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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: Bullock, T.H.;

    Two classes of electrical activity in the central nervous system have been known for a long time: spikes with synaptic potentials and "slow" fluctuations (components mainly below ca. 100 Hz). Their relations to each other are still little known and an unfortunate schism persists in mutual disparagement by investigators who chiefly study one class or the other. The news I wish to highlight is that this schism is waning and this essay will be outmoded as more workers study both. I focus here on the class of slow potentials which in certain respects is the more neglected. This class should extend down into the less-known "infraslow" domain (power mainly below 0.1 Hz) - omnipresent, higher in amplitude and clearly significant functionally.

    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/ Cognitive Sciences e...arrow_drop_down
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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/ Cognitive Sciences e...arrow_drop_down
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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
    Authors: VENTRUCCI, MASSIMO; A. Bowman; C. Miller; J. Gross; +1 Authors

    Magnetoencephalography (MEG) is a non-invasive technique which measures the electromagnetic activity in the brain by recording the magnetic fields outside the head. Data is acquired by sensitive devices embedded in a helmet placed over the human head. The high temporal resolution of MEG (in the order of milliseconds) is optimal for studying the transient magnetic fields associated with the highly dynamic processes of brain activations. The goal is the identification of spatio-temporal components in the signal that correspond to certain cognitive processes that ideally are carefully manipulated in the respective experiment. Several kinds of noise and artifacts can distort the desired signal. Filtering procedures and averaging across many trials, i.e. replicates of the experiment, are methods usually adopted to summarize the data and increase the signal to noise ratio. However, uncertainty of estimates are usually not taken into account. In this work linear smoothing estimation based on a local fitting approach is applied to smooth the data both in time and space, i.e. the helmet surface, in order to reduce sensor noise. Maps depicting the mean response by smoothing out the sensor noise, plus standard errors for the mean, are produced to help in identifying the time and location where a dipole pattern occurs, which indicates activation. Computational issues are successfully addressed by considering the array representation of the data (Currie et al. 2006). The estimator actually realizes a local mean averaging of the data both over time and space by simply pre-multiplying and post-multiplying the data matrix by a smoothing matrix relative to the space and the time dimension respectively. Standard errors are evaluated by considering, at each time slice, the data as arising from a spatial process, and fitting a covariogram model to the residuals. Analogously, an autoregressive model can be fitted to the residual time series at each sensor. Such a method is helpful in order to address the common case where the sensor noise presents a spatial and temporal structure. The availability of standard errors allows a null hypothesis of null activation to be tested, and maps of t-statistics can be provided to highlight the strength of the detected dipole pattern. The methodology carried out allows single-trial analysis as a useful alternative to the usual practice of averaging raw MEG data from many trials, which usually show a great variability both in phase and amplitude. The benefit of applying moothing estimation at the single-trial level, rather than averaging raw data across replicates, was studied via simulation and also shown in real data examples. Building on this, future research might go in two directions. The first is the development of methodologies which adjust for the trial to trial variability and provide a more effective method of constructing a mean response surface. The second is to consider the multilevel structure of the MEG data in an attempt to model the response by including in the fitting process the variability at the different levels of the hierarchy generated by the experiment (subjects, conditions, trials).

    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 Archivio istituziona...arrow_drop_down
    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
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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 Archivio istituziona...arrow_drop_down
      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
  • 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: Goyal, Manu;

    Processed PET brain imaging data from prior studies as described in Goyal, Vlassenko et al. Cell Metabolism 2017.

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    Dataset . 2018
    Data sources: B2FIND
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    Mendeley Data
    Dataset . 2018
    License: CC BY
    Data sources: Mendeley Data
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    Mendeley Data; NARCIS
    Dataset . 2018
    License: CC BY
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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      Mendeley Data
      Dataset . 2018
      License: CC BY
      Data sources: Mendeley Data
      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/
      Mendeley Data; NARCIS
      Dataset . 2018
      License: CC BY
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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: Xuezhu Cai;

    This dataset contains raw MRI NIFTI images acquired for the published paper titled "Mild Repetitive Head Impacts Alter Perivascular Flow in the Midbrain Dopaminergic System in Awake Rats and Increase Microgliosis and AQP4 Expression and Depolarization". The dataset has a control group and a mild head impact group indicated by the folder name. Each subfolder has both T1-weighted axial and sagittal MRI images acquired using Fast Low Angle Shot (FLASH) sequence (TR/TE = 300/2.5ms and Flip Angle = 30°) and images were collected every 2 min and 41 sec for two hours after contrast administration. A contrast agent phantom was attached to the head coil during each scan for validation of image intensity rescaling.

