Dissertations / Theses on the topic 'Quantitative Neuroscience'

2011/2012
Abstract (English) The current limit of knowledge advancement in proteomic analysis of gliomas, the most common primary malignant brain tumors, is related to the high sensitivity required to detect specific biomarkers within few cells volumes. To address this problem we developed a quantitative approach to eventually enable precise, high throughput and low cost analysis of glial cells with potential capability of real-time pathological screening and subtyping of brain tumors. A device consisting in micro-fabricated wells capable to isolate and host living astrocytes was designed and functionalized. Then for the fabrication of a nanobiosensor, able to detect in small volumes the presence of specific biomarkers, ideally for multiplexing assays and meant to fit within the small dimensions of this microdevice, an approach consisting in DNA-directed-immobilization (DDI) of biotinylated antibodies (Abs) on a single stranded DNA (ssDNA) nanoarray, produced by Atomic Force Microscopy (AFM) nanografting, was carefully optimized. The proof of concept was realized with Abs specific for Glial Fibrillary Acidic Protein (GFAP), a biomarker which belongs to the family of intermediate filaments and is crucial in cell’s differentiation, within a platform ready for parallelization. Nanosized patches of thiol modified ssDNA were prepared by AFM-based nanografting inside a matrix of self assembled monolayers (SAM) of alkanethiol-modified gold surfaces. Subsequently a complementary DNA strand (cDNA) conjugated to streptavidin (STV) was allowed to covalently bind to the patch by sequence specific DNA hybridization. Finally the biotin binding sites of STV were exploited to immobilize biotinylated monoclonal GFAP Abs (already in use for ELISA assays) on the top of those nanopatches. The efficiency of those nano-immuno arrays was tested by successfully obtaining the immobilization of purified recombinant GFAP protein, down to a concentration of 4 nM, firstly in standard PBS then in multicells’ lysate obtained from U87 glial cultures. The immobilization was detected by means of AFM measuring step by step the increases in the height of the patches and excluding modification of the roughness of both the SAM and the nanopatches after incubation with the cells’ lysate through a signal to noise ratio analysis. Titration curves for a comparison of sensitivity between this technique and the conventional ELISA assays are provided, they indeed confirm that the sensitivity of our nanosensors is at least that of ELISA, with the advantage of the scalability of the device.
Abstract (Italiano) L’attuale limite di avanzamento dello stato dell’arte dell’analisi proteomica dei gliomi cerebrali, la classe istologica di tumori cerebrali più frequente ed aggressiva, è legato alla difficoltà di individuare specifici biomarkers in piccoli volumi cellulari. Per superare questo limite si è deciso di sviluppare un approccio nanoquantitativo che consenta un’analisi proteomica precisa, ad alta sensibilità e basso costo, degli astrociti tumorali, con potenzialità di screening in tempo reale e sottotipizzazione di tumori cerebrali. Previa fabbricazione e funzionalizzazione di micro pozzetti idonei ad ospitare cellule astrocitarie, ci si è dedicati alla realizzazione di biosensori in grado di riconoscere specifici biomarkers e di essere accoppiati ai micro pozzetti. Al fine di immobilizzare anticorpi specifici per proteine di interesse in ambito neuroncologico, è stato scelto un approccio basato sul nanografting con Microscopio a Forza Atomica (AFM) e sull’immobilizzazione diretta sul DNA di anticorpi (DDI). In particolare la prova concettuale è stata condotta con anticorpi specifici per la Glial Fibrillary Acidic Protein (GFAP), un marcatore della differenziazione astrocitaria appartenente alla famiglia dei filamenti intermedi intracellulari, su una piattaforma atta ad una successiva parallelizzazione. I nanocostrutti responsabili del riconoscimento della proteina d’interesse, sono stati realizzati partendo da molecole di DNA a singola elica (ssDNA) graftate in una matrice di monostrati autoassemblati (SAM) di superfici d’oro alchiltiolo modificato. Al fine di sfruttare la capacità della streptavidina (STV) di legarsi ad anticorpi biotinilati è stata successivamente indotta l’ibridazione di un filamento di DNA complementare (cDNA) a quello precedentemente immobilizzato sulla superficie nanoassemblata che presentasse anche una coda di STV. I siti di legame per la biotina intrinseci al tetramero di STV sono quindi stati sfruttati per immobilizzare sulla superficie dei nanocostrutti degli anticorpi monoclonali biotinilati specifici per GFAP (già in uso per i protocolli ELISA). L’efficienza dei nano-immuno costrutti così ottenuti è stata testata ottenendo l’immobilizzazione di GFAP ricombinante anche a basse concentrazioni (fino a 4nM), sia in presenza di standard PBS, sia in presenza di un lisato multicellulare ottenuto da colture gliali di cellule U87. L’immobilizzazione di GFAP è stata confermata dall’incremento in altezza dei nanocostrutti misurato all’AFM escludendo modificazioni del rapporto segnale/rumore sia del SAM che dei nanocostrutti prima e dopo aggiunta di lisato multicellulare. Il limite di sensibilità del prototipo così ottenuto è stato confrontato con quello raggiungibile con protocolli standard ELISA, mostrando una sensibilità almeno comparabile all’ELISA a fronte di un maggiore potenziale diagnostico legato alla sua scalabilità.
XXV Ciclo
1979

Empathy skills are necessary to form therapeutic relationships. Previous research showed that participating in the arts engaged similar neuropathways as those needed to produce empathy. The theoretical framework for this study was art therapy relational neuroscience. The purpose of this pretest, posttest quantitative research study, using the Toronto Empathy Questionnaire, was to examine if a single art session could effectively prime for empathy. Using nonprobability, convenience sampling method, 74 graduate counseling students completed online surveys. Four findings are of note: (a) a t-test showed a significant difference between mean values of pre-post test scores, (b) an independent groups t-test indicated no difference in empathy gain scores as related to gender, (c) a Pearson's correlation indicated that age and art experience were positively correlated to empathy gain scores, (d) a multiple regression indicated that none of the variables examined moderated each other or empathy. Age, and art experience, independently, were found to be positively correlated with empathy scores. The results suggest that the self-conducted art session could enhance empathy. This research is an important contribution to the existing literature and enhances social change by studying a previously underrepresented population and investigating the possible effectiveness of a single art session prime for empathy. Using art to enhance empathy in graduate counseling students may aid with securing graduation, licensure, and therapeutic alliances with future clients.

The value of diffusion-weighted magnetic resonance imaging (DW-MRI) for early diagnosis of human ischaemic stroke has triggered the rapid development of this imaging technique. The introduction of the diffusion tensor (DT) model provides a range of tools that permit the quantitative assessment of this and many other neurological disorders. This thesis addresses some of the methodological issues encountered when using DT-MRI to image acute stroke patients.

At present, there are few clinically effective pain therapies available to treat chronic pain. One reason is due to a lack of understanding about how pain emerges in the brain. Excitingly, an emerging body of work suggests that the perfusion imaging technique, arterial spin labelling (ASL), is particularly well-suited to investigate this issue. The primary aim of this thesis is to develop and optimise a quantitative perfusion imaging approach to investigate the neural correlates of both experimental and pathological tonic pain. In Chapter 2, we explore different methods of inducing ongoing pain in healthy subjects. Results from this study show that mechanically induced pain is well suited for use in ASL FMRI experiments. In Chapter 3, we compare currently available ASL FMRI approaches for investigating tonic states, using a range of sensory paradigms. Results from these experiments support the use of an optimised version of Continuous ASL (CASL) FMRI to obtain whole-brain perfusion. Additionally, we discuss our decision to proceed with the newly acquired pseudo-continuous ASL (pCASL); a novel ASL technique that benefits from maximal signal-to-noise (SNR) across a whole-brain volume. In Chapter 4 we implement the pCASL FMRI approach to image the neural correlates of ongoing experimental pain. Results from the investigation of parametrically modulated ongoing mechanical pain show robust pain-related activation of key pain related regions that are monotonically active with an increase in stimulus intensity. Additionally, data from this experiment shows the presence of complex perfusion dynamics relative to pain worthy of further study. In Chapter 5, we optimised the pCASL sequence to obtain absolute perfusion changes across the whole-brain volume, using multi-inversion times, so that we could investigate the perfusion dynamics observed in Chapter 4. Results show that absolute perfusion changes during tonic pain are considerably less than for regions recruited during a non- pain task. Additionally, dynamic perfusion changes show complex stimulus responses across all active regions regardless of stimulus type. We conclude that while the technique is well suited to quantify absolute perfusion, the mechanisms underlying the dynamic changes in CBF (neuronal signal, neurovascular coupling) need further study. Finally, in Chapter 6, we implement the absolute perfusion approach developed in Chaper 5 to interrogate the neural correlates of the genetic pain disease, Erythromelalgia, and pleasurable relief. The results of this study show pain-related activation (and relief-induced reduction) of key pain-related regions. We conclude from these results that the ASL technique developed over the course of this thesis can be used to study a range of pain pathologies. Taken together, the results of this thesis document the development of a powerful perfusion imaging technique capable of quantifying absolute perfusion changes across a whole-brain volume. The data presented here from investigations of both experimental and pathological pain states supports the use of this technique in future tonic pain studies, as well as other neuroscience applications. We are confident that implementation of this imaging approach will provide integral insight into the mechanisms of ongoing pain states; and further the development of novel efficacious pain treatment options.

