Structural MR Imaging in the Diagnosis of Alzheimer's Disease and Other Neurodegenerative Dementia: Current Imaging Approach and Future Perspectives

Park, Mina; Moon, Won-Jin

doi:10.3348/kjr.2016.17.6.827

Cited by 77 publications

(75 citation statements)

References 150 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…). Thus, estimates of the amount of iron deposition in the drain can be used for evaluating the presence and progression of AD (Park and Moon ). Studies have found increased levels of iron in the putamen, pulvinar thalamus, red nucleus, hippocampus, and the temporal cortex of AD patients but the clinical consequences of these findings remain unknown (Langkammer et al .…”

Section: Iron Imagingmentioning

confidence: 99%

“…Pathological studies of patients with AD show plaques, neurofibrillary tangles, and neurons that contain high amount of iron, which is indicative of disruption in iron homeostasis in the brain . Thus, estimates of the amount of iron deposition in the drain can be used for evaluating the presence and progression of AD (Park and Moon 2016). Studies have found increased levels of iron in the putamen, pulvinar thalamus, red nucleus, hippocampus, and the temporal cortex of AD patients but the clinical consequences of these findings remain unknown (Langkammer et al 2014).…”

Section: Iron Imagingmentioning

confidence: 99%

See 1 more Smart Citation

Dementia risk and prevention by targeting modifiable vascular risk factors

Tariq

Barber

2017

Journal of Neurochemistry

View full text Add to dashboard Cite

The incidence of dementia is expected to double in the next 20 years and will contribute to heavy social and economic burden. Dementia is caused by neuronal loss that leads to brain atrophy years before symptoms manifest. Currently, no cure exists and extensive efforts are being made to mitigate cognitive impairment in late life in order to reduce the burden on patients, caregivers, and society. The most common type of dementia, Alzheimer's disease (AD), and vascular dementia (VaD) often co-exists in the brain and shares common, modifiable risk factors, which are targeted in numerous secondary prevention trials. There is a growing need for non-pharmacological interventions and infrastructural support from governments to encourage psychosocial and behavioral interventions. Secondary prevention trials need to be redesigned based on the risk profile of individual subjects, which require the use of validated and standardized clinical, biological, and neuroimaging biomarkers. Multidomain approaches have been proposed in high-risk populations that target optimal treatment; clinical trials need to recruit individuals at the highest risk of dementia before symptoms develop, thereby identifying an enriched disease group to test preventative and disease modifying strategies. The underlying aim should be to reduce microscopic brain tissue loss by modifying vascular and lifestyle risk factors over a relatively short period of time, thus optimizing the opportunity for preventing dementia in the future. Collaboration between international research groups is of key importance to the optimal use and allocation of existing resources, and the development of new techniques in preventing dementia.

show abstract

Section: Iron Imagingmentioning

confidence: 99%

Section: Iron Imagingmentioning

confidence: 99%

Dementia risk and prevention by targeting modifiable vascular risk factors

Tariq

Barber

2017

Journal of Neurochemistry

View full text Add to dashboard Cite

show abstract

“…Structural imaging procedures have been successful in AD diagnostics (Chetelat and Baron, 2003;Frisoni, Fox, Jack, Scheltens, & Thompson, 2010;Park and Moon, 2016;Teipel et al, 2013). This is due to the fact that structural atrophy mirrors patterns of the characteristic regional pathology of this condition (Jack, Petersen, O'brien, & Tangalos, 1992;Jack et al, 1997;Scheltens et al, 1992).…”

