Functional Connectivity, Information Fusion and Explainable AI for Modelling Neural Synchronization in Dyslexia Using High-Performance and Energy-Efficient Computing Algorithms (HPEEC-DYSCONNEC)
Funded Research Project – PID2022-137461NB-C32 funded by MCIN /AEI /10.13039/501100011033 / FEDER, UE. Plan Nacional de I+D+i 2018. Ministerio de Ciencia, Innovación y Universidades
Abstract
The present project aims to continue the lines of research initiated with the previous project with two complementary objectives: 1) The development of exploratory techniques that allow us to delve into the neurobiological origin of learning disorders and 2) to transform the exploratory information into knowledge for the clinical use. In this case, the aim is to provide methods for an early and objective identification of DD allowing the development of individualized intervention tasks.
Specifically, we propose the development of methods and algorithms for functional connectivity estimation by means of local and longrange neural synchronization. Moreover, this connectivity information will be used for constructing network models to explain conditional, unconditional or causal interactions between brain regions. To do so, we will employ graph theory to infer the network that processes different phonological units. Indeed, complex network analysis allows computing descriptors regarding their efficiency, in the search of differential patterns between controls and DD subjects. Connectivity information can be fused to Heart Rate Variability (HRV) data and the results of behavioural tests to identify implications in reading performance. Additionally, the analysis of network dynamics would reveal differential patterns in neural adaptation.
These objectives require the combination of signal processing and explainable AI techniques that allow the extraction of knowledge with clinical implications. Moreover, the computational burden associated to the tasks proposed here requires the use of HPC architectures and the parallelization of the algorithms, striking a balance between performance and energetic efficiency.
EEG and fNIRS signals were acquired during a semi-resting state experiment, using a specific experimental procedure developed by our research team during our previous projects. Subjects for EEG/fNIRS acquisitions were selected from the leeduca database (https://leeduca.es), which currently contains behavioural results from more than 7000 subjects (4-7 years old). The multidisciplinary research team (consolidated in the leeduca project) and their synergistic interaction will enable the transference of the results, representing a game-change in the treatment of learning disabilities in children with the associated socioeconomic impact.