TYPE Original Research
PUBLISHED 08 January 2024
DOI 10.3389/fnagi.2023.1303036

OPEN ACCESSEDITED BY

Dan Wu,Shenzhen Institute of Advanced Technology(CAS), China
REVIEWED BY

Jo Wrigglesworth,Monash University, Australia
Murat Bilgel,
National Institute on Aging (NIH), United States
Tengfei Li,
University of North Carolina at Chapel Hill,
United States*CORRESPONDENCE

Caroline Dartora
caroline.dartora@ki.se
Eric Westmaneric.westman@ki.se
RECEIVED 27 September 2023

A deep learning model for brain
age prediction using minimally
preprocessed T1w images as
input
Caroline Dartora 1*, Anna Marseglia 1, Gustav Mårtensson 1,
Gull Rukh 2, Junhua Dang 2, J-Sebastian Muehlboeck 1,
Lars-Olof Wahlund 1, Rodrigo Moreno 3, José Barroso 4,
Daniel Ferreira 1,4, Helgi B. Schiöth 2 and Eric Westman 1,5*, for
the Alzheimer's Disease Neuroimaging Initiative, theAustralian Imaging Biomarkers and Lifestyle Flagship Studyof Ageing, the Japanese Alzheimer's Disease Neuroimaging
Initiative and the AddNeuroMed Consortium

ACCEPTED 04 December 2023
PUBLISHED 08 January 2024
CITATION

Dartora C, Marseglia A, Mårtensson G,
Rukh G, Dang J, Muehlboeck J-S, Wahlund
L-O, Moreno R, Barroso J, Ferreira D,
Schiöth HB and Westman E (2024) A deeplearning model for brain age prediction using
minimally preprocessed T1w images as input.
Front. Aging Neurosci. 15:1303036.
doi: 10.3389/fnagi.2023.1303036
COPYRIGHT© 2024 Dartora, Marseglia, Mårtensson, Rukh,
Dang, Muehlboeck, Wahlund, Moreno,
Barroso, Ferreira, Schiöth and Westman. Thisis an open-access article distributed under
the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other forums ispermitted, provided the original author(s) andthe copyright owner(s) are credited and thatthe original publication in this journal is cited,in accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply withthese terms.

Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology,
Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden, 2 Department of Surgical
Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden,
3
Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology,
Stockholm, Sweden, 4 Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias,
Las Palmas, España, 5 Department of Neuroimaging, Centre for Neuroimaging Sciences, Instituteof Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
1

Introduction: In the last few years, several models trying to calculate thebiological brain age have been proposed based on structural magnetic
resonance imaging scans (T1-weighted MRIs, T1w) using multivariate
methods and machine learning. We developed and validated a convolutional
neural network (CNN)-based biological brain age prediction model thatuses one T1w MRI preprocessing step when applying the model to external
datasets to simplify implementation and increase accessibility in researchsettings. Our model only requires rigid image registration to the MNI space,which is an advantage compared to previous methods that require more
preprocessing steps, such as feature extraction.
Methods: We used a multicohort dataset of cognitively healthy individuals(age range = 32.0–95.7 years) comprising 17,296 MRIs for training andevaluation. We compared our model using hold-out (CNN1) and crossvalidation (CNN2–4) approaches. To verify generalisability, we used two
external datasets with different populations and MRI scan characteristics toevaluate the model. To demonstrate its usability, we included the external
dataset’s images in the cross-validation training (CNN3). To ensure that ourmodel used only the brain signal on the image, we also predicted brain age
using skull-stripped images (CNN4).
Results: The trained models achieved a mean absolute error of 2.99, 2.67,
2.67, and 3.08 years for CNN1–4, respectively. The model’s performancein the external dataset was in the typical range of mean absolute error(MAE) found in the literature for testing sets. Adding the external dataset
to the training set (CNN3), overall, MAE is unaffected, but individual cohort
MAE improves (5.63–2.25 years). Salience maps of predictions reveal thatperiventricular, temporal, and insular regions are the most important for age
prediction.

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Discussion: We provide indicators for using biological (predicted) brain age
as a metric for age correction in neuroimaging studies as an alternative tothe traditional chronological age. In conclusion, using different approaches,
our CNN-based model showed good performance using one T1w brain MRI
preprocessing step. The proposed CNN model is made publicly available forthe research community to be easily implemented and used to study ageing
and age-related disorders.
KEYWORDS

brain age, neurodegeneration, normal ageing, CNN, UK Biobank, ageing
prediction

1 Introduction

and/or cognitive reserve) (Cole et al., 2019; Stern et al., 2020). Withthe unprecedented growth of the elderly population worldwide andthe expected increase in dementia cases (WHO guidelines, 2019), a
biological marker of brain age could play a key role in dementia
prevention (Brusini et al., 2022).
In recent years, several brain age models have been developed
using different methods (Bocancea et al., 2021; Baecker et al., 2021a).
Previous studies employed machine (Franke et al., 2010; Franke andGaser, 2012; Cole et al., 2017; Hwang et al., 2021) and deep learningtechniques, with a focus on convolutional neural networks (CNN)(Cole et al., 2017; Jonsson et al., 2019; Kolbeinsson et al., 2019; Lianget al., 2019; Bintsi et al., 2020; Lam et al., 2020; Niu et al., 2020; Gupta
et al., 2021; Wood et al., 2022). These approaches achieved good
performance regarding mean absolute error (MAE) between 2 and6 years, with CNNs exhibiting superior results using images with few
image preprocessing steps. However, the model type and the input
choice varied across these studies that used preprocessed magnetic
resonance imaging (MRI) data (T1-weighted, T1w, or T2w), going
through normalisation, corrections, segmentation steps, or image
feature extraction. Such a chain of steps is challenging to implement
in research and, in the long term, in clinical settings due to time- andresource-consuming constraints.
Developing a model to predict “biological brain age” hinges on
selecting training data. Typically, a model is trained on neuroimaging
data of healthy individuals sourced from one or multiple cohorts,
encompassing a broad age range. The “ideal” dataset would include:(1) detailed information and clinical data of study participants inorder to be as comprehensive as possible with the selection criteria;(2) a large set of images, which are required to train a CNN model(Sajedi and Pardakhti, 2019); (3) participants with a diverse
demographical background and a large, preferably uniform, age
distribution to apply the developed model in more datasets (i.e.,
increase generalisability); (4) images acquired with a wide range of
MRI scanners and protocols to improve generalisation to new
unseen data of the model (Mårtensson et al., 2020), and (5)longitudinal data to ensure that the model does not predict a lower
age at a later time point. Although initiatives to gather large-scale
population-based datasets are ongoing (e.g., UK Biobank), to thebest of our knowledge, no existing cohort possesses all the fivecharacteristics listed above.
In this study, we aimed to develop and validate a CNN model
based on brain images that uses one preprocessing step (i.e., rigid
registration of T1w MRIs to the Montreal NeurologicalIn recent years, the concept of an individual’s biological age—which can differ from the person’s chronological age—has sparked
great interest in the medical research community, as ageing is asignificant risk factor for several age-related health conditions andmortality. There is also substantial heterogeneity in health outcomesamongst individuals of the same chronological age (Jylhävä et al.,2017). During the past decades, the research highlighted that thebiological ageing process varies between people because of thecomplex interplay between genetic and environmental factors, such as
lifestyle behaviours (Cole et al., 2017, 2019; Fratiglioni et al., 2020).
Given the ongoing changes in the body and brain throughout theageing process, chronological age stands out as a key risk factor for
mortality, chronic diseases, and functional impairment (Jylhävä et al.,2017). Various age-related changes in the brain are closely linked tothe development of several neurodegenerative disorders, including
Alzheimer’s disease (AD) and vascular dementia (Hou et al., 2019).
Like other age-related health conditions and also in the dementia field,
there is significant heterogeneity in the manifestation of the symptoms
as well as underlying brain pathology between people of the same
chronological age (Ferreira et al., 2020). Therefore, quantifying thebiological age could be a useful additional metric than the traditional
chronological age to identify individuals at risk of developing
age-related diseases (Cole et al., 2019; Tian et al., 2023).
Parallel advancements in neuroscience and computational science
have enabled researchers to develop novel algorithms to determine thebiological age of the brain. A biological marker of brain age will enable
us to adjust neuroimaging studies for the person’s biological age
instead of chronological age, capturing anatomical and functional
heterogeneities present in groups of healthy individuals. Anotheradvantage is that this could lead to a deeper understanding ofpathological ageing mechanisms, which can culminate in dementia.
Dementia is a multifactorial syndrome in which decades ofaccumulating neuropathology precedes clinical manifestation (Jacket al., 2010). The loss of neurons and synapses during the preclinicaland prodromal stages can lead to brain atrophy and, therefore, to“older-looking” brains (when biological brain age, i.e., predicted age,is higher than chronological age) (Cole and Franke, 2017; Bashyam
et al., 2020; Cole, 2020; Elliott et al., 2021). In contrast, someindividuals will show a higher chronological age than the biologicalbrain age, thus showing a “younger-looking” brain, which could
reflect relatively preserved brain structures (e.g., brain maintenance

