2,652 research outputs found
Computational techniques to interpret the neural code underlying complex cognitive processes
Advances in large-scale neural recording technology have significantly improved the
capacity to further elucidate the neural code underlying complex cognitive processes.
This thesis aimed to investigate two research questions in rodent models. First, what
is the role of the hippocampus in memory and specifically what is the underlying
neural code that contributes to spatial memory and navigational decision-making.
Second, how is social cognition represented in the medial prefrontal cortex at the
level of individual neurons. To start, the thesis begins by investigating memory and
social cognition in the context of healthy and diseased states that use non-invasive
methods (i.e. fMRI and animal behavioural studies). The main body of the thesis
then shifts to developing our fundamental understanding of the neural mechanisms
underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging
and behaviour preprocessing computational pipelines were developed and optimised
for use in social interaction and spatial navigation experimental analysis. In parallel,
a review was conducted on methods for multivariate/neural population analysis. A
comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise
and behavioural complexity. Furthermore, the review visualises how NML can be
applied to disease states in the brain and introduces the secondary analyses that
can be used to enhance or characterise a neural manifold. Lastly, the preprocessing
and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored
how neural firing in the medial Prefrontal cortex changed as a function of the social
dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble
of behavioural-tuned neurons that fire preferentially during specific behaviours such
as "pushing" or "retreating" for the animalâs own behaviour and/or the competitorâs
behaviour. Furthermore, in dominant animals, the neural population exhibited greater
average firing than that of subordinate animals. Next, to investigate spatial memory,
a spatial recency task was used, where rats learnt to navigate towards one of three
reward locations and then recall the rewarded location of the session. During the
task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats
over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animalâs spatial position leading up to a rewarded location was also active
in the decision period before the animal navigates to the rewarded location. The result
posits that prospective replay of neural sequences in the hippocampal CA1 region
could provide a mechanism by which decision-making is supported
3D Innovations in Personalized Surgery
Current practice involves the use of 3D surgical planning and patient-specific solutions in multiple surgical areas of expertise. Patient-specific solutions have been endorsed for several years in numerous publications due to their associated benefits around accuracy, safety, and predictability of surgical outcome. The basis of 3D surgical planning is the use of high-quality medical images (e.g., CT, MRI, or PET-scans). The translation from 3D digital planning toward surgical applications was developed hand in hand with a rise in 3D printing applications of multiple biocompatible materials. These technical aspects of medical care require engineersâ or technical physiciansâ expertise for optimal safe and effective implementation in daily clinical routines.The aim and scope of this Special Issue is high-tech solutions in personalized surgery, based on 3D technology and, more specifically, bone-related surgery. Full-papers or highly innovative technical notes or (systematic) reviews that relate to innovative personalized surgery are invited. This can include optimization of imaging for 3D VSP, optimization of 3D VSP workflow and its translation toward the surgical procedure, or optimization of personalized implants or devices in relation to bone surgery
Modelling and Characterization of Force Plate Measurements on Subacute Post-Concussion Subjects Through Machine Learning
Mild traumatic brain injuries (mTBI) are one of the leading causes of neurological disorders. Symptoms after a mTBI may include headache, dizziness, and balance issues, among others, with vestibular disorders observed in up to 80% of these patients. These symptoms generally resolve in the first few weeks after the injury, but some patients may develop persistent symptoms. Patients with Post-Concussion Vestibular Dysfunction (PCVD) may present alterations in the peripheral and central vestibular systems. These alterations may then affect postural control and stability, which coupled with visual motion sensitivity, cause the prolonged symptomatology. In this study, we evaluated postural control strategies in Healthy Controls (HC) and Subacute PCVD patients (ST) to identify underlying changes in the postural control system. Sensory Organization Test (SOT) was employed to measure Centre Of Pressure (COP) signals under different sensory conditions. Analysis of traditional linear metrics and entropy metrics of the COP signals demonstrated significant differences between groups. Complexity index was reduced for the ST group during âEyes Closedâ condition, with a median value of 7.93 vs 9.59 for the HC in the Medial-Lateral direction (p=0.002), and 5.17 vs 6.22 Anterior-Posterior direction (p=0.0009). Moreover, analysis of these metrics through machine learning, showed indications of interactions between these variables that may be predictive of the health condition of the patient. These results remark the potential of these metrics for evaluating changes in postural dynamics in patients with PCVD, and opens a new path for analysis of the COP signals with the support of machine learning models.M.S
Anwendungen maschinellen Lernens fĂŒr datengetriebene PrĂ€vention auf Populationsebene
Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die PrĂ€vention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung fĂŒr ein prĂ€ventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und MaĂnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da fĂŒr die meisten Krankheiten keine Risikomodelle existieren und sich verfĂŒg- bare Modelle auf einzelne Krankheiten beschrĂ€nken. Weil fĂŒr deren Berechnung jeweils spezielle Sets an PrĂ€diktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe DatenmodalitĂ€ten, wie Bilder oder -omics- Messungen, systemische Informationen ĂŒber zukĂŒnftige GesundheitsverlĂ€ufe, mit poten- tieller Relevanz fĂŒr viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch fĂŒr die Risikomod- ellierung unzugĂ€nglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier DatenmodalitĂ€ten in der PrimĂ€rprĂ€ven- tion zu untersuchen: polygene Risikoscores fĂŒr die kardiovaskulĂ€re PrĂ€vention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- itĂ€t wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenĂŒber ĂŒblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der DatenmodalitĂ€t zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores fĂŒr die kardiovaskulĂ€re PrĂ€vention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen ĂŒber den zukĂŒnftigen Ausbruch von Krankheiten. Unter Einsatz einer phĂ€nomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prĂ€dik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert fĂŒr deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, prĂ€ventionsori- entierten Medizin zu verĂ€ndern
Ditransitives in germanic languages. Synchronic and diachronic aspects
This volume brings together twelve empirical studies on ditransitive constructions in Germanic languages and their varieties, past and present. Specifically, the volume includes contributions on a wide variety of Germanic languages, including English, Dutch, and German, but also Danish, Swedish, and Norwegian, as well as lesser-studied ones such as Faroese. While the first part of the volume focuses on diachronic aspects, the second part showcases a variety of synchronic aspects relating to ditransitive patterns. Methodologically, the volume covers both experimental and corpus-based studies. Questions addressed by the papers in the volume are, among others, issues like the cross-linguistic pervasiveness and cognitive reality of factors involved in the choice between different ditransitive constructions, or differences and similarities in the diachronic development of ditransitives. The volumeâs broad scope and comparative perspective offers comprehensive insights into well-known phenomena and furthers our understanding of variation across languages of the same family
Gut-brain interactions affecting metabolic health and central appetite regulation in diabetes, obesity and aging
The central aim of this thesis was to study the effects of gut microbiota on host energy metabolism and central regulation of appetite. We specifically studied the interaction between gut microbiota-derived short-chain fatty acids (SCFAs), postprandial glucose metabolism and central regulation of appetite. In addition, we studied probable determinants that affect this interaction, specifically: host genetics, bariatric surgery, dietary intake and hypoglycemic medication.First, we studied the involvement of microbiota-derived short-chain fatty acids in glucose tolerance. In an observational study we found an association of intestinal availability of SCFAs acetate and butyrate with postprandial insulin and glucose responses. Hereafter, we performed a clinical trial, administering acetate intravenously at a constant rate and studied the effects on glucose tolerance and central regulation of appetite. The acetate intervention did not have a significant effect on these outcome measures, suggesting the association between increased gastrointestinal SCFAs and metabolic health, as observed in the observational study, is not paralleled when inducing acute plasma elevations.Second, we looked at other determinants affecting gut-brain interactions in metabolic health and central appetite signaling. Therefore, we studied the relation between the microbiota and central appetite regulation in identical twin pairs discordant for BMI. Second, we studied the relation between microbial composition and post-surgery gastrointestinal symptoms upon bariatric surgery. Third, we report the effects of increased protein intake on host microbiota composition and central regulation of appetite. Finally, we explored the effects of combination therapy with GLP-1 agonist exenatide and SGLT2 inhibitor dapagliflozin on brain responses to food stimuli
Methodische AnsÀtze zur Analyse biomechanischer Zeitreihendaten
Die vorliegende Dissertation beschĂ€ftigt sich mit der Frage inwiefern der Einsatz von Zeitreihenmethoden - also Methoden, die den kontinuierlichen Charakter biomechanischer Zeitreihendaten berĂŒcksichtigen - einen aus methodischer Sicht gĂŒnstigeren Auswertungsansatz darstellt als herkömmliche, diskrete Methoden. Damit liefert die Arbeit einen wichtigen Beitrag zur Ăberwindung der postulierten Konfidenzkrise biomechanisch-bewegungswissenschaftlicher Forschung, nach der sich die Ergebnisse vieler Studien aufgrund u. a. methodischer Defizite nicht oder nur eingeschrĂ€nkt reproduzieren lassen. Dabei konnte in drei Teilstudien fĂŒr verschiedene methodische Bereiche (Inferenzstatistik, Klassifikation, Trenderkennung) gezeigt werden, dass der Einsatz von Zeitreihenmethoden bekannte Probleme diskreter Methoden erfolgreich addressieren kann. Die vorgestellte Arbeit regt ĂŒber das spezifische Thema hinaus zu einem kritischeren Umgang mit methodischen Aspekten an und zeigt mögliche LösungsansĂ€tze auf
Deep learning for Alzheimerâs disease: towards the development of an assistive diagnostic tool
