scholarly journals Independent morphological variables correlate with Aging, Mild Cognitive Impairment, and Alzheimer's Disease

Author(s):  
Fernanda Hansen Pacheco de Moraes ◽  
Felipe Sudo ◽  
Marina Monteiro Carneiro ◽  
Bruno R. P. de Melo ◽  
Paulo Mattos ◽  
...  

This manuscript presents a study with recruited volunteers that comprehends three sorts of events present in Alzheimer's Disease (AD) evolution (structural, biochemical, and cognitive) to propose an update in neurodegeneration biomarkers for AD. The novel variables, K, I, and S, suggested based on physics properties and empirical evidence, are defined by power-law relations between cortical thickness, exposed and total area, and natural descriptors of brain morphology. Our central hypothesis is that variable K, almost constant in healthy human subjects, is a better discriminator of a diseased brain than the current morphological biomarker, Cortical Thickness, due to its aggregated information. We extracted morphological features from 3T MRI T1w images of 123 elderly subjects: 77 Healthy Cognitive Unimpaired Controls (CTL), 33 Mild Cognitive Impairment (MCI) patients, and 13 Alzheimer's Disease (AD) patients. Moreover, Cerebrospinal Fluid (CSF) biomarkers and clinical data scores were correlated with K, intending to characterize health and disease in the cortex with morphological criteria and cognitive-behavioral profiles. K distinguishes Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Cognitive Unimpaired Controls globally and locally with reasonable accuracy (CTL-AD, 0.82; CTL-MCI, 0.58). Correlations were found between global and local K associated with clinical behavioral data (executive function and memory assessments) and CSF biomarkers (t-Tau, Aβ-40, and Aβ-42). The results suggest that the cortical folding component, K, is a premature discriminator of healthy aging, Mild Cognitive Impairment, and Alzheimer's Disease, with significant differences within diagnostics. Despite the non-concomitant events, we found correlations between brain structural degeneration (K), cognitive tasks, and biochemical markers.

2013 ◽  
Vol 9 ◽  
pp. P223-P224
Author(s):  
Sara Mohades ◽  
Jonathan Dubois ◽  
Maxime Parent ◽  
Laksanun Cheewakriengkrai ◽  
Jared Rowley ◽  
...  

2013 ◽  
Vol 9 ◽  
pp. P70-P70
Author(s):  
Sara Mohades ◽  
Jonathan Dubois ◽  
Maxime Parent ◽  
Laksanun Cheewakriengkrai ◽  
Jared Rowley ◽  
...  

2019 ◽  
Author(s):  
FR Farina ◽  
DD Emek-Savaş ◽  
L Rueda-Delgado ◽  
R Boyle ◽  
H Kiiski ◽  
...  

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.


2004 ◽  
Vol 25 ◽  
pp. S13
Author(s):  
Frederik Barkhof ◽  
Serge A. Rombouts ◽  
Rutger Goekoop ◽  
Philip Scheltens

2012 ◽  
Vol 30 (4) ◽  
pp. 857-874 ◽  
Author(s):  
Päivi Hartikainen ◽  
Janne Räsänen ◽  
Valtteri Julkunen ◽  
Eini Niskanen ◽  
Merja Hallikainen ◽  
...  

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