702 Matching Annotations
  1. Last 7 days
  2. Jun 2019
  3. May 2019
  4. Apr 2019
  5. Mar 2019
    1. toolbox from [15] (http://www.glaciology.net/wavelet-coherence).

      analysis software

    2. in-house ‘goodness-of-fit’ MatLab function upon the 10 RSN map templates from Smith et al. [22]

      analysis tool

    3. FEAT–a software from FMRIB Software Library (www.fmrib.ox.ac.uk/fsl)

      analysis software

    4. resting-state fMRI data were acquired using the whole brain single-shot multi-slice BOLD echo-planar imaging (EPI) sequence, with TR 2 s, TE 35 ms, flip angle 90°, voxel size 2 × 2 × 4 mm3, matrix 128 × 128, 32 contiguous transverse slices per volume, and 210 volumes per acquisition; resulting in total a resting-state acquisition of 7 min.

      resting state acquisition details

    5. 3.0-Tesla unit (Philips Achieva)

      MRI Scanner

    6. esting-state fMRI data from University of Leuven in Belgium, available on the Autism Brain Imaging Data Exchange (http://fcon_1000.projects.nitrc.org/indi/abide)

      Sample 2

    7. 15 adolescents with ASD and 18 age- and IQ-matched controls

      Sample 1

    1. Pearson correlation


    2. EEGLAB

      Analysis tool

    3. Preprocessed Connectome Project Quality Assurance Protocol (QAP)

      Analysis tool

    4. FCP/INDI

      Image Data Access

    5. ActiGraph wGT3X-BT

      actigraphy equipment

    6. Sony ICD-UX 533 digital voice recorder

      voice recording equipment

    7. 3.0 T Siemens Tim Trio

      MRI Scanner

    8. 1.5 T Siemens Avanto

      MRI scanner

    9. iView-X Red-m, SensoMotoric Instruments [SMI] GmbH

      eye-tracking equipment

    10. 128-channel EEG geodesic hydrocel system

      EEG equipment

    1. we enforced a condition that at least 50% of participants had to demonstrate a connection between the amygdala and a given target

      analyis detail

    2. R using a script specifically written for this study, based on the method outlined by Behrens et al

      local analysis script

    3. seed probabilistic tractography from each amygdala voxel

      analysis procedure

    4. FreeSurfer

      analysis tool

    5. FSL tool FIRST

      FSL tool

    6. FSL tool SIENAX

      specific FSL tool


      specific tool from FSL

    8. FMRIB Software Library (FSL) version 4.1

      analysis software

    9. TractoR version 2.1

      analysis software

    10. 1.5 T Siemens Magnetom Avanto

      MRI equipment

    11. Twenty‐six high‐functioning young adults who had previously received a clinical diagnosis of an ASD, and 26 age‐matched neurotypical controls


    1. We downloaded data from the publicly accessible ABIDE-1 database

      Data access

    2. 3T GE Signa

      MRI Scanner

    3. Forty-five were diagnosed with Autistic Disorder, seven with Asperger’s Disorder, one with PDD-NOS and two with ASD of undetermined subtype


    4. Fifty-five age-matched (14.1±3.1 y/o) subjects were MRI scanned as typical controls

      control subjects

    5. ABIDE-1 data-base

      data source

    6. ABIDE-1 database

      Data source

    1. All data generated and/or analyzed during this study are available from the corresponding author (BEY) on reasonable request.

      data accessibility statement

    2. Randomise v2.1 program as part of FSL


    3. FMIRB’s linear analysis of mixed effects (FLAME1+2)


    4. FILM (FMRIB’s Improved Linear Model)


    5. AFNI’s 3ddespike program


    6. FEAT (FMRIB’s Expert Analysis Tool), part of FMRIB’s Software Library (FSL) package


    7. Siemens Verio

      MRI Manufacturer

    8. 39 youth with ASD relative to 22 TDC

      subject population

    9. 3T functional magnetic resonance imaging (fMRI)

