Evidence map›Paper›PMID 36542452›Full record

ArticleSchizophrenia bulletin2023

Disorganization of Semantic Brain Networks in Schizophrenia Revealed by fMRI.

Yukiko Matsumoto, Satoshi Nishida, Ryusuke Hayashi, Shuraku Son, Akio Murakami, Naganobu Yoshikawa, Hiroyoshi Ito, Naoya Oishi, Naoki Masuda, Toshiya Murai and 3 more

Abstract read
In one paragraph

Article in Schizophrenia bulletin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

13 authors.

Yukiko MatsumotoDepartment of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
Satoshi NishidaCenter for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan.ORCID 0000-0003-0555-518X
Ryusuke HayashiHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan.ORCID 0000-0003-3866-3654
Shuraku SonDepartment of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Akio MurakamiDepartment of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Naganobu YoshikawaFaculty of Medicine, Osaka University, Suita, Osaka, Japan.
Hiroyoshi ItoHuman Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan.
Naoya OishiMedical Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Naoki MasudaDepartment of Mathematics, University at Buffalo, State University of New York, Buffalo, USA.
Toshiya MuraiDepartment of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Karl FristonThe Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK.ORCID 0000-0001-7984-8909
Shinji NishimotoCenter for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan.
Hidehiko TakahashiDepartment of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesSchizophrenia is a mental illness that presents with thought disorders including delusions and disorganized speech. Thought disorders have been regarded as a consequence of the loosening of associations between semantic concepts since the term "schizophrenia" was first coined by Bleuler. However, a mechanistic account of this cardinal disturbance in terms of functional dysconnection has been lacking. To evaluate how aberrant semantic connections are expressed through brain activity, we characterized large-scale network structures of concept representations using functional magnetic resonance imaging (fMRI). STUDY

designWe quantified various concept representations in patients' brains from fMRI activity evoked by movie scenes using encoding modeling. We then constructed semantic brain networks by evaluating the similarity of these semantic representations and conducted graph theory-based network analyses. STUDY

resultsNeurotypical networks had small-world properties similar to those of natural languages, suggesting small-worldness as a universal property in semantic knowledge networks. Conversely, small-worldness was significantly reduced in networks of schizophrenia patients and was correlated with psychological measures of delusions. Patients' semantic networks were partitioned into more distinct categories and had more random within-category structures than those of controls.

conclusionsThe differences in conceptual representations manifest altered semantic clustering and associative intrusions that underlie thought disorders. This is the first study to provide pathophysiological evidence for the loosening of associations as reflected in randomization of semantic networks in schizophrenia. Our method provides a promising approach for understanding the neural basis of altered or creative inner experiences of individuals with mental illness or exceptional abilities, respectively.

Indexed as

SchizophreniaSemanticsBrainBrain MappingHumansMagnetic Resonance ImagingSemantic Websemantic networksmall-worldnessThought disordervoxelwise encoding modeling

Identifiers

PMID36542452
PMCPMC10016409

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.