A Comparative Study on the Modulatory Effects of Inhibition in the Mammalian and Avian Sound Localization Circuits

Sound localization is a critically important task for many animals including humans. Due to physical constraints acting on the circuits that process sound localization cues, many neural specializations have evolved. One of the key features and the focus of this dissertation is inhibitory input. To assess the impact of inhibition, I employ in vitro patch clamp techniques to observe cellular and synaptic physiology in brainstem circuits dedicated to sound localization processing. Using a mammalian model, I test the impact that GABAB receptor (GABABR) activation has on the inputs to the medial superior olive (MSO), the first area where sound localization computations take place. Activation of GABABRs modulates both excitatory and inhibitory inputs such that the magnitude of these inputs is decreased and the time course of inhibitory inputs is slowed. The functional significance of this modulation was tested using a bilateral stimulation protocol, which simulates the coincidence of in vivo excitatory inputs. Here, activation of GABABRs increased the sensitivity of MSO neurons to simulated interaural time disparity (ITD), the main cue for low frequency sound localization. To expand on these results, a computational model was used to show that each GABABR dependent modulation had a beneficial impact on ITD sensitivity in the MSO. In an avian system, I described the synaptic activity involving the superior olivary nucleus (SON), which provides the main inhibitory input in the avian sound localization circuit. At the SON itself, synaptic transmission consists of both GABA- and glycinergic components where glycine release is the result of co-release with GABA. I also show that functional glycine receptors localize at brainstem nuclei and that high frequency stimulation results in the release glycine onto nucleus magnocellularis neurons, a feature of the avian brainstem that has not been observed previously. In related experiments, I evaluate possible interactions that may occur when both GABA and glycine receptor systems are activated simultaneously. Here, a pre-activation of GlyRs leads the a decrease in conductance through the GABAAR likely due to changes in Cl- ion concentrations which manipulate the driving force of the ion

Cortical thickness, surface area and volume measures in Parkinson's disease, multiple system atrophy and progressive supranuclear palsy

OBJECTIVE Parkinson's disease (PD), Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP) are neurodegenerative diseases that can be difficult to distinguish clinically. The objective of the current study was to use surface-based analysis techniques to assess cortical thickness, surface area and grey matter volume to identify unique morphological patterns of cortical atrophy in PD, MSA and PSP and to relate these patterns of change to disease duration and clinical features. METHODS High resolution 3D T1-weighted MRI volumes were acquired from 14 PD patients, 18 MSA, 14 PSP and 19 healthy control participants. Cortical thickness, surface area and volume analyses were carried out using the automated surface-based analysis package FreeSurfer (version 5.1.0). Measures of disease severity and duration were assessed for correlation with cortical morphometric changes in each clinical group. RESULTS Results show that in PSP, widespread cortical thinning and volume loss occurs within the frontal lobe, particularly the superior frontal gyrus. In addition, PSP patients also displayed increased surface area in the pericalcarine. In comparison, PD and MSA did not display significant changes in cortical morphology. CONCLUSION These results demonstrate that patients with clinically established PSP exhibit distinct patterns of cortical atrophy, particularly affecting the frontal lobe. These results could be used in the future to develop a useful clinical application of MRI to distinguish PSP patients from PD and MSA patients

Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

Pulse Sequence Resilient Fast Brain Segmentation

Accurate automatic segmentation of brain anatomy from $T_1$-weighted~($T_1$-w) magnetic resonance images~(MRI) has been a computationally intensive bottleneck in neuroimaging pipelines, with state-of-the-art results obtained by unsupervised intensity modeling-based methods and multi-atlas registration and label fusion. With the advent of powerful supervised convolutional neural networks~(CNN)-based learning algorithms, it is now possible to produce a high quality brain segmentation within seconds. However, the very supervised nature of these methods makes it difficult to generalize them on data different from what they have been trained on. Modern neuroimaging studies are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is not possible to standardize the whole gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input $T_1$-w acquisition. Our approach relies on building approximate forward models of $T_1$-w pulse sequences that produce a typical test image. We use the forward models to augment the training data with test data specific training examples. These augmented data can be used to update and/or build a more robust segmentation model that is more attuned to the test data imaging properties. Our method generates highly accurate, state-of-the-art segmentation results~(overall Dice overlap=0.94), within seconds and is consistent across a wide-range of protocols.Comment: Accepted at MICCAI 201