The past decade has witnessed rapid advances at the intersection of machine learning and medicine. Owing to the tremendous amount of digitized hospital data, machine learning is poised to bring innovation to the traditional healthcare workflow. Though machine learning models have strong predictive power, it is challenging to translate a research project into a clinical tool partly due to the lack of a rigorous validation framework. In this dissertation, I presented a range of machine learning models that were trained to classify Alzheimerâs disease - a condition with an insidious onset - using routinely collected clinical data. In addition to reporting the model performance, I discussed several considerations, including feature selection, data harmonization, effect of confounding variables, diagnostic scope, model interpretability and validation, which are critical to the design, development, and validation of machine learning models. From the methodological standpoint, I presented a multidisciplinary collaboration in which medical domain knowledge which was obtained from experts and tissue examinations was tightly integrated with the interpretable outcomes derived from our machine learning frameworks. I demonstrated that the model, which generalized well on multiple independent cohorts, achieved diagnostic performance on par with a group of medical professionals. The interpretable analysis of our model showed that its underlying decision logic corresponds with expert ratings and neuropathological findings. Taken together, this work presented a machine learning system for classification of Alzheimerâs disease, marking an important milestone towards a translatable clinical application in the future
Audiovisual speech perception in cochlear implant patients
Hearing with a cochlear implant (CI) is very different compared to a normal-hearing (NH) experience, as the CI can only provide limited auditory input. Nevertheless, the central auditory system is capable of learning how to interpret such limited auditory input such that it can extract meaningful information within a few months after
implant switch-on. The capacity of the auditory cortex to adapt to new auditory stimuli is an example of intra-modal plasticity â changes within a sensory cortical region as a result of altered statistics of the respective sensory input. However, hearing deprivation before implantation and restoration of hearing capacities after implantation can also induce cross-modal plasticity â changes within a sensory cortical region as a result of altered statistics of a different sensory input. Thereby, a preserved cortical region can, for example, support a deprived cortical region, as in the case of CI users which have been shown to exhibit cross-modal visual-cortex activation for purely auditory stimuli. Before implantation, during the period of hearing deprivation, CI users typically rely on additional visual cues like lip-movements for understanding speech. Therefore, it has been suggested that CI users show a pronounced binding of the auditory and visual systems, which may allow them to integrate auditory and visual speech information more efficiently. The projects included in this thesis investigate auditory, and particularly audiovisual speech processing in CI users. Four event-related potential (ERP) studies approach the matter from different perspectives, each with a distinct focus.
The first project investigates how audiovisually presented syllables are processed by CI users with bilateral hearing loss compared to NH controls. Previous ERP studies employing non-linguistic stimuli and studies using different neuroimaging techniques found distinct audiovisual interactions in CI users. However, the precise timecourse
of cross-modal visual-cortex recruitment and enhanced audiovisual interaction for speech related stimuli is unknown. With our ERP study we fill this gap, and we present differences in the timecourse of audiovisual interactions as well as in cortical source configurations between CI users and NH controls.
The second study focuses on auditory processing in single-sided deaf (SSD) CI users. SSD CI patients experience a maximally asymmetric hearing condition, as they have a CI on one ear and a contralateral NH ear. Despite the intact ear, several behavioural studies have demonstrated a variety of beneficial effects of restoring binaural hearing, but there are only few ERP studies which investigate auditory processing in SSD CI users. Our study investigates whether the side of implantation affects auditory processing and whether auditory processing via the NH ear of SSD CI users works similarly as in NH controls.
Given the distinct hearing conditions of SSD CI users, the question arises whether there are any quantifiable differences between CI user with unilateral hearing loss and bilateral hearing loss. In general, ERP studies on SSD CI users are rather scarce, and there is no study on audiovisual processing in particular. Furthermore, there are no reports on lip-reading abilities of SSD CI users. To this end, in the third project we extend the first study by including SSD CI users as a third experimental group. The study discusses both differences and similarities between CI users with bilateral hearing loss and CI users with unilateral hearing loss as well as NH controls and provides â for the first time â insights into audiovisual interactions in SSD CI users.
The fourth project investigates the influence of background noise on audiovisual interactions in CI users and whether a noise-reduction algorithm can modulate these interactions. It is known that in environments with competing background noise listeners generally rely more strongly on visual cues for understanding speech and that such situations are particularly difficult for CI users. As shown in previous auditory behavioural studies, the recently introduced noise-reduction algorithm "ForwardFocus" can be a useful aid in such cases. However, the questions whether employing the algorithm is beneficial in audiovisual conditions as well and whether using the algorithm has a measurable effect on cortical processing have not been investigated yet. In this ERP study, we address these questions with an auditory and audiovisual syllable discrimination task.
Taken together, the projects included in this thesis contribute to a better understanding of auditory and especially audiovisual speech processing in CI users, revealing distinct processing strategies employed to overcome the limited input provided by a CI. The results have clinical implications, as they suggest that clinical hearing assessments, which are currently purely auditory, should be extended to audiovisual assessments. Furthermore, they imply that rehabilitation including audiovisual training methods may be beneficial for all CI user groups for quickly achieving the most effective CI implantation outcome
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