Evidence map›Paper›PMID 37961617›Full record

ArticlebioRxiv : the preprint server for biology2023

Estimating multimodal brain variability in schizophrenia spectrum disorders: A worldwide ENIGMA study.

Wolfgang Omlor, Finn Rabe, Simon Fuchs, Giacomo Cecere, Stephanie Homan, Werner Surbeck, Nils Kallen, Foivos Georgiadis, Tobias Spiller, Erich Seifritz and 66 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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

5 · Who and what money

Authors and funding

76 authors.

Wolfgang Omlor
Finn Rabe
Simon Fuchs
Giacomo Cecere
Stephanie Homan
Werner Surbeck
Nils Kallen
Foivos Georgiadis
Tobias Spiller
Erich Seifritz
Thomas Weickert
Jason Bruggemann
Cynthia Weickert
Steven Potkin
Ryota Hashimoto
Kang Sim
Kelly Rootes-Murdy
Josselin Houenou
Nerisa Banaj
Daniela Vecchio
Fabrizio Piras
Federica Piras
Gianfranco SpallettaORCID 0000-0002-7432-4249
Raymond Salvador
Andriana Karuk
Edith Pomarol-Clotet
Amanda Rodrigue
Godfrey Pearlson
David Glahn
David Tomecek
Filip Spaniel
Antonin Skoch
Matthias KirschnerORCID 0000-0002-9486-1439
Stefan Kaiser
Feng-Mei Fan
Pierre Berthet
Fleur Howells
Anne Uhlmann
Freda Scheffler
Dan Stein
Felice Iasevoli
Murray J Cairns
Vaughan J Carr
Stanley V Catts
Maria A Di Biase
Assen Jablensky
Melissa J Green
Frans A Henskens
Paul Klauser
Carmel Loughland
Patricia T Michie
Bryan Mowry
Christos Pantelis
Paul E Rasser
Ulrich Schall
Rodney Scott
Andrew Zalesky
Andrea de Bartolomeis
Annarita Barone
Mariateresa Ciccarelli
Arturo Brunetti
Sirio Cocozza
Giuseppe Pontillo
Mario Tranfa
Annabella Di Giorgio
Sophia I Thomopoulos
Neda Jahanshad
Paul M Thompson
Theo van Erp
Jessica Turner

Funding

SOLAR-Eclipse Computational Tools for Imaging GeneticsR01EB015611 · NIBIB · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KOCHUNOV, PETER V. · 2012 to 2024
$5.0M
Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine LearningRF1MH123163 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V., THOMPSON, PAUL M · 2021 to 2021
$1.2M
Hybrid GPU/CPU Computing Resource to Support Connectomic and GenomicsS10OD023696 · OD · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V. · 2018 to 2018
$593k
NIBIB NIH HHS R01 EB015611NIH HHS S10 OD023696NIMH NIH HHS RF1 MH123163
6 · The paper itself

Abstract

Objective: Schizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia. Methods: This meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy. Results: We found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region. Conclusions: Higher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.

Identifiers

PMID37961617
PMCPMC10634976

What Socratic 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 Socratic graph.