Neuroimaging in Dementias

Mahalingam, Sowmya; Chen, Ming-Kai

doi:10.1055/s-0039-1678580

Cited by 18 publications

(10 citation statements)

References 72 publications

(82 reference statements)

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“…The analyses presented in this study provide preliminary evidence that machine learning approaches may be helpful in the task of optimising the accuracy of big data assets which, although potentially highly informative, are also vulnerable to the presence of inaccuracies. When in vivo diagnostic tools such as molecular ligands [53][54][55] become widely available, it will be possible to address and retrospectively to ameliorate diagnostic inaccuracies in large clinical dementia datasets. This will, in turn, lead to new insights into disease risks, mechanisms, the discovery of endophenotypes and patient stratification.…”

Section: Discussionmentioning

confidence: 99%

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

et al. 2021

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Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients.

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Section: Discussionmentioning

confidence: 99%

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

et al. 2021

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“…For example, three radioligands which bind to amyloid-β in neuritic plaques in Alzheimer's disease have been widely studied and approved by the U.S. Food and Drug Administration. 87 At this time, these PET tracers are approved to rule out Alzheimer's disease. That is, in an older person with cognitive impairment, a "positive" amyloid PET does not rule in the diagnosis of Alzheimer's disease, whereas a "negative" amyloid PET indicates that it is unlikely that the patient's cognitive impairment is caused by Alzheimer's disease pathology.…”

Section: Fluid and Neuroimaging Biomarkers For Chronic Traumatic Encementioning

confidence: 99%

Clinical Presentation of Chronic Traumatic Encephalopathy

et al. 2020

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Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with repetitive head impacts (RHI), such as those received in contact/collision sports, blast injury in military veterans, and domestic violence. Currently, CTE can only be diagnosed following death. Although the clinical features of former boxers have been described for almost a century, and there is increasing evidence of long-term cognitive and neuropsychiatric impairments in living former American football players, the specific clinical presentation associated with underlying CTE neuropathology remains unclear. These features include diverse and nonspecific changes in cognition, mood, behavior, and motor functioning. Currently, there are no validated and widely accepted clinical diagnostic criteria. Proposed criteria are primarily based on retrospective telephonic interviews with the next of kin of individuals who were diagnosed with CTE postmortem. Prospective studies involving individuals presumably at high risk for CTE are underway; these will hopefully clarify the clinical features and course of CTE, allow the diagnostic criteria to be refined, and lead to the development and validation of in vivo biomarkers. This article reviews what is currently known about the clinical presentation of CTE and describes the evolution of this knowledge from early case reports of “punch drunk” boxers through larger case series of neuropathologically confirmed CTE. This article concludes with a discussion of gaps in research and future directions to address these areas.

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“…In the older age group (>65 years), Alzheimer's disease is one of several dreaded neurodegenerative diseases that cause dementia. Others include frontotemporal dementia, dementia with Lewy bodies, vascular dementia, progressive supranuclear palsy, alcoholic dementia, Creutzfeldt-Jakob disease, and corticobasal degeneration ( Figure 1) [4]. Alzheimer's disease has two forms: early-onset and late-onset.…”

Section: Introductionmentioning

confidence: 99%

Human-Induced Pluripotent Stem Cells and Herbal Small-Molecule Drugs for Treatment of Alzheimer’s Disease

Wuli

Tsai

Chiou

et al. 2020

IJMS

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Alzheimer’s disease (AD) is characterized by extracellular amyloid plaques composed of the β-amyloid peptides and intracellular neurofibrillary tangles and associates with progressive declines in memory and cognition. Several genes play important roles and regulate enzymes that produce a pathological accumulation of β-amyloid in the brain, such as gamma secretase (γ-secretase). Induced pluripotent stem cells from patients with Alzheimer’s disease with different underlying genetic mechanisms may help model different phenotypes of Alzheimer’s disease and facilitate personalized drug screening platforms for the identification of small molecules. We also discuss recent developments by γ-secretase inhibitors and modulators in the treatment of AD. In addition, small-molecule drugs isolated from Chinese herbal medicines have been shown effective in treating Alzheimer’s disease. We propose a mechanism of small-molecule drugs in treating Alzheimer’s disease. Combining therapy with different small-molecule drugs may increase the chance of symptomatic treatment. A customized strategy tailored to individuals and in combination with therapy may be a more suitable treatment option for Alzheimer’s disease in the future.

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Neuroimaging in Dementias

Cited by 18 publications

References 72 publications

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

Clinical Presentation of Chronic Traumatic Encephalopathy

Human-Induced Pluripotent Stem Cells and Herbal Small-Molecule Drugs for Treatment of Alzheimer’s Disease

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