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Institute—MNI—template space) for brain age prediction when using
external datasets. This minimal preprocessing feature has theadvantage and strength of simplifying the model’s implementation andincreasing accessibility in research settings. When publicly available,
the model can be quickly used for any T1w MRI scan without timeand resource-consuming preprocessing steps. To evaluate our model,
we used a large dataset of cognitively healthy individuals from six
cohorts to address the “ideal” dataset criteria. The CNN model wascompared using hold-out and cross-validation approaches. To verify
the model’s generalisability, we tested the abovementioned approaches
using two external datasets containing different scanners anddemographic characteristics from the training set. Furthermore,
we included the two cohorts used as external datasets in the crossvalidation loop to verify the model’s usability with different cohorts.
Finally, we employed the cross-validated model to predict brain age inskull-stripped images to ensure our model accurately predicted basedon the brain image signal. We then evaluated the model’s performanceusing two external datasets.

neurological and psychiatric disorders. Furthermore, individuals
had to have a clinical dementia rating (CDR) score equal to zero, amini-mental state examination (MMSE) score ≥ 24, self-reported
good health (this last when available), or ICD-9 or − 10 (details on
the used ICD codes can be found in Supplementary Table S1),
depending on the available data in each cohort.

2.2 Convolutional neural networks
The implemented supervised CNN uses PyTorch’s deep learning
framework (Paszke et al., 2019). The network architecture of ourmodel was based on the ResNet architecture (He et al., 2016), with 26
layers in total but with 3D kernels. Each convolutional operation isfollowed by batch normalisation and a Rectified Linear Unit (ReLU)
activation. The network was trained for 20 epochs with stochastic
gradient descent and an initial learning rate of 𝜆 = 0.002 that decreases
by a factor of 10 every five epochs. We used five independent models
during CNN development and the trained networks as an ensemble
model. Data augmentation was performed during model training in
70% of the training set and included random scaling, cropping offsets,
rotations (−5 to 5 degrees), affine, and gamma transformations(ranging from 0.5 to 2). In the data augmentation process, each image
was cropped to a dimension of 80 × 96 × 80 voxels with 2 × 2 × 2 mm3
resolution, thresholded to the 5th and 95th percentiles of the voxel
values, and scaled so that all voxels’ values were in the interval [−1, 1].
For training the model, a T1w brain MRI registered to the MNI
space and the individual’s chronological age were used. We streamlined
the image preprocessing to improve the model’s accessibility andprocessing speed. The sole preprocessing step involved is a rigid
registration (with six degrees of freedom) of the T1w MPRAGE MRI
to the MNI template space using FSL FLIRT 6.0 (FMRIB’s Linear
Image Registration Tool) for training the model and testing in external
datasets. A rigid registration is more straightforward and quicker thanan elastic registration. Omitting this step resulted in worse
performance despite implementing heavy data augmentation in the

2 Materials and methods
2.1 Study population
For this study, we included 17,296 T1w MRIs from 15,289(1,176 are 1.5 T and 16,120 are 3 T) cognitively healthy participantsfrom six cohorts: the Alzheimer’s Disease Neuroimaging Initiative(ADNI), the Australian Imaging, Biomarker & Lifestyle FlagshipStudy of Ageing (AIBL), the AddNeuroMed, the Group of
Neuropsychological Studies from the Canary Islands (GENIC,
from Grupo de Estudios Neuropsicologicos de las Islas Canarias),
the Japanese ADNI, and the UK Biobank (Figure 1). The
description of each cohort and information about image
acquisition protocols and scanners are in Supplementary material,
Section 1. A cognitively healthy status was defined based on theabsence of dementia, mild cognitive impairment, and other

FIGURE 1

Flowchart describing the data included in this study, which were available in our database system at the time of the study, for the different cohorts.
Where age ± SD is the mean age and standard deviation. Age ± SD and age range are in years.

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FIGURE 2

Scheme of our 3D CNN for training (A) and using for external dataset (B). In (A), the input is a T1w brain MRI previously registered to the MNI space, thesubject’s chronological age, and the output is the predicted brain age of the subject. Training and developing (dev) datasets are also shown, with data
augmentation being performed in 70% of the training set. In (B), the input is only a T1w MRI previously registered to the MNI space before using thetrained network (model’s weight) to predict brain age in the new and unseen data of an external dataset. Conv3D, 3D convolution; BatchNorm3d, 3D
batch normalisation; Dev, development dataset (for testing model’s performance while training); (Leaky)ReLu, (leaky) rectified linear unit; MaxPool3D,
3D max pooling; AvgPool3D, 3D average pooling; FC, fully connected layer; ResNet, residual network block.

training process (data not shown). Figure 2 shows a schematic
representation of the CNN model, its input for training, and whenusing it with external datasets.
Four separate models were developed in this project: one model
based on a hold-out approach of CNN (CNN1) and three models
based on a cross-validation approach (CNN2–4). Figure 3 displays theevaluation process scheme of our CNN model. Each one of theturquoise rectangles represents 1/10 of the primary dataset, composedof 16,734 raw MRIs from ADNI, AIBL, GENIC, and UKB cohorts.
Light blue rectangles indicate the 149 MRIs from AddNeuroMed
cohort, whereas lilac rectangles indicate the 413 MRIs from
J-ADNI. The CNN1 model is based on a hold-out approach with thetraining (80%), development (10%), and test (10%) datasets indicatedby the arrows. CNN2 and CNN4 models incorporated all data from
ADNI, AIBL, GENIC, and UKB cohorts in their cross-validation loop,
while AddNeuromed and J-ADNI were used for external validation.
The CNN3 model was similar to CNN2 and 4, except that
AddNeuroMed and J-ADNI were also incorporated in the crossvalidation loop—thus, no external datasets were used formodel evaluation.

ADNI, AIBL, GENIC, and UKB cohorts, was randomly split into a
training (Nimg = 15,052, Nsubj = 13,612, subsequently split into internal
validation set for the development of each model) and a hold-out test
(Nimg = 1,682, Nsubj = 1,503) set. If subjects had undergone multiple
scans, all their images were assigned to the same set. The test set wasevaluated after a satisfactory performance on the internal validation
set to reduce the risk of model overfitting. The data distribution intrain, development, and test sets for each cohort can be found in
Supplementary Figure S1. After training the CNN1 model, we applied
this model in the AddNeuroMed and J-ADNI cohorts to assess themodel’s performance and generalisability in external datasets.

