Table 1.

Comparison of clinical hypothesis-testing using a biomarker vs. the testing of multiple, simultaneous hypotheses using routine clinical EEG.

	Biomarker	Clinical EEG
		Hypothesis 1	Hypothesis 2	Hypothesis 3	Scanning for incidental findings
Patient Population	Children with ASD, 8–12y, with stereotypies	School-aged, typically developing child with parent-reported staring spells
Question Being Asked	Response to “Persevegon”	Absence epilepsy	Focal epilepsy	Non-epileptic staring spells	Unspecified
Recording Standards and Data Cleaning	HAPPE (Harvard Automated Preprocessing Pipeline for EEG), which is specifically optimized for EEG preprocessing in children with neurodevelopmental disorders (25)	1. ACNS/IFCN recording guidelines 2. Maybe some filtering 3. Expertise and bias of reader toward discounting/not mis-interpreting artifact
EEG Feature	Event-related modulation of motor beta activity	3 Hz generalized spike-wave	Focal sharp waves (± focal slowing	Absence of relevant abnormal findings	Anything pathological in EEG (or EKG)
Threshold (pos/neg)	Predefined based on prior data	Presence/absence*	Presence/absence*	Presence/absence	Presence/absence
Post-Test Probability†	Defined by the validation study	If EEG negative‡: Assuming good recording, post-test probability close to 0. If EEG positive: post-test probability close to 100%	If EEG negative‡: does not update pre-test probability much If EEG positive: increases probability of focal epilepsy If indeterminate (“sharp transients”): probably does not change the mind of the clinician much	Dependent on probability of absence and focal epilepsy	Depends

^*But what is the quality of the morphology, and how many examples do you need to see in order to consider it “real”?

^†For clinical EEG, in how many instances are we actually able to put numbers on post-test probabilities?

^‡If the technical standards are sub-optimal, may need to discount the effect of a “negative” test.