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  • 2014-2023
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  • Neuroinformatics

  • Authors: Gdański Uniwersytet Medyczny;

    This is the histopathological image of BRAIN tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format. The detailed information about the patient, sample, and diagnosis are as follows: Patient:Age: 65Clinical description: Aphasia. HIV+.Gender: FemaleDiagnosis:Classification: ICD-O_3.2Classification code: MATURE B-CELL LYMPHOMASDiagnosis: Diffuse large B-cell lymphoma, NOSResult of the histopathological examination: Diffuse large B-cell lymphoma of the brain (DLBCL, primary cerebral type). IHC: LCA+, CD20+, CD3(-), bcl2+, bcl6+, CD10(-), MUM1+, Ki67 85%.Sample:Material: FFPECollecting method: Surgical specimenTopography: EYE, BRAIN AND OTHER PARTS OF CENTRAL NERVOUS SYSTEMOrgan: BRAINTissue: Parietal lobeType of staining: positive/IHCStaining: Not applicableAntibody: CD 10Technology:Equipment: Pannoramic 250 3DHistechLens: 20xOrganization:Source: Medical University Gdańsk

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  • Authors: Gdański Uniwersytet Medyczny;

    This is the histopathological image of BRAIN tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format. The detailed information about the patient, sample, and diagnosis are as follows: Patient:Age: 45Clinical description: Brain tumor.Gender: MaleDiagnosis:Classification: ICD-O_3.2Classification code: GLIOMASDiagnosis: Astrocytoma, anaplastic, IDH-mutantResult of the histopathological examination: Diffuse anaplastic astrocytoma (G3), IDH1 - mutated. IHC: IDH1R132+, p53 - overexpressed, ATRX +/-, Ki67 up to 5%, CDKN2A+ .Sample:Material: FFPECollecting method: Surgical specimenTopography: EYE, BRAIN AND OTHER PARTS OF CENTRAL NERVOUS SYSTEMOrgan: BRAINTissue: Parietal lobeType of staining: positive/IHCStaining: Not applicableAntibody: ATRXTechnology:Equipment: Pannoramic 250 3DHistechLens: 20xOrganization:Source: Medical University Gdańsk

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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: Sainz Martinez, Cristina; Bach Cuadra, Meritxell; Jorge, João;

    BigBrain-MR is a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI contrasts and properties that affect image generation. This phantom was generated from the publicly available BigBrain histological dataset and from lower-resolution in-vivo 7T-MRI data, using a new image processing framework that allows mapping the general properties of in-vivo data into the fine anatomical scale of BigBrain. The dataset includes: BigBrain original contrast and a new atlas with 20 ROIs; T1-weighted image and T1 map; T2*-weighted images and R2* map; Magnetic susceptibility map (QSM); Background magnetic field map; Complex coil sensitivity maps (32ch-receive RF array); Bias field map. Information about each image/map (including data type and amplitude scaling) is provided in data_info.txt. Additionally, we have included a script with usage examples in Python that illustrate how the data can be loaded, processed and combined for diverse simulation purposes. BigBrain-MR is presented, described and tested in the following peer-reviewed article: C. Sainz Martinez, M. Bach Cuadra, J. Jorge. BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development. NeuroImage 2023. DOI: 10.1016/j.neuroimage.2023.120074

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    ZENODO
    Dataset . 2022
    License: CC BY NC SA
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY NC SA
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY NC SA
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY NC SA
      Data sources: Datacite
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  • Authors: Blank, B.; Andriamonje, S.; Del Moral, R.; Dufour, J.P.; +21 Authors

    Nucleus is C H2. GSI.

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    HEPData
    Dataset . 2015
    Data sources: Datacite
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      HEPData
      Dataset . 2015
      Data sources: Datacite
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    Authors: Legros, Alexandre; Modolo, Julien; Brown, Samantha; Robertson, John; +2 Authors