2.2.2 Cross-validated approach
To allow comparability to the hold-out model (CNN1), we used thesame 16,734 MRIs from ADNI, AIBL, GENIC, and UK Biobank in the
cross-validation approach training loop (CNN2). Stratification by cohort
was applied in splitting the 10-fold for training and testing. After 10-foldcross-validation, the trained model was evaluated in AddNeuroMed and
J-ADNI (external cohorts). Furthermore, to ensure that our model’s
prediction was based on the brain and not on other features (e.g., head
shape), we trained a 10-fold cross-validation model using skull-stripped
brain images (CNN4). For CNN4 model image input, Freesurfer 6.0.0 wasused to perform skull-stripping, applying the algorithm recon-all, andselect the image generated before brain parcellation (brain.finalsurfs.
mgz). Images were motion- and bias-corrected, transformed to Tailarach

2.2.1 Hold-out approach
Our CNN-based model was first trained in a hold-out fashion(CNN1). To not violate the test set and ensure comparability between
the models, the primary dataset, composed of 16,734 MRIs from

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FIGURE 3

Scheme of evaluation of our CNN model in this study. We primarily used ADNI, AIBL, GENIC, and the UK Biobank to develop our model (turquoise
scale). CNN1 works in a hold-out approach (data split: 80% train, 10% development, 10% test set, each set of the data indicated by arrows). CNN2 wastrained as a 10-fold cross-validation model using the data of the four primary cohorts (turquoise scale) in the training loop. To evaluate theperformance of these two models, we used AddNeuroMed (light blue) and J-ADNI (lilac) as external test sets. In CNN3, we added the external test setsin the 10-fold cross-validation. For comparison reasons, we evaluated our CNN2 scheme with skull-stripped T1w MRIs (CNN4). HO, hold-out; CV,cross-validation; ED, external test set; SS, skull-stripped images.

space, intensity-normalised, and skull-stripped. To reduce the size of the
final processed image and for comparative reasons, all skull-stripped
images were rigidly registered to the MNI space. Similar to CNN2, theskull-stripped CNN4 model was externally validated in AddNeuroMed
and J-ADNI. Finally, to ensure the cross-validated model’s usability when
including more diverse data, we trained a last model, CNN3, that included
all cohorts (ADNI, AIBL, GENIC, and UK Biobank plus AddNeuroMed
and J-ADNI) to the ensemble of images within the 10-foldcross-validation.

model that, regardless of input data, only outputs a single
predicted age of, e.g., 70. This will yield a suboptimal MAE, butwhen “correcting” for age, the MAE will be 0 between predicted
and chronological age. Given the widespread use of the UK
Biobank in the literature as a common dataset for training
and evaluating brain age models, we conducted additional
assessments of our model’s performance using only the UK
Biobank cohort.

2.3.2 Relevant regions for brain age prediction
To explain the model’s brain age prediction, we generated 3D
gradient-based saliency maps of each subject using the SmoothGrad(Smilkov et al., 2017) algorithm. Salience maps visualise the importantvoxels in individual predictions based on the computation of the gradientof the prediction with respect to the smoothed image. For gradientcomputation, we used the image with 15% noise added. The 3D gradientmaps were averaged through the whole image sample, and only 1% of thehigher salient values were shown to verify the most critical regions(Levakov et al., 2020; Mouches et al., 2022). For individual extrapolation,
we plotted the 1% normalised higher values of the salience maps andoverlayed them onto an arbitrary T1w brain MRI. The salience maps arepresented according to their brain age difference in the CNN1 model,
calculated from the chronological age, from −8 to +8 years of difference
from the chronological age.

2.3 Analyses
2.3.1 Model performance
Model performance was assessed using the MAE, defined as:
MAE 

1 N 
 y  y c ,iN i iwhere yc,i is the chronological age of participant i and y i thepredicted age. MAE’s values close to zero indicate the model’sgood performance, with predicted brain age being similar or
almost equal to chronological age. Consequently, the evaluationof the model’s performance includes assessing the distribution
and correlations between chronological and predicted brain age,in addition to MAE. Adjusting for age-dependent predicted brain
age differences from the chronological age (brain age difference/
gap—BAG) is problematic and can artificially inflate model
performance (Butler et al., 2021). This can be illustrated with a

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2.3.3 Differences in cortical thickness across age
groups based on chronological and predicted
brain age
To analyse the influence of correcting an individual’s brain age inneuroimaging studies, we visualise how brain age predictions are

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related to cortical thickness values. We ran surface group analysis with
QDEC (Query, Design, Estimate, Contrast) in FreeSurfer 6.0.0.
We used a smoothing kernel of full width at half maximum of 10 mm,
used sex as a covariate, and adjusted for false discovery rate at athreshold of 0.05. We grouped subjects of ages 60, 65, 70, 75, and 80(±1 year) based on chronological and predicted ages. These groups
were contrasted to a reference group of 55 ± 1 years old (based oncorresponding chronological or predicted brain age) individuals in thegeneral linear model. Since these groups are of different sizes—andp-values are influenced by group size—we present figures overlayedwith z-scores.

between chronological and predicted brain age in training (0.83) andtest sets (0.80), with MAE = 2.70 years in the training set and
MAE = 2.99 years in the testing set.
Scatterplots of the brain age predictions for the CV approach for
the CNN2 (CNN1 cohorts), CNN3 (CNN1 cohorts + J-ADNI and
AddNeuroMed), and CNN4 (skull-stripped images from CNN1
cohorts) are shown in Figure 5. For CNN2 and CNN3 models, results
show a strong correlation (0.83, for both CNNs) between chronologicaland predicted brain age with a MAE within the lower range ofpreviously published models (MAE = 2.67 years, for both CNNs).
However, CNN4 presents a less strong correlation (0.77) and a higher
MAE (3.08 years) within the presented models in this study. Age biascorrected scatterplots of all models are presented in
Supplementary material, Section 4, Supplementary Figures S2, S3.
Bland–Altman analysis of chronological and predicted brain age ispresented
inSupplementarymaterial,
Section5.
Supplementary Figure S4 illustrates that most of our predictions fit
within the two standard deviations, with disagreements between
models varying between −0.05 and 0.18.
In order to allow comparison between hold-out and crossvalidation approaches, we evaluated only the individuals used in the
training/test set of the CNN1 model (hold-out) in the CNN2 (crossvalidation approach) (Figure 6). Figure 6A shows the performance of
CNN1 (left) and CNN2 (right) using data from the test set. The crossvalidation approach (CNN2) shows better performance (smaller

3 Results
3.1 Model performance
Model performance was evaluated through the model’s mean
absolute error and correlation coefficients to verify the relationshipbetween chronological and predicted brain age. The results arepresented in Figures 4–8, with MAE ranging from 5.63 to 2.25 years
and correlation coefficients ranging from 0.77 to 0.90.
Scatterplots of the brain age predictions on the CNN1 (hold-out)
approach for the training and testing set of healthy individuals areshown in Figure 4. The results show that there is a strong correlation

FIGURE 4

Scatterplots of the predicted brain age in the CNN1 model (hold-out approach) in the training and test datasets. Each colored dot represents an
individual, and each color is a different cohort. The red line is a linear regression based on the predicted brain age.

FIGURE 5

Scatterplots of the predicted brain age in CNN2, 3, and 4 (cross-validation approach). Each dot represents an individual, and the color code used foreach cohort is presented in the legend. CNN2 was run using 4 cohorts, CNN3 with 6 cohorts, and CNN4 with 4 cohorts but with skull-stripped images.
The red line is the linear regression based on the predicted brain age.

