"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

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

Tuesday, October 29: 11 am – 12 pm

L-BFGS package

not limited

letter of intent is not required

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)

Expenses for foreign travel must be exceptionally well justified.

maximum project period is 2 years

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

Technology Development

Hypothesis Testing

areas

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.

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

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

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.

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.

and running the .exe file.

Reproducibility and Replicability in Science

slower

Neuroinformatics get together – Marseille, May 21st

toolbox from [15] (http://www.glaciology.net/wavelet-coherence).

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

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

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.

3.0-Tesla unit (Philips Achieva)

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)

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

Pearson correlation

EEGLAB

Preprocessed Connectome Project Quality Assurance Protocol (QAP)

FCP/INDI

ActiGraph wGT3X-BT

Sony ICD-UX 533 digital voice recorder

3.0 T Siemens Tim Trio

1.5 T Siemens Avanto

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

128-channel EEG geodesic hydrocel system

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

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

seed probabilistic tractography from each amygdala voxel

FreeSurfer

FSL tool FIRST

FSL tool SIENAX

FSL's BEDPOSTX

FMRIB Software Library (FSL) version 4.1

TractoR version 2.1

1.5 T Siemens Magnetom Avanto

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

We downloaded data from the publicly accessible ABIDE-1 database

3T GE Signa

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

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

ABIDE-1 data-base

ABIDE-1 database

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

Randomise v2.1 program as part of FSL

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

FILM (FMRIB’s Improved Linear Model)

AFNI’s 3ddespike program

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

Siemens Verio

39 youth with ASD relative to 22 TDC

3T functional magnetic resonance imaging (fMRI)

SAS Version 9.4

FreeSurfer 5.3.0

TRACULA) tool within FreeSurfer

3T Philips Achieva

17 adolescent boys with ASD and 17 typically developing boys

Supplementary Materials

Data Availability Statement

Supplementary Materials

scikit-learn

in-house code provided by LP

Statistical analyses in SPSS 22.0

Stata version 14

Codes used in the present study are available upon request.

Supplementary methods

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

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.

CONN toolbo

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).

repeated-measures ANOVA with follow-up t-tests

SPSS

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

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).

FMRIB Software Library (FSL

MRIConvert

3.0 T Siemens Magnetom Trio scanner (Erlangen, Germany)

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

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

functional magnetic resonance imaging (fMRI)

P < .05

χ2 test

t test

Brain Connectivity Toolbox

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

Diffusion Toolkit

FMRIB Diffusion Toolbox (FSL, version 4.1

GIFT software package

in-house MATLAB code.

SPM8

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

resting-state functional magnetic resonance imaging (fMRI)

SPM8

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

xjView toolbox

SPM Anatomy Toolbox v2

SPM8

‘lme4’ (Bates et al., 2015) in R

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.

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

R (version 3.3.3)

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)

3 T

Siemens

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

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)

new analyses of previously published data

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

functional magnetic resonance imaging

CC400 atlas

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

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

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

397 males under 31 years

Autism Brain Imaging Data Exchange Consortium

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.

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

Bonferroni-corrected p ≤ .0125

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

FreeSurfer 5.3

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.

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

8 high functioning ASD and 35 typically developing (TD)

Go to:Data Availability

Statistics and Machine Learning Toolbox or Effect Size Toolbox

SPSS 23.0,

Mann-Whitney U test

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

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

and corrected when necessary

Ubuntu OS

FreeSurfer version 5.1

Preprocessed Connectomes Project (PCP) resource [52]

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

18 TD and 20 ASD participants in the study

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.

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

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

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

SPSS (version 20,

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

method proposed by Wu et al. (2013)

Statistical Parametric Mapping (SPM8)

Data Processing Assistant for Resting-State fMRI (DPARSF)

The Autism Brain Imaging Data Exchange (ABIDE)

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

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

3-tesla MRI scanner (Philips Healthcare

15 children with ASD and 18 TDC

PALS-B12 brain atlas

method described by Schaer et al. (2008)

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

sphere radius was set to 25 mm

5.3 of FreeSurfer

MPRAGE sequence (176 slices, 1 mm3 voxels).

Siemens Trio 3 Tesla

Thirty-seven typically developing