Exploring dynamic functional connectivity alterations in the preclinical stage of Alzheimer's disease: an exploratory study from SILCODE

DySCo: A general framework for dynamic functional connectivity

A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional brain recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across brain areas change over time. However, the main dFC approaches have been developed and applied mostly empirically, lacking a common theoretical framework and a clear view on the interpretation of the results derived from the dFC matrices. Moreover, the dFC community has not been using the most efficient algorithms to compute and process the matrices efficiently, which has prevented dFC from showing its full potential with high-dimensional datasets and/or real-time applications. In this paper, we introduce the Dynamic Symmetric Connectivity Matrix analysis framework (DySCo), with its associated repository. DySCo is a framework that presents the most commonly used dFC measures in a common language and implements them in a computationally efficient way. This allows the study of brain activity at different spatio-temporal scales, down to the voxel level. DySCo provides a single framework that allows to: (1) Use dFC as a tool to capture the spatio-temporal interaction patterns of data in a form that is easily translatable across different imaging modalities. (2) Provide a comprehensive set of measures to quantify the properties and evolution of dFC over time: the amount of connectivity, the similarity between matrices, and their informational complexity. By using and combining the DySCo measures it is possible to perform a full dFC analysis. (3) Leverage the Temporal Covariance EVD algorithm (TCEVD) to compute and store the eigenvectors and values of the dFC matrices, and then also compute the DySCo measures from the EVD. Developing the framework in the eigenvector space is orders of magnitude faster and more memory efficient than naïve algorithms in the matrix space, without loss of information. The methodology developed here is validated on both a synthetic dataset and a rest/N-back task experimental paradigm from the fMRI Human Connectome Project dataset. We show that all the proposed measures are sensitive to changes in brain configurations and consistent across time and subjects. To illustrate the computational efficiency of the DySCo toolbox, we performed the analysis at the voxel level, a task which is computationally demanding but easily afforded by the TCEVD.

Dynamic functional connectivity is modulated by the amount of p-Tau231 in blood in cognitively intact participants

INTRODUCTION

Electrophysiology and plasma biomarkers are early and non-invasive candidates for Alzheimer's disease detection. The purpose of this paper is to evaluate changes in dynamic functional connectivity measured with magnetoencephalography, associated with the plasma pathology marker p-tau231 in unimpaired adults.

METHODS

73 individuals were included. Static and dynamic functional connectivity were calculated using leakage corrected amplitude envelope correlation. Each source's strength entropy across trials was calculated. A data-driven statistical analysis was performed to find the association between functional connectivity and plasma p-tau231 levels. Regression models were used to assess the influence of other variables over the clusters' connectivity.

RESULTS

Frontotemporal dynamic connectivity positively associated with p-tau231 levels. Linear regression models identified pathological, functional and structural factors that influence dynamic functional connectivity.

DISCUSSION

These results expand previous literature on dynamic functional connectivity in healthy individuals at risk of AD, highlighting its usefulness as an early, non-invasive and more sensitive biomarker.

Distinct Neuropsychological Correlates of Apathy Sub-Domains in Multiple Sclerosis

Background: Apathy is relatively frequent and significantly associated with clinical and cognitive outcomes in Multiple Sclerosis (MS), even if previous research has produced mixed results. This varied picture could be due to most studies treating apathy as a unitary construct, despite the evidence showing that apathy is a multifaceted syndrome including three different sub-domains (i.e., cognitive, affective, and behavioral). This study aims to investigate the neuropsychological correlates of apathy fractionated into its three sub-domains in participants with MS. Methods: Eighty-five participants with MS underwent a comprehensive neuropsychological battery. The severity of apathy symptoms was assessed by the self-report version of the Apathy Evaluation Scale. Results: Correlational analysis showed that cognitive apathy sub-domain scores had a high correlation with the performances obtained at cognitive tests tapping into inhibitory control (i.e., IML and Strop test-interference task), whereas the affective apathy sub-domain scores had a high correlation with the performances obtained at cognitive test tapping into the use of executive functions in visuospatial abilities (i.e., Clock Drawing Test). Moreover, linear regression analysis results showed that the cognitive apathy sub-domain scores predicted executive functioning domain scores and that the cognitive and affective apathy sub-domains scores predicted visuospatial abilities domain scores. Conclusion: These results confirm that apathy is a multidimensional concept with important neuropsychological correlates, visible only when it is fractionated into its sub-domains.

Individual prediction of hemispheric similarity of functional connectivity during normal aging

In the aging process of normal people, the functional activity pattern of brain is in constant change, and the change of brain runs through the whole life cycle, which plays a crucial role in the track of individual development. In recent years, some studies had been carried out on the brain functional activity pattern during individual aging process from different perspectives, which provided an opportunity for the problem we want to study. In this study, we used the resting-state functional magnetic resonance imaging (rs-fMRI) data from Cambridge Center for Aging and Neuroscience (Cam-CAN) database with large sample and long lifespan, and computed the functional connectivity (FC) values for each individual. Based on these values, the hemispheric similarity of functional connectivity (HSFC) obtained by Pearson correlation was used as the starting point of this study. We evaluated the ability of individual recognition of HSFC in the process of aging, as well as the variation trend with aging process. The results showed that HSFC could be used to identify individuals effectively, and it could reflect the change rule in the process of aging. In addition, we observed a series of results at the sub-module level and find that the recognition rate in the sub-module was different from each other, as well as the trend with age. Finally, as a validation, we repeated the main results by human brainnetome atlas (BNA) template and without global signal regression, found that had a good robustness. This also provides a new clue to hemispherical change patterns during normal aging.