Article

Brain Changes in Older Adults at Very Low Risk for Alzheimer's Disease

Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0317 Oslo, Norway, Multimodal Imaging Laboratory and Department of Neurosciences, University of California, San Diego, California 92093, and Department of Radiology, University of California, San Diego, California 92103.
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience (Impact Factor: 6.34). 05/2013; 33(19):8237-42. DOI: 10.1523/JNEUROSCI.5506-12.2013
Source: PubMed

ABSTRACT

Alzheimer's disease (AD) has a slow onset, so it is challenging to distinguish brain changes in healthy elderly persons from incipient AD. One-year brain changes with a distinct frontotemporal pattern have been shown in older adults. However, it is not clear to what extent these changes may have been affected by undetected, early AD. To address this, we estimated 1-year atrophy by magnetic resonance imaging (MRI) in 132 healthy elderly persons who had remained free of diagnosed mild cognitive impairment or AD for at least 3 years. We found significant volumetric reductions throughout the brain. The sample was further divided into low-risk groups based on clinical, biomarker, genetic, or cognitive criteria. Although sample sizes varied, significant reductions were observed in all groups, with rates and topographical distribution of atrophy comparable to that of the full sample. Volume reductions were especially pronounced in the default mode network, closely matching the previously described frontotemporal pattern of changes in healthy aging. Atrophy in the hippocampus predicted change in memory, with no additional default mode network contributions. In conclusion, reductions in regional brain volumes can be detected over the course of 1 year even in older adults who are unlikely to be in a presymptomatic stage of AD.

1 Follower
 · 
9 Reads
  • Source
    • "Although the performance improvement did not reach significance according to the corrected repeated t-test, the average t over all the classifiers was significantly different from zero, verifying the findings in the AD vs. NC classification ofDukart et al (2011)and in the MCI-to-AD conversion prediction ofMoradi et al (2015). The rationale for age-removal stemmed from strong evidence of overlapping effects of normal aging and dementia on brain atrophy (Fjell et al 2013;Dukart et al 2011). We note that there was no stratification according to age or gender when dividing the data into two sets A i and B i . "
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer’s disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.
    Full-text · Article · Jan 2016 · Neuroinformatics
    • "Both the surface-based analyses and YO-OO comparison suggested strong involvement in selected temporal cortices and the hippocampus. Entorhinal cortex and hippocampus are considered regions most M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 16 vulnerable to decline in both normal aging and early Alzheimer's disease (AD) (Fjell et al., 2012;Fjell et al., 2013;Fjell et al., 2014) and our data now suggest that such vulnerability extends beyond the age of 90 years. Structures involved in the default mode network have also been regarded as prime targets in both normal and pathologic aging (Buckner et al., 2008;Fjell et al., 2014). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Successful brain aging in the oldest old (≥90 years) is under-explored. This study examined cross-sectional brain morphological differences from eighth to eleventh decades of life in non-demented individuals by high-resolution magnetic resonance imaging. 277 non-demented community dwelling participants (71-103 years) from Sydney Memory and Ageing Study and Sydney Centenarian Study comprised the sample, including a subsample of 160 cognitively high-functioning elders. Relationships between age and MRI-derived measurements were studied using general linear models; and structural profiles of the ≥90 years were delineated. In full sample and the sub-sample, significant linear negative relationship of grey matter with age was found, with the greatest age effects in the medial temporal lobe and parietal and occipital cortices. This pattern was further confirmed by comparing directly the ≥90 years to the 71-89 years groups. Significant quadratic age effects on total white matter and white matter hyperintensities were observed. Our study demonstrated heterogeneous differences across brain regions between the oldest old and young old, with an emphasis on hippocampus, temporo-posterior cortex and white matter hyperintensities.
    No preview · Article · Jan 2016
    • "Both the surface-based analyses and YO-OO comparison suggested strong involvement in selected temporal cortices and the hippocampus. Entorhinal cortex and hippocampus are considered regions most M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 16 vulnerable to decline in both normal aging and early Alzheimer's disease (AD) (Fjell et al., 2012;Fjell et al., 2013;Fjell et al., 2014) and our data now suggest that such vulnerability extends beyond the age of 90 years. Structures involved in the default mode network have also been regarded as prime targets in both normal and pathologic aging (Buckner et al., 2008;Fjell et al., 2014). "

    No preview · Article · Jul 2015
Show more