      MRI Field strength and type of study

    1. Supplementary Materials

      AMARES algorithm implemented in jMRUI software FSL v5 in-house MATLAB for ASL FreeSurfer 5.3

    2. scikit-learn

      analysis software

    3. in-house code provided by LP

      analysis software

    4. Statistical analyses in SPSS 22.0

      statistical software

    5. Stata version 14

      Stat software

    1. Codes used in the present study are available upon request.

      software availability

    2. Supplementary methods of the Configurable Pipeline for the Analysis of Connectomes [15] (C-PAC, http://fcp-indi.github.com/C-PAC/), which integrates tools from AFNI (http://afni.nimh.nih.gov/afni), FSL (http://fmrib.ox.ac.uk) and Advanced Normalization Tools (ANTs; http://stnava.github.io/ANTs) using Nipype (http://nipype.readthedocs.io/en/latest/).

    3. version of the Configurable Pipelines for the Analysis of Connectomes (C-PAC)


    4. 357 neurotypical (NT) males and 471 NT females from the 1000 Functional Connectome Project and 360 males with ASD and 403 NT males from the Autism Brain Imaging Data Exchange.


    1. Model-based Neuroscience Summer School; August 5 – August 9, 2019; University of Amsterdam

    1. CONN toolbo

      Analysis software

    2. we included whether the subject was from the Temple or Geisinger cohort as a covariate, in order to minimize the impact of any differences between the two groups. We control for family wise error using Bonferroni correction (10 comparisons = critical p value of 0.05/10 = 0.005).

      statistical details

    3. repeated-measures ANOVA with follow-up t-tests

      statistical detail

    4. SPSS

      statistical software

    5. regional definitions from an independent data set created by the Kanwisher Lab

      Can these be obtained?

    6. Preprocessing steps included stripping non-brain material using the Brain Extraction Tool (BET) and motion correction, B0 unwarping, and slice time correction with FSL FEAT (fMRI Expert Analysis Tool) version 5.0.8. Images were normalized to 2 mm space via FLIRT and smoothed using a 5 mm Gaussian kernel. Four categorical regressors indicated whether the stimulus for each block was a face, place, food, or clock. Categorical regressors were boxcar functions at stimulus onset convolved with a double gamma function. Six estimated motion parameters were also included as nuisance regressors. Parameter estimate maps for each individual were then transformed into standardized t-statistic maps for each contrast (Faces, Places, Food, & Clocks).

      Analysis detail

    7. FMRIB Software Library (FSL

      analysis software

    8. MRIConvert

      Analysis software

    9. 3.0 T Siemens Magnetom Trio scanner (Erlangen, Germany)

      MRI Scanner

    10. 3.0 T Siemens Verio scanner (Erlangen, Germany) using a Siemens twelve-channel phased-array head coil

      MRI Scanner

    11. Forty-eight healthy adults (24 females; mean age 22) were included in the group analysis

      subject population

    12. functional magnetic resonance imaging (fMRI)


    1. P < .05

      stat detail

    2. χ2 test


    3. t test


    4. Brain Connectivity Toolbox

      analysis software

    5. 0.2. Tractography was terminated if it turned at an angle exceeding 45° or reached a voxel with a fractional anisotropy less than 0.2

      analysis detail

    6. Diffusion Toolkit

      analysis software

    7. FMRIB Diffusion Toolbox (FSL, version 4.1

      Analysis software

    1. GIFT software package

      analysis software

    2. in-house MATLAB code.

      analysis software

    3. SPM8

      analysis software

    4. our training set consists of 776 resting state scans: 491 were taken from healthy controls and 279 from patients.

      training set

    5. resting-state functional magnetic resonance imaging (fMRI)