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FIGURE 6

Comparison of the CNN1 (hold-out approach) and CNN2 (cross-validation approach) models. Data used for the comparison is based only on theindividuals used in the test dataset of CNN1. In (A), we present the test dataset’s predicted brain age (y-axis) for both models correlated tochronological age (x-axis). In (B), we present the correlation between the predicted brain age estimated by the two models (CNN1 on the x-axis and
CNN2 on the y-axis) in the training (left) and test (right) datasets split by CNN1. In (C), we present the correlation between the brain age difference
estimated by the two models (CNN1 on the x-axis and CNN2 on the y-axis) in the training and test datasets.

MAE) and correlation coefficient (0.86 compared to 0.80 from CNN1)
than hold-out. Figures 6B,C illustrate the correlations between brain
age predictions for both hold-out (CNN1, x-axis) and cross-validation(CNN2, y-axis) approaches in the training (left) and test (right)
datasets. The correlation of brain age predictions for CNN1 and
CNN2 is shown in Figure 6B, while Figure 6C illustrates thecorrelation between BAG of both models. Both predicted brain age
and BAG show a high correlation between models.
We also evaluate the performance of our model in the external
datasets AddNeuroMed and J-ADNI (CNN1, 2, and 4). The age
prediction distribution is shown in Figure 7 and shows the variability
in age prediction for each one of the trained models when applied tounseen data (external dataset).
The calculated MAE of each cohort for all models is presented inTable 1 and ranges from 5.63 to 2.25 years. Normalised MAE andcoefficient of determination for each cohort of the trained models canFrontiers in Aging Neurosciencebe found in Supplementary Tables S2, S3 and range from 1.04 to 0.35
and 0.89 to 0.55, respectively.
To understand how our model performed compared toexisting models, we further assessed our brain age models’performance only within the UK Biobank. Then we compared theMAEs we achieved with previous studies that evaluated theirmodels only in the UK Biobank cohort or using different cohorts(Table 2). Our CNN models achieved MAEs ranging between
2.66 and 3.03 years only using images of the UK Biobank cohort.
These are very similar to the MAEs achieved by CNN models(Table 2) developed in previous studies (ranging between 2.13
and 4.36). We also present the coefficient of determination
between predicted brain age and the identity line for all theavailable studies.
To understand the noise levels from our models and their ability
to capture subtle changes as a result of the ageing process, we plotted

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FIGURE 7

Comparison of brain age prediction distribution for chronological and predicted brain age in CNN1, 2, and 4 for the AddNeuroMed (A,B) and J-ADNI(C,D) cohorts. In (A,C), we present the boxplot of the predicted brain age for each model compared to the chronological age. In (B,D), the predicted
brain age distribution is compared to the chronological age.

TABLE 1 Calculated MAE for each cohort for each of the trained models.

Model
CNN11

MAE per cohort (years)
ADNI

AIBL

GENIC

UK Biobank

AddNeuroMed

J-ADNI

2.56

2.85

4.20

2.75

4.03

5.63

2

CNN2

2.40

2.69

3.96

2.66

3.67

5.30

CNN32

2.41

2.69

3.98

2.67

3.02

2.25

CNN41

2.98

3.33

4.42

3.03

3.64

4.14

The numbers in italics (AddNeuroMed and J-ADNI cohorts for models CNN1, 2, and 4) were calculated as external datasets for the model. Statistical differences were found (value of p < 0.05)
comparing the CNN absolute error; 2No statistical differences were found in the comparison between the absolute error of CNN2 and CNN3 (value of p = 0.37). Statistical differences between
the models were calculated using Friedman’s test with Conover’s post hoc test, adjusting for multiple comparisons with the Bonferroni method.
1longitudinal trajectories for the participants with multiple time
points. The longitudinal predictions, supported by an average
longitudinal brain age gap between 0.05 and − 0.18, align with the
expected pattern, demonstrating an increase in predicted age overtime (Figure 8).

variability in these patterns for CNN4. A complementary view ofsalience map slices is presented in Supplementary Figure S5.
To identify regions of importance for predicting biologicalbrain age, 18 individuals were randomly selected according to thefollowing criteria: being in the test dataset of the CNN1, being
between 64 and 66 years, and having an age range difference
between chronological and predicted brain age in the CNN1
model between −8 and + 8 years (Figure 10). The brain age gap foreach individual, predicted in each model, is presented in
Supplementary Table S4.
Generally, the averaged salience map across the entire image
sample aligns with the highlighted regions in the BAG analysis
for the same chronological age (65 years). The BAG analysis
reveals similar important regions for predictions, albeit with

3.2 Relevant regions for brain age
prediction
For the explicability of our model, the salience map of eachindividual prediction was generated. The averaged overlayed saliencemaps for each CNN model are presented in Figure 9, showing similar
patterns of relevant regions for predictions for CNN1-3 and higher

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TABLE 2 Comparison of our models MAE with the literature.

Study

Model

Modality

Preprocessing

MAE

R2

Only using the UK Biobank
Dartora et al.

CNN1

T1

Rigid reg.

2.75

0.88

Dartora et al.

CNN2

T1

Rigid reg.

2.66

0.89

Dartora et al.

CNN3

T1

Rigid reg.

2.67

0.89

Dartora et al.

CNN4

T1

Bias-field and motion cor., Skull-strip., Rigid MNI reg.

3.03

0.85

Bintsi et al. (2020)

CNN (3D ResNet)

T1

Skull-strip., non-linear MNI reg.

2.64

0.77

Bintsi et al. (2020)

CNN (patches)

T1

Skull-strip., non-linear MNI reg.

2.13

0.85

Kolbeinsson et al. (2019)

CNN

T1

Skull-strip., non-linear MNI reg.

2.58

-

Lam et al. (2020)

R-CNN

T1

Bias-field cor., Skull-strip., Rigid MNI reg.

2.86

0.87

Lam et al. (2020)

CNN

T1

Bias-field cor., Skull-strip., Rigid MNI reg.

4.36

0.66

Gupta et al. (2021)

CNN (slices)

T1

Bias-field cor., Skull-strip., Rigid MNI reg.

2.82

-

Dinsdale et al. (2021)

CNN (Female population)

T1

UK Biobank pipeline w/linear registration to MNI

2.86

0.87

Dinsdale et al. (2021)

CNN (Male population)

T1

UK Biobank pipeline w/linear registration to MNI

3.09

0.86

Jonsson et al. (2019)

CNN (transfer learning)

T1

Bias-field cor., Skull-strip., Dartel MNI reg., tissue maps

3.63

0.61

Peng et al. (2021)

SFCN

T1

Bias-field cor., Skull-strip., Rigid MNI reg.

2.14

0.39

Peng et al. (2021)

CNN

T1

Bias-field cor., Skull-strip., Rigid MNI reg.

2.38

-

Study

Model

Modality

Samples/
Cohorts

Preprocessing

MAE

R2

Using different cohorts
Dartora et al.

CNN1

T1

16734/4

Rigid reg.

2.99

0.80

Dartora et al.

CNN2

T1

16734/4

Rigid reg.

2.67

0.83

Dartora et al.

CNN3

T1

17296/6

Rigid reg.

2.67

0.83

Dartora et al.

CNN4

T1

16734/4

Bias-field and motion cor., Skull-strip., Rigid MNI

3.08

0.77

3.05

-

3.65

-

reg.
Wood et al. (2022)

3D DenseNet

T211735/2

Resampling, cropping, padding, intensity
normalisation.

Wood et al. (2022)

3D DenseNet

T211735/2

Resampling, cropping, padding, intensity
normalisation, skull-strip.

