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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: Shi, Yundi; Budin, Francois; Yapuncich, Eva; Rumple, Ashley; +8 Authors

    Computational anatomical atlases have shown to be of immense value in neuroimaging as they provide age appropriate reference spaces alongside ancillary anatomical information for automated analysis such as subcortical structural definitions, cortical parcellations or white fiber tract regions. Standard workflows in neuroimaging necessitate such atlases to be appropriately selected for the subject population of interest. This is especially of importance in early postnatal brain development, where rapid changes in brain shape and appearance render neuroimaging workflows sensitive to the appropriate atlas choice. We present here a set of novel computation atlases for structural MRI and Diffusion Tensor Imaging as crucial resource for the analysis of MRI data from non-human primate rhesus monkey (Macaca mulatta) data in early postnatal brain development. Forty socially-housed infant macaques were scanned longitudinally at ages 2 weeks, 3, 6, and 12 months in order to create cross-sectional structural and DTI atlases via unbiased atlas building at each of these ages. Probabilistic spatial prior definitions for the major tissue classes were trained on each atlas with expert manual segmentations. In this article we present the development and use of these atlases with publicly available tools, as well as the atlases themselves, which are publicly disseminated to the scientific community.

    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/ Frontiers in Neurosc...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/
    Frontiers in Neuroscience
    2017 . Peer-reviewed
    Data sources: Frontiers
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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/ Frontiers in Neurosc...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/
      Frontiers in Neuroscience
      2017 . Peer-reviewed
      Data sources: Frontiers
  • 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: Bernal-Rusiel, Jorge L.; Rannou, Nicolas; Gollub, Randy L.; Pieper, Steve; +4 Authors

    In this paper we present a web-based software solution to the problem of implementing real-time collaborative neuroimage visualization. In both clinical and research settings, simple and powerful access to imaging technologies across multiple devices is becoming increasingly useful. Prior technical solutions have used a server-side rendering and push-to-client model wherein only the server has the full image dataset. We propose a rich client solution in which each client has all the data and uses the Google Drive Realtime API for state synchronization. We have developed a small set of reusable client-side object-oriented JavaScript modules that make use of the XTK toolkit, a popular open-source JavaScript library also developed by our team, for the in-browser rendering and visualization of brain image volumes. Efficient realtime communication among the remote instances is achieved by using just a small JSON object, comprising a representation of the XTK image renderers' state, as the Google Drive Realtime collaborative data model. The developed open-source JavaScript modules have already been instantiated in a web-app called MedView, a distributed collaborative neuroimage visualization application that is delivered to the users over the web without requiring the installation of any extra software or browser plugin. This responsive application allows multiple physically distant physicians or researchers to cooperate in real time to reach a diagnosis or scientific conclusion. It also serves as a proof of concept for the capabilities of the presented technological solution.

    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/ Frontiers in Neuroin...arrow_drop_down
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    Frontiers in Neuroinformatics
    2017 . Peer-reviewed
    Data sources: Frontiers
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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/ Frontiers in Neuroin...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/
      Frontiers in Neuroinformatics
      2017 . Peer-reviewed
      Data sources: Frontiers
  • 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: Schiffler, Patrick; Tenberge, Jan-Gerd; Wiendl, Heinz; Meuth, Sven G.;

    The investigation of specific white matter areas is a growing field in neurological research and is typically achieved through the use of atlases. However, the definition of anatomically based regions remains challenging for the white matter and thus hinders region-specific analysis in individual subjects. In this article, we focus on creating a whole white matter parcellation method for individual subjects where these areas can be associated to cortex regions. This is done by combining cortex parcellation and fiber tracking data. By tracking fibers out of each cortex region and labeling the fibers according to their origin, we populate a candidate image. We then derive the white matter parcellation by classifying each white matter voxel according to the distribution of labels in the corresponding voxel from the candidate image. The parcellation of the white matter with the presented method is highly reliable and is not as dependent on registration as with white matter atlases. This method allows for the parcellation of the whole white matter into individual cortex region associated areas and, therefore, associates white matter alterations to cortex regions. In addition, we compare the results from the presented method to existing atlases. The areas generated by the presented method are not as sharply defined as the areas in most existing atlases; however, they are computed directly in the DWI space of the subject and, therefore, do not suffer from distortion caused by registration. The presented approach might be a promising tool for clinical and basic research to investigate modalities or system specific micro structural alterations of white matter areas in a quantitative manner.

