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Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients


Substantial clinical heterogeneity and comorbidity inherent amongst mental disorders limit the identification of neuroimaging biomarkers that can reliably track clinical symptoms. Strategies that enable generation of meaningful and replicable neurobiological markers at the individual level will push the field of neuropsychiatry forward in developing efficacious personalized treatment. The current study included 142 adult patients with a primary diagnosis of schizophrenia (SCZ), bipolar (BP), or attention deficit/hyperactivity disorder (ADHD), and 67 patient ratings across four behavioral measures. Using functional connectivity derived from a personalized fMRI approach, we identified several candidate imaging markers related to dimensional phenotypes across disorders, assessed the internal and external generalizability of these markers, and compared the probability of replicating findings across datasets using individual and group-averaged defined functional regions. We identified subject-specific connections related to three different clinical domains (attention deficit, appetite-energy, psychosis-positive) in a discovery dataset. Importantly, these connectivity biomarkers were robust and were reproduced in an independent validation dataset. For markers related to neurovegetative symptoms (attention deficit, appetite-energy symptoms), the brain connections involved showed similar connectivity patterns across the different diagnoses. However, psychosis-positive symptoms were associated with connections of varying strength across disorders. Finally, we found that markers for symptom domains were replicable for individually-specified connections, but not for group template-derived connections. Our personalized strategies allowed us to identify meaningful and generalizable imaging markers for symptom domains in patients who exhibit high levels of heterogeneity. These biomarkers may shed new light on the connectivity underpinnings of psychiatric symptoms and lead to personalized interventions.

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Fig. 1: Workflow for identifying robust imaging markers of clinical symptom domains.
Fig. 2: Identification and validation of symptom-related imaging makers.
Fig. 3: Replicable markers related to clinical symptom domains.
Fig. 4: Marker for psychosis-positive symptoms showed different connectivity strengths across diagnoses.
Fig. 5: Imaging markers derived from group-level functional regions did not generalize to new data.


  1. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Dysconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev. 2011;35:1110–24.

    Article  PubMed  Google Scholar 

  2. Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci USA. 2009;106:1279–84.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Cole MW, Anticevic A, Repovs G, Barch D. Variable global dysconnectivity and individual differences in schizophrenia. Biol Psychiatry. 2011;70:43–50.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Downar J, Blumberger DM, Daskalakis ZJ. The neural crossroads of psychiatric illness: an emerging target for brain stimulation. Trends Cogn Sci. 2016;20:107–20.

    Article  PubMed  Google Scholar 

  5. Wang D, Li M, Wang M, Schoeppe F, Ren J, Chen H, et al. Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Mol Psychiatry. 2020;25:2119–29.

    Article  PubMed  Google Scholar 

  6. Craddock N, Owen MJ. Rethinking psychosis: the disadvantages of a dichotomous classification now outweigh the advantages. World Psychiatry. 2007;6:84–91.

    PubMed  PubMed Central  Google Scholar 

  7. Bilder RM, Sabb FW, Cannon TD, London ED, Jentsch JD, Parker DS, et al. Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience. 2009;164:30–42.

    Article  PubMed  CAS  Google Scholar 

  8. Mueller S, Wang D, Fox MD, Yeo BT, Sepulcre J, Sabuncu MR, et al. Individual variability in functional connectivity architecture of the human brain. Neuron. 2013;77:586–95.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, et al. Precision functional mapping of individual human brains. Neuron. 2017;95:791–807.e7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Li M, Wang D, Ren J, Langs G, Stoecklein S, Brennan BP, et al. Performing group-level functional image analyses based on homologous functional regions mapped in individuals. PLoS Biol. 2019;17:e2007032.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lauren AM, Lebois, Li M, Baker JT, Wolff JD, Wang D, et al. Large-scale functional brain network architecture changes associated with trauma-related dissociation. Am J Psychiatry. 2020;0:19060647.

    Google Scholar 

  12. Poldrack RA, Huckins G, Varoquaux G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry. 2020;77:534–40.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Poldrack RA, Congdon E, Triplett W, Gorgolewski KJ, Karlsgodt KH, Mumford JA, et al. A phenome-wide examination of neural and cognitive function. Sci Data. 2016;3:160110.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, et al. Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci USA. 2009;106:13040–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Li M, Dahmani L, Wang D, Ren J, Stocklein S, Lin Y, et al. Co-activation patterns across multiple tasks reveal robust anti-correlated functional networks. NeuroImage. 2021;227:117680.

    Article  PubMed  Google Scholar 

  16. Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE. Intrinsic and task-evoked network architectures of the human brain. Neuron. 2014;83:238–51.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Chen RH, Ito T, Kulkarni KR, Cole MW. The human brain traverses a common activation-pattern state space across task and rest. Brain Connect. 2018;8:429–43.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:1125–65.

    Article  PubMed  Google Scholar 

  19. Murtagh F, Legendre P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J Classif. 2014;31:274–95.

    Article  Google Scholar 

  20. Carter JD, Bizzell J, Kim C, Bellion C, Carpenter KL, Dichter G, et al. Attention deficits in schizophrenia–preliminary evidence of dissociable transient and sustained deficits. Schizophr Res. 2010;122:104–12.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Katzman MA, Bilkey TS, Chokka PR, Fallu A, Klassen LJ. Adult ADHD and comorbid disorders: clinical implications of a dimensional approach. BMC Psychiatry. 2017;17:302.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Robinson LJ, Ferrier IN. Evolution of cognitive impairment in bipolar disorder: a systematic review of cross-sectional evidence. Bipolar Disord. 2006;8:103–16.

