Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest

Bi, Xia-an; Hu, Xi; Wu, Hao; Wang, Yan

doi:10.1109/jbhi.2020.2973324

Cited by 99 publications

(66 citation statements)

References 42 publications

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“…Some studies have suggested that the precuneus is involved in extended face recognition 54,55 and participates in facial emotion perception and context integration 56 . Based on a new neural network analysis model, 57 researchers found that the abnormality of precuneus combines with some risk genes has a good recognition ability for MCI and AD 58,59 . A previous study showed that a decrease in confrontation naming task scores in patients with early frontotemporal lobe degeneration was associated with decreased fMRI signals in the precuneus 60 .…”

Section: Discussionmentioning

confidence: 99%

Changes in local brain function in mild cognitive impairment due to semantic dementia

et al. 2021

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Aims Mild cognitive impairment due to semantic dementia represents the preclinical stage, involving cognitive decline dominated by semantic impairment below the semantic dementia standard. Therefore, studying mild cognitive impairment due to semantic dementia may identify changes in patients before progression to dementia. However, whether changes in local functional activity occur in preclinical stages of semantic dementia remains unknown. Here, we explored local functional changes in patients with mild cognitive impairment due to semantic dementia using resting‐state functional MRI. Methods We administered a battery of neuropsychological tests to twenty‐two patients with mild cognitive impairment due to semantic dementia (MCI‐SD group) and nineteen healthy controls (HC group). We performed structural MRI to compare gray matter volumes, and resting‐state functional MRI with multiple sub‐bands and indicators to evaluate functional activity. Results Neuropsychological tests revealed a significant decline in semantic performance in the MCI‐SD group, but no decline in other cognitive domains. Resting‐state functional MRI revealed local functional changes in multiple brain regions in the MCI‐SD group, distributed in different sub‐bands and indicators. In the normal band, local functional changes were only in the gray matter atrophic area. In the other sub‐bands, more regions with local functional changes outside atrophic areas were found across various indicators. Among these, the degree centrality of the left precuneus in the MCI‐SD group was positively correlated with general semantic tasks (oral sound naming, word‐picture verification). Conclusion Our study revealed local functional changes in mild cognitive impairment due to semantic dementia, some of which were located outside the atrophic gray matter. Driven by functional connectivity changes, the left precuneus might play a role in preclinical semantic dementia. The study proved the value of frequency‐dependent sub‐bands, especially the slow‐2 and slow‐3 sub‐bands.

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Section: Discussionmentioning

confidence: 99%

Changes in local brain function in mild cognitive impairment due to semantic dementia

et al. 2021

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“…Logistic regression (Mao et al, 2018) is based on a multinomial logistic regression model with a ridge estimator. Random forests are an ensemble learning method for classification based on a multitude of decision trees (Bi et al, 2020). Naïve Bayes is based on classification mechanisms that use Gaussian processes (Wei et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches

et al. 2021

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Background: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. Aims: This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. Methods: Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. Results: The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels ( r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score ( B = 0.003, 95% confidence interval 0.002–0.005, p < 0.001). Conclusions: Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.

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“…It was identified that non-linear SVM with RBF kernel showed better performance than some other classifiers. Brain region-gene pairs were proposed as the multimodal fusion features to detect AD in [55]. SNP and fMRI were used to detect correlation between genes and brain regions and build the fusion features.…”

Section: ) Ml-based Approaches In Ad Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest

Cited by 99 publications

References 42 publications

Changes in local brain function in mild cognitive impairment due to semantic dementia

Changes in local brain function in mild cognitive impairment due to semantic dementia

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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