156 research outputs found

    Support vector machine classification of arterial volumeâ weighted arterial spin tagging images

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    IntroductionIn recent years, machineâ learning techniques have gained growing popularity in medical image analysis. Temporal brainâ state classification is one of the major applications of machineâ learning techniques in functional magnetic resonance imaging (fMRI) brain data. This article explores the use of support vector machine (SVM) classification technique with motorâ visual activation paradigm to perform brainâ state classification into activation and rest with an emphasis on different acquisition techniques.MethodsImages were acquired using a recently developed variant of traditional pseudocontinuous arterial spin labeling technique called arterial volumeâ weighted arterial spin tagging (AVAST). The classification scheme is also performed on images acquired using blood oxygenationâ level dependent (BOLD) and traditional perfusionâ weighted arterial spin labeling (ASL) techniques for comparison.ResultsThe AVAST technique outperforms traditional pseudocontinuous ASL, achieving classification accuracy comparable to that of BOLD contrast images.ConclusionThis study demonstrates that AVAST has superior signalâ toâ noise ratio and improved temporal resolution as compared with traditional perfusionâ weighted ASL and reduced sensitivity to scanner drift as compared with BOLD. Owing to these characteristics, AVAST lends itself as an ideal choice for dynamic fMRI and realâ time neurofeedback experiments with sustained activation periods.In this article, we test the performance of our recently introduced method for dynamic arterial blood volume imaging (AVAST) in the context of functional MRI data classification. AVAST is compared with blood oxygenationâ level dependent (BOLD) and arterial spin labeling (ASL) perfusion data collected during a simple motor task using a support vector machine algorithm to classify the brain state. Findings suggest that the AVAST technique has similar performance as BOLD imaging, while preserving the statistical benefits of ASL techniques.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135476/1/brb3549_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135476/2/brb3549.pd

    Age Differences in Interhemispheric Interactions: Callosal Structure, Physiological Function, and Behavior

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    There is a fundamental gap in understanding how brain structural and functional network connectivity are interrelated, how they change with age, and how such changes contribute to older adults’ sensorimotor deficits. Recent neuroimaging approaches including resting state functional connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) have been used to assess brain functional (fcMRI) and structural (DTI) network connectivity, allowing for more integrative assessments of distributed neural systems than in the past. Declines in corpus callosum size and microstructure with advancing age have been well documented, but their contributions to age deficits in unimanual and bimanual function are not well defined. Our recent work implicates age-related declines in callosal size and integrity as a key contributor to unimanual and bimanual control deficits. Moreover, our data provide evidence for a fundamental shift in the balance of excitatory and inhibitory interhemispheric processes that occurs with age, resulting in age differences in the relationship between functional and structural network connectivity. Training studies suggest that the balance of interhemispheric interactions can be shifted with experience, making this a viable target for future interventions

    Resting state cortico-cerebellar functional connectivity networks: a comparison of anatomical and self-organizing map approaches.

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    The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Krienen and Buckner, 2009; O'Reilly et al., 2010; Buckner et al., 2011). However, none of this work has taken an anatomically-driven lobular approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., 2011), it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven connectivity atlas of the cerebellar lobules. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into "motor" and "non-motor" regions. We also used a self-organizing map (SOM) algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our SOM algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not necessarily indicative of functional boundaries, though anatomical divisions can be useful. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure

    Lifespan Differences in Cortico-Striatal Resting State Connectivity

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    Distinctive cortico-striatal circuits that serve motor and cognitive functions have been recently mapped based on resting state connectivity. It has been reported that age differences in cortico-striatal connectivity relate to cognitive declines in aging. Moreover, children in their early teens (i.e., youth) already show mature motor network patterns while their cognitive networks are still developing. In the current study, we examined age differences in the frontal-striatal ?cognitive? and ?motor? circuits in children and adolescence, young adults (YAs), and older adults (OAs). We predicted that the strength of the ?cognitive? frontal-striatal circuits would follow an inverted ?U? pattern across age; children and OAs would have weaker connectivity than YAs. However, we predicted that the ?motor? circuits would show less variation in connectivity strength across the lifespan. We found that most areas in both the ?cognitive? and ?motor? circuits showed higher connectivity in YAs than children and OAs, suggesting general inverted ?U?-shaped changes across the lifespan for both the cognitive and motor frontal-striatal networks.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140317/1/brain.2013.0155.pd

    Response of a climate model to tidal mixing parameterization under present day and last glacial maximum conditions

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    Author Posting. © Elsevier B.V., 2007. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Ocean Modelling 19 (2007): 125-137, doi:10.1016/j.ocemod.2007.06.009.Experiments with a climate model were conducted under present day and last glacial maximum conditions in order to examine the model’s response to a vertical mixing scheme based on internal tide energy dissipation. The increase in internal tide energy flux caused by a 120 m reduction in sea level had the expected effect on diffusivity values, which were higher under lower sea level conditions. The impact of this vertical diffusivity change on the Atlantic meridional overturning is not straightforward and no clear relationship between diffusivity and overturning is found. There exists a weak positive correlation between overturning and changes to the power consumed by vertical mixing. Most of the climatic response generated by sea level change was not related to alterations in the internal tide energy flux but rather to the direct change in sea level itself.Funding received from CFCAS through the CLIVAR and Polar Climate Stability Research networks. SRJ was supported by the U.S. National Science Foundation under Grant No. OCE-0241061

    Continent-Wide Survey Reveals Massive Decline in African Savannah Elephants

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    African elephants (Loxodonta africana) are imperiled by poaching and habitat loss. Despite global attention to the plight of elephants, their population sizes and trends are uncertain or unknown over much of Africa. To conserve this iconic species, conservationists need timely, accurate data on elephant populations. Here, we report the results of the Great Elephant Census (GEC), the first continent-wide, standardized survey of African savannah elephants. We also provide the first quantitative model of elephant population trends across Africa. We estimated a population of 352,271 savannah elephants on study sites in 18 countries, representing approximately 93% of all savannah elephants in those countries. Elephant populations in survey areas with historical data decreased by an estimated 144,000 from 2007 to 2014, and populations are currently shrinking by 8% per year continent-wide, primarily due to poaching. Though 84% of elephants occurred in protected areas, many protected areas had carcass ratios that indicated high levels of elephant mortality. Results of the GEC show the necessity of action to end the African elephants’ downward trajectory by preventing poaching and protecting habitat
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