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  • Neuroinformatics
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Terri L. Petkau; Jake Blanco; Blair R. Leavitt;

    Progranulin deficiency due to heterozygous null mutations in the GRN gene is a common cause of familial frontotemporal lobar degeneration (FTLD), while homozygous loss-of-function GRN mutations cause neuronal ceroid lipofuscinosis (NCL). Aged progranulin-knockout mice display highly exaggerated lipofuscinosis, microgliosis, and astrogliosis, as well as mild cell loss in specific brain regions. Progranulin is a secreted glycoprotein expressed in both neurons and microglia, but not astrocytes, in the brain. We generated conditional progranulin-knockout mice that lack progranulin in nestin-expressing cells (Nes-cKO mice), which include most neurons as well as astrocytes. We confirmed near complete knockout of progranulin in neurons in Nes-cKO mice, while microglial progranulin levels remained similar to that of wild-type animals. Overall brain progranulin levels were reduced by about 50% in Nes-cKO, and no Grn was detected in primary Nes-cKO neurons. Nes-cKO mice aged to 12 months did not display any increase in lipofuscin deposition, microgliosis, or astrogliosis in the four brain regions examined, though increases were observed for most of these measures in Grn-null animals. We conclude that neuron-specific loss of progranulin is not sufficient to cause similar neuropathological changes to those seen in constitutive Grn-null animals. Our results suggest that increased lipofuscinosis and gliosis in Grn-null animals are not caused by intrinsic progranulin deficiency in neurons, and that microglia-derived progranulin may be sufficient to maintain neuronal health and homeostasis in the brain.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Neurobiology of Dise...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Neurobiology of Disease
    Article . 2017 . Peer-reviewed
    License: Elsevier TDM
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DOAJ-Articles
    Article . 2017
    Data sources: DOAJ-Articles
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Neurobiology of Dise...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Neurobiology of Disease
      Article . 2017 . Peer-reviewed
      License: Elsevier TDM
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DOAJ-Articles
      Article . 2017
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Xia, Frances; Kheirbek, Mazen A.;

    Mood and anxiety disorders are complex heterogeneous syndromes that manifest in dysfunctions across multiple brain regions, cell types, and circuits. Biomarkers using brain-wide activity patterns in humans have proven useful in distinguishing between disorder subtypes and identifying effective treatments. In order to improve biomarker identification, it is crucial to understand the basic circuitry underpinning brain-wide activity patterns. Leveraging a large repertoire of techniques, animal studies have examined roles of specific cell types and circuits in driving maladaptive behavior. Recent advances in multiregion recording techniques, data-driven analysis approaches, and machine-learning-based behavioral analysis tools can further push the boundary of animal studies and bridge the gap with human studies, to assess how brain-wide activity patterns encode and drive emotional behavior. Together, these efforts will allow identifying more precise biomarkers to enhance diagnosis and treatment.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Other literature type . 2020
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    Trends in Neurosciences
    Article . 2020 . Peer-reviewed
    License: Elsevier TDM
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      Europe PubMed Central
      Other literature type . 2020
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Trends in Neurosciences
      Article . 2020 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: B. Ogan Mancarci; Lilah Toker; Shreejoy J. Tripathy; Brenna Li; +3 Authors

    Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single-cell RNA-sequencing (RNA-seq) studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets (MGSs) in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at www.neuroexpresso.org. Visual Abstract

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2017
    Data sources: PubMed Central
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    bioRxiv
    Preprint . 2016
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    eNeuro
    Article . Preprint
    License: CC BY
    Data sources: UnpayWall
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    eNeuro
    Article . 2017 . Peer-reviewed
    License: CC BY NC SA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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      Europe PubMed Central
      Article . 2017
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      bioRxiv
      Preprint . 2016
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      eNeuro
      Article . Preprint
      License: CC BY
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      eNeuro
      Article . 2017 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ryan M. Jones; Lulu Deng; Kogee Leung; Dallan McMahon; +2 Authors