    fMRI full experiment 3000 microTesla part 1fMRI full experiment 3000 microTesla part 1-10: MRI and fMRI DICOM images corresponding to the full study conducted with a 3000 microTesla exposure. These DICOM files have been produced by a Siemens 3T Verio (Siemens, Germany) MRI, as described in the paper. DICOM is a standard format for MRI images, and these can be analyzed with software packages such as BrainVoyager (Brain Innovation, The Netherlands), which we have used in the paper; or an open-access software package such as FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). The analysis procedure for the fMRI data is fully described in the Methods section of the paper. DICOM files are available for each subject pre- and post-exposure, for each of the two tasks presented in the paper (finger tapping and mental rotation). Two tasks were used in this experiment: a mental rotation task and a finger tapping task. The subject who have properly conducted the mental rotation task and have been included in the analysis are: S02, S03, S04, S06, S07, S09, S10, S11, S12, S13, S14, S16, S17, S18, S20, S21, S23, S24, S25, S27, S29. The subject who have properly conducted the finger tapping task and have been included in the analysis are: S03, S04, S06, S07, S08, S09, S10, S12, S13, S14, S16, S17, S20, S22, S23, S24, S25, S26, S27, S29. Please feel free to contact the authors if further guidance is required.fMRI full experiment 3000 microTesla part 2fMRI full experiment 3000 microTesla part 3fMRI full experiment 3000 microTesla part 4fMRI full experiment 3000 microTesla part 5fMRI full experiment 3000 microTesla part 6fMRI full experiment 3000 microTesla part 7fMRI full experiment 3000 microTesla part 8fMRI full experiment 3000 microTesla part 9fMRI full experiment 3000 microTesla part 10fMRI_Pilot_1800_microTesla_DICOM part 1fMRI_Pilot_1800_microTesla_DICOM part 1-2: MRI and fMRI DICOM images corresponding to the pilot study conducted with a 1800 microTesla exposure These DICOM files have been produced by a Siemens 1.5T Avanto (Siemens, Germany) MRI, as described in the paper. DICOM is a standard format for MRI images, and these can be analyzed with software packages such as BrainVoyager (Brain Innovation, The Netherlands), which we have used in the paper; or an open-access software package such as FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). The analysis procedure for the fMRI data is fully described in the Methods section of the paper. DICOM files are available for each subject pre- and post-exposure, for each of the two tasks presented in the paper (finger tapping and mental rotation). Please feel free to contact the authors if further guidance is required.fMRI_Pilot_1800_microTesla_DICOM part 2 Several aspects of the human nervous system and associated motor and cognitive processes have been reported to be modulated by extremely low-frequency (ELF, < 300 Hz) time-varying Magnetic Fields (MF). Due do their worldwide prevalence; power-line frequencies (60 Hz in North America) are of particular interest. Despite intense research efforts over the last few decades, the potential effects of 60 Hz MF still need to be elucidated, and the underlying mechanisms to be understood. In this study, we have used functional Magnetic Resonance Imaging (fMRI) to characterize potential changes in functional brain activation following human exposure to a 60 Hz MF through motor and cognitive tasks. First, pilot results acquired in a first set of subjects (N=9) were used to demonstrate the technical feasibility of using fMRI to detect subtle changes in functional brain activation with 60 Hz MF exposure at 1800 μT. Second, a full study involving a larger cohort of subjects tested brain activation during 1) a finger tapping task (N=20), and 2) a mental rotation task (N=21); before and after a one-hour, 60 Hz, 3000 μT MF exposure. The results indicate significant changes in task-induced functional brain activation as a consequence of MF exposure. However, no impact on task performance was found. These results illustrate the potential of using fMRI to identify MF-induced changes in functional brain activation, suggesting that a one-hour 60 Hz, 3000 μT MF exposure can modulate activity in specific brain regions after the end of the exposure period (i.e., residual effects). We discuss the possibility that MF exposure at 60 Hz, 3000 μT may be capable of modulating cortical excitability via a modulation of synaptic plasticity processes.

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    DANS-EASY
    Dataset . 2015
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2015
      Data sources: B2FIND
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  • Authors: Gdański Uniwersytet Medyczny;

    This is the histopathological image of BRAIN tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format. The detailed information about the patient, sample, and diagnosis are as follows: Patient:Age: 25Clinical description: Diffuse brain tumourGender: MaleDiagnosis:Classification: ICD-10_10-20Classification code: Chapter II - NeoplasmsDiagnosis: Brain, unspecifiedResult of the histopathological examination: Diffuse glioma, IDH1-wildtype Astrocytoma (II WHO). Ki67 ca 3 %,Sample:Material: FFPECollecting method: Surgical specimenTopography: EYE, BRAIN AND OTHER PARTS OF CENTRAL NERVOUS SYSTEMOrgan: BRAINTissue: CerebrumType of staining: positive/IHCStaining: Not applicableAntibody: Ki-67Technology:Equipment: Pannoramic 250 3DHistechLens: 20xOrganization:Source: Medical University Gdańsk

