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Genetic and symptomatic risks associated with longitudinal brain morphometry in bipolar disorder

Nature Mental Health volume 2, pages 209–217 (2024)Cite this article

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Abstract

Offspring of parents with bipolar disorder (BD offspring) with subthreshold symptoms are particularly vulnerable to future disease onset, yet most of them remain clinically unaffected until adulthood, implicating potential compensatory processes. This longitudinal study (average 6-year follow-up) determined the brain surface features related to combined BD genetic and symptomatic risks, and tested whether the identified features related to mood disorder onset at follow-up. We found at baseline young BD offspring with subthreshold symptoms (N = 49, age 17.4 ± 5.9 years) showed higher cortical thickness in widespread regions than non-BD offspring with subthreshold symptoms (N = 47, age 16.9 ± 4.1 years), while those regions showed reduced cortical thickness in patients with BD (N = 51, age 17.7 ± 1.5 years) than healthy controls (N = 72, age 16.6 ± 3.3 years). Among those regions, the frontotemporal cortex showed reduced baseline cortical thickness among individuals who developed mood disorders at follow-up, compared with those who remained undiagnosed. Our findings provide important evidence on the brain surface features related to BD risk and potential compensatory processes.

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Fig. 1: Between-group differences of regional cortical thickness at baseline (core analyses).
Fig. 2: Between-group differences of regional cortical thickness at baseline (other complementary analyses).
Fig. 3: Baseline cortical thickness differences between the converted and nonconverted participants.

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Data availability

The data included in this work are available in Supplementary Information. Further inquiry about the data can be addressed to the corresponding author (K.L., klin@gzhmu.edu.cn).

Code availability

MRI images were preprocessed using Freesurfer v. 7.2 (https://surfer.nmr.mgh.harvard.edu/). Surface registration utilized Workbench v. 1.5.0 (ref. 58). The ANCOVA model was constructed using Brainstat v. 0.4.2 (https://github.com/MICA-MNI/BrainStat). Parcel-wise significance testing was performed with BrainSmash v. 0.11.0 (ref. 62). MRI visualizations were generated using BrainSpace v. 0.1.10 (ref. 60).

References

  1. Birmaher, B. et al. Lifetime psychiatric disorders in school-aged offspring of parents with bipolar disorder: the Pittsburgh Bipolar Offspring study. Arch. Gen. Psychiatry 66, 287–296 (2009).

    PubMed  PubMed Central  Google Scholar 

  2. Mesman, E., Nolen, W. A., Reichart, C. G., Wals, M. & Hillegers, M. H. The Dutch bipolar offspring study: 12-year follow-up. Am. J. Psychiatry 170, 542–549 (2013).

    PubMed  Google Scholar 

  3. Duffy, A., Goodday, S., Keown-Stoneman, C. & Grof, P. The emergent course of bipolar disorder: observations over two decades from the Canadian high-risk offspring cohort. Am. J. Psychiatry 176, 720–729 (2019).

    PubMed  Google Scholar 

  4. Hafeman, D. M. et al. Assessment of a person-level risk calculator to predict new-onset bipolar spectrum disorder in youth at familial risk. JAMA Psychiatry 74, 841–847 (2017).

    PubMed  PubMed Central  Google Scholar 

  5. Li, X. et al. Integrity of the uncinate fasciculus is associated with the onset of bipolar disorder: a 6-year followed-up study. Transl. Psychiatry 11, 111 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Birmaher, B. et al. Psychiatric disorders in preschool offspring of parents with bipolar disorder: the Pittsburgh Bipolar Offspring Study (BIOS). Am. J. Psychiatry 167, 321–330 (2010).

    PubMed  PubMed Central  Google Scholar 

  7. Hillegers, M. H. et al. Five‐year prospective outcome of psychopathology in the adolescent offspring of bipolar parents. Bipolar Disord. 7, 344–350 (2005).

    PubMed  Google Scholar 

  8. Zhang, W. et al. Individual prediction of symptomatic converters in youth offspring of bipolar parents using proton magnetic resonance spectroscopy. Eur. Child. Adolesc. Psychiatry 30, 55–64 (2021).

    PubMed  Google Scholar 

  9. Roberts, G. et al. Abnormalities in left inferior frontal gyral thickness and parahippocampal gyral volume in young people at high genetic risk for bipolar disorder. Psychol. Med. 46, 2083–2096 (2016).

    CAS  PubMed  Google Scholar 

  10. De Zwarte, S. M. et al. The association between familial risk and brain abnormalities is disease specific: an ENIGMA-relatives study of schizophrenia and bipolar disorder. Biol. Psychiatry 86, 545–556 (2019).

