The role of network interactions in timing-dependent plasticity within the human motor cortex induced by paired associative stimulation

Spike timing-dependent plasticity (STDP) has been suggested as one of the key mechanism underlying learning and memory. Due to its importance, timing-dependent plasticity studies have been approached in the living human brain by means of non-invasive brain stimulation (NIBS) protocols such as paired associative stimulation (PAS). However, contrary to STDP studies at a cellular level, functional plasticity induction in the human brain implies the interaction among target cortical networks and investigates plasticity mechanisms at a systems level. This thesis comprises of two independent studies that aim at understanding the importance of considering broad cortical networks when predicting the outcome of timing-dependent associative plasticity induction in the human brain. In the first study we developed a new protocol (ipsilateral PAS (ipsiPAS)) that required timing- and regional-specific information transfer across hemispheres for the induction of timing-dependent plasticity within M1 (see chapter 3). In the second study, we tested the influence of individual brain structure, as measured with voxel-based cortical thickness, on a standard PAS protocol (see chapter 4). In summary, we observed that the near-synchronous associativity taking place within M1 is not the only determinant influencing the outcome of PAS protocols. Rather, the online interaction of the cortical networks integrating information during a PAS intervention determines the outcome of the pairing of inputs in M1

MEG-Untersuchungen somatosensorisch evozierter Aktivität im Kontext der interhemisphärischen Inhibition und Reduktionsmechanismen in Diskriminationsaufgaben

Die vorliegende experimentelle Arbeit hatte die Untersuchung der diskriminatorischen Reizverarbeitung, Lateralität und interhemisphärischer Inhibitionsprozesse im somatosensorischen System zum Gegenstand. Dazu wurden magnetoenzephalografische Messungen an gesunden Probanden durchgeführt, wobei nach taktiler Stimulation beider Zeige- und Mittelfinger somatosensorisch evozierte Felder (SEF) des primär somatosensorischen Kortex (S1) abgeleitet wurden. Die Studie erbrachte neue Erkenntnisse über die Beeinflussung der SEF einer Hand durch zusätzliche Stimulation der anderen Hand. In die Untersuchung konnten 24 gesunde Probanden eingeschlossen werden, die dem Edinburgh Handedness Inventory (EHI) zufolge ausnahmslos rechtshändig waren. Den Teilnehmern wurden über Luftdruckstimulatoren Berührungsreize an den Fingerbeeren der Zeige- und Mittelfinger beider Hände appliziert, wobei die sensorische Reizung der Finger einzeln und in pseudorandomisierter Reihenfolge erfolgte. Die vorgelegte Untersuchung zeigt, dass sich der Reduktionsmechanismus in Diskriminationsaufgaben innerhalb des mechanosensiblen Systems bei bilateraler taktiler Reizung im MEG abbildet - und zwar sowohl an der rechten, hier dominanten als auch - erstmals - an der linken, hier subdominanten Hand. Die Resultate untermauern den der Signalminderung zugrunde liegenden Mechanismus der lateralen Inhibition als global wirkendes Funktionsprinzip der somatosensorischen Reizverarbeitung. Die Ergebnisse bestätigen weiterhin den weitaus größeren Einfluss der Ausführung einer Diskriminationsaufgabe auf die primären SEF der rechten im Vergleich zur linken Hand und stützen die Hypothese einer vermehrten lateralen Inhibition bei Reizdiskrimination am rechten Zeige- und Mittelfinger. Eine Signalminderung aufgrund einer - von der zusätzlich stimulierten Gegenseite ausgehenden - interhemisphärischen Inhibition (IHI) konnte hingegen nicht nachgewiesen werden

Kalorische Stimulation in der funktionellen Magnetresonanztomographie (fMRT): Detektion vestibulär assoziierter Kortexareale und deren hämodynamische Antwort durch die Independent Component Analysis

