INTRODUCTION RESULTS METHODS CONCLUSIONS Research partially supported by CONICET, CONICYT/FONDECYT Regular ( ), National Health and Medical Research Council of Australia Career Development Fellowship (APP to O.P.), FONCyT- PICT , FONCyT-PICT , and the INECO Foundation. c The behavioral variant of the Frontotemporal Dementia: a multicenter study of its brain network organization. L. Reynaldo a, L. de la Fuente a, H. Desmaras a, S. Abrevaya a,b, A. Marcotti a, I. García Cordero a, L. Sedeño a,b,c, S. Baez a,b,c, B. Couto a,c, T. Torralva a,c, F. Manes a,b,c,e, A. Ibáñez a,b,c,d,e a. LPEN, INECO, U. Favaloro, Bs. As., Argentina. b. CONICET, Bs. As., Argentina. c. NUFIN Core on Neuroscience, UDP, Santiago, Chile. d. U. Autónoma del Caribe, Barranquilla, Colombia. e. CCD, Australian Research Council, New South Wales, Australia. Cognitive and behavioral impairments of the behavioral variant Frontotemporal Dementia (bvFTD) are related to alteration of long-distance brain networks (1). This is supported by results based on graph theory approach, which have shown altered information integration in patients (2). Our aim was to analyze whether network alterations in bvFTD are consistent across different research centers. This analysis is a fundamental step to challenge the possible value of network approaches to develop tools for dementia diagnosis. 1. Global measures suggest a deficit in the exchange of information between local and long-range connections. 2. Local indexes highlight that regions are, on average, more disconnected between each others. The main affected areas are associated with the classical pattern of atrophy in bvFTD. 3. Network centrality represents a counterintuitive result. This finding could indicate that the disconnection of links between regions entails an increased in the centrality of those nodes that are still unimpaired or less affected. 4. In addition, correlation results point out the link between cognitive performance and brain network topological organization. 5. Finally, and the most important, all of these results are consistent across different centers. This highlights the potential value of graph theory for bvFTD evaluation and diagnosis. -Participants: Patients with probable bvFTD (3) and healthy controls (age- and educational- matched) were recruited from three different centers (18 bvFTD and 19 controls from the Institute of Cognitive Neurology, Argentina; 17 bvFTD and 29 controls from Intellectus Memory and Cognition Center, Colombia and 19 bvFTD and 17 controls from the Australian Research Council Centre of Excellence in Cognition and its Disorders, Australia). All participants underwent a ten minutes fMRI resting-state recording. Patients from Argentina and Colombia were assessed with the INECO Frontal Screening (IFS) (4) and patients from Australia completed the Addenbrookes Cognitive Examination Revised (ACE-R) (5). -Graph Theory Analysis: this analysis constitutes a sensitive approach to study neurodegeneration (1). From these metrics, se selected two global network measures: a) the characteristic path length (P): it’s an index of integration, a short value of P ensures prompt transfer of information; b) average clustering coefficient (C): a measure of segregation, represents how strongly is a network locally interconnected. We also analyzed measures to characterize the local behavior of each region in the network: c) local efficiency: measures the efficiency of the local information processing; d) closeness centrality: how close is on average a region from the others; e) network centrality: indicates the importance of a node in the global context of a network. RESULTS 1) Measures of global integration and segregation Fig. 1 Figure 1: bvFTD patients consistently presented reduced clustering coefficient (except for Australia) and increased characteristic path length relative to controls in all research centers. These results suggest a loss of efficiency in information transfer between long-range connections (P) and a general decreased of local interconnectivity in patients. Fig. 2 2) Local network analysis 4) Correlation between topological measures and cognitive tasks Fig. 3 Figure 3: it shows the main regions in which patients presented impairments in closeness centrality. Although these results are not as consistent as the previous ones, patients from all the centers exhibited affectations in frontal and temporal areas. This is consistent with the main pattern of atrophy in bvFTD (blue and green colors are associated with frontal regions, yellow and pink with temporal areas, orange with parietal cortices and red with occipital regions and cerebellum. 3) Pattern of local network affectation (closeness centrality) Figure 4: Characteristic path length (figure 4) and clustering coefficient significant correlated with the decreased performance of bvFTD in the cognitive screenings. Hence, these results might indicate a close association between alteration in network topology and functional deficits in patients (bvFTD are in red, healthy controls in blue). Fig. 4 r= -0.43; p< 0.01 r= -0.44; p< 0.01r= -0.51; p< Pievani, M. et al., Lancet Neurol, 2011 // 2. Agosta, F. et al., Neurology, // 3. Rascovsky, K. et al., Brain, // 4. Torralva, T. et al., J Int Neuropsychol Soc, // 5. Mioshi, E. et al., Int J Geriatr Psychiatry, 2006 Figure 2: The decreased of the average local efficiency and closeness centrality suggest an overall loss of connections between close and distant regions in patients. However, the average network centrality presents a paradoxical finding given that it indicates that, in general, regions in bvFTD have a higher centrality in the network compared to controls. REFERENCES