In the last decade, Quantitative Susceptibility Mapping (QSM) has been proven a promising Magnetic Resonance Imaging (MRI) tool for the non-invasive quantification of clinically relevant biomarkers, such as iron stores and myelination. The relative simplicity of QSM implementation, which does not require dedicated hardware or acquisition sequence, and its validation with histological evidence favored the diffusion of this technique in the clinical practice, particularly in the diagnosis and follow-up of neurodegenerative diseases. In this thesis, we discussed a critical issue affecting quantification, namely the dependence on acquisition parameters, and its implications for clinical and fundamental research. Specifically, we investigated QSM potential in the study of synucleinopathies, that is a group of neurodegenerative disorders including Multiple System Atrophy (MSA) and Parkinson’s disease (PD), and its capability of detecting brain function via functional QSM (fQSM). As a first step, we assessed how TE-dependence affects QSM intra- and inter-scanner reproducibility by performing repeated measurements on the same participants acquired with both a 3T and a 7T scanner. Then, we explored the impact of TE on the diagnostic accuracy of this technique by acquiring multi-echo data at 7T on MSA patients with Parkinsonian and cerebellar phenotypes and a group of Healthy Controls (HC). In this study, we also assessed the potential of histogram analysis in enhancing QSM diagnostic power. In a third work, we aimed to identify a presymptomatic biomarker in patients at risk for synucleinopathies using 7T QSM. Specifically, we measured and compared iron deposition in nigrosome 1 (a small ovoid-shaped structure located within the dorsolateral portion of the Substantia Nigra pars compacta (SNc)) of PD, idiopathic Rapid Eye Movement (REM) sleep Behavior Disorder (iRBD) patients and HC. Finally, we implemented fQSM and explored its potential compared to fMRI using xii 7T MRI, a stimulation paradigm for tonotopic mapping, and univariate and multivariate analysis approaches. Overall, these studies emphasize the importance of QSM in both structural and functional studies and prove that QSM is a versatile and powerful tool for a wide range of neuroimaging applications.

Parallel processing begins in the retina, where input from photoreceptors is transmitted to 12 types of bipolar cell. Bipolar cells are interneurons that propagate visual signals to over 17 types of ganglion cell, which are output neurons of the retina. In this way various vertical pathways are formed that deliver different sensory signals to the brain. This thesis comprises a detailed map of the cell types that contribute to parallel processing in primate retina. Chapter 1 introduces the structure of the primate retina and describes the morphology of retinal cells and their contribution to visual processing. Chapter 2 provides a survey of ganglion cell types in marmoset retina. Ganglion cells were transfected with a plasmid for the expression of a synaptic marker conjugated to green fluorescent protein. At least 17 morphological types of ganglion cell were identified. The contribution of widefield ganglion cells is greater to peripheral than to foveal vision, whereas the fovea is dominated by midget and parasol cells. Outside the fovea ganglion cell diversity in marmoset retina is likely as great as that reported for non-primates. In Chapter 3 particle-mediated gene transfection was applied to post mortem human retina. Human retinas maintained their morphology and immunohistochemical properties for at least 3 days in culture. This study showed that gene transfection can be used to target cells in the human retina, with the potential to study their connectivity and structural changes in diseases. Chapter 4 provides a quantitative analysis of the major cell populations in the inner nuclear layer (INL) of normal human retina. Immunohistochemical markers were applied to vertical sections to label and quantify horizontal, bipolar, amacrine and Müller cells across the retina. Cone photoreceptors and ganglion cells were also counted. With the exception of the fovea, the proportion of different cell populations in the INL is comparable across all eccentricities and comparable to non-human primates and other mammals. The cone to cone bipolar cell ratio was constant across the retina suggesting that convergence and divergence do not change with eccentricity. The data provided in this thesis will serve as a reference for the interpretation of abnormalities in disease, and the informed targeting of treatments in human retinas.

Dopamine is an important neurotransmitter that is involved in several human functions such as reward, cognition, emotions and movement. Abnormalities of the neurotransmitter itself, or the dopamine receptors through which it exerts its actions, contribute to a wide range of psychiatric and neurological disorders such as Parkinson’s disease and schizophrenia. Thus far, despite the great interest and extensive research, the exact role of dopamine and the causalities of dopamine related disorders are not fully understood. Here we have developed multimodal imaging methods, to investigate the release of dopamine and the distribution of the dopamine D2-like receptor family in-vivo in healthy humans. We use the [¹¹C]PHNO PET ligand, which enables exploration of dopamine-related parameters in striatal regions, and for the first time in extrastriatal regions, that are known to be associated with distinctive functions and disorders. Our methods involve robust approaches for the manual and automated delineation of these brain regions, in terms of structural and functional organisation, using information from structural and diffusion MRI images. These data have been combined with [¹¹C]PHNO PET data for quantitative dopamine imaging. Our investigation has revealed the distribution and the relative density of the D3R and D2R sites of the dopamine D2-like receptor family, in healthy humans. In addition, we have demonstrated that the release of dopamine has a functional rather than a structural specificity and that the relative densities of the D3R and D2R sites do not drive this specificity. We have also shown that the dopamine D3R receptor is primarily distributed in regions that have a central role in reward and addiction. A finding that supports theories that assigns a primarily limbic role to the D3R.

Understanding the biophysical processes underlying biological and biotechnological processes is a prerequisite for therapeutic treatments and technological innovation. With the exponential growth of computational processing speed, experimental findings in these fields have been complemented by dynamic simulations of developmental signaling and genetic interactions. Models provide means to evaluate "emergent" properties of systems sometimes inaccessible by reductionist approaches, making them test beds for biological inference and technological refinement.

The complexity and interconnectedness of biological processes pose special challenges to modelers; biological models typically possess a large number of unknown parameters relative to their counterparts in other physical sciences. Estimating these parameter values requires iterative testing of parameter values to find values that produce low error between model and data. This is a task whose length grows exponentially with the number of unknown parameters. Many biological systems require spatial representation (i.e., they are not well-mixed systems and change over space and time). Adding spatial dimensions complicates parameter estimation by increasing computational time for each model evaluation. Defining error for model-data comparison is also complicated on spatial domains. Different metrics compare different features of data and simulation, and the desired features are dependent on the underlying research question.

This dissertation documents the modeling, parameter estimation, and simulation of two spatiotemporal modeling studies. Each study addresses an unanswered research question in the respective experimental system. The former is a 3D model of a nanoscale amperometric glucose biosensor; the model was used to optimize the sensor's design for improved sensitivity to glucose. The latter is a 3D model of the developmental gap gene system that helps establish the bodyplan of Drosophila melanogaster; I wished to determine if the embryo's geometry alone was capable of accounting for observed spatial distributions of gap gene products and to infer feasible genetic regulatory networks (GRNs) via parameter estimation of the GRN interaction terms. Simulation of the biosensor successfully predicted an optimal electrode density on the biosensor surface, allowing us to fabricate improved biosensors. Simulation of the gap gene system on 1D and 3D embryonic demonstrated that geometric effects were insufficient to produce observed distributions when simulated with previously reported GRNs. Noting the effects of the error definition on the outcome of parameter estimation, I conclude with a characterization of assorted error definitions (objective functions), describe data characteristics to which they are sensitive, and end with a suggested procedure for objective function selection. Choice of objective function is important in parameter estimation of spatiotemporal system models in varied biological and biotechnological disciplines.

