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Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry

Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Pers. Med. 2022, 12(11), 1850;
Submission received: 7 September 2022 / Revised: 1 November 2022 / Accepted: 1 November 2022 / Published: 5 November 2022
(This article belongs to the Special Issue The Current State of Psychiatry: Personalized Medicine and Treatment)


It is now possible to estimate an individual’s brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.

1. Aging, Disease, and the Brain

The aging process in humans is associated with the progressive decline of various physiological and organ functions [1], and many diseases including cancer, cardiovascular disease, diabetes, and dementia are associated with aging [2]. It is not uncommon for elderly people to suffer from multiple diseases simultaneously. Since humans’ bodies change with age and as humans are living longer in several regions of the world, the aging process has become a key issue in public health, disease prevention, and treatment. Many discussions concerning the pathological meaning of aging in the context of epigenetic change, proteotoxic or oxidative stress, and telomere damage have thus been conducted [3].
The brain is also affected by aging [4]. In the early stage of life, the aging process is regarded as brain development in which the brain matures, and children usually experience an increase in their cognitive ability along with their physical growth. During late adulthood, the brain aging process has different effects, e.g., a decline of cognitive function, and advancing age is associated with neurodegeneration, particularly Alzheimer’s disease and other forms of dementia [5,6]. If the aging process of the brain could be measured precisely and accurately, the findings may have potential as biomarkers for neuropsychiatric disorders. In fact, frameworks to quantify the age of a human brain have been attempted for several decades [7]. Today, advances in medical imaging and analytical methods (especially machine learning) have allowed the calculation of an individual’s biological age from the extracted biological features [8]. The frameworks that are now used to estimate the age of an individual’s brain have the potential to provide useful, objective, and personalized biomarkers for neurological and psychiatric disorders.

2. Neuroimaging-Based Brain-Age Estimation

Telomere-related and epigenetics-related biomarkers have not shown sufficient predictive and deterministic value for estimating brain ages, and it has been suggested that phenotype-based estimation can generate a much closer indicator of brain age [4]. Neuroimaging is a widely available, less-invasive method to investigate the whole brain of humans, and with neuroimaging, the brain’s morphological and microstructural features can be obtained; these features are speculated to be suitable material for the estimation of the age of an individual brain. In fact, neuroimaging-based brain-age estimation has been increasingly applied to individuals with various neuropsychiatric disorders and general populations [8]. In this narrative review, we examined over 100 studies and introduce the recent findings and methodologies of this emerging technique. We conducted a search of the PubMed database in May 2022 using “brain-age estimation” and/or “brain-age prediction” as keywords, although we did not adopt rigorous systematic selection criteria of studies for this narrative review.

3. Theory and Methodology

3.1. Theory of Neuroimaging-Based Brain-Age Estimation

In 2010, Katja Franke and her peers developed a prediction model that was able to estimate a subject’s age based on brain imaging data and the use of a regression machine-learning model [9]. The output of a brain-age estimation framework has been called the “brain age-delta,” “brain predicted age difference (Brain-PAD),” “brain age gap estimation (BrainAGE),” and “brain age gap (BAG),” each of which is computed by deducting the estimated brain age from the subject’s chronological age. In this review, we refer to “brain age-delta” as the output of a brain-age estimation framework. The brain age-delta is known as a heritable biomarker for both monitoring cognitively healthy aging and identifying age-associated disorders [8]. There are three possibilities for a brain age-delta value: (i) a brain age-delta close to zero, representing normal brain aging, (ii) a positive brain age-delta (i.e., estimated brain age > chronological age), representing an older-appearing brain, and (iii) a negative brain age-delta (i.e., estimated brain age < chronological age), representing a younger-appearing brain.
A brain-age estimation study is generally composed of three main stages: (i) creating a prediction model by using extracted brain features and a regression machine-learning model, validation, and bias correction; (ii) computing the brain age and brain age-delta for the subject under study; and (iii) interpreting the results, including the use of a within-group and/or a between-groups analysis. Figure 1 depicts the pipeline of a typical brain-age estimation study.
In the literature, the typical accuracy of the prediction of brain ages is from 2 years to 10 years in terms of mean absolute error (MAE) [10,11]. The prediction accuracy in a brain-age estimation framework depends on variables such as the type of input data, the feature extraction, and reduction strategies [12] and bias adjustment techniques [13], and machine-learning models [14]. In the following subsections, we provide a general overview of these variables.