    PubMed  PubMed Central  Google Scholar 

  11. Hanford, L. C., Nazarov, A., Hall, G. B. & Sassi, R. B. Cortical thickness in bipolar disorder: a systematic review. Bipolar Disord. 18, 4–18 (2016).

    PubMed  Google Scholar 

  12. Hanford, L. C., Hall, G. B., Minuzzi, L. & Sassi, R. B. Gray matter volumes in symptomatic and asymptomatic offspring of parents diagnosed with bipolar disorder. Eur. Child. Adolesc. Psychiatry 25, 959–967 (2016).

    PubMed  Google Scholar 

  13. Hanford, L. C., Sassi, R. B., Minuzzi, L. & Hall, G. B. Cortical thickness in symptomatic and asymptomatic bipolar offspring. Psychiatry Res. Neuroimaging 251, 26–33 (2016).

    PubMed  Google Scholar 

  14. Lin, K. et al. A multi-dimensional and integrative approach to examining the high-risk and ultra-high-risk stages of bipolar disorder. EBioMedicine 2, 919–928 (2015).

    PubMed  PubMed Central  Google Scholar 

  15. Roberts, G. et al. Accelerated cortical thinning and volume reduction over time in young people at high genetic risk for bipolar disorder. Psychol. Med. 52, 1344–1355 (2022).

    CAS  PubMed  Google Scholar 

  16. Lin, K. et al. Illness, at-risk and resilience neural markers of early-stage bipolar disorder. J. Affect. Disord. 238, 16–23 (2018).

    PubMed  Google Scholar 

  17. Wiggins, J. L. et al. Neural markers in pediatric bipolar disorder and familial risk for bipolar disorder. J. Am. Acad. Child Adolesc. Psychiatry 56, 67–78 (2017).

    PubMed  Google Scholar 

  18. Kunz, L. et al. Reduced grid-cell–like representations in adults at genetic risk for Alzheimer’s disease. Science 350, 430–433 (2015).

    CAS  PubMed  ADS  Google Scholar 

  19. Papanastasiou, E. et al. Examination of the neural basis of psychoticlike experiences in adolescence during reward processing. JAMA Psychiatry 75, 1043–1051 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. Pereira, J. B. et al. Assessment of cortical degeneration in patients with Parkinson’s disease by voxel‐based morphometry, cortical folding, and cortical thickness. Hum. Brain Mapp. 33, 2521–2534 (2012).

    PubMed  ADS  Google Scholar 

  21. Winkler, A. M. et al. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage 53, 1135–1146 (2010).

    PubMed  Google Scholar 

  22. Lerch, J. P. & Evans, A. C. Cortical thickness analysis examined through power analysis and a population simulation. NeuroImage 24, 163–173 (2005).

    PubMed  Google Scholar 

  23. Papmeyer, M. et al. Cortical thickness in individuals at high familial risk of mood disorders as they develop major depressive disorder. Biol. Psychiatry 78, 58–66 (2015).

    PubMed  Google Scholar 

  24. Bertocci, M. A. et al. Clinical, cortical thickness and neural activity predictors of future affective lability in youth at risk for bipolar disorder: initial discovery and independent sample replication. Mol. Psychiatry 24, 1856–1867 (2019).

    PubMed  Google Scholar 

  25. Li, W. et al. Morphological abnormalities in youth with bipolar disorder and their relationship to clinical characteristics. J. Affect. Disord. 338, 312–320 (2023).

    PubMed  Google Scholar 

  26. Cattarinussi, G., Di Giorgio, A., Wolf, R. C., Balestrieri, M. & Sambataro, F. Neural signatures of the risk for bipolar disorder: a meta‐analysis of structural and functional neuroimaging studies. Bipolar Disord. 21, 215–227 (2019).

    PubMed  Google Scholar 

  27. Cattarinussi, G., Kubera, K. M., Hirjak, D., Wolf, R. C. & Sambataro, F. Neural correlates of the risk for schizophrenia and bipolar disorder: a meta-analysis of structural and functional neuroimaging studies. Biol. Psychiatry 92, 375–384 (2022).

    PubMed  Google Scholar 

  28. Townsend, J. & Altshuler, L. L. Emotion processing and regulation in bipolar disorder: a review. Bipolar Disord. 14, 326–339 (2012).

    PubMed  Google Scholar 

  29. Whalley, H. C. et al. Review of functional magnetic resonance imaging studies comparing bipolar disorder and schizophrenia. Bipolar Disord. 14, 411–431 (2012).

