Table 1.
List of previous research with the data and the methods used, and the performances obtained
| # subjects | Best Performance | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Author | Title | Data | Method | (c+d) | (a+b) | (a+c)/(a+b+c+d) | a/(a+b) | c/(c+d) | a/(a+d) | c/(b+c) | a | b | c | d |
| Healthy Control | Patient (SZ, AD, CA, ..) | Prediction Accuracy (%) (overall) | Sensitivity, (PD,%) (%) | Specificity, (1-PFA,%) | PPV (+ predictive value | NPV (- predictive value | TP (true +) | FP (false +) | TN (true -) | FN (false -) | ||||
| Ford et al. (2002) | A combined structural-functional classification of schizophrenia using hippocampal volume plus fMRI activation. | fMRI-sMRI | Fisher's linear discriminate (FLD) analysis | 8 | 15 | 83-87 | ? | ? | ? | ? | ? | ? | ? | ? |
| Ford et al. (2003) | Patient Classification of fMRI Activation Maps. | fMRI | Fisher's linear discriminate (FLD) analysis | 10 | 15 | 60-80 | ? | ? | ? | ? | ? | ? | ? | ? |
| Job et al. (2006) | Grey matter changes can improve the prediction of schizophrenia in subjects at high risk. | vMRI | Measure of change in grey matter with masks and thresholding in ROIs. | 57 | 8 | 89 | 38 | 96 | 60 | 92 | 3 | 5 | 55 | 2 |
| Shinkareva et al. (2006) | Classification of functional brain images with a spatio-temporal dissimilarity map. | fMRI | Temporal dissimilarity using an RV-coefficient | 7 | 7 | 86 | 86 | 86 | 86 | 86 | 6 | 1 | 6 | 1 |
| Calhoun et al. (2007) | Temporal lobe and default hemodynannic brain modes discriminate between schizophrenia and bipolar disorder. | fMRI | ICA, Euclidian distance | 26 | SZ 21 BP 14 |
91 | 90 | 95 | ? | ? | ? | ? | ? | ? |
| Demirci et al. (2007) | A Projection Pursuit Algorithm to Classify Individuals Using fMRI Data: Application to Schizophrenia. | fMRI | ICA and Projection Pursuit | 36 | 34 | 80-90 | 91-97 | 89 | 89 | 91 | 31 | 3 | 32 | 4 |
| Pokrajac etal. (2005) | Applying spatial distribution analysis techniques to classification of 3D medical images. | fMRI | Mahalanobis distance, Kullback-Leibler (KL) divergence and maximum likelihood | 9 | 9 | 68-80 | 77-79 | 57-83 | 70 | 75 | 7 | 2 | 6 | 3 |
| Kortos et al. (2004) | Detecting discriminative functional MRI activation patterns using space filling curves. | fMRI | Hilbert space filling curves, neural networks | 9 | 9 | 82-100 | 79-100 | 74-100 | 100 | 100 | 9 | 0 | 9 | 0 |
| Wang et al. (2004) | Application of time series techniques to data mining and analysis of spatial patterns in 3D images. | fMRI | Time series domain techniques (Euclidian distance, Singular Value Decomposition) | 9 | 9 | 80-100 | ? | ? | ? | ? | ? | ? | ? | ? |
| Georgopoulos et al. (2007) | Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders. | MEG | Autoregressive integrative moving average (ARIMA) model | 89 | SZ 19 AD 9 CA 3 SS 1D MS 12 |
77 | ? | ? | ? | ? | ? | ? | ? | ? |
| Pardo etal. (2006) | Classification of adolescent psychotic disorders using linear discriminant analysis. | sMRI | Linear Discriminant Analysis (LDA) | 8 | SZ 10 BP 10 |
96.4 | ? | ? | ? | ? | ? | ? | ? | ? |
| Fan et al. (2007) | Multivariate examination of brain abnormality using both structural and functional MRI. | fMRI-sMRI | Pearson correlation coefficient, statistical regiona features {histograms) and PCA | 24 | 25 | 88-92 | ? | ? | ? | ? | ? | ? | ? | ? |
The missing data in some cells are approximated using the data in others when possible.
fMRI: functional MRI, sMRI: structural MRI, vMRI: volumetric MRI, MEG: Magnetoencephalography