3.2. Input Data and Feature-Extraction Methodologies of Neuroimaging

One of the key concerns among researchers attempting to develop a brain-age estimation framework is the selection of the input data. Each modality offers unique insights into the brain. For example, fluorodeoxyglucose-positron emission tomography (FDG-PET) scans provide information about the brain’s glucose metabolism, whereas magnetic resonance imaging (MRI) data provide information about the anatomy of the brain. Among the different brain, MRI modalities such as T1-weighted MRI images (T1w MRI), T2-weighted MRI images (T2w MRI), resting-state functional (f)MRI, and fluid-attenuated inversion recovery (FLAIR), the majority of brain-age estimation studies have used T1w MRI data. The main reason for using T1w MRI is because it is more readily available than other modalities [15]. Brain age frameworks generally require a large dataset for training a prediction model, and many public neuroimaging datasets such as ADNI (, accessed on 31 October 2022), PPMI (, accessed on 31 October 2022), IXI (, accessed on 31 October 2022), and OASIS (, accessed on 31 October 2022) have provided a great number of T1W MRI scans for research studies.
Each brain imaging modality requires a specific feature extraction strategy. The feature extraction approaches for T1w MRI data can be classified into two categories: (i) voxel-wise methods (e.g., statistical parametric mapping [SPM],, accessed on 31 October 2022) [8,16,17], which use gray matter (GM) and/or white matter (WM) signal intensities as brain features; and (ii) region-wise methods (e.g., FreeSurfer,, accessed on 31 October 2022) [18], which use the subcortical and cortical and measurements of volume, surface, and thickness values as brain features. Both voxel-wise and region-wise feature extraction approaches have been widely used in T1-w MRI-driven brain-age estimation studies [19,20,21].
A direct comparison of voxel-wise and region-wise metrics as well as their integration in the accuracy of brain age has been conducted [12]. For functional MRI-driven brain-age frameworks, the extracted features can be functional connectivity (FC) measures between brain regions or intrinsic connectivity networks and voxel-wise whole-brain FC measures (e.g., FSLNets,, accessed on 31 October 2022) [22,23]. In terms of the PET modality, the extracted features for estimating brain ages include measurements of brain metabolism (i.e., PET regional total glucose, aerobic glycolysis, oxygen) and cerebral blood flow [22,23]. White-matter microstructure measurements such as mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity have been employed as brain features for a diffusion tensor imaging (DTI)-based brain age framework [24].

3.3. Data Reduction, Validation, and Bias Adjustment Neuroimaging Methodologies

The ‘curse of dimensionality’ is one of the major concerns in developing a brain-age estimation framework, particularly when the number of brain features is far higher than the number of samples (e.g., in voxel-based feature extraction strategies). High-dimensional data can give rise to some substantial issues in a prediction model, such as overfitting and decreased computational efficiency. A data reduction technique that can decrease the high dimensionality of data and diminish redundant information is thus required. In the area of brain-age estimation, most studies have used the principal component analysis (PCA) strategy, which is an unsupervised learning technique [9,19]. The effect of the number of principal components on the accuracy of brain-age predictions has been investigated [9]. The number of principal components may influence the prediction accuracy in a brain age estimation framework. However, it can be adjusted to achieve maximum accuracy in the training set [9].
After a prediction model is developed, it is critical to validate the model’s prediction accuracy. Most studies in the field of brain-age estimation have used a K-fold cross-validation strategy (e.g., K = 5 or 10) to assess the prediction performance on a training set [12,14,16,21]. In the K-fold cross-validation technique, the data are randomly divided into K folds, and the learning process is repeated K times so that K-1 folds are used for training a prediction model, and the remaining fold is used as a test for each iteration. To assess the prediction accuracy, researchers generally use the coefficient of determination (R2) between the subjects’ chronological age and estimated age, the MAE, and root mean square error (RMSE) metrics.
Many brain-age estimation studies have reported age dependency on the prediction outputs, and this is considered a substantial issue in brain-age frameworks [13,21]. This bias, which could be a result of regression dilution bias, may adversely affect the predicted values and alter the interpretation of results. Several techniques have been proposed to diminish this bias (i.e., age dependency) [13,21,25]. For instance, Le and colleagues proposed using chronological age as a covariate in the statistical analyses and interpreting the results [26]. However, it should be highlighted that Le’s method is appropriate for group comparison only and not able to deliver bias-free brain age values at the individual level. A bias adjustment strategy is proposed in [21] (i.e., Cole’s method) that uses the intercept and slope of a linear regression model of estimated brain age against chronological derived from the training set. The bias-free Brain-age values in the test sets are then calculated by subtracting the intercept from the predicted brain age and dividing by the slope [21]. The most recent bias adjustment technique is suggested in [13] (i.e., Beheshti’s method) which computes offset values for test subjects on the basis of the intercept and slope of a linear regression model of brain age-delta against chronological age achieved from the training set. Then, the bias-free Brain-age values are computed by subtracting the offset values from the estimated brain-age values [13]. A direct comparison of these bias adjustment techniques has shown that Beheshti’s method greatly reduces the variance of the predicted ages, whereas Cole’s method increases it [13].

