Evidence map›Paper›PMID 39242921›Full record

ReviewNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2024

Revisiting the role of computational neuroimaging in the era of integrative neuroscience.

Alisa M Loosen, Ayaka Kato, Xiaosi Gu

Erratum issuedAbstract readReview
In one paragraph

Review in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  20. Naturalistic computational psychiatry: How to get there?Journal of psychiatry & neuroscience : JPN
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Alisa M Loosen *Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. alisa.loosen@mssm.edu.ORCID 0000-0002-4295-3817
Ayaka Kato *Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. ayaka.kato@mssm.edu.ORCID 0000-0002-6306-6600
Xiaosi GuDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0002-9373-987X

Funding

Neurocomputational mechanisms of proactive social behavior deficits in autism spectrum disorderR01MH122611 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FOSS-FEIG, JENNIFER, GU, XIAOSI · 2020 to 2024
$3.9M
Neural, computational and behavioral characterization of dynamic social behavior in borderline and avoidant personality disorderR01MH123069 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GU, XIAOSI, KOENIGSBERG, HAROLD W · 2021 to 2025
$3.1M
Delineating proactive social behaviors in dynamic and multidimensional social spaceR21MH120789 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GU, XIAOSI, SCHILLER, DANIELA · 2019 to 2020
$466k
MEXT | Japan Society for the Promotion of Science (JSPS) overseas research fellowshipNIMH NIH HHS R01 MH122611NIMH NIH HHS R01 MH123069NIMH NIH HHS R21 MH120789U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R21MH120789, R01MH122611,R01MH123069
6 · The paper itself

Abstract

Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. However, concerns persist regarding the ecological validity of lab-based neuroimaging studies and whether their spatiotemporal resolution is not sufficient for capturing neural dynamics. This review aims to re-examine the utility of computational neuroimaging, particularly in light of the growing prominence of alternative neuroscientific methods and the growing emphasis on more naturalistic behaviors and paradigms. Specifically, we will explore how computational modeling can both enhance the analysis of high-dimensional imaging datasets and, conversely, how neuroimaging, in conjunction with other data modalities, can inform computational models through the lens of neurobiological plausibility. Collectively, this evidence suggests that neuroimaging remains critical for human neuroscience research, and when enhanced by computational models, imaging can serve an important role in bridging levels of analysis and understanding. We conclude by proposing key directions for future research, emphasizing the development of standardized paradigms and the integrative use of computational modeling across neuroimaging techniques.

Indexed as

NeuroimagingNeurosciencesAnimalsBrainComputer SimulationHumansModels, Neurological

Identifiers

PMID39242921
PMCPMC11525590

What OpenQuestion holds

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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.