Wood et al. (2022)

3D DenseNet

T1

2387/1

Skull-strip., MNI registration

3.83

0.95

Wood et al. (2022)

3D DenseNet

T1

2387/1“Raw T1”*

4.86

0.90

He et al. (2022)

2D CNNT1

8379/8Bias-field cor., field of view norm., Skull-stripp.,

2.70

-

3.00

-

4.20**-

registration to SRI atlas, cropping.
He et al. (2021)

3D FUS CNN

T1 split

16705/8

Bias-field cor., field of view norm., Skull-stripp.,
multi-site data harmonisation with histogram
matching to SRI atlas, split in two channels (contrast
and morphometric), intensity normalisation.

Lee et al. (2022)

3D DenseNet

T1/PET

4127/1

MRI: intensity correction, and segmentation oftissues, intensity normalisation; PET: registration to
MRI and MCALT space, scale to SUVRpons,segmentation in meta-ROI.

CNN, convolutional neural network; CNN1, our CNN model in a hold-out approach; CNN2, our CNN model in a cross-validation approach; CNN3, our CNN model in a cross-validation
approach, including two cohorts in the dataset; CNN4, our CNN model in a cross-validation approach, using skull-stripped images; R-CNN, recurrent CNN; SFCN, simple fully convolutional
network; R2 = coefficient of determination. Dartoral et al. refer to the current study.
*The authors do not specify if any processing was done. For the “raw” T2 images in the same study, resampling, cropping, and intensity normalisation were performed.
**Calculated MAE only for the MRI. SUVRpons, standardised uptake volume ratio in relation to the pons; Meta-ROI, meta region of interest.

variations in the intensity of importance amongst them. The
negligible (low importance values) importance observed forspecific regions may be attributed to the smaller number ofFrontiers in Aging Neuroscience

images used to train CNN1. It is crucial to note that the BAGs
depicted in the top row of Figure 10 are based on CNN1
predictions for the plotted individuals.

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FIGURE 8

Brain age prediction in longitudinal trajectories for all the models. The average brain age gap/difference (Avg. BAG) in the presented population was
calculated and shows a trend towards zero for the brain age difference between predicted and chronological age. In CNN1, only the individuals withlongitudinal data in the test set are presented.

FIGURE 9

Relevant regions for age prediction. The absolute values of the salience maps for each model were averaged through the whole MRI sample andnormalized between 0 and 1 for better visualisation. The SmoothGrad method was used to generate all salience maps. Overlayed salience map colours
are normalized for each individual between 0 and 1.

FIGURE 10

Relevant regions for the brain age prediction based on 1% of the salience maps of individuals with an average age of 65 years overlayed to a random
T1w MRI sample for each of the four CNN models. The individuals were randomly selected in the test dataset used in CNN1, according to their brain
age difference and proximity to the average age of our sample (65 years). Each row represents the salience maps of one CNN model, where thecolumns show the same individual’s salience map in each of the four models. The SmoothGrad method was used to generate all salience maps. The
brain age difference is shown in the top row of the image, going from −8 to +8 years. Overlaid salience map colours are normalized for each individual
between 0 and 1.

In comparison, the CNN4 model exhibits BAG with greater
variability than the other three models (CNN1–3). The intensity ofimportance, with higher importance depicted in yellow colours, proves
more significant for predicting younger ages (e.g., −6 years), particularly

Frontiers in Aging Neuroscience

near the ventricles (Bintsi et al., 2021) and insular cortex (Lee et al., 2022).
Conversely, when predicting older ages, greater importance is assigned to
the right side of the insular cortex (Lee et al., 2022) and the frontal–
occipital region.

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FIGURE 11

Atrophy patterns in ageing according to chronological and predicted brain age in the cross-validation approach (CNN2). Analysis shows cortical
thickness z-stat maps corrected for false discovery rate with a threshold of 0.05 of cross-sectional differences between a reference group of 55 (±1.0)
years old and older subjects. Individuals were grouped by age ± 1.0 years. The QDEC analyses were done with a 10 mm smoothing kernel and sex
added as a covariate. Blue colours represent lower cortical thickness (increased atrophy) in the age group compared to the 55-year-old group, and red
colours represent an increased cortical thickness compared to the younger group. It is possible to verify that the atrophy patterns start in parietal–
frontal regions in the 65-year-old group. The approach also captures the increased thickness (in red) around the lingual gyrus.

3.3 Differences in cortical thickness acrossage groups based on chronological andpredicted brain age

et al., 2021; Kolbeinsson et al., 2021; Peng et al., 2021; Ren et al., 2022).
Our CNN1 model achieved an MAE of 2.99 years in the test set, which
agrees with the MAE reported in the literature for hold-out test setsusing 1–3 cohorts (MAE of 2.14–4.65 years) but several timeconsuming preprocessing steps (Cole et al., 2017; Jonsson et al., 2019;
Lam et al., 2020; Dinsdale et al., 2021; Gupta et al., 2021; Kolbeinsson
et al., 2021; Peng et al., 2021; Ren et al., 2022). Our calculated MAE isalso in the range of the available MAEs of the CNN models (Jonsson
et al., 2019; Bintsi et al., 2020; Lam et al., 2020; Dinsdale et al., 2021;
Gupta et al., 2021; He et al., 2021, 2022; Kolbeinsson et al., 2021; Peng
et al., 2021; Lee et al., 2022; Wood et al., 2022) available in the literaturethat used hold-out approaches and only data from the UK Biobank
but performed several preprocessing steps as opposed to our CNN
model. The current debate surrounding the development of brain age
models lacks a thorough evaluation of CNN-based brain age models
across different external datasets. In such a scenario, it is difficult andrelatively unfair to compare the performance of different models
without using the same evaluation dataset (Sajedi and Pardakhti,
2019). To overcome such a challenge, we evaluated the performanceof our CNN models in two external datasets, AddNeuroMed and
J-ADNI, showing that CNN1’s MAE performance is still within arange of 1.5 years when compared to the other CNNs.
Deep learning models, like CNNs, tested in out-of-distribution data
result in performance dropping, increasing underestimation in new
unseen data. However, including more diverse and variable data in themodel’s training increases the model’s robustness/reliability (Mårtensson
et al., 2020). One way of including variability in the model is by running
it in a cross-validation fashion. We have done this in CNN2, where thesame data used for training, validating, and testing CNN1 was used fortraining a 10-fold cross-validation model (CNN2). In general, theCNN2 model had better performance than CNN1. The correlation withchronological age was higher, the MAE was smaller in CNN2 in all casesof age prediction (training and test sets), and the calculated brain age
difference (BAG) was smaller when comparing both models.

Figure 11 shows the age-related differences in cortical thickness forchronological and biological (predicted) age groups by the CNN2
model. The number of individuals in each age group is presented in
Supplementary Table S5. Differences in atrophy patterns between
chronological and predicted brain age are observed in the older groups,
from the age of 70 years, with mid-frontal and parietal–occipital regionsof broader and more widespread differences around the cortex.

