Biology subjects

Pievani, M.

Publications and source records attributed to Pievani, M..

3 recordsLinked to original sources

bioRxiv2022

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↗

bioRxiv2022

Fingerprinting of brain disease: Connectome identifiability in cognitive decline and neurodegeneration

In analogy to the friction ridges of a human finger, the functional connectivity patterns of the human brain can be used to identify a given individual from a population. In other words, functional connectivity patterns constitute a marker of human identity, or a brain fingerprint. Yet remarkably, very little is known about whether brain fingerprints are preserved in brain ageing and in the presence of cognitive decline due to Alzheimers disease (AD). Using fMRI data from two independent datasets of healthy and pathologically ageing subjects, here we show that individual functional connectivity profiles remain unique and highly heterogeneous across early and late stages of cognitive decline due to AD. Yet, the patterns of functional connectivity making subjects identifiable, change across health and disease, revealing a functional reconfiguration of the brain fingerprint. We observed a fingerprint change towards between-functional system connections when transitioning from healthy to dementia, and to lower-order cognitive functions in the earliest stages of the disease. These findings show that functional connectivity carries important individualised information to evaluate regional and network dysfunction in cognitive impairment and highlight the importance of switching the focus from group differences to individual variability when studying functional alterations in AD. The present data establish the foundation for clinical fingerprinting of brain diseases by showing that functional connectivity profiles maintain their uniqueness, yet go through functional reconfiguration, during cognitive decline. These results pave the way for a more personalised understanding of functional alterations during cognitive decline, moving towards brain fingerprinting in personalised medicine and treatment optimization during cognitive decline.

neuroscience↗

bioRxiv2020

Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer's disease

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimers disease (n=206) and mild cognitive impairment (n=354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. In order to promote open science, we are making all scripts and the trained models available to the wider research community.

neuroscience↗