The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Parcellating cortical functional networks in individuals
Open accessAbstract
The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.
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
“Precision brain-mapping, which involves the collection of several hours of imaging data from individual patients, has shown individual differences in the organization of functional networks across the cerebral cortex,104–107 as well as in subcortical structures such as the amygdala.108”
From Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders (2021) · cites this paper as [105]
The cited source says what the claim attributes to it.
In the citing paper
“In recent years, it has become possible to construct a resting-state functional magnetic resonance imaging (rsfMRI)-based connectome map with high test–retest reliability in an individual subject (36, 37).”
In the citing paper
“Studies with these long scan times have revealed that both task-based and resting-state brain activity measures can be obtained with high reliability and precision, allowing researchers to focus on individuals rather than on groups.”
From Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders (2021) · cites this paper as [105]
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
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Self-citationThe human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Self-citationDistributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Self-citationSpontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Self-citation
Research works printed but not linked: 1
- Brodmann, K. Brodmann’s: Localisation in the Cerebral Cortex 3rd edn. (Springer, 2006).
Cited by
Sorted by citations
Items 401–500 of 526
Verbal memory network mapping in individual patients predicts postoperative functional impairments
Wang, F., Ren, J., Cui, W., Zhou, Y. C., Yao, P., Lai, X., Pang, Y., Chen, Z., Lin, Y., & Liu, H. (2024) · Human Brain Mapping · doi:10.1002/hbm.26691 · Cited by 2
Self-citationIndividual connectivity-based parcellations reflect functional properties of human auditory cortex
Hakonen, M., Dahmani, L., Lankinen, K., Ren, J., Barbaro, J., Blazejewska, A. I., Cui, W., Kotlarz, P. L., Li, M., Polimeni, J. R., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00486 · Cited by 2
Self-citationDevelopment of areal-level individualized homologous functional parcellations in youth
Li, J., Zhang, Y., Xin, W., Xue, M., Wang, Z., Lv, S., Yang, R., Zhu, W., Li, X., Yan, T., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08509-7 · Cited by 2
GWM-HFN, a Gray-White Matter heterogeneous fusion network for functional connectomes
Hu, K., Cai, Z., Xu, Y.-E., Chen, Y., Zheng, C., & Linli, Z. (2025) · Communications Biology · doi:10.1038/s42003-025-09231-0 · Cited by 2
Individualized functional connectivity markers for motor and mood symptoms of Parkinson’s disease
Dahmani, L., Bai, Y., Zhang, W., Ren, J., Li, S., Hu, Q., Fu, X., Ma, J., Wei, W., Wang, M., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121631 · Cited by 2
Self-citationHan, K., Zhou, Y., Huang, J., Ren, J., Lu, H., Liao, X., Zhu, Y., Zhang, H., & Liu, H. (2025) · BMJ Open · doi:10.1136/bmjopen-2025-100792 · Cited by 2
Self-citationPersonalized functional network connectivity abnormalities in chronic insomnia disorder
Li, X., Zhai, Y., Qin, T., Zhang, Z., Zhao, Z., Deng, H., Yang, S., Zhang, J., & Gong, L. (2026) · Psychoradiology · doi:10.1093/psyrad/kkag001 · Cited by 2
Mamah, D. T., Chen, S., Harms, M. P., Curtis, M., & Dong, F. (2026) · Schizophrenia · doi:10.1038/s41537-026-00736-z · Cited by 2
A personalized cortical atlas for functional regions of interest
Molloy, M. F., & Osher, D. E. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00108.2023 · Cited by 2
Mamah, D. T., Chen, S., Gordon, E. M., Kandala, S., Barch, D. M., & Harms, M. P. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100386 · Cited by 2
Silverstein, S. M., Demmin, D. L., & Fradkin, S. I. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg05 · Cited by 2
Sacchet, M. D., Valenti, J. L., Keshava, P., Walsh, S. W., Smoski, M. J., Krystal, A. D., & Pizzagalli, D. A. (2025) · Journal of Mood and Anxiety Disorders · doi:10.1016/j.xjmad.2025.100126 · Cited by 2
