Evidence map›Paper›PMID 39835577›Full record

ArticleHuman brain mapping2025

Reward Decision Network Disconnection in Poststroke Apathy: A Prospective Multimodality Imaging Study.

Yirong Fang, Xian Chao, Jinjing Wang, Zeyu Lu, Dawei Yin, Ran Shi, Peng Wang, Xinfeng Liu, Wen Sun

Abstract read
In one paragraph

Article in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yirong FangDepartment of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.ORCID 0009-0002-9209-4966
Xian ChaoDepartment of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Jinjing WangDepartment of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Zeyu LuDepartment of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Dawei YinDepartment of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Ran ShiDepartment of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Peng WangDepartment of Radiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Xinfeng LiuDepartment of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Wen SunDepartment of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.

Funding

Joint Fund for Medical Artificial Intelligence MAI2022Q018National Natural Science Foundation of China U20A20357National Natural Science Foundation of China U22A20341Natural Science Foundation of Anhui Province 2108085MH271Research Funds of Centre for Leading Medicine and Advanced Technologies of IHM 2023IHM01051Research Funds of Centre for Leading Medicine and Advanced Technologies of IHM 2023IHM01054
6 · The paper itself

Abstract

Apathy is a common neuropsychiatric symptom following stroke, characterized by reduced goal-directed behavior. The reward decision network (RDN), which plays a crucial role in regulating goal-directed behaviors, is closely associated with apathy. However, the relationship between poststroke apathy (PSA) and RDN dysfunction remains unclear due to apathy heterogeneity, the confounding effect of depression and individual variability in lesion impacts. This study aims to dissect the heterogeneity of PSA and explore the link between lesion-induced RDN damage and PSA. We prospectively recruited 207 patients with acute ischemic infarction and 60 demographically matched healthy controls. Participants underwent neuroimaging and longitudinal neuropsychiatric assessments. To characterize PSA heterogeneity, we employed multivariate analysis and clustering algorithms based on whole-brain functional connectivity and clinical assessments to classify patients into different PSA biotypes. We embedded each patient's lesion into a structural connectome atlas to obtain white matter (WM) disconnection maps. On this basis, WM disconnection scores were calculated for each brain region to quantify lesion-induced WM damage. We employed the XGBoost model to predict PSA biotypes based on WM disconnection scores, comparing the performance of models focusing on RDN-specific versus whole-brain WM disconnection. Additionally, we explored WM damage patterns across different biotypes by comparing disconnection scores in critical brain regions. We identified four PSA biotypes with unique clinical trajectories and neurobiological underpinnings. Biotype 4 was characterized by persistent apathy with depressive symptoms. Biotype 2 showed persistent apathy. Biotype 3 was non-apathetic. Biotype 1 exhibited delayed-onset apathy. The XGBoost models, when focused on the RDN-specific WM disconnection, performed significantly better in predicting PSA biotypes compared to the whole-brain WM disconnection model (t(164.66) = 8.871, p < 0.001). Analysis of WM disconnection patterns revealed that Biotype 4 exhibited more extensive RDN damage in crucial regions, Biotype 1 had a unique pattern of damage in the anterior cingulate cortex (t(61) = 1.874, p = 0.032), and Biotype 2 had a unique pattern of damage in the orbitofrontal cortex (t(53)= 1.827, p = 0.036). This study dissected PSA heterogeneity and demonstrated that RDN damage is a critical factor in PSA variability. We found that lesion-induced WM disconnections in anterior cingulate cortex and orbitofrontal cortex can lead to delayed-onset and persistent apathy, respectively. Furthermore, our findings revealed that apathy not only has distinct pathogenic mechanisms, but also shares neurobiological substrates with depression.

Indexed as

ApathyConnectomeDecision MakingIschemic StrokeNerve NetRewardStrokeWhite MatterAgedFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedMultimodal ImagingProspective Studiesapathydiffusion tensor imagingneuroimagingresting‐state fMRIreward decision networkstrokewhite matter disconnection

Identifiers

PMID39835577
PMCPMC11747988

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.