Evidence map›Paper›PMID 34284335›Full record

ArticleNeuroImage. Clinical2021

Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder.

Guoshi Li, Yujie Liu, Yanting Zheng, Ye Wu, Danian Li, Xinyu Liang, Yaoping Chen, Ying Cui, Pew-Thian Yap, Shijun Qiu and 2 more

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 12% of its field
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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. Functional connectivity of salience and affective networks among remitted depressed patients predicts episode recurrence.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023
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  5. A Large-scale Neural Model Inversion Framework for Effective Connectivity Estimation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026
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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

12 authors at 5 institutions in 2 countries.

Guoshi LiDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
Yujie LiuDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA; The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
Yanting ZhengDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA; The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
Ye WuDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
Danian LiCerebropathy Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Xinyu LiangThe First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Yaoping ChenThe First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Ying CuiCerebropathy Center, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Pew-Thian YapDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
Shijun QiuDepartment of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China. Electronic address: qiu-sj@163.com.
Han ZhangDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA. Electronic address: hanzhang.bit@gmail.com.
Dinggang ShenDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC USA. Electronic address: dinggang.shen@gmail.com.
University of North Carolina at Chapel Hill · USFirst Affiliated Hospital of Guangzhou University of Chinese Medicine · CNGuangzhou University of Chinese Medicine · CNSun Yat-sen University · CNThird Affiliated Hospital of Guangzhou Medical University · CN

Funding

Comprehensive MR Fingerprinting for Infants and Young Children at Risk for Developmental Delays.R01HD112923 · NICHD · DUKE UNIVERSITY · PI Dan Ma, DEANNE E WILSON-COSTELLO · 2024 to 2026
$1.7M
Diagnosis of Alzheimer's Disease Using Dynamic High-Order Brain NetworksR01EB022880 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2016 to 2018
$1.1M
NIBIB NIH HHS R01 EB022880NICHD NIH HHS R01 HD112923
6 · The paper itself

Abstract

Major depressive disorder (MDD) represents a grand challenge to human health and society, but the underlying pathophysiological mechanisms remain elusive. Previous neuroimaging studies have suggested that MDD is associated with abnormal interactions and dynamics in two major neural systems including the default mode - salience (DMN-SAL) network and the executive - limbic (EXE-LIM) network, but it is not clear which network plays a central role and which network plays a subordinate role in MDD pathophysiology. To address this question, we refined a newly developed Multiscale Neural Model Inversion (MNMI) framework and applied it to test whether MDD is more affected by impaired circuit interactions in the DMN-SAL network or the EXE-LIM network. The model estimates the directed connection strengths between different neural populations both within and between brain regions based on resting-state fMRI data collected from normal healthy subjects and patients with MDD. Results show that MDD is primarily characterized by abnormal circuit interactions in the EXE-LIM network rather than the DMN-SAL network. Specifically, we observe reduced frontoparietal effective connectivity that potentially contributes to hypoactivity in the dorsolateral prefrontal cortex (dlPFC), and decreased intrinsic inhibition combined with increased excitation from the superior parietal cortex (SPC) that potentially lead to amygdala hyperactivity, together resulting in activation imbalance in the PFC-amygdala circuit that pervades in MDD. Moreover, the model reveals reduced PFC-to-hippocampus excitation but decreased SPC-to-thalamus inhibition in MDD population that potentially lead to hypoactivity in the hippocampus and hyperactivity in the thalamus, consistent with previous experimental data. Overall, our findings provide strong support for the long-standing limbic-cortical dysregulation model in major depression but also offer novel insights into the multiscale pathophysiology of this debilitating disease.

Indexed as

Major Depressive DisorderBrainBrain MappingHumansMagnetic Resonance ImagingNeural PathwaysParietal LobeBrain networksEffective connectivityGenetic algorithmMajor depressive disorderNeural mass modelResting-state fMRI

Identifiers

PMID34284335
PMCPMC8313604
OpenAlexW3185857046

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 OpenQuestion graph.