Prediction of long-term memory scores in MCI based on resting-state fMRI

Djalel-Eddine Meskaldji, Maria Giulia Preti, Thomas Aw Bolton, Marie-Louise Montandon, Cristelle Rodriguez, Stephan Morgenthaler, Panteleimon Giannakopoulos, Sven Haller, Dimitri Van De Ville, Djalel-Eddine Meskaldji, Maria Giulia Preti, Thomas Aw Bolton, Marie-Louise Montandon, Cristelle Rodriguez, Stephan Morgenthaler, Panteleimon Giannakopoulos, Sven Haller, Dimitri Van De Ville

Abstract

Resting-state functional MRI (rs-fMRI) opens a window on large-scale organization of brain function. However, establishing relationships between resting-state brain activity and cognitive or clinical scores is still a difficult task, in particular in terms of prediction as would be meaningful for clinical applications such as early diagnosis of Alzheimer's disease. In this work, we employed partial least square regression under cross-validation scheme to predict episodic memory performance from functional connectivity (FC) patterns in a set of fifty-five MCI subjects for whom rs-fMRI acquisition and neuropsychological evaluation was carried out. We show that a newly introduced FC measure capturing the moments of anti-correlation between brain areas, discordance, contains key information to predict long-term memory scores in MCI patients, and performs better than standard measures of correlation to do so. Our results highlighted that stronger discordance within default mode network (DMN) areas, as well as across DMN, attentional and limbic networks, favor episodic memory performance in MCI.

Keywords: Cross-validation partial least square regression; Extreme value modeling; Functional brain connectivity; Long term memory; Medial temporal lobe; Mild cognitive impairment.

Figures

Fig. 1
Fig. 1
Illustration of the concepts used in the definition of accordance and discordance FC estimator. The figure shows two normalized time-courses. Only parts above the positive threshold (red parts) and below the negative threshold (blue parts) are considered as significant activations and deactivations, respectively. The overlapping of activation parts represents co-activation, and the overlapping of deactivation parts represents co-deactivation. These parts contribute in computing accordance value. The overlapping of activation parts of one time-course with deactivation parts of the second time-course (purple) contributes in computing discordance values. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Processing pipeline to derive prediction models and evaluate their performances.
Fig. 3
Fig. 3
Mean connectivity matrices (across subjects) corresponding to the Pearson correlation, accordance and discordance measures, respectively.
Fig. 4
Fig. 4
Predicted vs measured memory scores corresponding to different measures (Pearson correlation, accordance and discordance). Predicted values are obtained by performing LOO PLSR models, in which co-variables are the connectivity values. We also report the correlation between predicted and measured values for each model.
Fig. 5
Fig. 5
T-statistic maps corresponding to the coefficients of the PLSR models for the different measures (Pearson correlation, accordance and discordance).
Fig. 6
Fig. 6
Significant connections obtained by thresholding p-values (at level 0.005) corresponding to the coefficients of the discordance based model. The connection color represents the sign of the coefficient corresponding to each connection in the PLSR model (orange for positive coefficients and blue for negative coefficients). The node size is proportional to the node degree in the absolute t-map. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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