A decline in memory, loss of attention, and lack of ability to perform daily activities indicate a high probability of having dementia subjects with these symptoms are usually evaluated with neuropsychological standardized tests. 1 The timely detection of changes in brain tissue caused by MCI could prompt actions aimed at preventing or delaying the progression of the disease, either from normal subjects to MCI or from MCI to AD. This latter pathology develops primarily in subjects aged 65 and older and affects approximately 25 million people worldwide. Studies have reported that between 10% and 64% of subjects with MCI are at risk of developing AD. 1Ī transitional stage prior to AD is known as the mild cognitive impairment (MCI) stage, 2 which is characterized by memory loss with cognitive disorder. Its diagnosis is based on the information provided by a careful clinical examination, a thorough interview of the patient and relatives, imaging, and a neuropsychological assessment. Such is the case of Alzheimer’s disease (AD) which is a neurodegenerative condition characterized by progressive cognitive deterioration that limits the performance of daily activities. A great variety of diseases can affect brain morphology either globally or in some specific regions. Attempts have been made to characterize brain shape using different metrics, but this continues to be an open challenge. Morphometric measures of brain structures can be useful in determining changes related to diverse pathologies. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages.
Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. In all cases, this index outperformed the discrimination capability of the NV. The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD).
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