    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/ Frontiers in Human N...arrow_drop_down
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    Frontiers in Human Neuroscience
    2017 . Peer-reviewed
    Data sources: Frontiers
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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/ Frontiers in Human N...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/
      Frontiers in Human Neuroscience
      2017 . Peer-reviewed
      Data sources: Frontiers
  • 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: Gnanasambandam, Radhakrishnan; Nielsen, Morten S.; Nicolai, Christopher; Sachs, Frederick; +2 Authors

    Researchers can investigate the mechanistic and molecular basis of many physiological phenomena in cells by analyzing the fundamental properties of single ion channels. These analyses entail recording single channel currents and measuring current amplitudes and transition rates between conductance states. Since most electrophysiological recordings contain noise, the data analysis can proceed by idealizing the recordings to isolate the true currents from the noise. This de-noising can be accomplished with threshold crossing algorithms and Hidden Markov Models, but such procedures generally depend on inputs and supervision by the user, thus requiring some prior knowledge of underlying processes. Channels with unknown gating and/or functional sub-states and the presence in the recording of currents from uncorrelated background channels present substantial challenges to such analyses. Here we describe and characterize an idealization algorithm based on Rissanen's Minimum Description Length (MDL) Principle. This method uses minimal assumptions and idealizes ion channel recordings without requiring a detailed user input or a priori assumptions about channel conductance and kinetics. Furthermore, we demonstrate that correlation analysis of conductance steps can resolve properties of single ion channels in recordings contaminated by signals from multiple channels. We first validated our methods on simulated data defined with a range of different signal-to-noise levels, and then showed that our algorithm can recover channel currents and their substates from recordings with multiple channels, even under conditions of high noise. We then tested the MDL algorithm on real experimental data from human PIEZO1 channels and found that our method revealed the presence of substates with alternate conductances.

    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/ Frontiers in Neuroin...arrow_drop_down
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    Frontiers in Neuroinformatics
    2017 . Peer-reviewed
    Data sources: Frontiers
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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/ Frontiers in Neuroin...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/
      Frontiers in Neuroinformatics
      2017 . Peer-reviewed
      Data sources: Frontiers
  • 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: Borsook, David; Veggeberg, Rosanna; Erpelding, Nathalie; Borra, Ronald; +3 Authors

    The insula, a cortical hub buried within the lateral sulcus, is involved in a number of processes including goal-directed cognition, conscious awareness, autonomic regulation, interoception, and somatosensation. While some of these processes are well known in the clinical presentation of migraine (i.e., autonomic and somatosensory alterations), other more complex behaviors in migraine, such as conscious awareness and error detection, are less well described. Since the insula processes and relays afferent inputs from brain areas involved in these functions to areas involved in higher cortical function such as frontal, temporal, and parietal regions, it may be implicated as a brain region that translates the signals of altered internal milieu in migraine, along with other chronic pain conditions, through the insula into complex behaviors. Here we review how the insula function and structure is altered in migraine. As a brain region of a number of brain functions, it may serve as a model to study new potential clinical perspectives for migraine 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/ NARCISarrow_drop_down
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    Other ORP type . 2016
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      Other ORP type . 2016
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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: Jalbrzikowski, Maria Elizabeth;

