Evidence map›Paper›PMID 40675918›Full record

ArticleThe journal of prevention of Alzheimer's disease2025

Multimodal neuroimaging unveils basal forebrain-limbic system circuit dysregulation in cognitive impairment with depression: a pathway to early diagnosis and intervention.

Xiaowen Xu, Xiereniguli Anayiti, Peiying Chen, Zhongfeng Xie, Mengling Tao, Yongsheng Xiang, Mingyu Tan, Yingying Liu, Ling Yue, Shifu Xiao and 1 more

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Article in The journal of prevention of Alzheimer's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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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

11 authors.

Xiaowen XuDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China.
Xiereniguli AnayitiDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China.
Peiying ChenDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China.
Zhongfeng XieDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China.
Mengling TaoDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China.
Yongsheng XiangDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China.
Mingyu TanDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China.
Yingying LiuDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China.
Ling YueDepartment of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China. Electronic address: bellinthemoon@hotmail.com.
Shifu XiaoDepartment of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China. Electronic address: xiaoshifu@msn.com.
Peijun WangDepartment of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China; Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai 200092, China. Electronic address: tongjipjwang@vip.sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) frequently co-occurs with depressive symptoms, exacerbating both cognitive decline and clinical complexity, yet the neural substrates linking this co-occurrence remain poorly understood. We aimed to investigate the role of basal forebrain-limbic system circuit dysregulation in the interaction between cognitive impairment and depressive symptoms, identifying potential biomarkers for early diagnosis and intervention.

methodsThis cross-sectional study included participants stratified into normal controls (NC), cognitive impairment without depression (CI-nD), and cognitive impairment with depression (CI-D). Multimodal MRI (structural, diffusion, functional, perfusion, iron-sensitive imaging) and plasma biomarkers were analyzed. Machine learning models classified subgroups using neuroimaging features.

resultsCI-D exhibited distinct basal forebrain-limbic circuit alterations versus CI-nD and NC: (1) Elevated free-water fraction (FW) in basal forebrain subregions (Ch123/Ch4, p < 0.04), indicating early neuroinflammation; (2) Increased iron deposition in the anterior cingulate cortex and entorhinal cortex (p < 0.05); (3) Hyperperfusion and functional hyperactivity in Ch123 and amygdala; (4) Plasma neurofilamentlightchain exhibited correlated with hippocampal inflammation in CI-nD (p = 0.03) but linked to basal forebrain dysfunction in CI-D (p < 0.05). Multimodal support vector machine achieved 85 % accuracy (AUC=0.96) in distinguishing CI-D from CI-nD, with Ch123 and Ch4 as key discriminators. Pathway analysis in the CI-D group further revealed that FW-related neuroinflammation in the basal forebrain (Ch123/Ch4) indirectly contributed to cognitive impairment via structural atrophy.

conclusionWe identified a neuroinflammatory-cholinergic pathway in the basal forebrain as an early mechanism driving depression-associated cognitive decline. Multimodal imaging revealed distinct spatiotemporal patterns of circuit dysregulation, suggesting neuroinflammation and iron deposition precede structural degeneration. These findings position the basal forebrain-limbic system circuit as a therapeutic target and provide actionable biomarkers for early intervention in AD with depressive symptoms.

Indexed as

Basal ForebrainCognitive DysfunctionDepressionLimbic SystemNeuroimagingAgedBiomarkersCross-Sectional StudiesEarly DiagnosisFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedMultimodal ImagingBiomarkersAlzheimer’s diseaseBasal forebrain-limbic circuitCognitive impairmentDepressionMachine learningMultimodal MRI

Identifiers

PMID40675918
PMCPMC12413736

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Registered trials

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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 OpenQuestion graph.