Whole-brain cortical parcellation: A hierarchical method based on dMRI tractography

In den modernen Neurowissenschaften ist allgemein anerkannt, dass die Gehirnfunktionen auf dem Zusammenwirken von verschiedenen Regionen in Netzwerken beruhen und die strukturelle Konnektivität daher großer Bedeutung ist. Daher kann die Abgrenzung funktioneller Hirnbereiche auf der Grundlage der Diffusions-Magnet-Resonanz-Tomographie (dMRT) und der Traktografie zu wertvollen Hirnkarten führen.Existierende Verfahren versuchen eine fest vorgegebene Anzahl von Regionen zu finden und/oder sind auf kleine Bereiche der grauen Substanz beschränkt. Im Allgemeinen ist es jedoch unwahrscheinlich, dass eine einzelne Parzellierung des Kortex, eine ausreichende Darstellung der funktio- anatomischen Organisation des Gehirns erlaubt. In dieser Arbeit schlagen wir eine hierarchische Clusteranalyse vor um diese Einschränkungen zu überwinden und das gesamte Gehirn zu parzellieren. Wir zeigen, dass dieses Verfahren die Eigenschaften der zugrundeliegenden Struktur auf allen Granularitätstufen des hierarchischen Baums (Dendrogramm) kodieren kann. Weiterhin entwickeln wir eine optimale Verarbeitungspipeline zur Erstellung dieses Baums, die dessen Komplexität mit minimalem Informationsverlust reduziert. Wir zeigen wie diese Datenstrukturen verwendet werden können um die Ähnlichkeitstruktur von verschiedenen Probanden oder Messungen zu vergleichen und wie man daraus verschiedene Parzellierungen des Gehirns erhalten kann.Unser neuer Ansatz liefert eine ausführlichere Analyse der anatomischen Strukturen und bietet eine Methode zur Parzellierung des ganzen Gehirns.In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps.Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely that a single parcellation dividing the brain into a finite number of areas is an adequate representation of the function-anatomical organization of the brain. In this work, we propose hierarchical clustering as a solution to overcome these limitations and achieve whole-brain parcellation. We demonstrate that this method encodes the information of the underlying structure at all granularity levels in a hierarchical tree or dendrogram. We develop an optimal tree building and processing pipeline that reduces the complexity of the tree with minimal information loss. We show how these trees can be used to compare the similarity structure of different subjects or recordings and how to extract parcellations from them.Our novel approach yields a more exhaustive representation of the real underlying structure and successfully tackles the challenge of whole-brain parcellation

Longitudinal Cerebrospinal Fluid Biomarkers of Alzheimer Disease: Movement Toward the Diagnosis, Prognosis and Staging of Disease

Alzheimer’s disease (AD) is a devastating neurodegenerative disease that slowly claims the memories and experiences that comprise the life experiences of individuals that suffer from the disease. Despite a continually accelerating pace of research and discovery, a viable therapeutic intervention for AD has yet to be realized. There are a multitude of factors that may contribute to this difficulty including the challenge of separating the overall disease of Alzheimer’s from the clinically recognizable memory loss that occurs in what is now known to be the end-stage of the disease. Efforts to treat AD have increasingly turned toward very early disease states, before clinical signs and symptoms become apparent, as a number of clinical trials have failed to meet cognitive endpoints over the last 5-10 years – potentially due to the sole recruitment of individuals already experiencing significant cognitive decline. One important aspect of AD treatment is identification. It is now recognized that the disease begins more than a decade before the signature symptoms of cognitive impairment become apparent. Identifying individuals in this “preclinical” disease state has become a primary focus of many investigators who believe that AD must be targeted and fought well before the clinical manifestations of memory impairment appear. Biomarkers, indicators of normal biological or pathological processes that may be studied as a means to give individuals a disease diagnosis, prognosis, or theragnosis – provided a treatment is available for the disease in question – are of paramount importance in many diseases. AD has proved a difficult target to nail down reliable, sensitive, and specific biomarkers. This is in part due to analytical difficulties in major, core biomarkers of disease and in part due to setbacks in clinical trials of promising therapeutic candidates. The current work begins with an overview of biomarker modalities used in AD; however, the primary focus is on protein biomarkers in cerebrospinal fluid (CSF). CSF provides an intimate window to the central nervous system that, in the case of AD, has shown the ability to identify and monitor disease progress over time in cohorts of cognitively normal and demented individuals. In an effort to pinpoint AD before clinical signs and symptoms manifest, biomarker research in preclinical AD has become a robust area of investigation. CSF biomarkers of amyloid pathology, neuronal damage, and neuroinflammation are discussed in two independent cohorts: the Adult Children Study (ACS) from Washington University in St. Louis and the Alzheimer’s Disease Neuroimaging Initiative. The ACS cohort is comprised of middle-aged, cognitively normal individuals recruited on a volunteer basis from community dwelling participants with and without a family history of AD. The ADNI cohort is comprised of older individuals also recruited on a volunteer basis from community dwelling participants, though participants are recruited with respect to clinical status and include cognitively normal individuals, individuals with mild cognitive impairment, and individuals with AD, in addition to being older than the ACS cohort. In both cohorts, it was found that CSF markers of amyloid plaques – one of two required pathological hallmarks that indicate AD – changed earlier than those of tau tangles, the second required pathological hallmark. Currently, examining biomarkers on a group-wide basis is the best way to get an accurate picture of biomarkers at baseline and followup lumbar punctures (LPs). As the goal is to be able to give individual people a diagnosis and prognosis of their disease, the behavior of biomarkers is particularly interesting because studies have found that CSF Aβ42 changes up to 15 or more years before cognitive signs and symptoms become apparent and, hopefully, beginning treatment in this period will be helpful not only for diagnosing for individuals with AD dementia, but also for individuals with very early disease