    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/ Mendeley Data; NARCI...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/
    Mendeley Data; NARCIS
    Dataset . 2021
    License: CC BY
    Data sources: Datacite; NARCIS
    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/
    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    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/
    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Mendeley Data
    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/
    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Mendeley Data
    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/
    DANS-EASY
    Dataset . 2021
    Data sources: B2FIND
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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/ Mendeley Data; NARCI...arrow_drop_down
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      Mendeley Data; NARCIS
      Dataset . 2021
      License: CC BY
      Data sources: Datacite; NARCIS
      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/
      Mendeley Data
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      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/
      Mendeley Data
      Dataset . 2021
      License: CC BY
      Data sources: Mendeley Data
      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/
      Mendeley Data
      Dataset . 2021
      License: CC BY
      Data sources: Mendeley Data
      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/
      DANS-EASY
      Dataset . 2021
      Data sources: B2FIND
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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: Lamm, Claus; Bauer, Herbert; Vitouch, Oliver; Durec, Susanne; +2 Authors

    As is known from psychometrics, restriction of task processing time by the instruction to respond as quickly and accurately as possible leads to task-unspecific cognitive processing. Since this task processing mode is used in most functional neuroimaging studies of human cognition, this may evoke cortical activity that is functionally not essential for the particular task under investigation. Using topographic recordings of event-related slow cortical potentials, two experiments have been performed to investigate whether cortical activity during processing of a visuo-spatial imagery task is substantially influenced by the time provided to process the task. Furthermore, it was investigated whether this effect is additionally modulated by a subject’s task-specific ability. The instruction to respond as quickly and accurately as possible led to increased negative slow cortical potential amplitudes over parietal and frontal regions and significantly interacted with task-specific ability. While cortical activity recorded over parietal and frontal regions was different between subjects with low and high spatial ability when processing time was unrestricted, no such differences were found between ability groups when subjects were instructed to answer both quickly and accurately. These results suggest that restricting processing time has considerable effects on the amount and the pattern of brain activity during cognitive processing and should be taken into account more explicitly in the experimental design and interpretation of neuroimaging studies of cognition.

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    Authors: Winkler, A;

    This thesis is divided into three main parts. In the first, we discuss that, although permutation tests can provide exact control of false positives under the reasonable assumption of exchangeability, there are common examples in which global exchangeability does not hold, such as in experiments with repeated measurements or tests in which subjects are related to each other. To allow permutation inference in such cases, we propose an extension of the well known concept of exchangeability blocks, allowing these to be nested in a hierarchical, multi-level definition. This definition allows permutations that retain the original joint distribution unaltered, thus preserving exchangeability. The null hypothesis is tested using only a subset of all otherwise possible permutations. We do not need to explicitly model the degree of dependence between observations; rather the use of such permutation scheme leaves any dependence intact. The strategy is compatible with heteroscedasticity and can be used with permutations, sign flippings, or both combined. In the second part, we exploit properties of test statistics to obtain accelerations irrespective of generic software or hardware improvements. We compare six different approaches using synthetic and real data, assessing the methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). In the third part, we investigate and compare the different methods for assessment of cortical volume and area from magnetic resonance images using surface-based methods. Using data from young adults born with very low birth weight and coetaneous controls, we show that instead of volume, the permutation-based non-parametric combination (NPC) of thickness and area is a more sensitive option for studying joint effects on these two quantities, giving equal weight to variation in both, and allowing a better characterisation of biological processes that can affect brain morphology.

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    Oxford University Research Archive
    Other literature type . 2017
    License: CC BY
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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/ Oxford University Re...arrow_drop_down
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      Oxford University Research Archive
      Other literature type . 2017
      License: CC BY
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    The aim of this study was to characterize the magnetization transfer (MT) effects in locus coeruleus (LC) imaging by applying high spatial resolution quantitative MT (qMT) imaging to create parametric maps of the macromolecular content of the LC and surrounding tissues. Healthy volunteers (n=26; sex=17F/9M; age range=20-72 years) underwent brain MRI on a 3.0 Tesla scanner. qMT data were acquired using a 3D MT-prepared spoiled gradient echo sequence. A traditional Neuromelanin (NM) scan consisting of a T1-weighted turbo spin echo sequence with MT preparation was also acquired. PSR was estimated for each voxel using a single-point qMT approach (Yarnykh VL, 2012, doi: 10.1002/mrm.23224). This repository includes the qMT datasets for the 26 participants. The dataset for each subject includes the following preprocessed images: - B0map.nii - B1map.nii - NM_2_qMT.nii (NM scan using 2D multislice TSE) - qMT_1pt_mc.nii (motion corrected qMT data) - qMT_2kHz.nii (MT-weighted image) - qMT_ref.nii (Reference image - No MT) - T1mfa.nii (multiple flip angle images to calculate the T1 map) All the above are coregistered to qMT_2kHz.nii. The dataset also includes the following processed images: - PSR_kba12p5.nii (PSR map) - T1obs.nii (T1 map) Matlab code required for data analysis has been posted at https://github.com/smithalexk/pulsed_qMT/tree/5ec63095c3b4e426a076b5730bffcab415df9c95 When using the code, please cite - Smith AK, et al., Neuroimage, 2014; doi: 10.1016/j.neuroimage.2014.03.005 - Trujillo P, et al., Neuroradiology, 2017; doi: 10.1007/s00234-017-1911-2

