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

Abstract

Background Alzheimer’s 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. Methods We investigated 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 bootstrapping correlation and interaction analyses. Results 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, and 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 significant 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). Conclusions 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.

Mapping the multidimensional geometric landscape of graded phenotypic variation and progression in neurodegenerative syndromes

“…Furthermore, these phenotype geometries readily relate to everyday clinic observations to explain variation across canonical presentations and clinical “chameleons” 32 such as bvFTD with AD-like severe amnesia 33,34 or FTLD-motor syndromes with aphasia, 35 accommodate locations of broad categorical descriptors such as “mixed aphasia” or “atypical dementia”, and relate them to different neural-pathological mechanisms. 31,36,37 In short, leveraging phenotypic heterogeneity to clinical advantage remedies limitations of categorical-only descriptive approaches, models fluid intersections and dynamic trajectories of syndromes, and reveals the large landscape of nuanced inter-individual clinical variations in AD/FTLD.…”

Transdiagnostic brain module dysfunctions across sub-types of frontotemporal dementia: a connectome-based investigation

“…The VAN’s role in detecting and filtering relevant information from the environment, as well as its involvement in attention shifting and salient stimulus processing, has been consistently reported in prior studies [42, 43]. BV-FTD exhibited significant alterations in VAN connectivity compared to HCs and showed even more profound disruptions in VAN than AD, possibly due to the VAN’s association with psychiatric disorders [11, 20, 41]. Similarly, the VAN exhibited dysconnectivity in SV-FTD compared to HCs [44, 45].…”

“…Disruptions in these networks, DMN, FPN, and VAN, have been described as a “triple model” observed in various diseases, including autism, schizophrenia, and AD [40]. This model suggests that the VAN acts as an interface between the DMN and FPN, regulating their competing inter-network activities and promoting appropriate behavioral responses [40, 41]. Our intra- and inter-module analyses identified altered connectivity patterns within the VAN, such as VAN-AN, VAN-DMN, and VAN-SUB.…”

Inferring Multi-Organ Genetic Causal Connections using Imaging and Clinical Data through Mendelian Randomization

“…For example, Alzheimer's disease and dementia exhibited bidirectional causal relationships with fMRI traits and DTI parameters. Prior studies have consistently reported that resting fMRI connectivity patterns are disrupted in patients with Alzheimer's disease 101,102 , particularly in brain regions involved in memory and cognitive function 103,104 .…”