Structural disconnection-based prediction of poststroke depression

Pan, Chensheng; Li, Guo; Jing, Ping; Chen, Guohua; Sun, Wenzhe; Miao, Jinfeng; Wang, Yanyan; Lan, Yan; Qiu, Xiuli; Zhao, Xin; Mei, Jie; Huang, Shanshan; Lian, Lifei; Wang, He; Zhu, Zhou; Zhu, Suiqiang

doi:10.1038/s41398-022-02223-2

Cited by 17 publications

(23 citation statements)

References 55 publications

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“…In this study, gender, education level, diabetes mellitus, alcohol drinking, Barthel Index, FT3 differ significantly in PSD and non-PSD, which may be attributed to the occurrence of PSD compared with non-PSD. Many researchers have investigated a series of scoring systems from PSD prediction ( Pan et al, 2022 ; Yi et al, 2022 ). Prospective studies assessed the predictive power of HAMD score for PSD, which were commonly used in clinical ( Kang et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Predictive microbial feature analysis in patients with depression after acute ischemic stroke

Yao

Xie

Wang

et al. 2023

Front. Aging Neurosci.

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IntroductionPost-stroke depression (PSD) is the most common emotional problem following a stroke, which requires early diagnosis to improve the prognosis. Gut microbiota plays important role in the pathological mechanisms of acute ischemic stroke and influences the outcome of patients. However, the relationship between PSD and gut microbiota remains unknown. Here, we explored whether the microbial signatures of gut microbiota in the patients with stroke could be an appropriate predictor of PSD.MethodsFecal samples were collected from 232 acute ischemic stroke patients and determined by 16s rRNA sequencing. All patients then received 17-Hamilton Depression Rating Scale (HAMD-17) assessment 3 months after discharge, and were further divided into PSD group and non-PSD group. We analyzed the differences of gut microbiota between these groups. To identify gut microbial biomarkers, we then established microbial biomarker model.ResultsOur results showed that the composition of gut microbiota in the PSD patients differed significantly from that in non-PSD patients. The genus Streptococcus, Akkermansia, and Barnesiella were significantly increased in PSD patients compared to non-PSD, while the genus Escherichia-Shigella, Butyricicoccus, and Holdemanella were significantly decreased. Correlation analyses displayed that Akkermansia, Barnesiella, and Pyramidobacter were positively correlated with HAMD score, while Holdemanella was negatively correlated with HAMD score. The optimal microbial markers were determined, and the combination achieved an area under the curve (AUC) value of 0.705 to distinguish PSD from non-PSD.ConclusionsOur findings suggest that PSD patients had distinct gut microbiota compared to non-PSD patients, and explore the potential of microbial markers, which might provide clinical decision-making in PSD.

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Section: Discussionmentioning

confidence: 99%

Predictive microbial feature analysis in patients with depression after acute ischemic stroke

Yao

Xie

Wang

et al. 2023

Front. Aging Neurosci.

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“…The lesion masks were supervised by another well-trained neurologist (Wenzhe Sun) for agreement. Spatial normalization to Montreal Neurological Institute (MNI 152) template was performed for original MRI/CT images and native lesion masks with Clinical Toolbox [ 14 ] of Statistical Parametric Mapping (SPM12, Wellcome Trust Centre for Neuroimaging, London, United Kingdom) running on MATLAB R2021a (The MathWorks, Inc, Natick, MA). Visual inspection of the normalized lesion maps, as well as manual correction if necessary, were performed (Chensheng Pan and Wenzhe Sun).…”

Section: Methodsmentioning

confidence: 99%

“…There is a growing recognition that some biopsychosocial risk factors for depression may not be equally related to all depressive symptoms [ 12 , 13 ]. For example, the association between lesion characteristics and PSD was found symptom-specific [ 14 ]. Therefore, investigating neural substrates of PSD at the level of individual symptoms or aspects has been recommended in the field of PSD research [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Psychopathological network for early-onset post-stroke depression symptoms

Pan

Sun

et al. 2023

BMC Psychiatry

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Background Post-stroke depression (PSD) can be conceptualized as a complex network where PSD symptoms (PSDS) interact with each other. The neural mechanism of PSD and interactions among PSDS remain to be elucidated. This study aimed to investigate the neuroanatomical substrates of, as well as the interactions between, individual PSDS to better understand the pathogenesis of early-onset PSD. Methods A total of 861 first-ever stroke patients admitted within 7 days poststroke were consecutively recruited from three independent hospitals in China. Sociodemographic, clinical and neuroimaging data were collected upon admission. PSDS assessment with Hamilton Depression Rating Scale was performed at 2 weeks after stroke. Thirteen PSDS were included to develop a psychopathological network in which central symptoms (i.e. symptoms most strongly correlated with other PSDS) were identified. Voxel-based lesion-symptom mapping (VLSM) was performed to uncover the lesion locations associated with overall PSDS severity and severities of individual PSDS, in order to test the hypothesis that strategic lesion locations for central symptoms could significantly contribute to higher overall PSDS severity. Results Depressed mood, Psychiatric anxiety and Loss of interest in work and activities were identified as central PSDS at the early stage of stroke in our relatively stable PSDS network. Lesions in bilateral (especially the right) basal ganglia and capsular regions were found significantly associated with higher overall PSDS severity. Most of the above regions were also correlated with higher severities of 3 central PSDS. The other 10 PSDS could not be mapped to any certain brain region. Conclusions There are stable interactions among early-onset PSDS with Depressed mood, Psychiatric anxiety and Loss of interest as central symptoms. The strategic lesion locations for central symptoms may indirectly induce other PSDS via the symptom network, resulting in higher overall PSDS severity. Trial registration URL: http://www.chictr.org.cn/enIndex.aspx; Unique identifier: ChiCTR-ROC-17013993.

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“…We believe that local spatial relationships in MRI brain images can assist in the understanding of brain structure and lead to improved prediction of survival times. Additionally, many papers [9,10,11,12,13] about the global structure of the brain has proven its correlation with survival prediction in glioblastoma patients. Firstly, the disruptions in the normal-appearing brain beyond the lesion could mediate the topological alteration of the connectome associated with worse patient survival [9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Survival of Glioblastoma Patients Using Local Spatial Relationships and Global Structure Awareness in FLAIR MRI Brain Images

et al. 2023

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This article introduces a framework for predicting the survival of brain tumor patients by analyzing magnetic resonance images. The prediction of brain tumor survival is challenging due to the limited size of available datasets. To overcome the issue of overfitting, we propose a self-supervised learning method that involves identifying image patches from the same or different images. By recognizing intra-and inter-image differences, the network can learn the relationships between local spatial windows in the same image and across different images. In addition to analyzing local information, we also incorporate a global structure awareness network to capture global information from the entire image. Our proposed method shows a strong correlation between local spatial relationships and survivor class prediction in FLAIR MRI brain images. We evaluate our method using the BraTS 2020 validation dataset and observe that our method outperforms others in accuracy and SpearmanR correlation metrics.

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Structural disconnection-based prediction of poststroke depression

Cited by 17 publications

References 55 publications

Predictive microbial feature analysis in patients with depression after acute ischemic stroke

Predictive microbial feature analysis in patients with depression after acute ischemic stroke

Psychopathological network for early-onset post-stroke depression symptoms

Prediction of Survival of Glioblastoma Patients Using Local Spatial Relationships and Global Structure Awareness in FLAIR MRI Brain Images

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