Spherical parameterization for genus zero surfaces using Laplace-Beltrami eigenfunctions

International audienceIn this work, we propose a fast and simple approach to obtain a spherical parameterization of a certain class of closed surfaces without holes. Our approach relies on empirical findings that can be mathematically investigated, to a certain extent, by using Laplace-Beltrami Operator and associated geometrical tools. The mapping proposed here is defined by considering only the three first non-trivial eigenfunctions of the Laplace-Beltrami Operator. Our approach requires a topological condition on those eigenfunctions, whose nodal domains must be 2. We show the efficiency of the approach through numerical experiments performed on cortical surface meshes

Adolescent brain maturation and cortical folding: evidence for reductions in gyrification

Evidence from anatomical and functional imaging studies have highlighted major modifications of cortical circuits during adolescence. These include reductions of gray matter (GM), increases in the myelination of cortico-cortical connections and changes in the architecture of large-scale cortical networks. It is currently unclear, however, how the ongoing developmental processes impact upon the folding of the cerebral cortex and how changes in gyrification relate to maturation of GM/WM-volume, thickness and surface area. In the current study, we acquired high-resolution (3 Tesla) magnetic resonance imaging (MRI) data from 79 healthy subjects (34 males and 45 females) between the ages of 12 and 23 years and performed whole brain analysis of cortical folding patterns with the gyrification index (GI). In addition to GI-values, we obtained estimates of cortical thickness, surface area, GM and white matter (WM) volume which permitted correlations with changes in gyrification. Our data show pronounced and widespread reductions in GI-values during adolescence in several cortical regions which include precentral, temporal and frontal areas. Decreases in gyrification overlap only partially with changes in the thickness, volume and surface of GM and were characterized overall by a linear developmental trajectory. Our data suggest that the observed reductions in GI-values represent an additional, important modification of the cerebral cortex during late brain maturation which may be related to cognitive development

Brain structural concomitants of resting state heart rate variability in the young and old: evidence from two independent samples

Previous research has shown associations between brain structure and resting state high-frequency heart rate variability (HF-HRV). Age affects both brain structure and HF-HRV. Therefore we sought to examine the relationship between brain structure and HF-HRV as a function of age. Data from two independent studies were used for the present analysis. Study 1 included 19 older adults (10 male, age range 62-78 years) and 19 younger adults (12 male, age range 19-37). Study 2 included 23 older adults (13 males; age range 55-75) and 27 younger adults (19 males; age range 18-34). The rootmean- square of successive R-R-interval differences (RMSSD) from ECG recordings was used as timedomain measure of HF-HRV. MRI scans were performed on a 3.0-T Siemens Magnetom Trio scanner. Cortical reconstruction and volumetric segmentation were performed with the Freesurfer image analysis suite, including 12 regions as regions-of-interests (ROI). Zero-order and partial correlations were used to assess the correlation of RMSSD with cortical thickness in selected ROIs. Lateral orbitofrontal cortex (OFC) cortical thickness was significantly associated with RMSSD. Further, both studies, in line with previous research, showed correlations between RMSSD and anterior cingulate cortex (ACC) cortical thickness. Meta-analysis on adjusted correlation coefficients from individual studies confirmed an association of RMSSD with the left rostral ACC and the left lateral OFC. Future longitudinal studies are necessary to trace individual trajectories in the association of HRV and brain structure across aging

Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE

Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating high-dimensional distributions, in particular Variational Autoencoders (VAEs), opened up new avenues for probabilistic modeling. Modelling of volumetric data has remained a challenge, however, because constraints on available computation and training data make it difficult effectively leverage VAEs, which are well-developed for 2D images. We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We do so by estimating the sample mean and covariance in the latent space of the 2D model over the slice direction. This combined model lets us sample new coherent stacks of latent variables to decode into slices of a volume. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy. We demonstrate that our proposed model is competitive in generating high quality volumes at high resolutions according to both traditional metrics and our proposed evaluation.Comment: accepted for publication at MICCAI 2020. Code available https://github.com/voanna/slices-to-3d-brain-vae

Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm

The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice’s index of (Formula presented.) (Formula presented.) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas