A new feature extraction method for identification of affected regions and diagnosis of cognitive disorders

Cognitive disorders like AD progressively disintegrate neurons and their interconnections in the brain; thus gradually deteriorating cognitive functions. Automated diagnosis is very important in the early diagnosis of cognitive disorders. Early diagnosis allows in taking measures helping the person to move on. Clinical diagnosis is inefficient as the symptoms start to manifest only after significant atrophy of the cortical structures. This makes management of the conditions difficult. Resent findings have revealed the potential of Neuroimaging as a highly effective tool in the early detection of these disorders as structural changes in the brain set in much before the manifestation of observable symptoms. The disorders are a reflection of degeneration of the cortical structures and hence can be detected by analysis of the structural images of the brain. Therefore, analysis of T1-weighted MRI has become a popular method of early diagnosis of AD. The work proposes a feature extraction method that enables simultaneous identification of the afflicted cortical structures and diagnosis of disorders. The method proposed is based on sparse logistic regression and linear discriminant analysis. The results obtained were better than or comparable with many of the works reported in literature.

I. Introduction

A major reason for mental health disorders which affect the cognitive abilities of a person is AD(Alzheimer's disease). Neurons and their interconnection are gradually disintegrated by AD; thus gradually deteriorating the cognitive functions. AD accounts for 60–80 percentage of dementia cases related to old age [1]. At present, approximately 5.2 million people in the US and 35 million people worldwide are affected by AD, which is foreseen to twofold by 2030 and more than triple by 2050 [2]. Cognitive decline which is greater than normal age related problems but falls short of severe dementia are clinically classified as Mild Cognitive Impairment (MCI). More than half of those diagnosed with MCI converts to AD, but some MCI cases may remain stable and carry on their normal life over time. Many studies have concluded that yearly MCI to AD conversion rate is about 10%-15%[3]. The current clinical management that diagnose the disease from the symptoms exhibited by the patients has its limitations. The symptoms appear only during the later stages of these diseases. Even a highly experienced physician can identify the disease only when the patient starts showing the physical symptoms. Clinical symptoms begin to show only after significant degeneration of the neurons in brain. Therefore, diagnosing the impairment using these changes can provide a much better help in the early treatment of the disease.