• Journal of Integrative Natural Science
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• v.11 no.3
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• pp.139-147
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• 2018

Hippocampal atrophy is a well-established imaging biomarker of Alzheimer disease (AD). However, hippocampus is a non-homogenous structure with cytoarchitecturally and functionally distinct sub-regions or subfield, with each region performing distinct functions. Certain regions of the subfield have shown selective vulnerability to AD. Here, we are interested in studying the effects of normal aging and mild cognitive impairment on these sub-regional volumes. With a reliable automated segmentation technique, we segmented these subregions of the hippocampus in 101 cognitively normal (CN), 135 early mild cognitive impairment (EMCI), 67 late mild cognitive impairment (LMCI) and 48 AD subjects. Thereby, dividing the hippocampus into hippocampal tail (tail), subiculum (SUB), cornu ammonis 1 (CA1), hippocampal fissure (fissure), presubiculum (PSUB), parasubiculum (ParaSUB), molecular layer (ML), granule cells/molecular layer/dentate gyrus (GCMLDG), cornu ammonis 3(CA3), cornu ammonis 4(CA4), fimbria and hippocampal-amygdala transition area (HATA). In this cross sectional study of 351 ADNI subjects, no differences in terms of age, gender, and years of education were observed among the groups. Though, the groups had statistically significant differences (p < 0.05 after the multiple comparison correction) in the Mini-Mental State Examination (MMSE) scores. There was asymmetrical volume loss in the early stages of AD with the left hemisphere showing volume loss in regions that were unaffected in the right hemisphere. Bilateral parasubiculum, right cornu ammonis 1, 3 and 4, right fimbria and right HATA regions did not show any volume loss till the late MCI stages. Our findings suggest that the hippocampal subfield regions are selectively vulnerable to AD and also that these vulnerabilities are asymmetrical especially during the early stages of AD.

• Korean Journal of Biological Psychiatry
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• v.20 no.4
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• pp.119-128
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• 2013

Objectives Memory impairment is a very important mental health issue for elderly and adults. Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Early detection of the prodromal stage of patients with AD is an important topic of interest for both mental health clinicians and policy makers. Methods Electroencephalograpgy (EEG) has been used as a possible biological marker for patients with MCI, and AD. In this review, we will summarize the clinical implications of EEG and ERP as a biological marker for AD and MCI. Results EEG power density, functional coupling, spectral coherence, synchronization, and connectivity were analyzed and proved their clinical efficacy in patients with the prodromal stage of AD. Serial studies on late event-related potentials (ERPs) were also conducted in MCI patients as well as healthy elders. Even though these EEG and ERP studies have some limitations for their design and method, their clinical implications are increasing rapidly. Conclusion EEG and ERP can be used as biological markers of AD and MCI. Also they can be used as useful tools for early detection of AD and MCI patients. They are useful and sensitive research tools for AD and MCI patients. However, some problems remain to be solved until they can be practical measures in clinical setting.

• Smart Media Journal
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• v.12 no.3
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• Journal of Yeungnam Medical Science
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• v.23 no.1
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• pp.118-123
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• 2006

Dentatorubropallidoluysian atrophy (DRPLA) is a rare neurodegenerative disorder usually inherited in an autosomal dominant pattern. DRPLA has been shown to be associated with expansion of an unstable cytosine-adenine-guanine (CAG) trinucleotide repeat in a gene on chromosome 12p. We evaluated a family with DRPLA that affected three members; A 35-year-old female presented with seven year history of gait ataxia, dysarthria and mild cognitive impairment. The MRI of the brain revealed diffuse cerebellar atrophy with an incidental lipoma in the midbrain. Her 30-year-old brother presented with progressive cerebellar ataxia that developed at the age of 20. Her grandmother and mother were reported to have developed ataxia during the late period of their life, and died at the age of 60 and 55, respectively. The demonstration of an expanded CAG repeat in the gene for DRPLA was used to confirm the diagnosis.