Developmental Patterns Based Individualized Parcellation of Infant Cortical Surface
Li, G., Wang, L., Lin, W., & Shen, D. G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_8 · Cited by 2
Spectral Resolution Clustering for Brain ParcellationPreprint
Dillon, K., & Wang, Y.-P. (2018) · arXiv.org · doi:10.48550/arxiv.1810.04026 · Cited by 2
Frank, L. E., & Zeithamová, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.02.518897 · Cited by 2
Subject-specific brain functional fingerprints are preserved during general anesthesiaPreprint
Liu, H., Fu, X., Ren, J., Wei, W., Li, S., Wang, D., Wang, Y., Yang, Z., Zhang, Y., Zhang, P., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3332757/v1 · Cited by 2
Self-citationMonosynaptic connections link functionally similar regions in human cortexPreprint
Xu, R., Bush, A., Takahashi, A., Sisterson, N. D., Walton, A. E., Hutchinson, S., Kammen, A., Neudorfer, C., Kokkinos, V., Nanda, P., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.19.726279 · Cited by 2
Ren, W., Jia, C., Zhou, Y. C., Zhao, J., Wang, B., Yu, W., Li, S., Hu, Y., & Zhang, H. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.981653 · Cited by 1
Distinct neural pathway and its information flow for blind individual's Braille reading
Wang, R., Gong, J., Zhao, C., Xu, Y., & Hong, B. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120852 · Cited by 2
Self-citationHu, D., Han, K., Cheng, J., & Li, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72069-7_9 · Cited by 1
Chapter 9. Translational Neurobiological Approaches to Precision Medicine for Fear and PTSD
Harnett, N. G., Seligowski, A. V., Pine, D. S., & Ressler, K. J. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg09 · Cited by 1
Gao, C., Wu, X., Ma, L., Li, D., Wang, Y., Guo, C., Li, W., Wang, H., Chu, C., Madsen, K. H., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121399 · Cited by 1
Cyr, C., Holubecki, A. M., Shi, L., Lakshman, M., Salvo, J. J., Anderson, N. L., Kragel, J. E., Lurie, S. M., Voss, J. L., Kokkinos, V., et al. (2026) · Journal of Neurophysiology · doi:10.1152/jn.00589.2025 · Cited by 1
Mapping Functional Brain Organization Using Artificial Intelligence
Zhu, T., Mohapatra, S., Tan, S., Ouyang, M., & Huang, H. (2025) · Chemical & Biomedical Imaging · doi:10.1021/cbmi.5c00092 · Cited by 1
Yang, C., Wang, P., Zhu, Z., Wu, H., Su, Q., Zhu, S., Shao, Y., Lin, H., & Biswal, B. B. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03726-4 · Cited by 1
Yu, M., Rao, B., Zhao, W., Feng, X., Li, H., Zhao, J., Zhou, Y., Cheng, S., Xu, Z., & Xu, H. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-07969-3 · Cited by 2
Liu, Q., Zhang, J., Duan, X., Zhang, P., Yang, Y., Yao, G., Jiaerheng, B., Shou, X., Yong He, A., Han, K., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03596-2 · Cited by 1
Self-citationIndividualized mapping of functional brain networks in older adulthood
Hughes, C. S., Krendl, A. C., French, R. C., Risacher, S. L., Wu, Y., Saykin, A. J., & Betzel, R. F. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1285 · Cited by 1
Chen, M., Chen, T., Zhuang, Y., Cao, C., (23775), J. Z., Liu, T., Zhang, L., & Zhu, D. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122037 · Cited by 3
Xu, N., Xing, Y., Li, A., Liu, S., Gao, J., Liu, X., Xie, W., Guo, N., Chen, Y., Sun, X., et al. (2026) · Brain stimulation · doi:10.1016/j.brs.2026.103142 · Cited by 1
Self-citationBillot, A., Jhingan, N., Varkanitsa, M., Wolna, A., Shain, C., Blank, I., Ryskin, R. A., Kiran, S., & Fedorenko, E. G. (2026) · Nature Communications · doi:10.1038/s41467-026-76598-x · Cited by 6
Scanning for Justice With Functional Magnetic Resonance Imaging
Yarnell, S. C., Westphal, A., & Ross, D. A. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.06.014 · Cited by 1
PRAGMA: Interactively Constructing Functional Brain Parcellations
Bayrak, R. G., Hoang, N., Hansen, C. B., Chang, C. E., & Berger, M. (2020) · Visualization · doi:10.1109/vis47514.2020.00016 · Cited by 1
Fotiadis, P., & Bassett, D. S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00020-8 · Cited by 1
Guo, Y., Ren, J., Cui, W., Dahmani, L., Wang, D., Fu, X., Li, M., Li, S., Zhang, Y., Lin, X., et al. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14404 · Cited by 1