3.4. Machine-Learning Methodologies

One of the important steps in developing a brain-age estimation framework is choosing a regression machine-learning model. A regression model establishes a pattern between independent variables (here, brain features) and the corresponding dependent variable (a subject’s chronological age) based on the training dataset, and the model uses this pattern to predict the brain age based on unseen data (i.e., independent test datasets). The most widely used traditional regression algorithms include support vector regression (SVR) [19,23], relevance vector regression (RVR) [9], Gaussian process regression [21], an ensemble of gradient-boosted regression trees [25], and XGBoost [25]. It has been demonstrated that the type of regression algorithm used influences the prediction accuracy and the interpretation of outcomes in brain-age frameworks [14].
In addition to the traditional regression algorithms, deep learning models have become a prominent methodology in the area of brain-age estimation [11], as they can be used to develop more accurate prediction models. A major advantage of deep learning models is that they can be applied directly with 3D brain image data and incorporate feature extraction, data reduction, and prediction stages into a unified system. The main challenge of deep learning-based brain-age estimation frameworks is that this methodology requires a large dataset to train a model. In 2017, James Cole and his peers developed the first deep learning-based brain-age estimation framework, with a 3D convolutional neural network (CNN) that uses 3D gray matter and 3D white matter intensity maps as the input data [11]. Other deep learning architectures used in brain-age estimation frameworks include feed-forward neural networks, VGGNet [27], ResNet [28], U-Net [29], and an ensemble of CNN architectures [30].

4. Applications in Neuropsychiatry

4.1. Alzheimer’s Disease, Dementia, and Memory Impairment

One of the most active areas of brain-age research concerns Alzheimer’s disease (AD) and mild cognitive impairment (MCI) (Table 1) [17,23,31,32,33,34,35,36,37], because of their strong association with aging. Alzheimer’s disease is the most common cause of dementia, which is also a relevant issue in aging societies in many developed countries. The early detection of AD is important in terms of early cognitive intervention [38,39,40] as well as the future development of disease-modifying therapy [41], and neuroimaging plays key roles in ensuring accurate and early diagnoses, revealing the underlying pathophysiology, and monitoring the disease. An increased BAG in individuals with AD has been consistently reported, ranging from +2.88 to +9.29 years [17,20,37], and correlations between an increased BAG and cognitive dysfunction or white matter hyperintensity were also found [17,36]. Importantly, the predictability of progression from MCI to AD and the detectability of preclinical AD based on brain-age measures are also confirmed and would be clinically significant [31,34,37].

4.2. Other Neurological Diseases

Parkinson’s disease (PD) is a common neurodegenerative movement disorder characterized by the degeneration of dopaminergic neurons in the substantia nigra [55]. Overall, it seems that an increase in brain age by 2–3 years occurs in PD [20,42,43], and such an increase is associated with cognitive or motor impairment (Table 1). A comparison study between PD and AD revealed a significant increase in the BAG in AD compared to PD.
Epilepsy is also a common neurological disorder, characterized by recurrent seizures associated with abnormal electrical activity in the brain. A brain with chronic epilepsy tends to present a BAG of +4 to +8 years (Table 1) [19,44,45,46], and comorbid psychosis may further increase the BAG by several additional years [19]. Interestingly, epilepsy surgery may reduce the abnormal BAG increase, regardless of postsurgical seizure freedom [46].
Multiple sclerosis (MS) is an autoimmune disease involving damage to the myelin sheaths of the brain and spinal cord [56]. The BAG in MS is relatively high at +6.5 to 10.3 years on average (Table 1), and it is particularly higher in secondary progressive MS (+13.3 years) [47,48]. An increased BAG is also suggested to predict MS progression.
A brain-age framework has also been applied to neurological and related disorders including traumatic brain injury [49,50], pain [51,52], Prader-Willi syndrome [53], and HIV infection [54] (Table 1).

4.3. Schizophrenia and Psychotic Disorders

Schizophrenia is a serious psychiatric disorder presenting symptoms that include psychosis, cognitive dysfunction, and negative symptoms. Increased brain age in schizophrenia and first-episode psychosis (FEP) has been reported (Table 2) [57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72], and consistent findings of a BAG increased by approx. 3–6 years in schizophrenia have been confirmed, with possible associations with cognitive dysfunction or polygenic risk. The BAG in individuals with FEP may be lower than that in schizophrenia, and, according to longitudinal studies, an acceleration of brain aging over time is suggested in this population. The BAG is also associated with schizotypal symptoms in relatives of patients with psychosis [72]. Early medication may reduce the BAG in psychosis [71].

