## 3 CAD Systems of Brain Disorders Based on Multimodal Fusion
### Critical Discussion about the Multimodal Diagnosis of AD  
Several CAD systems [144; 150; 151; 152; 153; 156; 7; 154; 155; 156] have been implemented over the past decade for the diagnosis of AD or its early-stage MCI. To distinguish patients with AD from those in normal aging, the researchers used machine learning methods such as SVM, ANN and naive Bayes classifier. However, it should be noted that these techniques have been used for single-modal images (MRI or PET) in the majority of the work. Unfortunately, few works have exploited multimodality for the diagnosis of AD like [10; 17; 46; 154; 184; 186; 187; 206; 207], which seems to be better achieved by serving the advantages of several types of images, each measuring a different type of structural or functional characteristic. In reality, the various imaging environments and modalities offer complementary information that is useful when used in conjunction. This improves the performance of the diagnostic system compared to the system using a single modality. In this context, some research groups have adapted machine learning algorithms to several types of modalities (or to additional clinical/cognitive data), by concatenating the structural and functional characteristics of each subject into a single vector of characteristics. However, this type of approach causes strong growth in the distribution dimension. In addition, the principle prompts us to find the right standardization for each modality in order to preserve its informational content and avoid overwhelming the characteristics derived from one or the other type of image. The difficulty in this case is in the evaluation of the relevance of the content of each modality, which allows the optimization of the classifier. To remedy this problem, several researchers [186; 187; 203; 204; 208] have exploited the idea of carrying out multi-kernel learning (MKL) based on the combination of several kernels. This learning applied to MRI/PET multimodal images is more flexible, thanks to the use of different weights on the modality biomarkers. In addition, it provides a unified means for combining heterogeneous data when a different data type cannot be directly concatenated. However, despite its performance in terms of cross-validation, this feature selection method requires having the same number of features calculated for each modality. In addition to the use of MRI/PET multimodal neuroimaging data, studies have attempted to integrate other types of data from other biomarkers and test these prognostic capacities such as CSF, Pittsburgh compound B (PIB)-PET, apolipoprotein E (ApoE) and genetic data. The advantage of these measures developed for the diagnosis of AD is that the number of its characteristics is different, and that they often contain relevant and complementary pathological information which can help in the future diagnosis of AD and increase the performance of the classification [292]. For example, researchers, as in [10,206], proposed a kernel-based combination to integrate the CSF with the FDG-PET and MRI modalities. They have shown that the morphometric changes in AD and MCI are linked to the CSF, which offers additional information [293]. Additionally, the [11C] PIB-PET biomarker has been used by several longitudinal studies and therapeutic interventions to estimate the evolution of AD. The PET imaging tracer, PIB, was developed to identify the cerebral amyloid. However, quantitative analysis of PIB [11C] data requires the definition of regional interest volumes. In this context, to define the regions for a PIB-PET analysis, researchers as in [188,294] have shown that the integration of MRI or PET offers similar results. This avoids the need for an MRI that takes time and increases costs. Therefore, MRI analysis remains more appropriate for clinical research, while the application of a PET model to [11C] PIB is adequate for clinical diagnosis.