    22q11.2 Microdeletion syndrome (22qDS) is caused by a recurrent genetic mutation associated with a high degree of social impairment, and also represents one of the greatest known genetic risk factors for schizophrenia. This syndrome therefore represents an excellent model for investigating how a known genetic "lesion" may lead to abnormal development of social behavior and to the expression of a diagnosable psychotic disorder. However, little is known about vulnerabilities that contribute to the development of psychosis in this population, nor have the neurobiological substrates of social cognitive impairment been explored in 22qDS. The present investigation sought to examine social cognitive risk factors, at the level of both behavior and neuroanatomy, which may contribute to psychotic symptomatology in adolescents and young adults with 22qDS. We conducted three separate studies to investigate these questions. In the first study (22qDS= 31, controls=31), using behavioral measures, we sought to determine whether social cognition better predicts positive symptoms than does non-social cognition in 22qDS. The primary aims of study 2 (22qDS=31, controls= 34) were: 1) to investigate neuroanatomic alterations in socially relevant brain regions (i.e., amygdala, fusiform gyrus, superior temporal gyrus, insula, anterior cingulate, and frontal regions), using structural magnetic resonance imaging (sMRI); and 2) to determine whether such alterations were associated with psychotic symptoms and social cognition in 22qDS patients. Finally, in study 3 (22qDS=26, controls=23), we used diffusion tensor imaging (DTI) to: 1) examine alterations in white matter tracts connecting these `social brain' regions in 22qDS patients relative to typically developing youth, and 2) to determine whether white matter microstructural abnormalities were associated with psychotic symptoms and social cognition in 22qDS patients. Several novel findings emerged from these studies. First, in study 1, we found that Theory of Mind (ToM) performance was the best predictor of positive symptoms in 22qDS, accounting for 39% of the variance in symptom severity. In study 2, in comparison to typically developing controls, 22qDS participants showed disruptions in multiple brain regions associated with social cognition. In particular, those with 22qDS had increased cortical volumes in bilateral orbitofrontal cortices and insula, which appeared to be driven by increased cortical thickness in these regions, and decreased cortical volume in bilateral fusiform gyrus and anterior cingulate, which appeared to be driven by decreased surface area in these regions. We also found that increased cortical thickness in the right medial orbitofrontal cortex was significantly associated with increased positive symptom severity in 22qDS, while increased right amygdala volumes were associated with better social cognition performance in 22qDS. Finally, in study 3, in comparison to typically developing controls, 22qDS participants showed reduced white matter integrity in the left inferior frontal fasciculus and right uncinate fasciculus, fiber tracts that connect occipital to the temporal lobes and medial temporal with orbitofrontal regions, respectively. 22qDS participants also had significantly decreased axial diffusivity, a putative index of axonal damage, in multiple tracts, including the bilateral inferior and superior longitudinal fasciculus (which connects the parietal to the frontal lobes), and the uncinate fasciculus. Greater severity of positive symptoms was associated with decreased axial diffusivity in the left inferior frontal fasciculus and right superior longitudinal fasciculus; in contrast, increased axial diffusivity in the left inferior longitudinal fasciculus was associated with better social cognition in 22qDS. Considering that both social impairment and neuroanatomic abnormalities predate the onset of psychosis in 22qDS, these findings provide novel information about the relationship between social cognition and psychosis risk in 22qDS. Importantly, study 2 is the first to investigate multiple measures of structural neuroanatomy (i.e., volume, cortical thickness, surface area) in 22qDS and provides important information about functionally distinct subcomponents that may contribute to alterations in cortical volume in social relevant neuroanatomic regions. Also, when testing the joint contribution of behavioral and neuroanatomic measures to prediction of positive symptoms in 22qDS, we found that right medial orbitofrontal cortical thickness and ToM task performance accounted for 43% of the variance in positive symptoms in 22qDS, significantly improving the prediction of positive symptoms in comparison to the ToM behavioral measure alone. Study 3 represents one of the first investigations of multiple DTI indices (i.e., fractional anisotropy, axial and radial diffusivity) in 22qDS. Our pattern of results suggests that white matter microstructural disruption in 22qDS may be driven by axonal damage, rather than demyelination. Finally, given that ToM was a robust predictor of positive symptoms in our sample and exploratory analyses found relationships between positive symptoms and neuroanatomic regions associated with social cognition in 22qDS, these findings suggest that social cognition may be a valuable intermediate trait for predicting the development of psychosis.

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    Authors: Ousdal, Olga Therese; Argyelan, Miklos; Narr, Katherine L.; Abbott, Christopher; +30 Authors

    Background Electroconvulsive therapy (ECT) is associated with volumetric enlargements of corticolimbic brain regions. However, the pattern of whole-brain structural alterations following ECT remains unresolved. Here, we examined the longitudinal effects of ECT on global and local variations in gray matter, white matter, and ventricle volumes in patients with major depressive disorder as well as predictors of ECT-related clinical response. Methods Longitudinal magnetic resonance imaging and clinical data from the Global ECT-MRI Research Collaboration (GEMRIC) were used to investigate changes in white matter, gray matter, and ventricle volumes before and after ECT in 328 patients experiencing a major depressive episode. In addition, 95 nondepressed control subjects were scanned twice. We performed a mega-analysis of single subject data from 14 independent GEMRIC sites. Results Volumetric increases occurred in 79 of 84 gray matter regions of interest. In total, the cortical volume increased by mean ± SD of 1.04 ± 1.03% (Cohen’s d = 1.01, p < .001) and the subcortical gray matter volume increased by 1.47 ± 1.05% (d = 1.40, p < .001) in patients. The subcortical gray matter increase was negatively associated with total ventricle volume (Spearman’s rank correlation ρ = −.44, p < .001), while total white matter volume remained unchanged (d = −0.05, p = .41). The changes were modulated by number of ECTs and mode of electrode placements. However, the gray matter volumetric enlargements were not associated with clinical outcome. Conclusions The findings suggest that ECT induces gray matter volumetric increases that are broadly distributed. However, gross volumetric increases of specific anatomically defined regions may not serve as feasible biomarkers of clinical response.