4.4. Mood Disorders

There have been several studies of brain age in mood disorders (Table 2), including bipolar affective disorder (BPAD) and major depressive disorder (MDD) [73,74,75,76,77,78,79,80,81]. Unlike schizophrenia, some studies reported no significant difference in the BAG in mood disorders [59,62,73], while others found an increase of approx. +2 to +4 years [76,77,79,80,81]. Overall, the aging abnormality in mood disorders would be mild to moderate. Two studies that focused on MDD in late life reported significantly increased brain age [76,77]. Interestingly, the BAG may be reduced by medications, such as lithium for BPAD or antidepressants for MDD [74,80].

4.5. Other Psychiatric Disorders

The brain age in other psychiatric disorders such as obsessive-compulsive disorder (OCD) and specific phobias has been investigated [82,83] (Table 2), and a relatively large study reported contributions of both severe mental illness and cardiometabolic disorders to an increased BAG [84].

4.6. Comprehensive Studies

Brain-age findings across various neuropsychiatric disorders have been obtained in comprehensive studies [85,86,87,88] (Table 2). Overall, these studies successfully identified neuropsychiatric disorders and risk factors by using brain age, and it was indicated that a multimodal imaging model may have high accuracy [88]. In particular, an investigation of a large sample (>10,000 patients and 35,000 healthy controls) revealed the effect sizes of a BAG in various conditions, which should be regarded as a reliable standard of BAG scores so far [85]. According to this study, the strongest aging of the brain is seen in dementia, followed by MS, schizophrenia, and MCI.

5. Applications to General Populations

Targeting a general population or individuals without neuropsychiatric diagnoses is another important topic in neuroimaging-based brain-age framework research, as it may clarify how to keep our brains healthy and avoid the risks of accelerated aging [15,16,21,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129]. The most consistent significant risk factor could be diabetes (Table 3). In fact, diabetes has been consistently reported to adversely affect the aging of the brain. Alcohol consumption and smoking were also associated with an increased BAG in some studies [15,16,121,122,123,128]. Other factors that were suggested to be associated with an increased BAG include mortality, allostatic load, lung function, exposure to famine in early gestation, recidivism, chronic pain, cardiovascular risk, chemotherapy for cancer, lead exposure in childhood, hypertension, premature birth, male sex, worry and rumination, neighborhood disadvantage, sleep apnea, obesity, and physical strength (Table 3).
In addition, several studies reported potentially protective factors associated with a reduced BAG: long-term meditation (−7.5 years), music composition (approx. −4 years), physical activity, taking ibuprofen, and life satisfaction (Table 3). Interestingly, it has been observed in more than one study that childbirth decreases the BAG in women, not only during the postpartum period but also in later life [100].
Thus, although the studies are diverse in terms of the methodologies used and the targeted factors, cumulative evidence will further expand our knowledge of how to improve the aging process of human brains. The strong and consistent risk for brain aging appears to be diabetes, followed by alcohol consumption. Other lifestyle-related risk factors, e.g., smoking or hypertension, may also be harmful but less consistent. Regarding beneficial effects, though the research focuses were diverse across studies, physical, mental, or creative activities may likely improve our brain age. It is unclear whether neuropsychiatric disorders, particularly dementia, could be prevented by improving brain aging. Further research is necessary to obtain real-world evidence regarding the utility of brain-age studies for this question.

6. Strengths, Controversies, and Future Direction

As described above, a neuroimaging-based brain-age estimation can provide a reliable neuropsychiatric biomarker at the single-subject level. In addition, brain MRI—particularly T1-weighted structural MRI—is a widely available examination in most countries, which may support easier and wider clinical applications of brain-age analyses. Given that many studies have successfully used public databases to build a brain-age prediction model, the reproducibility and external validity of a brain-age model should be at an acceptable level. Thus, the strengths of brain age as a biomarker would be its use as a single-subject-level marker, widely available examination, and acceptable reproducibility. The processing of MRI scans, including normalization and machine-learning analysis, may require advanced techniques and could be a possible barrier for most facilities, but currently, there are several public tools, e.g., BARACUS (, accessed on 31 October 2022) and brainageR (, accessed on 31 October 2022) which would help us apply a brain age model to the patients.
Controversies and limitations of the use of brain ages have also been suggested, particularly for the interpretation of study results. As reviewed herein, there is a level of overlapping of BAG scores across various disorders, which might limit the usefulness of the brain age for differential diagnoses in clinical analyses. It was also reported that individual variations in brain age were associated with early-life factors rather than longitudinal changes [119]. Moreover, the methodology is quite diverse in terms of imaging modalities, processing, and choice of machine-learning algorithm, and there is no established consensus about the optimal protocol for determining brain ages. Future studies should address these controversies and limitations.
In conclusion, neuroimaging-based brain-age estimation has been widely and increasingly researched for over 10 years, and many studies revealed its usefulness for neuropsychiatry. Considering the utility, availability, and reproducibility of neuroimaging-based brain-age estimations for single patients, brain age can be expected to become a useful personalized biomarker in neuropsychiatry.

