Estimating brain age from structural MRI and MEG data: insights from dimensionality reduction techniques

Brain age prediction studies aim at reliably estimating the difference between the chronological age of an individual and their predicted age based on neuroimaging data, which has been proposed as an informative measure of disease and cognitive decline. As most previous studies relied exclusively on magnetic resonance imaging (MRI) data, we hereby investigate whether combining structural MRI with functional magnetoencephalography (MEG) information improves age prediction using a large cohort of healthy subjects (N = 613, age 18–88 years) from the Cam-CAN repository. To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle the high dimensionality of neuroimaging data. Using MEG features (mean absolute error (MAE) of 9.60 years) yielded worse performance when compared to using MRI features (MAE of 5.33 years), but a stacking model combining both feature sets improved age prediction performance (MAE of 4.88 years). Furthermore, we found that PCA resulted in inferior performance, whereas CCA in conjunction with Gaussian process regression models yielded the best prediction performance. Notably, CCA allowed us to visualize the features that significantly contributed to brain age prediction. We found that MRI features from subcortical structures were more reliable age predictors than cortical features, and that spectral MEG measures were more reliable than connectivity metrics. Our results provide an insight into the underlying processes that are reflective of brain aging, yielding promise for the identification of reliable biomarkers of neurodegenerative diseases that emerge later during the lifespan

Knowledge and awareness about optometry profession among rural versus urban population in Eastern India: a survey-based study

Background: The main purpose of this study was to determine the awareness of optometry and to create awareness of optometry among rural and urban population of Kolkata.Methods: A cross-sectional descriptive study was conducted among rural and urban population. A total of 671 participants- male=378 and female=293, were enrolled in this study. An amplified self-structured questionnaire was used to collect data from the participants through a survey camp.Results: Out of 671 subjects, (328 rural and 343 urban subjects), 62 (18.9%) and 166 (48.3%) (95%, CI: 1.93-1.88) were know about optometry in rural and urban population respectively. 4.9% and 14% (95%, CI: 1.97-1.94) did knew the difference between optometrist and ophthalmologists, while in participants opinion optometrist is assistant of ophthalmologist was 3.4% and 1.5% (95%, CI: 2.69-2.61) in rural and urban participants respectively. However, 18.2% (rural) and 32% (urban) (95%, CI: 2.31-2.17) participants think that optometrist is trained in detection and recognition of eye diseases while 22.6% and 26.8% (95%, CI: 3.20-3.02) thinks they can prescribe spectacles and contact lens independently in rural and urban participants respectively. All factors mentioned were found to be statistically significant (p<0.05) with the Chi square and ANOVA test in SPSS version 21.Conclusions: These findings seem to indicate lack of awareness and knowledge about optometry profession. There is a need to increase campaign in these areas via educational programs, awareness survey camp, a poster explaining the eye care, social media regarding eye care provider’s duties and practices, exhibitions for public concerning the profession of optometry

