## 3 CAD Systems of Brain Disorders Based on Multimodal Fusion
### Critical Discussion about the Multimodal Diagnosis of AD
Studies have been interested in the application of the ApoE4 genotype that has been integrated into the MRI and CSF in several works [184,185], which have tested the performance of the classification after stratification of ApoE4. This biomarker was commonly applied as a stratification factor or covariate that contributes to the adjustment of heterogeneity in sporadic AD, since non-e4 status in EOAD patients is correlated with typicity. Biological or genetic biomarkers [184,185] have also been developed for the diagnosis of AD or its early-stage MCI. Studies by genetic endophenotype by imaging would make it possible to develop the link between genetics and the topography of AD by focusing on the areas of the brain most associated with pathological genotypes. However, the use of these biomarkers differs from one research center to another and is subject to various factors, such as the cost of local availability and historical patterns of use [207]. In general, several deductions have been noted by researchers from these centers, namely: (1) The increase in total tubulin-associated unit (t-tau) proteins and hyperphosphorylated tau at the level of threonine 181 (p-tau) in the CSF is associated with the pathology of neurofibrillary tangling; (2) the decrease in the amyloid \(\beta\) (A\(\beta\)42) is indicative of the pathology of the amyloid plaque; and (3) the presence of the \(\varepsilon\)4 allele of APOE can predict cognitive decline or conversion to AD [184]. However, the change in the proteins t-tau and p-tau in the CSF does not really express the extent of the tau pathology in the [PERSON]'s brain. These CSF biomarkers are only peripheral substitutes for the current pathology of the tau protein. Recently, several ligands of the tau protein are being developed. Such technology offers a much more precise measure of the pathology of the tau protein, which significantly promotes better understanding and classification of the early and presymptomatic stages of AD. It should be added that most large-scale works have exploited multimodal techniques which target the exploration of the relationship between various modalities for the same participants, neglecting the useful relationship between the different participants. In another sense, the techniques for selecting the multitasking functionalities are used only to jointly select the common functionalities through different modalities, ignoring the information relating to the distribution of the data in each of these modalities, which is important for a later classification. Some researchers as in [186,187] have tried to tackle this problem, by proposing a method for learning multiple regularized multitasking functionalities, which makes it possible to preserve both the inherent relationship between the data of the different modalities and the information relating to the distribution of data in each modality. However, we can generally suggest a few remarks found in most of the works cited in the literature:All the classification models performed very well, distinguishing participants with normal aging from the patients with AD. The performance was worse for MCI vs. AD determination, which proved more difficult, probably due to the MCI biomarker scheme which is quite similar to that observed in AD. However, multimodal classification had better diagnostic and forecasting power than single-modal classification. The majority of clinical studies in recognized biomedical laboratories have focused on binary classification problems (i.e., AD vs. HC and MCI vs. HC), neglecting to test the power of the models proposed for multi-class classification of AD, MCI and normal controls. It is true that the latter type of classification is more difficult to verify than the binary classification, but it is crucial to diagnose the different stages of dementia. In addition, longitudinal data may contain essential information for classification, while the proposed studies can only deal with basic data.