Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease

Abstract

Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely Multi-Modal Multi-Task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality, and (2) a multi-modal support vector machine which fuses the above-selected features from all modalities to predict multiple (regression and classification) variables. To validate our method, we perform two sets of experiments on ADNI baseline MRI, FDG-PET, and cerebrospinal fluid (CSF) data from 45 AD patients, 91 MCI patients, and 50 healthy controls (HC). In the first set of experiments, we estimate two clinical variables such as Mini Mental State Examination (MMSE) and Alzheimer’s Disease Assessment Scale - Cognitive Subscale (ADAS-Cog), as well as one categorical variable (with value of ‘AD’, ‘MCI’ or ‘HC’), from the baseline MRI, FDG-PET, and CSF data. In the second set of experiments, we predict the 2-year changes of MMSE and ADAS-Cog scores and also the conversion of MCI to AD from the baseline MRI, FDG-PET, and CSF data. The results on both sets of experiments demonstrate that our proposed M3T learning scheme can achieve better performance on both regression and classification tasks than the conventional learning methods.

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

“…In future research, we plan to use the cortical thickness and diffusion properties (Irimia, Torgerson, Goh, & Van Horn, 2015) and to combine MRI features with nonimaging characteristics (DeCarlo, Tuokko, Williams, Dixon, & MacDonald, 2014), not only to address this limitation but also to further develop the brain‐age technique toward the goal of revealing the earliest indications of AD in symptomatic and asymptomatic individuals. Several studies have successfully investigated multimodal data such as MRI, PET for developing a high accurate AD classification or MCI conversion prediction frameworks (Ortiz, Munilla, Álvarez‐Illán, Górriz, & Ramírez, 2015; Zhang & Shen, 2012; Zhang, Wang, Zhou, Yuan, & Shen, 2011). Another direction for future study may be to use multimodal data (i.e., MRI and PET) to present a high accurate and robust brain‐age model.…”

Joint prediction of multiple scores captures better individual traits from brain images

“…125 Zhang et al (2012) have tackled multiple source/output prediction as joint feature selection with a multi-task / multi-modal model. More in detail, they use multi-output models to perform a feature selection across scores on each modality.…”

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

“…Importantly, the classification performance is not significantly degraded if same-modality data are collected in different centers [249]. Recent results based on multimodal approaches have achieved encouraging results in discriminating AD and MCI subjects [250,251]. In the coming years, machine learning algorithms will be incorporated into scanner software to enhance the semi-automated detection of prodromal AD stages based on high-dimensional pattern recognition.…”