    PubMed  Google Scholar 

  30. Lapomarda, G. et al. Out of control: an altered parieto-occipital-cerebellar network for impulsivity in bipolar disorder. Behav. Brain Res. 406, 113228 (2021).

    PubMed  Google Scholar 

  31. Hibar, D. P. et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol. Psychiatry 23, 932–942 (2018).

    CAS  PubMed  Google Scholar 

  32. McWhinney, S. R. et al. Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness—ENIGMA study in 2436 individuals. Bipolar Disord. 24, 509–520 (2022).

    CAS  PubMed  Google Scholar 

  33. Hajek, T. et al. Brain structural signature of familial predisposition for bipolar disorder: replicable evidence for involvement of the right inferior frontal gyrus. Biol. Psychiatry 73, 144–152 (2013).

    PubMed  Google Scholar 

  34. Sugranyes, G. et al. Brain structural trajectories in youth at familial risk for schizophrenia or bipolar disorder according to development of psychosis spectrum symptoms. J. Child Psychol. Psychiatry 62, 780–789 (2021).

    PubMed  Google Scholar 

  35. Lotter, L. D. et al. Human cortex development is shaped by molecular and cellular brain systems. Preprint at bioRxiv https://doi.org/10.1101/2023.05.05.539537 (2023).

  36. Piccolo, L. R. et al. Age-related differences in cortical thickness vary by socioeconomic status. PLoS ONE 11, e0162511 (2016).

  37. Bethlehem, R. A. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Baldessarini, R. J. et al. Age at onset versus family history and clinical outcomes in 1,665 international bipolar-I disorder patients. World Psychiatry 11, 40–46 (2012).

    PubMed  PubMed Central  Google Scholar 

  39. Savitz, J. B., Price, J. L. & Drevets, W. C. Neuropathological and neuromorphometric abnormalities in bipolar disorder: view from the medial prefrontal cortical network. Neurosci. Biobehav. Rev. 42, 132–147 (2014).

    CAS  PubMed  Google Scholar 

  40. van Staalduinen, E. K. & Zeineh, M. M. Medial temporal lobe anatomy. Neuroimaging Clin. N. Am. 32, 475–489 (2022).

    PubMed  Google Scholar 

  41. Altshuler, L. L. et al. An MRI study of temporal lobe structures in men with bipolar disorder or schizophrenia. Biol. Psychiatry 48, 147–162 (2000).

    CAS  PubMed  Google Scholar 

  42. Schneider, M. R., DelBello, M. P., McNamara, R. K., Strakowski, S. M. & Adler, C. M. Neuroprogression in bipolar disorder. Bipolar Disord. 14, 356–374 (2012).

    PubMed  Google Scholar 

  43. Abé, C., Liberg, B., Klahn, A. L., Petrovic, P., & Landén, M. Mania-related effects on structural brain changes in bipolar disorder—a narrative review of the evidence. Mol. Psychiatry 28, 2674–2682 (2023).

  44. Förster, K., Horstmann, R. H., Dannlowski, U., Houenou, J., & Kanske, P. Progressive grey matter alterations in bipolar disorder across the life span—a systematic review. Bipolar Disord. 25, 443–456 (2023).

  45. Förster, K. et al. Association of hospitalization with structural brain alterations in patients with affective disorders over nine years. Transl. Psychiatry 13, 170 (2023).

    PubMed  PubMed Central  Google Scholar 

  46. Lin, K. et al. Resting-state fMRI signals in offspring of parents with bipolar disorder at the high-risk and ultra-high-risk stages and their relations with cognitive function. J. Psychiatr. Res. 98, 99–106 (2018).

    PubMed  Google Scholar 

  47. Hamilton, M. A. X. The assessment of anxiety states by rating. Br. J. Med. Psychol. 32, 50–55 (1959).

    CAS  PubMed  Google Scholar 

  48. Hamilton, M. A. X. A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23, 56–62 (1960).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Youngstrom, E. A., Gracious, B. L., Danielson, C. K., Findling, R. L. & Calabrese, J. Toward an integration of parent and clinician report on the Young Mania Rating Scale. J. Affect. Disord. 77, 179–190 (2003).

    PubMed  Google Scholar 

  50. Overall, J. E. & Gorham, D. R. The brief psychiatric rating scale. Psychol. Rep. 10, 799–812 (1962).

    Google Scholar 

  51. Kaufman, J. et al. Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): initial reliability and validity data. J. Am. Acad. Child Adolesc. Psychiatry 36, 980–988 (1997).

    CAS  PubMed  Google Scholar 

  52. Maxwell, M. E. Manual for the Family Interview for Genetic Studies (FIGS) (National Institute of Mental Health, 1992).

