Evidence map›Paper›PMID 41109569›Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2026

Connectome-Based Predictive Models Optimized for Sleep Differentiate Patients With Depression From Psychiatrically Healthy Controls.

Anurima Mummaneni, Carolyn Amir, Nicholas B Allen, Tiffany C Ho

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

4 authors.

Anurima MummaneniDepartment of Psychology, University of California, Los Angeles, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California.
Carolyn AmirDepartment of Psychology, University of California, Los Angeles, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California.
Nicholas B AllenDepartment of Psychology, University of Oregon, Eugene, Oregon.
Tiffany C HoDepartment of Psychology, University of California, Los Angeles, Los Angeles, California; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California. Electronic address: tiffanycho@ucla.edu.

Funding

Inflammatory and Glutamatergic Mechanisms of Sustained Threat in Adolescents with Depression: Toward Predictors of Treatment Response and Clinical CourseR01MH127176 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TIFFANY CHEING HO · 2022 to 2026
$3.1M
Doctoral Training in Brain and Behavioral Development During Adolescence (UCLA BBDA)T32HD091059 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Mirella Dapretto, ANDREW J FULIGNI · 2018 to 2026
$1.7M
The Roles of Inflammatory and Glutamatergic Processes in the Neurodevelopmental Mechanisms Underlying Adolescent DepressionK01MH117442 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HO, TIFFANY CHEING · 2018 to 2022
$881k
NICHD NIH HHS T32 HD091059NIMH NIH HHS K01 MH117442NIMH NIH HHS R01 MH127176
6 · The paper itself

Abstract

backgroundIt is unknown whether brain-based predictive models derived from sleep features are useful for the clinical diagnosis of major depressive disorder (MDD).

methodsUsing resting-state functional magnetic resonance imaging data from the curated ABCD (Adolescent Brain Cognitive Development) Study Data Release 3.0, we trained a connectome-based predictive model (CPM) on 35,778 pairwise connections (Pearson's r) from 2349 (237 participants with at least 1 psychiatric disorder and 2112 control participants) participants ages 11 to 12 to predict sleep duration (measured from a Fitbit tracker). Linear regression models were used to compare the predicted values from these CPMs with self-reported sleep duration and diagnostic group status in an independent cohort of 78 participants (57 participants with MDD and 21 control participants) ages 14 to 18.

resultsThe ABCD-based CPM predicted self-reported sleep duration in the independent cohort of participants with MDD (partial r = 0.332, p = .009). Even though self-reported sleep duration did not significantly differ between diagnostic groups (t

conclusionsCPMs trained to predict objective sleep duration are robust and generalizable. Intrinsic functional connectivity differences between clinically depressed and psychiatrically healthy adolescents are detectable by CPMs optimized for sleep prediction, underscoring the shared neural bases between sleep health and depression. Future work will test whether sleep-based CPMs are predictive of clinical course and if they generalize to other disorders beyond depression.

Indexed as

BrainConnectomeMajor Depressive DisorderSleepAdolescentChildFemaleHumansMagnetic Resonance ImagingMaleAdolescenceDepressionPredictive modelingResting-state fMRISleep

Identifiers

PMID41109569
PMCPMC13242708

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.