# Age Estimation from fMRI Data Using Recurrent Neural Network
## 6 Conclusions  
To estimate the age from fMRI images, we proposed the deep learning model based on GRU and Transformer. The preprocessing technique is also provided to reduce the data dimension. Since we have only 795 pieces of data, we use cross-validation to determine hyperparameters during training. Despite the lack of data, our model provides a reasonable estimation of age compared to the previous entropy-based method. Throughout the research, we found a relationship between brain activity and age, which can be extended to other brain disease research. Limitations: Since the proposed GRU-based model takes a preprocessed input, the region extraction method is crucial. The proposed framework is based on the AAL atlas, an anatomical (structural) parcellation. We would like to mention that other representations such as functionally-defined brain regions of interest [61; 62; 63] might be more sensitive to analyzing fMRI. However, our framework is universal in the sense that we can apply the same normalization technique and GRU-based model even with functionally-defined regions of interest. Another limitation is the diversity of data samples obtained from different projects. Although we normalized samples during preprocessing, this may cause inter-project errors. In future work, we plan to collect samples from controlled experiments. Conceptualization, A.N. ; data curation, Y.G. ; formal analysis, Y.G. and A.N. ; funding acquisition, A.N. ; investigation, Y.G. and A.N. ; methodology, Y.G. and A.N. ; project administration, A.N. ; resources, Y.G. and A.N. ; software, Y.G. and A.N. ; supervision, A.N. ; validation, Y.G. and A.N. ; visualization, Y.G. ; writing--original draft, Y.G. ; writing--review and editing, A.N. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea, funded by the Korean Government (MSIT) under Grant NRF-2017R1C1B5018298. Figure 5: Ablation Study. The \(x\)-axis represents the actual age of the subject, whereas the \(y\)-axis represents the estimated age. (**a**) MSE loss function without dropout; (**b**) \(\ell_{1}\) loss function with dropout; (**c**) first 100 repetition time points only (with MSE loss function and dropout). The solid line is the best diagonal when the actual age matches the estimated age. The dotted line refers to the deviation line 12 years away from the actual age. The second line of figures are the Bland–Altman (B&A) plot corresponding to the above. (**d**) MSE without dropout (B&A plot); (**e**) \(\ell_{1}\) with dropout (B&A plot); (**f**) 100 TRs (B&A plot). The \(x\)-axis is the average of each sample’s actual and predicted age, and the \(y\)-axis is the difference between the exact age and the predicted age of the sample. The solid line is the average of the difference. The dashed line is the upper and lower limits of 95% agreement, i.e., \(\pm 1.96\) standard deviations. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** All 26 projects are from the 1000 Functional Connectomes Project ([[http://fcon1000.projects.nitrc.org](http://fcon1000.projects.nitrc.org)]([http://fcon1000.projects.nitrc.org](http://fcon1000.projects.nitrc.org)), accessed on 20 November 2021). Furthermore, SALD project is from the Southwest University Adult Lifespan Dataset (SALD) ([[http://fcon_1000.projects.nitrc.org/indi/retro/sald.html](http://fcon_1000.projects.nitrc.org/indi/retro/sald.html)]([http://fcon_1000.projects.nitrc.org/indi/retro/sald.html](http://fcon_1000.projects.nitrc.org/indi/retro/sald.html)), accessed on 20 November 2021) [54]. **Acknowledgments:** The authors would like to thank [PERSON] for their helpful discussions. **Conflicts of Interest:** The authors declare no conflict of interest in this paper. * _[PERSON] et al. (2003)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Mapping cortical change across the human life span. _Nat. Neurosci._**2003**, \(6\), 309.