Nuclear Magnetic Resonance (NMR) is a phenomenon in which certain nuclei in the presence of a magnetic field and radiofrequency (RF) radiation emit a certain amount of signal at a frequency equal to that of the RF radiation. Proton Magnetic Resonance Spectroscopy (1H-MRS) is an NMR technique capable of measuring the chemical composition, often referred to as metabolites, of the human body non-invasively and in vivo. It is commonly used as a research tool in the investigation of neurological disorders such as multiple sclerosis, brain tumors, stroke, clinical depression, and schizophrenia. Accurate quantification of the metabolites of interest requires a reference standard of known and fixed concentration. Brain tissue water has been previously reported to have a fairly constant and known concentration, and so has been suggested to be a suitable reference concentration in absolute quantitative 1H-MRS of the human brain. In practice, however, it is challenging to measure the actual tissue water concentration; hence, some studies choose to use estimates of tissue water concentration from the literature. These literature values are usually averages from a healthy study group. There are however indications that brain tissue water content could vary widely in certain disease conditions such as in brain tumors and inflammation. In such situations, absolute metabolite quantification using the literature estimates of tissue water content will be inaccurate while the measurement of cerebral water content using the available techniques will be impractical for the patients due to scanning time considerations. It is therefore necessary to develop a technique that can be used to quantify both the reference water and metabolite concentrations, simultaneously without subject tolerance issues. The main objective of this thesis was to investigate the response of water NMR signal from human brain tissue under various measurement conditions using the single-voxel 1H-MRS technique. As part of the investigation, the thesis also focused on the development of methods for the absolute quantification of cerebral water and metabolite concentrations. A standard 1H-MRS water-suppressed acquisition on the General Electric (GE) MR scanner acquires some unsuppressed-water spectra at the beginning of the PRESS pulse sequence. Using the Spectroscopy Analysis by GE (SAGE) software package (version 7), this thesis developed methods to optimise the unsuppressed-water and suppressed-water signals from which, respectively, cerebral water and metabolite concentrations were estimated. The unsuppressed-water signal response characteristics were investigated in experiments at 3 T that involved: 1) variation of the MRS voxel position over a three-dimensional RF field within an eight-channel head coil; 2) measurement of the relaxation times of brain tissue water using standard saturation recovery and multi spin-echo MRS techniques; 3) measurement of brain tissue water content in peripheral inflammation; and 4) estimation of the BOLD effect on the water spectral peak. The stability of the MR scanner used for all the investigations was assessed. Over the project period, the worst precision measurements of the scanner (for both water and metabolite signals) were observed to be about 12 % and 26 % in serial phantom and human studies, respectively. The MRI/MRS scanner was therefore found to measure water and metabolite signals with good precision, both in vivo and in vitro. By recording the water NMR signal responses at various locations within the phased-array head coil, RF sensitivity profile (voxel position-dependent) equations of the head coil were obtained. The coordinates of any in vivo voxel could be substituted into an appropriate profile equation to estimate an unsuppressed-water signal area that could be used as a reference signal to quantify brain tissue water content. This novel technique of quantifying cerebral water content is superior to the previous techniques of performing multi-echo unsuppressed-water signal acquisitions. The method does not require extra unsuppressed-water acquisitions, or corrections for variations in the sensitivity of the eight-channel head coil as both the in vivo and reference signals are acquired from the same voxel position. Brain tissue water content was subsequently quantified accurately using the newly developed method of referencing. In frontal brain voxels, the average water content, WC of grey matter, GM was found to be higher than that of white matter, WM (GM/WM WC ± SE = 46.37 ± 2.58/42.86 ± 2.46 mol/kg; p = 0.02); parietal voxels also showed a similar comparison (GM/WM WC ± SE = 37.23 ± 1.70/34.14 ± 2.02 mol/kg; p = 0.03). These findings were consistent with previous reports of cerebral water content. For regions of mixed proportions of grey and white matter tissues, the average water contents of each tissue type considered separately (by voxel segmentation) and together were found to compare with literature estimates. Using data from five voxel positions, average brain tissue water content was observed to be uniformly distributed across the human brain by one-way ANOVA (p = 0.60), and did not vary significantly with gender (p > 0.05) and age (p > 0.05). For the first time, cerebral water content was observed in this thesis to remain fairly constant in psoriatic arthritis, a peripheral inflammatory condition (one-way ANOVA, p = 0.63). Among five brain metabolites quantified in the psoriasis patients, only the mean concentration of creatine, Cr was found to be significantly lower in the frontal grey matter voxels of the patients, PsA compared to healthy controls, HC at baseline (PsA/HC ± SE = 6.34 ± 0.38/7.78 ± 0.38 mM/kg; p = 0.01) and post-TNF-alpha blockade medication (PsA/HC ± SE = 6.69 ± 0.25/7.78 ± 0.38 mM/kg; p = 0.03). None of the metabolite concentrations, including Cr (p = 0.27), changed significantly with medication. The condition of PsA was not observed to affect the mood of the patients, as indicated by their BDI scores. The significant finding of Cr concentration alteration in psoriatic arthritis thus suggests that Cr may not be a reliable denominator in studies of psoriasis that express the metabolite levels as ratios. The T1 and T2 relaxation times of water and the metabolites were measured in the prefrontal grey matter (T1/T2 ± SE = 1574 ± 61/147 ± 6 ms) and bilateral Hippocampi (T1/T2 ± SE; left = 1475 ± 68/178 ± 83 ms, right = 1389 ± 58/273 ± 98 ms). The relaxation time estimates for the metabolites were in agreement with literature values; relaxation times for water however were measured for the first time in those regions and at 3 T. The measured relaxation times were used to correct the water and metabolite signals for relaxation effects during their absolute quantification, and could as well serve the same purpose in future studies. There is increasing interest in the BOLD response of cerebral metabolites and water during tasks. This thesis thus also assessed changes in brain tissue metabolite and water contents while a subject experienced a visual stimulus. In the presence of the visual stimulus, the BOLD effects on the metabolite and water spectral peaks were found to be comparable, as has been observed in previous studies. For the first time, this thesis further investigated the impact of temporal resolution (determined by NEX) on the amount of the BOLD signal acquired from cerebral water and metabolites. In a single visual activation paradigm, the BOLD effect resulted in increased water peak area which differed significantly between NEX values of 2 and 8 (p < 0.01); this observation also was true for NAA and Glu. The findings thus suggest that temporal resolution of the MRS data could result in significant differences in the results of functional MRS studies.

Cette thèse a pour objectif l’étude d’un lien effectif potentiel entre la motilité cellulaire, la mécanique cellulaire, et l’activité biochimique de ces mêmes cellules. Ce couplage a été étudié dans divers systèmes biologiques, et aussi bien dans des cultures de cellules qu’à l’intérieur de tissus plus complexes. Notamment, nous avons particulièrement cherché à détecter un couplage électromécanique dans des neurones qui pourrait être impliqué dans la propagation du message nerveux.Pour ce faire, nous avons dû développer deux microscopes optiques à la sensibilité extrême. Ces microscopes se composent de deux parties principales. La première sert à détecter des mouvements axiaux plus petits que la longueur d’onde optique, soit en dessous de 100 nanomètres. La deuxième partie permet la détection d’un signal de fluorescence, offrant la possibilité de suivre l’évolution biochimique de la cellule. Avec ces deux microscopes multimodaux, il est donc possible de suivre de manière simultanée un contraste de motilité, un contraste mécanique, un contraste structurel et un contraste biochimique. Si l’un de ces systèmes est basé sur la tomographie de cohérence optique plein champ et permet de faire de telles mesures en 3-D et en profondeur dans les tissus biologiques, le second ne permet que des mesures dans des cultures de cellules, mais est bien plus robuste au bruit mécanique. Dans ce manuscrit, nous allons essentiellement décrire le développement de ces deux appareils, et préciser les contrastes auxquels ils sont sensibles spécifiquement.Nous développerons également deux des applications principales de ces microscopes que nous avons étudié dans le détail au cours de cette thèse. La première application développe l’intérêt d’un de nos microscopes pour la détection sans marquage des principaux composants cellulaires et structuraux de la cornée et de la rétine. La seconde application tend à détecter et à suivre des ondes électromécaniques dans des neurones de mammifères
This PhD project aims to explore the relationship that might exist between the dynamic motility and mechanical behavior of different biological systems and their biochemical activity. In particular,we were interested in detecting the electromechanical coupling that may happen in active neurons, and may assist in the propagation of the action potential. With this goal in mind, we have developed two highly sensitive optical microscopes that combine one modality that detects sub-wavelength axial displacements using optical phase imaging and another modality that uses a fluorescence path. Therefore, these multimodal microscopes can combine a motility, a mechanical,a structural and a biochemical contrast at the same time. One of this system is based ona multimodal combination of full-field optical coherence tomography (FF-OCT) and allows the observation of such contrast inside thick and scattering biological tissues. The other setup provides a higher displacement sensitivity, but is limited to measurements in cell cultures. In this manuscript, we mainly discuss the development of both systems and describe the various contrastst hey can reveal. Finally, we have largely used our systems to investigate diverse functions of the eye and to look for electromechanical waves in cell cultures. The thorough description of both biological applications is also provided in the manuscript

Friedreich's Ataxia (FA) is an autosomal-recessive, neurodegenerative disease characterized by progressive lower extremity muscle weakness and sensory loss, balance deficits, limb and gait ataxia, and dysarthria. FA is considered a sensory ataxia because the dorsal root ganglia and spinal cord dorsal columns are involved early in the disease, whereas the cerebellum is affected later. Balance deficits and gait ataxia are often evaluated clinically and in research using clinical rating scales. Recently, quantitative tools such as the Biodex Balance System SD and the GAITRite Walkway System have become available to objectively assess balance and gait, respectively. However, there are limited studies using instrumented measures to quantitatively assess and characterize balance and gait disturbances in FA, and longitudinal, quantitative analyses of both balance and gait have not been investigated in this patient cohort. The purpose of the present study was to characterize gait patterns of adults with FA and to identify changes in gait and balance over time using clinical rating scales and quantitative measures. Additionally, this study investigated the relationship between disease duration, clinical rating scale scores and objective measures of gait and balance. This study used a longitudinal research design to investigate changes in balance and gait in 8 adults with genetically confirmed FA and 8 healthy controls matched for gender, age, height, and weight. Subjects with FA were evaluated using the Berg Balance Scale (BBS), the Friedreich's Ataxia Rating Scale (FARS) and instrumented gait and balance measures at baseline, 6 months, 12 months and 24 months. Controls underwent the same tests at baseline and 12 months. Gait parameters were measured utilizing the GAITRite Walkway system with a focus on gait velocity, cadence, step and stride lengths, step and stride length variability and percent of the gait cycle in swing, stance and double limb support. Balance was assessed using the BBS and the Biodex Balance System; the latter included tests of postural stability and limits of stability. At baseline, there were significant differences in gait and balance parameters, BBS scores and FARS total scores between FA subjects and controls as determined using paired t-tests (p This is the first longitudinal study to demonstrate changes over time in gait and balance of adults with FA using both quantitative measures and clinical rating scales. This study provided a detailed characterization of the gait pattern and balance of adults with FA. The GAITRite Walkway system proved to be a sensitive measure, and able to detect subtle changes in gait parameters over time in adults with FA. In addition, the BBS was an appropriate and sensitive assessment to detect changes in static and dynamic balance in this patient cohort. Finally, results revealed a strong and consistent relationship between clinical rating scale scores, postural stability indices, limits of stability scores, and step and stride length variability in individuals with FA.