    1. SPM8


    2. wise threshold of P < 0.001 (uncorrected) and a cluster extent threshold ensuring q < 0.05 (false discovery rate (FDR)-corrected)

      stat detail

    3. xjView toolbox

      visualization software

    4. SPM Anatomy Toolbox v2

      analysis software

    5. SPM8


    6. ‘lme4’ (Bates et al., 2015) in R

      specific test

    7. interaction between condition and group. Participant and item were included as random effects, and we fit an intercept for each participant and for each item, allowing the intercept to vary across individuals and items. To assess the importance of our predictors of interest, we performed likelihood ratio tests (LRTs) to test whether the model including a given predictor would provide a better fit to the data than a model without that term.

      stat details

    8. condition (physical harm vs. psychological harm vs. neutral act) and group (NT vs. ASD).

      stat design

    9. R (version 3.3.3)

      behavioral analysis software

    10. motion-corrected, realigned, normalized onto a common brain space (Montreal Neurological Institute, MNI, template), spatially smoothed using a Gaussian filter (full-width half-maximum = 8 mm kernel) and high-pass filtered (128 s)

      processing details

    11. 3 T

      Field strength for MRI

    12. Siemens

      Scanner manufacturer

    13. ASD group consisted of 16 adults between the ages of 20 and 46 (M = 31.13, SD = 8.21; 2 women)

      ASD Group

    14. The NT group consisted of 25 adults from the Boston area between the ages of 18 and 50 (M = 28.56, SD = 10.10; 7 women)

      NT Adult group

    15. new analyses of previously published data

      primary data report elsewhere

    16. Altogether, these results reveal neural sensitivity to the distinction between psychological harm and physical harm.


    17. functional magnetic resonance imaging


  6. Feb 2019
    1. DIPY WORKSHOP 2019; 11 - 15th March 2019; Bloomington - Indiana

    1. CC400 atlas


    2. final dataset used in our analysis was composed of 397 males (mean age ± standard deviation, 16.29 ± 5.61 years) distributed along 19 datasets collected from 16 international sites

      Final dataset

    3. To pre-process the fMRI data, we used the Athena pipeline (http://www.nitrc.org/plugins/mwiki/index.php/neurobureau:AthenaPipeline)

      preprocessing tool

    4. ABIDE Consortium website (http://fcon_1000.projects.nitrc.org/indi/abide/)

      Data source

    5. 397 males under 31 years


    6. Autism Brain Imaging Data Exchange Consortium

      Data source

    1. We investigated group differences in the relations of age with the four diffusion measures, averaged across all tracts, using ANOVA with factors for age, group and their interaction.

      stat 'model'

    2. Statistical Parametric Connectome (SPC; Meskaldji et al., 2015

      stat software

    3. Bonferroni-corrected p ≤ .0125

      stat detail

    4. TRActs Constrained by UnderLying Anatomy (TRACULA; Yendiki et al., 2011)


    5. FreeSurfer 5.3


    6. Anatomical images were acquired using a 3D multiecho magnetization-prepared rf-spoiled rapid gradient-echo MEMPRAGE (T1 weighted) sequence with EPI based volumetric navigators for real time motion correction (Tisdall et al., 2012; van der Kouwe et al., 2008) TR = 2530 ms, Flip Angle = 7°, TEs = 1.74 ms/3.6 ms/5.46 ms/7.32 ms, iPAT = 2; FOV = 56 mm; 176 in-plane sagittal slices; voxel size = 1 mm3 isotropic; scan duration 6 m 12 s. DW-MRI scans were acquired using standard echo-planar imaging (TR = 8020 ms, TE = 83 ms, b = 700 s/mm2; 10 non-diffusion weighted T2 images acquired with b = 0; 60 diffusion directions; 128 × 128 matrix; 2 × 2 mm in-plane resolution; 64 axial oblique (AC-PC) slices; 2 mm slice thickness (0 mm gap); scan duration 9 m 47 s.

      scanning details

    7. 51 individuals with ASD without intellectual disability and 36 TD controls, aged 8–25, participated.


