problems solution future let’s step backwards Train Test SPM Extract Cluster Means Linear Regression Predict Step 1: Split data into a training and test set Step 2: Find signiﬁcant clusters Step 3: Extract mean intensity for the test and training sets Step 4: Use training data to run a linear regression Step 5: Use the model and the test data to predict lsas‐Δ score. Folds Data Oliver Doehermann, Anisha Keshavan, Franzi H.

problems solution future let’s step backwards Train Test SPM Extract Cluster Means Linear Regression Predict Step 1: Split data into a training and test set Step 2: Find signiﬁcant clusters Step 3: Extract mean intensity for the test and training sets Step 4: Use training data to run a linear regression Step 5: Use the model and the test data to predict lsas‐Δ score. Folds Data Oliver Doehermann, Anisha Keshavan, Franzi H.

problems solution future let’s step backwards Train Test SPM Extract Cluster Means Linear Regression Predict Step 1: Split data into a training and test set Step 2: Find signiﬁcant clusters Step 3: Extract mean intensity for the test and training sets Step 4: Use training data to run a linear regression Step 5: Use the model and the test data to predict lsas‐Δ score. Folds Data Oliver Doehermann, Anisha Keshavan, Franzi H.

problems solution future let’s step backwards Train Test SPM Extract Cluster Means Linear Regression Predict Step 1: Split data into a training and test set Step 2: Find signiﬁcant clusters Step 3: Extract mean intensity for the test and training sets Step 4: Use training data to run a linear regression Step 5: Use the model and the test data to predict lsas‐Δ score. Folds Data Oliver Doehermann, Anisha Keshavan, Franzi H.

problems solution future let’s step backwards Train Test SPM Extract Cluster Means Linear Regression Predict Step 1: Split data into a training and test set Step 2: Find signiﬁcant clusters Step 3: Extract mean intensity for the test and training sets Step 4: Use training data to run a linear regression Step 5: Use the model and the test data to predict lsas‐Δ score. Folds Data Oliver Doehermann, Anisha Keshavan, Franzi H.

problems solution future let’s step backwards Train Test SPM Extract Cluster Means Linear Regression Predict Step 1: Split data into a training and test set Step 2: Find signiﬁcant clusters Step 3: Extract mean intensity for the test and training sets Step 4: Use training data to run a linear regression Step 5: Use the model and the test data to predict lsas‐Δ score. Folds Data Oliver Doehermann, Anisha Keshavan, Franzi H.

problems solution future let’s step backwards Train Test SPM Extract Cluster Means Linear Regression Predict Step 1: Split data into a training and test set Step 2: Find signiﬁcant clusters Step 3: Extract mean intensity for the test and training sets Step 4: Use training data to run a linear regression Step 5: Use the model and the test data to predict lsas‐Δ score. Folds Data Enough details? Oliver Doehermann, Anisha Keshavan, Franzi H.

problems solution future let’s step backwards Train Test SPM Extract Cluster Means Linear Regression Predict Step 1: Split data into a training and test set Step 2: Find signiﬁcant clusters Step 3: Extract mean intensity for the test and training sets Step 4: Use training data to run a linear regression Step 5: Use the model and the test data to predict lsas‐Δ score. Folds Data Enough details? No Oliver Doehermann, Anisha Keshavan, Franzi H.

problems solution future let’s step backwards What else? Where did the input data come from? What procedure was used to collect clinical data? What were the process parameters?

problems solution future the scientiﬁc process Generate hypotheses Design experiment Collect data Analyze data Interpret results Publish 18% 9% 18% 27% 9% 18% Hypothesize Design Collect Analyze Interpret Publish dramatization. not to be taken too seriously

different algorithms different assumptions different platforms different interfaces different ﬁle formats problems solution future the world of neuroimaging analysis software data source: pymvpa.org 1990 92 94 96 98 2000 02 04 06 08 2010 Afni Brainvoyager Freesurfer R Caret Fmristat FSL MVPA NiPy ANTS SPM Brainvisa

problems solution future the scientiﬁc process “The scientiﬁc method’s central motivation is the ubiquity of error - the awareness that mistakes and self-delusion can creep in absolutely anywhere and that the scientist’s effort is primarily expended in recognizing and rooting out error.” Donoho et al. (2009) assumed veracity of publications dependence on peer review as a proxy for testing

special populations : enables aggregation of large data sets (e.g., Autism, ADHD, Schizophrenia) cross-discipline interaction : problems solution future but why share data and code?

special populations : enables aggregation of large data sets (e.g., Autism, ADHD, Schizophrenia) cross-discipline interaction : - provides data to test their algorithms problems solution future but why share data and code?

special populations : enables aggregation of large data sets (e.g., Autism, ADHD, Schizophrenia) cross-discipline interaction : - provides data to test their algorithms - increases sample size for learning algorithms problems solution future but why share data and code?

special populations : enables aggregation of large data sets (e.g., Autism, ADHD, Schizophrenia) cross-discipline interaction : - provides data to test their algorithms - increases sample size for learning algorithms pedagogy : problems solution future but why share data and code?

special populations : enables aggregation of large data sets (e.g., Autism, ADHD, Schizophrenia) cross-discipline interaction : - provides data to test their algorithms - increases sample size for learning algorithms pedagogy : provides easy mechanism to train new personnel problems solution future but why share data and code?

most publications do not include data, code some journals mandate but provide no infrastructure for storage, distribution problems solution future current barriers

most publications do not include data, code some journals mandate but provide no infrastructure for storage, distribution most scientists do not have the time to curate data problems solution future current barriers

most publications do not include data, code some journals mandate but provide no infrastructure for storage, distribution most scientists do not have the time to curate data no standard ontology for describing experiments, data, derived data, workﬂows problems solution future current barriers

problems solution future data sharing Neuroimaging Tools and Resources Clearinghouse (NITRC) XNAT (Wash U) + HID (BIRN) + IDA (LONI) databases Brain Map (brainmap.org) National Database for Autism Research (NDAR) Personal web sites