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    Authors: Pizarro, Ricardo A.; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; +8 Authors

    High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

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    Frontiers in Neuroinformatics
    2016 . Peer-reviewed
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      Frontiers in Neuroinformatics
      2016 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Curcic-Blake, Branislava; Ford, Judith M.; Hubl, Daniela; Orlov, Natasza D.; +9 Authors

    Auditory verbal hallucinations (AVH) occur in psychotic disorders, but also as a symptom of other conditions and even in healthy people. Several current theories on the origin of AVH converge, with neuroimaging studies suggesting that the language, auditory and memory/limbic networks are of particular relevance. However, reconciliation of these theories with experimental evidence is missing. We review 50 studies investigating functional (EEG and fMRI) and anatomic (diffusion tensor imaging) connectivity in these networks, and explore the evidence supporting abnormal connectivity in these networks associated with AVH. We distinguish between functional connectivity during an actual hallucination experience (symptom capture) and functional connectivity during either the resting state or a task comparing individuals who hallucinate with those who do not (symptom association studies). Symptom capture studies clearly reveal a pattern of increased coupling among the auditory, language and striatal regions. Anatomical and symptom association functional studies suggest that the interhemispheric connectivity between posterior auditory regions may depend on the phase of illness, with increases in non-psychotic individuals and first episode patients and decreases in chronic patients. Leading hypotheses involving concepts as unstable memories, source monitoring, top-down attention, and hybrid models of hallucinations are supported in part by the published connectivity data, although several caveats and inconsistencies remain. Specifically, possible changes in fronto-temporal connectivity are still under debate. Precise hypotheses concerning the directionality of connections deduced from current theoretical approaches should be tested using experimental approaches that allow for discrimination of competing hypotheses. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

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    Other ORP type . 2017
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      Other ORP type . 2017
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    Authors: Bilenko, Natalia Y.; Gallant, Jack L.;

    In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model.

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    Frontiers in Neuroinformatics
    2016 . Peer-reviewed
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      Frontiers in Neuroinformatics
      2016 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Shi, Yundi; Budin, Francois; Yapuncich, Eva; Rumple, Ashley; +8 Authors

    Computational anatomical atlases have shown to be of immense value in neuroimaging as they provide age appropriate reference spaces alongside ancillary anatomical information for automated analysis such as subcortical structural definitions, cortical parcellations or white fiber tract regions. Standard workflows in neuroimaging necessitate such atlases to be appropriately selected for the subject population of interest. This is especially of importance in early postnatal brain development, where rapid changes in brain shape and appearance render neuroimaging workflows sensitive to the appropriate atlas choice. We present here a set of novel computation atlases for structural MRI and Diffusion Tensor Imaging as crucial resource for the analysis of MRI data from non-human primate rhesus monkey (Macaca mulatta) data in early postnatal brain development. Forty socially-housed infant macaques were scanned longitudinally at ages 2 weeks, 3, 6, and 12 months in order to create cross-sectional structural and DTI atlases via unbiased atlas building at each of these ages. Probabilistic spatial prior definitions for the major tissue classes were trained on each atlas with expert manual segmentations. In this article we present the development and use of these atlases with publicly available tools, as well as the atlases themselves, which are publicly disseminated to the scientific community.