With increasing amounts of data being collected in various fields of neuroscience, there is a growing need for robust techniques for the analysis of this information. This thesis focuses on the evaluation and development of quantitative frameworks for the analysis and classification of neuronal data from a variety of contexts. Firstly, I investigate methods for analysing spontaneous neuronal network activity recorded on microelectrode arrays (MEAs). I perform an unbiased evaluation of the existing techniques for detecting ‘bursts’ of neuronal activity in these types of recordings, and provide recommendations for the robust analysis of bursting activity in a range of contexts using both existing and adapted burst detection methods. These techniques are then used to analyse bursting activity in novel recordings of human induced pluripotent stem cell-derived neuronal networks. Results from this review of burst analysis methods are then used to inform the development of a framework for characterising the activity of neuronal networks recorded on MEAs, using properties of bursting as well as other common features of spontaneous activity. Using this framework, I examine the ontogeny of spontaneous network activity in in vitro neuronal networks from various brain regions, recorded on both single and multi-well MEAs. I also develop a framework for classifying these recordings according to their network type, based on quantitative features of their activity patterns. Next, I take a multi-view approach to classifying neuronal cell types using both the morphological and electrophysiological features of cells. I show that a number of multi-view clustering algorithms can more reliably differentiate between neuronal cell types in two existing data sets, compared to single-view clustering techniques applied to either the morphological or electrophysiological ‘view’ of the data, or a concatenation of the two views. To close, I examine the properties of the cell types identified by these methods.

There is a limited research base on low voltage brain conditions, which are characterized by electrical activity being measured at below 20 microvolts. The purpose of this study was to examine the relationship between saliva cortisol levels and voltage using an EGG in a college student population. Illuminating this relationship is important to inform how low voltage conditions can affect daily memory and cognitive functioning of undergraduate college students that may be a result of stress. The college student population may be vulnerable to the low voltage condition because of stress from the transition between teenage and adult life and related social and academic pressures. Sapolsky's theory of stress, which hypothesized that high cortisol levels will manifest as a low voltage condition, guided this study. The sample included 60 undergraduate students recruited by flyers distributed on the campus of a liberal arts college. A multiple regression analysis was used to examine the relationship between explain the variables. Although no low voltage was found in this study sample, the study results contribute to positive social change by providing a better understanding for students and staff of brain functioning when exposed to chronic stress and encourage the implementation of programs for managing stress and prevention of stress before it reaches a chronic state and negatively impacts brain functioning.

Traumatic brain injury (TBI) incidence rates are increasing among the U.S. population and represent substantial acute and chronic care costs. A confounding factor in TBI treatment is the incidence rates of concomitant mental health disorders including depression, anxiety, and posttraumatic stress disorder (PTSD). Clinical data establish that the prevalence of any of these 3 diagnoses complicates the treatment of TBI regardless of whether the diagnosis was pre-existing or occurred because of the TBI, such that prognosis and recovery are negatively impacted. Despite this evidence, psychological assessment is not a first line step in the approach to TBI. The purpose of this research was to assess the prevalence of psychological screening among TBI patients for depression, anxiety, and PTSD to enable conclusions about the current standard of care in TBI management. Meta-analysis of peer reviewed journals on TBI management was used to determine if there was considerable evidence to support that depression, anxiety, and PTSD were being addressed as the standard of care in TBI management. Mean analysis of literature search results established that there was not considerable evidence to support a conclusion that depression, anxiety, and PTSD assessment were standard of care in TBI management. Among the recommendations resulting from this finding were for additional studies on TBI points of care to determine how mental health is currently being managed among TBI patients, and for a change in current TBI treatment protocols to incorporate mental health assessment as part of overall TBI management. If these, and the remaining recommendations, were implemented, it was affirmed that these would have a positive social impact resulting in improved patient outcomes, decreased healthcare costs, and better healthcare delivery for TBI patients.

Spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disorder that is the leading genetic cause of infantile death. SMA is caused by homozygous deletion or mutation of the survival of motor neuron 1 gene (SMN1). The SMN2 gene is nearly identical to SMN1, however is alternatively spliced. The close relationship to SMN1 results in SMN2 being a very power genetic modifier of SMA disease severity and a target for therapies. In this study we attempt to characterize novel chemical compounds identified as potential activators of the SMN2 gene. Additionally, we sought to determine the regulatory role individual HDAC proteins use to control expression of full length protein from the SMN2 gene. We used quantitative PCR to determine the effects of novel compounds and shRNA silencing of individual HDACs on the steady state levels of a SMN2-luciferase reporter transcripts. We determined that the compounds identified in multiple reporter high throughput screens increased SMN protein levels via transcriptional activation of the SMN2 gene. Other compounds identified in the same screen functioned post-transcriptionally, possibly stabilizing the SMN protein itself by decreasing degradation. Furthermore, we determined that reduction of individual HDAC proteins was sufficient to increase SMN protein levels in a transgenic reporter system. Knockdown of class I HDAC proteins preferentially activated the reporter by increased promoter transcription. Silencing of class II HDAC proteins maintained transcriptional activity; however silencing of HDAC 5 and 6 also appeared to enhance inclusion of an alternatively spliced exon. This collective work defines a quantitative RNA based protocol to determine mechanism of SMN reporter increase in response to any chosen treatment method. Additionally, this work highlights HDAC proteins 2 and 6 as excellent investigative targets. These data are important to the basic understanding of SMN expression regulation and the refinements of current therapeutic compounds as well as the development of novel SMA therapeutics.

In the last decade, (non)prescription opioid abuse, opioid use disorder (OUD) diagnoses, and opioid-related overdoses have risen and represent a significant public health concern. One method of understanding OUD is as a disorder of choice that requires choosing opioid rewards at the expense of other nondrug rewards. The characterization of OUD as a disorder of choice is important as it implicates decision- making processes as therapeutic targets, such as the valuation of opioid rewards. However, reward-value measurement and interpretation are traditionally different in substance abuse research compared to related fields such as economics, animal behavior, and neuroeconomics and may be less effective for understanding how opioid rewards are valued. The present research therefore used choice procedures in line with behavioral/neuroeconomic studies to determine if drug-associated decision making could be predicted from economic choice theories. In Experiment 1, rats completed an isomorphic food-food probabilistic choice task with dynamic, unpredictable changes in reward probability that required constant updating of reward values. After initial training, the reward magnitude of one choice subsequently increased from one to two to three pellets. Additionally, rats were split between the Signaled and Unsignaled groups to understand how cues modulate reward value. After each choice, the Unsignaled group received distinct choice-dependent cues that were uninformative of the choice outcome. The Signaled group also received uninformative cues on one option, but the alternative choice produced reward-predictive cues that informed the trial outcome as a win or loss. Choice data were analyzed at a molar level using matching equations and molecular level using reinforcement learning (RL) models to determine how probability, reward magnitude, and reward-associated cues affected choice. Experiment 2 used an allomorphic drug versus food procedure where the food reward for one option was replaced by a self-administered remifentanil (REMI) infusion at doses of 1, 3 and 10 μg/kg. Finally, Experiment 3 assessed the potential for both REMI and food reward value to be commonly scaled within the brain by examining changes in nucleus accumbens (NAc) Oxygen (O2) dynamics. Results showed that increasing reward probability, magnitude, and the presence of reward-associated cues all independently increased the propensity of choosing the associated choice alternative, including REMI drug choices. Additionally, both molar matching and molecular RL models successfully parameterized rats’ decision dynamics. O2 dynamics were generally commensurate with the idea of a common value signal for REMI and food with changes in O2 signaling scaling with the reward magnitude of REMI rewards. Finally, RL model-derived reward prediction errors significantly correlated with peak O2 activity for reward delivery, suggesting a possible neurological mechanism of value updating. Results are discussed in terms of their implications for current conceptualizations of substance use disorders including a potential need to change the discourse surrounding how substance use disorders are modeled experimentally. Overall, the present research provides evidence that a choice model of substance use disorders may be a viable alternative to the disease model and could facilitate future treatment options centered around economic principles.

This dissertation explores the surprising phenomenon of listeners' unconsciously breathing in time to music, inspiring and expiring at select moments of specific works. When and how the experience of hearing music might produce stimulus-synchronous respiratory events is studied through Repeated Response Case Studies, gathering participants' respiratory sequences during repeated listenings to recorded music, and through Audience Response Experiments, responses for participants experiencing live music together in a concert hall.