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  • Authors: Qihua, Li;

    BRATS2015TestingV2

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    Authors: Shiwaku, Hironari; Yamashita, Kanefumi; Ohmiya, Toshihiro; Nimura, Satoshi; +3 Authors

    Background and Study Aims: Detecting esophageal achalasia remains a challenge. We describe the diagnostic utility of corona appearance, a novel endoscopic finding specific to esophageal achalasia. Patients and Methods: Corona appearance and seven conventional endoscopic findings were compared for sensitivity and consistency (-value) among 53 untreated esophageal achalasia patients who underwent endoscopy at our hospital. The following criteria had to be met during lower esophageal sphincter examination using the attached ST Hood short-type for positive corona appearance: A) congestion inside the hood, B) ischemic change around the hood, and C) palisade vessels outside the hood. Results: Corona appearance had the highest sensitivity (91%; -value, 0.71). Other findings in descending order of sensitivity included 1) functional stenosis of the esophagogastric junction (EGJ; 86%; -value, 0.58), 2) mucosal thickening and whitish change (71%; -value, 0.27), 3) abnormal contraction of the esophageal body (59%; -value, 0.32), 4) dilation of the esophageal lumen (58%; -value, 0.53), 5) liquid remnant (57%; -value, 0.51), 6) Wrapping around EGJ (49%; -value, 0.14), and 7) food remnant (30%; -value, 0.88). Even in 22 patients with poor (grade 1) intraluminal expansion, corona appearance had highest sensitivity (88%) compared to other endoscopic findings (-value, 0.63). Conclusions: Among endoscopic findings using a ST Hood short-type to diagnose esophageal achalasia, corona appearance had the highest sensitivity and its consistency (-value) among endoscopists was substantial compared to other endoscopic findings. Similar results were obtained for esophageal achalasia cases with poor expansion. Endoscopic diagnosis of esophageal achalasia with hood attached is useful. Video Supporting_file_01[1]Video for CA was defined as positive when all of the following three criteria were met upon observation of the LES performed with the attached ST Hood short-type: a) Congestion inside the hood, b) ischemic change around the hood, and c) palisade vessels outside the hood.Supporting_file_01[1].wmv

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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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    DRYAD; ZENODO; NARCIS
    Dataset . 2018
    License: CC 0
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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      DRYAD; ZENODO; NARCIS
      Dataset . 2018
      License: CC 0
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    Authors: Andy R. Eugene; Wayne T. Nicholson;

    Propranolol has been found to be therapeutically effective, to obtain a clinical response by beta-adrenoceptor blockade, at plasma levels of greater than 20 ng/mL(Coltart et al., 1971; Frishman, 1988; Johnsson and Regàrdh, 1976). Thus, to display the data, we used highlighted plasma concentration where the pharmacokinetic curve falls below 20ng/mL threshold for therapeutic efficacy in the patient’s plasma. https://www.edusoft.ro/brain/index.php/brain/issue/view/30

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    ZENODO
    Image . 2015
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Image . 2015
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Image . 2015
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Image . 2015
      License: CC BY
      Data sources: ZENODO
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    Authors: Vagal, Achala S.; Wintermark, Max; Nael, Kambiz; Bivard, Andrew; +3 Authors

    Recent positive trials have thrust acute cerebral perfusion imaging into the routine evaluation of acute ischemic stroke. Updated guidelines state that in patients with anterior circulation large vessel occlusions presenting beyond 6 hours from time last known well, advanced imaging selection including perfusion based selection is necessary. Centers that receive acute stroke patients must now have the capability to perform and interpret CT or MR perfusion imaging, or provide rapid transfer to centers with the capability of selecting patients for a highly impactful endovascular therapy, particularly in delayed time windows. Many stroke centers are quickly incorporating the use of automated perfusion processing software to interpret perfusion raw data. As CT perfusion is being assimilated in real world clinical practice, it is essential to understand the basics of perfusion acquisition, quantification and interpretation. It is equally important to recognize the common technical and clinical diagnostic challenges of automated CTP including ischemic core and penumbral misclassifications that could result in underestimation or overestimation of the core and penumbra volumes. This review highlights the pitfalls of automated CT perfusion along with practical pearls to address the common challenges. This is particularly tailored to aid the acute stroke clinician who must interpret automated perfusion studies, in an emergency setting to make time-dependent treatment decisions for acute ischemic stroke patients.

