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Although a number of cytoskeletal derangements have been described in the setting of traumatic axonal injury (TAI), little is known of early structural changes that may serve to initiate a cascade of further axonal degeneration. Recent work by the authors has examined conformational changes in cytoskeletal constituents of neuronal axons undergoing traumatic axonal injury (TAI) following focal compression through confocal imaging data taken in vitro and in situ. The present study uses electron microscopy to understand and quantify in vitro alterations in the ultrastructural composition of microtubules and neurofilaments within neuronal axons of rats following focal compression. Standard transmission electron microscopy processing methods are used to identify microtubules, while neurofilament identification is performed using antibody labeling through gold nanoparticles. The number, density, and spacing of microtubules and neurofilaments are quantified for specimens in sham Control and Crushed groups with fixation at <1min following load. Our results indicate that the axon caliber dependency known to exist for microtubule and neurofilament metrics extends to axons undergoing TAI, with the exception of neurofilament spacing, which appears to remain constant across all Crushed axon diameters. Confidence interval comparisons between Control and Crushed cytoskeletal measures suggests early changes in the neurofilament spatial distributions within axons undergoing TAI may precede microtubule changes in response to applied loads. This may serve as a trigger for further secondary damage to the axon, representing a key insight into the temporal aspects of cytoskeletal degeneration at the component level, and suggests the rapid removal of neurofilament sidearms as one possible mechanism.

The social and economic consequences of poor mental health in the developing world are presumed to be significant, yet are largely under-researched. The authors argue that mental health modules can be meaningfully added to multi-purpose household surveys in developing countries, and used to investigate this relationship. Data from nationally representative surveys in Bosnia and Herzegovina, Indonesia, and Mexico, along with special surveys from India and Tonga, show similar patterns of association between mental health and socioeconomic characteristics across countries. Individuals who are older, female, widowed, and report poor physical health are more likely to report worse mental health outcomes. Individuals living with others with poor mental health are also significantly more likely to report worse mental health themselves. In contrast, there is little observed relationship between mental health and poverty or education, common measures of socio-economic status. The results instead suggest that economic and multi-dimensional shocks such as illness or crisis can have a greater impact on mental health than overall levels of poverty. This may have important implications for social protection policy. The authors also find significant associations between poor mental health and lowered labor force participation (especially for women) and higher frequency visits to health centers, suggesting that poor mental health can have significant economic consequences for households and the health system. Finally, the paper discusses how measures of mental health are distinct from general subjective welfare measures such as happiness and indicate useful directions of future research.

Metastasis of ovarian carcinoma to the central nervous system occurs in <2% of cases and classically localizes within the brain parenchyma. Moreover, leptomeningeal spread of these tumors is an exceedingly rare phenomenon. Here, we conduct a systematic review of the current literature on the natural history, treatment options, and proposed pathogenic mechanisms of leptomeningeal carcinomatosis in ovarian carcinoma. We also report a case of a 67-year-old female with stage IV metastatic ovarian serous carcinoma initially confined to the peritoneal cavity with a stable disease burden over the course of three years. Follow-up imaging demonstrated an intracranial lesion, which was resected via craniotomy, and pathology was consistent with the original diagnosis. Three months after surgery, she developed rapidly progressive dizziness, generalized weakness, fatigue, and ataxia. Repeat MRI demonstrated interval development of extensive and diffusely enhancing dural nodularity, numerous avidly enhancing supratentorial and infratentorial lesions, enhancement of the bilateral trigeminal nerves, internal auditory canals, and exit wound from the surgical site into the posterior aspect of the right-sided neck musculature consistent with diffuse leptomeningeal dissemination. The present case highlights that leptomeningeal dissemination of ovarian carcinoma is a potential yet rare consequence following surgical resection of an ovarian parenchymal metastasis. Progressive clinical symptomatology that develops postoperatively in this patient population should prompt urgent workup to rule out leptomeningeal disease and an expedited radiation oncology consultation if identified. Published version