Understanding mechanisms related to psychosis in Motor Neurone Disease

Psychosis is a challenging feature of the syndromes of motor neurone disease (MND), frontotemporal dementia (FTD) and their overlap (FTD-MND). Clinically evident psychosis is not common, except in those with C9orf72+ expansions. However, subthreshold psychosis or pre-psychosis processes are common and provide the opportunity to study the mechanisms of psychosis in MND and FTD-MND. My aim was to identify the prevalence and the cognitive and neural correlates of psychosis, and related processes, in MND. I used a tiered cohort study approach. Tier 1 introduced screening as standard in a regional MND clinic (N=111) using the Edinburgh Cognitive and Behavioural ALS Screen and Cambridge Behavioural Inventory-Revised (CBI-R). In Tier 2, 60 patients and 30 controls underwent neuropsychological assessment, including (i) evidence-based decision-making, to quantify jumping to conclusions (JTC), (ii) attentional control and associative learning, (iii) perceptual inference, and (iv) psychiatric screening with Neuropsychiatric Inventory (NPI), Brief Psychiatric Rating Scale (BRPS), and the Comprehensive Assessment of At-Risk Mental States (CAARMS). Tier 3 included magnetic resonance imaging of 30 patients and 20 controls. Carer reports in Tier 1 indicated that 10% of patients exhibited features suggestive of psychosis and 40% exhibited behavioural change. In Tier 2, many patients manifested abnormal behaviours (CBI-R 41%; NPI showed 19%; BPRS 24%), with 12-16% showing psychosis-specific symptoms (CBI-R and NPI psychosis index scores). In the jumping to conclusions task, patients made decisions based on less evidence than controls and were insensitive to negative feedback. Carer ratings of patient behaviour correlated with performance on the jumping to conclusions task when decisions were rewarded or costs fixed. Attentional shifting and perceptual inference were normal in MND. A principal component analysis (PCA) of questionnaires revealed two component scores, reflecting distinct patients’ and carers’ perspectives. The imaging analyses focused on the correlates of jumping to conclusions and insensitivity to negative feedback, as a potential risk profile for psychosis, with exploratory analyses of the correlates of the CBI-R psychosis index, and carers’ ratings of behaviour from the PCA. Using a Freesurfer regions-of-interest approach, grey matter volume correlated inversely with CBI-R psychosis index in the caudate, amygdala, cingulate and hippocampus. Using tract-based spatial statistics, increased mean diffusivity (MD) of diffusion weighted imaging correlated with the CBI-R psychosis responses in inferior longitudinal and uncinate fasciculi. Cost sensitivity in the JTC task correlated with cingulate and cerebellar grey matter volumes. White matter correlates of cost sensitivity included reduced FA with increasing cost sensitivity in white matter connecting the inferior frontal lobe in controls and patients. Although overt psychosis is uncommon in MND, many patients displayed abnormal behaviour or cognitive symptoms, including suboptimal reasoning biases and inferential impulsivity. Degeneration of cerebellar, cingulate and striatal grey matter, and adjacent major white matter tracts, may underlie these cognitive impairments and together represent a vulnerability to develop psychosis. Compromised reasoning and inference have implications for clinical management, including decisions around treatment options and management of well-being in MND.This research was funded by Alzheimer’s Research UK Studentship (PhD2017-26); Medical Research Council (SUAG 051/ G101400); and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care