Section: Oscarmentioning

confidence: 99%

Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease

Suresh

Fischl

Salat

et al. 2017

Human Brain Mapping

View full text Add to dashboard Cite

There is great value to use of structural neuroimaging in the assessment of Alzheimer's disease (AD). However, to date, predictive value of structural imaging tend to range between 80% and 90% in accuracy and it is unclear why this is the case given that structural imaging should parallel the pathologic processes of AD. There is a possibility that clinical misdiagnosis relative to the gold standard pathologic diagnosis and/or additional brain pathologies are confounding factors contributing to reduced structural imaging classification accuracy. We examined potential factors contributing to misclassification of individuals with clinically diagnosed AD purely from cortical thickness measures. Correctly classified and incorrectly classified groups were compared across a range of demographic, biological, and neuropsychological data including cerebrospinal fluid biomarkers, amyloid imaging, white matter hyperintensity (WMH) volume, cognitive, and genetic factors. Individual subject analyses suggested that at least a portion of the control individuals misclassified as AD from structural imaging additionally harbor substantial AD biomarker pathology and risk, yet are relatively resistant to cognitive symptoms, likely due to "cognitive reserve," and therefore clinically unimpaired. In contrast, certain clinical control individuals misclassified as AD from cortical thickness had increased WMH volume relative to other controls in the sample, suggesting that vascular conditions may contribute to classification accuracy from cortical thickness measures. These results provide examples of factors that contribute to the accuracy of structural imaging in predicting a clinical diagnosis of AD, and provide important information about considerations for future work aimed at optimizing structural based diagnostic classifiers for AD.

show abstract

“…In recent years, considerable efforts have been made to identify neuroimaging biomarkers, with the aim of providing objective measures of disease pathology before symptom onset (Varghese, Sheelakumari, James, & Mathuranath, 2013). Most of this work has been carried out using structural Magnetic Resonance Imaging (MRI) (Park & Moon, 2016). Estimates of the sensitivity and specificity of MRI markers in detecting AD range from 82-89% and 86-97%, respectively (see Beheshti, Demirel, Matsuda, & Initiative, 2017, for a summary of recent studies).…”

Section: Introductionmentioning

confidence: 99%

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Farina

Emek‐Savaş

Rueda‐Delgado

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder characterised by severe cognitive decline and loss of autonomy. AD is the leading cause of dementia. AD is preceded by mild cognitive impairment (MCI). By 2050, 68% of new dementia cases will occur in low- and middle-income countries. In the absence of objective biomarkers, psychological assessments are typically used to diagnose MCI and AD. However, these require specialist training and rely on subjective judgements. The need for low-cost, accessible and objective tools to aid AD and MCI diagnosis is therefore crucial. Electroencephalography (EEG) has potential as one such tool: it is relatively inexpensive (cf. magnetic resonance imaging; MRI) and is portable. In this study, we collected resting state EEG, structural MRI and rich neuropsychological data from older adults (55+ years) with AD, with MCI and from healthy controls (n~60 per group). Our goal was to evaluate the utility of EEG, relative to MRI, for the classification of MCI and AD. We also assessed the performance of combined EEG and behavioural (Mini-Mental State Examination; MMSE) and structural MRI classification models. Resting state EEG classified AD and HC participants with moderate accuracy (AROC=0.76), with lower accuracy when distinguishing MCI from HC participants (AROC=0.67). The addition of EEG data to MMSE scores had no additional value compared to MMSE alone. Structural MRI out-performed EEG (AD vs HC, AD vs MCI: AROCs=1.00; HC vs MCI: AROC=0.73). Resting state EEG does not appear to be a suitable tool for classifying AD. However, EEG classification accuracy was comparable to structural MRI when distinguishing MCI from healthy aging, although neither were sufficiently accurate to have clinical utility. This is the first direct comparison of EEG and MRI as classification tools in AD and MCI participants.

show abstract

Structural MR Imaging in the Diagnosis of Alzheimer's Disease and Other Neurodegenerative Dementia: Current Imaging Approach and Future Perspectives

Cited by 77 publications

References 150 publications

Dementia risk and prevention by targeting modifiable vascular risk factors

Dementia risk and prevention by targeting modifiable vascular risk factors

Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Contact Info

Product

Resources

About