437 research outputs found
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
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The Monogenic Architecture of Retinal and Neurological Diseases
Monogenic diseases, or single-gene disorders, are clinical manifestations that can be traced to genetic variation in a single gene that alters the biologically intended (wildtype) function of its protein (or mRNA) product. Although the causal gene and its function are well-understood in many monogenic diseases, this knowledge alone often does not fully encapsulate the extensive clinical spectrum of phenotypes seen in patients. This is due in part to the numerous types of pathogenic variants that can arise in a single gene, all of which can have distinct effects on disease expression. Understanding the relationship between the vast number of possible genotypes and corresponding disease phenotypes defines a gene’s monogenic disease architecture—an important but poorly understood concept that can yield informative mechanistic and clinical insight.
This doctoral dissertation integrates traditional sequencing approaches with in-depth characterization of patient phenotypes to elucidate the monogenic disease architecture of three etiologically distinct disorders: retinal degeneration caused by autosomal recessive variation in ABCA4 and neurodevelopmental disease entities caused by autosomal dominant variants in CERT1 and PUM1. Genetic modifiers are identified as a significant factor in the penetrance of the major disease-causing allele of ABCA4 and several other genetic inconsistencies are resolved to create a coherent genotype-phenotype model for the disease. Insight from this model is then applied to demonstrate the effect of allele differences in disease progression and evaluation of treatment efficacy in patients. A large cohort of affected individuals with CERT1 variation is assembled to (1) validate the causal role of CERT1 in disease, (2) delineate the precise mechanism of CERT protein dysfunction in sphingolipid metabolism and (3) demonstrate therapeutic efficacy of an inhibitor compound for a newly described syndrome.
Finally, the mutational spectrum of PUM1 is expanded to previously unattributed variant classes with unexpected pathophysiological consequences to patients. Not only do the findings in this dissertation advance the prospects of delivering personalized, precision medicine to patients, the overall impact underscores the importance of this integrated approach in reconciling knowledge gaps between observations at the molecular and organismal level
Natural language processing (NLP) for clinical information extraction and healthcare research
Introduction: Epilepsy is a common disease with multiple comorbidities. Routinely collected health care data have been successfully used in epilepsy research, but they lack the level of detail needed for in-depth study of complex interactions between the aetiology, comorbidities, and treatment that affect patient outcomes. The aim of this work is to use natural language processing (NLP) technology to create detailed disease-specific datasets derived from the free text of clinic letters in order to enrich the information that is already available. Method: An NLP pipeline for the extraction of epilepsy clinical text (ExECT) was redeveloped to extract a wider range of variables. A gold standard annotation set for epilepsy clinic letters was created for the validation of the ExECT v2 output. A set of clinic letters from the Epi25 study was processed and the datasets produced were validated against Swansea Neurology Biobank records. A data linkage study investigating genetic influences on epilepsy outcomes using GP and hospital records was supplemented with the seizure frequency dataset produced by ExECT v2. Results: The validation of ExECT v2 produced overall precision, recall, and F1 score of 0.90, 0.86, and 0.88, respectively. A method of uploading, annotating, and linking genetic variant datasets within the SAIL databank was established. No significant differences in the genetic burden of rare and potentially damaging variants were observed between the individuals with vs without unscheduled admissions, and between individuals on monotherapy vs polytherapy. No significant difference was observed in the genetic burden between people who were seizure free for over a year and those who experienced at least one seizure a year. Conclusion: This work presents successful extraction of epilepsy clinical information and explores how this information can be used in epilepsy research. The approach taken in the development of ExECT v2, and the research linking the NLP outputs, routinely collected health care data, and genetics set the way for wider research
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Conformable transistors for bioelectronics
The diversity of network disruptions that occur in patients with neuropsychiatric disorders creates a strong demand for personalized medicine. Such approaches often take the form of implantable bioelectronic devices that are capable of monitoring pathophysiological activity for identifying biomarkers to allow for local and responsive delivery of intervention. They are also required to transmit this data outside of the body for evaluation of the treatment’s efficacy.
