Table 1.

Sources of dirty data.

Category	Problem	Examples
Phenotypic measures	Measures are subjective	• Poor inter-rater reliability and high variability in gold-standard diagnostic tools and behavioral measures [33–35, 81]
	Measures are nonspecific	• High false-positive rate on ADOS in adults with schizophrenia [36]
	Measures focus on the tails of behavior	• Healthy controls will be zero inflated on questionnaire data [37, 38]
Participants	Comorbidity	• Symptoms of psychiatric disorders often overlap across diagnoses, while the majority of predictive models in psychiatry rely on more binary classification approaches
	Medication	• Psychiatric medications have the ability to alter BOLD signal patterns. This becomes difficult to study the psychiatric phenomena of interest as signals are confounded
	Episodic symptoms	• Symptoms change as a function of disease state. Data from scans based on one day may be vastly different in brain states relative to scans based on another day
Data collection	Multi-site	• Inter-scanner differences can induce significant variability [82, 83], and the complexity of the data analysis workflows could affect reproducibility [84]
	Missing data	• Subjects not completing questionnaires • Inability to complete behavioral testing or scan sessions in clinical populations [85, 86]