Activity Analysis, a new statistical technique, supported the development and definition of discrete phase components of the breath cycle that come into coordination: the onsets of inspiration and expiration, the intervals of high flow during these two main phases, and the post-expiration pause. Alignment in these components across listenings illuminate when the naturalistic complex stimuli can attract or cue listener respiration events.

Four patterns of respiratory phase alignment are identified through detailed analysis of stimuli and responses. Participants inspired with the inspirations of vocalists and wind performers, suggesting embodied perception and imagined action may exert influence on their quiet breathing. Participants suppressed and delayed inspirations when the music was highly unpredictable, suggesting adaptation in aid of auditory attention. Similar behaviour occurred with sustained sounds of exceptional aesthetic value. Participants inspired with recurring motivic material and similar high salience events, as if marking them in recognition or amplifying their affective impact. And finally, participants occasionally breathed following structural endings, suggesting a sigh-like function of releasing the respiratory system from cortical control.

These instances of music-aligned respiratory phase alignment seemed to be stronger in participants who were typically active with heard music, but the impacts of training and expertise was not a simple condition for this behaviour. Contrasts between case study participants showed highly idiosyncratic patterns of respiratory alignment and differences in susceptibility along side moments of shared effect. In the audience experiments, alignment within phase components was measurable and significant, but rarely involved more than a quarter of participants in any given instance. These levels of concurrent activity in respiration underline the subtlety of this bodily response to music.

RL has been transformative for neuroscience by providing a normative anchor for interpreting neural and behavioral data. End-to-end RL methods have scored impressive victories with minimal compromises in autonomy, hand-engineering, and generality. The cost of this minimalism in practice is that model-free RL methods are slow to learn and generalize poorly. Humans and animals exhibit substantially improved flexibility and generalize learned information rapidly to new environment by learning invariants of the environment and features of the environment that support fast learning rapid transfer in new environments. An important question for both neuroscience and machine learning is what kind of ``representational objectives'' encourage humans and other animals to encode structure about the world. This can be formalized as ``representation feature learning,'' in which the animal or agent learns to form representations with information potentially relevant to the downstream RL process. We will overview different representational objectives that have received attention in neuroscience and in machine learning. The focus of this overview will be to first highlight conditions under which these seemingly unrelated objectives are actually mathematically equivalent. We will use this to motivate a breakdown of properties of different learned representations that are meaningfully different and can be used to inform contrasting hypotheses for neuroscience. We then use this perspective to motivate our model of the hippocampus. A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity, and policy dependence in place cells suggests that the representation is not purely spatial. We approach the problem of understanding hippocampal representations from a reinforcement learning perspective, focusing on what kind of spatial representation is most useful for maximizing future reward. We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. We go on to argue that entorhinal grid cells encode a low-dimensional basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.

Alcoholism and alcohol use disorders are a major problem worldwide. Excessive alcohol consumption has been associated with cognitive impairment not only in drinkers but also in their offspring. Previous clinical reports have suggested that inherited drug use behavior in individuals, including the overall amount of alcohol consumed, originates from parents who engage in the consumption of alcohol both during and prior to conception. For example, mothers exposed to alcohol during pregnancy have been shown to produce offspring with neurodevelopmental, physiological, and behavioral deficits, in rodents. Additionally, several studies now support the idea that fathers exposed to alcohol prior to mating produce male offspring with developmental, physiological, and cognitive deficits as well. With this said, alcohol exposed fathers appear to pass different phenotypes to their sons than they do their daughters. To date, little research has been dedicated to examining cognitive deficits associated with paternal alcohol exposure or the volume of alcohol intake in daughters of male alcoholics. The purpose of this set of experiments is to explore possible changes in cognitive function and alcohol acceptance in both male and female offspring of alcohol-exposed fathers. Adult male rats were exposed to a repeated binge dose of alcohol and later mated with non-manipulated females. Offspring of exposed fathers were assessed for levels of alcohol consumption via Intraoral Cannulation, followed by a series of cognitive function tests via T-maze task performance. Accuracy percentage within the T- maze and volume of alcohol accepted were compared and analyzed using an ANOVA. Our findings suggest that paternal binge doses of ethanol exposure prior to breeding results in offspring that consume significantly more ethanol than controls, exhibit greater latency time to reach criterion in a T-maze, and having significantly fewer percent correct responses in T-maze task performance when including all trials. The results presented here add to the growing body of literature aimed at understanding the consequences of paternal pre-conception ethanol exposure and the effects on subsequent generations.

This thesis seeks to address the critical bottlenecks of current technologies that have slowed the neuroscience research in C. elegans. The objective of this research is to enhance the currently developed systems through the design and construction of simple microdevices and quantitative analytical tools for high-throughput phenotyping C. elegans to investigate functions of nervous systems. First, we developed and used the integrated system combining user-friendly single-layer microfluidics and quantitative analytical tools to study the genetic regulation of target gene expression. We found several putative mutants based on large-scale screens, which would have previously been too labor-intensive to attempt. Second, we developed a simple mathematical model that describes the regulation of a target gene expression. Using the model developed, we simulated phenotypical space of hypothetical mutants to suggest plausible genetic pathways some isolated mutants may affect. Lastly, we developed a high-density multichannel device for rapid trapping, parallel selective stimulating, long-term culturing, and (often repeatedly). We used this integrated system to study the neurodegenerative process based on selective ablation of multiple animals using an optogenetic tool, which would have been taken at least 1 order of magnitude longer. Taken together, we expect that these developments will greatly facilitate a broad range of fundamental, and application studies including aging, neurodegeneration, circuit and behavior.

Nous proposons une généralisation des modèles actuels, utilisés en neuroscience pour l'analyse des statistiques de trains de potentiels d'action, et basé sur le paradigme de maximisation de l'entropie statistique sous contraintes. Notre méthode permet d'estimer des distributions de Gibbs avec un potentiel paramétrique arbitraire, généralisant les modèles actuels (Ising ou chaines de Markov du premier ordre). Notre méthodologie permet de tenir compte des effets de mémoire dans la dynamique. Elle fournit de manière directe la divergence de Kullback-Leibler entre le statistique empirique et le modèle statistique. Elle ne présuppose pas une forme spécifique du potentiel de Gibbs et ne nécessite pas l'hypothèse de bilan détaillé En outre, elle permet la comparaison de différents modèles statistiques et offre un contrôle des effets de taille finie, propres à la statistique empirique, par le biais de résultats des grandes déviations. Nous avons également développé un logiciel implémentant cette méthode et nous présentons des résultats d'application à des données biologiques issues d'enregistrements par multi-électrode sur des cellules ganglionnaires de rétines animaux. De plus, notre formalisme permet d'étudier l'évolution de la distribution des potentiels d'action lors de la variation des poids synaptiques induits par plasticité. Nous montrons une application a l'analyse de données synthétiques issues d'un réseau neuronal simulé soumis a de la plasticité de type (STDP).

This thesis is focused on the human brain microstructure mapping using quantitative and diffusion MRI. The T1/T2 quantitative imaging relies on sequences dedicated to the mapping of T1 and T2 relaxation times. Their variations within the tissue are linked to the presence of different water compartments defined by a specific organization of the tissue at the cell scale. Measuring these parameters can help, therefore, to better characterize the brain microstructure. The dMRI, on the other hand, explores the brownian motion of water molecules in the brain tissue, where the water molecules' movement is constrained by natural barriers, such as cell membranes. Thus, the information on their displacement carried by the dMRI signal gives access to the underlying cytoarchitecture. Combination of these two modalities is, therefore, a promising way to probe the brain tissue microstructure. The main goal of the present thesis is to set up the methodology to study the microstructure of the white matter of the human brain in vivo. The first part includes the acquisition of a unique MRI database of 79 healthy subjects (the Archi/CONNECT), which includes anatomical high resolution data, relaxometry data, diffusion-weighted data at high spatio-angular resolution and functional data. This database has allowed us to build the first atlas of the anatomical connectivity of the healthy brain through the automatic segmentation of the major white matter bundles, providing an appropriate anatomical reference for the white matter to study individually the quantitative parameters along each fascicle, characterizing its microstructure organization. Emphasis was placed on the construction of the first atlas of the T1/T2 profiles along the major white matter pathways. The profiles of the T1 and T2 relaxation times were then correlated to the quantitative profiles computed from the diffusion MRI data (fractional anisotropy, radial and longitudinal diffusivities, apparent diffusion coefficient), in order to better understand their relations and to explain the observed variability along the fascicles and the interhemispheric asymmetries. The second part was focused on the brain tissue modeling at the cell scale to extract the quantitative parameters characterizing the geometry of the cellular membranes, such as the axonal diameter and the axonal density. A diffusion MRI sequence was developed on the 3 Teslas and 7 Teslas Siemens clinical systems of NeuroSpin which is able to apply any kind of gradient waveforms to fall within an approach where the gradient waveform results from an optimization under the hypothesis of a geometrical tissue model, hardware and time constraints induced by clinical applications. This sequence was applied in the study of fourteen healthy subjects in order to build the first quantitative atlas of the axonal diameter and the local axonal density at 7T. We also proposed a new geometrical model to model the axon, dividing the axonal compartment, usually modelled using a simple cylinder, into two compartments: one being near the membranes with low diffusivity and one farer from the membranes, less restricted and with higher diffusivity. We conducted a theoretical study showing that under clinical conditions, this new model allows, in part, to overcome the bias induced by the simple cylindrical model leading to a systematic overestimation of the smallest diameters. Finally, in the aim of going further in the physiopathology of the autism, we added to the current 3T imaging protocol the dMRI sequence developed in the framework of this thesis in order to map the axonal diameter and density. This study is ongoing and should validate shortly the contribution of these new quantitative measures of the microstructure in the comprehension of the atrophies of the corpus callosum, initially observed using less specific diffusion parameters such as the generalized fractional anisotropy. There will be other clinical applications in the future.