    Focused ultrasound (FUS)-mediated blood-brain barrier (BBB) opening recently entered clinical testing for targeted drug delivery to the brain. Sources of variability exist in the current procedures, motivating the development of real-time monitoring and control techniques to improve treatment safety and efficacy. Here we used three-dimensional (3D) transcranial microbubble imaging to calibrate FUS exposure levels for volumetric BBB opening. Methods: Using a sparse hemispherical transmit/receive ultrasound phased array, pulsed ultrasound was focused transcranially into the thalamus of rabbits during microbubble infusion and multi-channel 3D beamforming was performed online with receiver signals captured at the subharmonic frequency. Pressures were increased pulse-by-pulse until subharmonic activity was detected on acoustic imaging (psub), and tissue volumes surrounding the calibration point were exposed at 50-100%psub via rapid electronic beam steering. Results: Spatially-coherent subharmonic microbubble activity was successfully reconstructed transcranially in vivo during calibration sonications. Multi-point exposures induced volumetric regions of elevated BBB permeability assessed via contrast-enhanced magnetic resonance imaging (MRI). At exposure levels ≥75%psub, MRI and histological examination occasionally revealed tissue damage, whereas sonications at 50%psub were performed safely. Substantial intra-grid variability of FUS-induced bioeffects was observed via MRI, prompting future development of multi-point calibration schemes for improved treatment consistency. Receiver array sparsity and sensor configuration had substantial impacts on subharmonic detection sensitivity, and are factors that should be considered when designing next-generation clinical FUS brain therapy systems. Conclusion: Our findings suggest that 3D subharmonic imaging can be used to calibrate exposure levels for safe FUS-induced volumetric BBB opening, and should be explored further as a method for cavitation-mediated treatment guidance.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2018
    Data sources: PubMed Central
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    Europe PubMed Central
    Article . 2018
    Data sources: PubMed Central
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Theranostics
    Article . 2018 . Peer-reviewed
    Data sources: Crossref
    https://pubmed.ncbi.nlm.nih.go...
    Other literature type . 2018
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      Europe PubMed Central
      Article . 2018
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      Europe PubMed Central
      Article . 2018
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      Theranostics
      Article . 2018 . Peer-reviewed
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      https://pubmed.ncbi.nlm.nih.go...
      Other literature type . 2018
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    Authors: Eskildsen, S.F.; Coupe, P.; Fonov, V.; Manjon, J.V.; +8 Authors

    Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834 ± 0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781 ± 0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514, and the Dana Foundation.

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    Other literature type . Article . 2012
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    Article . 2011
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      Other literature type . Article . 2012
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      Article . 2011
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    Authors: Elena, Choleris; Liisa A M, Galea; Farida, Sohrabji; Karyn M, Frick;

    Abstract Biological differences between males and females are found at multiple levels. However, females have too often been under-represented in behavioral neuroscience research, which has stymied the study of potential sex differences in neurobiology and behavior. This review focuses on the study of sex differences in the neurobiology of social behavior, memory, emotions, and recovery from brain injury, with particular emphasis on the role of estrogens in regulating forebrain function. This work, presented by the authors at the 2016 meeting of the International Behavioral Neuroscience Society, emphasizes varying approaches from several mammalian species in which sex differences have not only been documented, but also become the focus of efforts to understand the mechanistic basis underlying them. This information may provide readers with useful experimental tools to successfully address recently introduced regulations by granting agencies that either require (e.g. the National Institutes of Health in the United States and the Canadian Institutes of Health Research in Canada) or recommend (e.g. Horizon 2020 in Europe) the inclusion of both sexes in biomedical research.

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    Europe PubMed Central
    Other literature type . 2018
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    Neuroscience & Biobehavioral Reviews
    Article . 2018 . Peer-reviewed
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      Europe PubMed Central
      Other literature type . 2018
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      Neuroscience & Biobehavioral Reviews
      Article . 2018 . Peer-reviewed
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    Authors: Parikshat Sirpal; Rafat Damseh; Ke Peng; Dang Khoa Nguyen; +1 Authors

    AbstractIn this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model’s fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

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    Neuroinformatics
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    Neuroinformatics; PolyPublie
    Other literature type . Article . 2021 . 2022 . Peer-reviewed
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      Other literature type . Article . 2021 . 2022 . Peer-reviewed
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    Authors: Hatton, Sean N; Huynh, Khoa H; Bonilha, Leonardo; Abela, Eugenio; +70 Authors

    AbstractThe epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analyzed from 1,069 non-epileptic controls and 1,249 patients: temporal lobe epilepsy with hippocampal sclerosis (N=599), temporal lobe epilepsy with normal MRI (N=275), genetic generalized epilepsy (N=182) and nonlesional extratemporal epilepsy (N=193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fiber tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at p<0.001). Across “all epilepsies” lower fractional anisotropy was observed in most fiber tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. Less robust effects were seen with mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Those with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced differences in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and in mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of microstructural abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibers in a large multicentre study of epilepsy. Overall, epilepsy patients showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding new insights into pathological substrates that may be used to guide future therapeutic and genetic studies.

