The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood
Open accessAbstract
Background There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention.Methods Ninety-two individuals (68 depressed patients, 24 never-depressed control subjects) completed a sustained positive mood induction during functional magnetic resonance imaging. Directed functional connectivity paths within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation (GIMME), a method shown to accurately recover the direction and presence of connectivity paths in individual participants. During model selection, individuals were clustered using community detection on neural connectivity estimates. Subgroups were externally tested across multiple levels of analysis.Results Two connectivity-based subgroups emerged: subgroup A, characterized by weaker connectivity overall, and subgroup B, exhibiting hyperconnectivity (relative to subgroup A), particularly among ventral affective regions. Subgroup predicted diagnostic status (subgroup B contained 81% of patients; 50% of control subjects; χ 2 = 8.6, p = .003) and default mode network connectivity during a separate resting-state task. Among patients, subgroup B members had higher self-reported symptoms, lower sustained positive mood during the induction, and higher negative bias on a reaction-time task. Symptom-based depression subgroups did not predict these external variables.Conclusions Neural connectivity-based categorization travels with diagnostic category and is clinically predictive, but not clinically deterministic. Both patients and control subjects showed heterogeneous, and overlapping, profiles. The larger and more severely affected patient subgroup was characterized by ventrally driven hyperconnectivity during positive processing. Data-driven parsing suggests heterogeneous substrates of depression and possible resilience in control subjects in spite of biological overlap.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Equally noteworthy, however, is the marked heterogeneity of depressed patients, which is evident at the neural network 143,144 , cognitive 145 , information processing 145,146 , and clinical 147 levels, and may help to explain the fact that no known intervention strategy is effective for all depressed patients.”
From Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model (2020) · cites this paper as [144]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Karalunas, S. L., Fair, D., Musser, E. D., Aykes, K., Iyer, S. P., & Nigg, J. T. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.763 · Cited by 340
RetractedDiagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,845
(1975) · Medical Entomology and Zoology · Cited by 7,245
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
The Endophenotype Concept in Psychiatry: Etymology and Strategic Intentions
Gottesman, I. I., & Gould, T. D. (2003) · American Journal of Psychiatry · doi:10.1176/appi.ajp.160.4.636 · Cited by 5,615
Cognitive Therapy and the Emotional Disorders
Jaffe, B. (1977) · Psychosomatics · doi:10.1016/s0033-3182(77)71107-7 · Cited by 4,730
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747
Functional and effective connectivity in neuroimaging: a synthesis
Friston, K. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020107 · Cited by 2,452
The brain basis of emotion: a meta-analytic review
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11000446 · Cited by 2,320
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.0071 · Cited by 2,281
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–106 of 106
Cross-species neuroimaging intermediate phenotypes deepen our understanding of depressionPreprint
Guo, H., Dong, S., Xiao, Y., Yang, J., Zhao, P., Zhao, T., Cai, A., Wang, H., Hua, R., Liu, R., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2414671/v2
Gao, H., Ma, Y., Qian, R., Jin, T., Gu, B., Qiu, B., Ye, S., Huang, M., Wu, D., & Zhao, Z. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.05.686774
Womer, F. Y., Xiao, Y., Ngo, V., Zheng, J., & Wang, F. (2025) · Journal of Translational Internal Medicine · doi:10.1515/jtim-2025-0045 · Cited by 1
Wang, X., Shao, J., & Lin, P. (2026) · Behavioural Brain Research · doi:10.1016/j.bbr.2026.116135
Functional connectivity during positive mood and anxiety treatment response in children
Mattoni, M., McMakin, D. L., Sequeira, S. L., Forbes, E. E., Silk, J. S., Kendall, P. C., Siegle, G. J., Ladouceur, C. D., Ryan, N. D., Dahl, R. E., et al. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121897
Self-citationPatient Stratification in Psychiatry: Current and Future Approaches
Liston, C. M., & Dzirasa, K. (2026) · Springer eBooks · doi:10.1007/978-3-032-31497-0_4
Metadata
- Authors
- Price, R. B., Lane, S., Gates, K., Kraynak, T. E., Horner, M. S., Thase, M. E., & Siegle, G. J.
- Affiliations
- University of Pittsburgh (US)
- University of North Carolina at Chapel Hill (US)
- Philadelphia VA Medical Center (US)
- Corresponding author
- Price, Rebecca B.
- ORCID
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 81
- Issue
- 4
- Pages
- 347-357
- ISSN
- 0006-3223 (ISSN-L)
- PMC
- PMC5215983
- OpenAlex
- W2463383187
- Cited by
- 106
- References
- 56
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 7 · 2023: 15 · 2022: 9 · 2021: 18 · 2020: 17 · 2019: 9 · 2018: 10 · 2017: 8
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biological Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (T32HL007560)
- National Institute of Mental Health (R01MH069618, K02MH082998, R01MH058397, K23MH100259, R01MH074807, R01MH058356)
- National Institute of Biomedical Imaging and Bioengineering (R21EB015573)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- depression, positive mood, neuronal heterogeneity, directed functional connectivity, GIMME, community detection, default mode network, mood induction, negative bias, resilience
- MeSH terms
- Adult, Affect, Brain, Brain Mapping, Depressive Disorder, Major, Major Depressive Disorder, Female, Humans, Magnetic Resonance Imaging, Male, Neural Pathways, Psychiatric Status Rating Scales, Resilience, Psychological
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