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    Frontiers in Neuroscience
    2017 . Peer-reviewed
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      Frontiers in Neuroscience
      2017 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Bernal-Rusiel, Jorge L.; Rannou, Nicolas; Gollub, Randy L.; Pieper, Steve; +4 Authors

    In this paper we present a web-based software solution to the problem of implementing real-time collaborative neuroimage visualization. In both clinical and research settings, simple and powerful access to imaging technologies across multiple devices is becoming increasingly useful. Prior technical solutions have used a server-side rendering and push-to-client model wherein only the server has the full image dataset. We propose a rich client solution in which each client has all the data and uses the Google Drive Realtime API for state synchronization. We have developed a small set of reusable client-side object-oriented JavaScript modules that make use of the XTK toolkit, a popular open-source JavaScript library also developed by our team, for the in-browser rendering and visualization of brain image volumes. Efficient realtime communication among the remote instances is achieved by using just a small JSON object, comprising a representation of the XTK image renderers' state, as the Google Drive Realtime collaborative data model. The developed open-source JavaScript modules have already been instantiated in a web-app called MedView, a distributed collaborative neuroimage visualization application that is delivered to the users over the web without requiring the installation of any extra software or browser plugin. This responsive application allows multiple physically distant physicians or researchers to cooperate in real time to reach a diagnosis or scientific conclusion. It also serves as a proof of concept for the capabilities of the presented technological solution.

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    Frontiers in Neuroinformatics
    2017 . Peer-reviewed
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      Frontiers in Neuroinformatics
      2017 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Schiffler, Patrick; Tenberge, Jan-Gerd; Wiendl, Heinz; Meuth, Sven G.;

    The investigation of specific white matter areas is a growing field in neurological research and is typically achieved through the use of atlases. However, the definition of anatomically based regions remains challenging for the white matter and thus hinders region-specific analysis in individual subjects. In this article, we focus on creating a whole white matter parcellation method for individual subjects where these areas can be associated to cortex regions. This is done by combining cortex parcellation and fiber tracking data. By tracking fibers out of each cortex region and labeling the fibers according to their origin, we populate a candidate image. We then derive the white matter parcellation by classifying each white matter voxel according to the distribution of labels in the corresponding voxel from the candidate image. The parcellation of the white matter with the presented method is highly reliable and is not as dependent on registration as with white matter atlases. This method allows for the parcellation of the whole white matter into individual cortex region associated areas and, therefore, associates white matter alterations to cortex regions. In addition, we compare the results from the presented method to existing atlases. The areas generated by the presented method are not as sharply defined as the areas in most existing atlases; however, they are computed directly in the DWI space of the subject and, therefore, do not suffer from distortion caused by registration. The presented approach might be a promising tool for clinical and basic research to investigate modalities or system specific micro structural alterations of white matter areas in a quantitative manner.

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    Frontiers in Human Neuroscience
    2017 . Peer-reviewed
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      Frontiers in Human Neuroscience
      2017 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Gnanasambandam, Radhakrishnan; Nielsen, Morten S.; Nicolai, Christopher; Sachs, Frederick; +2 Authors

    Researchers can investigate the mechanistic and molecular basis of many physiological phenomena in cells by analyzing the fundamental properties of single ion channels. These analyses entail recording single channel currents and measuring current amplitudes and transition rates between conductance states. Since most electrophysiological recordings contain noise, the data analysis can proceed by idealizing the recordings to isolate the true currents from the noise. This de-noising can be accomplished with threshold crossing algorithms and Hidden Markov Models, but such procedures generally depend on inputs and supervision by the user, thus requiring some prior knowledge of underlying processes. Channels with unknown gating and/or functional sub-states and the presence in the recording of currents from uncorrelated background channels present substantial challenges to such analyses. Here we describe and characterize an idealization algorithm based on Rissanen's Minimum Description Length (MDL) Principle. This method uses minimal assumptions and idealizes ion channel recordings without requiring a detailed user input or a priori assumptions about channel conductance and kinetics. Furthermore, we demonstrate that correlation analysis of conductance steps can resolve properties of single ion channels in recordings contaminated by signals from multiple channels. We first validated our methods on simulated data defined with a range of different signal-to-noise levels, and then showed that our algorithm can recover channel currents and their substates from recordings with multiple channels, even under conditions of high noise. We then tested the MDL algorithm on real experimental data from human PIEZO1 channels and found that our method revealed the presence of substates with alternate conductances.

