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When Age Tips The Balance: a Dual Mechanism Affecting Hemispheric Specialization for Language

Poster E89 in Poster Session E, Thursday, October 26, 10:15 am - 12:00 pm CEST, Espace Vieux-Port

Loic Labache1, Elise Roger2,3,4, Noah Hamlin5,6, Jordanna Kruse5,6, Monica Baciu4, Gaelle E. Doucet5,6; 1Yale University, 2Institut Universitaire de Gériatrie de Montréal, Montreal, CA, 3University of Montreal, Montreal, CA, 4Université Grenoble Alpes, Grenoble, FR, 5Boys Town National Research Hospital, Omaha, US-NE, 6Creighton University School of Medicine, Omaha, US-NE

As people age, there is a decline in the specificity, selectivity, and lateralization of functional brain responses. Language-related neural networks are predominantly left-lateralized in healthy adults, and there have been reports of significant changes in these networks with age. However, the evolution of hemispheric specialization for language with aging, the underlying mechanisms, and their effects on cognition remain to be further clarified. Cognitive and MRI data from 759 healthy adults (18 to 88yo) were included. We preprocessed T1w and resting state data using fMRIprep. We extracted the normalized volume (vol_norm) and the main functional gradient values (G1), revealing the ability of brain regions to process high-order information, for each ROI defined in the Language-and-Memory network [LMN; 10 Language Cores (LCORE); 27 Language-verbal Memory (LM)]. We modeled the asymmetry of G1 across ages by applying the Generalized Additive Mixed Models method. ROIs with a significant FDR-corrected hemisphere*age interaction were selected and then classified (k-medoids) according to their asymmetry profile (left minus right hemisphere). We assessed the relationship between brain variables and cognitive language performance using partial Canonical Correlation Analyses (CCA). Brain variables were first reduced using a Principal Component Analysis (PC). We identified correlations between CCA modes and tested their significance. 25/37 LMN regions showed an asymmetry change in G1 with aging (p<0.02, FDR corrected). Changes occurred gradually, according to a dual mechanism described by two main clusters, with a switch at 53 years old. The first cluster (C1) included 14 regions whose gradient asymmetry evolved from a left-sided specialization to a tendency towards bilateralization. Left hemisphere G1 capacity remained stable across ages, but right hemisphere heteromodality increased sharply. In the second cluster (C2), G1 asymmetry evolved from bilateral towards a left hemisphere dominance with age through increased left hemisphere heteromodal specialization, while the right hemisphere remains stable. 86% of the LCORE regions that showed G1 disruption with age followed the C1 pattern. LM regions, on the other hand, showed both types of patterns (45% C1; 55% C2). Regarding behavior, language production was more strongly impacted by age than comprehension and decreased around the switching point of G1. Multimodal CCA revealed that the dual mechanism of asymmetry changes was significantly related to language production (r=.28, p<.001 for C1 and C2). Good language performance was characterized by: for C1, a leftward asymmetry of the volume of the dorsal pathway (PC1) and the heteromodality of the ventral pathway (PC2); and for C2, both heteromodal (LM regions; PC2) and volumic (lateral regions; PC1) bilateralization. As the opposite scheme was observed with advancing age, the closer older adults maintained a brain pattern to that of younger adults, the better their language production. Functional asymmetry in integrating high-level information helps optimize the functioning of neural processes involved in language. Changes in asymmetries are related to the difficulties in language production often reported in typical aging. Our results support and expand on previous research on interhemispheric reorganization, providing a comprehensive, multimodal, and dynamic view of brain plasticity during healthy aging.

Topic Areas: Language Development/Acquisition, Computational Approaches

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