Researchers and clinicians who are interested in data mining approach to classify dementia-like diseases. Alzheimer Disease (AD) and Vascular Dementia (VaD) are two of the most common neurodegenerative kinds of dementia disease. Over the years, advanced structural and functional MRI techniques, such as Diffusion Tensor Imaging (DTI) and resting state functional MRI (rs-fMRI) have emerged as powerful methods to investigate the signs of neurodegeneration^1,2. However, despite a large number of studies, efforts to define a clear profile of cognitive impairment for VaD, as well as its differentiation from AD, are still poor³. In this study we tested the power of imaging metrics and adopted a data mining approach, based on DTI and rs-fMRI, to assess the reliability of machine learning approaches for the automated diagnosis of AD and VaD. 31 subjects affected by AD (age 72.88±7.33, MMSE=15.97±6.38) and 27 subjects diagnosed with VaD (age 76.67±7.77, MMSE=17.94±4.22) underwent MRI examination using a 3T Siemens Skyra MR scanner (Siemens, Erlangen, Germany) with a 32-channels head-coil. All patients were selected based on their neuropsychological examination. 35 healthy controls (HC), age 69.43±9.65 and MMSE=28.56±1.45, were also studied. MRI acquisitions: 1) DTI: twice refocused SE-EPI (TR/TE=10000/97 ms, 70 slices with no gap, acquisition matrix=120×120, voxel size=2x2x2mm³, 64 directions, b=1200s/mm², 10 volumes with no diffusion weighting); 2) rs-fMRI: EPI 2D BOLD sequence (TR/TE=3010/20ms, voxel size=2.5mm isotropic, FOV=224mm, 60 slices for a total of 120 volumes); 3) a 3DT1-weighted scan was also collected with a MPRAGE sequence (TR/TE/TI=2300/2.95/900ms, flip angle 9°, 256 slices, slice, voxel size=1x1x1.2mm³, FOV=270mm) for anatomical reference. From DTI we calculated fractional anisotropy (FA) and mean diffusivity (MD) maps using FSL⁴. Using these maps we extracted mean FA and MD values of 10 brain areas (Fig.1) that resulted as being particularly relevant from a previous Tract Based Spatial Statistics (TBSS) analysis.⁵ For each subject, rs-fMRI images were preprocessed using FSL⁴ and then parcelled using the automated anatomical labelling (AAL⁶) atlas into 116 areas. For each AAL area we calculated both mean FC value (by averaging the mean value of signals of all the voxels within the region) and graph metrics using the BCT⁷ toolbox. Extracted measures from DTI (mean FA and MD of areas in Fig.1) and from rs-fMRI (mean FC values and graph metrics of the AAL regions) were used as input features to compare 4 different classifiers, all implemented in Matlab: Support Vector Machine (SVM)⁸, Multilayer Perceptron (MLP)⁸, Radial Basis Function network (RBF network)⁸ and Adaptive Neuro-Fuzzy Inference System (ANFIS)⁹. Each classifier was run testing multiple input feature sets: i) DTI indexes, ii) FC indexes, iii) graph metrics, iv) DTI+FC, v)DTI+graph metrics. This step allowed us to identify the optimal input set of features to maximise the classification accuracy. In order to further optimise the classification performance we applied a feature selection algorithm (ReliefF⁸) to rank and then select the features with higher discrimination ability to construct the classifier.The machine learning algorithms were able to classify AD and HC with accuracy (ACC)=94%, VaD and HC with ACC=96%, AD and VaD with ACC=92% (Fig.2). In particular, comparing AD vs HC the best classification performance was obtained using ANFIS using a combined input set of features of DTI indexes and graph metrics (Fig.2,3), while SVM (with RBF kernel) showed high classification accuracy using only graph metrics as input features (AC=93%). Also, results from this comparison confirmed the key role of the hippocampi and default mode network (DMN) impairment in AD. SVM gave the highest classification performances when using combined DTI+graph metrics (AC=96%) and DTI+FC indexes (AC=93%) in discriminating VaD vs HC (Fig.2,4). Furthermore, DTI+graph metrics used as feature dataset for ANFIS obtained the best classification accuracy when comparing VaD vs AD (AC=92%, Fig.2,5). Our results showed that machine learning approaches were able to accurately discriminate both AD and VaD from HC as well as AD from VaD, while also confirming a differential involvement of the hippocampi and DMN regions in AD, compared to HC. The achieved accuracy (ACC>90%) is superior to reported values based on volumetric measurements¹⁰. SVM and ANFIS emerged as the most efficient classifiers. Moreover, these algorithms significantly improved their classification accuracy when using multimodal feature sets (e.g. DTI+graph metrics) rather than single modality feature sets (e.g DTI indexes). In particular, the feature set composed by DTI indexes and graph theoretical measures allowed to accurately discriminate AD, VaD and HC with the best classification performances (>90%). Results suggest therefore that machine learning algorithms represent a suitable approach to build an automated image-based system for advising on clinical diagnosis of dementia-like diseases.