Table 7.

A brief description of the datasets used for the validation of various multimodality based AD classification frameworks

	Subjects					Type	Classification algorithm	Database	Classification accuracy
	AD	MCI	pMCI	sMCI	CN				AD/CN	MCI/CN	AD/MCI	sMCI/pMCI
(Fan et al., 2008b)	—	15	—	—	15	SFC	SVM	BLSA	—	90.00	—	—
(Vemuri et al., 2008)	190	—	—	—	190	SFC	SVM	ADNI	89.30	—	—	—
(Kohannim et al., 2010)	158	264	—	—	213	SFC	SVM	ADNI	93.81	75.49	—	—
(Davatzikos et al., 2011)	—	—	69	170	—	SFC	SVM	ADNI	—	—	—	61.70
(Dukart et al., 2011a)	21	—	—	—	13	SFC	SVM	Leipzig	100.00	—	—	—
(Cui et al., 2011)	96	—	56	87	111	SFC	SVM	ADNI	—	—	—	67.13
(Cui et al., 2012)	—	79	—	—	204	SFC	SVM	SMAS	—	71.09	—	—
(Dukart et al., 2013)	49	—	—	—	41	SFC	SVM	ANDI+Leipzig	90.001	—	—	—
(Zhang et al., 2014)	24	57	—	—	97	SFC	Kernel SVM decision-tree	OASIS	96.00	85.00	88.00	—
(Zhu et al., 2014)	51	99	—	—	52	SFC	SVM	ADNI	95.90	82.00	—	—
(Li et al., 2014a)	21	—	—	—	15	SFC	SVM	TH	94.30	—	—	—
(Apostolova et al., 2014)	95	182	—	—	111	SFC	SVM	ADNI	85.00	79.00	70.00	—
(Moradi et al., 2015)	200	—	164	100	231	SFC	LDS,Random forest	ADNI	—	—	—	81.72
(Zheng et al., 2015)	163	—	104	94	189	SFC	SVM	ADNI	92.11	—	—	79.37
(Tang et al., 2016)	29	—	—	—	23	SFC	LDA+SVM	TH	94.60	—	—	—
(Schouten et al., 2016)	77	—	—	—	173	SFC	Elastic net classifier	PRODEM	93.00	—	—	—
(Clark et al., 2016)	—	24	—	—	—	SFS	Ensemble classifier	ADRC	—	—	87.202	—
(Hinrichs et al., 2011)	48	119	—	—	66	SFS	Multi-kernel SVM	ADNI	92.40	—	—	—
(Zhang et al., 2011)	51	99	—	—	52	SFS	Multi-kernel SVM	ADNI	93.20	76.40	—	—
(Dai et al., 2012)	16	—	—	—	22	SFS	Ensemble of MU-LDA	XWH	89.47	—	—	—
(Zhang, Shen, 2012)	45	91	—	—	50	SFS	Multi-kernel SVM	ADNI	93.33	83.20	—	—
(Young et al., 2013)	63	—	47	96	73	SFS	Gaussian process classifier	ADNI	—	—	—	74.00
(Gray et al., 2013)	37	—	34	41	35	SFS	Random forest	ADNI	89.00	74.60	—	58.00
(Casanova et al., 2013)	171	—	153	182	188	SFS	RLR	ADNI	87.10	—	—	63.00
(Liu et al., 2014)	50	—	—	—	70	SFS	Multi-kernel SVM	ADNI	87.12	—	—	—
(Xu et al., 2015)	113	—	27	83	117	SFS	SRC	ADNI	94.80	74.50	—	77.80
(Zu et al., 2015)	51	—	43	56	52	SFS	Multi-kernel SVM	ADNI	95.95	80.26	—	69.78
(Cheng et al., 2015b)	51	—	43	56	52	SFS	Domain transfer SVM	ADNI	—	86.40	82.70	79.40
(Cheng et al., 2015a)	51	—	43	56	52	SFS	M2TL	ADNI	—	—	—	80.10
(Dyrba et al., 2015b)	28	—	—	—	25	SFS	Multi-kernel SVM	EDSD	85.00	—	—	—
(Korolev et al., 2016)	—	—	139	120	—	SFS	Probabilistic multi-kernel	ADNI	—	—	—	80.00
(Yu et al., 2016)	50	97	—	—	52	SFS	Multi-task learning	ADNI	92.60	80.00	—	—

SFC = Straightforward feature concatenation

SFS = Specialized fusion strategies LDS = Low density separation

SRC = Sparse representation-based classification

MU-LDA = Maximum uncertainty-linear discriminant analysis

M2TL = Multimodal manifold-regularized transfer learning

TH = Tongji hospital, Wuhan, China

SMAS = Sydney memory and aging study

XWH = Xuan wu hospital, Beijing, China

PRODEM = Prospective registry on dementia in Austria

ADRC = Alzheimer disease research center, Washington University school of medicine, St. Louis, Missouri

¹= Accuracy for the combined ADNI+Leipzig cohort

²= Prediction of conversion from MCI to AD (AUC)