Evidence map›Paper›PMID 42327262›Full record

ArticlebioRxiv : the preprint server for biology2026

Mechanistic corticostriatal circuit model predicts learning-dependent fMRI dynamics and individual reward bias in humans.

Simon Carter, Zeming Kuang, Anthony G Chesebro, Haris Organtzidis, Sadia A Jumana, Stephen J Burke, Anand Pathak, Eva-Maria Ratai, Earl K Miller, Richard H Granger and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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

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.

Simon CarterDepartment of Applied Mathematics, State University of New York at Stony Brook; Stony Brook, NY USA.
Zeming KuangLaufer Center for Physical and Quantitative Biology, State University of New York at Stony Brook; Stony Brook, NY USA.
Anthony G ChesebroLaufer Center for Physical and Quantitative Biology, State University of New York at Stony Brook; Stony Brook, NY USA.
Haris OrgantzidisLaufer Center for Physical and Quantitative Biology, State University of New York at Stony Brook; Stony Brook, NY USA.
Sadia A JumanaLaufer Center for Physical and Quantitative Biology, State University of New York at Stony Brook; Stony Brook, NY USA.
Stephen J BurkeAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Boston, MA USA.
Anand PathakDepartment of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
Eva-Maria RataiAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Boston, MA USA.
Earl K MillerDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
Richard H GrangerDepartment of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
Helmut H StreyLaufer Center for Physical and Quantitative Biology, State University of New York at Stony Brook; Stony Brook, NY USA.
Lilianne R Mujica-ParodiLaufer Center for Physical and Quantitative Biology, State University of New York at Stony Brook; Stony Brook, NY USA.ORCID 0000-0002-3752-5519

Funding

The Harvard Clinical and Translational Science CenterUL1TR002541 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2018 to 2022
$93.0M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM008444 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI FROHMAN, MICHAEL A. · 1992 to 2024
$12.6M
NCATS NIH HHS UL1 TR002541NIGMS NIH HHS T32 GM008444
6 · The paper itself

Abstract

Circuit-level computational models can do more than explain existing data; they can generate novel hypotheses and capture individual differences in human populations. We demonstrate this using a biophysical corticostriatal model, transforming simulated neuronal activity from local field potential (LFP) into functional magnetic resonance imaging (fMRI) signals via the balloon model, and finally generating behavioral outcomes. We then validate the results against those obtained from human subjects. The model generates a counterintuitive yet testable prediction. Although prefrontal-striatal coherence demonstrates an

Identifiers

PMID42327262
PMCPMC13278121

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.