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Erin D. Bigler - One of the best experts on this subject based on the ideXlab platform.
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Neuroimaging and Neuropsychology
Physician's Field Guide to Neuropsychology, 2019Co-Authors: Erin D. BiglerAbstract:Neuroimaging has revolutionized all of the clinical neurosciences because of the exquisite manner in which the living brain can now be visualized (Bigler, Neuropsychol Rev 23(3):169–209, 2013; Kloppel et al. NeuroImage 61(2):457–463, 2012). But it is more than just neuroanatomy; contemporary Neuroimaging permits mapping of structure with function as well as some aspects of brain metabolism. The fact that the 1979 Nobel Prize in Physiology or Medicine involved computed tomography (CT) and the 2003 prize was for magnetic resonance imaging (MRI) is a testament of the contribution that these imaging techniques have made to medicine and neuroscience.
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Structural Neuroimaging in sport-related concussion.
International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 2017Co-Authors: Erin D. BiglerAbstract:Abstract Structural Neuroimaging of athletes who have sustained a sports-related concussion (SRC) can be viewed as either standard clinical imaging or with advanced Neuroimaging methods that quantitatively assess brain structure. Negative findings from conventional computed tomography (CT) or magnetic resonance imaging (MRI) are the norm in SRC. Nonetheless, these conventional measures remain the first line of Neuroimaging of the athlete as they do detect clinically significant pathologies, when present, such as hemorrhagic abnormalities in the form of hematomas, contusions and mircobleeds along with regions of focal encephalomalacia or other signal abnormalities, with CT best capable of detecting skull fractures. However, advanced Neuroimaging techniques hold particular promise in detecting subtle neuropathology in the athlete which standard clinical Neuroimaging cannot. To best understand what conventional as well as quantitative Neuroimaging methods are detecting in SRC, this review begins by covering basic neuroanatomical principles associated with mild traumatic brain injury (mTBI) and the brain regions most vulnerable to injury from SRC, as these regions define where advanced Neuroimaging methods most likely detect abnormalities. Advanced MRI techniques incorporate quantitative metrics that include volume, shape, thickness along with diffusion parameters that provide a more fine-grained analysis of brain structure. With advancements in image analysis, multiple quantitative Neuroimaging metrics now can be utilized in assessing SRC. Such multimodality approaches are particularly relevant and important for assessing white matter and network integrity of the brain following injury, including SRC. This review focuses just on the structural side of Neuroimaging in SRC, but these techniques also are being integrated with functional Neuroimaging, where the combination of the two approaches may provide superior methods in assessing the pathological effects of SRC.
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Neuroimaging and neuropathology of TBI.
NeuroRehabilitation, 2011Co-Authors: Erin D. Bigler, William L. MaxwellAbstract:Neuroimaging at all stages of a traumatic brain injury (TBI) provides information about gross brain pathology. In this review, post-mortem TBI cases are matched to Neuroimaging findings from TBI survivors to demonstrate the close correlation between observable pathology with in vivo Neuroimaging to the underlying neuropathology. An emphasis of this review focuses on Neuroimaging identification of trauma induced cortical and white matter degeneration along with hydrocephalus ex vacuo expansion of the ventricular system as the injured brain exhibits atrophic changes. The role of hippocampal atrophy and thalamic injury along with the vulnerability of the corpus callosum in TBI are also reviewed. The aim of this review is to provide pathological confirmation of observable Neuroimaging abnormalities that relate directly to trauma-induced effects of the injury.
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Neuroimaging in Mild Traumatic Brain Injury
Psychological Injury and Law, 2010Co-Authors: Erin D. BiglerAbstract:Neuroimaging in mild traumatic brain injury (mTBI) is reviewed. While computed tomography remains the acute standard for Neuroimaging of mTBI, it is only sensitive to gross abnormalities and is typically performed as a measure to rule out more serious and life-threatening injury. Magnetic resonance imaging (MRI), especially at field strength of 3.0 T, is the follow-up Neuroimaging standard for assessing potential underlying structural injury to the brain. Several MRI sequences are particularly sensitive to subtle hemorrhagic lesions and signal abnormalities in white matter, sensitive enough to detect pathology when present in mTBI. Clinical correlation of neuropsychological outcome with Neuroimaging findings is discussed along with the future potential for functional Neuroimaging in evaluating the mTBI patient.