4 Discussion
In this study, we developed a CNN-based model using one
preprocessing step (rigid registration to MNI space), when using
new and unseen data in the model, to predict the person’s biologicalbrain age from T1w images to be easily implemented and used.
Furthermore, the study was performed as an effort to attend to thehighlighted points related to an “ideal” image database (which
includes diverse image data) to develop brain age prediction
models. Finally, our model evaluation was performed using
different approaches—hold-out (CNN1) and cross-validation(CNN2)—and generalisability was tested in external datasets. The
usability (CNN3) of the model was also assessed by adding moredata (from two external datasets) in the training loop, and toensure that the previously trained models were using the brain
signal for age prediction, we also trained the model using skullstripped images (CNN4).
Due to the complexity and time-consuming nature of the training
networks with large amounts of data, the hold-out method used in
CNN1 is the most common approach in the literature (Cole et al.,2017; Jonsson et al., 2019; Lam et al., 2020; Dinsdale et al., 2021; Gupta

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Comparing the performance of the different cohorts used in thedevelopment of this study, we see a tendency for individual smaller
MAEs in CNN2. To confirm the robustness of the model, CNN2 wasalso evaluated in external datasets, confirming the decrease inunderestimation, as proved by the increase in the model’s performancein AddNeuroMed and J-ADNI cohorts when compared to CNN1. Even
though CNN1 and CNN2 have similar performance in age prediction,
the increased variability in the model’s training reduced the average andcalculated MAE for each cohort. This is further proved by CNN3, where
CNN2 and CNN3 performed similarly (p > 0.05). Both models
presented the same MAE of 2.67 years and a coefficient of determinationof 0.83, but different performances in out-of-distribution data.
Specifically, evaluating the predictions in the J-ADNI cohort in CNN3,
it is possible to verify that adding the cohort to the training set decreased
the predicted MAE for this cohort, going from an MAE of 5.63 in
CNN1 to 2.25 in CNN3. This reinforces our hypothesis that the small
number of data does not increase the absolute error across all thecohorts (AddNeuroMed and J-ADNI represent ~3% of the total numberof images used in CNN3) but increases the performance in a specificcohort. This increases the model’s usability, as data with more variability
will be included in the training set, ameliorating the age prediction inexternal cohorts (Mårtensson et al., 2020). Therefore, additional tests
with a greater variety of cohorts are necessary to understand how wide
the model’s generalisability is and how it can change by adding moredata to the training set.
To ensure that our model was using only brain signals from the
T1w MRI and that predictions were not depending on the headmorphology or bone tissue, we trained and evaluated CNN4. Thismodel is comparable to CNN2, apart from that skull-stripped brain
images were used as input. Regarding performance, CNN2 presented
a smaller MAE and a better correlation to chronological age than
CNN4. The same tendency is also present in evaluating data only fromthe UK Biobank. This shows that our CNN works better withminimally preprocessed brain MRIs when compared to heavily
preprocessed images. However, for evaluating the external dataset,
J-ADNI, CNN4 presented a smaller MAE than CNN2. We hypothesisethat using heavily preprocessed images as in CNN4 could remove or
decrease the effects of bias field and skull size/format and partiallymitigate the inhomogeneity present in the external dataset.
The lower limit of the MAE score is unknown and depends on
both the inter-subject variability and age distribution of both training
and test datasets. By using stricter exclusion criteria for what is
considered “cognitively healthy,” the variability and theoretical MAE
lower bound decrease. This makes comparisons between studies ofmodels evaluated on different datasets challenging. Several studies
have trained and evaluated their model on the UK Biobank cohort,which enables rough comparisons. However, this restricts the model
to cross-sectional image data (at least for the first wave of the UK
Biobank data) from a “homogeneous” population from theUnited Kingdom acquired in standard equipment (Siemens
Magnetom Skyra Syngo MR D13) with 3 T MRI following the same
protocol, which is not the reality for datasets and clinical/researchsettings. Also, the performance of CNNs trained on medical images
from one cohort may produce systematically different predictions on
images outside the training data distribution (Mårtensson et al., 2020).
Comparing our results with the literature applied only to UK Biobank
images, we observed that using several time-consuming image
preprocessing steps, none of the models achieved a MAE smaller thanFrontiers in Aging Neuroscience

2.13. The CNN4, which uses skull-stripped images, showed the worst
performance within our different approaches using the same CNN
architecture. For a more accurate comparison of the model’s
performance using the MAE metric, normalised MAE should be used.
However, not all the selected papers for comparison presented theaverage age of the used subgroup of UK Biobank data, limiting the
calculation of a normalised MAE. For future comparisons to ourstudy, the normalised MAE for all four different approaches ispresented in Supplementary material, Section 6.
Essentially, all our trained models showed MAE levels comparable
to those reported in previous literature, which is typically in the rangeof 2.13–6 (Jonsson et al., 2019; Kolbeinsson et al., 2019; Sajedi andPardakhti, 2019; Bintsi et al., 2020; Cole, 2020; Lam et al., 2020;
Baecker et al., 2021a,b; Dinsdale et al., 2021; Gupta et al., 2021;
Tanveer et al., 2022). This indicates that our models have good
performance, with the advantage of requiring only one
preprocessing step.
A “perfect” model for brain age prediction in cognitively
unimpaired individuals should show smooth and non-declining
trajectories within the same individual at different time points, assumingthat a healthy person’s brain age does not vary rapidly or decrease/increase substantially. Visually, our CNN model in different approaches
seems to generate smooth predictions that increase with chronologicalage, with some noisy predictions deviating from the trajectory. For
quantitative analysis, we also calculate the mean age gap (BAG) of all
individuals, the mean squared error, and R2. The models present mean
brain age differences between chronological and predicted brain ages
smaller than 0.5 years. However, the predictions also show a potential
confounder in subsequent analyses of brain age predictions: noise. As it
can be observed, for some subjects, fluctuations in brain age differedsome years between two scanning sessions that were 1 year apart. It
seems unlikely that this is a biological phenomenon, but instead it isattributed to the input data being noisy or of low quality. Most datasets
do not have abundant longitudinal data to sort “bad” predictions from“good” predictions. If the data and group sizes are extensive, some levelof noise is acceptable and might not affect the interpretation of theresults. The maximum average difference between chronological andbrain age of all longitudinal plots was −0.11, with R2’s higher than 0.88.
However, this effect can have a non-negligible impact when analysing
datasets with small group sizes or running longitudinal analyses withfew follow-up scans. This is important to remember when conducting
future studies related to brain age, for example, when investigating theassociation between brain age and neurological disorders with a low
prevalence in the population.
The analysis of important brain regions for prediction was realised
qualitatively by the plots of the salience maps. The salience maps showthat important regions for predicting subjects as older were on theright side of the insular cortex and in the frontal–occipital regions. In
contrast, for predicting subjects as younger, highlighted regions arelocated around the ventricles and insular cortex. The salience maps
also show regions symmetrical and asymmetrical, mainly on the left
side of the brain (Roe et al., 2021) and around the ventricles (Bintsi
et al., 2021), as important for age prediction. Left brain asymmetries
with ageing are a typical pattern found in ageing studies of cortical
thickness (Koelkebeck et al., 2014; Frangou et al., 2022), corticalvolume, and surface area (Koelkebeck et al., 2014). In agreement with
Lee et al. (2022), who plotted salience maps for different decades,
regions with a higher contribution for age prediction were in the

