Evidence map›Paper›PMID 42036576›Full record

ReviewAdvances in experimental medicine and biology2026

Molecular, Structural, and Functional Neuroimaging in Major Depression.

Chien-Han Lai

Abstract readReview
PubMed Publisher
In one paragraph

Review in Advances in experimental medicine and biology, 2026. 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

1 author.

Chien-Han LaiPhD Psychiatry & Neuroscience Clinic, Taoyuan, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural and functional imaging have been applied to understand the pathophysiological alterations of brain in major depressive disorder (MDD) for several decades. The interaction between gene and brain in the pathophysiology of MDD can be explored by molecular imaging. Structural imaging studies support the crucial role of fronto-limbic model. Gray matter alterations may include frontal cortex, anterior cingulate cortex, amygdala, hippocampus, and parietal lobe. White matter alterations may include superior longitudinal fasciculus, uncinate fasculus, and cingulum. Structural connectome combined with functional connectome might be helpful for diagnosing MDD with an acceptable accuracy. Functional imaging studies reveal the importance of fronto-limbic network and default mode network. However, magnetic resonance spectroscopy, electroencephalography, and functional near-infrared spectroscopy mostly found the functional alterations in the frontal cortex. Positron emission tomography seems to have consistent findings in the fronto-limbic regions of translocator protein radioligand imaging, not the glucose metabolism and serotonin receptor density imaging. Molecular imaging findings are significantly influenced by the focused genetic polymorphism and imaging modality. Most findings still report the crucial role of fronto-limbic model and beyond fronto-limbic regions, such as default mode network. Fronto-limbic network should still be the core regions of pathophysiology for MDD. However, beyond fronto-limbic regions should not be ignored and more efforts should be executed to clarify the architecture of MDD pathophysiology using structural, functional, and molecular imaging.

Indexed as

BrainFunctional NeuroimagingMajor Depressive DisorderMolecular ImagingConnectomeHumansMagnetic Resonance ImagingPositron-Emission TomographyAnterior cingulate cortex (ACC)Dorsolateral prefrontal cortex (DLPFC)FrontalFunctional connectivityLimbicMRI-basedTask

Identifiers

What OpenQuestion holds

Textmetadata
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.