Deep learning as an investigation tool in neuroimaging

Following the successful use of deep learning (DL) in the field of computer vision and natural language processing, Convolutional Neural Networks (CNN) have been increasingly applied to neuroimaging studies to differentiate diseased population from healthy subjects. DL has been applied to datasets acquired from different modalities such as electroencephalography (EEG), Magnetic Resonance Imaging (MRI) and magnetoencephalography (MEG). Although DL models have outperformed competing methods in various contexts, the usefulness of the trained models to improve our understanding of the underlying neural substrates remains limited. This is primarily due to the difficulty associated with visualizing and interpreting the predictive features learned by DL models.Our main aim was to leverage DL models to reveal subtle discriminative brain patterns from neuroimaging data and subsequently develop methods to visualize these patterns. We investigated the applicability of DL to 2 specific studies: (1) an EEG dataset obtained from a small number of participants to understand the add-on effects of cardiovascular exercise on motor learning in healthy subjects, (2) a large-scale MRI and MEG study to characterize the process of healthy brain aging.This thesis is comprised of two distinct experiments. (1) We leveraged the end-to-end learning ability of CNNs to investigate the differences in EEG activity between an exercise and a control group from data collected while subjects performed isometric handgrips. Subsequently, we developed a method to visualize the task-specific, discriminatory EEG patterns between the two groups. (2) We investigated machine learning models coupled with several multivariate associativity techniques to predict the age of healthy individuals from their T1-weighted MRI and resting-state MEG data. We also explored Graph Convolutional Networks (GCNs) to incorporate the topological information of the data into the prediction models.We found that CNNs can be reliably trained on a dataset collected from a relatively small number of participants using an adversarial training strategy. In addition, we were able to identify relevant frequency bands and brain regions that were modulated by exercise. We observed (i) a finer frequency band within the wider beta-band that was modulated by exercise, as well as (ii) a significant modulation of the event-related desynchronization in this frequency band located in bilateral sensorimotor cortices and contralateral prefrontal regions to the moving hand. Therefore, our approach demonstrates the feasibility of identifying subtle discriminative features in a completely data-driven manner using deep learning. In the context of brain age prediction, we found that the subcortical regions were more reliable predictors of age as compared to cortical regions. In addition, we also observed how the functional organization of the brain changed with age.Our approach demonstrated the feasibility of identifying subtle discriminative features in a completely data-driven manner using DL. We believe these results hold a significant contribution to the methodological advances for small-scale neuroimaging studies where a small number of subjects are traditionally tested, e.g. neurorehabilitation. Our findings from the brain age prediction expanded upon previous work in the field and provided useful insights into the brain areas that are reliably affected with age. In addition, the graph network architecture demonstrated ways to include the topology of the brains functional organization while analyzing age-related effects on a diverse set of neuroimaging features. Collectively, the findings and methods presented in this thesis demonstrated the wide scope of using DL models in the analysis of various modalities of neuroimaging data.Suite à l’utilisation réussie de l’apprentissage en profondeur (DL) dans le domaine de la vision par ordinateur et du traitement du langage naturel, les réseaux de neurones à convolution (CNN) ont été de plus en plus utilisés dans les études en neuroimagerie afin de différencier les populations en santé de celles présentant une maladie neurodégénérative. Des modèles d’apprentissage en profondeur ont été appliqués à des ensembles de données acquis à partir de différentes modalités telles que l’électroencéphalographie (EEG), l’imagerie par résonance magnétique (IRM) et la magnétoencéphalographie (MEG). Bien que les modèles DL aient surperformé les méthodes concurrentes dans divers contextes, l’utilité des modèles conus pour améliorer notre compréhension des substrats neuronaux sous-jacents reste limitée. Notre objectif principal était dexploiter des modèles DL pour révéler des caractéristiques cérébrales subtiles discriminantes à partir de données de neuroimagerie, puis de développer des méthodes permettant de les visualiser. Nous avons étudié l’applicabilité des modèles DL dans le cadre de deux études spécifiques utilisant la neuroimagerie soient: (1) un ensemble de données EEG obtenu à partir d’un nombre restreint de participants afin d’étudier les effets additifs de l’exercice cardiovasculaire aigu sur l’apprentissage moteur auprès de sujets en santé, et (2) une étude à partir de données dIRM et de MEG à grande échelle pour caractériser le processus du vieillissement cérébral sain.Cette thèse est composée de deux expériences distinctes. (1) Nous avons exploité la capacité dapprentissage de bout en bout des CNN pour étudier les différences dans les activités EEG entre un groupe assujetti à lexercice cardiovasculaire et un groupe contrôle. Les données ont été collectées pendant que les sujets exécutaient des contractions manuelles isométriques. Par la suite, nous avonsdéveloppé une méthode permettant de visualiser les formes EEG pouvant discriminer les deux groupes et étant spécifiques à la tâche. (2) Nous avons étudié des modèles dapprentissage machine associés à divers techniques dassociativité à variables multiples pour prédire lâge des individus en bonne santé à partir de leurs données dIRM et de MEG. Nous avons constaté que les CNN peuvent être entraı̂nés de manière fiable sur un ensemble de données collecté auprès d’un nombre relativement restreint de participants à l’aide d’une stratégie de formation contradictoire. De plus, nous avons pu identifier des bandes de fréquences pertinentes et des régions du cerveau modulées par l’exercice. En utilisant le pipeline basé sur les CNN sur le même ensemble de données, nous avons observé (i) une bande de fréquence plus fine modulée par l’exercice dans la bande bêta, ainsi que (ii) une modulation significative de la désynchronisation liée à l’événementdans cette bande de fréquence située dans les cortex sensorimoteurs bilatéraux et les régions préfrontales controlatérales à la main en mouvement. Dans le contexte de la prédiction de l’âge du cerveau, nous avons constaté que les régions sous-corticales étaient des prédicteurs plus fiables de l’âge en comparaison aux régions corticales. Notre approche a démontré la faisabilité d’identifier des caractéristiques discriminantes subtiles d’une manière entièrement basée sur les données en utilisant la DL. Nous pensons que ces résultats apportent une contribution significative aux avancées méthodologiques pour les études en neuroimagerie à petite échelle dans lesquelles un nombre habituellement restreint de sujets est testé. Nos conclusions tirées de la prédiction de l’âge du cerveau complémentent celles détudes antérieurs se penchant sur la même question et ont fourni des informations utiles sur les zones du cerveau systématiquement affectées par l’âge. Ensemble, les résultats et les méthodes présentés dans cette thèse ont démontré le large champ dutilisation des DL dans lanalyse de diverses modalités de données de neuroimagerie

