Evidence map›Paper›PMID 35720774›Full record

ArticleFrontiers in computational neuroscience2022

Treatment Response Prediction for Major Depressive Disorder Patients

Hanxiaoran Li, Sutao Song, Donglin Wang, Danning Zhang, Zhonglin Tan, Zhenzhen Lian, Yan Wang, Xin Zhou, Chenyuan Pan, Yue Wu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in computational neuroscience, 2022. 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
0.5field-weighted citation impact, top 41% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

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

10 authors at 5 institutions in 1 country.

Hanxiaoran LiInstitutes of Psychological Sciences, College of Education, Hangzhou Normal University, Hangzhou, China.
Sutao SongSchool of Information Science and Engineering, Shandong Normal University, Jinan, China.
Donglin WangInstitutes of Psychological Sciences, College of Education, Hangzhou Normal University, Hangzhou, China.
Danning ZhangShandong Mental Health Center, Shandong University, Jinan, Shandong, China.
Zhonglin TanDepartment of Psychiatry, Hangzhou Seventh People's Hospital, Hangzhou, China.
Zhenzhen LianInstitutes of Psychological Sciences, College of Education, Hangzhou Normal University, Hangzhou, China.
Yan WangInstitutes of Psychological Sciences, College of Education, Hangzhou Normal University, Hangzhou, China.
Xin ZhouInstitutes of Psychological Sciences, College of Education, Hangzhou Normal University, Hangzhou, China.
Chenyuan PanInstitutes of Psychological Sciences, College of Education, Hangzhou Normal University, Hangzhou, China.
Yue WuDepartment of Translational Psychiatry Laboratory, Hangzhou Seventh People's Hospital, Hangzhou, China.
Hangzhou Normal University · CNAffiliated Hospital of Hangzhou Normal University · CNHangzhou Seventh Peoples Hospital · CNShandong Normal University · CNShandong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antidepressant treatment, as an important method in clinical practice, is not suitable for all major depressive disorder (MDD) patients. Although magnetic resonance imaging (MRI) studies have found thalamic abnormalities in MDD patients, it is not clear whether the features of the thalamus are suitable to serve as predictive aids for treatment responses at the individual level. Here, we tested the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) of the thalamus using multivariate pattern analysis (MVPA). A total of 74 MDD patients and 44 healthy control (HC) subjects were recruited. Thirty-nine MDD patients and 35 HC subjects underwent scanning twice. Between the two scanning sessions, patients in the MDD group received selective serotonin reuptake inhibitor (SSRI) treatment for 3-month, and HC group did not receive any treatment. Gaussian process regression (GPR) was trained to predict the percentage decrease in the Hamilton Depression Scale (HAMD) score after treatment. The percentage decrease in HAMD score after SSRI treatment was predicted by building GPRs trained with baseline thalamic data. The results showed significant correlations between the true percentage of HAMD score decreases and predictions (

Indexed as

major depressive disorder (MDD)MVPAstructural magnetic resonance imaging (sMRI)thalamustreatment response prediction

Identifiers

PMID35720774
PMCPMC9199000
OpenAlexW4281832217

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.