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    Frontiers in Neuroinformatics
    2017 . Peer-reviewed
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      Frontiers in Neuroinformatics
      2017 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Borsook, David; Veggeberg, Rosanna; Erpelding, Nathalie; Borra, Ronald; +3 Authors

    The insula, a cortical hub buried within the lateral sulcus, is involved in a number of processes including goal-directed cognition, conscious awareness, autonomic regulation, interoception, and somatosensation. While some of these processes are well known in the clinical presentation of migraine (i.e., autonomic and somatosensory alterations), other more complex behaviors in migraine, such as conscious awareness and error detection, are less well described. Since the insula processes and relays afferent inputs from brain areas involved in these functions to areas involved in higher cortical function such as frontal, temporal, and parietal regions, it may be implicated as a brain region that translates the signals of altered internal milieu in migraine, along with other chronic pain conditions, through the insula into complex behaviors. Here we review how the insula function and structure is altered in migraine. As a brain region of a number of brain functions, it may serve as a model to study new potential clinical perspectives for migraine treatment.

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    Authors: Jalbrzikowski, Maria Elizabeth;

    22q11.2 Microdeletion syndrome (22qDS) is caused by a recurrent genetic mutation associated with a high degree of social impairment, and also represents one of the greatest known genetic risk factors for schizophrenia. This syndrome therefore represents an excellent model for investigating how a known genetic "lesion" may lead to abnormal development of social behavior and to the expression of a diagnosable psychotic disorder. However, little is known about vulnerabilities that contribute to the development of psychosis in this population, nor have the neurobiological substrates of social cognitive impairment been explored in 22qDS. The present investigation sought to examine social cognitive risk factors, at the level of both behavior and neuroanatomy, which may contribute to psychotic symptomatology in adolescents and young adults with 22qDS. We conducted three separate studies to investigate these questions. In the first study (22qDS= 31, controls=31), using behavioral measures, we sought to determine whether social cognition better predicts positive symptoms than does non-social cognition in 22qDS. The primary aims of study 2 (22qDS=31, controls= 34) were: 1) to investigate neuroanatomic alterations in socially relevant brain regions (i.e., amygdala, fusiform gyrus, superior temporal gyrus, insula, anterior cingulate, and frontal regions), using structural magnetic resonance imaging (sMRI); and 2) to determine whether such alterations were associated with psychotic symptoms and social cognition in 22qDS patients. Finally, in study 3 (22qDS=26, controls=23), we used diffusion tensor imaging (DTI) to: 1) examine alterations in white matter tracts connecting these `social brain' regions in 22qDS patients relative to typically developing youth, and 2) to determine whether white matter microstructural abnormalities were associated with psychotic symptoms and social cognition in 22qDS patients. Several novel findings emerged from these studies. First, in study 1, we found that Theory of Mind (ToM) performance was the best predictor of positive symptoms in 22qDS, accounting for 39% of the variance in symptom severity. In study 2, in comparison to typically developing controls, 22qDS participants showed disruptions in multiple brain regions associated with social cognition. In particular, those with 22qDS had increased cortical volumes in bilateral orbitofrontal cortices and insula, which appeared to be driven by increased cortical thickness in these regions, and decreased cortical volume in bilateral fusiform gyrus and anterior cingulate, which appeared to be driven by decreased surface area in these regions. We also found that increased cortical thickness in the right medial orbitofrontal cortex was significantly associated with increased positive symptom severity in 22qDS, while increased right amygdala volumes were associated with better social cognition performance in 22qDS. Finally, in study 3, in comparison to typically developing controls, 22qDS participants showed reduced white matter integrity in the left inferior frontal fasciculus and right uncinate fasciculus, fiber tracts that connect occipital to the temporal lobes and medial temporal with orbitofrontal regions, respectively. 22qDS participants also had significantly decreased axial diffusivity, a putative index of axonal damage, in multiple tracts, including the bilateral inferior and superior longitudinal fasciculus (which connects the parietal to the frontal lobes), and the uncinate fasciculus. Greater severity of positive symptoms was associated with decreased axial diffusivity in the left inferior frontal fasciculus and right superior longitudinal fasciculus; in contrast, increased axial diffusivity in the left inferior longitudinal fasciculus was associated with better social cognition in 22qDS. Considering that both social impairment and neuroanatomic abnormalities predate the onset of psychosis in 22qDS, these findings provide novel information about the relationship between social cognition and psychosis risk in 22qDS. Importantly, study 2 is the first to investigate multiple measures of structural neuroanatomy (i.e., volume, cortical thickness, surface area) in 22qDS and provides important information about functionally distinct subcomponents that may contribute to alterations in cortical volume in social relevant neuroanatomic regions. Also, when testing the joint contribution of behavioral and neuroanatomic measures to prediction of positive symptoms in 22qDS, we found that right medial orbitofrontal cortical thickness and ToM task performance accounted for 43% of the variance in positive symptoms in 22qDS, significantly improving the prediction of positive symptoms in comparison to the ToM behavioral measure alone. Study 3 represents one of the first investigations of multiple DTI indices (i.e., fractional anisotropy, axial and radial diffusivity) in 22qDS. Our pattern of results suggests that white matter microstructural disruption in 22qDS may be driven by axonal damage, rather than demyelination. Finally, given that ToM was a robust predictor of positive symptoms in our sample and exploratory analyses found relationships between positive symptoms and neuroanatomic regions associated with social cognition in 22qDS, these findings suggest that social cognition may be a valuable intermediate trait for predicting the development of psychosis.