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    Authors: Klein, Arno; Ghosh, Satrajit;

    The proposed research develops new computational tools to identify, diagnose, and predict treatment outcome for different mental illnesses. The research will be applied first to major depressive disorder, which affects millions of Americans, but is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain structure, activity, or connectivity between any two populations.

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    Authors: Anderson, Prof. Michael L.;

    The creative re-use of existing cognitive capacities may have played a significant role in the evolutionary development of the brain. There are obvious evolutionary advantages to such redeployment, and the data presented here confirm three important empirical predictions of this account of the development of cognition: (1) a typical brain area will be utilized by many cognitive functions in diverse task categories, (2) evolutionarily older brain areas will be deployed in more cognitive functions and (3) more recent cognitive functions will utilize more, and more widely scattered brain areas. These findings have implications not just for our understanding of the evolutionary origins of cognitive function, but also for the practice of both clinical and experimental neuroscience.

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    Authors: Klein, Arno; Ghosh, Satrajit;

    Figure 2 - Examples of automatically extracted features (MRI) (a) Example structural features (left lateral views of volumes, surfaces, curves, and points) (b) Schematic feature hierarchy: 3-D gyrii surround a 2-D sulcal ribbon with 1-D fundus containing 0-D pits

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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: Bullock, T.H.;

    Two classes of electrical activity in the central nervous system have been known for a long time: spikes with synaptic potentials and "slow" fluctuations (components mainly below ca. 100 Hz). Their relations to each other are still little known and an unfortunate schism persists in mutual disparagement by investigators who chiefly study one class or the other. The news I wish to highlight is that this schism is waning and this essay will be outmoded as more workers study both. I focus here on the class of slow potentials which in certain respects is the more neglected. This class should extend down into the less-known "infraslow" domain (power mainly below 0.1 Hz) - omnipresent, higher in amplitude and clearly significant functionally.

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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
    Authors: VENTRUCCI, MASSIMO; A. Bowman; C. Miller; J. Gross; +1 Authors

    Magnetoencephalography (MEG) is a non-invasive technique which measures the electromagnetic activity in the brain by recording the magnetic fields outside the head. Data is acquired by sensitive devices embedded in a helmet placed over the human head. The high temporal resolution of MEG (in the order of milliseconds) is optimal for studying the transient magnetic fields associated with the highly dynamic processes of brain activations. The goal is the identification of spatio-temporal components in the signal that correspond to certain cognitive processes that ideally are carefully manipulated in the respective experiment. Several kinds of noise and artifacts can distort the desired signal. Filtering procedures and averaging across many trials, i.e. replicates of the experiment, are methods usually adopted to summarize the data and increase the signal to noise ratio. However, uncertainty of estimates are usually not taken into account. In this work linear smoothing estimation based on a local fitting approach is applied to smooth the data both in time and space, i.e. the helmet surface, in order to reduce sensor noise. Maps depicting the mean response by smoothing out the sensor noise, plus standard errors for the mean, are produced to help in identifying the time and location where a dipole pattern occurs, which indicates activation. Computational issues are successfully addressed by considering the array representation of the data (Currie et al. 2006). The estimator actually realizes a local mean averaging of the data both over time and space by simply pre-multiplying and post-multiplying the data matrix by a smoothing matrix relative to the space and the time dimension respectively. Standard errors are evaluated by considering, at each time slice, the data as arising from a spatial process, and fitting a covariogram model to the residuals. Analogously, an autoregressive model can be fitted to the residual time series at each sensor. Such a method is helpful in order to address the common case where the sensor noise presents a spatial and temporal structure. The availability of standard errors allows a null hypothesis of null activation to be tested, and maps of t-statistics can be provided to highlight the strength of the detected dipole pattern. The methodology carried out allows single-trial analysis as a useful alternative to the usual practice of averaging raw MEG data from many trials, which usually show a great variability both in phase and amplitude. The benefit of applying moothing estimation at the single-trial level, rather than averaging raw data across replicates, was studied via simulation and also shown in real data examples. Building on this, future research might go in two directions. The first is the development of methodologies which adjust for the trial to trial variability and provide a more effective method of constructing a mean response surface. The second is to consider the multilevel structure of the MEG data in an attempt to model the response by including in the fitting process the variability at the different levels of the hierarchy generated by the experiment (subjects, conditions, trials).

