17,454 research outputs found

    Improved physiological noise regression in fNIRS: a multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis

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    For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. -55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.Published versio

    High-density microfibers as a deep brain bidirectional optical interface

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    Optical interrogation and manipulation of neural dynamics is a cornerstone of systems neuroscience. Genetic targeting enable delivering fluorescent indicators and opsins to specific neural subpopulations. Optic probes can fluorescently sense and convey calcium, voltage, and neurotransmitter dynamics. This optical toolkit enables recording and perturbing cellular-resolution activity in thousands of neurons across a field of view. Yet these techniques are limited by the light scattering properties of tissues. The cutting edge of microscopy, three-photon imaging, can record from intact tissues at depths up to 1 mm, but requires head-fixed experimental paradigms. To access deeper layers and non-cortical structures, researchers rely on optical implants, such as GRIN lenses or prisms, or the removal of superficial tissue. In this thesis, we introduce a novel implant for interfacing with deep brain regions constructed from bundles of hundreds or thousands of dissociated, small diameter (<8 µm) optical fibers. During insertion into the tissue, the fibers move independently, splaying through the target region. Each fiber achieves near total internal reflection, acting as a bidirectional optical interface with a small region of tissue near the fiber aperture. The small diameter and flexibility of the fibers minimize tissue response, preserving local connectivity and circuit dynamics. Histology and immunohistochemistry from implants into zebra finch basal ganglia (depth 2.9 mm) show the splaying of the fibers and the presence of NeuN-stained cells in close proximity to the fiber tips. By modeling the optical properties of the fibers and tissue, we simulate the interface properties of a bundle of fibers. Overlap in the sensitivity between nearby fibers allows application of blind source separation to extract individual neural traces. We describe a nonnegative independent component analysis algorithm especially suited to the interface. Finally, experimental data from implants in transgenic mice yield proof of principle recordings during both cortical spreading depolarization and forepaw stimulation. Collectively, the data presented here paint a compelling picture of splaying microfibers as a deep brain interface capable of sampling or perturbing neural activity at hundreds or thousands of points throughout a 3D volume of tissue while eliciting less response than existing optical implants

    Auto detection in autism

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    Autism is a neurobiological disorder in which, certain regions of the brain are affected. The main features of autism are impairment in communication, social interaction, language and deficit in imitation and theory of mind. Using Functional Magnetic Resonance Imaging (fMRI), haemodynamic responses during a bilateral finger tapping task are analyzed for both autistic subjects and normal control subjects. fMRI is a noninvasive technique to image the activity of the brain related to a specific task. Generally, the active voxels in the IMRI images are detected using parametric or non-parametric statistical methods in which the fMRI response is assumed to have a model. Such methods are not applicable to detect the active voxels when the fMRI response is unknown. The data driven methods are also used for analyzing the fMRI data. The data driven methods are computationally expensive. In this study, a method for detecting activated voxels without using prior knowledge of the input stimulus is presented. The assumption in this method is that the activation typically involves larger region comprising of several voxels and that these neighboring activated voxels are also temporally correlated. To validate the accuracy of this method, Principal component Analysis and Independent Component Analysis are also performed. A significant overlap in the sensorimotor cortex is found between the various methods suggesting that the automatic detecting method presented does provide accurate detection and localization

    Advanced deep learning for medical image segmentation:Towards global and data-efficient learning

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    Advanced deep learning for medical image segmentation:Towards global and data-efficient learning

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    Dynamic Construction of Stimulus Values in the Ventromedial Prefrontal Cortex

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    Signals representing the value assigned to stimuli at the time of choice have been repeatedly observed in ventromedial prefrontal cortex (vmPFC). Yet it remains unknown how these value representations are computed from sensory and memory representations in more posterior brain regions. We used electroencephalography (EEG) while subjects evaluated appetitive and aversive food items to study how event-related responses modulated by stimulus value evolve over time. We found that value-related activity shifted from posterior to anterior, and from parietal to central to frontal sensors, across three major time windows after stimulus onset: 150–250 ms, 400–550 ms, and 700–800 ms. Exploratory localization of the EEG signal revealed a shifting network of activity moving from sensory and memory structures to areas associated with value coding, with stimulus value activity localized to vmPFC only from 400 ms onwards. Consistent with these results, functional connectivity analyses also showed a causal flow of information from temporal cortex to vmPFC. Thus, although value signals are present as early as 150 ms after stimulus onset, the value signals in vmPFC appear relatively late in the choice process, and seem to reflect the integration of incoming information from sensory and memory related regions
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