Ischemic Stroke in a Seven-Year-Old Female With Down Syndrome and Newly Discovered Moyamoya Syndrome.

Moyamoya represents a rare, progressive cerebrovascular disease, characterized by a gradual stenosis of the intracranial internal carotid arteries, thereby increasing the risk of stroke. Down syndrome is known to be a predisposing factor for Moyamoya syndrome. This review discusses a distinctive case of a seven-year-old female with Down syndrome who manifested with Moyamoya syndrome, evident from acute stroke-like symptoms.

Using artificial neural networks to ask 'why' questions of minds and brains.

Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question 'why' brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these 'why' questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.

A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition.

Prior work has identified cortical regions selectively responsive to specific categories of visual stimuli. However, this hypothesis-driven work cannot reveal how prominent these category selectivities are in the overall functional organization of the visual cortex, or what others might exist that scientists have not thought to look for. Furthermore, standard voxel-wise tests cannot detect distinct neural selectivities that coexist within voxels. To overcome these limitations, we used data-driven voxel decomposition methods to identify the main components underlying fMRI responses to thousands of complex photographic images. Our hypothesis-neutral analysis rediscovered components selective for faces, places, bodies, and words, validating our method and showing that these selectivities are dominant features of the ventral visual pathway. The analysis also revealed an unexpected component with a distinct anatomical distribution that responded highly selectively to images of food. Alternative accounts based on low- to mid-level visual features, such as color, shape, or texture, failed to account for the food selectivity of this component. High-throughput testing and control experiments with matched stimuli on a highly accurate computational model of this component confirm its selectivity for food. We registered our methods and hypotheses before replicating them on held-out participants and in a novel dataset. These findings demonstrate the power of data-driven methods and show that the dominant neural responses of the ventral visual pathway include not only selectivities for faces, scenes, bodies, and words but also the visually heterogeneous category of food, thus constraining accounts of when and why functional specialization arises in the cortex.

Predicting individual task contrasts from resting-state functional connectivity using a surface-based convolutional network.

Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.

NeuroGen: Activation optimized image synthesis for discovery neuroscience.

Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a priori hypotheses, limited to the set of images presented to the individual while they are in the scanner, are subject to noise in the observed brain responses, and may vary widely across individuals. In this work, we propose a novel computational strategy, which we call NeuroGen, to overcome these limitations and develop a powerful tool for human vision neuroscience discovery. NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation. We demonstrate that the reduction of noise that the encoding model provides, coupled with the generative network's ability to produce images of high fidelity, results in a robust discovery architecture for visual neuroscience. By using only a small number of synthetic images created by NeuroGen, we demonstrate that we can detect and amplify differences in regional and individual human brain response patterns to visual stimuli. We then verify that these discoveries are reflected in the several thousand observed image responses measured with fMRI. We further demonstrate that NeuroGen can create synthetic images predicted to achieve regional response patterns not achievable by the best-matching natural images. The NeuroGen framework extends the utility of brain encoding models and opens up a new avenue for exploring, and possibly precisely controlling, the human visual system.

Cortical response to naturalistic stimuli is largely predictable with deep neural networks.

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.

Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction.

The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on preprocessing choices, such as the parcellation scheme used to define regions of interest (ROIs). In this study, we critically evaluate the effect of brain parcellations on machine learning models applied to rs-fMRI data. Our experiments reveal an intriguing trend: On average, models with stochastic parcellations consistently perform as well as models with widely used atlases at the same spatial scale. We thus propose an ensemble learning strategy to combine the predictions from models trained on connectivity data extracted using different (e.g., stochastic) parcellations. We further present an implementation of our ensemble learning strategy with a novel 3D Convolutional Neural Network (CNN) approach. The proposed CNN approach takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. Our ensemble CNN framework overcomes the limitations of traditional machine learning models for connectomes that often rely on region-based summary statistics and/or linear models. We showcase our approach on a classification (autism patients versus healthy controls) and a regression problem (prediction of subject's age), and report promising results.

Machine learning in resting-state fMRI analysis.

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.

A Switch and Wave of Neuronal Activity in the Cerebral Cortex During the First Second of Conscious Perception.

Conscious perception occurs within less than 1 s. To study events on this time scale we used direct electrical recordings from the human cerebral cortex during a conscious visual perception task. Faces were presented at individually titrated visual threshold for 9 subjects while measuring broadband 40-115 Hz gamma power in a total of 1621 intracranial electrodes widely distributed in both hemispheres. Surface maps and k-means clustering analysis showed initial activation of visual cortex for both perceived and non-perceived stimuli. However, only stimuli reported as perceived then elicited a forward-sweeping wave of activity throughout the cerebral cortex accompanied by large-scale network switching. Specifically, a monophasic wave of broadband gamma activation moves through bilateral association cortex at a rate of approximately 150 mm/s and eventually reenters visual cortex for perceived but not for non-perceived stimuli. Meanwhile, the default mode network and the initial visual cortex and higher association cortex networks are switched off for the duration of conscious stimulus processing. Based on these findings, we propose a new "switch-and-wave" model for the processing of consciously perceived stimuli. These findings are important for understanding normal conscious perception and may also shed light on its vulnerability to disruption by brain disorders.

Polarons Explain Luminescence Behavior of Colloidal Quantum Dots at Low Temperature.

Luminescence properties of colloidal quantum dots have found applications in imaging, displays, light-emitting diodes and lasers, and single photon sources. Despite wide interest, several experimental observations in low-temperature photoluminescence of these quantum dots, such as the short lifetime on the scale of microseconds and a zero-longitudinal optical phonon line in spectrum, both attributed to a dark exciton in literature, remain unexplained by existing models. Here we propose a theoretical model including the effect of solid-state environment on luminescence. The model captures both coherent and incoherent interactions of band-edge exciton with phonon modes. Our model predicts formation of dressed states by coupling of the exciton with a confined acoustic phonon mode, and explains the short lifetime and the presence of the zero-longitudinal optical phonon line in the spectrum. Accounting for the interaction of the exciton with bulk phonon modes, the model also explains the experimentally observed temperature-dependence of the photoluminescence decay dynamics and temperature-dependence of the photoluminescence spectrum.

Revalidation of the Sat-Chit-Ananda Scale.

Sat-Chit-Ananda is an indigenous construct that refers to absolute bliss and consciousness. The present study aimed to strengthen the psychometric properties of the newly developed Sat-Chit-Ananda scale (Singh et al. in Int J Vedic Found Manag 1(2):54-74, 2014). A total of 398 students aged 17-36 years (mean age = 21.33 years, SD = 2.2, 70% males) participated in this study. An exploratory as well as confirmatory factor analysis was computed for the 17-item scale. Its' concurrent validity was established by assessing its correlation with other well-being measures, namely Flourishing (Diener et al. in Soc Indic Res 97:143-156 2010) and Scale of Positive and Negative Experience (Diener et al. 2010). Satisfactory results were obtained from both exploratory and confirmatory factor analyses. Sat-Chit-Ananda and its factors were found to be significantly positively correlated with Flourishing and Positive Experiences and were negatively correlated with Negative Experiences. Thus, the validity of the Sat-Chit-Ananda (Singh et al. 2014) scale stands further substantiated-offering this scale as a promising new assessment tool.