Joseph C. Masdeu - One of the best experts on this subject based on the ideXlab platform.
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education research neuroradiology curriculum in neurology residency training programs how we teach Neuroimaging
Neurology, 2019Co-Authors: Paul Johnson, Joseph C. Masdeu, Stefan Sillau, Douglas E Ney, Pearce KorbAbstract:Objective To better understand how the essential skill of interpreting various Neuroimaging studies is taught to neurology residents in Accreditation Council for Graduate Medical Education (ACGME)-accredited training programs. Methods A 22-question survey was sent electronically to 150 ACGME adult neurology program directors. We collected data regarding the presence of a Neuroimaging curriculum, frequency of review sessions and testing, resource availability, and program director confidence in Neuroimaging skills of graduating residents. We collected average scores on the Neuroimaging section of the Resident In-service Training Examination of graduating residents for the past 3 years, which we attempted to correlate with resource availability. Results One-third of neurology residency programs do not have a Neuroimaging curriculum, and half of training programs do not require a Neuroimaging rotation. On average, trainees spend 1 hour per week reviewing imaging with radiologists. Program directors believed trainees receive insufficient Neuroimaging training, with a median satisfaction rating on a Likert scale (0–100) of 35 (interquartile range 27–47). Few programs take advantage of online training resources. Conclusion Opportunities exist to improve Neuroimaging education in neurology resident education. This can be done by closer adherence to the American Academy of Neurology Neuroimaging curriculum guidelines, especially by expanding access to online resources and additional emphasis on imaging review with neurology subspecialists.
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Neuroimaging curriculum for neurology trainees: report from the Neuroimaging Section of the AAN.
Journal of neuroimaging : official journal of the American Society of Neuroimaging, 2003Co-Authors: Rohit Bakshi, Andrei V. Alexandrov, Camilo R. Gomez, Joseph C. MasdeuAbstract:Abstract Neuroimaging plays a major role in the evaluation of patients with neurological disorders. Surveys of neurologists have revealed that most rely on their own readings of images for patient management, and a majority believe that neurologists should be allowed to officially interpret and bill for scan reviews. The importance of Neuroimaging training for neurology residents has been stressed by the Association of University Professors of Neurology. Although there is a desire to promote the Neuroimaging education of neurologists, no curricula have existed previously. The Neuroimaging Section of the American Academy of Neurology (AAN) developed a task force of practicing neuroimagers to provide a Neuroimaging curriculum for neurological trainees and training directors. The resulting curriculum is available on the Web sites of the AAN (http://www.aan.com) and the American Society of Neuroimaging (http://www.asnweb.org/education/curriculum.shtml) and will be updated as the need arises through evolving technology or breadth of applications. This curriculum should help in the design of neurology residency and fellowship programs and subspecialty pathways in which adequate Neuroimaging education and training are desired for various reasons, including certification and the demonstration of competency and proficiency.
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American Academy of Neurology Neuroimaging training guidelines
Neurology, 1997Co-Authors: Joseph C. MasdeuAbstract:These guidelines are intended to reflect the content of Neuroimaging training in many neurology programs. They emphasize the importance of Neuroimaging in the training of neurologists. These guidelines are not intended to replace or supplement the Essentials for Residency Training Programs promulgated by the Neurology Residency Review Committee of the Accreditation Council for Graduate Medical Education. 1. Background and scope of Neuroimaging * Neuroimaging is an integral part of the clinical evaluation of patients with neurologic disorders. Neuroimaging, unlike some of the more specialized neurophysiologic techniques, is often integrated in the neurologic evaluation and therefore is best learned by the residents and fellows as part of their clinical experience. * Three levels of competence in Neuroimaging can be defined: 1. Diagnostic expertise for most neurologic disorders. This level is generally achieved by the end of residency training in neurology. 2. Expertise in all clinical and basic aspects of a given modality. Depending on specific circumstances, this degree of expertise allows operation of an imaging laboratory independently. This level will usually require fellowship training. 3. Research expertise in a particular aspect of Neuroimaging. It is advisable for neurology residents and imperative for Neuroimaging fellows that they conduct research in some area of Neuroimaging. 2. Guidelines for Neuroimaging training during neurology residency * Given the central role of Neuroimaging in the practice of neurology, it is highly desirable that the residency training experience enables the graduate of a neurology program to interpret Neuroimaging studies of all common neurologic disorders. This experience may include all those modalities that are used in the practice of neurology (e.g., MRI, magnetic resonance angiography, CT, neurosonology). * Neuroimaging curriculum for the residency 1. Neuroimaging is best learned as an integrated aspect of the clinical evaluation of a given patient. Most of the Neuroimaging experience of the resident will be acquired during the 18 months of required adult …
Arthur W. Toga - One of the best experts on this subject based on the ideXlab platform.