Author Contributions

Both D.S. and I.B. contributed equally to this article including searching for literature and writing a manuscript. All authors have read and agreed to the published version of the manuscript.


This study was supported by grants from the Japan Society for the Promotion of Science (KAKENHI) no. JP21K15720, the Japan Epilepsy Research Foundation (JERF TENKAN 22007), and the Uehara Memorial Foundation (all to D.S.).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


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Table 1. Neuroimaging-based brain age studies for dementia, cognitive impairment, and other neurological disorders.
Table 1. Neuroimaging-based brain age studies for dementia, cognitive impairment, and other neurological disorders.
First Author
YearCohortImaging ModalityML AlgorithmMain Findings
Alzheimer’s Disease and Cognitive Impairment
Gaser [31]2013133 pMCI, 62 sMCIT1WIRVRBAG predicts conversion to AD, 10% greater risk of developing AD by each 1 additional yr of BAG
Lowe [32]2016150 AD, 112 pMCI, 36 sMCI, 107HCT1WIRVREffect of APOEe4 on BrainAGE changing rates over time
Beheshti [17]2018147 AD, 112 pMCI, 102 sMCI, 146 HCsT1WISVRBAG: +5.36 yr in AD, +3.15 yr in pMCI, +2.38 yr in sMCI. Correlation with cognitive function
Wang [33]20193688 people (middle age to elderly)T1WICNNBAG: related to incident dementia
Mohajer [35]202048 AD, 222 MCI, 60 HCsT1WISVRBAG was elevated in MCI and AD but was not associated with sleep-disordered breathing.
Ly [34]202074 AD, 283 MCI, 51 preclinical AD, 83 HCsT1WIGPRBAG differentiated cognitively unimpaired Amyloid (+) from Amyloid (−).
Beheshti [23]2021292 AD, 440 MCI, 548 HCsFDG-PETSVRYounger BAG in females than in males in HCs group but not in MCI or AD groups
Habes [36]20211932 MCI/AD, 8284 HCsT1WIRBF-kernelBAG associated with WMH as well as cognitive function
Parkinson’s disease
Beheshti [20]2020160 PD, 129 AD, 839 HCsT1WISVRGM-based BAG: +1.50 yr in PD, +9.29 yr in AD. WM-based BAG: +2.47 yr in PD, +8.85 yr in AD. WM-based BAG > GM-based BAG in PD
Eickhoff [42]2021372 PD, 172 HCsT1WISVRBAG: +2.9 yr in PD. Associated with disease duration and cognitive and motor impairment.
Charisse [43]202283 PD-NC, 78 PD-MCI, 17 PD-D, 84 HCsT1WISVRRBA: +2.38 yr in PD-NC, +1.90 yr in PD-MCI, +3.52 yr in PD-D. Associated with attention deficits and working memory
Pardoe [44]201742 new FE, 94 refractory FE, 74 HCsT1WIGPRBAG: +4.5yr in refractory FE, no significance in new FE
Hwang [45]2020104 TLE, 151 HCsT1WI, fMRISVRT1-based BAG: +6.6 yr in TLE. fMRI-based BAG: +8.3 yr in TLE
Association with clinical data
Sone [19]2021318 epilepsy, 1,196 HCsT1WISVRBAG: >+4 yr in all types of epilepsies, +10.9 yr in TLE with psychosis
de Bézenac [46]202248 TLE, 37 HCsT1WIGPRBAG: +7.97 yr in TLE, postsurgical reduction of BAG
Multiple sclerosis
Cole [47]20201204 MS/CIS, 150 HCsT1WIGPRBAG: +10.3 yr in MS, +13.3 yr in SPMS, predictive value for progression
Jacobs [48]2021179 MST1WIGPRBAG: +6.54 yr in MS, associated with a physical disability
Traumatic brain injury
Gan [49]2021116 mTBI, 63 HCsDTIRVRBAG: +2.59 yr in mTBI, associated with post-concussion complaints
Hellstrom [50]2021123 mTBIT1WI, DTIXGBoostNo significant difference in BAG between APOEe4 carriers and non-carriers after mTBI
Yu [51]202131 CLBP, 32 HCsT1WIGPRDiscrepancy in BAG between HCs and CLBP was greater in older individuals
Hung [52]202245 TN, 52 OA, 50 CLBP, 812 HCsT1WIGPRBAG: +6.48 yr in TN, +9.80 yr in OA, no significance in BP. Female-driven elevation in BAG
Azor [53]201920 PWS, 40 HCsT1WIGPRBAG: +7.24 yr in PWS, Not associated with IQ, hormonal or psychotropic medications, or abnormal behaviors
Cole [54]2017162 HIV(+), 105 HIV(−)T1WIGPRBAG: +2.15 yr in HIV(+), associated with cognitive performance