Il y a dans le cerveau humain environ 100 sillons corticaux, et plus de 100 milliards de faisceaux de matière blanche. Si le nombre, la configuration et la fonction de ces deux structures anatomiques diffèrent, elles possèdent toutefois une propriété géométrique commune: ce sont des courbes ouvertes continues. Cette thèse se propose d'étudier comment les caractéristiques des courbes ouvertes peuvent être exploitées afin d'analyser quantitativement les sillons corticaux et les faisceaux de matière blanche. Les quatre caractéristiques d'une courbe ouverte-forme, taille, orientation et position- ont des propriétés différentes, si bien que l'approche usuelle est de traiter chacune séparément à l'aide d'une métrique ad hoc. Nous introduisons un cadre riemannien adapté dans lequel il est possible de fusionner les espaces de caractéristiques afin d'analyser conjointement plusieurs caractéristiques. Cette approche permet d'apparier et de comparer des courbes suivant des distances géodésiques. Les correspondances entre courbes sont établies automatiquement en utilisant une métrique élastique. Dans cette thèse, nous validerons les métriques introduites et nous montrerons leurs applications pratiques, entre autres dans le cadre de plusieurs problèmes cliniques importants. Dans un premier temps, nous étudierons spécifiquement les fibres du corps calleux, afin de montrer comment le choix de la métrique influe sur le résultat du clustering. Nous proposons ensuite des outils permettant de calculer des statistiques sommaires sur les courbes, ce qui est un premier pas vers leur analyse statistique. Nous représentons les groupes de faisceaux par la moyenne et la variance de leurs principales caractéristiques, ce qui permet de réduire le volume des données dans l'analyse des faisceaux de matière blanche. Ensuite, nous présentons des méthodes permettant de détecter les changements morphologiques et les atteintes de la matière blanche. Quant aux sillons corticaux, nous nous intéressons au problème de leur labellisation.

Children growing up in poor areas with high crime rates are shown to easily get involved in violent actions and criminal gangs. In South Africa, despite considerable efforts to reduce youth delinquency, youth crime rates are still disturbingly high – specifically, in the townships of the Cape Flats. This paper points out an important aspect previously unaddressed by most youth crime prevention: the subconscious roots of youth crime. What if we could develop youth crime prevention programs that manage to impact the subconscious behavioral patterns of youth in high crime areas? This paper proposes a  promising and cost-effective approach that has great potential to affect multipe causes of crime: mindfulness meditation. Built upon newest findings in Neuroscience, this paper suggests that mindfulness meditation classes are associated with a reduction in aggressive behavior, a risk factor for youth crime, and an increase in self-efficacy, a protective factor. The impact of mindfulness classes at a high school in Khayelitsha, a poor and violent-stricken township of Cape Town, is analyzed. Self-reported aggression and self-efficacy are measured via a psychometric survey questionnaire created from two well-tested and validated scales. Regression analyses of 384 survey answers provided mixed results. Whilst novice meditators were not associated with higher self-efficacy and lower aggression, long-term meditators performed better in several dimensions of self-efficacy and aggression, yet no significant relationship was found. Further research specifically needs to investigate the moderating effect of age (a proxy for psychological development) on meditation. This study aims to bridge the gap between the outdated paradigms of youth crime prevention and ancient wisdom via ground-breaking new evidence from the field of Neuroscience. This study furthermore hopes to point policy makers toward developing new, integrative and sustainable approaches to youth crime prevention – approaches that give back agency to our youth.

Anders Westholm har inget med betygssättningen att göra annat än i rent formellt hänseende (examinator). Det är han som rapporterar in och skriver under men i sak är det seminarieledaren som har beslutet i sin hand. Statsvetenskapliga institutet har som princip att skilja på handledning och examination vilket innebär att handledaren inte får vara seminarieledare. Seminarieledare och personen som satt betygget var i det här fallet Sven Oskarsson: Sven.Oskarsson@statsvet.uu.se

Neurons have both a fast and slow mode of signaling. Fast signals are communicated by transmembrane voltage changes, while calcium levels within the cell communicate information on a much slower time scale. Calcium acts as a second messenger responsible for modulating neuronal excitability in many ways including the mediation of gene transcription in the cell and the sensitivity of the cell to further stimulus. I develop a voltage model of the neuron's electrical signal with ion diffusion and drift which includes voltage-gated calcium currents and calcium-dependent potassium currents. The influx of calcium resulting from the voltage model will prime the endoplasmic reticulum with calcium. A model of the dynamics of calcium induced calcium release from the endoplasmic reticulum via IP3 receptors which includes diffusion of calcium and IP3 as well as calcium buffering by the mitochondria results in a calcium wave similar to what has been observed experimentally.

Neurons have both a fast and slow mode of signaling. Fast signals are communicated by transmembrane voltage changes, while calcium levels within the cell communicate information on a much slower time scale. Calcium acts as a second messenger responsible for modulating neuronal excitability in many ways including the mediation of gene transcription in the cell and the sensitivity of the cell to further stimulus. I propose a means of determining calcium conductance density of the cell membrane from intracellular calcium concentration measurement data using a two step process. The first step is the inference of calcium current density from calcium concentration measurements using a least squares fit to the data. Once an estimate of the calcium current density is determined, the minimum value over time is used to determine the calcium conductance density. I develop a voltage model of the neuron's electrical signal with ion diffusion and drift which includes voltage-gated calcium currents and calcium-dependent potassium currents. The influx of calcium resulting from the voltage model will prime the endoplasmic reticulum with calcium. A model of the dynamics of calcium induced calcium release from the endoplasmic reticulum via IP3 receptors which includes diffusion of calcium and IP3 as well as calcium buffering by the mitochondria results in a calcium wave similar to what has been observed experimentally. Finally, I use a branch structure together with IP3 generation, calcium buffers in the cytosol and ER, cell membrane calcium transports (voltage-gated calcium channels, pumps, exchangers, and store-operated channels), and ER calcium transports (IP3 receptors, ryanodine receptors, pumps, leak channels) to show that calcium waves initiate in the apical trunk at the point where the stimulated oblique branches off.

abstract: During the past five decades neurosurgery has made great progress, with marked improvements in patient outcomes. These noticeable improvements of morbidity and mortality can be attributed to the advances in innovative technologies used in neurosurgery. Cutting-edge technologies are essential in most neurosurgical procedures, and there is no doubt that neurosurgery has become heavily technology dependent. With the introduction of any new modalities, surgeons must adapt, train, and become thoroughly familiar with the capabilities and the extent of application of these new innovations. Within the past decade, endoscopy has become more widely used in neurosurgery, and this newly adopted technology is being recognized as the new minimally invasive future of neurosurgery. The use of endoscopy has allowed neurosurgeons to overcome common challenges, such as limited illumination and visualization in a very narrow surgical corridor; however, it introduces other challenges, such as instrument "sword fighting" and limited maneuverability (surgical freedom). The newly introduced concept of surgical freedom is very essential in surgical planning and approach selection and can play a role in determining outcome of the procedure, since limited surgical freedom can cause fatigue or limit the extent of lesion resection. In my thesis, we develop a consistent objective methodology to quantify and evaluate surgical freedom, which has been previously evaluated subjectively, and apply this model to the analysis of various endoscopic techniques. This model is crucial for evaluating different endoscopic surgical approaches before they are applied in a clinical setting, for identifying surgical maneuvers that can improve surgical freedom, and for developing endoscopic training simulators that accurately model the surgical freedom of various approaches. Quantifying the extent of endoscopic surgical freedom will also provide developers with valuable data that will help them design improved endoscopes and endoscopic instrumentation.
Dissertation/Thesis
Ph.D. Neuroscience 2014

Worldwide, as many as 1 billion people suffer from neurological disorders. Fundamentally, neurological disorders are caused by dysregulation of biochemical signaling within neurons, leading to deficits in learning and memory formation. To identify better preventative and therapeutic strategies for patients of neurological disorders, we require a better understanding of how biochemical signaling is regulated within neurons.

Biochemical signaling at the connections between neurons, called synapses, regulates dynamic shifts in a synapse’s size and connective strength. Called synaptic plasticity, these shifts are initiated by calcium ion (Ca²⁺) flux into message-receiving structures called dendritic spines. Within dendritic spines, Ca²⁺ binds sensor proteins such as calmodulin (CaM). Importantly, Ca²⁺/CaM may bind and activate a wide variety of proteins, which subsequently facilitate signaling pathways regulating the dendritic spine’s size and connective strength.

