1,387 research outputs found
Genetic and Neuroanatomical Support for Functional Brain Network Dynamics in Epilepsy
Focal epilepsy is a devastating neurological disorder that affects an
overwhelming number of patients worldwide, many of whom prove resistant to
medication. The efficacy of current innovative technologies for the treatment
of these patients has been stalled by the lack of accurate and effective
methods to fuse multimodal neuroimaging data to map anatomical targets driving
seizure dynamics. Here we propose a parsimonious model that explains how
large-scale anatomical networks and shared genetic constraints shape
inter-regional communication in focal epilepsy. In extensive ECoG recordings
acquired from a group of patients with medically refractory focal-onset
epilepsy, we find that ictal and preictal functional brain network dynamics can
be accurately predicted from features of brain anatomy and geometry, patterns
of white matter connectivity, and constraints complicit in patterns of gene
coexpression, all of which are conserved across healthy adult populations.
Moreover, we uncover evidence that markers of non-conserved architecture,
potentially driven by idiosyncratic pathology of single subjects, are most
prevalent in high frequency ictal dynamics and low frequency preictal dynamics.
Finally, we find that ictal dynamics are better predicted by white matter
features and more poorly predicted by geometry and genetic constraints than
preictal dynamics, suggesting that the functional brain network dynamics
manifest in seizures rely on - and may directly propagate along - underlying
white matter structure that is largely conserved across humans. Broadly, our
work offers insights into the generic architectural principles of the human
brain that impact seizure dynamics, and could be extended to further our
understanding, models, and predictions of subject-level pathology and response
to intervention
A Comparison of Neuroelectrophysiology Databases
As data sharing has become more prevalent, three pillars - archives,
standards, and analysis tools - have emerged as critical components in
facilitating effective data sharing and collaboration. This paper compares four
freely available intracranial neuroelectrophysiology data repositories: Data
Archive for the BRAIN Initiative (DABI), Distributed Archives for
Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. These
archives provide researchers with tools to store, share, and reanalyze
neurophysiology data though the means of accomplishing these objectives differ.
The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are
utilized by these archives to make data more accessible to researchers by
implementing a common standard. While many tools are available to reanalyze
data on and off the archives' platforms, this article features Reproducible
Analysis and Visualization of Intracranial EEG (RAVE) toolkit, developed
specifically for the analysis of intracranial signal data and integrated with
the discussed standards and archives. Neuroelectrophysiology data archives
improve how researchers can aggregate, analyze, distribute, and parse these
data, which can lead to more significant findings in neuroscience research.Comment: 25 pages, 8 figures, 1 tabl
BrainStat: A toolbox for brain-wide statistics and multimodal feature associations
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation
Standards for data acquisition and software‐based analysis of in vivo electroencephalography recordings from animals. A TASK1‐WG5 report of the AES/ILAE Translational Task Force of the ILAE
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/1/epi13909.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139127/2/epi13909_am.pd
Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data
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