AD: Alzheimer’s disease, BAG: brain age gap, CIS: clinically isolated syndrome, CLBP: chronic lower back pain, CNN: convolutional neural network, DTI: diffusion tensor imaging, FDG-PET: 18F-fluorodeoxyglucose PET, FE: focal epilepsy, fMRI: functional MRI, GPR: Gaussian process regression, HCs: healthy controls, ML: machine learning, MS: multiple sclerosis, mTBI: mild traumatic brain injury, NC: normal cognition, OA: osteoarthritis, PD: Parkinson disease, PD-D: PD with dementia, pMCI: progressive mild cognitive impairment, PWS: Prader-Willi syndrome, RBF: radial basis function, RVR: relevance vector regression, sMCI: stable mild cognitive impairment, SPMS: secondary progressive MS, SVR: support vector regression, T1WI: T1-weighted image, TLE: temporal lobe epilepsy, TN: trigeminal neuralgia.
Table 2. Neuroimaging-based brain age studies for psychiatric disorders.
Table 2. Neuroimaging-based brain age studies for psychiatric disorders.
First Author
YearCohortImaging ModalityML AlgorithmMain Findings
Schizophrenia and Psychosis
Koutsouleris [57]2014141 SZ, 104 MDD, 57B PD, 89 ARMS, 127 HCsT1WISVRBAG: +5.5 yr in SZ, +4.0 yr in MDD, +3.1 yr in BPD, +1.7 yr in ARMS.
Schnack [58]2016341 SZ, 386 HCsT1WISVRBAG: +3.36 yr in SZ, acceleration just after illness onset
Nenadic [59]201745 SZ, 22 BPAD, 70 HCsT1WIRVRBAG: +2.56 yr in SZ, no significance in BPAD
Kolenic [60]2018120 FEP, 114 HCsT1WIRVRBAG: +2.64 yr in FES, associated with obesity
Hajek [62]201943 FES, 43 HCs, 96 offspring of BPAD (48 affected, 48 unaffected), 60 HCsT1WIRVRBAG: +2.64 yr in FES, no significance in early BPAD
Chung [61]2019476 CHRN/AN/ABAG predicts conversion to psychosis in a univariate analysis but not in a multivariate analysis
Shahab [63]201981 SZ, 53 BPAD, 91 HCsT1WI, DTIRFBAG: +7.8–8.2 yr in SZ, no significance in BPAD
Kuo [64]202026 SZ, 30 MDD, 19AD, 109 HCsT1WILASSO, ICABAG: +5.69 yr in SCZ, +3.25 yr in AD, no significance in MDD. Association with large-scale structural covariance network
Tønnesen [65]2020668 SZ, 185 BPAD, 990 HCsDTIXGBoostIncreased BAG in SZ (Cohen’s d = −0.29) and BPAD (Cohen’s d = 0.18)
Lee [66]202190 SZ, 200 HCs, 76 SZ, 87 HCsT1WIOLS, Ridge, LASSO, Elastic-Net, SVR, RVRBAG: +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]202129 SZ, 61 HCsT1WISVRBAG: +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]2021183FEP, 155 HCsT1WIRVRBAG: +3.39 yr in FEP at baseline, longitudinal worsening was associated with clinical outcomes or higher baseline BMI
Teeuw [69]2021193 SZ, 218 HCsT1WISVRBAG: correlation with polygenic risk, no correlation with epigenetic aging
Wang [70]2021166 SZ, 107 HCsDTIRFBAG: +5.903 in SZ >30 yrs old. Association with working memory and processing speed
Xi [71]202160 FES, 60 HCsDTIRVRBAG: +4.932 yr in FES, +2.718. Decreased BAG after early medication
Demro [72]2022163 psychosis, 103 relatives, 66 HCsT1WISVR/RFBAG increase in psychosis more than HCs or relatives. Associated with cognition or schizotypal symptoms in relatives
Mood disorders
Bestteher [73]201938 MDD, 40 HCsT1WIRVRBAG: no significant change in MDD
Van Gestel [74]201984 BPAD, 45 HCsT1WIRVRBAG: +4.28 yr in BPAD without Li treatment, no significance in BPAD with Li treatment or HCs
de Nooij [75]2019283AYAT1WIRVRReduction of BAG in young high-risk individuals who developed a mood disorder over 2-yr follow-up
Christman [76]202076 MDD (middle-age), 118 MDD (elderly), 130 HCsT1WICNNBAG: +3.69 yrs in geriatric MDD, no increase in mid-life MDD. Associated with cognitive and functional deficits in elderly
Ahmed [77]202195 late-life depressionT1WICNNBAG: +4.36 yrs in late-life depression. Not associated with treatment response.
Ballester [78]2021160 MDD, 111 HCsT1WIGPRBAG: higher in older MDD than in younger MDD, associated with BMI in MDD, not associated with treatment response
Han [79]20212675 MDD, 4314 HCsT1WIRidge regressionBAG: +1.08 yr in MDD with no specific association with clinical characteristics
