# Age Estimation from fMRI Data Using Recurrent Neural Network
###### Abstract  
Finding a biomarker that indicates the subject's age is one of the most important topics in biology. Several recent studies tried to extract a biomarker from brain imaging data including fMRI data. However, most of them focused on MRI data, which do not provide dynamics and lack attempts to apply recently proposed deep learning models. We propose a deep neural network model that estimates the age of a subject from fMRI images using a recurrent neural network (RNN), more precisely, a gated recurrent unit (GRU). However, applying neural networks is not trivial due to the high dimensional nature of fMRI data. In this work, we propose a novel preprocessing technique using the Automated Anatomical Labeling (AAL) atlas, which significantly reduces the input dimension. The proposed dimension reduction technique allows us to train our model with 640 training and validation samples from different projects under mean squared error (MSE). Finally, we obtain the correlation value of 0.905 between the predicted age and the actual age on 155 test samples. The proposed model estimates the age within the range of \(\pm\)12 on most of the test samples. Our model is written in Python and is freely available for download. a 202212749. [[https://doi.org/10.3390/app12020749](https://doi.org/10.3390/app12020749)]([https://doi.org/10.3390/app12020749](https://doi.org/10.3390/app12020749))  
neurodegenerative diseases [45]. Thus, an age estimation based on fMRI has an advantage over structural MRI. [PERSON] et al. showed the relationship between the entropy of the brain and age [46] including time-varying dynamics. The authors first divided the fMRI image into 90 regions using the Automated Anatomical Labeling atlas (AAL) [47], then transformed the fMRI image to time series data. Finally, they extracted correlation coefficients among all the pairs of the time series and computed the entropy of the empirical distribution of coefficients. However, the correlation between the entropy and the actual age is not strong, and it is hard to interpret the meaning of the entropy of coefficients. Another area related to time series data is natural language processing (NLP). Recurrent neural networks (RNN) [48], long short-term memory (LSTM) [49], and gated recurrent units (GRU) [50] dominated the field due to their structure being similar to time series. Later, Transformer [51], based on a simple and clear attention structure [52; 53], was proposed, which showed more concise and efficient performance than traditional deep networks. Transformer achieves better results due to its ability to capture long-term dependencies. In this paper, we propose a deep neural network (DNN) model to estimate the age from fMRI. Since fMRI data can be viewed as a time series of 3D images, it is natural to use network models from NLP, such as recurrent neural networks (RNN) or Transformer. The proposed model is based on GRU since fMRI data have weaker long-term dependencies. We would like to emphasize that applying an existing DNN model is not trivial, mainly due to the data shortage issue. The cost of collecting fMRI data is high, and it is hard to obtain a sufficient amount of data to train the DNN model. Moreover, the dimension of fMRI data is huge (e.g., \(64\times 64\times 34\times 100\)) compared to the number of trainable data. To overcome the data shortage, we propose a new dimension reduction technique for fMRI data. More precisely, we use fMRI data from 795 publicly available samples. We first apply the clustering algorithm to the data to reduce the dimension. This provides time series data that are a 94 dimension vector at each repetition time point. Then, we train DNN models that estimate the age, where the input is preprocessed fMRI data. We compare the proposed model with other DNN models including vanilla RNN, Transformer, and multilayer perceptron (MLP). The correlation coefficient between the estimated age and the actual age is \(r=0.905\), which outperforms the other DNN models. The paper is organized as follows.