Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies

被引:12
作者
Blake, Kimberly, V [1 ,7 ]
Ntwatwa, Ziphozihle [1 ]
Kaufmann, Tobias [2 ,3 ,4 ]
Stein, Dan J. [1 ,5 ]
Ipser, Jonathan C. [1 ]
Groenewold, Nynke A. [1 ,6 ]
机构
[1] Univ Cape Town, Neurosci Inst, Fac Hlth Sci, Dept Psychiat & Mental Hlth, Cape Town, Western Cape, South Africa
[2] Univ Tubingen, Tubingen Ctr Mental Hlth, Dept Psychiat & Psychotherapy, Tubingen, Germany
[3] Univ Oslo, Oslo Univ Hosp, Div Mental Hlth & Addict, NORMENT, Oslo, Norway
[4] Univ Oslo, Inst Clin Med, Oslo, Norway
[5] Univ Cape Town, SA MRC Unit Risk & Resilience Mental Disorders, Cape Town, Western Cape, South Africa
[6] Univ Cape Town, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, SA MRC Unit Child & Adolescent Hlth, Cape Town, Western Cape, South Africa
[7] Groote Schuur Hosp, UCT Neurosci Ctr, Anzio Rd, ZA-7925 Cape Town, Western Cape, South Africa
关键词
Biological age; Brain morphometry; Diffusion tensor imaging; Machine learning; Mental illness; BIPOLAR DISORDER; WHITE-MATTER; SCHIZOPHRENIA; AGE;
D O I
10.1016/j.jpsychires.2022.11.011
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Evidence suggests that psychopathology is associated with an advanced brain ageing process, typically mapped using machine learning models that predict an individual's age based on structural neuroimaging data. The brain predicted age difference (brain-PAD) captures the deviation of brain age from chronological age. Substantial heterogeneity between studies has introduced uncertainty regarding the magnitude of the brain-PAD in adult psychopathology. The present meta-analysis aimed to quantify structural MRI-based brain-PAD in adult psy-chotic and mood disorders, while addressing possible sources of heterogeneity related to diagnosis subtypes, segmentation method, age and sex. Clinical factors influencing brain ageing in axis 1 psychiatric disorders were systematically reviewed. Thirty-three studies were included for review. A random-effects meta-analysis revealed a brain-PAD of +3.12 (standard error = 0.49) years in psychotic disorders (n = 16 studies), +2.04 (0.10) years in bipolar disorder (n = 5), and +0.90 (0.20) years in major depression (n = 7). An exploratory meta-analysis found a brain-PAD of +1.57 (0.67) in first episode psychosis (n = 4), which was smaller than that observed in psychosis and schizophrenia (n = 10, +3.87 (0.61)). Patient mean age significantly explained heterogeneity in effect size estimates in psychotic disorders, but not mood disorders. The systematic review determined that clinical factors, such as higher symptom severity, may be associated with a larger brain-PAD in psychopathology. In conclusion, larger structural MRI-based brain-PAD was confirmed in adult psychopathology. Preliminary evidence was ob-tained that brain ageing is greater in those with prolonged duration of psychotic disorders. Accentuated brain ageing may underlie the cognitive difficulties experienced by some patients, and may be progressive in nature.
引用
收藏
页码:180 / 191
页数:12
相关论文
共 55 条
  • [1] Ad-Dabbagh Y, 2006, P 12 ANN M ORG HUM B
  • [2] Ageing of the brain
    Anderton, BH
    [J]. MECHANISMS OF AGEING AND DEVELOPMENT, 2002, 123 (07) : 811 - 817
  • [3] Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report
    Ballester, Pedro L.
    Suh, Jee Su
    Nogovitsyn, Nikita
    Hassel, Stefanie
    Strother, Stephen C.
    Arnott, Stephen R.
    Minuzzi, Luciano
    Sassi, Roberto B.
    Lam, Raymond W.
    Milev, Roumen
    Muller, Daniel J.
    Taylor, Valerie H.
    Kennedy, Sidney H.
    Frey, Benicio N.
    [J]. NEUROIMAGE-CLINICAL, 2021, 32
  • [4] Brain age in mood and psychotic disorders: a systematic review and meta-analysis
    Ballester, Pedro L.
    Romano, Maria T.
    de Azevedo Cardoso, Taiane
    Hassel, Stefanie
    Strother, Stephen C.
    Kennedy, Sidney H.
    Frey, Benicio N.
    [J]. ACTA PSYCHIATRICA SCANDINAVICA, 2022, 145 (01) : 42 - 55
  • [5] MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide
    Bashyam, Vishnu M.
    Erus, Guray
    Doshi, Jimit
    Habes, Mohamad
    Nasralah, Ilya
    Truelove-Hill, Monica
    Srinivasan, Dhivya
    Mamourian, Liz
    Pomponio, Raymond
    Fan, Yong
    Launer, Lenore J.
    Masters, Colin L.
    Maruff, Paul
    Zhuo, Chuanjun
    Volzke, Henry
    Johnson, Sterling C.
    Fripp, Jurgen
    Koutsouleris, Nikolaos
    Satterthwaite, Theodore D.
    Wolf, Daniel
    Gur, Raquel E.
    Gur, Ruben C.
    Morris, John
    Albert, Marilyn S.
    Grabe, Hans J.
    Resnick, Susan
    Bryan, R. Nick
    Wolk, David A.
    Shou, Haochang
    Davatzikos, Christos
    [J]. BRAIN, 2020, 143 : 2312 - 2324
  • [6] Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging
    Besteher, Bianca
    Gaser, Christian
    Nenadic, Igor
    [J]. PSYCHIATRY RESEARCH-NEUROIMAGING, 2019, 290 : 1 - 4
  • [7] Chen C.L., MEDRXIV
  • [8] Generalization of diffusion magnetic resonance imaging ?based brain age prediction model through transfer learning
    Chen, Chang-Le
    Hsu, Yung-Chin
    Yang, Li-Ying
    Tung, Yu-Hung
    Luo, Wen-Bin
    Liu, Chih-Min
    Hwang, Tzung-Jeng
    Hwu, Hai-Gwo
    Tseng, Wen-Yih Isaac
    [J]. NEUROIMAGE, 2020, 217
  • [9] Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression
    Christman, Seth
    Bermudez, Camilo
    Hao, Lingyan
    Landman, Bennett A.
    Boyd, Brian
    Albert, Kimberly
    Woodward, Neil
    Shokouhi, Sepideh
    Vega, Jennifer
    Andrews, Patricia
    Taylor, Warren D.
    [J]. TRANSLATIONAL PSYCHIATRY, 2020, 10 (01)
  • [10] Use of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk
    Chung, Yoonho
    Addington, Jean
    Bearden, Carrie E.
    Cadenhead, Kristin
    Cornblatt, Barbara
    Mathalon, Daniel H.
    McGlashan, Thomas
    Perkins, Diana
    Seidman, Larry J.
    Tsuang, Ming
    Walker, Elaine
    Woods, Scott W.
    McEwen, Sarah
    van Erp, Theo G. M.
    Cannon, Tyrone D.
    [J]. JAMA PSYCHIATRY, 2018, 75 (09) : 960 - 968