Handwriting Profiling Using Generative Adversarial Networks

Handwriting is a skill learned by humans from a very early age. The ability to develop one’s own unique handwriting as well as mimic another person’s handwriting is a task learned by the brain with practice. This paper deals with this very problem where an intelligent system tries to learn the handwriting of an entity using Generative Adversarial Networks (GANs). We propose a modified architecture of DCGAN (Radford, Metz, and Chintala 2015) to achieve this. We also discuss about applying reinforcement learning techniques to achieve faster learning. Our algorithm hopes to give new insights in this area and its uses include identification of forged documents, signature verification, computer generated art, digitization of documents among others. Our early implementation of the algorithm illustrates a good performance with MNIST datasets

The effects of the NMDAR co-agonist d-serine on the structure and function of optic tectal neurons in the developing visual system

Abstract The N-methyl-d-aspartate type glutamate receptor (NMDAR) is a molecular coincidence detector which converts correlated patterns of neuronal activity into cues for the structural and functional refinement of developing circuits in the brain. d-serine is an endogenous co-agonist of the NMDAR. We investigated the effects of potent enhancement of NMDAR-mediated currents by chronic administration of saturating levels of d-serine on the developing Xenopus retinotectal circuit. Chronic exposure to the NMDAR co-agonist d-serine resulted in structural and functional changes in the optic tectum. In immature tectal neurons, d-serine administration led to more compact and less dynamic tectal dendritic arbors, and increased synapse density. Calcium imaging to examine retinotopy of tectal neurons revealed that animals raised in d-serine had more compact visual receptive fields. These findings provide insight into how the availability of endogenous NMDAR co-agonists like d-serine at glutamatergic synapses can regulate the refinement of circuits in the developing brain

Amphiphilic Rhenium-Oxo Corroles as a New Class of Sensitizers for Photodynamic Therapy

A set of rhenium(V)-oxo meso-triarylcorroles bearing ester and carboxylic acid functionalities were synthesized with a view to determining their potential for photodynamic therapy. Toward this end, we measured their near-IR phosphorescence and their ability to sensitize singlet oxygen formation. The two esters studied, ReVO 5,10,15-tris(meta-carbomethoxyphenyl)corrole and ReVO 5,10,15-tris(para-carbomethoxyphenyl)corrole, were found to exhibit phosphorescence quantum yields of around 1% and fairly long phosphorescence lifetimes of about 60 μs in toluene. The corresponding carboxylic acids, which were examined in ethanolic/aqueous media, in contrast, showed much lower phosphorescence quantum yields on the order of 0.01% and somewhat shorter phosphorescent lifetimes. The quantum yields for singlet oxygen formation, on the other hand, turned out to be equally high (0.72 ± 0.02) for the esters and corresponding carboxylic acids. For the two carboxylic acids, we also carried out photocytotoxicity measurements on rat bladder cancer cells (AY27) and human colon carcinoma cells (WiDr). Cell viability measurements (MTT assays) indicated 50% cell death (LD50) for AY27 cells upon 5 min of blue light exposure with the meta carboxylic acid and upon 7 min of exposure with the para carboxylic acid; complete cell death resulted after 20 min for both compounds. The WiDr cells proved less sensitive, and LD50 values were reached after 8 and 12 min illumination with the meta and para carboxylic acids, respectively