Neuroimaging | EPFL Graph Search

Neuroimaging is the use of quantitative (computational) techniques to study the structure and function of the central nervous system, developed as an objective way of scientifically studying the healthy human brain in a non-invasive manner. Increasingly it is also being used for quantitative research studies of brain disease and psychiatric illness. Neuroimaging is highly multidisciplinary involving neuroscience, computer science, psychology and statistics, and is not a medical specialty. Neuroimaging is sometimes confused with neuroradiology. Neuroradiology is a medical specialty and uses non-statistical brain imaging in a clinical setting, practiced by radiologists who are medical practitioners. Neuroradiology primarily focuses on recognising brain lesions, such as vascular disease, strokes, tumors and inflammatory disease. In contrast to neuroimaging, neuroradiology is qualitative (based on subjective impressions and extensive clinical training) but sometimes uses basic quanti

Bodily self and human grid cell-like activity: probing spatial representation with immersive virtual reality

Hyukjun Moon

Grid cells are place-modulated neurons that encode the location of an agent in space, relying on the integration of various sensorimotor signals from the body. Of note, the integration of those signals is also fundamental for the agent's conscious experience of selfhood, referred to as bodily self-consciousness (BSC). Although both grid cells and BSC systems are related to the brain mechanisms of self-location and are based on multimodal signal integration, their interplay has never been investigated. My thesis aimed at revealing their links in humans by assessing the impact of BSC modulations on grid cell-like representation (GCLR): an fMRI proxy of grid cell activity in entorhinal cortex (EC). In Study 1, I assessed whether manipulation on BSC, specifically enhancing self-identification, affects GCLR during spatial navigation in a virtual reality (VR) environment. I implemented a novel fMRI paradigm integrating a spatial navigation task with online BSC manipulation through a motion-mimicking avatar. The data showed a significant reduction of GCLR as well as improved spatial navigation performance in the condition with the self-identified avatar, linking entorhinal grid cell activity and BSC through bodily self-motion cues provided online during spatial navigation. These behavioral and neural changes were further associated with increased activity in posterior parietal and retrosplenial cortex, collectively suggesting the impact of BSC on a distributed spatial navigation networks in the human brain. Consistent with the previous BSC research, in Study1, I observed illusory drifts in experienced self-location toward the self-identified avatar. In Study2, I further investigated whether illusory self-location changes would be sufficient to elicit GCLR in the absence of active spatial navigation. Adopting the Full-body illusion (FBI) paradigm to the MRI scanner, my data extend changes in self-location related to multisensory stimulation and demonstrated that these indeed evoke GCLR. Although the amplitude of GCLR during the FBI was smaller than the one during virtual navigation, their grid orientations were similar. These data suggest that the brain mechanisms underlying the BSC-induced illusory self-location are similar and comparable, albeit weaker, to the ones during virtual navigation.Temporal Interference (TI) stimulation is a novel method that can excite or inhibit neurons in the deep brain by combining high-frequency electrical fields. Building on the previous studies, in study3, I probed the causal link between GCLR and spatial navigation by applying TI stimulation to EC of participants while they were performing a VR navigation task in the scanner. The preliminary results showed that their spatial navigation was improved by excitatory stimuli compared to the control, while it deteriorated during the inhibition. Besides, I found that modulated GCLR was also associated with improved performance in the excitatory condition. Together, these data provide the first causal link between GCLR and spatial navigation, showing potential future avenues for navigation enhancement in humans. To summarize, I investigated BSC and grid cell systems jointly in humans and, for the first time, uncovered their links through robust evidence based on both behavioral and neuroimaging data. My thesis shed new light on the research of both systems which are closely intermingled but have hitherto been considered separately.

EPFL2021

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Pablo Maceira Elvira

In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.

2018

Questions and controversies in the study of time-varying functional connectivity in resting fMRI

Daniel Kessler, Raphaël Pierre Liégeois, Jonathan Raël Lurie

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

MIT PRESS2020