    8. 8 high functioning ASD and 35 typically developing (TD)


    1. Go to:Data Availability

      initial data access

    2. Statistics and Machine Learning Toolbox or Effect Size Toolbox

      stat tool

    3. SPSS 23.0,

      stats software

    4. Mann-Whitney U test

      stat test

    5. The algorithm was implemented using routines written in MATLAB 8.3 (R2014a)

      local software

    6. Mandelbulber software (https://github.com/buddhi1980/mandelbulber2)


    7. and corrected when necessary

      manual intervention

    8. Ubuntu OS


    9. FreeSurfer version 5.1

      analysis tool

    10. Preprocessed Connectomes Project (PCP) resource [52]


    11. High-resolution structural images were obtained using T1-weighted pulse sequences at 3T MR scanners at all sites, on Tim Trio at UCLA_1, USM, and Yale and on Allegra (Siemens, Erlangen, Germany) at NYU, and on a Signa (GE Medical Systems, Milwaukee, WI) at UM_1

      MRI acquisition details

    12. 18 TD and 20 ASD participants in the study

      Final N

    13. we chose the 20 youngest male participants with ASD and matched them as closely as possible on VIQ scores and cerebellar volumes (using left and right gray matter volume in mm3) to 20 male TD participants of similar age, as follows.

      subject selection

    14. below 12 years old who were male, and whose verbal IQ (as well as Full Scale and Performance IQ) was greater than 70.

      Subselection criteria

    15. Autism Brain Imaging Data Exchange (ABIDE) (http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html)

      Data source

    1. he deconvolution code in MATLAB is publicly available at http://users.ugent.be/~dmarinaz/HRF_deconvolution.html.

      Deconvolution code

    2. SPSS (version 20,

      Statistics software

    3. blind deconvolution technique developed for rs-fMRI by Wu et al. (2013)

      analysis 'method'

    4. method proposed by Wu et al. (2013)

      Analysis 'method'

    5. Statistical Parametric Mapping (SPM8)

      analysis software

    6. Data Processing Assistant for Resting-State fMRI (DPARSF)

      Analysis software

    7. The Autism Brain Imaging Data Exchange (ABIDE)

      Data source

    8. we also hypothesized that such alterations will lead to differences in estimated functional connectivity in fMRI space compared to latent neural space


    9. we hypothesized that this will lead to voxel-specific, yet systematic differences in HRF shape between ASD and healthy controls.


  7. Jan 2019
    1. PALS-B12 brain atlas


    2. method described by Schaer et al. (2008)

      analysis 'tool'

    3. brain maps were smoothed using a 10 mm full-width half-maximum Gaussian kernel


    4. sphere radius was set to 25 mm


    5. 5.3 of FreeSurfer

      analysis tool

    6. MPRAGE sequence (176 slices, 1 mm3 voxels).

      mri protocol

    7. Siemens Trio 3 Tesla


    8. Thirty-seven typically developing

      number of controls

    9. The AS-NoSOD group (N = 27) presented normal speech onset, whereas the AS-SOD group (N = 28) was characterized by a SOD.


    10. AS individuals (N = 55)

      number of subjects

    1. SPSS

      stat tool

    2. Brain Connectivity Toolbox

      software tool

    3. Automated Anatomical Labeling Atlas (AAL)

      software tool

    4. The FA threshold of 0.15

      analysis parameter

    5. Each line was propagated by 0.25 mm to the next point in space, at which point the process was repeated. Each of these streamlines was terminated when FA < 0.15 or when the angular deviation from paths was >55° to prevent streamlines from looping back