    Article  PubMed  Google Scholar 

  23. Vasterling JJ, Brailey K, Constans JI, Sutker PB. Attention and memory dysfunction in posttraumatic stress disorder. Neuropsychology. 1998;12:125–33.

    Article  PubMed  CAS  Google Scholar 

  24. Posner MI, Rothbart MK, Vizueta N, Levy KN, Evans DE, Thomas KM, et al. Attentional mechanisms of borderline personality disorder. Proc Natl Acad Sci USA. 2002;99:16366–70.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Sha Z, Wager TD, Mechelli A, He Y. Common dysfunction of large-scale neurocognitive networks across psychiatric disorders. Biol Psychiatry. 2019;85:379–88.

    Article  PubMed  Google Scholar 

  26. Petersen SE, Posner MI. The attention system of the human brain: 20 years after. Annu Rev Neurosci. 2012;35:73–89.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349–56.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Uddin LQ, Nomi JS, Hébert-Seropian B, Ghaziri J, Boucher O. Structure and function of the human insula. J Clin Neurophysiol. 2017;34:300–6.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Lis M, Stańczykiewicz B, Liśkiewicz P, Misiak B. Impaired hormonal regulation of appetite in schizophrenia: a narrative review dissecting intrinsic mechanisms and the effects of antipsychotics. Psychoneuroendocrinology. 2020;119:104744.

    Article  PubMed  CAS  Google Scholar 

  30. Platzer M, Fellendorf FT, Bengesser SA, Birner A, Dalkner N, Hamm C, et al. The relationship between food craving, appetite-related hormones and clinical parameters in bipolar disorder. Nutrients. 2020;13:76.

  31. Simmons WK, Burrows K, Avery JA, Kerr KL, Bodurka J, Savage CR, et al. Depression-related increases and decreases in appetite: dissociable patterns of aberrant activity in reward and interoceptive neurocircuitry. Am J Psychiatry. 2016;173:418–28.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kringelbach ML. The human orbitofrontal cortex: linking reward to hedonic experience. Nat Rev Neurosci. 2005;6:691–702.

    Article  PubMed  CAS  Google Scholar 

  33. Diler RS, Daviss WB, Lopez A, Axelson D, Iyengar S, Birmaher B. Differentiating major depressive disorder in youths with attention deficit hyperactivity disorder. J Affect Disord. 2007;102:125–30.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Weiss M, Worling D, Wasdell M. A chart review study of the inattentive and combined types of ADHD. J Atten Disord. 2003;7:1–9.

    Article  PubMed  CAS  Google Scholar 

  35. Burrows T, Kay-Lambkin F, Pursey K, Skinner J, Dayas C. Food addiction and associations with mental health symptoms: a systematic review with meta-analysis. J Hum Nutr Dietetics. 2018;31:544–72.

    Article  CAS  Google Scholar 

  36. Lamme VA, Supèr H, Spekreijse H. Feedforward, horizontal, and feedback processing in the visual cortex. Curr Opin Neurobiol. 1998;8:529–35.

    Article  PubMed  CAS  Google Scholar 

  37. Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N, et al. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol. 2019;17:e3000042.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Fortin JP, Parker D, Tunç B, Watanabe T, Elliott MA, Ruparel K, et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage. 2017;161:149–70.

    Article  PubMed  Google Scholar 

  39. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23:28–38.

    Article  PubMed  CAS  Google Scholar 

  40. Boeke EA, Holmes AJ, Phelps EA. Toward robust anxiety biomarkers: a machine learning approach in a large-scale sample. Biol Psychiatry: Cogn Neurosci Neuroimaging. 2020;5:799–807.

    Google Scholar 

  41. Jin D, Zhou B, Han Y, Ren J, Han T, Liu B, et al. Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer’s disease. Adv Sci. 2020;7:2000675.

    Article  CAS  Google Scholar 

  42. Gordon EM, Laumann TO, Adeyemo B, Petersen SE. Individual variability of the system-level organization of the human brain. Cereb Cortex. 2017;27:386–99.

    PubMed  Google Scholar 

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This work was supported by Changping Laboratory and the Ministry of Science and Technology of China (2021B-01-01), National Natural Science Foundation of China grants Nos. 81790652, 81790650, and NIH grants P50MH106435, 1R01DC017991, 5K01MH111802. LD is supported by a Canadian Institutes of Health Research postdoctoral fellowship, FRN: MFE-171291. These data were obtained from the OpenfMRI database ds000030, funded by Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants).

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ML and HL conceived the study; ML and LD performed the analyses with support from YH and HL; ML, LD, DW, MW, CSH and HL wrote the manuscript. All authors commented on the manuscript.

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Correspondence to Meiyun Wang or Hesheng Liu.

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Li, M., Dahmani, L., Hubbard, C.S. et al. Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients. Neuropsychopharmacol. (2022).

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