Evidence map›Paper›PMID 42431323›Full record

ArticleJournal of affective disorders2026

Resting-state functional connectivity and alexithymia: Preliminary predictive evidence.

Anika Holton, Tianye Zhai, Elise Shealy, Samuel Lucero, Laura Murray, Kevin Noemer, Yihong Yang, Betty Jo Salmeron, Amy Janes

Abstract read
In one paragraph

Article in Journal of affective disorders, 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

9 authors.

Anika HoltonNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
Tianye ZhaiNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA. Electronic address: tianye.zhai@nih.gov.
Elise ShealyNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
Samuel LuceroNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
Laura MurrayNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
Kevin NoemerNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
Yihong YangNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
Betty Jo SalmeronNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA.
Amy JanesNeuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, 251 Bayview Blvd, Baltimore, MD, 21224, USA.

Funding

Defining neurobiological links between substance use and mental illnessZIADA000641 · NIDA · NATIONAL INSTITUTE ON DRUG ABUSE · PI JANES, AMY · 2022 to 2025
$8.8M
Intramural NIH HHS ZIA DA000641
6 · The paper itself

Abstract

Alexithymia, defined by reduced emotional awareness, is prevalent in psychiatric and substance use disorders; however, its relationship with brain circuitry remains insufficiently understood. Investigating alexithymia in healthy adults enables identification of core brain networks underlying disrupted emotional awareness, independent of comorbid pathology, and may inform the development of targeted interventions. In this study, 150 healthy individuals were assessed using the Toronto Alexithymia Scale (TAS-20) and underwent resting-state functional magnetic resonance imaging. Predictive modeling was applied to whole-brain functional connectivity data to identify network patterns predictive of individual differences in alexithymia. Distinct brain patterns emerged for the TAS-20 total score and the Difficulty Describing Feelings (DDF) subscale. For total alexithymia, the right thalamus and left dorsolateral prefrontal cortex (dlPFC) were implicated. Higher total alexithymia was associated with reduced connectivity between the thalamus and posterior insula and sensorimotor regions, as well as decreased connectivity between the dlPFC and the cerebellum, medial temporal lobe, and frontal cortex. For the DDF subscale, the left premotor cortex (PMC) was identified as a central node, with higher DDF scores linked to reduced connectivity between the left PMC and both the anterior insula and dorsal anterior cingulate cortex. Overall, decreased connectivity among sensory integration, cognitive control, and motor planning networks modestly predicted core features of alexithymia. These findings provide preliminary evidence for the neural networks underlying individual differences in emotional processing and provide a framework for future research on vulnerability to psychiatric disorders and the development of targeted, circuit-based interventions.

Indexed as

Affective SymptomsBrainAdultBrain MappingDorsolateral Prefrontal CortexEmotionsFemaleHumansMagnetic Resonance ImagingMaleNerve NetNeural PathwaysThalamusYoung AdultAffective processingAlexithymiaEmotion regulationNeural correlatesPredictive modelingResting-state functional connectivity

Identifiers

PMID42431323
PMCPMC13446112

What OpenQuestion holds

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LicenceCC BY
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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.