Dynamic neural circuit disruptions associated with antisocial behaviors

Jiang, Weixiong; Zhang, Han; Zeng, Ling‐Li; Shen, Hui; Qin, Jian; Thung, Kim-Han; Yap, Pew Thian; Liu, Huasheng; Hu, Dewen; Wang, Wei; Shen, Dinggang

doi:10.1002/hbm.25225

Cited by 7 publications

(7 citation statements)

References 116 publications

(188 reference statements)

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“…We also found an interesting result that rFPN-mVN variation increased after the TMS treatment, which was positively correlated with visuospatial function. Currently, investigating the temporal variability of dFNC is believed to be an effective method to characterize intrinsic temporal fluctuations in functional connectivity and to associate network activities and behaviors [ 35 , 36 ]. The fluctuation in connectivity over time could reflect brain network flexibility, which could be essential for cognitive abilities [ 37 – 39 ].…”

Section: Discussionmentioning

confidence: 99%

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

Chen,

Li,

Qin

et al. 2023

Eur Radiol Exp

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Background Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer’s disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response. Methods Sixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline. Results We found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54). Conclusion Our findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response. Relevance statement Dynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease. Key points • rTMS at the left angular gyrus could induce cognitive improvement. • rTMS could suppress primary network connections to compensate for higher-order networks. • Dynamic reorganization of brain networks could predict individual treatment response to rTMS. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

Chen,

Li,

Qin

et al. 2023

Eur Radiol Exp

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“… 48 Such a soft margin method has been well-validated, extensively used and demonstrated superior performance even with a small sample size. 34 , 35 , 49 Herein, we used a linear kernel with a hyperparameter C = 1 34 , 50 , 51 and other hyperparameters were kept as defaults to make the model more robust, 35 including insensitivity = 0 and an eInsensitive loss function. 34 , 50 , 51 …”

Section: Methodsmentioning

confidence: 99%

“… 34 , 35 , 49 Herein, we used a linear kernel with a hyperparameter C = 1 34 , 50 , 51 and other hyperparameters were kept as defaults to make the model more robust, 35 including insensitivity = 0 and an eInsensitive loss function. 34 , 50 , 51 …”

Section: Methodsmentioning

confidence: 99%

Neural alterations in opioid-exposed infants revealed by edge-centric brain functional networks

Jiang

Merhar

Zeng

et al. 2022

Brain Communications

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Prenatal opioid exposure has been linked to adverse effects spanning multiple neurodevelopmental domains, including cognition, motor development, attention, and vision. However, the neural basis of these abnormalities is largely unknown. A total of 49 infants, including 21 opioid-exposed and 28 controls, were enrolled and underwent MR imaging (43 ± 6 days old) after birth, including resting state functional MRI. Edge-centric functional networks based on dynamic functional connections were constructed and machine-learning methods were employed to identify neural features distinguishing opioid-exposed infants from unexposed controls. An accuracy of 73.6% (sensitivity 76.25% and specificity 69.33%) was achieved using 10 times ten-fold cross-validation, which substantially outperformed those obtained using conventional static functional connections (accuracy 56.9%). More importantly, we identified that prenatal opioid exposure preferentially affects inter- rather than intra-network dynamic functional connections, particularly with the visual, subcortical and default mode networks. Consistent results at the brain regional and connection levels were also observed, where the brain regions and connections associated with visual and higher order cognitive functions played pivotal roles in distinguishing opioid-exposed infants from controls. Our findings support the clinical phenotype of infants exposed to opioids in utero and may potentially explain the higher rates of visual and emotional problems observed in this population. Finally, our findings suggested that edge-centric networks could better capture the neural differences between opioid-exposed infants and controls by abstracting the intrinsic co-fluctuation along edges, which may provide a promising tool for future studies focusing on investigating the effects of prenatal opioid exposure on neurodevelopment.

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“…Dynamic functional connectivity (dFC), commonly evaluated using sliding temporal windows correction, 21–23 has been adopted to quantify the time‐varying functional connectivity patterns of rs‐fMRI signals across spatially separate brain regions. Previous studies demonstrated that the temporal property of the dFC has been found quite informative in detecting the switch of cognitive states 24 and sensitive to many brain diseases 25,26 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic functional connectivity changes associated with decreased memory performance in betel quid dependence

Guo,

Xu,

et al. 2023

Addiction Biology

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The temporal variability of the dynamic functional connectivity (dFC) has been suggested as a useful metric for studying abnormal cognitive function. This study aimed to explore the associations between the temporal properties of dFC and memory performance in betel quid dependence (BQD). Sixty‐four BQD individuals and 47 gender‐ and age‐matched healthy controls (HCs) underwent functional magnetic resonance imaging and a series of neuropsychological assessments. The dFC was constructed by calculating the Pearson correlation coefficients within a sliding window and was clustered into three functional connectivity states using k‐means clustering. The dFC temporal properties derived from the cluster results were compared between the BQD and HC groups. The results showed that States 1 and 3 featured more frequent and weak connectivity, and State 2 featured less frequent and strong connectivity. There were significant differences for mean dwell time (MDT) in State 3 (p = 0.022) and fraction of time in State 2 (p = 0.018) between the BQD and HC groups. Pearson correlation analyses showed that the MDT in State 1 was negatively correlated with long delay free recall and short delay free recall, and the MDT in State 3 was positively correlated with false positive of long delay recall. Our findings provide strong evidence that MDT match the memory performance and suggest new insights into the pathophysiological mechanism of memory disorders in BQD individuals.

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Dynamic neural circuit disruptions associated with antisocial behaviors

Cited by 7 publications

References 116 publications

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

Neural alterations in opioid-exposed infants revealed by edge-centric brain functional networks

Dynamic functional connectivity changes associated with decreased memory performance in betel quid dependence

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