Table 1.

Summary of Machine Learning Results

		SVM			LDA			NB
Measure	Frequency band	Accuracy	AUC	Features	Accuracy	AUC	Features	Accuracy	AUC	Features
RSFC	Slow-2	57.14	0.52	57	63.87	0.62	3	61.34	0.60	3
	Slow-3	65.55	0.67	14	63.03	0.62	12	63.03	0.61	21
	Slow-4	52.10	0.44	3	53.78	0.43	3	52.94	0.42	3
	Slow-5	75.63	0.80	10	75.63	0.80	8	76.47	0.79	11
	Slow-4 + 5	84.87	0.86	31	81.51	0.86	36	83.19	0.88	29
	LFO	72.27	0.72	5	69.75	0.71	5	73.95	0.79	60
	All	72.27	0.72	37	69.75	0.73	27	68.07	0.69	26
ALFFs	Slow-2	52.94	0.43	25	53.78	0.49	34	57.98	0.56	33
	Slow-3	63.03	0.59	4	67.23	0.69	1	66.39	0.67	1
	Slow-4	69.75	0.71	17	68.91	0.72	17	69.75	0.73	17
	Slow-5	78.99	0.81	11	77.31	0.81	13	73.11	0.76	12
	Slow-4 + 5	64.71	0.65	3	67.23	0.68	3	64.71	0.67	3
	LFO	73.95	0.72	14	78.15	0.81	14	69.75	0.72	15
	All	62.18	0.56	6	61.34	0.61	8	63.87	0.64	10
fALFFs	Slow-2	53.78	0.46	22	59.66	0.58	2	60.50	0.57	2
	Slow-3	54.62	0.42	12	73.11	0.78	6	72.27	0.75	6
	Slow-4	64.71	0.55	2	63.87	0.64	2	65.55	0.65	2
	Slow-5	70.59	0.70	6	68.07	0.70	6	64.71	0.69	2
	Slow-4 + 5	56.30	0.46	6	55.46	0.53	25	56.30	0.53	24
	LFO	58.82	0.50	16	55.46	0.54	3	63.03	0.57	20

Bold values represent the best results per category.

The three resting-state measures and seven frequency bands tested are organized in the leftmost columns. The three traditional machine learning models trained are organized in the top row. The accuracies are the LOOCV accuracies. “Features” column indicates the number of features selected from the RFE feature selection. Best LOOCV accuracies were achieved with Slow-4 + 5 RSFC features.

ALFFs, amplitude of low-frequency fluctuations; AUC, area-under-the-curve; fALFFs, fractional ALFFs; LDA, linear discriminant analysis; LFO, low-frequency oscillation; LOOCV, leave-one-out cross-validation; NB, naive Bayes; RFE, recursive feature elimination; RSFC, resting-state functional connectivity; SVM, support vector machine.