169,946 research outputs found
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
The charm of structural neuroimaging in insanity evaluations. guidelines to avoid misinterpretation of the findings
Despite the popularity of structural neuroimaging techniques in twenty-first-century research, its results have had limited translational impact in real-world settings, where inferences need to be made at the individual level. Structural neuroimaging methods are now introduced frequently to aid in assessing defendants for insanity in criminal forensic evaluations, with the aim of providing âconvergenceâ of evidence on the mens rea of the defendant. This approach may provide pivotal support for judgesâ decisions. Although neuroimaging aims to reduce uncertainty and controversies in legal settings and to increase the objectivity of criminal rulings, the application of structural neuroimaging in forensic settings is hampered by cognitive biases in the evaluation of evidence that lead to misinterpretation of the imaging results. It is thus increasingly important to have clear guidelines on the correct ways to apply and interpret neuroimaging evidence. In the current paper, we review the literature concerning structural neuroimaging in court settings with the aim of identifying rules for its correct application and interpretation. These rules, which aim to decrease the risk of biases, focus on the importance of (i) descriptive diagnoses, (ii) anatomo-clinical correlation, (iii) brain plasticity and (iv) avoiding logical fallacies, such as reverse inference. In addition, through the analysis of real forensic cases, we describe errors frequently observed due to incorrect interpretations of imaging. Clear guidelines for both the correct circumstances for introducing neuroimaging and its eventual interpretation are defined
The mechanisms of tinnitus: perspectives from human functional neuroimaging
In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural substrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power
Neuroimaging Research into Disorders of Consciousness: Moral Imperative or Ethical and Legal Failure?
This article explores the ethical and legal implications of enrolling individuals with disorders of consciousness (DOC) in neuroimaging research studies. Many scientists have strongly emphasized the need for additional neuroimaging research into DOC, characterizing the conduct of such studies as morally imperative. On the other hand, institutional review boards charged with approving research protocols, scientific journals deciding whether to publish study results, and federal agencies that disburse grant money have limited the conduct, publication, and funding of consciousness investigations based on ethical and legal concerns. Following a detailed examination of the risks and benefits of neuroimaging research involving individuals with DOC, the author urges IRBs, scientific journals, and funding agencies to no longer stall the conduct, publication, and funding of neuroimaging research into DOC if certain criteria designed to protect the health and safety of individuals with DOC are satisfied
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Capacity and tendency: A neuroscientific framework for the study of emotion regulation.
It is widely accepted that the ability to effectively regulate one's emotions is a cornerstone of physical and mental health. As such, it should come as no surprise that the number of neuroimaging studies focused on emotion regulation and associated processes has increased exponentially in the past decade. To date, neuroimaging research on this topic has examined two distinct but complementary features of emotion regulation - the capacity to effectively utilize a strategy to regulate emotion and to a lesser extent, the tendency to choose to regulate. However, theoretical accounts of emotion regulation have only recently begun to distinguish capacity from tendency. In the present review, we provide a novel framework for conceptualizing these two intertwined, yet distinct, facets of emotion regulation. First we characterize brain regions that support emotion generation and are thus targeted by emotion regulation. Next, we synthesize findings from the dozens of neuroimaging studies that have examined emotion regulation capacity, focusing in particular on the most commonly studied emotion regulation strategy - reappraisal. Finally, we discuss emerging neuroimaging research examining state and trait regulatory tendencies. We conclude by integrating findings from neuroimaging research on emotion regulation capacity and tendency and suggest ways that this integrated model can inform basic and translational neuroscientific research on emotion regulation
Random forest prediction of Alzheimer's disease using pairwise selection from time series data
Time-dependent data collected in studies of Alzheimer's disease usually has
missing and irregularly sampled data points. For this reason time series
methods which assume regular sampling cannot be applied directly to the data
without a pre-processing step. In this paper we use a machine learning method
to learn the relationship between pairs of data points at different time
separations. The input vector comprises a summary of the time series history
and includes both demographic and non-time varying variables such as genetic
data. The dataset used is from the 2017 TADPOLE grand challenge which aims to
predict the onset of Alzheimer's disease using including demographic, physical
and cognitive data. The challenge is a three-fold diagnosis classification into
AD, MCI and control groups, the prediction of ADAS-13 score and the normalised
ventricle volume. While the competition proceeds, forecasting methods may be
compared using a leaderboard dataset selected from the Alzheimer's Disease
Neuroimaging Initiative (ADNI) and with standard metrics for measuring
accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy
of 0.73. The results show that the method is effective and comparable with
other methods.Comment: 6 pages, 1 figure, 6 table
1st INCF Workshop on NeuroImaging Database Integration
The goal of this meeting was to map existing neuroimaging databases, particularly databases containing primary data, and to identify mechanisms that could facilitate integrated use of such databases, including possible fusion of databases. The report provides an overview of existing neuroimaging databases that were discussed during the workshop and examines the feasibility of database federations. The report includes several recommendations for future developments
Neuroimaging and Eyewitness Testimony
This paper will explore how breakthroughs in neuroscience, specifically neuroimaging, can be used to validate eyewitness testimony. Though the use of direct evidence is decreasing, due to findings of numerous wrongful convictions that were based on eyewitness testimonies, it is still an element of many criminal trials today. Cross-examination is used to validate eyewitness testimony because memories are fallible. Cross-examination can successfully determine if a witness is telling the truth, but it cannot determine if a memory is true. This has resulted in juries convicting individuals based on questionable eyewitness testimony. Neuroscientists have found that neuroimaging methods, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) scans, can be used to distinguish between true and false memories and can determine if a witness is telling the truth. Both prosecutors and defense attorneys alike stand to benefit from using neuroimaging to validate eyewitness testimony that is brought into trial. Though the jury can use neuroimaging evidence to more accurately assess eyewitness testimony, as with all scientific data, the jury should be properly instructed when neuroimages are used, in order to reduce the prejudicial value of the evidence
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