|Category||Term||Definition/Description||Examples and Additional Resources|
|Analyses||Descriptive||Rely on existing observations to identify underlying patterns that can describe and summarize the data in an interpretable manner||Identification of distinct subtypes of autism (|
133), depression (
135), psychosis (
136), schizophrenia (
137), and bipolar and schizoaffective disorders (
Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study.
Biol Psychiatry. 2020; 87: 282-293
|Predictive||Establish associations between independent and dependent variables in existing data, uses learned associations to predict the trends and properties in unobserved samples||Prediction of clinical, cognitive, and personality traits across different psychiatric illnesses (|
|Prescriptive||Identify patterns and trends in the underlying data and recommend personalized interventions that can most effectively alleviate individual specific patterns of concern||Prediction of clinical responses to medications (|
41) and cognitive behavioral therapy (
|Algorithms||Supervised||Rely on pre-observed ground truth labels to learn associations between the input and output data||Prediction of treatment response in social anxiety disorder (|
|Semi-Supervised||Combine a small amount of labeled data and a large amount of unlabeled data to identify associations between the input and output data||Identification of subtypes of internalizing disorders in youth (|
|Unsupervised||Group and interpret patterns in unlabeled data to characterize data||Investigation of overlapping and specific correlates of common psychiatric illnesses (|
|Supervised Algorithms||Classification||Establish relationships between the input variables and discrete binary or multi-class categorical labels||Distinguishing healthy and clinical populations|
145) for a review on the application of clinically informative classifiers
|Regression||Predict continuous variables based on the input data||Prediction of disease severity|
146) for a review on the use of regression algorithms to elucidate brain-behavior relationships
|Unsupervised Algorithms||Clustering||Enable the characterization of data into distinct groups (clusters) based on shared underlying patterns||Biologically driven disease subtype identification|
|Models||Classical Machine Learning||Relatively ‘simple’ models that rely on linear or non-linear and sparse or non-sparse approaches to derive associations between the input and output data||Linear regression, logistic regression, support vector machines|
|Deep Learning||Subset of machine learning models that rely on one or more neural networks (e.g., perceptrons) to progressively extract higher-level features from the input data to make predictions about the output||Feedforward neural networks, convolutional neural networks|
|Variables||Binary||Discrete data that can fall in one of two categories||Presence or absence of an illness|
|Categorical||Discrete data that can fall in one of several categories||Subtypes of an illness|
|Continuous||Data can take on any value within a continuous range||Dimensional measures of impairment|
|Regularization||L1 (Sparse)||Uses the absolute value of magnitude of the parameters as the penalty term, thus encouraging sparsity by shrinking least important coefficients to zero|
|L2 (Non-Sparse)||Uses the squared magnitude of the parameters as the penalty term, thus reducing the magnitudes of parameters|
|Model Properties||Complexity||A more complex model has the capacity for more complicated relationship among the features and target variables|
|Transparency||Ability to understand and interpret the brain-behavior relationships captured by the model|
|Model Performance||Accuracy||Correlation (or correspondence) between true and predicted values when evaluating a model on a (held out) test set that is from the same population (or dataset) as the training set|
|Generalizability||Correlation (or correspondence) between true and predicted values when evaluating a model on a population (or dataset) that is unique from the population in which it was trained|
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Sample Sizes and Transfer Learning
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