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    bioRxiv
    Preprint . 2019
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      bioRxiv
      Preprint . 2019
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    Authors: Liang Wang; Yanfang Li; Paul D. Metzak; Yong He; +1 Authors

    In this study we used functional magnetic resonance imaging to investigate age-related changes in large-scale brain functional networks during memory encoding and recognition in 12 younger and 16 older adults. For each participant, functional brain networks were constructed by computing temporal correlation matrices of 90 brain regions and analyzed using graph theoretical approaches. We found the age-related changes mainly in the long-range connections with widespread reductions associated with aging in the fronto-temporal and temporo-parietal regions, and a few age-related increases in the posterior parietal regions. Graph theoretical analysis revealed that the older adults had longer path lengths linking different regions in the functional brain networks as compared to the younger adults. Further analysis indicated that the increases in shortest path length in the networks were combined with the loss of long-range connections. Finally, we showed that for older adults, frontal areas played reduced roles in the network (reduced regional centrality), whereas several default-mode regions played increased roles relative to younger subjects (increased regional centrality). Together, our results suggest that normal aging is associated with disruption of large-scale brain systems during the performance of memory tasks, which provides novel insights into the understanding of age-related decline in multiple cognitive functions.

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    NeuroImage
    Article . 2010 . Peer-reviewed
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    NeuroImage
    Article . 2009
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      NeuroImage
      Article . 2010 . Peer-reviewed
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      NeuroImage
      Article . 2009
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    Authors: Parikshat Sirpal; Ali Kassab; Philippe Pouliot; Dang Khoa Nguyen; +1 Authors

    Abstract. In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database—a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2019
    Data sources: PubMed Central
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    Journal of Biomedical Optics; PolyPublie
    Other literature type . Article . 2019 . Peer-reviewed
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    Journal of Biomedical Optics
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      Journal of Biomedical Optics; PolyPublie
      Other literature type . Article . 2019 . Peer-reviewed
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      Journal of Biomedical Optics
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Terri L. Petkau; Jake Blanco; Blair R. Leavitt;

    Progranulin deficiency due to heterozygous null mutations in the GRN gene is a common cause of familial frontotemporal lobar degeneration (FTLD), while homozygous loss-of-function GRN mutations cause neuronal ceroid lipofuscinosis (NCL). Aged progranulin-knockout mice display highly exaggerated lipofuscinosis, microgliosis, and astrogliosis, as well as mild cell loss in specific brain regions. Progranulin is a secreted glycoprotein expressed in both neurons and microglia, but not astrocytes, in the brain. We generated conditional progranulin-knockout mice that lack progranulin in nestin-expressing cells (Nes-cKO mice), which include most neurons as well as astrocytes. We confirmed near complete knockout of progranulin in neurons in Nes-cKO mice, while microglial progranulin levels remained similar to that of wild-type animals. Overall brain progranulin levels were reduced by about 50% in Nes-cKO, and no Grn was detected in primary Nes-cKO neurons. Nes-cKO mice aged to 12 months did not display any increase in lipofuscin deposition, microgliosis, or astrogliosis in the four brain regions examined, though increases were observed for most of these measures in Grn-null animals. We conclude that neuron-specific loss of progranulin is not sufficient to cause similar neuropathological changes to those seen in constitutive Grn-null animals. Our results suggest that increased lipofuscinosis and gliosis in Grn-null animals are not caused by intrinsic progranulin deficiency in neurons, and that microglia-derived progranulin may be sufficient to maintain neuronal health and homeostasis in the brain.

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    Neurobiology of Disease
    Article . 2017 . Peer-reviewed
    License: Elsevier TDM
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    DOAJ-Articles
    Article . 2017
    Data sources: DOAJ-Articles
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      Neurobiology of Disease
      Article . 2017 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DOAJ-Articles
      Article . 2017
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    Authors: Xia, Frances; Kheirbek, Mazen A.;

    Mood and anxiety disorders are complex heterogeneous syndromes that manifest in dysfunctions across multiple brain regions, cell types, and circuits. Biomarkers using brain-wide activity patterns in humans have proven useful in distinguishing between disorder subtypes and identifying effective treatments. In order to improve biomarker identification, it is crucial to understand the basic circuitry underpinning brain-wide activity patterns. Leveraging a large repertoire of techniques, animal studies have examined roles of specific cell types and circuits in driving maladaptive behavior. Recent advances in multiregion recording techniques, data-driven analysis approaches, and machine-learning-based behavioral analysis tools can further push the boundary of animal studies and bridge the gap with human studies, to assess how brain-wide activity patterns encode and drive emotional behavior. Together, these efforts will allow identifying more precise biomarkers to enhance diagnosis and treatment.

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    Europe PubMed Central
    Other literature type . 2020
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