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    Authors: Ousdal, Olga Therese; Argyelan, Miklos; Narr, Katherine L.; Abbott, Christopher; +30 Authors

    Background Electroconvulsive therapy (ECT) is associated with volumetric enlargements of corticolimbic brain regions. However, the pattern of whole-brain structural alterations following ECT remains unresolved. Here, we examined the longitudinal effects of ECT on global and local variations in gray matter, white matter, and ventricle volumes in patients with major depressive disorder as well as predictors of ECT-related clinical response. Methods Longitudinal magnetic resonance imaging and clinical data from the Global ECT-MRI Research Collaboration (GEMRIC) were used to investigate changes in white matter, gray matter, and ventricle volumes before and after ECT in 328 patients experiencing a major depressive episode. In addition, 95 nondepressed control subjects were scanned twice. We performed a mega-analysis of single subject data from 14 independent GEMRIC sites. Results Volumetric increases occurred in 79 of 84 gray matter regions of interest. In total, the cortical volume increased by mean ± SD of 1.04 ± 1.03% (Cohen’s d = 1.01, p < .001) and the subcortical gray matter volume increased by 1.47 ± 1.05% (d = 1.40, p < .001) in patients. The subcortical gray matter increase was negatively associated with total ventricle volume (Spearman’s rank correlation ρ = −.44, p < .001), while total white matter volume remained unchanged (d = −0.05, p = .41). The changes were modulated by number of ECTs and mode of electrode placements. However, the gray matter volumetric enlargements were not associated with clinical outcome. Conclusions The findings suggest that ECT induces gray matter volumetric increases that are broadly distributed. However, gross volumetric increases of specific anatomically defined regions may not serve as feasible biomarkers of clinical response.

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    Authors: Pizarro, Ricardo A.; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; +8 Authors

    High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

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    Frontiers in Neuroinformatics
    2016 . Peer-reviewed
    Data sources: Frontiers
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      Frontiers in Neuroinformatics
      2016 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Curcic-Blake, Branislava; Ford, Judith M.; Hubl, Daniela; Orlov, Natasza D.; +9 Authors

    Auditory verbal hallucinations (AVH) occur in psychotic disorders, but also as a symptom of other conditions and even in healthy people. Several current theories on the origin of AVH converge, with neuroimaging studies suggesting that the language, auditory and memory/limbic networks are of particular relevance. However, reconciliation of these theories with experimental evidence is missing. We review 50 studies investigating functional (EEG and fMRI) and anatomic (diffusion tensor imaging) connectivity in these networks, and explore the evidence supporting abnormal connectivity in these networks associated with AVH. We distinguish between functional connectivity during an actual hallucination experience (symptom capture) and functional connectivity during either the resting state or a task comparing individuals who hallucinate with those who do not (symptom association studies). Symptom capture studies clearly reveal a pattern of increased coupling among the auditory, language and striatal regions. Anatomical and symptom association functional studies suggest that the interhemispheric connectivity between posterior auditory regions may depend on the phase of illness, with increases in non-psychotic individuals and first episode patients and decreases in chronic patients. Leading hypotheses involving concepts as unstable memories, source monitoring, top-down attention, and hybrid models of hallucinations are supported in part by the published connectivity data, although several caveats and inconsistencies remain. Specifically, possible changes in fronto-temporal connectivity are still under debate. Precise hypotheses concerning the directionality of connections deduced from current theoretical approaches should be tested using experimental approaches that allow for discrimination of competing hypotheses. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

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    Authors: Bilenko, Natalia Y.; Gallant, Jack L.;

    In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model.

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    Frontiers in Neuroinformatics
    2016 . Peer-reviewed
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