Evidence map›Paper›PMID 36073155›Full record

ArticleSchizophrenia bulletin2022

Abnormal Information Flow in Schizophrenia Is Linked to Psychosis.

Yingxin Jia, Kiwamu Kudo, Leighton B N Hinkley, Melissa Fisher, Sophia Vinogradov, Srikantan Nagarajan, Karuna Subramaniam

Open access · hybridAbstract read
In one paragraph

Article in Schizophrenia bulletin, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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 at 3 institutions in 2 countries.

Yingxin JiaDepartment of Radiology and Biomedical Imaging, University of California, San, Francisco, CA 94143, USA.
Kiwamu KudoDepartment of Radiology and Biomedical Imaging, University of California, San, Francisco, CA 94143, USA.
Leighton B N HinkleyDepartment of Radiology and Biomedical Imaging, University of California, San, Francisco, CA 94143, USA.
Melissa FisherDepartment of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA.
Sophia VinogradovDepartment of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA.
Srikantan NagarajanDepartment of Radiology and Biomedical Imaging, University of California, San, Francisco, CA 94143, USA.
Karuna SubramaniamDepartment of Psychiatry, University of California, San Francisco, CA 94143, USA.
University of California, San Francisco · USUniversity of Minnesota · USRicoh (Japan) · JP

Funding

Using a computational and network neuroscience framework to study pharmacological manipulations of state representation processes in early psychosisP50MH119569 · NIMH · UNIVERSITY OF MINNESOTA · PI A DAVID REDISH, Sophia Vinogradov · 2020 to 2026
$24.5M
Causal Role of Medial Prefrontal Neural Activity in Self-Agency in Schizophrenia (Admin Suppl)R01MH122897 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SUBRAMANIAM, KARUNA · 2020 to 2024
$4.4M
NIMH NIH HHS P50 MH119569NIMH NIH HHS R01 MH122897NIMH NIH HHS R01MH122897
6 · The paper itself

Abstract

background and hypothesisPrior research has shown that patients with schizophrenia (SZ) show disruption in brain network connectivity that is thought to underlie their cognitive and psychotic symptoms. However, most studies examining functional network disruption in schizophrenia have focused on the temporally correlated coupling of the strength of network connections. Here, we move beyond correlative metrics to assay causal computations of connectivity changes in directed neural information flow, assayed from a neural source to a target in SZ. STUDY

designThis study describes a whole-brain magnetoencephalography-imaging approach to examine causal computations of connectivity changes in directed neural information flow between brain regions during resting states, quantified by phase-transfer entropy (PTE) metrics, assayed from a neural source to an endpoint, in 21 SZ compared with 21 healthy controls (HC), and associations with cognitive and clinical psychotic symptoms in SZ. STUDY

resultsWe found that SZ showed significant disruption in information flow in alpha (8-12 Hz) and beta (12-30 Hz) frequencies, compared to HC. Reduced information flow in alpha frequencies from the precuneus to the medio-ventral occipital cortex was associated with more severe clinical psychopathology (ie, positive psychotic symptoms), while reduced information flow between insula and middle temporal gyrus was associated with worsening cognitive symptoms.

conclusionsThe present findings highlight the importance of delineating dysfunction in neural information flow in specific oscillatory frequencies between distinct regions that underlie the cognitive and psychotic symptoms in SZ, and provide potential neural biomarkers that could lead to innovations in future neuromodulation treatment development.

Indexed as

Psychotic DisordersSchizophreniaBrainHumansMagnetic Resonance ImagingMagnetoencephalographyphasepsychotic symptomsrestingstate MEGtransfer entropy

Identifiers

PMID36073155
PMCPMC9673273
OpenAlexW4294992698

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.