenmodes was proposed to detect hierarchical modules in brain networks and describe segregation and integration across multiple levels 29 , different from the classical graph measures (e.g., modularity and the participant coefficient) at a single level 30 . More importantly, the NSP method has been found to yield better neural signatures than graph theory for linking brain features to cognitive functions and attention-deficit/hyperactivity disorder (ADHD) symptoms 31,32,33 . It is thus expected that an NSP-based analysis may better reveal the opposite neural biomarkers that underlie positive and negative symptoms in schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

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Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Wang¹,

Zhao²,

Wang

2022

Preprint

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Schizophrenia is a severe psychotic disorder clinically characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional connectivity networks in schizophrenia patients and healthy controls, and we studied the dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable global integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Attention systems were associated with hallucinations, and default-mode network (DMN) and control systems were related to avolition. In a machine-learning model, NSP-based features outperformed classical graph measures at predicting positive and negative symptom scores. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms on brain networks was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.

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Dynamic segregation and integration of brain functional networks associated with emotional arousal

Zhou¹,

Xie²,

Wang³

et al. 2023

iScience

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Lifespan associations of resting-state brain functional networks with ADHD symptoms

Cited by 9 publications

References 79 publications

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

Dynamic segregation and integration of brain functional networks associated with emotional arousal

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