In this thesis, I use computational models to characterize molecular mechanisms regulating Ca²⁺-dependent protein signaling within the dendritic spine. Specifically, I explore how Ca²⁺/CaM differentially activates binding partners and how these binding partners transduce signals downstream. For this, I present deterministic models of Ca²⁺, CaM, and CaM-dependent proteins, and in analyzing model output I demonstrate in-part that competition for CaM-binding alone may be sufficient to set the Ca²⁺ frequency-dependence of protein activation. Subsequently, I adapt my deterministic models into particle-based, spatial-stochastic frameworks to quantify how spatial effects influence model output, showing evidence that spatial gradients of Ca²⁺/CaM may set spatial gradients of activated proteins downstream. Additionally, I incorporate into my models the most detailed model to-date of Ca²⁺/CaM-dependent protein kinase II (CaMKII), a multi-subunit protein essential to synaptic plasticity. With this detailed model of CaMKII, my analysis suggests that the many subunits of CaMKII provide avidity effects that significantly increase the protein’s effective affinity for binding partners, particularly Ca²⁺/CaM. Altogether, this thesis provides a detailed analysis of Ca²⁺-dependent signaling within dendritic spines, characterizing molecular mechanisms that may be useful for the development of novel therapeutics for patients of neurological disorders.

Research Doctorate - Doctor of Philosophy (PhD)
This thesis uses quantitative approaches to process behavioural and neural data in order to understand spatial cognition learning and cognitive control. Quantitative measurement was used to clearly identify two distinct strategies for improvement in the mental rotation task, one a departure from mental transformation, the other improvement of mental rotation. Using data from from an experiment on learning in mental rotation, a quantitative model of mental rotation was developed. The model was able to account for the RT distribution and error rates using an LBA decision model and a scale adjusted gamma distribution to account for rotation time. The following two chapters apply a modified version of a previously established signal processing technique to model the change in cued task-switching ERPs as a function of RT. Using this approach we modeled a switch-specific ERP component that increases with RT prior to target onset, providing evidence for switch-specific proactive control. We then used the same approach to investigate how interference following target onset is dealt with, reporting ERPs that suggest reactive control is actively used to resolve both target conflict and cue related processing. The final chapter extends the modeling approach used in the previous two chapters, by making modifications to the algorithm. This new method was evaluated on a simulated dataset, and then applied to neural data from the mental rotation experiment to demonstrate its utility. Although results were encouraging, more testing and development is necessary to optimise this new technique.

Les études d’imagerie par résonance magnétique fonctionnelle (IRMf) ont pour prémisse générale l’idée que le signal BOLD peut être utilisé comme un succédané direct de l’activation neurale. Les études portant sur le vieillissement cognitif souvent comparent directement l’amplitude et l’étendue du signal BOLD entre des groupes de personnes jeunes et âgés. Ces études comportent donc un a priori additionnel selon lequel la relation entre l’activité neurale et la réponse hémodynamique à laquelle cette activité donne lieu restent inchangée par le vieillissement. Cependant, le signal BOLD provient d’une combinaison ambiguë de changements de métabolisme oxydatif, de flux et de volume sanguin. De plus, certaines études ont démontré que plusieurs des facteurs influençant les propriétés du signal BOLD subissent des changements lors du vieillissement. L’acquisition d’information physiologiquement spécifique comme le flux sanguin cérébral et le métabolisme oxydatif permettrait de mieux comprendre les changements qui sous-tendent le contraste BOLD, ainsi que les altérations physiologiques et cognitives propres au vieillissement. Le travail présenté ici démontre l’application de nouvelles techniques permettant de mesurer le métabolisme oxydatif au repos, ainsi que pendant l’exécution d’une tâche. Ces techniques représentent des extensions de méthodes d’IRMf calibrée existantes. La première méthode présentée est une généralisation des modèles existants pour l’estimation du métabolisme oxydatif évoqué par une tâche, permettant de prendre en compte tant des changements arbitraires en flux sanguin que des changements en concentrations sanguine d’O2. Des améliorations en terme de robustesse et de précisions sont démontrées dans la matière grise et le cortex visuel lorsque cette méthode est combinée à une manipulation respiratoire incluant une composante d’hypercapnie et d’hyperoxie. Le seconde technique présentée ici est une extension de la première et utilise une combinaison de manipulations respiratoires incluant l’hypercapnie, l’hyperoxie et l’administration simultanée des deux afin d’obtenir des valeurs expérimentales de la fraction d’extraction d’oxygène et du métabolisme oxydatif au repos. Dans la deuxième partie de cette thèse, les changements vasculaires et métaboliques liés à l’âge sont explorés dans un groupe de jeunes et aînés, grâce au cadre conceptuel de l’IRMf calibrée, combiné à une manipulation respiratoire d’hypercapnie et une tâche modifiée de Stroop. Des changements de flux sanguin au repos, de réactivité vasculaire au CO2 et de paramètre de calibration M ont été identifiés chez les aînés. Les biais affectant les mesures de signal BOLD obtenues chez les participants âgés découlant de ces changements physiologiques sont de plus discutés. Finalement, la relation entre ces changements cérébraux et la performance dans la tâche de Stroop, la santé vasculaire centrale et la condition cardiovasculaire est explorée. Les résultats présentés ici sont en accord avec l’hypothèse selon laquelle une meilleure condition cardiovasculaire est associée à une meilleure fonction vasculaire centrale, contribuant ainsi à l’amélioration de la santé vasculaire cérébrale et cognitive.
Functional MRI (fMRI) studies using the BOLD signal are done under the general assumption that the BOLD signal can be used as a direct index of neuronal activation. Studies of cognitive aging often compare BOLD signal amplitude and extent directly between younger and older groups, with the additional assumption that the relationship between neuronal activity and the hemodynamic response is unchanged across the lifespan. However, BOLD signal arises from an ambiguous mixture of changes in oxidative metabolism, blood flow and blood volume. Furthermore, previous studies have shown that several BOLD signal components may be changed during aging. More physiologically-specific information on blood flow and oxidative metabolism would allow a better understanding of these signal changes and of the physiological and cognitive changes seen with aging. The work presented here demonstrates techniques to estimate oxidative metabolism at rest and during performance of a task. These techniques are extensions of previous calibrated fMRI methods and the first method presented is based on a generalization of previous models to take into account both arbitrary changes in blood flow and blood O2 content. The improved robustness and accuracy of this method, when used with a combined hypercapnia and hyperoxia breathing manipulation, is demonstrated in visual cortex and grey matter. The second technique presented builds on the generalization of the model and uses a combination of breathing manipulations including hypercapnia, hyperoxia and both simultaneously, to obtain experimentally-determined values of resting oxygen extraction fraction and oxidative metabolism. In the second part of this thesis, age-related vascular and metabolic changes are explored in a group of younger and older adults using a calibrated fMRI framework with a hypercapnia breathing manipulation and a modified Stroop task. Changes in baseline blood flow, vascular reactivity to the CO2 challenge and calibration parameter M were identified in the older participants. Potential biases in BOLD signal measurements in older adults arising from these physiological changes are discussed. Finally, the relationship between these cerebral changes and performance on the modified Stroop task, central vascular health and cardiovascular fitness are explored. The results of this thesis support the hypothesis that greater cardiovascular fitness is associated with improvements in central vascular function, contributing in turn to improved brain vascular health and cognition.

The cerebral cortex, the outer part of the brain that has expanded in humans, has layers whose differentiation varies within gradients. Along those gradients, we can define cortical types which range in number of layers and degrees of laminar differentiation. From least to most elaborate types there is an increase in the presence of granular layer IV, a shift in relative prominence of deep (layers V–VI) to superficial layers (layers II–III), and shift in location of large pyramidal neurons from deep (layers V–VI) to superficial layers (layers II–III), an increase in differentiation of deep layers (layers V–VI), and an increase in a defined boundary between layers I–II. According to this criteria, the following cortical types were defined: agranular, dysgranular, eulaminate I, and eulaminate II. In addition, primary areas in the cerebral cortex show distinct cortical features and are named koniocortices. Prior studies have shown that cortical types are related to epigenetics, synaptic plasticity, connections, pathologies, and evolution. Therefore, an algorithm to determine cortical type across areas in the human cortex will be a useful tool for the study of normal and pathological cortical networks. The Nissl stain, a standard histological staining method, was used in this study to observe differences in cortical type characteristics across the cerebral cortex. Qualitative analysis was performed on several cortical regions of an established neuroanatomical atlas, the prefrontal cortex of a post-mortem human, and the cerebral cortex of a rhesus macaque. Five laminar features were identified and used to group cortical regions into types, with less than 5% of disagreement amongst at least three experienced neuroanatomists. From these cortical type characteristics, an algorithm was created that can be used to systematically to determine cortical type throughout the cerebral cortex of humans and rhesus macaques. Additionally, quantitative analyses were performed in order to see if this cortical type classification could be an automated practice, that can be performed by individuals who are not experienced neuroanatomists. These quantitative measurements showed varying ability to classify cortical types; therefore, further studies will need to be performed in order to find the optimal quantitative measures of cortical type. A NMDS study was performed to summarize results of the various quantitative measurements, which showed an undisputable gradual trend of cortical types throughout the prefrontal cortex of the human brain. Overall, this study provides a cortical type classification algorithm that reliably and reproducibly identifies different cortical types in the cerebral cortex of human and rhesus macaque brains.