However, the ability to perform these demanding electronic functions in the complex physiological environment with minimum disruption to the biological tissue remains a big challenge. An optimal fully implantable bioelectronic device would require each component from the front-end to the data transmission to be conformable and biocompatible. For this reason, organic material-based conformable electronics are ideal candidates for components of bioelectronic circuits due to their inherent flexibility, and soft nature.
In this work, first an organic mixed-conducting particulate composite material (MCP) able to form functional electronic components and non-invasively acquire high–spatiotemporal resolution electrophysiological signals by directly interfacing human skin is presented. Secondly, we introduce organic electrochemical internal ion-gated transistors (IGTs) as a high-density, high-amplification sensing component as well as a low leakage, high-speed processing unit.
Finally, a novel wireless, battery-free strategy for electrophysiological signal acquisition, processing, and transmission that employs IGTs and an ionic communication circuit (IC) is introduced. We show that the wirelessly-powered IGTs are able to acquire and modulate neurophysiological data in-vivo and transmit them transdermally, eliminating the need for any hard Si-based electronics in the implant
Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback
Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories
Identification and Functional Assessment of Novel Neuromuscular Disease-Causing Genes
Inherited neuromuscular diseases comprise a highly heterogeneous group of disorders characterized by the impairment of the neural structures or motor unit components responsible for the generation of movement.
While as single gene-associated disorder the majority of them are rare, taken together their estimated prevalence reaches 1 – 3 cases / 1000 individuals. Due to their elevated morbidity and mortality, they represent a significant health burden for the affected individuals, their families, and the healthcare systems. Moreover, their clinical and genetic heterogeneity makes their diagnosis a long and complex process, which often requires specialized diagnostic procedures and poses a challenge in about half of the cases. However, thanks to decreasing costs and increased availability of next-generation sequencing technologies, the last years had witnessed a rise in the number of novel genes associated to neuromuscular disorders.
In this study, we identified three novel neuromuscular disease-causing genes: PIEZO2, whose biallelic loss-of-function mutations cause distal arthrogryposis with impaired proprioception and touch; VAMP1, whose biallelic loss-of-function mutations cause a novel presynaptic congenital myasthenic syndrome; CAPRIN1, whose specific p.Pro512Leu mutation causes a neurodegenerative disorder characterized by ataxia and muscle weakness.
For PIEZO2, we identified biallelic loss-of-function mutations using exome sequencing, SNPchip-based linkage analysis, DNA microarray, and Sanger sequencing in ten affected individuals of four independent families showing arthrogryposis, hypotonia, respiratory insufficiency at birth, scoliosis, and delayed motor development. This phenotype is clearly distinct from distal arthrogryposis with ocular anomalies which characterize the autosomal dominant distal arthrogryposis 3 (DA3), distal arthrogryposis 5 (DA5), and Marden-Walker syndrome (MWKS). While these disorders are caused by heterozygous gain-of-function mutations in PIEZO2, the novel reported mutations result in the loss of PIEZO2, since they lead to nonsense-mediated mRNA decay in patient-derived fibroblast cell lines. PIEZO2 is a mechanosensitive ion channel playing a major role in light-touch sensation and proprioception. Mice ubiquitously depleted of PIEZO2 die postnatally because of respiratory distress, while individuals lacking PIEZO2 develop a neuromuscular disorder, likely due to the loss of proprioception inputs in muscles.
For VAMP1, we identified biallelic loss-of-function mutations using exome or genome sequencing in two pairs of siblings from two independent families affected by a novel congenital myasthenic syndrome. Electrodiagnostic examination showed severely low compound muscle action potentials and presynaptic impairment. The two described homozygous mutations are a frameshift and a missense mutation of a highly conserved residue, therefore are likely to result in the loss of VAMP1 function. Indeed, the phenotype is resembled by VAMP1lew/lew mice, which carry a homozygous VAMP1 truncating mutation and show neurophysiological features of presynaptic impairment.
