Towards non-invasive diagnostic imaging of early-stage Alzheimer's disease

Viola, Kirsten L.; Sbarboro, James; Sureka, Ruchi; De, Mrinmoy; Bicca, Maíra A.; Wang, Jane; Vasavada, Shaleen; Satpathy, Sreyesh; Wu, Summer; Joshi, Hrushikesh M.; Velasco, Pauline T.; MacRenaris, Keith W.; Waters, Emily A.; Lu, Changyuan; Phan, Joseph; Lacor, Pascale N.; Prasad, Pottumarthi V.; Dravid, Vinayak P.; Klein, William L.

doi:10.1038/nnano.2014.254

Cited by 164 publications

(127 citation statements)

References 44 publications

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“…Using PET for diagnosis of AD, Viola et al [41] found that Aβ oligomers are responsible for memory loss in patients with AD. The oligomer-specific antibodies, binding to Aβ oligomers, are induced into brain tissues and cells.…”

Section: Clinical Neurophysiological Diagnosis Of Admentioning

confidence: 99%

Clinical Neurophysiological and Automated EEG-Based Diagnosis of the Alzheimer's Disease

Bhat¹,

Acharya

Dadmehr³

et al. 2015

Eur Neurol

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Alzheimer's disease (AD) is a progressive disorder affecting intellectual, behavioral and functional abilities. It is associated with age and pathological alterations leading to the formation of amyloid plaques and tangles. It is the most common source of dementia in the older population, which varies in its degrees of severity. We are yet to find efficient methods of diagnosis of AD, as its symptoms vary among individuals. This paper presents a review of recent research on the clinical neurophysiological and automated electroencephalography-based diagnosis of the AD. Various therapeutic measures are also discussed briefly.

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Section: Clinical Neurophysiological Diagnosis Of Admentioning

confidence: 99%

Clinical Neurophysiological and Automated EEG-Based Diagnosis of the Alzheimer's Disease

Bhat¹,

Acharya

Dadmehr³

et al. 2015

Eur Neurol

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“…Structural magnetic resonance imaging (sMRI), provides high accuracy when diagnosing patients with AD vs. controls, especially in advanced stages of the disease [3,4]. However, early diagnostic accuracy for identifying AD and prognostic value for patients diagnosed with early AD using sMRI remains problematic [5].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

Collij¹,

Heeman²,

Kuijer³

et al. 2016

Radiology

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This manuscript is an original research paperWord count: 3015 Advances in Knowledge:-Automated machine learning methods can be trained to distinguish between subjects with subjective cognitive decline (SCD), patients with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) based on arterial spin labeling (ASL) images with high classification training accuracy (range, 83.8% -89.0% , p < .01).-Classifiers based on these trainings can predict the diagnosis of single subjects with high diagnostic accuracy (area under the receiver operating curve range, .89 -.96, p < .001). Implications for Patient Care:-Automated classification of 3D pseudo-continuous ASL scans that detects AD patients with high accuracy (> 82%) may support image-based diagnosis, especially in centres without experienced (neuro)radiologists.-Automated classification of 3D pseudo-continuous ASL scans may be used for AD screening purposes without compromising diagnostic accuracy.Summary statement: Automated classification of perfusion maps enable distinguishing patients with various stages of Alzheimer's disease with high accuracy.ABSTRACT Purpose: This current study investigates whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients withAlzheimer's disease (AD), mild cognitive impairment (MCI), and subjective cognitive decline (SCD), after using the W-score method to remove confounding effects of gender and age. Materials and Methods:The local institutional review board approved the study. Subjects classifier used diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction-set. Prediction performance was assessed by means of a ROC analysis, generating an area under the curve (AUC) and sensitivity/specificity distribution.Results: Single-subject diagnosis in the prediction-set using the discrimination maps yielded excellent performance for AD vs. SCD (AUC .96, p < .01), good performance for AD vs. MCI (AUC = 0.89, p < .01), and poor performance for MCI vs. SCD (AUC = 0.63, p = .06).Application of the AD vs. SCD discrimination map for prediction of MCI subgroups resulted in good performance for MCIc vs. SCD (AUC = .84, p < .01) and fair performance for MCIc vs.MCIs (AUC = .71, p > .05). Conclusion:Using automated methods, age-and gender adjusted ASL perfusion maps can be used to classify and predict diagnoses of AD, MCI-converters, stable MCI patients and SCD subjects with good to excellent accuracy and AUC values.

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“…This antibody-conjugated MNP quickly reached the oligomers, owing to active targeting and has shown clear intense MRI signals in the hippocampus region of brain in contrast to wild-type control mouse which showed absence of signals. This substantiated its use as a probe in AD pathology [50].…”

Section: Theranostic Approach Of Mnps For Neurodegenerative Diseasementioning

confidence: 71%

Therapeutic approaches of magnetic nanoparticles for the central nervous system

Dilnawaz

Sahoo

2015

Drug Discovery Today

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Towards non-invasive diagnostic imaging of early-stage Alzheimer's disease

Cited by 164 publications

References 44 publications

Clinical Neurophysiological and Automated EEG-Based Diagnosis of the Alzheimer's Disease

Clinical Neurophysiological and Automated EEG-Based Diagnosis of the Alzheimer's Disease

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

Therapeutic approaches of magnetic nanoparticles for the central nervous system

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