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Human Neuroimaging as a "Big Data" science
Brain Imaging and Behavior, 2014Co-Authors: John Darrell Van Horn, Arthur W. TogaAbstract:The maturation of in vivo Neuroimaging has led to incredible quantities of digital information about the human brain. While much is made of the data deluge in science, Neuroimaging represents the leading edge of this onslaught of "big data". A range of Neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Yet few, if any, common solutions exist to support the science of Neuroimaging. In this article, we discuss how modern Neuroimaging research represents a multifactorial and broad ranging data challenge, involving the growing size of the data being acquired; sociological and logistical sharing issues; infrastructural challenges for multi-site, multi-datatype archiving; and the means by which to explore and mine these data. As Neuroimaging advances further, e.g. aging, genetics, and age-related disease, new vision is needed to manage and process this information while marshalling of these resources into novel results. Thus, "big data" can become "big" brain science.
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Human Neuroimaging as a “Big Data” science
Brain Imaging and Behavior, 2013Co-Authors: John Darrell Van Horn, Arthur W. TogaAbstract:The maturation of in vivo Neuroimaging has led to incredible quantities of digital information about the human brain. While much is made of the data deluge in science, Neuroimaging represents the leading edge of this onslaught of “big data”. A range of Neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Yet few, if any, common solutions exist to support the science of Neuroimaging. In this article, we discuss how modern Neuroimaging research represents a multifactorial and broad ranging data challenge, involving the growing size of the data being acquired; sociological and logistical sharing issues; infrastructural challenges for multi-site, multi-datatype archiving; and the means by which to explore and mine these data. As Neuroimaging advances further, e.g. aging, genetics, and age-related disease, new vision is needed to manage and process this information while marshalling of these resources into novel results. Thus, “big data” can become “big” brain science.
Daniel S Marcus - One of the best experts on this subject based on the ideXlab platform.
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Data sharing in Neuroimaging research
Frontiers in neuroinformatics, 2012Co-Authors: Jean-baptiste Poline, Krzysztof J. Gorgolewski, Janis L. Breeze, Satrajit S. Ghosh, Yaroslav O. Halchenko, Michael Hanke, Christian Haselgrove, Karl G. Helmer, David Keator, Daniel S MarcusAbstract:Significant resources around the world have been invested in Neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of Neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for Neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing Neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived Neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of Neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of Neuroimaging.
Max Coltheart - One of the best experts on this subject based on the ideXlab platform.
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How Can Functional Neuroimaging Inform Cognitive Theories
Perspectives on Psychological Science, 2013Co-Authors: Max ColtheartAbstract:Work on functional Neuroimaging of cognition falls into two categories. The first aims at localizing specific cognitive subsystems in specific brain regions. In this research, the cognitive subsystems in question need to be defined independently of the Neuroimaging data because the interpretation of the data requires such definition; so functional Neuroimaging is informed by cognitive theories rather than informing them. The second category uses Neuroimaging data to test cognitive theories. As cognitive theories are expressed in cognitive terms, such theories have to be embellished by explicit proposals about relationships between cognition and the brain if they are to become capable of generating predictions about the results of experiments that use functional Neuroimaging. Whether functional Neuroimaging can succeed in informing a cognitive theory depends critically upon the plausibility of such supplementary proposals. It is also critical to avoid the "consistency fallacy." When Neuroimaging data from an experiment are consistent with predictions from a particular cognitive theory, this cannot be offered as evidence in support of that theory unless it can be shown that there were possible other outcomes of the experiment that are inconsistent with the theory-outcomes that would have falsified predictions from the theory had they been obtained.