For CAPRIN1, we identified the identical de novo c.1535C>T (p.Pro512Leu) missense variant using trio exome sequencing in two unrelated individuals displaying early-onset ataxia, dysarthria, cognitive decline and muscle weakness. This mutation causes the substitution of a highly conserved residue and in silico tools predict an increase in the protein aggregation propensity. Overexpression of CAPRIN1-P512L caused the formation of insoluble ubiquitinated aggregates, sequestrating proteins associated with neurodegenerative disorders, such as ATXN2, GEMIN5, SNRNP200, and SNCA. Upon differentiation in cortical neurons of induced pluripotent stem cell (iPSC) lines where the CAPRIN1-P512L was introduced via CRISPR/Cas9, reduced neuronal activity and altered stress granules dynamics were observed in the lines harboring the mutation. Moreover, nano-differential scanning fluorimetry revealed that CAPRIN1-P512L adopts an extended conformation, and fluorescence microscopy demonstrated that RNA greatly enhances its aggregation in vitro.
Taken together, this study associates: (1) biallelic loss-of-function mutations in PIEZO2 with the autosomal recessive distal arthrogryposis with impaired proprioception and touch; (2) biallelic loss-of-function mutations in VAMP1 with an autosomal recessive presynaptic congenital myasthenic syndrome; (3) a recurrent de novo p.Pro512Leu mutation of CAPRIN1 with a neurodegenerative disorder characterized by ataxia and muscle weakness
Leveraging EEG-based speech imagery brain-computer interfaces
Speech Imagery Brain-Computer Interfaces (BCIs) provide an intuitive and flexible way of interaction via brain activity recorded during imagined speech. Imagined speech can be decoded in form of syllables or words and captured even with non-invasive measurement methods as for example the Electroencephalography (EEG). Over the last decade, research in this field has made tremendous progress and prototypical implementations of EEG-based Speech Imagery BCIs are numerous. However, most work is still conducted in controlled laboratory environments with offline classification and does not find its way to real online scenarios. Within this thesis we identify three main reasons for these circumstances, namely, the mentally and physically exhausting training procedures, insufficient classification accuracies and cumbersome EEG setups with usually high-resolution headsets. We furthermore elaborate on possible solutions to overcome the aforementioned problems and present and evaluate new methods in each of the domains. In detail we introduce two new training concepts for imagined speech BCIs, one based on EEG activity during silently reading and the other recorded during overtly speaking certain words. Insufficient classification accuracies are addressed by introducing the concept of a Semantic Speech Imagery BCI, which classifies the semantic category of an imagined word prior to the word itself to increase the performance of the system. Finally, we investigate on different techniques for electrode reduction in Speech Imagery BCIs and aim at finding a suitable subset of electrodes for EEG-based imagined speech detection, therefore facilitating the cumbersome setups. All of our presented results together with general remarks on experiences and best practice for study setups concerning imagined speech are summarized and supposed to act as guidelines for further research in the field, thereby leveraging Speech Imagery BCIs towards real-world application.Speech Imagery Brain-Computer Interfaces (BCIs) bieten eine intuitive und flexible Möglichkeit der Interaktion mittels Gehirnaktivität, aufgezeichnet während der bloßen Vorstellung von Sprache. Vorgestellte Sprache kann in Form von Silben oder Wörtern auch mit nicht-invasiven Messmethoden wie der Elektroenzephalographie (EEG) gemessen und entschlüsselt werden. In den letzten zehn Jahren hat die Forschung auf diesem Gebiet enorme Fortschritte gemacht, und es gibt zahlreiche prototypische Implementierungen von EEG-basierten Speech Imagery BCIs. Die meisten Arbeiten werden jedoch immer noch in kontrollierten Laborumgebungen mit Offline-Klassifizierung durchgeführt und finden nicht denWeg in reale Online-Szenarien. In dieser Arbeit identifizieren wir drei Hauptgründe für diesen Umstand, nämlich die geistig und körperlich anstrengenden Trainingsverfahren, unzureichende Klassifizierungsgenauigkeiten und umständliche EEG-Setups mit meist hochauflösenden Headsets. Darüber hinaus erarbeiten wir mögliche Lösungen zur Überwindung der oben genannten Probleme und präsentieren und evaluieren neue Methoden für jeden dieser Bereiche. Im Einzelnen stellen wir zwei neue Trainingskonzepte für