Evidence map›Paper›PMID 32016430›Full record

ArticleSchizophrenia bulletin2020

Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study.

Jie Yang, Weidan Pu, Guowei Wu, Eric Chen, Edwin Lee, Zhening Liu, Lena Palaniyappan

Abstract read
In one paragraph

Article in Schizophrenia bulletin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

7 authors.

Jie YangInstitute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China.
Weidan PuMedical Psychological Center, the Second Xiangya Hospital, Central South University, Changsha, P.R. China.
Guowei WuInstitute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China.
Eric ChenDepartment of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong.
Edwin LeeDepartment of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong.
Zhening LiuInstitute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China.
Lena PalaniyappanInstitute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWorking memory (WM) deficit is a key feature of schizophrenia that relates to a generalized neural inefficiency of extensive brain areas. To date, it remains unknown how these distributed regions are systemically organized at the connectome level and how the disruption of such organization brings about the WM impairment seen in schizophrenia.

methodsWe used graph theory to examine the neural efficiency of the functional connectome in different granularity in 155 patients with schizophrenia and 96 healthy controls during a WM task. These analyses were repeated in another independent dataset (81 patients and 54 controls). Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. A machine-learning approach was adopted to study the ability of multivariate connectome features from one dataset to discriminate patients from controls in the second dataset.

resultsSmall-worldness of the whole-brain connectome was significantly increased in schizophrenia during the WM task; this increase is related to better (though subpar) WM accuracy in patients with more severe negative symptom burden. There was a shift in the degree distribution to a more homogeneous form in patients. The machine-learning approach classified a new set of patients from controls with 84.3% true-positivity rate for schizophrenia and 71.6% overall accuracy.

conclusionsWe demonstrate a putative mechanistic link between connectome topology, hub redistribution, and impaired n-back performance in schizophrenia. The task-dependent modulation of the connectome relates to, but remains inefficient in, improving the performance above par in the presence of severe negative symptoms.

Indexed as

ConnectomeAdultCognitive DysfunctionFemaleHumansMachine LearningMagnetic Resonance ImagingMaleMemory DisordersMemory, Short-TermNerve NetSchizophreniaYoung Adultgraph theoryneural efficiencyschizophrenia/functional connectomeworking memory

Identifiers

PMID32016430
PMCPMC7345823

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.