Hippocampal atrophy based Alzheimer’s disease diagnosis via machine learning methods

Uysal, Gokce; Öztürk, Muzaffer

doi:10.1016/j.jneumeth.2020.108669

Cited by 86 publications

(38 citation statements)

References 14 publications

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“…The rostral and caudal sections of the hippocampus are functionally involved in learning and memory (40). Functional MRI (fMRI) studies have shown two sub-networks within the medial temporal lobe, involving the rostral hippocampus and the medial hippocampus in the memory system (41)(42)(43)(44). Recent research has indicated the importance of reduced hippocampal volume as an indicator of the presence of AD (45).…”

Section: Discussionmentioning

confidence: 99%

Plasma p-Tau181 Outperforms Cerebrospinal Fluid Total Tau in Terms of Alzheimer’s Disease Diagnosis

Nabizadeh

Balabandian

Rostami

et al. 2021

Preprint

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Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dementia, and is a serious concern for the health of individuals and government health care systems worldwide. Gray matter atrophy and white matter damage are major contributors to cognitive deficits experienced by patients with AD, as seen through magnetic resonance imaging (MRI). Many of these brain changes associated with AD begin to occur about 15 years before the onset of initial clinical symptoms. Therefore, it is critical to find biomarkers reflective of these brain changes associated with AD to identify this disease and monitor its prognosis and development. The level of hyperphosphorylated tau 181 (p-Tau181) in the plasma has been recently considered as a novel biomarker for the presence of AD, with increased levels in patients with AD, preclinical AD, and those experiencing mild cognitive impairment (MCI). In the current study, we examined the association of cerebrospinal fluid (CSF) and plasma levels of p-Tau181 with structural brain changes pertaining to cortical thickness, cortical volume, surface area, and subcortical volume in MCI patients. In this cross-sectional study we included the information of 461 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The results of voxel-wise partial correlation analyses showed a significant negative correlation between the increased levels of plasma p-Tau181, CSF total tau, and CSF p-Tau181 and structural changes in widespread brain regions. These results provide evidence for the use of plasma p-Tau181 as a diagnostic marker for structural changes in the brain associated with the early stages of AD and neurodegeneration.

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Section: Discussionmentioning

confidence: 99%

Plasma p-Tau181 Outperforms Cerebrospinal Fluid Total Tau in Terms of Alzheimer’s Disease Diagnosis

Nabizadeh

Balabandian

Rostami

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…These results match those observed in earlier studies. The rostral and caudal hippocampus are functionally involved in learning and memory, and functional MRI studies showed two subnetworks within the medial temporal lobe, involving the rostral hippocampus and the medial hippocampus in the memory system (36)(37)(38)(39). Recent research demonstrated the importance of the hippocampus volumetric reduction as an indicator of AD (40).…”

Section: Discussionmentioning

confidence: 99%

CSF total tau, plasma and CSF p tau 181 associated with structural changes in the early stage of Alzheimer's disease

Nabizadeh

Rostami

Balabandian

et al. 2020

Preprint

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Alzheimer's disease (AD) is the most important cause of dementia and is a serious concern for individuals and governments worldwide. Changes in the brain appear about 15 years before the first clinical symptoms; with this in mind, it can clear the role of biomarkers in monitoring Alzheimer's development. P tau 181 level in plasma recently emerged as a new biomarker and rises obviously in AD patients, preclinical AD, and MCI patients. The role of gray matter atrophy and white matter damages in cognitive decline is well established, which is detectable by magnetic resonance imaging (MRI). In this investigation, we measured the association between CSF (total tau, and p tau 181) and plasma p tau 181 with structural changes (cortical thickness, cortical volume, surface area, and subcortical volume) in MCI patients. We performed a cross-sectional study on the ADNI cohort between 461 MCI patients. Results of voxel-wise partial correlation analysis in our participants showed a significant correlation between plasma p tau 181, CSF total tau and p tau 181 with changes in structural values in different regions. Our study revealed a significant correlation between plasma p tau and structural changes in the brain regions associated with Alzheimer's disease physiopathology. These results provide evidence for using plasma p tau 181 as a diagnostic factor in the early onset of AD patients and neurodegeneration.

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“…Considering volumetric information of right and left hippocampus of brain, age, and gender, AD prediction approach was proposed in [58]. The performance of the proposed study was investigated by six different ML classifiers.…”

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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Hippocampal atrophy based Alzheimer’s disease diagnosis via machine learning methods

Cited by 86 publications

References 14 publications

Plasma p-Tau181 Outperforms Cerebrospinal Fluid Total Tau in Terms of Alzheimer’s Disease Diagnosis

Plasma p-Tau181 Outperforms Cerebrospinal Fluid Total Tau in Terms of Alzheimer’s Disease Diagnosis

CSF total tau, plasma and CSF p tau 181 associated with structural changes in the early stage of Alzheimer's disease

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

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