Evidence map›Paper›PMID 29105239›Full record

ArticleHuman brain mapping2018

Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function.

Jérémy Lefort-Besnard, Danielle S Bassett, Jonathan Smallwood, Daniel S Margulies, Birgit Derntl, Oliver Gruber, Andre Aleman, Renaud Jardri, Gaël Varoquaux, Bertrand Thirion and 2 more

Open access · greenAbstract read
In one paragraph

Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 3 pooled it
2.8field-weighted citation impact, top 9% 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

25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 44 citations in OpenAlex.

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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

12 authors at 9 institutions in 5 countries.

Jérémy Lefort-BesnardDepartment of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Germany.ORCID 0000-0002-8033-4953
Danielle S BassettDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.ORCID 0000-0002-6183-4493
Jonathan SmallwoodDepartment of Psychology, University of York, Heslington, United Kingdom.
Daniel S MarguliesMax Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103, Germany.
Birgit DerntlDepartment of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Germany.
Oliver GruberDepartment of Psychiatry, University of Heidelberg, Germany.
Andre AlemanBCN Neuroimaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Renaud JardriDivision of Psychiatry, University of Lille, CNRS UMR9193, SCALab & CHU Lille, Fontan Hospital, CURE platform, Lille, 59000, France.
Gaël VaroquauxParietal Team, INRIA/Neurospin Saclay, France.
Bertrand ThirionParietal Team, INRIA/Neurospin Saclay, France.ORCID 0000-0001-5018-7895
Simon B EickhoffInstitute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany.
Danilo BzdokDepartment of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Germany.
Institut national de recherche en sciences et technologies du numérique · FRKlinik für Psychosomatik · DECentre National de la Recherche Scientifique · FRForschungszentrum Jülich · DEHeidelberg University · DEMax Planck Institute for Human Cognitive and Brain Sciences · DEUniversity Medical Center Groningen · NLUniversity of Pennsylvania · USUniversity of York · GB

Funding

Linguistic and NonLinguistic Functions of Frontal CortexR01DC009209 · NIDCD · UNIVERSITY OF PENNSYLVANIA · PI THOMPSONSCHILL, SHARON L · 2008 to 2018
$5.2M
Virtual Resection to Treat EpilepsyR01NS099348 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2016 to 2019
$2.3M
Evolution of the Linked Architecture of Network Control and Executive Function in AdolescenceR21MH106799 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, SATTERTHWAITE, THEODORE · 2016 to 2017
$515k
CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic NetworksR01HD086888 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH · 2015 to 2018
$506k
NICHD NIH HHS R01 HD086888NIMH NIH HHS R21 MH106799NINDS NIH HHS R01 NS099348
6 · The paper itself

Abstract

Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal-directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyperconnectivity and hypoconnectivity within the DMN, and both increased and decreased DMN coupling with the multimodal saliency network (SN) and dorsal attention network (DAN). This study systematically revisited network disruption in patients with schizophrenia using data-derived network atlases and multivariate pattern-learning algorithms in a multisite dataset (n = 325). Resting-state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co-occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network coupling were used to characterize healthy participants and patients with schizophrenia, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN-DAN coupling, while structural covariation results highlighted aberrant DMN-SN coupling. Our findings reframe the role of the DMN core and its relation to canonical networks in schizophrenia. We thus underline the importance of large-scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients.

Indexed as

AlgorithmsBrainBrain MappingHumansMagnetic Resonance ImagingNeural PathwaysRestSchizophreniadefault mode network properfunctional connectivitymachine learningneuroimagingschizophreniasparse inverse covariance estimationsparsitystructural covariance

Identifiers

PMID29105239
PMCPMC5764781
OpenAlexW2765769685

What OpenQuestion holds

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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.