Evidence map›Paper›PMID 41646384›Full record

ArticleResearch square2026

Aberrant Recovery of Timescale-Aligned Amplitude Balance Links to Symptoms and Cognition in Schizophrenia.

Sir-Lord Wiafe, Spencer Kinsey, Najme Soleimani, Raymond O Nsafoa, Nigar Khasayeva, Amritha Harikumar, Robyn Miller, Vince D Calhoun

Abstract readPreprint
In one paragraph

Article in Research square, 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

5 · Who and what money

Authors and funding

8 authors.

Sir-Lord WiafeTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.ORCID 0009-0002-8583-7987
Spencer KinseyTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.ORCID 0009-0002-4743-380X
Najme SoleimaniTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
Raymond O NsafoaKwame Nkrumah University of Science and Technology (KNUST) Hospital, Kumasi, 00233, Ghana.
Nigar KhasayevaTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
Amritha HarikumarTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
Robyn MillerTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
Vince D CalhounTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.ORCID 0000-0001-9058-0747

Funding

Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivityR01MH123610 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2021 to 2025
$3.1M
NIMH NIH HHS R01 MH123610
6 · The paper itself

Abstract

Schizophrenia has long been linked to impaired coordination of brain activity, yet most frameworks overlook two key dimensions: the amplitude of brain signals and the differing timescales on which regions operate. These factors are critical in disorders where neural activity is exaggerated and slowed. In healthy adults, networks compensate for mismatched processing speeds to maintain proportionate activity, but this process is poorly understood in schizophrenia. We developed a timescale-aligned, time-resolved framework that separates temporal distortions from genuine amplitude differences, enabling measurement of amplitude balance between networks across timescales. This approach was applied to large-scale fMRI datasets, including the Human Connectome Project and a multi-site schizophrenia cohort. Patients with schizophrenia showed greater amplitude imbalance, especially during fast fluctuations, along with more frequent re-entry into unbalanced states and slower recovery to stable coordination. We further identified a flexible intermediate state that patients occupied more often, and that predicted better working-memory performance. Across cohorts, amplitude imbalance was associated with greater symptom severity and poorer reasoning ability. These findings provide a new mechanistic view of dyscoordination in schizophrenia grounded in timescale-normalized amplitude dynamics, highlight aberrant recovery of amplitude balance as a core feature of the illness, and suggest that timescale-aligned amplitude imbalance may serve as a promising target for biomarker development.

Indexed as

BOLD amplitudedynamic time warpingfMRISchizophreniatimescale alignment

Identifiers

PMID41646384
PMCPMC12869618

What Socratic holds

Textmetadata
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.