A low dimensional cognitive-network space in Alzheimer's disease and frontotemporal dementia

Pini, L.; de Lange, S.; Pizzini, F. B.; Boscolo Galazzo, I.; Manenti, R.; Cotelli, M.; Galluzzi, S.; Cotelli, M. S.; Corbetta, M.; Van den Heuvel, M.; Pievani, M.

doi:10.1101/2022.08.29.504748

bioRxiv · 10.1101/2022.08.29.504748

A low dimensional cognitive-network space in Alzheimer's disease and frontotemporal dementia

Pini, L.·de Lange, S.·Pizzini, F. B.·Boscolo Galazzo, I.·Manenti, R.·Cotelli, M.·Galluzzi, S.·Cotelli, M. S.·Corbetta, M.·Van den Heuvel, M.·Pievani, M.

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Abstract

Network neuroscience is a promising approach to explore cognitive processes in neurological disorders. Alzheimers disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated. In this study, we aimed to investigate the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71{+/-}10), 22 AD (age: 72{+/-}6) and 20 controls (age: 72{+/-}6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN) and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a robust convergent correlation-wise and interaction analyses. Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (p<0.002), and bvFTD scored worse than AD in cogPC2 (p=0.031). At the network level, the DMN showed a robust association in the whole group with cogPC1 and cogPC2, and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation). These results suggest that a low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components.

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Pini, L., de Lange, S., Pizzini, F. B., Boscolo Galazzo, I., Manenti, R., Cotelli, M., Galluzzi, S., Cotelli, M. S., Corbetta, M., Van den Heuvel, M., Pievani, M.. 2022-08-29. A low dimensional cognitive-network space in Alzheimer's disease and frontotemporal dementia. https://doi.org/10.1101/2022.08.29.504748

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