Han [80]2021220 MDD/Anxiety, 65 HCsT1WIRidge regressionBAG: +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]2021109 MDD, 710 HCsfMRISVRBAG: +2.11 yr in MDD, associated with impulsivity and symptom severity
Liu [82]202290 OCD, 106 HCsT1WIGPRBAP: +0.826 yr in OCD, associated with disease duration
Niu [83]202270 SP, 77 SAD, 70 MDD, 44 PTSD, 48 ODD, 81 ADHDT1WIRidge regressionMultidimensional brain-age index is sensitive to distinct regional change patterns
Ryan [84]20221618 SMI, 11,849 HCsDTIRF, gradient boosting regression, LASSOAdditive effect of SMI and cardiometabolic disorders on brain aging, the greater effect of SMI than CMD
Kaufmann [85]201910,141 patients, 35,474 HCsT1WIXGBoostBAG: 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]2020353 AD, 833 MCI, 387 SZ, 12,689 HCsT1WICNNSuccessful discrimination for neuropsychiatric disorders
Kolbeinsson [87]202012,196 people who had not been stratified for healthT1WICNNIdentified risk factors, e.g., MS, diabetes, and beneficial factors, e.g., physical strength
Rokicki [88]202154 AD, 90 MCI, 56 SCI, 159 SZ, 135 BPAD, 750 HCsT1WI, T2WI, ASLRFHighest 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.
Table 3. Neuroimaging-based brain age studies for general populations or those without neuropsychiatric diagnoses.
Table 3. Neuroimaging-based brain age studies for general populations or those without neuropsychiatric diagnoses.
First Author
YearCohortImaging ModalityML AlgorithmMain Findings
Franke [89]2013185 peopleT1WIRVRBAG: +4.6 yr in T2DM, Acceleration by +0.2 yr per year
Franke [90]2014228 elderlyT1WIRVRBAG associated with health markers with gender-specific pattern
Franke [91]20158 womenT1WIRVRBAG changes during the course of the menstrual cycle
Luders [92]201650 LTM, 50 HCsT1WIRVRBAG: −7.5 yr in LTM
Cole [21]2018669 peopleT1WIGPRHigher BAG was associated with weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load, and increased mortality risk.
Franke [93]2018118 elderlyT1WIRVRBAG: +4.3 yr in males whose mothers were exposed to famine in early gestation
Hatton [94]2018359 menT1WISVRBAG associated with negative fateful life events in midlife
Kiehl [95]20181332 incarcerated malesT1WIICABrain age predicts recidivism, particularly when combined with other data.
Le [96]201820 healthy peopleT1WISVRBAG: −1.15 or −1.18 yr by taking ibuprofen
Luders [97]201814 healthy women after childbirthT1WIRVRBrain age became younger in late postpartum by 5.4 yr.
Rogenmoser [98]201842 pro-musician, 45 amateurs, 38HCsT1WIRVRBAG: −3.70 to −4.51 yr in musicians
Scheller [99]201834 elderlyT1WIRVRinteraction of BAG and APOE variants, suggesting a compensation mechanism in the elderly
de Lange [100]201912,021 womenT1WIXGBoostBAG decrease with the number of previous childbirths
Cruz-Almeida [101]201947 elderlyT1WIGPRIncreased BAG in elderly with chronic pain
Cole [15]202014,701 peopleT1WI, FLAIR, T2*, DTI, fMRILASSOBAS associated with stroke history, diabetes, smoking, alcohol, and cognitive measures
de Lange [102]2020473 peopleT1WI, DTI, fMRIXGBoostAssociated with cardiovascular risk
de Lange [103]202019,787 womenT1WIXGBoostBAG decrease with the number of previous childbirths. Involvement of brain subcortical regions
Henneghan [104]202043 breast cancer with chemotherapy, 50 HCsT1WISVR/RFTrend-level increase on BAG after chemotherapy for breast cancer
Reuben [105]2020564 people at 45 yrT1WISVR/RFBAG: +0.77 yr in those who had lead exposure in childhood
Seidel [106]202020 sepsis survivors with cognitive deficits, 44 HCsT1WIKernel regressionBAG: +4.5 yr in sepsis survivor, associated with the severity of dyscognition
Anaturk [107]2021537 elderlyT1WI, DTI, FLAIRXGBoostRelationship with cumulative lifestyle measures independent of cognitive age
Bittner [108]2021622 elderlyT1WIRVRBAG: +5.04 months by combined lifestyle risk, +0.6 month by smoking, −0.55 month by physical activity
Cherbuin [109]2021335 middle age, 351 elderlyT1WIRVRBAG: +51.1–65.7days by every additional 10-mmHg increase in BP
