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Written by Pan Chensheng, Zhu Zhou
Responsible editor—Wang Sizhen, Fang Yiyi
Editor—Summer Leaf
Post-stroke depression (PSD) occurs in about one-third of stroke people, and is associated with poor post-stroke outcomes, higher long-term mortality, and a heavier burden of family social care [1]
。 Most scholars believe that PSD is not only a psychological stress response to a new stroke and physical disability, but is also directly related
to damage to specific neuroanatomy.
Since the 70s of the last century, dozens of studies have attempted to reveal the association between stroke site and PSD, but have not been able to reach consistent conclusions [1].
With the advent of the era of the brain connectome/brain network group, researchers have gradually realized that many neuropsychiatric disorders, including depressive disorders, should be characterized and predicted by brain network disconnection [2].
In the context of this paradigm change, the authors believe that the study of the neural mechanism of PSD should also be expanded from focusing solely on the local focus of lesions to focusing on the extensive brain network damage
caused by lesions.
Methods such as disconnectome and lesion network mapping combine brain lesions with open-source connectome data in normal people to indirectly estimate the brain network disconnection caused by lesions.
The correlation between disconnection and post-stroke neuropsychiatric symptoms was analyzed [2, 3].
Such studies have shown that a variety of behavioral defects after stroke (eg, post-stroke aphasia, amnesia, limb movement disorders, etc.
) can be predicted by structural disconnection (SDC) indicators [3] However, thestructural network mechanism of PSD is currently unclear
.
Revealing the neural network mechanism of PSD is of great significance
for early prediction, objective quantitative diagnosis, and targeted and precise intervention of PSD.
Recently, Professor Zhu Suiqiang and Professor Zhu Zhou's research group from the Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, published a report entitled "Structural disconnection-based prediction of poststroke" at Translational Psychiatry depression", via voxel-based disconnection-symptom mapping, VDSM) identified the characteristic structural disconnection pattern associated with PSD, and found that damage to a wide structural network consisting of bilateral hemispheric prefrontal, temporal and posterior parietal lobes is involved in the occurrence of PSD, and a new imaging predictor based on this model was designed.
" The SDC score can significantly enhance the classification performance
of existing PSD predictive models.
1 Correlation between stroke lesion site and PSD
In the past 40 years, research on the neural mechanism of PSD has focused on the role of lesion sites (left and right, anterior and posterior, whether a brain region is involved, etc.) in the development of PSD [4].
The authors first performed a standard lesion analysis
with reference to previous literature in the field.
Comparison of lesion localization in PSD and non-PSD groups (left hemisphere stroke/right hemisphere stroke/bilateral hemisphere stroke/ subtentorial stroke), no significant between-group differences
were found.
Comparing the incidence of PSD in the two subgroups of left hemisphere stroke and right hemisphere stroke separately, no left-right lateral risk of PSD was found
.
The spatial distribution of lesions across the cohort (Figure 1a), PSD group (Figure 1b) is shown on a standard anatomical template lesion probability maps for the non-PSD groups (Figure 1c) and the difference between the two groups ( Lesion Subtraction Plot, Figure 1d).
Subsequent voxel-based lesion-symptom mapping (VLSM) analysis found that lesions in multiple brain regions in the frontal, parietal, and temporal lobes of the right hemisphere were associated with higher lesions PSD risk is associated (Figure 1f), and these positive clumps (inside the white circle) are associated with lesion differential plots (Figure 1d).
The peak regions coincide
.
After conducting literature research, the authors found that the results of multiple similar studies at the voxel level in the past 20 years showed a high degree of heterogeneity, and believed that VLSM and other lesion analysis methods only focused on the lesion itself, and ignored the influence of the lesion on the widely distributed brain network of the human brain, resulting in unstable statistical analysis results The neural mechanisms of PSD may need to be elucidated
from the perspective of brain network disconnection.
Figure 1: Routine lesion analysis
(Source: Pan C, et al.
