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. 2022 Nov 5;12(11):1850. doi: 10.3390/jpm12111850

Table 2.

Neuroimaging-based brain age studies for psychiatric disorders.

First Author
[ref.]
Year Cohort Imaging Modality ML Algorithm Main Findings
Schizophrenia and Psychosis
Koutsouleris [57] 2014 141 SZ, 104 MDD, 57B PD, 89 ARMS, 127 HCs T1WI SVR BAG: +5.5 yr in SZ, +4.0 yr in MDD, +3.1 yr in BPD, +1.7 yr in ARMS.
Schnack [58] 2016 341 SZ, 386 HCs T1WI SVR BAG: +3.36 yr in SZ, acceleration just after illness onset
Nenadic [59] 2017 45 SZ, 22 BPAD, 70 HCs T1WI RVR BAG: +2.56 yr in SZ, no significance in BPAD
Kolenic [60] 2018 120 FEP, 114 HCs T1WI RVR BAG: +2.64 yr in FES, associated with obesity
Hajek [62] 2019 43 FES, 43 HCs, 96 offspring of BPAD (48 affected, 48 unaffected), 60 HCs T1WI RVR BAG: +2.64 yr in FES, no significance in early BPAD
Chung [61] 2019 476 CHR N/A N/A BAG predicts conversion to psychosis in a univariate analysis but not in a multivariate analysis
Shahab [63] 2019 81 SZ, 53 BPAD, 91 HCs T1WI, DTI RF BAG: +7.8–8.2 yr in SZ, no significance in BPAD
Kuo [64] 2020 26 SZ, 30 MDD, 19AD, 109 HCs T1WI LASSO, ICA BAG: +5.69 yr in SCZ, +3.25 yr in AD, no significance in MDD. Association with large-scale structural covariance network
Tønnesen [65] 2020 668 SZ, 185 BPAD, 990 HCs DTI XGBoost Increased BAG in SZ (Cohen’s d = −0.29) and BPAD (Cohen’s d = 0.18)
Lee [66] 2021 90 SZ, 200 HCs, 76 SZ, 87 HCs T1WI OLS, Ridge, LASSO, Elastic-Net, SVR, RVR BAG: +3.8–5.2yr in SZ cohort 1, +4.5–11.7 yr in SZ cohort 2. Algorithm choice can be a cause of inter-study variability.
Lieslehto [67] 2021 29 SZ, 61 HCs T1WI SVR BAG: +1.3 yr at baseline, +7.7 yr at follow-up in SZ. It was suggested that BA captured treatment-related and global brain alterations.
McWhinney [68] 2021 183FEP, 155 HCs T1WI RVR BAG: +3.39 yr in FEP at baseline, longitudinal worsening was associated with clinical outcomes or higher baseline BMI
Teeuw [69] 2021 193 SZ, 218 HCs T1WI SVR BAG: correlation with polygenic risk, no correlation with epigenetic aging
Wang [70] 2021 166 SZ, 107 HCs DTI RF BAG: +5.903 in SZ >30 yrs old. Association with working memory and processing speed
Xi [71] 2021 60 FES, 60 HCs DTI RVR BAG: +4.932 yr in FES, +2.718. Decreased BAG after early medication
Demro [72] 2022 163 psychosis, 103 relatives, 66 HCs T1WI SVR/RF BAG increase in psychosis more than HCs or relatives. Associated with cognition or schizotypal symptoms in relatives
Mood disorders
Bestteher [73] 2019 38 MDD, 40 HCs T1WI RVR BAG: no significant change in MDD
Van Gestel [74] 2019 84 BPAD, 45 HCs T1WI RVR BAG: +4.28 yr in BPAD without Li treatment, no significance in BPAD with Li treatment or HCs
de Nooij [75] 2019 283AYA T1WI RVR Reduction of BAG in young high-risk individuals who developed a mood disorder over 2-yr follow-up
Christman [76] 2020 76 MDD (middle-age), 118 MDD (elderly), 130 HCs T1WI CNN BAG: +3.69 yrs in geriatric MDD, no increase in mid-life MDD. Associated with cognitive and functional deficits in elderly
Ahmed [77] 2021 95 late-life depression T1WI CNN BAG: +4.36 yrs in late-life depression. Not associated with treatment response.
Ballester [78] 2021 160 MDD, 111 HCs T1WI GPR BAG: higher in older MDD than in younger MDD, associated with BMI in MDD, not associated with treatment response
Han [79] 2021 2675 MDD, 4314 HCs T1WI Ridge regression BAG: +1.08 yr in MDD with no specific association with clinical characteristics
Han [80] 2021 220 MDD/Anxiety, 65 HCs T1WI Ridge regression BAG: +2.78 yr in MDD, +2.91 yr in Anxiety. Association with somatic symptoms (+4.21 yr) and antidepressant use (−2.53 yr)
Dunlop [81] 2021 109 MDD, 710 HCs fMRI SVR BAG: +2.11 yr in MDD, associated with impulsivity and symptom severity
Others
Liu [82] 2022 90 OCD, 106 HCs T1WI GPR BAP: +0.826 yr in OCD, associated with disease duration
Niu [83] 2022 70 SP, 77 SAD, 70 MDD, 44 PTSD, 48 ODD, 81 ADHD T1WI Ridge regression Multidimensional brain-age index is sensitive to distinct regional change patterns
Ryan [84] 2022 1618 SMI, 11,849 HCs DTI RF, gradient boosting regression, LASSO Additive effect of SMI and cardiometabolic disorders on brain aging, the greater effect of SMI than CMD
Comprehensive
Kaufmann [85] 2019 10,141 patients, 35,474 HCs T1WI XGBoost BAG: d = +1.03 in dementia, +0.41 in MCI, +0.10 in MDD, +0.74 in MS, +0.29 in BPAD, +0.51 in SZ, +0.06 in ADHD, +0.07 in ASD
Bashyam [86] 2020 353 AD, 833 MCI, 387 SZ, 12,689 HCs T1WI CNN Successful discrimination for neuropsychiatric disorders
Kolbeinsson [87] 2020 12,196 people who had not been stratified for health T1WI CNN Identified risk factors, e.g., MS, diabetes, and beneficial factors, e.g., physical strength
Rokicki [88] 2021 54 AD, 90 MCI, 56 SCI, 159 SZ, 135 BPAD, 750 HCs T1WI, T2WI, ASL RF Highest accuracy by multimodal imaging model

AD: Alzheimer’s disease, ARMS: at-risk mental state, ASL: arterial spin labeling, AYA: adolescence and young adult, BAG: brain age gap, BPAD: bipolar affective disorder, BPD: borderline personality disorder, CHR: clinical high-risk state for psychosis, CMD; cardiometabolic disease, CNN: convolutional neural network, DTI: diffusion tensor imaging, FEP: first episode psychosis, FES: first-episode schizophrenia, fMRI: functional MRI, GPR: Gaussian process regression, HCs: healthy controls, ICA: independent component analysis, MDD: major depressive disorder, ML: machine learning, OCD: obsessive-compulsive disorder, ODD: oppositional defiant disorder, OLS: ordinary least squares, PTSD: posttraumatic stress disorder, RF: random forest, RVR: relevance vector regression, SAD: social anxiety disorder, SMI: severe mental illness, SP: specific phobias, SVR: support vector regression, SZ: schizophrenia, T1WI: T1-weighted image, T2WI: T2-weighted image.