The ACLeeve is a portable and easy to use electromyography (EMG) device to be used during ACL injury recovery to aid in the rehabilitation process. The ACLeeve is wrapped around the user's leg using a knee sleeve and electrodes, and then wirelessly transfers data to the user's phone or laptop. To achieve this, the ACLeeve must be small and lightweight, with electronics and software capable of detecting EMG readings and then transmitting them over a secure wireless connection. As well, the ACLeeve must be safe for the user and conform to all relevant standards. ACLeeve will monitor the user's movements of both quadriceps using surface EMGs (SEMGs) and Strain Sensors. These devices will be actively transmitting the collected data to a microprocessor, which will then process and send the data to an external device for further software analysis. The ACLeeve will provide real-time feedback to the user (audio or visual) regarding the performance of their movements, which will allow the user to physically adjust in order to achieve better long-term results.Our software will perform long-term analyses which will evaluate the progress of obtaining the goal of 80-90% asymmetry between the user's ACL-injured quadricep and their other healthy quadricep.

Background: Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. Aims: Evaluate discriminative performance and generalizability of functional connectivity markers for BPD. Method: Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain—global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity—and evaluated the generalizability of the classification in the left-out portion of non-matched data. Results: Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61–70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70–75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. Limitations: The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. Conclusions: Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population. Published version

This paper analyzes the effects of all-weather rural roads on households' net output prices, education and health in a poor, drought-prone region of India. Of 30 villages originally surveyed in 2001-02, when two had such roads, a further nine received them between January 2007 and December 2009 under the program Pradhan Mantri Gram Sadak Yojana. Cross-section comparisons involving all villages and 'before and after' comparisons in the nine yielded these findings: (i) net output prices were 5 per cent or more higher; (ii) substantially fewer days of schooling were lost due to bad weather, largely because teachers had fewer absences; (iii) the acutely sick received more timely treatment and were more likely to be treated in a hospital than in the nearest primary health clinic; and (iv) the respondents ranked the resulting benefits in the domains of health and education at least as highly as the 'commercial' ones.

Identifying and tracking cell location in long-term longitudinal studies is critical for identifying large-scale time-dependent neuronal activity variations. Population cell tracking across multiple sessions is complicated by non-rigid transformations induced by noise, cell movement and imaging field shifts. In this text we develop SCOUT (Single-Cell SpatiOtemporal LongitUdinal Tracking), a fast, robust cell tracking method utilizing multiple cell-cell similarity metrics, probabilistic inference, and an adaptive clustering methodology to perform cell identification across multiple calcium imaging sessions. We then apply SCOUT to study variations of firing activity coincident with contextual discrimination and neural circuit negation. Next, we investigate the relationship between firing activity and transcriptomics in a single cell type, showing that transcriptional gradients can be associated to subtle variations in neuronal firing activity, which then motivates the development of scGradient, a machine learning algorithm for identifying continuous transcriptional gradients across and within cell types.

