Functional Imaging in Healthy Aging and Alzheimer's Disease

2004 ◽  
pp. 62-95
Author(s):  
Nicole D. Anderson ◽  
Cheryl L. Grady
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Imaging ◽  
Healthy Aging

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

Functional Imaging of Visuospatial Processing in Alzheimer's Disease

NeuroImage ◽  
2002 ◽  
Vol 17 (3) ◽  
pp. 1403-1414 ◽  
Author(s):  
D. Prvulovic ◽  
D. Hubl ◽  
A.T. Sack ◽  
L. Melillo ◽  
K. Maurer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Imaging ◽  
Visuospatial Processing

Functional Imaging in Alzheimer’s Disease

2000 ◽  
pp. 149-168
Author(s):  
Reisa A. Sperling ◽  
Thomas A. Sandson ◽  
Keith A. Johnson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Imaging

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

2019 ◽  
Author(s):  
FR Farina ◽  
DD Emek-Savaş ◽  
L Rueda-Delgado ◽  
R Boyle ◽  
H Kiiski ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Healthy Aging ◽  
Neurodegenerative Disorder ◽  
Low Cost ◽  
Structural Mri ◽  
Lower Accuracy

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.

scholarly journals Smell and taste recognition in early stages of late-onset Alzheimer’s disease

SURG Journal ◽  
2010 ◽  
Vol 4 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Nila Ilhamto ◽  
Lisa M Duizer
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Health Status ◽  
Healthy Aging ◽  
Late Onset ◽  
Word Recall ◽  
Early Stages ◽  
Predictive Variables ◽  
Healthy Elderly ◽  
Inadequate Nutrition

Problems of inadequate nutrition and energy intake are common in the aging population. Smell and taste deficits associated with Late-Onset Alzheimer’s Disease (LOAD) may accentuate the decline in nutritional status of elderly individuals and indirectly enhance progression of cognitive problems in LOAD. The objective of this study was to explore and characterize smell and taste recognition abilities in early stages of LOAD, beyond that of normal healthy aging. A total of 29 healthy-younger subjects aged 18-40 (HY), 13 healthy-elderly (HA) and six elderly adults diagnosed with LOAD (AD) aged 60-85, were recruited from the Guelph community. The Sniffin’ Sticks Screening Test (SSST) and Taste Strips were used to test olfactory and gustatory functions, respectively. Participants also completed the mini-mental state examination (MMSE), clock test and word recall tests to assess cognitive/memory skills. Compared to HA individuals, people with AD had significant odour recognition impairment. Correlation analysis also revealed an age-associated decline in overall taste ability. When specific tastes were examined, impairments in sour and bitter identification were observed with increasing age. However, no significant differences in specific taste abilities were found between HA and AD individuals. In predicting health status (ie. presence or absence of LOAD), an assessment of all variables in this study was conducted using Generalized Linear Model (GLM). Results showed that sweet recognition and clock test scores were the best predictive variables of health status. However, this is a preliminary model that needs refinement through further research using more individuals.

scholarly journals Phenotypic regional functional imaging patterns during memory encoding in mild cognitive impairment and Alzheimer's disease

2013 ◽  
Vol 9 (3) ◽  
pp. 284-294 ◽  
Author(s):  
Jeffrey N. Browndyke ◽  
Kelly Giovanello ◽  
Jeffrey Petrella ◽  
Kathleen Hayden ◽  
Ornit Chiba-Falek ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Imaging ◽  
Memory Encoding

O1-02-06 Deactivation in healthy aging, mild cognitive impairment and Alzheimer's disease

2004 ◽  
Vol 25 ◽  
pp. S13
Author(s):  
Frederik Barkhof ◽  
Serge A. Rombouts ◽  
Rutger Goekoop ◽  
Philip Scheltens
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Healthy Aging
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