, Transl Psychiatry, 2022).
2 Correlation between stroke-induced structural disconnection and PSD
The investigators embedded each subject's lesion in the Human Connectome Project-842 (HCP-842) fiber map to determine the fibers involved in the lesion, thereby indirectly estimating the lesion-induced SDC caused by the lesion。 The spatial frequency distribution of SDCs across the cohort is shown on a standard anatomical template (Figure 2a), where at least 5% of patients will develop disconnected voxels (Figure 2b red region) included voxel-based disconnection-symptom mapping (VDSM).
), that is, the association between disconnection and PSD was tested on a voxel basis, and it was found that lesions in the PSD group were more likely to have fiber connections with bilateral dorsolateral prefrontal, posterior parietal, and temporal lobes than in the non-PSD group (Figure 2C).
These regions have spatial similarities to the depressive functional circuits identified in previous studies of dysfunctional (lesion network mapping) [5] and may be important components of
the complex structural network associated with depressive disorders.
Subsequently, the researchers performed structural disconnection group analysis at the fiber bundle level, that is, to test whether there was a significant difference between PSD and non-PSD groups in the severity of disconnection of 70 fiber bundles in the human brain, and found VDSM The positive results of the main fiber bundles involved in the positive voxels and the horizontal disconnection group analysis of fiber bundles have good consistency, all involving the main fiber pathways such as human brain association pathway, projection pathway, and commutation pathway, and the damage of these interlobar or interhemispheric connections may be an important mechanism for the occurrence of
PSD.
Figure 2 Structural disconnectome analysis based on voxels
(Source: Pan C, et al.
, Transl Psychiatry, 2022).
3.
Generalizability of research results and predictive value analysis of SDC
The researchers used split-half validation (2-fold cross-validation) to verify whether VDSM results were reliable and generalizable
.
The above VDSM analysis was repeated separately in 2 randomly divided subdatasets and positive results were found to have good reproducibility (Figure 3a-b).
。 Given that patients in the PSD group were more likely to involve these positive voxels in the disconnection range, the researchers quantified the degree to which each patient's disconnection map overlapped with the VDSM positive result plot as a structural disconnection score (SDC score).
）
。 During cross-validation, the SDC score for each patient in one dataset was calculated based on the VDSM analysis results of the other dataset, and it was found that: SDC for the PSD group throughout the cohort The score was significantly higher than that of the non-PSD group (Figure 3c); The actual incidence and severity of depression in the low, intermediate, and high PSD risk groups divided according to the SDC score triplete increased gradually, and there were significant between-group differences (Figure 3d-e).
, suggesting that SDC score can predict PSD risk and severity in a single indicator
.
Multivariate Logistic regression found that the SDC score remained the same after adjusting for sociodemographic, clinical, psychological, and imaging covariates associated with PSD PSD is independently correlated
.
In the process of cross-validation, the authors further construct and validate two prediction models: a reference model containing 8 known predictors and an enhanced model with SDC score, comparing the prediction performance of the two models and finding the improvement of net reclassification.
The comprehensive differentiation improvement degree was statistically significant, suggesting that the introduction of SDC score significantly improved the classification performance of conventional models
.
A dominance analysis of the enhanced model found that SDC score ranked first in importance in PSD prediction.
Exceeds currently accepted predictors (Figure 3F).
Figure 3: Cross-validation of the main results
(Source: Pan C, et al.
, Transl Psychiatry, 2022).
The potential causes of inconsistency in previous lesion studies are pointed out from the perspective of brain network disconnection, which provides new evidence for the brain network mechanism and bio-psychosocial model of PSD, based on the new predictor SDC score of structural disconnection Existing predictive models
can be effectively improved.
This study provides new perspectives and directions
for the early prediction, objective quantitative diagnosis and neural stimulation target identification of PSD.