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    DRYAD; ZENODO
    Dataset . 2020
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2020
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      Data sources: Datacite; ZENODO
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  • Authors: Gdański Uniwersytet Medyczny;

    This is the histopathological image of BRAIN tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format. The detailed information about the patient, sample, and diagnosis are as follows: Patient:Age: 65Clinical description: Aphasia. HIV+.Gender: FemaleDiagnosis:Classification: ICD-O_3.2Classification code: MATURE B-CELL LYMPHOMASDiagnosis: Diffuse large B-cell lymphoma, NOSResult of the histopathological examination: Diffuse large B-cell lymphoma of the brain (DLBCL, primary cerebral type). IHC: LCA+, CD20+, CD3(-), bcl2+, bcl6+, CD10(-), MUM1+, Ki67 85%.Sample:Material: FFPECollecting method: Surgical specimenTopography: EYE, BRAIN AND OTHER PARTS OF CENTRAL NERVOUS SYSTEMOrgan: BRAINTissue: Parietal lobeType of staining: positive/IHCStaining: Not applicableAntibody: CD 10Technology:Equipment: Pannoramic 250 3DHistechLens: 20xOrganization:Source: Medical University Gdańsk

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  • Authors: Gdański Uniwersytet Medyczny;

    This is the histopathological image of BRAIN tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format. The detailed information about the patient, sample, and diagnosis are as follows: Patient:Age: 45Clinical description: Brain tumor.Gender: MaleDiagnosis:Classification: ICD-O_3.2Classification code: GLIOMASDiagnosis: Astrocytoma, anaplastic, IDH-mutantResult of the histopathological examination: Diffuse anaplastic astrocytoma (G3), IDH1 - mutated. IHC: IDH1R132+, p53 - overexpressed, ATRX +/-, Ki67 up to 5%, CDKN2A+ .Sample:Material: FFPECollecting method: Surgical specimenTopography: EYE, BRAIN AND OTHER PARTS OF CENTRAL NERVOUS SYSTEMOrgan: BRAINTissue: Parietal lobeType of staining: positive/IHCStaining: Not applicableAntibody: ATRXTechnology:Equipment: Pannoramic 250 3DHistechLens: 20xOrganization:Source: Medical University Gdańsk

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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: Sainz Martinez, Cristina; Bach Cuadra, Meritxell; Jorge, João;

    BigBrain-MR is a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI contrasts and properties that affect image generation. This phantom was generated from the publicly available BigBrain histological dataset and from lower-resolution in-vivo 7T-MRI data, using a new image processing framework that allows mapping the general properties of in-vivo data into the fine anatomical scale of BigBrain. The dataset includes: BigBrain original contrast and a new atlas with 20 ROIs; T1-weighted image and T1 map; T2*-weighted images and R2* map; Magnetic susceptibility map (QSM); Background magnetic field map; Complex coil sensitivity maps (32ch-receive RF array); Bias field map. Information about each image/map (including data type and amplitude scaling) is provided in data_info.txt. Additionally, we have included a script with usage examples in Python that illustrate how the data can be loaded, processed and combined for diverse simulation purposes. BigBrain-MR is presented, described and tested in the following peer-reviewed article: C. Sainz Martinez, M. Bach Cuadra, J. Jorge. BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development. NeuroImage 2023. DOI: 10.1016/j.neuroimage.2023.120074

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    ZENODO
    Dataset . 2022
    License: CC BY NC SA
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY NC SA
    Data sources: Datacite
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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/
      ZENODO
      Dataset . 2022
      License: CC BY NC SA
      Data sources: ZENODO
      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/
      ZENODO
      Dataset . 2022
      License: CC BY NC SA
      Data sources: Datacite
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  • Authors: Blank, B.; Andriamonje, S.; Del Moral, R.; Dufour, J.P.; +21 Authors

    Nucleus is C H2. GSI.

