Evidence map›Paper›PMID 33048435›Full record

ArticleHuman brain mapping2021

Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis.

George Gifford, Nicolas Crossley, Sarah Morgan, Matthew J Kempton, Paola Dazzan, Gemma Modinos, Matilda Azis, Carly Samson, Ilaria Bonoldi, Beverly Quinn and 10 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human brain mapping, 2021. 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
0.2field-weighted citation impact, top 49% 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, 3 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

20 authors at 8 institutions in 4 countries.

George GiffordDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.ORCID 0000-0002-3133-1019
Nicolas CrossleyDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Sarah MorganDepartment of Psychiatry, University of Cambridge, Cambridge, UK.
Matthew J KemptonDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Paola DazzanDepartment of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Gemma ModinosDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.ORCID 0000-0002-7870-066X
Matilda AzisDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Carly SamsonDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Ilaria BonoldiDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Beverly QuinnCAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK.
Sophie E SmartDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Mathilde AntoniadesDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Matthijs G BossongDepartment of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands.
Matthew R BroomeInstitute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK.
Jesus PerezCAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK.
Oliver D HowesDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
James M StoneDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Paul AllenDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.ORCID 0000-0001-8510-878X
Anthony A GraceDepartments of Neuroscience, Psychiatry and Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Philip McGuireDepartment of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
King's College London · GBCambridgeshire and Peterborough NHS Foundation Trust · GBMount Sinai Hospital · USPontificia Universidad Católica de Chile · CLUniversity of Birmingham · GBUniversity of Cambridge · GBUniversity of Pittsburgh · USUtrecht University · NL

Funding

Department of HealthMedical Research Council MC_U120097115Medical Research Council MR/N027078/1MRF_ C0439Wellcome TrustWellcome Trust 091667Wellcome Trust 202397/Z/16/Z
6 · The paper itself

Abstract

The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as 'integrated' FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the 'cartographic profile' of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub-network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub-network comprised brain areas implicated in bottom-up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk.

Indexed as

Prodromal SymptomsAdolescentAdultBrainConnectomeFemaleHumansLongitudinal StudiesMagnetic Resonance ImagingMaleNerve NetPredictive Value of TestsPsychomotor PerformancePsychotic DisordersRisk FactorsYoung Adultcartographic profileclinical high-risk for psychosisnetwork analysisnetwork based statisticsnetwork integrationtask fMRI

Identifiers

PMID33048435
PMCPMC7775992
OpenAlexW3091952538

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.