      analysis parameters

    6. Deterministic tractography was performed using the Diffusion Toolkit in PANDA, a MATLAB

      software tool

    7. FSL's “dtifit” tool

      software tool

    8. “auto_warp” command in AFNI

      software tool

    9. “fsl_prepare_fieldmap” tool in FSL

      software tool

    10. skull-stripped using the Brain Extraction Tool

      software tool

    11. “eddy” tool from FSL

      software tool

    12. Diffusion tensor imaging data was acquired using a single-shot spin echo echo-planar imaging (EPI) sequence with 30 gradient directions and the following acquisition parameters: repetition time (TR) = 7700 ms; echo time (TE) = 90 ms; b = 1000 s/mm2; acquisition matrix = 204 × 204; voxel size = 2.0 × 2.0 × 2.0 mm, 60 contiguous axial slices and scan time = 8 min 22 s. High-resolution T1-weighted structural images were also acquired by collecting 176 contiguous sagittal slices using a three-dimensional magnetization prepared rapid gradient echo imaging (3D MPRAGE) sequence with the following parameters: repetition time (TR) = 2250 ms; inversion time (TI) = 850 ms; echo time (TE) = 3.98 ms; field of view (FOV) = 256 mm; acquisition matrix = 256 × 256; voxel size = 1.0 × 1.0 × 1.0 mm; slice thickness = 1.0 mm; flip angle = 9°. A field map was also recorded with a gradient echo sequence with the parameters of repetition time (TR) = 488 ms; echo time 1 (TE 1) = 4.92 ms; echo time 2 (TE 2) = 7.38 ms; voxel size = 3.0 × 3.0 × 3.0 mm; field of view (FOV) = 204 mm; slice thickness = 3.0 mm; 40 slices; flip angle = 60° to measure field inhomogeneities and compensate for geometrical distortions that result from standard EPI sequences.

      Acquisition details

    13. 3 T Siemens Trio MRI scanner using a 12-channel head coil


    1. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

      Data availability statement

    2. GWCi = β0 + β1Gj + β2 agej + β3 agej2 + β4 agej3 + β5 (agej × groupj) + β6 (agej2 × groupj) + β7 (agej3 × groupj) + β8 IQj + εi, where ε denotes the residual error.

      stat model

    3. Corrections for multiple comparisons across the whole brain were performed using random-field theory (RFT)-based cluster-corrected analysis for non-isotropic images using a p < 0.05 (two-tailed) cluster-significance threshold

      stat detail: multiple compariuson

    4. F-test for nested model comparisons was performed at each vertex

      stat test

    5. linear, quadratic, and cubic effects of age, in addition to the main effect of group in a vertex-wise fashion

      stat detail

    6. SurfStat toolbox (http://www.math.mcgill.ca/keith/surfstat/) for Matlab (R2016a; www.mathworks.com)

      Statistical analysis

    7. smoothed using a 10-mm full-width at half-maximum (FWHM) surface-based Gaussian kernel prior to statistical analyses

      analysis detail

    8. final sample size of 153 participants (n = 77 individuals with ASD and n = 76 TD controls)

      final sample size

    9. FreeSurfer v5.3.0

      Analysis tool

    10. 3-Tesla GE Signa System (General-Electric, Milwaukee, WI) with full-head coverage, 196 contiguous slices (1.1-millimetre (mm) thickness, with 1.09 × 1.09 mm in-plane resolution), a 256 × 256 × 196 matrix, and a repetition time/echo time (TR/TE) of 7/2.8 milliseconds (ms) (flip angle = 20°, FOV = 28 cm). A (birdcage) head coil was used for radiofrequency transmission and reception.

      Acquisition details

    11. aged 7 to 25

      age range

    12. Eighty-two (82) right-handed males with ASD and eighty-two (82)

      N Subjects

  8. Dec 2018
    1. FORCE11 Scholarly Communications Institute; Aug 5-9, 2019; UCLA, Los Angeles, CA, USA

  9. Nov 2018
  10. Oct 2018
    1. These data are obtained from the Human Connectome Project and thus we must adhere to their data use terms (https://www.humanconnectome.org/study/hcp-young-adult/data-use-terms). They provide data access at the following link: https://db.humanconnectome.org/.

      Data source

    2. independent samples t-tests and analyses of covariance (ANCOVAs; controlling for age and ICV) to test for gender differences in FFM traits and amygdala/hippocampal volumes, respectively

      Stat method

    3. Hippocampal subfield segmentation was derived using the automated algorithm available in FreeSurfer version 6.0

      software tool