A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction

Abstract

Increasing evidence has suggested that the dynamic properties of functional brain networks are related to individual behaviors and cognition traits. However, current fMRI-based approaches mostly focus on statistical characteristics of the windowed correlation time course, potentially overlooking subtle time-varying patterns in dynamic functional connectivity (dFC). Here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to capture temporal and spatial features of functional connectivity sequences simultaneously. The results on a large cohort (Human Connectome Project, n = 1,050) demonstrated that our model could achieve a high classification accuracy of about 93% in a gender classification task and prediction accuracies of 0.31 and 0.49 (Pearson's correlation coefficient) in fluid and crystallized intelligence prediction tasks, significantly outperforming previously reported models. Furthermore, we demonstrated that our model could effectively learn spatiotemporal dynamics underlying dFC with high statistical significance based on the null hypothesis estimated using surrogate data. Overall, this study suggests the advantages of a deep learning model in making full use of dynamic information in resting-state functional connectivity, and highlights the potential of time-varying connectivity patterns in improving the prediction of individualized characterization of demographics and cognition traits.

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

“…Additional documents were retrieved from a recent literature review (Dizaji et al 2021), co-authored by B.H.V. and C.E.G.S., and another study (Fan et al 2020, Table 1) that provide a comparison between similar studies.…”

Representation learning of resting state fMRI with variational autoencoder

“…Most applications are in the regime of supervised learning. Typically, a neural network takes an fMRI-based input data and is trained to generate an output that optimally matches the ground truth for a task, such as individual identification ( Chen and Hu, 2018 ; Wang et al, 2019 ), prediction of gender, age, or intelligence ( Fan et al, 2020 ; Gadgil et al, 2020 ; Plis et al, 2014 ), disease classification ( Seo et al, 2019 ; Suk et al, 2016 ; Wang et al, 2020 ; Yang et al, 2019 ; Zou et al, 2017 ). The labels required for supervised learning are often orders of magnitude smaller in size than the fMRI data itself, which has a high dimension in both space and time.…”

“…The labels required for supervised learning are often orders of magnitude smaller in size than the fMRI data itself, which has a high dimension in both space and time. As a result, the prior studies often limit the model capacity by using a shallow network and/or limit the input data to activity at the region of interest (ROI) level ( Chen and Hu, 2018 ; Dvornek et al, 2018 ; Koppe et al, 2019 ; Matsubara et al, 2019 ; Suk et al, 2016 ; Wang et al, 2019 ; Wang et al, 2020 ) or reduce it to functional connectivity ( D’Souza et al, 2019 ; Fan et al, 2020 ; Kawahara et al, 2017 ; Kim and Lee, 2016 ; Riaz et al, 2020 ; Seo et al, 2019 ; Venkatesh et al, 2019 ; Yang et al, 2019 ; Zhao et al, 2018 ). It is also uncertain to what extent representations learned for a specific task would be generalizable to other tasks.…”

Representation Learning of Resting State fMRI with Variational Autoencoder

“…Most applications are in the regime of supervised learning. Typically, a neural network takes an fMRI-based input data and is trained to generate an output that optimally matches the ground truth for a task, such as individual identification (Chen and Hu, 2018;Wang et al, 2019), prediction of gender, age, or intelligence (Fan et al, 2020;Gadgil et al, 2020;Plis et al, 2014), disease classification (Seo et al, 2019;Suk et al, 2016;Wang et al, 2020;Yang et al, 2019;Zou et al, 2017). The labels required for supervised learning are often orders of magnitude smaller in size than the fMRI data itself, which has a high dimension in both space and time.…”

“…The labels required for supervised learning are often orders of magnitude smaller in size than the fMRI data itself, which has a high dimension in both space and time. As a result, the prior studies often limit the model capacity by using a shallow network and/or limit the input data to activity at the region of interest (ROI) level (Chen and Hu, 2018;Dvornek et al, 2018;Koppe et al, 2019;Matsubara et al, 2019;Suk et al, 2016;Wang et al, 2019;Wang et al, 2020) or reduce it to functional connectivity (D'Souza et al, 2019;Fan et al, 2020;Kawahara et al, 2017;Kim and Lee, 2016;Riaz et al, 2020;Seo et al, 2019;Venkatesh et al, 2019;Yang et al, 2019;Zhao et al, 2018). It is also uncertain to what extent representations learned for a specific task would be generalizable to other tasks.…”