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Open science resources for `Big Data' Analyses of the human connectome


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Neuroimaging has become a `Big Data' pursuit that requires very large datasets and high throughput computational tools. In this talk I will highlight many open science resources for acquiring the necessary data. This is from a lecture that I gave in 2015 at the USC Neuroimaging and Informatics Institute.

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Open science resources for `Big Data' Analyses of the human connectome

  1. 1. Open science resources for ‘Big Data’ analyses of the human connectome Cameron Craddock, PhD Computational Neuroimaging Lab Center for Biomedical Imaging and Neuromodulation Nathan S. Kline Institute for Psychiatric Research Center for the Developing Brain Child Mind Institute
  2. 2. The Human Connectome • The sum total of all of the brain’s connections – Structural connections: synapses and fibers • Diffusion MRI – Functional connections: synchronized physiological activity • Resting state functional MRI • Nodes are brain areas • Edges are connections Craddock et al. Nature Methods, 2013.
  3. 3. Connectomics is Big Data
  4. 4. Discovery science of human brain function 1. Characterizing inter-individual variation in connectomes (Kelly et al. 2012) 2. Identifying biomarkers of disease state, severity, and prognosis (Craddock 2009) 3. Re-defining mental health in terms of neurophenotypes, e.g. RDOC (Castellanos 2013) Data is often shared only in its raw form – must be preprocessed to remove nuisance variation and to be made comparable across individuals and sites.
  5. 5. No consensus on preprocessing Non-white noise in fMRI: Does modelling have an impact? Torben E. Lund,a,* Kristoffer H. Madsen,a,b Karam Sidaros,a Wen-Lin Luo,c and Thomas E. Nicholsd a Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidoure, Kettegaard Alle´ 30, 2650 Hvidovre, Denmark b Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark c Merck & Co., Inc., Whitehouse Station, New Jersey 08889-0100, USA d Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA Received 16 December 2004; revised 1 July 2005; accepted 6 July 2005 Available online 11 August 2005 The sources of non-white noise in Blood Oxygenation Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) are many. Familiar sources include low-frequency drift due to hardware imperfections, oscillatory noisedueto respiration and cardiac pulsation and residual movement artefacts not accounted for by rigid body registration. These contributions give rise to temporal autocorrelation in theresidualsof thefMRI signal and invalidate thestatistical analysis as the errors are no longer independent. The low-frequency drift is often removed by high-pass filtering, and other effects are typically modelled as an autoregressive (AR) process. In this paper, we propose an alternative approach: Nuisance Variable Regression (NVR). By identically normally distributed (i.i.d.), thisobservation isimportant and hashad largeimpact on paradigm design and dataanalyses. With non-white noise, the i.i.d. assumption is no longer fulfilled, and if this is ignored, the estimated standard deviations will typically be negatively biased, resulting in invalid (liberal) statistical inferences. Another consequence is the difficulty in detecting signals when covered in noise. As we are normally interested in the GM signal, it is problematic that this is the region where structured noise is most pronounced. With physiological noise increasing with field strength (Kru¨ger and Glover, 2001; NeuroImage 29 (2006) 54 – 66 e noise in fMRI: Does modelling have an impact? nd,a,* Kristoffer H. Madsen,a,b Karam Sidaros,a c and Thomas E. Nicholsd tre for Magnetic Resonance, Copenhagen University Hospital, Hvidoure, Kettegaard Alle´ 30, 2650 Hvidovre, Denmark ematical Modelling, Technical University of Denmark, Lyngby, Denmark hitehouse Station, New Jersey 08889-0100, USA istics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA r 2004; revised 1 July 2005; accepted 6 July 2005 ugust 2005 hite noisein Blood Oxygenation Level Dependent magnetic resonance imaging (fMRI) are many. clude low-frequency drift due to hardware identically normally distributed (i.i.d.), thisobservation isimportant andhashadlargeimpact onparadigmdesignanddataanalyses. NeuroImage 29 (2006) 54 – 66 A component based noise correction method (CompCor) for BOLD and perfusion based fMRI Yashar Behzadi,a,b Khaled Restom,a Joy Liau,a,b and Thomas T. Liua,⁎ a UCSD Center for Functional Magnetic Resonance Imaging and Department of Radiology, 9500 Gilman Drive, MC 0677, La Jolla, CA 92093-0677, USA b Department of Bioengineering, University of California San Diego, La Jolla, CA, USA Received 18 December 2006; revised 23 April 2007; accepted 25 April 2007 Available online 3 May 2007 A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion- based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are neurovascular coupling mechanisms (Hoge et al., 1999). However, as the fMRI community has moved to higher field strengths, physiological noise has become an increasingly important confound limiting the sensitivity and the application of fMRI studies (Kruger and Glover, 2001; Liu et al., 2006). Physiological fluctuations have been shown to be a significant source of noise in BOLD fMRI experiments, with an even greater effect in perfusion-based fMRI utilizing arterial spin labeling NeuroImage 37 (2007) 90– 101 This is particularly complicated for “post-hoc” aggregated datasets
