Neuroimaging is a powerful tool for characterizing neurodegenerative process in the progression of Alzheimer’s disease (AD). Pattern classification methods have been widely employed to predict disease status using neuroimaging measures [2
]. Since AD is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions, regression models have been investigated to predict clinical scores from individual magnetic resonance imaging (MRI) and/or positron emission tomography (PET) scans [8
]. For example, in [9
], stepwise regression was performed in a pairwise fashion to relate each of MRI and FDG-PET measures of eight candidate regions to each of four Rey’s Auditory Verbal Learning Test (RAVLT) memory scores.
Predicting disease status and predicting memory performance, using neuroimaging data, are both important learning tasks. Prior research typically studied these tasks separately. One example is to first determine disease-relevant cognitive scores and then identify imaging biomarkers associated with these scores so that interesting pathways from brain structure to cognition to symptom can potentially be discovered. However, a specific cognitive function could be related to multiple imaging measures associated with different biological pathways (some of them are not related to AD). As a result, the identified imaging biomarkers are not necessarily all disease specific. To have a better understanding of the underlying mechanism specific to AD, an interesting topic would be to only discover imaging biomarkers associated with both cognitive function and AD status.
To identify AD-sensitive and cognition-relevant imaging biomarkers, we propose a new joint classification and regression learning model to simultaneously performing two heterogeneous tasks, i.e
., imaging-to-disease classification and imaging-to-cognition regression. We use magnetic resonance imaging (MRI) measures as predictors and cognitive memory scores and disease status as response variables. For each individual regression or classification task, we employ a multitask learning model [1
] in which tasks for predicting different memory performances (or those for predicting AD and control dummy variables in classification) are considered as homogeneous tasks. Different to LASSO and other related methods that mainly find the imaging features correlated to each individual memory score, our method selects the imaging features that tend to play an important role on influencing multiple homogenous tasks.
Our new method utilizes the sparse regularization to perform imaging biomarker selection and learn a sparse parameter matrix under a unified framework that integrates both heterogeneous and homogenous tasks. Specifically, by recognizing that the formation [6
] and maintenance [4
] of memory are synergically accomplished by a few brain areas, such as medial temporal lobe structures, medial and lateral parietal, as well as prefrontal cortical areas, we use the 2,1
-norm regularization to select features that can predict most memory scores and classify AD versus control. Empirical comparison with the existing methods demonstrates that the proposed method not only yields improved performance on predicting both cognitive scores and disease status, but also discovers a small set of AD-sensitive and cognition-relevant biomarkers in accordance with prior findings.