Imaging the subthalamic nucleus in Parkinson’s disease

This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time

Real-time brain-machine interface architectures : neural decoding from plan to movement

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 129-135).Brain-machine interfaces (BMI) aim to enable motor function in individuals with neurological injury or disease, by recording the neural activity, mapping or 'decoding' it into a motor command, and then controlling a device such as a computer interface or robotic arm. BMI research has largely focused on the problem of restoring the original motor function. The goal therefore has been to achieve a performance close to that of the healthy individual. There have been compelling proof of concept demonstrations of the utility of such BMIs in the past decade. However, performance of these systems needs to be significantly improved before they become clinically viable. Moreover, while developing high-performance BMIs with the goal of matching the original motor function is indeed valuable, a compelling goal is that of designing BMIs that can surpass original motor function. In this thesis, we first develop a novel real-time BMI for restoration of natural motor function. We then introduce a BMI architecture aimed at enhancing original motor function. We implement both our designs in rhesus monkeys. To facilitate the restoration of lost motor function, BMIs have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Moreover, both target and trajectory information are encoded in the motor cortical areas. These suggest that BMIs should be designed to combine these principal aspects of movement. We develop a novel two-stage BMI to decode jointly the target and trajectory of a reaching movement. First, we decode the intended target from neural spiking activity before movement initiation. Second, we combine the decoded target with the spiking activity during movement to estimate the trajectory. To do so, we use an optimal feedback-control design that aims to emulate the sensorimotor processing underlying actual motor control and directly processes the spiking activity using point process modeling in real time. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. This BMI also performs significantly better than linear regression approaches demonstrating the advantage of a design that more closely mimics the sensorimotor system.(cont.) While restoring the original motor function is indeed important, a compelling goal is the development of a truly "intelligent" BMI that can transcend such function by considering the higherlevel goal of the motor activity, and reformulating the motor plan accordingly. This would allow, for example, a task to be performed more quickly than possible by natural movement, or more efficiently than originally conceived. Since a typical motor activity consists of a sequence of planned movements, such a BMI must be capable of analyzing the complete sequence before action. As such its feasibility hinges fundamentally on whether all elements of the motor plan can be decoded concurrently from working memory. Here we demonstrate that such concurrent decoding is possible. In particular, we develop and implement a real-time BMI that accurately and simultaneously decodes in advance a sequence of planned movements from neural activity in the premotor cortex. In our experiments, monkeys were trained to add to working memory, in order, two distinct target locations on a screen, then move a cursor to each, in sequence. We find that the two elements of the motor plan, corresponding to the two targets, are encoded concurrently during the working memory period. Additionally, and interestingly, our results reveal: that the elements of the plan are encoded by largely disjoint subpopulations of neurons; that surprisingly small subpopulations are sufficient for reliable decoding of the motor plan; and that the subpopulation dedicated to the first target and their responses are largely unchanged when the second target is added to working memory, so that the process of adding information does not compromise the integrity of existing information. The results have significant implications for the architecture and design of future generations of BMIs with enhanced motor function capabilities.by Maryam Modir Shanechi.Ph.D

On the role of the hippocampus in the acquisition, long-term retention and semanticisation of memory

Institute for Adaptive and Neural ComputationA consensus on how to characterise the anterograde and retrograde memory processes that are lost or spared after hippocampal damage has not been reached. In this thesis, I critically re-examine the empirical literature and the assumptions behind current theories. I formulate a coherent view of what makes a task hippocampally dependent at acquisition and how this relates to its long-term fate. Findings from a neural net simulation indicate the plausibility of my proposals. My proposals both extend and constrain current views on the role of the hippocampus in the rapid acquisition of information and in learning complex associations. In general, tasks are most likely to require the hippocampus for acquisition if they involve rapid, associative learning about unfamiliar, complex, low salience stimuli. However, none of these factors alone is sufficient to obligatorily implicate the hippocampus in acquisition. With the exception of associations with supra-modal information that are always dependent on the hippocampus, it is the combination of factors that is important. Detailed, complex information that is obligatorily hippocampally-dependent at acquisition remains so for its lifetime. However, all memories are semanticised as they age through the loss of detailed context-specific information and because generic cortically-represented information starts to dominate recall. Initially hippocampally dependent memories may appear to become independent of the hippocampus over time, but recall changes qualitatively. Multi-stage, lifelong post-acquisition memory processes produce semanticised re-representations of memories of differing specificity and complexity, that can serve different purposes. The model simulates hippocampal and cortical interactions in the acquisition and maintenance of episodic and semantic events, and behaves in accordance with my proposals. In particular, conceptualising episodic and semantic memory as representing points on a continuum of memory types appears viable. Support is also found for proposals on the relative importance of the hippocampus and cortex in the rapid acquisition of information and the acquisition of complex multi-model information; and the effect of existing knowledge on new learning. Furthermore, episodic and semantic events become differentially dependent on cortical and hippocampal components. Finally, as a memory ages, it is automatically semanticised and becomes cortically dependent