Self-citationHuang, J., Ren, J., Xie, W., Pan, R., Xu, N., & Liu, H. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-081847 · Cited by 1
Self-citationLi, M., Lebois, L. A. M., Ridgewell, C., Palermo, C. A., Winternitz, S., Liu, H., Kaufman, M. L., & Shinn, A. K. (2024) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.06.009 · Cited by 1
Self-citationZhang, A., Yao, C., Zhang, Q., Zhao, Z., Qu, J., Lui, S., Zhao, Y., & Gong, Q. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1448145 · Cited by 1
Chapter 2. The Future Of Precision Transcranial Magnetic Stimulation In Psychiatry
Philip, N. S., Zandvakili, A., & Berlow, Y. A. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg02 · Cited by 1
Connectional differences between humans and macaques in the MT+ complex
Ruan, J., Yuan, Y., Qiao, Y., Qiu, M., Dong, X., Cui, Y. J., Wang, J., & Liu, N. (2024) · iScience · doi:10.1016/j.isci.2024.111617 · Cited by 2
Dynamic Changes in Brain Networks of Alzheimer's Disease Based on Co-Activation Patterns
Wang, M., Ma, Y., Wu, J., Funahashi, S., Liu, T., & Yan, T. (2024) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei64163.2024.10906206 · Cited by 1
Deep learning-based embedding of functional connectivity profiles for precision functional mapping
Tu, J. C., Kim, J., Lu, C., Luckett, P. H., Adeyemo, B., Shimony, J. S., Elison, J. T., Eggebrecht, A. T., & Wheelock, M. D. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.129 · Cited by 5
Connectivity-Driven Parcellation Methods for the Human Cerebral CortexPreprint
Arslan, S. (2017) · arXiv (Cornell University) · doi:10.25560/54760 · Cited by 1
Data driven methods for characterizing individual differences in brain MRI
Kumar, K. (2018) · Espace ÉTS (ETS) · doi:10.13140/rg.2.2.12193.71525 · Cited by 1
Hua, L., Gao, F., Xia, X., Guo, Q., Zhao, Y., & Yuan, Z. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.14.516364 · Cited by 1
Precision Neuromodulation with Real-Time Brain Decoding for Working Memory EnhancementPreprint
Khan, A., Li, H., Blaine, C., Grier, J., Hammet, E., Figueroa, A., Garcia, S., Duprat, R., Reber, J., Deluisi, J. A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.27.662056 · Cited by 1
Area 55b forms a cortical transition zone for sensorimotor-semantic transformation in human languagePreprint
Wu, G., Wang, X., Lyu, B., & Du, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-9934100/v1 · Cited by 1
Self-citationOberman, L. M., Penafiel, A. I., Dieterich, R., Phan, C. T., Chou, Y.-Y., Pham, D. L., Adamson, M. M., Hines, C. E., Rezaee, Z., Deng, Z.-D., et al. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1605157
Functional Imaging: Magnetic Resonance Imaging
Bandettini, P. A. (2016) · Springer eBooks · doi:10.1007/978-1-4614-6434-1_150-1
Neuroimaging in 2015: a turning point?
Corvin, A. P., & O’Donovan, M. C. (2016)
Sohn, W. S., Lee, T. Y., Yoo, K., Kim, M., Yun, J., Hur, J., Yoon, Y. B., Seo, S. W., Na, D. L., Jeong, Y., et al. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00238
Task-Based and Resting-State Functional MRI in DLGG
Krainik, A., & Cochereau, J. (2017) · Springer eBooks · doi:10.1007/978-3-319-55466-2_19
Individual Variation in Brain Network Topology Predicts Emotional IntelligencePreprint
George, L., Ivy, L., Guimond, S., Olivia, L., Neeraj, T., Dost, Ö., Shaun, E., Kathryn, L., Matcheri, K., & Roscoe, B. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/275768
2nd European Conference on Brain Stimulation in Psychiatry
Palm, U. (2018) · Nervenheilkunde · doi:10.1055/s-0038-1637822
Individual heterogeneity in the functional network topography of medial prefrontal cortexPreprint
Toro‐Serey, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17605/osf.io/4x6z8
Madole, J. W., Ritchie, S. J., Cox, S. R., Buchanan, C. R., Hernández, M. V., Maniega, S. M., Wardlaw, J. M., Harris, M. A., Bastin, M. E., Deary, I. J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.13.875559
Shu, S., Qin, L., Yin, Y., Han, M., Cui, W., & Gao, J. (2020) · Human Brain Mapping · doi:10.1002/hbm.24937
Concepts and Principles of Clinical Functional Magnetic Resonance Imaging
Holmes, A. J., & MacDonald, A. W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781316995808.016
Self-citationIndividualized Neuromarkers Track Multiple Symptom Domains in Early-Onset Schizophrenia
