Three-dimensional stereotactic surface projection in the statistical analysis of single photon emission computed tomography data for distinguishing between Alzheimer’s disease and depression

Table 1 Results of principal axis factor analysis (df = 21)

	Total of explained variance
Factors	Initial eigenvalues			Sums of squares of loadings after sampling			Sums of squares of loadings after rotation
	Total	Proportion of explained variance (%)	Cumulative proportion of explained variance (%)	Total	Proportion of explained variance (%)	Cumulative proportion of explained variance (%)	Total	Proportion of explained variance (%)	Cumulative proportion of explained variance (%)
Factor 1	2.422	34.603	34.603	1.961	28.017	28.017	1.805	25.780	25.780
Factor 2	1.223	17.478	52.081	0.982	14.025	42.041	1.067	15.248	41.029
Factor 3	1.166	16.656	68.738	0.484	6.920	48.961	0.555	7.932	48.961
Factor 4	0.805	11.502	80.240
Factor 5	0.598	8.538	88.778
Factor 6	0.500	7.144	95.922
Factor 7	0.285	4.078	100.000

Methods to extract factors: Principal axis factor analysis. Principal axis factor analysis with varimax rotation (df = 21) was performed for age, gender, HAM-D score, MMSE score, CGI, and the maximum Z-scores of AD-associated and depression-associated regions. Three significant factors with eigenvalues over 1 were identified. HAM-D: Hamilton Rating Scale for Depression; MMSE: Minimal Mental State Examination; CGI: Clinical Global Impression; AD: Alzheimer’s disease.