Augmentation of Brain Function: Facts, Fiction and Controversy. Volume III: From Clinical Applications to Ethical Issues and Futuristic Ideas

The final volume in this tripartite series on Brain Augmentation is entitled “From Clinical Applications to Ethical Issues and Futuristic Ideas”. Many of the articles within this volume deal with translational efforts taking the results of experiments on laboratory animals and applying them to humans. In many cases, these interventions are intended to help people with disabilities in such a way so as to either restore or extend brain function. Traditionally, therapies in brain augmentation have included electrical and pharmacological techniques. In contrast, some of the techniques discussed in this volume add specificity by targeting select neural populations. This approach opens the door to where and how to promote the best interventions. Along the way, results have empowered the medical profession by expanding their understanding of brain function. Articles in this volume relate novel clinical solutions for a host of neurological and psychiatric conditions such as stroke, Parkinson’s disease, Huntington’s disease, epilepsy, dementia, Alzheimer’s disease, autism spectrum disorders (ASD), traumatic brain injury, and disorders of consciousness. In disease, symptoms and signs denote a departure from normal function. Brain augmentation has now been used to target both the core symptoms that provide specificity in the diagnosis of a disease, as well as other constitutional symptoms that may greatly handicap the individual. The volume provides a report on the use of repetitive transcranial magnetic stimulation (rTMS) in ASD with reported improvements of core deficits (i.e., executive functions). TMS in this regard departs from the present-day trend towards symptomatic treatment that leaves unaltered the root cause of the condition. In diseases, such as schizophrenia, brain augmentation approaches hold promise to avoid lengthy pharmacological interventions that are usually riddled with side effects or those with limiting returns as in the case of Parkinson’s disease. Brain stimulation can also be used to treat auditory verbal hallucination, visuospatial (hemispatial) neglect, and pain in patients suffering from multiple sclerosis. The brain acts as a telecommunication transceiver wherein different bandwidth of frequencies (brainwave oscillations) transmit information. Their baseline levels correlate with certain behavioral states. The proper integration of brain oscillations provides for the phenomenon of binding and central coherence. Brain augmentation may foster the normalization of brain oscillations in nervous system disorders. These techniques hold the promise of being applied remotely (under the supervision of medical personnel), thus overcoming the obstacle of travel in order to obtain healthcare. At present, traditional thinking would argue the possibility of synergism among different modalities of brain augmentation as a way of increasing their overall effectiveness and improving therapeutic selectivity. Thinking outside of the box would also provide for the implementation of brain-to-brain interfaces where techniques, proper to artificial intelligence, could allow us to surpass the limits of natural selection or enable communications between several individual brains sharing memories, or even a global brain capable of self-organization. Not all brains are created equal. Brain stimulation studies suggest large individual variability in response that may affect overall recovery/treatment, or modify desired effects of a given intervention. The subject’s age, gender, hormonal levels may affect an individual’s cortical excitability. In addition, this volume discusses the role of social interactions in the operations of augmenting technologies. Finally, augmenting methods could be applied to modulate consciousness, even though its neural mechanisms are poorly understood. Finally, this volume should be taken as a debate on social, moral and ethical issues on neurotechnologies. Brain enhancement may transform the individual into someone or something else. These techniques bypass the usual routes of accommodation to environmental exigencies that exalted our personal fortitude: learning, exercising, and diet. This will allow humans to preselect desired characteristics and realize consequent rewards without having to overcome adversity through more laborious means. The concern is that humans may be playing God, and the possibility of an expanding gap in social equity where brain enhancements may be selectively available to the wealthier individuals. These issues are discussed by a number of articles in this volume. Also discussed are the relationship between the diminishment and enhancement following the application of brain-augmenting technologies, the problem of “mind control” with BMI technologies, free will the duty to use cognitive enhancers in high-responsibility professions, determining the population of people in need of brain enhancement, informed public policy, cognitive biases, and the hype caused by the development of brain- augmenting approaches