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A.1 Dataset-related Details
A.1.1 Dataset Documentation
We provide a datasheet that documents this data collection (in the Supplementary materials).
A.1.2 Access Link and Data Repositories
The brain network datasets can be accessed at: https://doi.org/10.17608/k6.auckland.21397377.
We have a Github repository at https://github.com/brainnetuoa/data_driven_network_neuroscience which includes all our preprocessing codes and a demo on how to convert a raw fMRI image to a brain network in a step-by-step manner with sample input images from a subject in TaoWu.
Figshare (https://www.figshare.com) is a citable, shareable, and discoverable open data repository used by many educational institutions around the world, including ours. Figshare fits the many requirements of NeurIPS, including long term preservations of the data, access to a persistent identifier (an assigned DOI), and clear licensing and data standards.
A.1.3 Author Statement
As authors, we confirm that we bear all responsibility in case of any violation of rights during the collection of the data or other work, and will take appropriate action when needed, to remove data with such issues.
A.1.4 License of our Brain Network Datasets
A.1.5 Consent Process for Neuroimages
ABIDE, TaoWu, and Neurocon all follow Creative Common licenses and their source neuroimages are open use. For ADNI and PPMI we have followed their policies (Data Usage and Publication Policy) on how to use and publish derived data and where appropriate, we have submitted our manuscripts for permission to publish. Both required proper acknowledgements of the entity and their funders in the paper, which we have included.
A.1.6 Computing Resources Used for Preprocessing
As stated in the main paper, fMRIPrep is computationally expensive. In our case, one run on one subject of fMRIPrep (with surface reconstruction enabled) using 8 threads on an Intel(R) Core(TM) i9-10940X CPU @ 3.30GHz takes on average 4-5 hours. Preprocessing of all data sources was split between two sites running on different internal cluster configurations. We are currently storing approximately 5 TB of data on these clusters, including the original neuroimages, preprocessed neuroimages, and our derived brain network data.
Site 1 cluster:
1. 3x Intel(R) Core(TM) i9-10940X CPU @ 3.30GHz
2. 1x Intel(R) Xeon(R) CPU ES-4020 v2 @ 2.60GHz
Site 2 cluster:
1. 5x Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
2. 1x Intel(R) Xeon(R) Gold 6230 CPU @ 2.10GHz
A.2 Experiment-related Details
A.2.1 Code
The code we used to run our entire pipeline and baseline experiments can be accessed on our Github: https://github.com/brainnetuoa/data_driven_network_neuroscience.
A.2.2 Parameters
As stated in the main paper we used scikit-learn for all our implementations and here we provide the full list of parameters (including those we used for Grid Search).
LogisticRegression(penalty:('l1', 'l2', 'elasticnet', 'none'), dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lcfgs', max_iter=1000000, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)
GaussianNB(priors=None, var_smoothing=1e-09)
$\mathbf{SVC}(C = 1, \text{kernel} = \text{'rbf', 'linear', 'poly', 'sigmoid', 'shortest path')}$ , degree  $= 3$ , gamma  $=$  'auto', coef  $= 0.0$ , shrinking  $=$  True, probability  $=$  False, tol  $= 0.001$ , cache_size  $= 200$ , class_weight  $=$  None, verbose  $=$  False, max_iter  $=$  -1, decision_function_shape  $=$  'ovr', break_ties  $=$  False, random_state  $=$  None)
KNeighborsClassifier(n_neighbors=5, weights=('uniform', 'distance'), algorithm='auto', leaf_size=30, p=(1,2), metric='minkowski', metric_parameters=None, n_jobs=None)
RandomForestClassifier(n_estimators=(50,100,150,200), criterion='entropy', max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=0, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)
A.2.3 Computing Resources Used for Baselines
Experiments on baselines were performed on the internal cluster at site 2 (stated above).
A.2.4 Connectivity Measure Comparison on TaoWu
Table 8: Connectivity Measure Comparison - Classification Accuracy (mean±standard deviation)
TaoWu - AAL116
Correlation	Covariance	Partial Correlation	Precision
Logistic Regression	82.5±12.7	82.5±12.7	62.5±5.0	65.0±12.2
Naive Bayes	61.5±19.0	61.5±19.0	45.0±16.2	43.5±16.2
SVC	85.0±9.4	85.0±9.4	65.0±20.0	65.0±14.6
kNN	60.0±21.5	60.0±14.6	52.5±5.0	57.5±6.1
Random Forest	72.5±12.2	70.0±10.0	65.0±18.4	62.5±7.9