  53. Endicott, J., Spitzer, R. L., Fleiss, J. L. & Cohen, J. The Global Assessment Scale: a procedure for measuring overall severity of psychiatric disturbance. Arch. Gen. Psychiatry 33, 766–771 (1976).

    CAS  PubMed  Google Scholar 

  54. Correll, C. U. et al. Differentiation in the preonset phases of schizophrenia and mood disorders: evidence in support of a bipolar mania prodrome. Schizophr. Bull. 33, 703–714 (2007).

    PubMed  PubMed Central  Google Scholar 

  55. Manjón, J. V., Coupé, P., Martí‐Bonmatí, L., Collins, D. L. & Robles, M. Adaptive non‐local means denoising of MR images with spatially varying noise levels. J. Magn. Reason. Imaging 31, 192–203 (2010).

    Google Scholar 

  56. Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).

    PubMed  Google Scholar 

  57. Van Essen, D. C., Glasser, M. F., Dierker, D. L., Harwell, J. & Coalson, T. Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22, 2241–2262 (2012).

    PubMed  Google Scholar 

  58. workbench. GitHub https://github.com/Washington-University/workbench (2021).

  59. Schaefer, A. et al. Local–global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).

    PubMed  Google Scholar 

  60. BrainSpace. GitHub https://github.com/MICA-MNI/BrainSpace (2022).

  61. Worsley, K. J. et al. SurfStat: a Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory. NeuroImage 47, S102 (2009).

  62. brainsmash. GitHub https://github.com/murraylab/brainsmash (2021).

Download references

Acknowledgements

This project was supported by grants from the National Natural Science Foundation of China (82171531, 81671347), the Science and Technology Program of Guangzhou, China (202007030012) and the Guangzhou Municipal Key Discipline in Medicine (2021–2023) to K.L., grant from the National Natural Science Foundation of China (21921004) to J. Wang, grant from the National Natural Science Foundation of China (82102031) to J. Wu, grant from the National Natural Science Foundation of China (82371558) to R.S., and grant from the Guangzhou Health science and Technology general guidance project (no. 20221A011052) to W.L.

Author information

Author notes

  1. These authors contributed equally: Weicong Lu, Jinfeng Wu.

Authors and Affiliations

  1. Department of Affective Disorder, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China

    Weicong Lu, Robin Shao, Ruoxi Zhang, Xiaoyue Li, Jiehua Kong, Xinhe Tian, Yanling Gao, Guiyun Xu & Kangguang Lin

  2. Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, People’s Republic of China

    Weicong Lu, Robin Shao, Wenjin Zou, Ruoxi Zhang, Xiaoyue Li, Jiehua Kong, Xinhe Tian, Yanling Gao, Guiyun Xu & Kangguang Lin

  3. Songjiang Research Institute, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Jinfeng Wu & Jie Wang

  4. Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, People’s Republic of China

    Jinfeng Wu, Danhao Zheng & Jie Wang

  5. Department of Radiology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China

    Wenjin Zou

  6. Department of Rehabilitation Sciences, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

    Sonata Yau

  7. Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

    Sonata Yau

  8. Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ontario, Canada

    Benjamin Goldstein

  9. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada

    Benjamin Goldstein

  10. School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong Province, People’s Republic of China

    Kwok-Fai So & Kangguang Lin

  11. Ministry of Education Joint International Research Laboratory of CNS Regeneration, Jinan University, Guangzhou, Guangdong Province, People’s Republic of China

    Kwok-Fai So

  12. Institute of Neuroscience and Brain Diseases, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, People’s Republic of China

    Jie Wang

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Contributions

W.L., R.S., G.X., S.Y., B.G., K.-F.S., J. Wang and K.L. conceptualized and designed the study. W.L., W.Z., R.Z., X.L., J.K., D.Z., X.T. and Y.G. performed data collection. J. Wu, R.S., J. Wang and K.L. analysed data and interpreted findings. W.L., J. Wu, R.S., J. Wang and K.L. drafted the manuscript. All authors reviewed and evaluated the manuscript. All authors contributed substantially to the intellectual contents of the manuscript.

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Correspondence to Robin Shao, Jie Wang or Kangguang Lin.

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Nature Mental Health thanks Katharina Foerster, Fabio Sambataro and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Lu, W., Wu, J., Shao, R. et al. Genetic and symptomatic risks associated with longitudinal brain morphometry in bipolar disorder. Nat. Mental Health 2, 209–217 (2024). https://doi.org/10.1038/s44220-023-00194-x

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