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insular cortex (from 30- to 50-year groups), ventricular boundary (50to 60-year group), and cerebellum (90- to 100-year group). Further
studies are necessary to understand why CNN3 has higher variability
in the important regions for prediction. They should delve into thenuances of the CNN3 model, which uses the same image data as
CNN2 but incorporates additional preprocessing steps.
Interestingly, regions around the eyes were selected in the three
non-skull-stripped models (CNN1–3). We hypothesise that changes
in the soft tissue and liquid surrounding the orbital space, such as thebony orbit, which has differences in sex (i.e., men usually have agreater skeleton size than women) (Erkoç et al., 2015), as well as
general dimensions in orbital structures (Rana et al., 2022), could
be used in the model to predict age. For the model using skull-stripped
brain images as input (CNN4), right regions close to the cerebellum
and occipital lobe, outside the brain, were selected as important forprediction. We believe that the increased noise used for the
SmoothGrad in a region that could have a higher neurodegenerative
load could be leading to prediction importance outside the brain inthis model. More studies are necessary to understand the highlightsof this region for prediction. However, we believe this could be an
artefact generated by the SmoothGrad method due to the addition ofnoise in the image for the construction of the maps. Future studies
could use different methods to generate salience maps and use
different methods to generate skull-stripped images to verify if thisoutstanding region (right outside the skull region) continues tobe highlighted for prediction in this model.
One of the theoretical uses of biological age is for age correction
in neuroimaging studies. The hypothesis behind it is that correcting
neuroimaging studies for the biological (predicted) age of theindividuals will better handle the heterogeneity that we see in ageing,
incorporating diversity in longitudinal brain trajectories due tolifestyle, environmental, or even biological factors (Cole et al., 2019;
Tian et al., 2023). For the analysis of chronological and biological(predicted) age atrophy pattern differences, we can observe differences
mainly in the older groups (as of 70 years). In the comparison of thegroup of 70 years in chronological and biological (predicted) brain
age, atrophied areas of higher statistical significance are present in the
mid-frontal to parietal–occipital regions of the group based on
biological age. This agrees with the work of Thambisetty et al. (2010),
where an anterior–posterior gradient in age-related brain atrophy was
found, with frontal–parietal regions showing a greater decline. The
groups between 75 and 80 years have more similar atrophy patterns,
with higher spread to the parietal lobe in the biological (predicted)
brain age. Interestingly, a region of greater thickness in the oldest
groups, compared to the reference group (55 years), was found in the
primary visual cortex, located in the calcarine sulcus. The shrinkageof the visual cortex is still widely discussed in the literature, with ahandful of studies showing cortical thinning of the visual cortex to thesparing of this region. These studies suggest that the visual cortex
thickness is use-dependent instead of age-related (Burge et al., 2016;
Griffis et al., 2016; Jorge et al., 2020). We hypothesise that this can be acohort confounding effect, even individuals of 75 years being present
in all cohorts, but in larger amounts in ADNI and AIBL. Differencesin atrophy patterns between individuals grouped by theirchronological and biological (predicted) brain age need to be further
studied. However, our results already show different tendencies inatrophy patterns between them. Correcting for biological (predicted)
brain age in neuroimaging studies could be one step further in

Frontiers in Aging Neuroscience

understanding heterogeneity present in ageing and be used in early
diagnosis of neurological diseases, prognosis, and even monitoring oftreatment response, being one step further to precision medicine.
A key strength of this study is the use of minimally processed
images as input for the CNN model, which makes it feasible for
implementation in research and, in the long term, clinical settings.
Our model requires only registration in MNI space, which typicallytakes a few seconds and can be easily performed using an open brain
image processing software such as FSL or FreeSurfer. Additional
preprocessing steps would increase the likelihood of image exclusion
during quality control and preprocessing. This would limit the model’s
performance and the possibility of using cohorts with a small sample
size. CNN models can learn from the image data, including structureand shape, which may not be captured by summary metrics such asvolume or segmented tissue maps, without requiring pre-segmented
data (Liang et al., 2019; Niu et al., 2020). Our focus on one-step
preprocessing, using a rigid registration to the MNI space templatepreviously implemented by Cole et al. (2017), is to ensure accessibility
to our model in the future research.
This study includes cohorts to attend to the highlighted points
related to, what we believe to be, an ideal image database to develop brain
age prediction models. We used detailed information and clinical datafor inclusion criteria of many individuals (more than 16,000 T1w MRIs)
from different parts of the world (Asia, Australia, Europe, and America)
and with a diverse number of image acquisition protocols and MRI
scanners covering 1.5 T and 3 T scanners, which increases the model
usability due to its generalisation to new unseen data (Mårtensson et al.,2020). This model was compared to the model using only UK Biobank
as input with the same acquisition protocol. The results show that themodel performance is similar when we include not only different
scanners with different magnetic fields but also different acquisition
protocols from the different cohorts. We also used all the longitudinal
data available and showed that our model presents a reasonable age
prediction congruent with the individual timeline. Also, our focus on the
cognitive “healthy” status, rather than on the overall health status (e.g.,
excluding from the training set age-related diseases that affect the body’s
organs other than the brain) is a strength of our study as it makes a clear
separation between the outcome (brain age) and what leads to thatoutcome (risk factors, e.g., chronic cardiometabolic disease and risk
factors, affective/mood disorders, etc.). Furthermore, to validate thegeneralisability of the models, we used external datasets of cognitively
unimpaired individuals from two cohorts: AddNeuroMed, a crossEuropean study designed to find biomarkers for Alzheimer’s disease, and
J-ADNI, the Japanese version of the ADNI dataset. We chose to use thesecohorts because of differences in age distribution, e.g., AddNeuroMed
average age is approximately 10 years older than the average of our total
sample, different ethnicities, e.g., our sample is composed mainly ofEuropean and North American individuals, and both cohorts mostly use
images acquired in 1.5 T scanners, whereas the training dataset was
based mainly on 3 T MRI, i.e., 96.2% of all images used for training andtesting in the CNN1 approach.
Some limitations need to be acknowledged. The large dataset in
the current study hindered the possibility of performing extensive
quality control. For the CNN model, this would mean inspecting
whether the rigid registration was adequate. The random cases
we inspected suggested that the overall quality of the segmentations
was sufficient. However, tools to automate the quality control
process—such as Brusini et al. (2020) and Klapwijk et al. (2019)—will

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Data availability statement

be necessary for future studies on this data size. Regardless of the lackof extensive quality control of the images, our model showed robust
findings with only slightly worse performance when compared topreviously published works. A potential limitation of this type ofmodel, only trained in cognitively intact individuals, is its performancein neurodegenerative diseases, which warrants further investigation.
Even though we defined the cognitively “healthy” status asconsistently as possible across the cohorts, some variation exists, but
we acknowledge the clinical and cognitive assessments relied on similar
procedures across cohorts. Given our large training set, heavy data
augmentation, and running only 20 epochs in training, we minimised
the risk of overfitting. However, a crucial consideration for futurestudies is evaluating how the inclusion of different populations
influences brain age prediction models based on minimally processed
MRIs. To enhance model robustness, future training models should
encompass greater diversity, including cohorts from Asia, Africa, andLatin America, and involve a broader array of scanners (from 1.5 T to
7 T) and imaging protocols (Mårtensson et al., 2020). It is also importantto address differences in the distribution of chronological age and thenumber of subjects in each cohort, as these variations may contributeto overfitting some cohort-specific information and characteristics (e.g.,
the model may learn that images from GENIC generally fall within thelower age span). Finally, it is worth noting that there might
be sociodemographic differences between the cohorts since therecruitment of participants happened in different geographical areas(J-ADNI: Japan, AIBL: Australia, ADNI: North America, and UK
Biobank, GENIC, and AddNeuroMed: Europe). However, this is notnecessarily a limitation but rather a strength. Indeed, the developed
algorithm could be applied in the future research, in which the biologicalage of the brain is a focus, to some extent independent of the cohort
characteristics, thus increasing the generalisability of the model. Futurestudies need to further test this hypothesis and the impact of different
cultural backgrounds on the estimation of brain age.

The original contributions presented in the study are included in
the article/Supplementary material, further inquiries can be directedto the corresponding authors.

Ethics statement
The studies involving humans were approved by Ethics Review
Appeals Board DNR 2021-04428. The studies were conducted inaccordance with the local legislation and institutional requirements.
Written informed consent for participation was not required from theparticipants or the participants' legal guardians/next of kin in
accordance with the national legislation and institutional requirements.

