Emulative, coherent, and causal dynamics between large-scale brain networks are neurobiomarkers of Accelerated Cognitive Ageing in epilepsy.

Accelerated cognitive ageing (ACA) is an ageing co-morbidity in epilepsy that is diagnosed through the observation of an evident IQ decline of more than 1 standard deviation (15 points) around the age of 50 years old. To understand the mechanism of action of this pathology, we assessed brain dynamics with the use of resting-state fMRI data. In this paper, we present novel and promising methods to extract brain dynamics between large-scale resting-state networks: the emulative power, wavelet coherence, and granger causality between the networks were extracted in two resting-state sessions of 24 participants (10 ACA, 14 controls). We also calculated the widely used static functional connectivity to compare the methods. To find the best biomarkers of ACA, and have a better understanding of this epilepsy co-morbidity we compared the aforementioned between-network neurodynamics using classifiers and known machine learning algorithms; and assessed their performance. Results show that features based on the evolutionary game theory on networks approach, the emulative powers, are the best descriptors of the co-morbidity, using dynamics associated with the default mode and dorsal attention networks. With these dynamic markers, linear discriminant analysis could identify ACA patients at 82.9% accuracy. Using wavelet coherence features with decision-tree algorithm, and static functional connectivity features with support vector machine, ACA could be identified at 77.1% and 77.9% accuracy respectively. Granger causality fell short of being a relevant biomarker with best classifiers having an average accuracy of 67.9%. Combining the features based on the game theory, wavelet coherence, Granger-causality, and static functional connectivity- approaches increased the classification performance up to 90.0% average accuracy using support vector machine with a peak accuracy of 95.8%. The dynamics of the networks that lead to the best classifier performances are known to be challenged in elderly. Since our groups were age-matched, the results are in line with the idea of ACA patients having an accelerated cognitive decline. This classification pipeline is promising and could help to diagnose other neuropsychiatric disorders, and contribute to the field of psychoradiology.

Accelerated Cognitive Ageing in Epilepsy: A Neuropsychological Evaluation of Cognitive Deterioration.

OBJECTIVE: Shed light on cognitive deterioration in Accelerated Cognitive Ageing (ACA) in epilepsy from a neuropsychological point of view in order to improve clinical diagnostics. METHODS: We compared the IQ-profile including GAI, OPIE IV-premorbid IQ and deterioration-scores of 21 epilepsy patients with ACA with 21 matched epilepsy patients without ACA (Epilepsy Controls) and 16 age- and education-matched Healthy Controls. Memory was also evaluated. RESULTS: Premorbid IQs were equal in all groups. Deterioration was apparent in the ACA-group in the WAIS-IV FSIQ and PRI, whereas no deterioration was found in the two control groups. PSI was impaired in both epilepsy groups, though with more impairment seen in the ACA-group. The VCI remained unimpaired. The FSIQ-GAI discrepancy was equal in both patient groups and significantly larger than in the Healthy Controls. WMS-IV memory indices were of average level in all groups. Memory impairment in ACA was not statistically different from the Epilepsy Controls. 85.7% of ACA-patients could be correctly classified through factors DET_FSIQ and PSI. CONCLUSIONS: Cognitive deterioration in ACA is characterized by an average drop of 19 IQ-points in FSIQ and PRI. Verbal abilities remain unimpaired. Impairments in fluid functions compromise cognitive abilities in epilepsy, but only partially contribute to cognitive deterioration in ACA. PSI proved to have some diagnostic value in differentiating epilepsy patients from healthy controls, but fails to differentiate between ACA and Epilepsy Controls. A comparison made between OPIE-IV equations and obtained IQs leads to a significant better detection of cognitive deterioration in epilepsy than the use of GAI-FSIQ discrepancies alone.