Fan, Y., Li, H., Li, L., Wei, S., Wang, C., & Huafu, C. (2020) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip51612.2020.9317400
Examining the Heterogeneity in Schizophrenia Using Top-Down and Bottom-Up Approaches
Shahab, S. (2017) · TSpace (University of Toronto)
Functional Imaging: Magnetic Resonance Imaging
Bandettini, P. A. (2016) · Springer eBooks · doi:10.1007/978-1-4939-3474-4_150
Experimental and Biological Approaches
(2020) · Cambridge University Press eBooks · doi:10.1017/9781316995808.011
Diehl, M. M., Lempert, K. M., Parr, A. C., Ballard, I. C., Steele, V. R., & Smith, D. V. (2018) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/yvds9 · Cited by 2
Gao, C., Wu, X., Wang, Y., Li, G., Chu, C., Madsen, K. H., & Fan, L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.19.500596
Zhang, Y., Fan, L., Jiang, T., Dagher, A., & Bellec, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.30.510241
Whitman, E. T., Knodt, A. R., Elliott, M. L., Abraham, W. C., Cheyne, K. R., Hogan, S., Ireland, D. R., Keenan, R. J., Leung, J., Melzer, T. R., et al. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad109
Yan, C., Chen, X., Lu, B., Wang, Y., Li, X., Wang, Z., Li, H., Liao, Y., Blumberger, D. M., Castellanos, F. X., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2758676/v1
Self-citationLv, X., Funahashi, S., Li, C., & Wu, J. (2023) · Communications Biology · doi:10.1038/s42003-023-04804-3
How Can I Analyze Large-Scale Intrinsic Functional Networks with iEEG?
Kucyi, A., & Sadaghiani, S. (2023) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-031-20910-9_33
Namaky, N., Swearingen, H. R., Winter, J., Bozzay, M. L., Primack, J. M., Philip, N. S., & Barredo, J. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-070654
Dahmani, L., Bai, Y., Zhang, W., Ren, J., Li, S., Hu, Q., Fu, X., Ma, J., Wei, W., Wang, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.31.578238
Self-citationfMRI-based spatio-temporal parcellations of the human brain
Ling, Q., Liu, A., Li, Y., McKeown, M. J., & Chen, X. (2024) · Current Opinion in Neurology · doi:10.1097/wco.0000000000001280
Individualized Cortical Parcellation Using Masked Semi-supervised Graph Neural Network
Yang, Z., & Yan, T. (2023) · ICME International Conference on Complex Medical Engineering (CME) · doi:10.1109/cme60059.2023.10565404
Plachti, A., Balajoo, S. M., & Genon, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-820480-1.00192-3
Chapter 10. Latent Variable–Based Predictive And Explanatory Disease Models
Paulus, M. P. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg10
(2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.bm01
Da, C. M., Ribeiro, F., Felger, J. C., Treadway, M. T., Goldsmith, D. R., Haroon, E., & Miller, A. H. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg06
(2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.fm01
Chapter 7. Pharmacogenetic Testing: A Tool for Precision Prescribing in Psychiatry
Bousman, C., Forbes, M., & Dunlop, B. W. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg07
Chapter 8. Treatment Prediction Biomarkers For Major Depressive Disorder
Sacchet, M. D., Webb, C. A., & Pizzagalli, D. A. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg08
Chapter 3. Neural Mechanisms Of Bipolar Disorder: Toward Personalized Markers of Future Illness Risk
Phillips, M. L. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg03
Chapter 11. Computational Cognitive Methods For Precision Psychiatry
Huys, Q. J. M. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg11
Vinogradov, S., Mishra, J., Nahum, M., & Ramsay, I. S. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg04
(2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.index
Chapter 13. Moving From Precision to Personalized Psychiatry: Clinical Perspectives on the New Era
Perna, G., & Nemeroff, C. B. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg13
Chapter 1. A Neural Circuit–Informed Taxonomy For Precision Psychiatry
Hack, L. M., Ball, T. M., & Williams, L. M. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg01
Ross, D. A., Cooper, J. J., Arbuckle, M. R., Walker, A. E., & Travis, M. J. (2021) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9781615379118.lg14