In addition, the indirect brain network analysis method based on the normal human connectome atlas used in this article does not need to collect special neuroimaging sequences for subjects, and can be generalized to the study of the mechanism
of neuropsychiatric symptoms after various focal brain injuries except PSD 。 However, this method has certain limitations and cannot reflect factors other than stroke foci (eg, changes in cerebral small vessel disease such as white matter hyperintensity, microhemorrhage; comorbidities such as diabetes; The structure of the human brain is inherently different to individual differences, etc.
), so such findings may need to be supplemented and validated by other advanced research methods, including direct connectome analysis [6].
Original link: style="text-align:justify;text-indent: 0em;">
Pan Chensheng, a postdoctoral fellow at Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology, is the first author of the paper, and Professor Zhu Suiqiang and Professor Zhu Zhou are the corresponding authors of the paper, which was supported
by the National Key Research and Development Program of the Ministry of Science and Technology and the National Natural Science Foundation of China.
Corresponding authors: Zhu Suiqiang (left), Zhu Zhou (middle); First author: Pan Chensheng (right)
(Photo courtesy of Zhu Suiqiang and Zhu Zhou Laboratory)
Corresponding author bio (swipe up and down to read).
Suiqiang Zhu, male, M.
D.
, professor, chief physician, second-level professor, doctoral supervisor
.
Standing member of the Neurology Branch of the Chinese Medical Association, member of the Cerebrovascular Disease Group, deputy leader of the EEG and Epilepsy Group, member of the Neurologist Branch of the Chinese Medical Doctor Association, and vice chairman of the Epilepsy Professional Committee of
the Neurology Branch.
He is currently the director of the Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and his main research areas are cerebrovascular diseases, epilepsy, and difficult and critical diseases
of the nervous system.
He has published 90 SCI papers, and led the key special project of the National Key R&D Program "Research on the Prevention and Control of Major Chronic Non-communicable Diseases": the development and application of multi-dimensional screening and prevention technology for depression after stroke; Key clinical projects of hospitals under the Ministry of Health: construction of diagnosis and treatment platform for severe cerebral hemorrhage under multidimensional brain function monitoring; 3 projects of the National Natural Science Foundation of China; There are more than ten other provincial and ministerial scientific research projects
.
Zhou Zhu, female, M.
D.
, chief physician, associate professor, master supervisor
.
Member of Sleep Group of Chinese Neurology Association, Member of Psychosomatic Neurology Group of Psychosomatic Medicine Branch of Chinese Medical Association, Vice Chairman of Sleep Disorder Branch of Wuhan Medical Association, Standing Committee Member and Secretary of Youth Committee of Neurology Branch of Hubei Medical Association, Standing Director of Hubei Sleep Association, Standing Committee Member
of Psychosomatic Medicine Committee of Hubei Pathophysiology Society.
He presided over 2 projects of the National Natural Science Foundation of China, one science and technology support plan of Hubei Province, and participated in 2 national key research and development projects as a major backbone member
.
Published SCI as the first or corresponding author in authoritative journals such as eClinicalmedicine, JNNP, Glia, BBI, etc More than 30 papers
.
Welcome to scan the code to join Logical Neuroscience Literature Study 2
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24) Welcome to "Logical Neuroscience" [1] " Logical Neuroscience "Recruitment Associate Editor/Editor/Operation Position (Online Office)[2] Talent Recruitment - " Logical Neuroscience " Recruitment Article Interpretation/Writing Position ( Online Part-time, Online Office) Reference (swipe up and down to read).
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Nickel A, Thomalla G.
Post-Stroke Depression: Impact of Lesion Location and Methodological Limitations-A Topical Review.
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Padmanabhan JL, Cooke D, Joutsa J, Siddiqi SH, Ferguson M, Darby RRet al.
A Human Depression Circuit Derived From Focal Brain Lesions.
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Bobes MA, Van den Stock J, Zhan M, Valdes-Sosa M, de Gelder B.
Looking beyond indirect lesion network mapping of prosopagnosia: direct measures required.
Brain2021; 144:e75.
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