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    Authors: Goyal, Manu;

    Processed PET brain imaging data from prior studies as described in Goyal, Vlassenko et al. Cell Metabolism 2017.

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    Dataset . 2018
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    Mendeley Data
    Dataset . 2018
    License: CC BY
    Data sources: Mendeley Data
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    Mendeley Data; NARCIS
    Dataset . 2018
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      Dataset . 2018
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      Mendeley Data
      Dataset . 2018
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      Dataset . 2018
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    Authors: Xuezhu Cai;

    This dataset contains raw MRI NIFTI images acquired for the published paper titled "Mild Repetitive Head Impacts Alter Perivascular Flow in the Midbrain Dopaminergic System in Awake Rats and Increase Microgliosis and AQP4 Expression and Depolarization". The dataset has a control group and a mild head impact group indicated by the folder name. Each subfolder has both T1-weighted axial and sagittal MRI images acquired using Fast Low Angle Shot (FLASH) sequence (TR/TE = 300/2.5ms and Flip Angle = 30°) and images were collected every 2 min and 41 sec for two hours after contrast administration. A contrast agent phantom was attached to the head coil during each scan for validation of image intensity rescaling.

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    Mendeley Data; NARCIS
    Dataset . 2021
    License: CC BY
    Data sources: Datacite; NARCIS
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    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2021
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    Mendeley Data
    Dataset . 2021
    License: CC BY
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    DANS-EASY
    Dataset . 2021
    Data sources: B2FIND
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      Dataset . 2021
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      Dataset . 2021
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      Dataset . 2021
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    Authors: Lamm, Claus; Bauer, Herbert; Vitouch, Oliver; Durec, Susanne; +2 Authors

    As is known from psychometrics, restriction of task processing time by the instruction to respond as quickly and accurately as possible leads to task-unspecific cognitive processing. Since this task processing mode is used in most functional neuroimaging studies of human cognition, this may evoke cortical activity that is functionally not essential for the particular task under investigation. Using topographic recordings of event-related slow cortical potentials, two experiments have been performed to investigate whether cortical activity during processing of a visuo-spatial imagery task is substantially influenced by the time provided to process the task. Furthermore, it was investigated whether this effect is additionally modulated by a subject’s task-specific ability. The instruction to respond as quickly and accurately as possible led to increased negative slow cortical potential amplitudes over parietal and frontal regions and significantly interacted with task-specific ability. While cortical activity recorded over parietal and frontal regions was different between subjects with low and high spatial ability when processing time was unrestricted, no such differences were found between ability groups when subjects were instructed to answer both quickly and accurately. These results suggest that restricting processing time has considerable effects on the amount and the pattern of brain activity during cognitive processing and should be taken into account more explicitly in the experimental design and interpretation of neuroimaging studies of cognition.

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    Authors: Winkler, A;

    This thesis is divided into three main parts. In the first, we discuss that, although permutation tests can provide exact control of false positives under the reasonable assumption of exchangeability, there are common examples in which global exchangeability does not hold, such as in experiments with repeated measurements or tests in which subjects are related to each other. To allow permutation inference in such cases, we propose an extension of the well known concept of exchangeability blocks, allowing these to be nested in a hierarchical, multi-level definition. This definition allows permutations that retain the original joint distribution unaltered, thus preserving exchangeability. The null hypothesis is tested using only a subset of all otherwise possible permutations. We do not need to explicitly model the degree of dependence between observations; rather the use of such permutation scheme leaves any dependence intact. The strategy is compatible with heteroscedasticity and can be used with permutations, sign flippings, or both combined. In the second part, we exploit properties of test statistics to obtain accelerations irrespective of generic software or hardware improvements. We compare six different approaches using synthetic and real data, assessing the methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). In the third part, we investigate and compare the different methods for assessment of cortical volume and area from magnetic resonance images using surface-based methods. Using data from young adults born with very low birth weight and coetaneous controls, we show that instead of volume, the permutation-based non-parametric combination (NPC) of thickness and area is a more sensitive option for studying joint effects on these two quantities, giving equal weight to variation in both, and allowing a better characterisation of biological processes that can affect brain morphology.

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    Oxford University Research Archive
    Other literature type . 2017
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