Classifying Early and Late Mild Cognitive Impairment Stages of Alzheimer’s Disease by Analyzing Different Brain Areas

Uysal, Gokce; Öztürk, Muzaffer

doi:10.1109/tiptekno50054.2020.9299217

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…They discovered that the olfactory cortex, hippocampus, par hippocampal, amygdala, and superior parietal gyrus all showed lower node strength, local clustering coefficient, and local efficiency as well as increased local characteristic path length in AD patients. Uysal et al [28] employed the method of constructing a brain function network, and they found that the betweenness centrality in the right inferior temporal gyrus and the nodal degree in the left middle temporal gyrus was different in distinguishing between EMCI and LMCI. Luo et al [29] used graph theory to characterize the brain network abnormalities of AD and MCI with a Chinese brain template.…”

Section: Discussionmentioning

confidence: 99%

Functional Brain Network Measures for Alzheimer’s Disease Classification

Wang,

Sheng,

Zhang

et al. 2023

IEEE Access

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Background: Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. The therapy of AD depends heavily on an early diagnosis. In this study, our primary objective is to evaluate the classification framework, which combines graph theory and machine learning techniques for functional magnetic resonance imaging (fMRI), to distinguish AD, early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and healthy control (HC). Methods: A novel multi-feature selection method, incorporating the dual graph theoretical approach, is proposed for classification. This method utilizes three different feature selection methods after brain areas selection through graph-theory analyses in 96 subjects with brain parcellation by using the joint human connectome project multimodal parcellation (J-HCPMMP) of 180 areas per hemisphere. Results:The classification results show that the optimal features selected by the minimal redundancy maximal relevance (MRMR) based on support vector machine linear (SVM-linear) from graph measures for 36 areas of 360 areas. The classification accuracies for identifying HC vs. EMCI, HC vs. LMCI, HC vs. AD, EMCI vs. LMCI, LMCI vs. AD, and EMCI vs. AD, are 85.60%, 92.90%, 96.80%, 83.30%, 84.90% and 89.50%, respectively. Conclusion:The results indicate that the combination of graph measures and machine learning in fMRI connectivity analysis might be helpful for the diagnosis of AD, especially the use of local measures, which may better reflect functional changes in local brain regions because of cognitive impairment.

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Section: Discussionmentioning

confidence: 99%

Functional Brain Network Measures for Alzheimer’s Disease Classification

Wang,

Sheng,

Zhang

et al. 2023

IEEE Access

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“…Neuropsychological assessments provide essential information regarding the risk of cognitive impairment and remain the first line of choice for neurologists, whereas imaging features offer insight into cortical degeneration in AD. Uysal and Ozturk (2020) demonstrated that the efficient use of the brain with increasing age promotes the formation of new neuronal pathways and increases brain plasticity, resulting in elderly individuals with cortical atrophy but without cognitive impairment; this renders the performance of multi-class AD classification using structural MRI challenging. Although the subtlety of brain changes presents challenges for imaging-based classification, the combined use of clinical and imaging features is promising.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features

Qin

Cui

et al. 2022

Front. Aging Neurosci.

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Due to the clinical continuum of Alzheimer’s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD, and (2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, namely, cortical thickness (CTh), fractal dimension (FD), gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination (MMSE) scores at four-time points after the initial visit, we identified an optimal subset of 40 imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using the AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus (PHG), parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus (STS). Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice.

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“…Using FreeSurfer evaluation, they were able to identify 68 features related to the thickness of the cortical from each image. When handling sequential data, recurrent neural networks, particularly those with long short-term memory (LSTM), are powerful models [36]. The vanishing gradient problem is fixed, feature propagation is strengthened, and the number of parameters is decreased thanks to the DenseNet CNN architecture.…”

Section: Related Workmentioning

confidence: 99%

Dual Attention Aware Octave Convolution Network for Early-Stage Alzheimer's Disease Detection

Rangaraju,

Chinnadurai,

Natarajan

et al. 2024

ITC

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Some of the most fundamental human capabilities, including thought, speech, and movement, may be lost due to brain illnesses. The most prevalent form of dementia, Alzheimer's disease (AD), is caused by a steady decline in brain function and is now incurable. Despite the challenges associated with making a conclusive diagnosis of AD, the field has generally shifted toward making diagnoses justified by patient records and neurological analysis, such as MRI. Reports of studies utilizing machine learning for AD identification have increased in recent years. In this publication, we report the results of our most recent research. It details a deep learning-based, 3D brain MRI-based method for automated AD detection. As a result, deep learning models have become increasingly popular in recent years for analyzing medical images. To aid in detecting Alzheimer's disease at an initial phase, we suggest a dual attention-aware Octave convolution-based deep learning network (DACN). The three main parts of DACN are as follows: First, we use Patch Convolutional Neural Network (PCNN) to identify discriminative features within each MRI patch while simultaneously boosting the features of abnormally altered micro-structures in the brain; second, we use an Octave convolution to minimize the spatial redundancy and widen the field of perception of the brain's structure; and third, we use a dual attention aware convolution classifier to dissect the resulting depiction further. An outstanding test accuracy of 99.87% is reached for categorizing dementia phases by employing the suggested method in experiments on a publically available ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The proposed model was more effective, efficient, and reliable than the state-of-the-art models through our comparisons.

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Classifying Early and Late Mild Cognitive Impairment Stages of Alzheimer’s Disease by Analyzing Different Brain Areas

Cited by 5 publications

References 23 publications

Functional Brain Network Measures for Alzheimer’s Disease Classification

Functional Brain Network Measures for Alzheimer’s Disease Classification

Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features

Dual Attention Aware Octave Convolution Network for Early-Stage Alzheimer's Disease Detection

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