Im Gegensatz zu allen anderen sensorischen Systemen existiert beim Menschen kein primär vestibulärer Kortex. Die Verarbeitung vestibulärer Signale erfolgt in einer Reihe multisensorischer Areale. Funktionelle Reaktionen (wie der Nystagmus) infolge vestibulärer Stimulation treten erst mit einer Latenz von 30-40 s nach Stimulusdarbietung auf und können bis zu 120 s andauern. Bislang kamen in Bildgebungsstudien Stimulationsmethoden wie vestibulär evozierte myogene Potentiale und die kalorische und galvanische Stimulation zum Einsatz. Ebenso wurden statistische Modelle angewendet, um die kortikale Antwort zu analysieren. Allerdings wurden bisher noch keine datengestützten, modell-freien Analysemethoden zur Erforschung kortikaler Areale genutzt, die kein Vorwissen über zeitliche Abläufe der hämodynamischen Antwort erfordern. In der hier vorliegenden Arbeit wurde erstmalig eine solch statistische Methode verwendet, um aktivierte vestibuläre Kortexareale zu detektieren, die funktionell miteinander verbunden sind

Artificial Intelligence for Detection, Characterization, and Classification of Complex Visual Patterns in Medical Imaging; Applications in Pulmonary and Neuro-imaging

Medical imaging is widely used in current healthcare and research settings for various purposes such as diagnosis, treatment options, patient monitoring, longitudinal studies, etc. The two most commonly used imaging modalities in the United States are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Raw images acquired via CT or MRI need to undergo a variety of processing steps prior to being used for the purposes explained above. These processing steps include quality control, noise reduction, anatomical segmentation, tissue classification, etc. However, since medical images often include millions of voxels (smallest 3D units in the image containing information) it is extremely challenging to process them manually by relying on visual inspection and the experience of trained clinicians. In light of this, the field of medical imaging is seeking ways to automate data processing. With the impressive performance of Artificial Intelligence (AI) in the field of Computer Vision, researchers in the medical imaging community have shown increasing interest in utilizing this powerful tool to automate the task of processing medical imaging data. Despite AI’s significant contributions to the medical imaging field, large cohorts of data still remain without optimized and robust AI-based tools to process images efficiently and accurately. This thesis focuses on exploiting large cohorts of CT and MRI data to design AI-based methods for processing medical images using weakly-supervised and supervised learning strategies, as well as mathematical (and/or statistical) modeling and signal processing methods. In particular, we address four image processing problems in this thesis. Namely: 1) We propose a weakly-supervised deep learning method to automate binary quality control of diffusion MRI scans into ‘poor’ and ‘good’ quality classes; 2) We design a weakly-supervised deep learning framework to learn and detect visual patterns related to a set of different artifact categories considered in this work, in order to identify major artifact types present in dMRI volumes; 3) We develop a supervised deep learning method to classify multiple lung texture patterns with association to Emphysema disease on human lung CT scans; 4) We investigate and characterize the properties of two types of negative BOLD response elicited in human brain fMRI scans during visual stimulation using mathematical modeling and signal processing tools. Our results demonstrate that through the use of artificial intelligence and signal processing algorithms: 1) dMRI scans can be automatically categorized into two quality groups (i.e., ‘poor’ vs ‘good’) with a high classification accuracy, enabling rapid sifting of large cohorts of dMRI scans to be utilized in research or clinical settings; 2) Type of the major artifact present in ‘poor’ quality dMRI volumes can be identified robustly and automatically with high precision enabling exclusion/correction of corrupt volumes according to the artifact type contaminating them; 3) Multiple lung texture patterns related to Emphysema disease can be automatically and robustly classified across various large cohorts of CT scans enabling investigation of the disease through longitudinal studies on multiple cohorts; 4) Negative BOLD responses of different categories can be fully characterized on fMRI data collected from visual stimulation of human brain enabling researchers to better understand the human brain functionality through studying cohorts of fMRI scans

Somatics Research Bibliography: A Working Tool for Somatics and Somatic Psychology

