Probability-based prediction model using multivariate and LVQ-PNN for diagnosing dementia

Abstract

The aim of this study was to develop a prediction model that integrated various image features and neuropsychological scores to yield a single estimate reflecting the probability of dementia. Method:A total of 130 subjects belong to Normal control group, AD group, and MCI group, were recruited in this study. For these subjects, the multiple features obtained from different modalities, including structural MRI morphometry (volume / shape), rs-fMRI, and neuropsychological assessment measures (NPA) were used to explore an optimal set of predictors of conversion from MCI to AD. Unlike previous studies using logistic regression analysis, a new method based on learning vector quantization (LVQ) and probabilistic neural network (PNN) is proposed to establish a prediction model. Results:We test the baseline, 1-year follow-up, and 2-year follow-up scans of 17 AD subjects (M/ F=5/12), 22 normal controls (NC; 13/9), 16 subjects that remain stable MCI (MCI-s; 11/5), and 4 subjects convert to AD within a given timeframe (MCI-c; 2/2). This study found that the proposed quantitative indicator provides well-behaving AD state estimates, corresponding well with the actual diagnosis. Conclusion:According to the results, all of the test data have the trend that decreased over time. It has Neuropsychiatry (London) (2016) 6(6) 377 Research Jiann-Der Lee detection and diagnosis of .Brain atrophy typically starts in the medial temporal and limbic areas, subsequently extending to parietal association areas and finally to frontal and primary cortices. Early changes in hippocampus, amygdala, and entorhinal cortex have been demonstrated with the help of MRI and these changes are consistent with the underlying pathology of MCI and AD. Methods based on volumetric measurements [14-16], or on visual rating scales [17] have largely been used to assess cortex atrophy. Hippocampal volumes and entorhinal cortex measures have been found to be equally accurate in distinguishing between AD and normal cognitive elderly subjects [18]. However, the segmentation and identification of hippocampus or entorhinal cortex are usually time-consuming and prone to interrater and intra-rater variability. In addition, the enlargement of ventricles is also a significant characteristic of AD due to neuronal loss. Ventricles are filled with cerebro-spinal fluid (CSF) and surrounded by gray matter (GM) and white matter (WM). As a result, by measuring the ventricular enlargement, hemispheric atrophy rate shows higher correlation with the disease progression.In addition to the atrophy of brain regions, neuropsychological assessment (NPA) has featured prominently over the past 30 years in the characterization of dementia associated with Alzheimer disease (AD) [19,20]. As research has increasingly focused on earlier stages of illness, it has become clear that biological markers of AD can precede cognitive and behavioral symptoms by years, such as Mini Mental State Examination (MMSE) [21] and Clinical Dementia Rating scale (CDR) [22], the Cognitive Abilities Screening In...