Aghamoosa, S., Sims, S. A., Chen, A. A., Caulfield, K. A., Lopez, J. W., Rbeiz, K. S., Fleischmann, H. H., Horn, O., Antonucci, M. U., Revuelta, G. J., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5067527
Somato-cognitive action network in laryngeal and focal hand dystonia sensorimotor dysfunctionPreprint
Wang, Y., Huynh, B. P., Ren, J., Chen, M., Zhang, W., Hu, D., Li, S., Liu, H., & Kimberley, T. J. (2025) · medRxiv · doi:10.1101/2025.02.21.25322612
Self-citationGWM-HFN: A Novel Gray-White Matter Heterogeneous Fusion Network for Functional ConnectomesPreprint
Hu, K., Cai, Z., Xu, Y.-E., Chen, Y., Zheng, C.-C., & Linli, Z.-Q. (2025) · medRxiv · doi:10.1101/2025.03.05.25323471
Functional MRI from a Big Data Perspective
Xia, M., & Yong He, A. (2025) · Neuromethods · doi:10.1007/978-1-0716-4438-6_31
Supervised brain node and network construction under voxel-level functional imaging
Xu, W., Wang, S., Gao, S., Tian, X., Tan, C., Shen, X., Luo, W., Constable, R. T., Li, T., & Zhao, Y. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.56
Li, M., Yue, W., Yu, C., Wang, K., Wang, M., Li, Z., Zhang, P., Zhang, L., Kuang, W., Hu, Y., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-098155
Self-citationBolton, T. A. W., Schöttner, M., Patel, J. H., Fluhr, H., Alemán‐Gómez, Y., & Hagmann, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.20.671319 · Cited by 1
Current limitations in functional connectivity assessment: suggestions for analysis improvements
Xu, T., Bayrak, R. G., Fischbach, A. K., Bridgeford, E., Vogelstein, J. T., Noble, S., & Anteraper, S. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-19099-5.00002-7
Exploring functional connectivity using graph theoretical analysis
Jiang, T. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-19099-5.00006-4
Pierson, C. J., Khodaparast, N., McWade, M. A., Kuo, Y., Houghton, D. C., Rodriguez, S. L., Korschgen, V. L., Cunningham, K. A., & Wilkes, D. M. (2025) · Trials · doi:10.1186/s13063-025-09171-4
Clinical patterns in a neuroimaging-based predictive model of self-reported dissociation
Purcell, J. B., Ren, B., Palermo, C. A., Bair, Z. A., Marr, M. C., Modell, R. L., Pan, X., Robinson, M. A., Shanahan, M. E., Swee, M. B., et al. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.10.045
Zhao, J., Xu, Y., Cui, Z., Li, H., Sun, L., Liang, X., Han, M., Zeng, Z., Li, Q., Zhao, T., et al. (2026) · Nature Communications · doi:10.1101/2025.11.18.689151 · Cited by 1
Reproducible and Individualized Striatal Parcellation Based on Multi-Level Contrastive Learning
Jiang, Y., Li, Y., Chen, X., Xie, C., & Liu, A. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252680
Metadata
- Authors
- Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., Langs, G., Pan, R., Qian, T., Li, K., Baker, J. T., Stufflebeam, S. M., Wang, K., Wang, X., Hong, B., & Liu, H.
- Affiliations
- Harvard University (US)
- Beth Israel Deaconess Medical Center (US)
- Massachusetts Institute of Technology (US)
- Capital Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Beijing Institute for Brain Disorders (CN)
- Tsinghua University (CN)
- Corresponding author
- Hong, Bo
- Liu, Hesheng
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2015-11-09
- Volume
- 18
- Issue
- 12
- Pages
- 1853-1860
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC4661084
- OpenAlex
- W2212967118
- Cited by
- 527
- References
- 67
- Citations by year
- 2026: 42 · 2025: 47 · 2024: 54 · 2023: 48 · 2022: 52 · 2021: 65 · 2020: 44 · 2019: 44 · 2018: 46 · 2017: 51 · 2016: 33 · 2015: 1
- FWCI
- 26.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (5u54mh091657-05, 5k23mh104515-05)
- National Institute of Mental Health (1U54 MH091657, U54-MH091657, K23-MH104515, K01MH099232, P50MH106435)
- National Institute of Neurological Disorders and Stroke (K23NS083741, K25NS069805, R01NS091604, R01NS069696)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015902)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD067312)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- cortical parcellation, resting-state networks, interindividual variability, hemispheric lateralization, personalized medicine, task-based fMRI, reproducibility
- MeSH terms
- Adolescent, Adult, Brain Mapping, Cerebral Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Young Adult
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