  6. 6. A variety of analyses
  7. 7. The cost of discovery “Best practice” r-fMRI preprocessing: ~ 2 hours Discovery dataset: ~1,000 subjects “Point and click” processing: 2,000 person hours (1 year) Scripted processing: 2,000 CPU hours (84 days to minutes) Different derivatives and analyses add time Different preprocessing strategies scale time
  8. 8. Configurable Pipeline for the Analysis of Connectomes (CPAC) • Pipeline to automate preprocessing and analysis of large-scale datasets • Most cutting edge functional connectivity preprocessing and analysis algorithms • Configurable to enable “plurality” – evaluate different processing parameters and strategies • Automatically identifies and takes advantage of parallelism on multi-threaded, multi-core, and cluster architectures • “Warm restarts” – only re-compute what has changed • Open science – open source • Nypipe
  9. 9. • 33 datasets acquired with a variety of different test-retest designs – Intra- and inter-session re-tests – 1629 subjects – 3357 anatomical MRI scans – 5093 resting state fMRI scans – 1302 diffusion MRI scans
  10. 10. Why not share preprocessed data? • Make data available to a wider audience of researchers • Evaluate reproducibility of analysis results
  11. 11. ADHD-200 Preprocessed • 374 ADHD & 598 TDC – 7-21 years old • Two functional pipelines – Athena: FSL & AFNI, precursor to C-PAC – NIAK: MINC tools + NIAK using PSOM pipeline • Structural pipeline – Burner: SPM5 based VBM
  12. 12. ADHD-200 Preprocessed (2) • 9,500 downloads from 49 different users • Athena preprocessed data used by winning team of the ADHD Global competition • 31 peer reviewed publications, 2 dissertations and 1 patent – ( oups/4198361/adhd-200- preprocessed/) Figure 2. Overview of the ADHD-200 Preprocessed audience.
  13. 13. Beijing DTI Preprocessed • 180 healthy college students • 55 with Verbal, Performance, and Full IQ • Preprocessed using FSL – DTI scalars (FA, MD, etc…) – Probabilistic Tractography
  14. 14. ABIDE Preprocessed indexed by NDAR • 539 ASD and 573 typical – 6 – 64 years old – Some overlap with controls from ADHD-200 • 4 Functional Preprocessing Pipelines • 4 Preprocessing strategies – GSR, No-GSR, Filtering, No- Filtering • 4 Cortical thickness pipelines – ANTS, CIVET, Freesurfer, Mindboggle
  15. 15. ABIDE Preprocessed (2) DPARSF CCS CBRAIN Team Tools Analyses C-BRAIN CIVET, MINC Cortical Measures C-PAC AFNI, ANTs, FSL, Nipype R-fMRI, VBM, Cortical Measures CCS AFNI, Freesurfer, FSL R-fMRI, VBM, Cortical Measures DPARSF DPARSF, REST, SPM R-fMRI Mindboggle Mindboggle Cortical Measures NIAK II MINC, NIAK, PSOM R-fMRI
  16. 16. Quality Assessment Protocol • Spatial Measures – Contrast to Noise Ratio – Entropy Focus Criterion – Foreground to Background Energy Ratio – Smoothness (FWHM) – % Artifact Voxels – Signal-to-Noise Ratio • Temporal Measures – Standardized DVARS – Median distance index – Mean Functional Displacement – # Voxels with FD > 0.2m – % Voxels with FD > 0.2m
  17. 17. Quality Assessment Protocol (2) • Implemented in python • Normative datasets to help learn thresholds for quality control – ABIDE – CoRR
  18. 18. Regional Brainhacks • One event that linked 8 Cities, 3 Countries, 2 continents – Ann Arbor – Boston – Miami – Montreal – New York City – Porto Alegre, Brazil – Toronto – Washington DC
  19. 19. Acknowledgements CPAC Team: Daniel Clark, Steven Giavasis and Michael Milham. Quality Assessment Protocol: Zarrar Shehzad, Daniel Lurie, Steven Giavasis, and Sang Han Lee. ABIDE Preprocessed: Pierre Bellec, Yassine Benhajali, Francois Chouinard, Daniel Clark, R. Cameron Craddock, Alan Evans, Steven Giavasis, Budhachandra Khundrakpam, John Lewis, Qingyang Li, Zarrar Shezhad, Aimi Watanabe, Ting Xu, Chao-Gan Yan, Zhen Yang, Xinian Zuo, and the ABIDE consortium. Brainhack Organizers: Pierre Bellec, Daniel Margulies, Maarten Mennes, Donald McLauren, Satra Ghosh, Matt Hutchison, Robert Welsh, Scott Peltier, Jonathan Downer, Stephen Strother, Katie Dunlop, Angie Laird, Lucina Uddin, Benjamin De Leener, Julien Cohen-Adad, Andrew Gerber, Alex Franco, Caroline Froehlich, Felipe Meneguzzi, John VanMeter, Lei Liew, Ziad Saad, Prantik Kundu CPAC-NDAR integration was funded by a contract from NDAR. ABIDE Preprocessed data is hosted in a Public S3 Bucket provided by AWS.