Many years ago when Somatics magazine was young, it occurred to me that it would be valuable to collect and publish research article references in Somatics magazine that were relevant to the different somatics disciplines to encourage the development of the field. There were next to no studies devoted to Somatics itself, but there were many studies devoted to the elements of somatic practices. Somatics is a multidisciplinary field. It builds on the research findings from many fields, such as anatomy, physiology, neurophysiology, psychology, dance, biomechanics, and education. The references are selected to be suggestive to the interested researcher and practitioner for their purposes and of the many possible research avenues that are yet to be explored. I have collected these research references for more than four decades. I worked originally with Psychological Abstracts, then PsychInfo, and finally, PubMed. Over that time there has been more research done on the somatic disciplines themselves. The greatest amount of research has been done on yoga (the oldest and largest of the somatic disciplines) and yoga therapy. These studies are examples of the research that can be done with the other somatics disciplines as well. We are in an era that appreciates evidence-based practice and practice-based evidence. This is evidence. These research articles are selected according to the following criteria: The article combines both body and mind either in its research design or theoretical perspective; the research design incorporates convergent measures—that is, it includes physiological, behavioral, and psychological measures; subjective and objective measures; and the research focuses on the whole organism (human) from a somatic perspective—that is, the effect of a body therapy on a psychological state. Topics addressed include biofeedback, body psychotherapy, consciousness states, electrophysiology, kinesiology, mind and body, motor processes, neural basis of motor control, neuroscience, posture and emotion, psychophysiology, and yoga/yoga therapy

Characterisation of the Haemodynamic Response Function (HRF) in the neonatal brain using functional MRI

Background: Preterm birth is associated with a marked increase in the risk of later neurodevelopmental impairment. With the incidence rising, novel tools are needed to provide an improved understanding of the underlying pathology and better prognostic information. Functional Magnetic Resonance Imaging (fMRI) with Blood Oxygen Level Dependent (BOLD) contrast has the potential to add greatly to the knowledge gained through traditional MRI techniques. However, it has been rarely used with neonatal subjects due to difficulties in application and inconsistent results. Central to this is uncertainity regarding the effects of early brain development on the Haemodynamic Response Function (HRF), knowledge of which is fundamental to fMRI methodology and analysis. Hypotheses: (1) Well localised and positive BOLD functional responses can be identified in the neonatal brain. (2) The morphology of the neonatal HRF differs significantly during early human development. (3) The application of an age-appropriate HRF will improve the identification of functional responses in neonatal fMRI studies. Methods: To test these hypotheses, a systematic fMRI study of neonatal subjects was carried out using a custom made somatosensory stimulus, and an adapted study design and analysis pipeline. The neonatal HRF was then characterised using an event related study design. The potential future application of the findings was then tested in a series of small experiments. Results: Well localised and positive BOLD functional responses were identified in neonatal subjects, with a maturational tendency towards an increasingly complex pattern of activation. A positive amplitude HRF was identified in neonatal subjects, with a maturational trend of a decreasing time-to-peak and increasing positive peak amplitude. Application of the empirical HRF significantly improved the precision of analysis in further fMRI studies. Conclusions: fMRI can be used to study functional activity in the neonatal brain, and may provide vital new information about both development and pathology

Multimodal Investigation of Brain Network Systems: From Brain Structure and Function to Connectivity and Neuromodulation

