110 research outputs found
The Company Prosodic Deficits Keep Following Right Hemisphere Stroke: A Systematic Review
Objectives:
The aim of this systematic review was to identify the presence and nature of relationships between specific forms of aprosodia (i.e., expressive and receptive emotional and linguistic prosodic deficits) and other cognitive-communication deficits and disorders in individuals with right hemisphere damage (RHD) due to stroke.
Methods:
One hundred and ninety articles from 1970 to February 2020 investigating receptive and expressive prosody in patients with relatively focal right hemisphere brain damage were identified via database searches.
Results:
Fourteen articles were identified that met inclusion criteria, passed quality reviews, and included sufficient information about prosody and potential co-occurring deficits. Twelve articles investigated receptive emotional aprosodia, and two articles investigated receptive linguistic aprosodia. Across the included studies, receptive emotional prosody was not systematically associated with hemispatial neglect, but did co-occur with deficits in emotional facial recognition, interpersonal interactions, or emotional semantics. Receptive linguistic processing was reported to co-occur with amusia and hemispatial neglect. No studies were found that investigated the co-occurrence of expressive emotional or linguistic prosodic deficits with other cognitive-communication impairments.
Conclusions:
This systematic review revealed significant gaps in the research literature regarding the co-occurrence of common right hemisphere disorders with prosodic deficits. More rigorous empirical inquiry is required to identify specific patient profiles based on clusters of deficits associated with right hemisphere stroke. Future research may determine whether the co-occurrences identified are due to shared cognitive-linguistic processes, and may inform the development of evidence-based assessment and treatment recommendations for individuals with cognitive-communication deficits subsequent to RHD
Towards structured sharing of raw and derived neuroimaging data across existing resources
Data sharing efforts increasingly contribute to the acceleration of
scientific discovery. Neuroimaging data is accumulating in distributed
domain-specific databases and there is currently no integrated access mechanism
nor an accepted format for the critically important meta-data that is necessary
for making use of the combined, available neuroimaging data. In this
manuscript, we present work from the Derived Data Working Group, an open-access
group sponsored by the Biomedical Informatics Research Network (BIRN) and the
International Neuroimaging Coordinating Facility (INCF) focused on practical
tools for distributed access to neuroimaging data. The working group develops
models and tools facilitating the structured interchange of neuroimaging
meta-data and is making progress towards a unified set of tools for such data
and meta-data exchange. We report on the key components required for integrated
access to raw and derived neuroimaging data as well as associated meta-data and
provenance across neuroimaging resources. The components include (1) a
structured terminology that provides semantic context to data, (2) a formal
data model for neuroimaging with robust tracking of data provenance, (3) a web
service-based application programming interface (API) that provides a
consistent mechanism to access and query the data model, and (4) a provenance
library that can be used for the extraction of provenance data by image
analysts and imaging software developers. We believe that the framework and set
of tools outlined in this manuscript have great potential for solving many of
the issues the neuroimaging community faces when sharing raw and derived
neuroimaging data across the various existing database systems for the purpose
of accelerating scientific discovery
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
Behavioral changes are the earliest signs of a mental disorder, but arguably,
the dynamics of brain function gets affected even earlier. Subsequently,
spatio-temporal structure of disorder-specific dynamics is crucial for early
diagnosis and understanding the disorder mechanism. A common way of learning
discriminatory features relies on training a classifier and evaluating feature
importance. Classical classifiers, based on handcrafted features are quite
powerful, but suffer the curse of dimensionality when applied to large input
dimensions of spatio-temporal data. Deep learning algorithms could handle the
problem and a model introspection could highlight discriminatory
spatio-temporal regions but need way more samples to train. In this paper we
present a novel self supervised training schema which reinforces whole sequence
mutual information local to context (whole MILC). We pre-train the whole MILC
model on unlabeled and unrelated healthy control data. We test our model on
three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers
and four different studies. Our algorithm outperforms existing self-supervised
pre-training methods and provides competitive classification results to
classical machine learning algorithms. Importantly, whole MILC enables
attribution of subject diagnosis to specific spatio-temporal regions in the
fMRI signal.Comment: Accepted at MICCAI 2020. arXiv admin note: substantial text overlap
with arXiv:1912.0313
BioTorrents: A File Sharing Service for Scientific Data
The transfer of scientific data has emerged as a significant challenge, as datasets continue to grow in size and demand for open access sharing increases. Current methods for file transfer do not scale well for large files and can cause long transfer times. In this study we present BioTorrents, a website that allows open access sharing of scientific data and uses the popular BitTorrent peer-to-peer file sharing technology. BioTorrents allows files to be transferred rapidly due to the sharing of bandwidth across multiple institutions and provides more reliable file transfers due to the built-in error checking of the file sharing technology. BioTorrents contains multiple features, including keyword searching, category browsing, RSS feeds, torrent comments, and a discussion forum. BioTorrents is available at http://www.biotorrents.net
The impact of hypsarrhythmia on infantile spasms treatment response: Observational cohort study from the National Infantile Spasms Consortium
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141801/1/epi13937_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141801/2/epi13937.pd
The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data
<p>Abstract</p> <p>Background</p> <p>Neuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.</p> <p>Findings</p> <p>In this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.</p> <p>Conclusions</p> <p>The BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.</p
The Function Biomedical Informatics Research Network Data Repository
The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical datasets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 dataset consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 Tesla scanners. The FBIRN Phase 2 and Phase 3 datasets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN’s multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data
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