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    HEPData
    Dataset . 2015
    Data sources: Datacite
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      HEPData
      Dataset . 2015
      Data sources: Datacite
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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: Legros, Alexandre; Modolo, Julien; Brown, Samantha; Robertson, John; +2 Authors

    fMRI full experiment 3000 microTesla part 1fMRI full experiment 3000 microTesla part 1-10: MRI and fMRI DICOM images corresponding to the full study conducted with a 3000 microTesla exposure. These DICOM files have been produced by a Siemens 3T Verio (Siemens, Germany) MRI, as described in the paper. DICOM is a standard format for MRI images, and these can be analyzed with software packages such as BrainVoyager (Brain Innovation, The Netherlands), which we have used in the paper; or an open-access software package such as FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). The analysis procedure for the fMRI data is fully described in the Methods section of the paper. DICOM files are available for each subject pre- and post-exposure, for each of the two tasks presented in the paper (finger tapping and mental rotation). Two tasks were used in this experiment: a mental rotation task and a finger tapping task. The subject who have properly conducted the mental rotation task and have been included in the analysis are: S02, S03, S04, S06, S07, S09, S10, S11, S12, S13, S14, S16, S17, S18, S20, S21, S23, S24, S25, S27, S29. The subject who have properly conducted the finger tapping task and have been included in the analysis are: S03, S04, S06, S07, S08, S09, S10, S12, S13, S14, S16, S17, S20, S22, S23, S24, S25, S26, S27, S29. Please feel free to contact the authors if further guidance is required.fMRI full experiment 3000 microTesla part 2fMRI full experiment 3000 microTesla part 3fMRI full experiment 3000 microTesla part 4fMRI full experiment 3000 microTesla part 5fMRI full experiment 3000 microTesla part 6fMRI full experiment 3000 microTesla part 7fMRI full experiment 3000 microTesla part 8fMRI full experiment 3000 microTesla part 9fMRI full experiment 3000 microTesla part 10fMRI_Pilot_1800_microTesla_DICOM part 1fMRI_Pilot_1800_microTesla_DICOM part 1-2: MRI and fMRI DICOM images corresponding to the pilot study conducted with a 1800 microTesla exposure These DICOM files have been produced by a Siemens 1.5T Avanto (Siemens, Germany) MRI, as described in the paper. DICOM is a standard format for MRI images, and these can be analyzed with software packages such as BrainVoyager (Brain Innovation, The Netherlands), which we have used in the paper; or an open-access software package such as FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). The analysis procedure for the fMRI data is fully described in the Methods section of the paper. DICOM files are available for each subject pre- and post-exposure, for each of the two tasks presented in the paper (finger tapping and mental rotation). Please feel free to contact the authors if further guidance is required.fMRI_Pilot_1800_microTesla_DICOM part 2 Several aspects of the human nervous system and associated motor and cognitive processes have been reported to be modulated by extremely low-frequency (ELF, < 300 Hz) time-varying Magnetic Fields (MF). Due do their worldwide prevalence; power-line frequencies (60 Hz in North America) are of particular interest. Despite intense research efforts over the last few decades, the potential effects of 60 Hz MF still need to be elucidated, and the underlying mechanisms to be understood. In this study, we have used functional Magnetic Resonance Imaging (fMRI) to characterize potential changes in functional brain activation following human exposure to a 60 Hz MF through motor and cognitive tasks. First, pilot results acquired in a first set of subjects (N=9) were used to demonstrate the technical feasibility of using fMRI to detect subtle changes in functional brain activation with 60 Hz MF exposure at 1800 μT. Second, a full study involving a larger cohort of subjects tested brain activation during 1) a finger tapping task (N=20), and 2) a mental rotation task (N=21); before and after a one-hour, 60 Hz, 3000 μT MF exposure. The results indicate significant changes in task-induced functional brain activation as a consequence of MF exposure. However, no impact on task performance was found. These results illustrate the potential of using fMRI to identify MF-induced changes in functional brain activation, suggesting that a one-hour 60 Hz, 3000 μT MF exposure can modulate activity in specific brain regions after the end of the exposure period (i.e., residual effects). We discuss the possibility that MF exposure at 60 Hz, 3000 μT may be capable of modulating cortical excitability via a modulation of synaptic plasticity processes.

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    DANS-EASY
    Dataset . 2015
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2015
      Data sources: B2FIND
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