Visual Exploration of Dynamic Multichannel EEG Coherence Networks

Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline-based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time-annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Epileptogenesis in rodents leads to neural system dysfunction and loss of associative memory measured by auditory event related potentials.

Epilepsy is a common and disabling neurological condition affecting 1-2% of the world’s population. Individuals suffering from epilepsy are prone to cognitive dysfunctions with detrimental effects in neural processing and memory resulting in decreases in quality of life. An evaluation of inherent neural processes is valuable information to diagnose and clinically assess cognitive function, which could significantly improve the treatment possibilities and thereby the quality of life for epilepsy patients. An evaluation of cognitive functions during epileptogenesis was performed by experiments using auditory event related potentials (ERP) in rats before and after induction of status epilepticus (SE) using the Lithium-Pilocarpine model (LP) of epilepsy. The aim of this study was to assess changes in neural system function during epileptogenesis by evaluating inherent responses to auditory stimuli in three ERP tasks at different time periods: before SE (control state), one week-, one month- and two months- after SE (epileptic state). 1. Habituation- (a.) evaluate the ability to habituate to repeated auditory stimuli using the N70 peak response, (b.) examine the time-frequency response through inter-trial coherence (ITC) and event-related spectral perturbation (ERSP); 2. Chirp- evaluate the auditory steady state responses through ITC; and, 3. Mismatch-Negativity (MMN)- evaluate associative memory through ERP responses to regular or odd tones. Habituation tasks showed increased N70 peak magnitude during epileptogenesis from 1-week, 1-month, and 2-months after SE using repeated measures analysis of variance (rANOVA) with significant differences before and after SE (p\u3c0.05, 1-week, 2-months). ITC showed significant differences between groups during habituation from 0.5-20 Hz and ERSP from 60-100 Hz and 0.5-15 Hz, with baseline corrected ERSP revealing differences from 1-30 Hz. The habituation results indicate a diminished ability to properly habituate to repeated stimuli with abnormal neuronal firing in the epileptic state compared to the non-epileptic control state linking a possible mechanism with imbalances in neuronal inhibition and excitation during epileptogenesis. Chirp response ITC showed increased synchronous activity in high gamma band (\u3e40 Hz) during epileptogenesis indicating the neuronal response in epileptic groups are phase locked to the chirp stimuli at a higher incidence than controls. Epileptic MMN ERP responses for odd and regular tones exhibited a decrease in the response curves from 250-350ms post-stimulus indicating a loss of ability to distinguish tones and difficulties with their associative memory during epileptogenesis.Our results indicate that a proper EEG-based analysis of auditory ERPs are useful in evaluating neural systems during epileptogenesis showing clear imbalances in excitatory: inhibitory function, as well as an indication that associative memory is detrimentally affected. The ERP methods employed may help in the diagnosis of the epileptic patients with cognitive disabilities as their epilepsy progresses, as it is simple, non-invasive and cost effective

In a network state of mind

Scheltens, P. [Promotor]Stam, C.J. [Promotor]Flier, W.M. van der [Copromotor

Quantitative Electroencephalography and genetics as biomarkers of dementia in Parkinson’s disease

The importance of cognitive decline in Parkinson’s disease (PD), which eventually progresses to dementia (PD-D) in the majority of surviving patients, has been widely recognised during the last decade. PD-D is associated with a twofold increase in mortality, increased caregiver strain and increased healthcare costs. Thus, early and correct identification of the PD patients with a risk of dementia is a challenging problem of neurology, which has led to the suggestion of various markers of cognitive decline in PD. If validated, these markers would offer the opportunity for disease modification and therapeutic intervention at a critical early stage of the illness, when the viable neuronal population is greater. The focus of this thesis was to assess how various factors - quantitative electroencephalography (qEEG) changes, genetics, deep brain stimulation (DBS), olfactory function, etc. – may be related with the risk of cognitive decline in PD patients. We performed four clinical studies with various design. These studies included PD patients who were dementia-free on inclusion, and control participants. Principal findings are the following: (1) increase of global median relative power theta (4–8 Hz), executive and working memory dysfunction are independent prognostic markers of severe cognitive decline in PD patients over a period of 3 years. (2) DBS of the subthalamic nuclei in a group of PD patients with mean age 63.2 years, in comparison with a group of younger patients (52.9 years), causes higher incidence of psychiatric events over 2 years of observation. However, these events were transient and did not outweigh the benefits of surgery. (3) Worsening of verbal fluency performance is an early cognitive outcome of DBS of the subthalamic nuclei in PD patients. (4) Among early appearing non-motor signs of Parkinson’s disease, alteration of olfaction but not EEG spectrum correlates with motor function. (5) A composite score approach seems to be a realistic goal in the search for biomarkers of severe cognitive decline

XXII International Conference on Mechanics in Medicine and Biology - Abstracts Book

This book contain the abstracts presented the XXII ICMMB, held in Bologna in September 2022. The abstracts are divided following the sessions scheduled during the conference

Network Modeling of Motor Pathways from Neural Recordings

During cued motor tasks, for both speech and limb movement, information propagates from primary sensory areas, to association areas, to primary and supplementary motor and language areas. Through the recent advent of high density recordings at multiple scales, it has become possible to simultaneously observe activity occurring from these disparate regions at varying resolution. Models of brain activity generally used in brain-computer interface (BCI) control do not take into account the global differences in recording site function, or the interactions between them. Through the use of connectivity measures, however, it has been made possible to determine the contribution of individual recording sites to the global activity, as they vary with task progression. This dissertation extends those connectivity models to provide summary information about the importance of individual sites. This is achieved through the application of network measures on the adjacency structure determined by connectivity measures. Similarly, by analyzing the coordinated activity of all of the electrode sites simultaneously during task performance, it is possible to elucidate discrete functional units through clustering analysis of the electrode recordings. In this dissertation, I first describe a BCI system using simple motor movement imagination at single recording sites. I then incorporate connectivity through the use of TV-DBN modeling on higher resolution electrode recordings, specifically electrocorticography (ECoG). I show that PageRank centrality reveals information about task progression and regional specificity which was obscured by direct application of the connectivity measures, due to the combinatorial increase in feature dimensionality. I then show that clustering of ECoG recordings using a method to determine the inherent cluster count algorithmically provides insight into how network involvement in task execution evolves, though in a manner dependent on grid coverage. Finally, I extend clustering analysis to show how individual neurons in motor cortex form distinct functional communities. These communities are shown to be task-specific, suggesting that neurons can form functional units with distinct neural populations across multiple recording sites in a context dependent impermanent manner. This work demonstrates that network measures of connectivity models of neurophysiological recordings are a rich source of information relevant to the field of neuroscience, as well as offering the promise of improved degree-of-freedom and naturalness possible through direct BCI control. These models are shown to be useful at multiple recording scales, from cortical-area level ECoG, to highly localized single unit microelectrode recordings