The field of cognitive neuroscience has benefited greatly from multimodal investigations of the human brain, which integrate various tools and neuroimaging data to understand brain functions and guide treatments for brain disorders. In this dissertation, we present a series of studies that illustrate the use of multimodal approaches to investigate brain structure and function, brain connectivity, and neuromodulation effects. Firstly, we propose a novel landmark-guided region-based spatial normalization technique to accurately quantify brain morphology, which can improve the sensitivity and specificity of functional imaging studies. Subsequently, we shift the investigation to the characteristics of functional brain activity due to visual stimulations. Our findings reveal that the task-evoked positive blood-oxygen-level dependent (BOLD) response is accompanied by sustained negative BOLD responses in the visual cortex. These negative BOLD responses are likely generated through subcortical neuromodulatory systems with distributed ascending projections to the cortex. To further explore the cortico-subcortical relationship, we conduct a multimodal investigation that involves simultaneous data acquisition of pupillometry, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). This investigation aims to examine the connectivity of brain circuits involved in the cognitive processes of salient stimuli. Using pupillary response as a surrogate measure of activity in the locus coeruleus-norepinephrine system, we find that the pupillary response is associated with the reorganization of functional brain networks during salience processing. In addition, we propose a cortico-subcortical integrated network reorganization model with potential implications for understanding attentional processing and network switching. Lastly, we employ a multimodal investigation that involves concurrent transcranial magnetic stimulation (TMS), EEG, and fMRI to explore network perturbations and measurements of the propagation effects. In a preliminary exploration on brain-state dependency of TMS-induced effects, we find that the propagation of left dorsolateral prefrontal cortex TMS to regions in the lateral frontoparietal network might depend on the brain-state, as indexed by the EEG prefrontal alpha phase. Overall, the studies in this dissertation contribute to the understanding of the structural and functional characteristics of brain network systems, and may inform future investigations that use multimodal methodological approaches, such as pupillometry, brain connectivity, and neuromodulation tools. The work presented in this dissertation has potential implications for the development of efficient and personalized treatments for major depressive disorder, attention deficit hyperactivity disorder, and Alzheimer's disease

Simultaneous EEG-fMRI at ultra-high field for the study of human brain function

Scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have highly complementary domains, and their combination has been actively sought within neuroscience research. The important gains in fMRI sensitivity achieved with higher field strengths open exciting perspectives for combined EEG-fMRI; however, simultaneous acquisitions are subject to highly undesirable interactions between the two modalities, which can strongly compromise data quality and subject safety, and most of these interactions are increased at higher fields. The work described in this thesis was centered on the development of simultaneous EEG-fMRI in humans at 7T, covering aspects of subject safety, signal quality assessment, and quality improvement. Additionally, given the potential value of high-field EEG-fMRI to study the neuronal correlates of so-called negative BOLD responses, an initial fMRI study was dedicated to these phenomena. The initial fMRI study aimed to characterize positive (PBR) and negative BOLD responses (NBR) to visual checkerboard stimulation of varying contrast and duration, focusing on NBRs occurring in visual and in auditory cortical regions. Results showed that visual PBRs and both visual and auditory NBRs significantly depend on stimulus contrast and duration, suggesting a dynamic system of visual-auditory interactions, sensitive to stimulus contrast and duration. The neuronal correlates of these interactions could not be addressed in higher detail with fMRI alone, yet could potentially be clarified in future work with combined EEG-fMRI. Moving on to simultaneous EEG-fMRI implementation, the first stage comprised an assessment of potential safety concerns at 7T. The safety tests comprised numerical simulations of RF power distribution and real temperature measurements on a phantom during acquisition. Overall, no significant safety concerns were found for the setup tested. A characterization of artifacts induced on MRI data due to the presence of EEG components was then performed. With the introduction of the EEG system, functional and anatomical images exhibited general losses in spatial SNR, with a smaller loss in temporal SNR in fMRI data. B0 and B1 field mapping pointed towards RF pulse disruption as the major degradation mechanism affecting MRI data. The main part of this work focused on EEG artifacts induced by MRI. The first step focused on optimizing signal transmission between the EEG cap and amplifiers, to minimize artifact contamination at this important stage. Along this line, adequate cable shortening and bundling effectively reduced environment noise in EEG recordings. Simultaneous acquisitions were then performed on humans using the optimized setup. On average, EEG data exhibited clear alpha modulation and average visual evoked potentials (VEP), with concomitant BOLD signal changes. In the second step, a novel approach for head motion artifact detection was developed, based on a simple modification of the EEG cap, and simultaneous acquisitions were performed in volunteers undergoing visual checkerboard stimulation. After gradient artifact correction, EEG signal variance was found to be largely dominated by pulse artifacts, but contributions from spontaneous motion were still comparable to those of neuronal activity. Using a combination of pulse artifact correction, motion artifact correction and ICA denoising, strong improvements in data quality could be obtained, especially at a single-trial level