L’objectif de cette thèse était la validation de l’existence ainsi que la découverte de nouveaux sous-types au sein de la maladie d’Alzheimer, première cause de démence au monde. Afin d’explorer son hétérogénéité, nous avons employé des méthodes d’apprentissage profond appliquées à une modalité de neuroimagerie, l’imagerie par résonance magnétique structurelle.Cependant, la découverte de biais méthodologiques importants dans de nombreuses études de notre domaine, ainsi que l’absence de consensus de la communauté sur la manière d’interpréter les résultats des méthodes d’apprentissage profond a fait en partie dévier la thèse de son objectif principal pour s’orienter d’avantage vers des problématiques de validation, de robustesse et d’interprétabilité de l’apprentissage profond. Ainsi, trois études expérimentales ont été menées pour s’assurer de la capacité des réseaux profonds de correctement détecter la maladie. La première est une étude expérimentale de méthodes d’apprentissage profond pour la classification de la maladie d’Alzheimer et a permis d’établir une juste comparaison des méthodes. La seconde étude a permis de constater un manque de robustesse de la classification avec l’apprentissage profond en termes de motifs d’atrophie découverts à l’aide de méthodes d’interprétabilité. Enfin, la dernière étude propose une méthode de découverte de sous-types aidée par l’augmentation de données. Bien que fonctionnant sur des données synthétiques, celle-ci ne généralise pas aux données réelles.Une contribution majeure de la thèse est la librairie ClinicaDL, grâce à laquelle les résultats expérimentaux de la thèse ont été produits de manière à être reproductibles. The goal of this PhD was the validation of the existence and the discovery of new subtypes of Alzheimer’s disease, the first cause of dementia worldwide. Indeed, despite its discovery more than a century ago, this disease is still not well defined and existing treatments are only weakly effective, possibly because several phenotypes exist within the disease. In order to explore its heterogeneity, we employed deep learning methods applied to a neuroimaging modality, structural magnetic resonance imaging.However, the discovery of important methodological biases in many studies in our field, as well as the lack of consensus regarding deep learning interpretability, partly changed the main objective of the PhD to focus more on issues of validation, robustness and interpretability of deep learning. Then, to correctly assess the ability of deep learning to detect Alzheimer’s disease, three experimental studies were conducted. The first one is a study of deep learning methods for Alzheimer’s classification and allowed a fair comparison of the methods. The second study found a lack of robustness of classification with deep learning in terms of atrophy patterns discovered using interpretability methods. Finally, the last study proposed a subtype discovery method aided by data augmentation. Although it works on synthetic data, it does not generalize to real data.Experimental results of this PhD were obtained thanks to ClinicaDL, one major contribution of this PhD. It is an open source Python library that was used to improve the reproducibility of deep learning experiments.

Microcircuits in the neocortex are functionally organized along layers and columns, which are the fundamental modules of cortical information processing. While the function of cortical microcircuits has focused on neuronal elements, much less is known about the functional organization of astrocytes and their bidirectional interaction with neurons. Here, we show that Cannabinoid type 1 receptor (CB1R)-mediated astrocyte activation by neuron-released endocannabinoids elevate astrocyte Ca2+ levels, stimulate ATP/adenosine release as gliotransmitters, and transiently depress synaptic transmission in layer 5 pyramidal neurons at relatively distant synapses (˃20 μm) from the stimulated neuron. This astrocyte-mediated heteroneuronal synaptic depression occurred between pyramidal neurons within a cortical column and was absent in neurons belonging to adjacent cortical columns. Moreover, this form of heteroneuronal synaptic depression occurs between neurons located in particular layers, following a specific connectivity pattern that depends on a layer-specific neuron-to-astrocyte signaling. These results unravel the existence of astrocyte-mediated nonsynaptic communication between cortical neurons and that this communication is column- and layer-specific, which adds further complexity to the intercellular signaling processes in the neocortex.

Functional imaging of neural cell populations at mesoscale is criticalfor mapping intra- and inter-regional network dynamics across the dorsal neocortex. Past lab members produced a flexible work flow for producing high quality segmentations of functional structure across neocortex utilizing Independent Component Analysis (ICA). This unsupervised machine learning decomposition of densely sampled recordings of cortical calcium dynamics, results in a collection of components comprised of neuronal signal sources distinct from optical, movement, and vascular artifacts. In this body of work, I built a supervised learning classifier that automatically separates neural activity and artifact components, using a set of extracted spatial and temporal metrics that characterize the respective components. Control data recorded in glial cell reporter and non-fluorescent mouse lines validates human and machine identification of functional component class.Utilizing the insight from the machine learning analysis, I processed alarge dataset from three different transgenic mouse lines expressing calcium indicators in distinct sub-population of neurons: pan-neuronal, upper, and lower layer specific excitatory neuronal subpopulations. I was able to create domain maps to discretize the cortex into functional units and observe how each domain behaves. The application of this data-driven method ultimately reduced the number of time series, while maintaining the informative structure at a mesoscale. From all these animals, I gain insight into the developing structural changes of circuitry based on age-related functional changes observed in these domain maps.