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주요우울장애에서의 뇌연령 예측을 위한 자기공명영상 기반 머신러닝모델 연구의 고찰

A Review of Magnetic Resonance Imaging-Based Brain Age Prediction in Major Depressive Disorder

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주제분류정신과학

초록

Objectives Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Abstract

Objectives Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

참고문헌 (50)

  1. "Depressive disorder (depression)"[URL]
  2. Koo PC, "Combined cognitive, psychomotor and electrophysiological biomarkers in major depressive disorder", Eur Arch Psychiatry Clin Neurosci, 269, pp.823~832 (2019)
  3. Lorenzo EC, "Major depression and the biological hallmarks of aging", Ageing Res Rev, 83, pp.101805~ (2023)
  4. Al-Khatib Y, "Depression and metabolic syndrome : a narrative review", Cureus, 14, pp.e22153~ (2022)
  5. Zhang Y, "Depression and cardiovascular disease in elderly : current understanding", J Clin Neurosci, 47, pp.1~5 (2018)
  6. Diniz BS, "The effect of gender, age, and symptom severity in late-life depression on the risk of all-cause mortality : the BambuíCohort Study of Aging", Depress Anxiety, 31, pp.787~795 (2014)
  7. Isaev NK, "Accelerated aging and aging process in the brain", Rev Neurosci, 29, pp.233~240 (2018)
  8. Cole JH, "Brain age predicts mortality", Mol Psychiatry, 23, pp.1385~1392 (2018)
  9. Elliott ML, "Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort", Mol Psychiatry, 26, pp.3829~3838 (2021)
  10. Han LKM, "Contributing factors to advanced brain aging in depression and anxiety disorders", Transl Psychiatry, 11, pp.402~ (2021)
  11. Koutsouleris N, "Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers", Brain, 138(Pt 7), pp.2059~2073 (2015)
  12. Schnack HG, "Accelerated brain aging in schizophrenia : a longitudinal pattern recognition study", Am J Psychiatry, 173, pp.607~616 (2016)
  13. Clausen AN, "Assessment of brain age in posttraumatic stress disorder : findings from the ENIGMA PTSD and brain age working groups", Brain Behav, 12, pp.e2413~ (2022)
  14. Blake KV, "Advanced brain ageing in adult psychopathology : a systematic review and meta-analysis of structural MRI studies", J Psychiatr Res, 157, pp.180~191 (2023)
  15. Ballester PL, "Brain age in mood and psychotic disorders : a systematic review and meta-analysis", Acta Psychiatr Scand, 145, pp.42~55 (2022)
  16. Cole JH, "Predicting age using neuroimaging : innovative brain ageing biomarkers", Trends Neurosci, 40, pp.681~690 (2017)
  17. Luders E, "Estimating brain age using highresolution pattern recognition : younger brains in long-term meditation practitioners", Neuroimage, 134, pp.508~513 (2016)
  18. Bashyam VM, "MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide", Brain, 143, pp.2312~2324 (2020)
  19. Dunlop K, "Accelerated brain aging predicts impulsivity and symptom severity in depression", Neuropsychopharmacology, 46, pp.911~919 (2021)
  20. Han LKM, "Brain aging in major depressive disorder : results from the ENIGMA major depressive disorder working group", Mol Psychiatry, 26, pp.5124~5139 (2021)
  21. Han S, "The stage-specifically accelerated brain aging in never-treated first-episode patients with depression", Hum Brain Mapp, 42, pp.3656~3666 (2021)
  22. Koutsouleris N, "Accelerated brain aging in schizophrenia and beyond : a neuroanatomical marker of psychiatric disorders", Schizophr Bull, 40, pp.1140~1153 (2014)
  23. Luo Y, "Accelerated functional brain aging in major depressive disorder : evidence from a large scale fMRI analysis of Chinese participants", Transl Psychiatry, 12, pp.397~ (2022)
  24. Jha MK, "Accelerated brain aging in adults with major depressive disorder predicts poorer outcome with sertraline : findings from the EMBARC study", Biol Psychiatry Cogn Neurosci Neuroimaging, 8, pp.462~470 (2023)
  25. Besteher B, "Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging", Psychiatry Res Neuroimaging, 290, pp.1~4 (2019)
