Evidence map›Paper›PMID 32563920›Full record

ArticleNeuroImage. Clinical2020

Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis.

Yuhui Du, Hui Hao, Shuhua Wang, Godfrey D Pearlson, Vince D Calhoun

Abstract read
In one paragraph

Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
–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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. The overlap across psychotic disorders: A functional network connectivity analysis.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2024
    Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article
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

5 authors.

Yuhui DuSchool of Computer & Information Technology, Shanxi University, Taiyuan, China; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA. Electronic address: duyuhui@sxu.edu.cn.
Hui HaoSchool of Computer & Information Technology, Shanxi University, Taiyuan, China.
Shuhua WangSchool of Computer & Information Technology, Shanxi University, Taiyuan, China.
Godfrey D PearlsonDepartments of Psychiatry, Yale University, New Haven, CT, USA.
Vince D CalhounTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.

Funding

Transcranial Direct Current Stimulation for Treatment of Auditory Verbal HalluciP20GM103472 · NIGMS · THE MIND RESEARCH NETWORK · PI ABBOTT, CHRISTOPHER C · 2012 to 2017
$14.3M
NEURAL MECHANISMS OF SCHIZOPHRENIAP20RR021938 · NCRR · THE MIND RESEARCH NETWORK · PI HANLON, FAITH MARIE · 2008 to 2011
$9.4M
Unified multivariate data-driven solutions for static and dynamic brain connectivityR01EB020407 · NIBIB · THE MIND RESEARCH NETWORK · PI ADALI, TULAY, CALHOUN, VINCE D · 2015 to 2018
$2.7M
NCRR NIH HHS P20 RR021938NIBIB NIH HHS R01 EB020407NIGMS NIH HHS P20 GM103472
6 · The paper itself

Abstract

It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.

Indexed as

AdultBipolar DisorderBrainBrain MappingFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNerve NetNeural PathwaysPsychotic DisordersSchizophreniaBipolar disorderDynamic functional connectivityFunctional magnetic resonance imagingIndependent component analysisSchizoaffective disorderSchizophrenia

Identifiers

PMID32563920
PMCPMC7306624

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.