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de Lange, S.

Publications and source records attributed to de Lange, S..

2 recordsLinked to original sources

Insomnia subtypes have differentiating deviations in brain structural connectivity

ObjectiveInsomnia disorder is the most common sleep disorder. A better understanding of insomnia-related deviations in the brain could inspire better treatment. Insufficiently recognized heterogeneity within the insomnia population could obscure involved brain circuits. The present study investigated whether structural brain connectivity deviations differ between recently discovered and validated insomnia subtypes. MethodsStructural and diffusion weighted 3-Tesla MRI data of four independent studies were harmonized. The sample consisted of 73 controls without sleep complaints and 204 participants with insomnia grouped into five subtypes based on their fingerprint of personality and mood traits assessed with the Insomnia Type Questionnaire. Linear regression correcting for age, sex, and brain volume evaluated group differences in structural connectivity strength, indicated by fractional anisotropy and mean diffusivity, and evaluated within two different atlases. ResultsInsomnia subtypes showed differentiating profiles of deviating structural connectivity which moreover concentrated in different functional networks. Permutation testing against randomly drawn heterogeneous subsamples indicated significant specificity of deviation profiles in four of the five subtypes: highly distressed (p=0.019), moderately distressed reward insensitive (p=0.014), slightly distressed low reactive (p=0.006) and slightly distressed high reactive (p=0.006). ConclusionsOur results provide a first indication that different insomnia subtypes exhibit distinct profiles of deviations in structural brain connectivity. Subtyping of insomnia could be essential for a better understanding of brain mechanisms that contribute to insomnia vulnerability.

neuroscience↗

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

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.

neuroscience↗