  26. Kuo CY, "Largescale structural covariance networks predict age in middle-to-late adulthood : a novel brain aging biomarker", Cereb Cortex, 30, pp.5844~5862 (2020)
  27. Ballester PL, "Gray matter volume drives the brain age gap in schizophrenia : a SHAP study", Schizophrenia(Heidelb), 9, pp.3~ (2023)
  28. Wagenmakers MJ, "BrainAge of patients with severe late-life depression referred for electroconvulsive therapy", J Affect Disord, 330, pp.1~6 (2023)
  29. Ahmed R, "Structural MRI-based measures of accelerated brain aging do not moderate the acute antidepressant response in late-life depression", Am J Geriatr Psychiatry, 30, pp.1015~1025 (2022)
  30. Christman S, "Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression", Transl Psychiatry, 10, pp.317~ (2020)
  31. Ballester PL, "Accelerated brain aging in major depressive disorder and antidepressant treatment response : a CAN-BIND report", Neuroimage Clin, 32, pp.102864~ (2021)
  32. Mai N, "Determining the effects of LLD and MCI on brain decline according to machine learning and a structural covariance network analysis", J Psychiatr Res, 126, pp.43~54 (2020)
  33. Kaufmann T, "Common brain disorders are associated with heritable patterns of apparent aging of the brain", Nat Neurosci, 22, pp.1617~1623 (2019)
  34. Hahn T, "An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling", Sci Adv, 8, pp.eabg9471~ (2022)
  35. Kolenic M, "Obesity, dyslipidemia and brain age in first-episode psychosis", J Psychiatr Res, 99, pp.151~158 (2018)
  36. Schmaal L, "Subcortical brain alterations in major depressive disorder : findings from the ENIGMA major depressive disorder working group", Mol Psychiatry, 21, pp.806~812 (2016)
  37. Schmaal L, "Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA major depressive disorder working group", Mol Psychiatry, 22, pp.900~909 (2017)
  38. Hyun JW, "SGPP : spatial Gaussian predictive process models for neuroimaging data", Neuroimage, 89, pp.70~80 (2014)
  39. Abrol A, "Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning", Nat Commun, 12, pp.353~ (2021)
  40. Li Y, "Anatomical context protects deep learning from adversarial perturbations in medical imaging", Neurocomputing(Amst), 379, pp.370~378 (2020)
  41. Ning K, "Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants", Sci Rep, 10, pp.10~ (2020)
  42. Arnone D, "State-dependent changes in hippocampal grey matter in depression", Mol Psychiatry, 18, pp.1265~1272 (2013)
  43. Hoexter MQ, "Gray matter volumes in obsessivecompulsive disorder before and after fluoxetine or cognitive-behavior therapy : a randomized clinical trial", Neuropsychopharmacol, 37, pp.734~745 (2012)
  44. Kraus C, "Serotonin and neuroplasticity-links between molecular, functional and structural pathophysiology in depression", Neurosci Biobehav Rev, 77, pp.317~326 (2017)
  45. Lu H, "Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression : secondary analysis of a randomised sham-controlled clinical trial", Dialogues Clin Neurosci, 26, pp.38~52 (2024)
  46. Nuninga JO, "Volume increase in the dentate gyrus after electroconvulsive therapy in depressed patients as measured with 7T", Mol Psychiatry, 25, pp.1559~1568 (2020)
  47. Oltedal L, "Volume of the human hippocampus and clinical response following electroconvulsive therapy", Biol Psychiatry, 84, pp.574~581 (2018)
  48. Hogstrom LJ, "The structure of the cerebral cortex across adult life : age-related patterns of surface area, thickness, and gyrification", Cereb Cortex, 23, pp.2521~2530 (2013)
  49. Liem F, "Predicting brain-age from multimodal imaging data captures cognitive impairment", Neuroimage, 148, pp.179~188 (2017)
  50. Peng H, "Accurate brain age prediction with lightweight deep neural networks", Med Image Anal, 68, pp.101871~ (2021)

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