Author contributions
CD: Conceptualization, Data curation, Formal analysis,
Investigation, Methodology, Software, Validation, Visualization,
Writing – original draft, Writing – review & editing. AM:
Conceptualization, Data curation, Formal analysis, Investigation,
Methodology, Writing – original draft, Writing – review & editing.
GM: Conceptualization, Data curation, Methodology, Software,
Writing – original draft. GR: Writing – review & editing. JD: Writing– review & editing. J-SM: Data curation, Resources, Writing – review& editing. L-OW: Writing – review & editing. RM: Writing – review& editing. JB: Writing – review & editing. DF: Writing – review &editing. HS: Writing – review & editing. EW: Conceptualization,
Funding acquisition, Investigation, Project administration, Resources,
Supervision, Writing – review & editing.

Alzheimer's Disease Neuroimaging
Initiative

5 Conclusion
In this study, we developed a CNN-based model to predict
biological brain age using raw T1w MRI registered to the MNI space,with the goal of accessibility and simplicity in implementation. The
model was systematically evaluated using different approaches,comprising several datasets of cognitively healthy individuals withdifferent scans and population characteristics, as well as using crosssectional and longitudinal data. Our CNN-based model provides
results comparable to other validated methods in the literature (a.k.a.
state-of-the-art methods) but uses one preprocessing step when using
external datasets. The generalisability and usability of the model were
tested using external datasets with different demographic
characteristics, MRI protocols, and MRI scanners, proving therobustness of the model. In addition, we present the important regions
for brain age prediction. We also provide indicators for the use of
biological (predicted) brain age as a metric for age correction inneuroimaging studies as an alternative to the traditional chronologicalage based on the differences in cortical atrophy. Finally, the model’scode and trained CNN weights are made publicly available for the
research community to quickly implement and use in their research
to study ageing and age-related brain disorders.

Frontiers in Aging Neuroscience

Data used in the preparation of this article were obtained from the
Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.
loni.usc.edu). As such, the investigators within the ADNI contributed
to the design and implementation of ADNI and/or provided data but
did not participate in the analysis or writing of this report. A complete
listing of ADNI investigators can be found at: http://adni.loni.usc.edu/
wp-content/uploads/how_to_apply/ADNI_Acknowledgement_
List.pdf.

Australian Imaging Biomarkers andLifestyle Flagship Study of Ageing
Data used in the preparation of this article was obtained from theAustralian Imaging Biomarkers and Lifestyle Flagship Study of Aging(AIBL) funded by the Commonwealth Scientific and Industrial
Research Organisation (CSIRO), which was made available at theADNI database (www.loni.usc.edu/ADNI). The AIBL researchers
contributed data but did not participate in the analysis or writing of
this report. AIBL researchers are listed at: https://aibl.csiro.au/.

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Japanese Alzheimer's Disease
Neuroimaging Initiative

Johnson & Johnson Pharmaceutical Research & Development LLC.;
Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.;
NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals
Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical
Company; and Transition Therapeutics. The Canadian Institutes of HealthResearch is providing funds to support ADNI clinical sites in Canada.
Private sector contributions are facilitated by the Foundation for the
National Institutes of Health (www.fnih.org). The grantee organisation is
the Northern California Institute for Research and Education, and thestudy is coordinated by the Alzheimer’s Therapeutic Research Institute at
the University of Southern California. ADNI data are disseminated by the
Laboratory for NeuroImaging at the University of Southern California.
J-ADNI was supported by the following grants: Translational Research
Promotion Project from the New Energy and Industrial Technology
Development Organization of Japan; Research on Dementia, Health Labor
Sciences Research Grant; Life Science Database Integration Project ofJapan Science and Technology Agency; Research Association of
Biotechnology (contributed by Astellas Pharma Inc., Bristol-Myers Squibb,
Daiichi-Sankyo, Eisai, Eli Lilly and Company, Merck-Banyu, Mitsubishi
Tanabe Pharma, Pfizer Inc., Shionogi & Co., Ltd., Sumitomo Dainippon,and Takeda Pharmaceutical Company), Japan; and a grant from ananonymous foundation. GENIC was funded by the Estrategia deEspecialización Inteligente de Canarias RIS3 de la Consejería de Economía,
Industria, Comercio y Conocimiento del Gobierno de Canarias, co-fundedby the Programa Operativo FEDER Canarias 2014–2020(ProID2020010063), and Universidad Fernando Pessoa Canarias. The data
used in the preparation of this article was taken from the GENIC database(Group of Neuropsychological Studies of the Canary Islands, University of
La Laguna, Spain). Principal investigators: José Barroso and Daniel
Ferreira (contact: daniel.ferreira.padilla@ki.se). The following collaborators
contributed to the GENIC database but did not participate in the analysis
or writing of this report (in alphabetic order by family name): Nira Cedrés,
Rut Correia, Patricia Díaz, Eloy García, Lissett González, Aída Figueroa,
Nerea Figueroa, Teodoro González, Zaira González, Cathaysa Hernández,
Edith Hernández, Nira Jiménez, Judith López, Cándida Lozano, Alejandra
Machado, María Mata, Yaiza Molina, Antonieta Nieto, Roraima YánezPérez, María Sabucedo, Elena Sirumal, Marta Suárez, Manuel Urbano, andPedro Velasco.

Data used in the preparation of this article were obtained from theJapanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI)
database deposited in the National Bioscience Database Center
Human Database, Japan (Research ID: hum0043.v1, 2016). As such,the investigators within J-ADNI contributed to the design andimplementation of J-ADNI and/or provided data but did notparticipate in the analysis or writing of this report. A complete listingof J-ADNI investigators can be found at: https://humandbs.
biosciencedbc.jp/en/hum0043-j-adni-authors.

Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. The authors wish
to thank the Swedish Research Council (VR), the Strategic Research
Programme in Neuroscience at Karolinska Institutet (StratNeuro), theCenter for Innovative Medicine (CIMED), the Foundation for GeriatricDiseases at Karolinska Institutet, the regional agreement on medical
training and clinical research (ALF) between Stockholm County
Council and Karolinska Institutet, the Swedish Brain Foundation, theSwedish Alzheimer Foundation, the Åke Wiberg Foundation, the Olle
Engkvist Byggmästare Foundation, the joint research funds of KTH
Royal Institute of Technology and Stockholm County Council (HMT),
Swedish Parkinson Foundation, King Gustaf V’s and Queen Victoria’sFoundation, David and Astrid Hageléns Foundation, Loo and Hans
Ostermans Foundation, Gun and Bertil Stohne’s Foundation, and the
NIA-supported Collaboratory on Research Definitions for reserve andresilience in cognitive ageing and dementia for financial support.

Acknowledgments
The authors wish to thank the NVIDIA Corporation for the donationof the Titan X Pascal used for this research. This research has been
conducted using the UK Biobank Resource under Application Number
30172. The UK Biobank study was approved by the National HealthService National Research Ethics Service (11/NW/0382). Moreinformation about the UK Biobank is available at www.ukbiobank.ac.uk.
The AddNeuroMed study was supported by InnoMed (Innovative
Medicines in Europe), an Integrated Project funded by the European
Union under the Sixth Framework Programme priority FP6-2004LIFESCIHEALTH-5, Life Sciences, Genomics, and Biotechnology for
Health. Data collection and sharing for this project were funded by the
Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutesof Health Grant U01 AG024904) and DOD ADNI (Department of
Defense award number W81XWH-12-2-0012). ADNI is funded by the
National Institute on Aging, the National Institute of Biomedical Imaging
and Bioengineering, and through generous contributions from thefollowing: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery
Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers
Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals,
Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. andits affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO
Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.;

Frontiers in Aging Neuroscience

Conflict of interest
The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could
be construed as a potential conflict of interest.

Publisher’s noteAll claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations,or those of the publisher, the editors and the reviewers. Any productthat may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material
The Supplementary material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fnagi.2023.1303036/
full#supplementary-material

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