Speech Imagery BCIs vor, von denen eines auf der Messung von EEG-Aktivität während des stillen Lesens und das andere auf der Aktivität während des Aussprechens bestimmter Wörter basiert. Unzureichende Klassifizierungsgenauigkeiten werden durch die Einführung des Konzepts eines Semantic Speech Imagery BCI angegangen, das die semantische Kategorie eines vorgestellten Wortes vor dem Wort selbst klassifiziert, um die Performance des Systems zu erhöhen. Schließlich untersuchen wir verschiedene Techniken zur Elektrodenreduktion bei Speech Imagery BCIs und zielen darauf ab, eine geeignete Teilmenge von Elektroden für die EEG-basierte Erkennung von vorgestellter Sprache zu finden, um so die umständlichen Setups zu erleichtern. Alle unsere Ergebnisse werden zusammen mit allgemeinen Bemerkungen zu Erfahrungen und Best Practices für Studien-Setups bezüglich vorgestellter Sprache zusammengefasst und sollen als Richtlinien für die weitere Forschung auf diesem Gebiet dienen, um so Speech Imagery BCIs für die Anwendung in der realenWelt zu optimieren
Interictal Network Dynamics in Paediatric Epilepsy Surgery
Epilepsy is an archetypal brain network disorder. Despite two decades of research
elucidating network mechanisms of disease and correlating these with outcomes, the clinical
management of children with epilepsy does not readily integrate network concepts. For
example, network measures are not used in presurgical evaluation to guide decision making
or surgical management plans.
The aim of this thesis was to investigate novel network frameworks from the perspective of
a clinician, with the explicit aim of finding measures that may be clinically useful and
translatable to directly benefit patient care. We examined networks at three different scales,
namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and
micro (single unit networks) scales, consistently finding network abnormalities in children
being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical
translation, using frameworks such as IDEAL to robustly assess the impact of these new
technologies on management and outcomes.
The thesis sets up a platform from which promising computational technology, that utilises
brain network analyses, can be readily translated to benefit patient care
Detection of pathological high-frequency oscillations in refractory epilepsy patients undergoing simultaneous stereo-electroencephalography and magnetoencephalography
BACKGROUND: Stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) have generally been used independently as part of the pre-surgical evaluation of drug-resistant epilepsy (DRE) patients. However, the possibility of simultaneously employing these recording techniques to determine whether MEG has the potential of offering the same information as SEEG less invasively, or whether it could offer a greater spatial indication of the epileptogenic zone (EZ) to aid surgical planning, has not been previously evaluated. METHODS: Data from 24 paediatric and adult DRE patients, undergoing simultaneous SEEG and MEG as part of their pre-surgical evaluation, was analysed employing manual and automated high-frequency oscillations (HFOs) detection, and spectral and source localisation analyses. RESULTS: Twelve patients (50%) were included in the analysis (4 males; mean age=25.08 years) and showed interictal SEEG and MEG HFOs. HFOs detection was concordant between the two recording modalities, but SEEG displayed higher ability of differentiating between deep and superficial epileptogenic sources. Automated HFO detector in MEG recordings was validated against the manual MEG detection method. Spectral analysis revealed that SEEG and MEG detect distinct epileptic events. The EZ was well correlated with the simultaneously recorded data in 50% patients, while 25% patients displayed poor correlation or discordance. CONCLUSIONS: MEG recordings can detect HFOs, and simultaneous use of SEEG and MEG HFO identification facilitates EZ localisation during the presurgical planning stage for DRE patients. Further studies are necessary to validate these findings and support the translation of automated HFO detectors into routine clinical practice
The 26th Annual Boston University Undergraduate Research (UROP) Abstracts
The file is available to be viewed by anyone in the BU community. To view the file, click on "Login" or the Person icon top-right with your BU Kerberos password. You will then be able to see an option to View.Abstracts for the 2023 UROP Symposium, held at Boston University on October 20, 2023 at GSU Metcalf Ballroom. Cover and logo design by Morgan Danna. Booklet compiled by Molly Power
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