Damage to the orbitofrontal cortex (OFC) is often accompanied by disorders in personality, mood and social behavior. The purpose of this thesis was to study the cellular composition and architecture of this very complex and functionally important area. The posterior part of the OFC (pOFC) has the most multimodal circuits of the OFC and robust connections with the amygdala, a key center of the brain for emotions. Moreover, because cortico-cortical connections can be predicted on the basis of architectonic features, analyzing the cytoarchitecture of this area is important in order to understand its connections and functions. Previous descriptions show that the architecture throughout the OFC varies along two major gradients of laminar differentiation. These gradients show that the size of layer IV decreases medially and posteriorly and that the most medial and posterior areas lack layer IV and are agranular. In this study we analyzed the transitional dysgranular cortex, which has a narrow layer IV, and lies between the anterior granular cortices, with well-developed layer IV, and the posterior agranular areas. For that purpose, three regions of interest (ROIs) along the mediolateral axis of the transitional pOFC were delineated: lateral, central, and medial. We used unbiased systematic sampling in our ROIs to estimate the densities of neurons, astrocytes, oligodendrocytes and microglia. We also estimated the densities of three classes of inhibitory neurons that are identified by the expression of calcium-binding proteins (calretinin, parvalbumin, and calbindin). We also found that neurons labeled for calretinin are the most common inhibitory neuron class. The density of calretinin labeled neurons in the transitional dysgranular part of pOFC also increases from medial to lateral at a comparable rate to the entire population of neurons. Parvalbumin and calbindin neuron densities also increase from medial to lateral, but the difference is less pronounced. Our findings show that, despite the density gradient from medial to lateral, the proportion of CR, PV, and CB neurons is comparable across the three ROIs. This shows that there is a balance of excitation and inhibition along the transitional dysgranular part of the pOFC, which is functionally important because it has been shown to be affected in disorders like autism.

Many cognitive and neurological disorders today, such as Autism Spectrum Disorders (ASD) and various forms of epilepsy such as infantile spasms (IS), manifest as changes in voltage activity recorded in scalp electroencephalograms (EEG). Diagnosis of brain disease often relies on the interpretation of complex EEG features through visual inspection by clinicians. Although clinically useful, such interpretation is subjective and suffers from poor inter-rater reliability, which affects clinical care through increased variability and uncertainty in diagnosis. In addition, such qualitative assessments are often binary, and do not parametrically measure characteristics of disease manifestations. Many cognitive disorders are grouped by similar behaviors, but may arise from distinct biological causes, possibly represented by subtle electrophysiological differences. To address this, quantitative analytical tools - such as functional network connectivity, frequency-domain, and time-domain features - are being developed and applied to clinically obtained EEG data to identify electrophysiological biomarkers. These biomarkers enhance a clinician’s ability to accurately diagnose, categorize, and select treatment for various neurological conditions. In the first study, we use spectral and functional network analysis of clinical EEG data recorded from a population of children to propose a cortical biomarker for autism. We first analyze a training set of age-matched (4–8 years) ASD and neurotypical children to develop hypotheses based on power spectral features and measures of functional network connectivity. From the training set of subjects, we derive the following hypotheses: 1) The ratio of the power of the posterior alpha rhythm (8–14 Hz) peak to the anterior alpha rhythm peak is significantly lower in ASD than control subjects. 2) The functional network density is lower in ASD subjects than control subjects. 3) A select group of edges provide a more sensitive and specific biomarker of ASD. We then test these hypotheses in a validation set of subjects and show that both the first and third hypotheses, but not the second, are validated. The validated features successfully classified the data with significant accuracy. These results provide a validated study for EEG biomarkers of ASD based on changes in brain rhythms and functional network characteristics. We next perform a follow-up study that utilizes the same group of ASD and neurotypical subjects, but focuses on differences between these two groups in the sleep state. Motivated by the results from the previous study, we utilize the previously validated biomarkers, including the alpha ratio and the subset of edges found to be a sensitive biomarker of ASD, and test their effectiveness in the sleep state. To complement these frequency domain features, we also investigate the efficacy of several time domain measures. This investigation did not lead to significant findings, which may have important implications for the differences between sleep and wake states in ASD, or perhaps generally for clinical assessment, as well as for the effect of noise on signal in clinically obtained data. Finally, we design a similar analysis framework to investigate a set of clinical EEG data recorded from a population of children with active infantile spasms (IS) (2-16 months), and age-matched neurotypical children, in both wake and sleep states. The goal of this analysis is to develop a quantitative biomarker from the EEG signal, which ultimately we will apply to predict the clinical outcome of children with IS. In addition to spectral and functional network analysis, we calculate time domain features previously found to correlate with seizures. We compare the two populations by each feature individually, test the effects of age on these features, use all features in a linear discriminant model to categorize IS versus neurotypical EEG, and test the findings using a leave-one-out validation test. We find almost every feature tested shows significant population differences between IS and control groups, and that taken together they serve as an effective classifier, with potential to be informative as to disease severity and long-term outcome. Furthermore, analysis of these features reveals two groups, indicating a possibility that these features reflect two distinct qualitative characteristics of IS and seizures.

BACKGROUND: A number of epilepsy syndromes are characterized by sleep-activated epileptiform discharges, however drivers of this process are not well understood. Previous research has found that thalamic injury in early life may increase the odds of sleep-activated spikes. Benign childhood epilepsy with centrotemporal spikes (BECTS) is among the most common pediatric-onset epilepsy syndromes, characterized by sleep-potentiated spike activity, a focal sensorimotor seizure semiology, and deficits in language, attention, and behavioral functioning. Though ictal and interictal electro-clinical activity resolves during mid-adolescence, adverse psychosocial outcomes may persist. Previous findings from monozygotic twin and neuroimaging studies suggest a multifactorial pattern of disease and raise suspicion for structural changes in thalamocortical connectivity focal to the seizure onset zone, though this has not been explored. OBJECTIVE: This research aims to (1) assess white matter differences in focal thalamocortical connectivity between BECTS cases and healthy controls using validated probabilistic tractography methods, (2) assess the association between spike burden and white matter connectivity focal to the seizure onset zone, and (3) evaluate longitudinal changes in thalamocortical connectivity across four cases. METHODS: 42 subjects ages 6-15 years were recruited between November 2015 and February 2018, including 23 BECTS cases and 19 healthy controls. Subjects underwent 3 Tesla structural and diffusion-weighted magnetic resonance imaging (2mm x 2mm x 2mm) with 64 gradient directions (b-value=2000) and 72 electrode sleep-deprived electroencephalographic (EEG) recordings. Seed and target regions of interest (ROIs) were created within each hemisphere using the Desikan-Killiany atlas, with the thalamus set as a seed ROI, and SOZ cortex and non-SOZ (NSOZ) cortex as target ROIs. Probabilistic tractography was executed using PROBTRACKX2 with 500 streamlines per seed voxel, 0.5 millimeter steps, and a curvature threshold of 0.2. All streamlines reaching the target ROI were summed and normalized by seed voxel count. Results for BECTS and healthy controls were plotted by age. The slope of thalamocortical connectivity versus age was computed for each group and compared between groups using nonparametric bootstrap analysis. Additionally, the association between SOZ connectivity and spike burden was assessed in a subgroup analysis using a linear regression model, controlling for age. RESULTS: A significant difference in the developmental trajectory of thalamocortical connectivity to the SOZ in BECTS cases compared to healthy controls was found (p=0.014), where the increase in connectivity with age observed in healthy controls was not present in BECTS children. These results did not extend to NSOZ thalamocortical connections (p=0.192). Longitudinal results support these observations, where all BECTS cases who underwent repeat imaging (N=4) showed a decrease in thalamocortical connectivity to the SOZ over the follow-up period. No relationship was found between thalamocortical connectivity and spike burden (p=0.840). CONCLUSIONS: These findings suggest that children with BECTS show subtle alterations in thalamocortical white matter development focal to the seizure onset zone. Thalamocortical connectivity to the SOZ does not appear to directly mediate non-REM sleep spike potentiation in BECTS. Limitations of this study include the potential for selection bias and limited power to detect sample differences. Additional research is needed to further characterize thalamocortical network changes and electrographic and neuropsychological correlates.

Alzheimer’s Disease is the most common form of dementia, affecting 48 million people worldwide. Despite this fact, only 45% of the patients have received the diagnose. The reason behind this is the fact that the cause of the disease is still unclear. Several hypotheses have been suggested, with main focus in the imbalance between the production and the clearance of Αβ in the brain (formation of plaques) or hyperphosphorylation of the tau protein (formation of tangles). In order to have a better understanding of what is actually happening in the brain, more biomarkers need to be developed. Keeping this in mind, we tried to develop a method to monitor the protein levels of SAP in the brain. SAP is a glycoprotein, normally produced by the liver in acute phase immune responses. SAP has been correlated with AD in the 1980s and quite recently it has been shown that SAP is elevated in AD patients, but not in individuals with plaques and no dementia. For this reason, we developed a mass spectrometry based targeted quantification method for monitoring SAP in the brain, as well as C9, a blood contamination reference protein. Our method is robust enough to be further used in large studies, in order to investigate the role of SAP in AD.