Dunas [110]2021351 peopleT1WI, DTI, fMRIOLS, BRR, LASSO, ENET, SVR, RVR, GPRBAG associated with current and past physical fitness and cognitive ability
Elliott [111]2021869 middle-ageT1WISVR/RFAssociated with cognitive function, impaired brain health at age 3, and other signs of aging
Hedderich [112]2021101 premature-born adults, 111 full-term controlsT1WIRVRBAG: +1.4 yr in premature-born adults, associated with low gestational age, low birth weight, and increased neonatal treatment intensity
Karim [113]202178 older adultsT1WI, T2WI, FLAIRGPRBAG associated with male sex, worry, and rumination
Rakesh [114]2021166 adolescentsT1WISVRincreased BAG by neighborhood disadvantage, modulated by effortful control
Rosemann [115]2021169 elderlyT1WIGPRNo association with age-related hearing loss
Salih [116]202115,335 HCsDTIBayesian ridge regressionLimbic tract-based BAG was most accurate and associated with daily life factors. Two SNPs were associated with BAG.
Sanders [117]2021122 elderlyT1WIXGBoostBAG decrease in more physically active women but not men
Subramaniapillai [118]20211067 elderlyT1WIElastic net regressionBrain age was more associated with AD risk factors in women than in men.
Vidal-Pineiro [119]20216950 peopleT1WILASSO, XGBoostNo association between cross-sectional brain age and longitudinal change. Association with congenital factors, suggesting a lifelong influence on brain structure from early life
Weihs [120]2021690 peopleT1WIOLSBrain age associated with AHI and ODI in PSG data
Angebrandt [121]2022240 HCs, 231 HCs (middle age)T1WISVR/RFDose-dependent relation between 90-day alcohol consumption and BAG
Beck [122]2022790 healthy peopleT1WI, DTIXGBoostT1-based BAG: associated with sBP, smoking, pulse, and CRP.
DTI-based BAG: associated with phosphate, MCV
Bourassa [123]2022910 people (midlife)T1WISVR/RFBAG in midlife is associated with smoking, obesity, and psychological problems during adolescence.
Giannakopoulos [124]202280 elderlyT1WIRVRBAG predicted a decrease in executive function over time.
Linli [125]202233,293 peopleT1WIXGBoostBAG: +1.19 yr in active regular smokers, associated with the amount of smoking
Sone [16]2022773 elderlyT1WISVRBAG: associated with life satisfaction, alcohol use, and diabetes
Vaughan [126]202257 elderlyT1WIGPRBAG: associated with leg strength, moderating the relationship between strength and mobility
Wang [127]2022165 elderlyT1WIRVRBAG: associated with female gender, higher education but not with APOE-e4 or family history of dementia
Whistel [128]2022712 peopleT1WISVRAssociation of BAG in mid- to late-life with heavier smoking and alcohol consumption in early mid-life
Zheng [129]20221676 HCsT1WIRBF-kernelBAG associated with worse cognitive outcomes over time
BAG: brain age gap, CNN: convolutional neural network, DTI: diffusion tensor imaging, ENET: efficient neural network, fMRI: functional MRI, GPR: Gaussian process regression, HCs: healthy controls, ICA: independent component analysis, LTM: long-term meditation practitioner, ML: machine learning, OLS: ordinary least squares, RBF: radial basis function, RF: random forest, RVR: relevance vector regression, SVR: support vector regression, T1WI: T1-weighted image, T2DM: type 2 diabetes mellitus, T2WI: T2-weighted image.
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Sone, D.; Beheshti, I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J. Pers. Med. 2022, 12, 1850.

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Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. Journal of Personalized Medicine. 2022; 12(11):1850.

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Sone, Daichi, and Iman Beheshti. 2022. "Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry" Journal of Personalized Medicine 12, no. 11: 1850.

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