739 Matching Annotations
  1. Last 7 days
  2. Dec 2019
    1. IBRO-RIKEN CBS Summer Program 2020; Intern Course: June 18 - August 21, 2020, Lecture Course: June 22 - 26, 2020; Saitama JAPAN

    1. "As much as I care what the phone call contained, I care more that my 401(k) is growing, because I have a child to put through college." Kim Alfano

      This seems to me as a classic example of 'the ends justify the means'.

  3. Nov 2019
  4. Oct 2019
    1. The over-arching goal of this NIDA/NIMH R25 program is to support educational activities that complement and/or enhance the training of a workforce to meet the nation’s biomedical, behavioral and clinical research needs in the use of ABCD data

      overarching aim

    1. Tuesday, October 29: 11 am – 12 pm

      OpenNeuro Webinar; Tuesday, October 29: 11 am – 12 pm PDT

    2. OpenNeuro Webinars; Thursday, October 24: 10 am – 11 am PDT

  5. Sep 2019
    1. not limited

      limits?

    2. letter of intent is not required

      LOI not required

    3. Indirect Costs (also known as Facilities & Administrative [F&A] Costs) are reimbursed at 8% of modified total direct costs (exclusive of tuition and fees and expenditures for equipment)

      capped IDR

    4. Expenses for foreign travel must be exceptionally well justified.

      foreign travel

    5. maximum project period is 2 years

      duration

    6. Funds Available and Anticipated Number of Awards The following NIH components intend to commit the following amounts in FY 2020: NIDA, $100,000, 1-2 awards NIMH, $200,000, 1-2 awards

      funds

    7. Technology Development

      encouragement areas

    8. Hypothesis Testing

      encouragement areas

    9. areas

      encouragement areas

    10. overall goals of this initiative are: Widening use of the ABCD dataset Enhancing rigor and reproducibility towards better predictive models Facilitating collaboration between clinical and computational researchers on normative and psychopathological neurodevelopment.

      overall goals

    11. Applications proposing prediction of outcomes within the baseline assessment (e.g., predictiing impulsivity scores at the baseline timepoint from neuroimaging measures) are encouraged to explicitly address validation strategies

      encouragement for validation

    12. Applications emphasizing the development of predictive models for identifying group/individual differences, with the overarching goal of predicting behavioral and clinical outcomes in future timepoints

      particular encouragement

    13. purpose of this FOA is to invite applications that involve research education on the use of ABCD data through meetings/workshops involving 1. Advanced seminars relevant to analysis of ABCD data, and 2. Hands on collaborative- or competition-style use of the ABCD dataset.

      purpose

    14. Two types of events are the focus of this initiative. The first includes competitive events where multiple research teams are pitted against each other towards a common challenge. The second includes collaborative events ranging from traditional workshops aimed at analysis-related training to hackathons or codeathons – where multiple participants gather to engage in collaborative computer programming.

      event types

  6. nda.nih.gov nda.nih.gov
    1. and running the .exe file.

      Well, not on MAC (or Linux). Install the package in the 'usual' fashion for your system...

  7. Aug 2019
    1. slower

      Slower, meaning 20 minutes using 7 cores of an 8 vcore MacBook Pro Intel Core i7...

  8. Jul 2019
  9. Jun 2019
  10. May 2019
  11. Apr 2019
  12. 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

      stat

    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

    7. FSL's BEDPOSTX

      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

      Subjects

    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

      patients

    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

      software

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

      software

    4. FILM (FMRIB’s Improved Linear Model)

      software

    5. AFNI’s 3ddespike program

      software

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

      Software

    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

      0.3.9.1 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 0.3.9.1 of the Configurable Pipelines for the Analysis of Connectomes (C-PAC)

      Software

    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.

      subjects

    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)

      modality

    1. P < .05

      stat detail

    2. χ2 test

      stat

    3. t test

      stat

    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)

      modality

    1. SPM8

      software

    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

      software

    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.

      Conclusion

    17. functional magnetic resonance imaging

      modality

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

    1. CC400 atlas

      resouce

    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

      Sample

    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)

      software

    5. FreeSurfer 5.3

      software

    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.

      Participants

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

      sample

    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)

      software

    7. and corrected when necessary

      manual intervention

    8. Ubuntu OS

      OS

    9. FreeSurfer version 5.1

      analysis tool

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

      preprocessing

    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

      hypothesis

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

      Hypothesis

  14. Jan 2019
    1. PALS-B12 brain atlas

      tool

    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

      parameter

    4. sphere radius was set to 25 mm

      parameter

    5. 5.3 of FreeSurfer

      analysis tool

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

      mri protocol

    7. Siemens Trio 3 Tesla

      scanner

    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.

      groups