Brevet FR3063378A1 — publié le 20180831

Method, device and program for determining at least a brain network involved in a realization of a given process. 1. Domain Cognitive deficits in Parkinson's disease are thought to be related to altered functional brain connectivity. To date, cognitive-related changes in Parkinson's disease have never been explored with dense-EEG with the aim of establishing a relationship between the degree of cognitive impairment, on the one hand, and alterations in the functional connectivity of brain networks, on the other hand. The invention relates to a method and a device for identifying altered brain networks associated with cognitive phenotypes in Parkinson's disease (and other diseases ) using dense- [[G data recorded during rest with eyes closed. The invention relates aims at building at least one static marker which is likely to be used by another method or device for identifying the presence of the absence of early signs of an apparition of the disease. In an exemplary implementation of the pro posed technique, three groups of Parkinson's disease patients (N=124) with different cognitive phenotypes coming from a data-driven cluster analysis, were studied: G1) cognitively intact patients (63), G2) patients with mild cognitive impairment (46) and G3) patients with severe cognitive impairment (15). Functional brain networks were identified using a dense-EEG source connectivity method. Pairwise functional connectivity is computed for 68 brain regions in different EEG frequency bands. Network statistics were assessed at both global (network topology) and local (inter-regional connections) level. Results revealed progressive disruptions in functional connectivity between the three patient groups, typically in the alpha band. Differences between G1 and G2 (p G2 (A), G1 > G3 (B) and G2 > G3 (C). Bottom row presents the proportion (%) of each type of connection in each subnetwork, as categorized according to the lobes each edge interconnects. F: Frontal, T: Temporal, P: Parietal, C: Central, and 0: Occipital. 20 Figure4: Edge-wise analysis (alpha 1). Subnetworks of functional connections showing a significant difference between the three groups at alpha 1. At each part, the top row presents graph-based representations of these subnetworks, with each region represented as a red sphere plotted according to the stereotactic coordinates of its centroid, and each suprathreshold edge represented as a dark green line. The size of the node represents the number of 25 significantly different connections from the node itself. For ail edges, connectivity was higher in G2 > G3 (A) and G1 > G3 (B). Bottom row presents the proportion (%) of each type of connection in each subnetwork, as categorized according to the lobes each edge interconnects. F: Frontal, T: Temporal, P: Parietal, C: Central, and 0: Occipital. Figure 5: Scatterplot of the association between the cognitive score and the edge-wise 30 connectivity index for the A) G1, G2 and G3 and B) G1 and G2. 3063378 16 N=124 n G1 G2 G3 p value Mean(SD) Mean (SD) Mean (.5D) 63 (50.81) 46 (37.10) 15 (12.10) Dei ov-ophic Sex (% male) 73.02 63.04 80 0.358 Handedness right) 84.12 91.3 93.33 0.36.) Age (y) 63.53 (7.97) 67.29 (7.73) 70.07 (6.01) 0,003 Formai educz-ttion (y) 13.32 (3.68) 11.52 (3.56) 9.47 (2.23) REVENDICATIONS1. Method for determining at least a brain network involved in a realization of a given process, said method comprising obtaining signal recoded by a signal acquisition device, said method further comprising at least one of the following steps: - preprocessing signais according to at least one preprocessing parameter, delivering data to analyze - power spectrum analyzing on data to analyze, according to at least one analyzing parameter; - functional connectivity analyzing by creating, from said power spectrum analyzed data, at least one functional connectivity matrix; - characterizing at least one network obtained from said at least one functional connectivity matrix; - realizing a statistical analysis of said at least one network; - obtaining a statistic marker based on said statistical analysis. 15 2. Device for determining at least a brain network involved in a realization of a given process, said device comprising means for obtaining signal recoded by a signal acquisition device, said device further comprising at least one of the following means for: - preprocessing signais according to at least one preprocessing parameter, delivering data to 20 analyze - power spectrum analyzing on data to analyze, according to at least one analyzing parameter; - functional connectivity analyzing by creating, from said power spectrum analyzed data, at least one functional connectivity matrix; - characterizing at least one network obtained from said at least one functional connectivity 25 matrix; - realizing a statistical analysis of said at least one network; - obtaining a statistic marker based on said statistical analysis. 3. A computer program product comprising computer executable program code recorded on a 30 computer readable non-transitory storage medium, the computer executable program code when executed, performing any method according to claim 1.