Neuroscientists have long been trying to understand the brain dynamics underlying cognition: how does the brain perform cognitive tasks? How do neurological diseases (i.e. multiple sclerosis, dementia ) impair cognitive functioning?
To answer, late neuroimaging studies have successfully modelled the brain as a functional network that describes how segregated long-distance brain regions interact to perform cognitive functions. Traditionally, we have investigated static functional networks that depict brain activity over a wide time window (a few seconds) – but this oversimplifies how the brain works. Recent research has revealed a temporally richer brain dynamics, where ‘microstates’ of 100–200ms activate and dissolve; cognitive processes evolve over only few milliseconds.
In this project, we design a study to pursue a 360° description of the brain dynamics underpinning cognition: we acquired high temporal resolution brain activity data, on which we carry out dynamic connectivity analyses.
The AIMS research group has collected a unique dataset: MEG, T1/T2/DWI MR and behavioural data for 100 MS patients and 50 HCs. Throughout the project, we consider the magnetoencephalography (MEG) technique; we assess the brain functionality by measuring the brain’s electromagnetic fields with the finest temporal resolution (ms) and a good spatial resolution. The MEG data were acquired during rest and while subjects performed cognitive tasks: the auditory oddball paradigm (attentional task), and n-back paradigm (working memory task). These tasks elicit a specific brain response, the event-related field (ERF), that is extensively studied in healthy and pathological populations – especially its EEG counterpart ERP (event-related potential). However, traditional neuroimaging studies propose mainly a temporal investigation of the brain response, lacking to characterize the temporal and spatial domains. Despite the late progress, some pitfalls in signal processing techniques hinder the optimal investigations (i.e. time-varying techniques often imply a trade-off between spectral and temporal resolution of analysis).
Throughout this project, we will employ the Hidden Markov Model (HMM) algorithm: a Bayesian statistic model that infers for each time point (ms) the active state, a functional brain network with a unique spatial, temporal, and spectral profile. This method allows a novel and more extensive characterization of the brain’s activity that underpins cognitive tasks: the states’ descriptors (temporal, spectral, and spatial) explain the network dynamics on a sub-second scale and trial level, exploiting the MEG data resolution. Applying this method, we want to overcome the pitfalls in traditional signal processing analyses and unveil some neurophysiological mechanisms underlying cognitive functioning.
Finally, understanding cognitive mechanisms can lead to identifying how a neurological disease, such as multiple sclerosis (MS), impairs them. MS is the most common neurodegenerative and inflammatory disease of the central nervous system, and about half of the MS population presents cognitive impairment. Assessing and following-up cognitive impairment is yet challenging, and researchers are trying to identify an objective and reliable biomarker that can overcome the pitfalls of traditional neuropsychological evaluations. Therefore, in this project, we will also focus on how multiple sclerosis affects attention and working memory: first by understanding which brain active states characterize attentional and working memory tasks, and then by detecting the brain dynamics features that MS alters. This approach will yield a biomarker – perhaps one of the states’ descriptors (spectral, temporal, or spatial) – to guide a more target-oriented assessment and treatment of cognitive impairment in MS.
After the bachelor’s degree in biomedical engineering at Politecnico di Milano, Chiara pursued her master studies within the Double Degree Program (Erasmus +), which allowed her to obtain the Master of Science in Biomedical Engineering at VUB (Vrije Universiteit Brussel) in 2019 and the Master of Science in Biomedical Engineering at Politecnico di Milano in 2020. In Brussels, she focused on neuroengineering, and in Milan, she deepened the fields of biosensors and bioelectronics. Chiara won the research grant in Fundamental Research by the Research Foundation Flanders (FWO) in 2020.