Voxel-based analysis derived from fractional anisotropy images of white matter volume changes with aging
Introduction
Age-related effects on the volume of the human brain tissues have been extensively studied both at post mortem (Rees, 1976, Meier-Ruge et al., 1992, Kemper, 1994, Aboitiz et al., 1996, Pakkenberg and Gundersen, 1997, Tang et al., 1997, Marner et al., 2003) and in vivo using magnetic resonance imaging (MRI) (Sowell et al., 2004, Raz and Rodrigue, 2006). The most shared hypothesis is that grey matter (GM) volume declines linearly with age (Sowell et al., 2004, Raz and Rodrigue, 2006), while white matter (WM) volume essentially remains steady or increases slowly through adulthood, peaking at the 40–50 year range (Courchesne et al., 2000, Bartzokis et al., 2001, Bartzokis et al., 2004, Jernigan et al., 2001, Ge et al., 2002, Allen et al., 2005, Fotenos et al., 2005, Walhovd et al., 2005), followed by a precipitous decline starting around 60 years of age (Guttmann et al., 1998, Salat et al., 1999, Courchesne et al., 2000, Bartzokis et al., 2001, Bartzokis et al., 2004, Jernigan et al., 2001, Ge et al., 2002, Liu et al., 2003, Allen et al., 2005, Fotenos et al., 2005, Walhovd et al., 2005).
The topographic patterns of age-related GM decline have been investigated using both global and regional MR-based approaches (Sowell et al., 2004, Raz and Rodrigue, 2006), whereas the actual topographic distribution of WM changes with aging is still controversial (Raz and Rodrigue, 2006). The remarkable heterogeneity between studies regarding WM volume changes with aging might be due to at least three reasons. First, these studies differ in the sample size and age ranges studied. In this context, the inclusion of adolescents should serve to clarify the impact of ongoing progressive volume changes that can be thought of as continuous with brain maturational effects (Sowell et al., 1999, 2002, 2004). This is particularly important for those WM fiber bundles that post mortem (Yakovlev and Lecours, 1967, Benes et al., 1994, Kemper, 1994) and in vivo MRI (Jernigan et al., 1991, Pfefferbaum et al., 1994, Reiss et al., 1996, Giedd et al., 1999, Courchesne et al., 2000, Bartzokis et al., 2001, Bartzokis et al., 2004, Sowell et al., 2002) studies have shown to progressively increase in size throughout childhood and into young adulthood. Second, the lack of a correlation between subjects’ age and whole WM volume might relate to an uneven distribution of WM loss across different brain regions (Salat et al., 1999, Bartzokis et al., 2001, Bartzokis et al., 2004, Jernigan et al., 2001, Allen et al., 2005, Lemaitre et al., 2005, Walhovd et al., 2005, Abe et al., 2008, Brickman et al., 2007, Smith et al., 2007). As a consequence, these regional changes might go undetected when using a global approach (Good et al., 2001, Sullivan et al., 2004, Abe et al., 2008, Agosta et al., 2007, Smith et al., 2007). Third, in spite of the optimization of methods to map GM density (Good et al., 2001), a careful standardization of MRI methods to assess WM morphometry is still lacking. To date, the majority of the studies evaluating age-related WM volume regional changes (Salat et al., 1999, Bartzokis et al., 2001, Jernigan et al., 2001, Good et al., 2001, Sato et al., 2003, Taki et al., 2004, Tisserand et al., 2004, Allen et al., 2005, Lemaitre et al., 2005, Walhovd et al., 2005, Abe et al., 2008, Brickman et al., 2007, Smith et al., 2007) were based on sequences and post-processing algorithms used for GM volumetry assessment, which may not represent the best approach to image the well-known pathological heterogeneity of the WM (Sullivan and Pfefferbaum 2006).
Diffusion tensor (DT) MRI is a non-invasive method which is sensitive to features of tissue microstructure, such as axonal density and axonal fiber orientational coherence. Diffusion is anisotropic in WM, and DT-derived maps allow to visualize anisotropic structures that are consistent with the anatomy of the major WM fiber bundles (Pierpaoli et al., 1996). Recently, we developed a voxel-based (VB) method to obtain estimates of WM fiber bundles volumetry using DT MRI (Pagani et al., 2007). Such an approach, which provides an index of atrophy derived from the transformation between a fractional anisotropy (FA) atlas (resuming average morphometry of a reference population) and individual subjects FA maps, has been shown to be sensitive to fiber bundles volume changes related to disease (Pagani et al., 2007).
Against this background, the aim of this study was to investigate the age-related changes of WM fiber bundles volumes at a VB resolution, using DT MRI obtained from a large sample of healthy subjects, spanning six decades of life. Since there is recent evidence that WM changes with aging may not be linear (Courchesne et al., 2000, Bartzokis et al., 2001, Bartzokis et al., 2004, Jernigan et al., 2001, Ge et al., 2002, Allen et al., 2005, Walhovd et al., 2005), we also tested for non-linear effects in the age-functions observed.
Section snippets
Subjects
We studied 84 healthy volunteers (36 women and 48 men, mean age = 44 years, range = 13–70 years), with no previous history of neurological dysfunction and a normal neurological exam. Eighty-one subjects (96.4%) were right-handers and three subjects (3.6%) were left-handers, according to the Edinburgh Handedness Questionnaire (Oldfield, 1971). All subjects had normal Mini-Mental State Examination scores (Folstein et al., 1975), after correction for age and education. All subjects were assessed
Results
One or more WMHs were seen on the T2-weighted MRI scans from 31 subjects (37%). Mean WMH loads per each age year decade are reported in Table 1. The characteristics of the brain lesions were always consistent with those of non-specific changes in the WM. No other major abnormalities such as infarct, vascular malformation, or tumor were found.
Table 2 reports parameter estimates of regressor equations, cluster sizes (i.e., the number of contiguous voxels passing the height threshold), F values,
Discussion
Previous reports on the relationship between age and brain WM volume changes have provided conflicting results, since some studies have reported a decrease in WM volume with aging (Guttmann et al., 1998, Salat et al., 1999, Courchesne et al., 2000, Bartzokis et al., 2001, Bartzokis et al., 2004, Jernigan et al., 2001, Ge et al., 2002, Liu et al., 2003, Allen et al., 2005, Fotenos et al., 2005, Lemaitre et al., 2005, Walhovd et al., 2005, Abe et al., 2008, Benedetti et al., 2006, Brickman et
References (84)
- et al.
Aging in the CNS: comparison of gray/white matter volume and diffusion tensor data
Neurobiol. Aging
(2008) - et al.
Evidence for cervical cord tissue disorganisation with aging by diffusion tensor MRI
NeuroImage
(2007) - et al.
Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region
Neurobiol. Aging
(2005) Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer's disease
Neurobiol Aging
(2004)- et al.
Heterogeneous age-related breakdown of white matter structural integrity: implications for cortical "disconnection" in aging and Alzheimer's disease
Neurobiol. Aging
(2004) - et al.
Estimation of the effective self-diffusion tensor from the NMR echo
J. Magn. Reson. B
(1994) - et al.
Regional white matter and neuropsychological functioning across the adult lifespan
Biol. Psychiatry
(2006) - et al.
Structural MRI covariance patterns associated with normal aging and neuropsychological functioning
Neurobiol. Aging
(2007) - et al.
Nonlinear regression in parametric activation studies
NeuroImage
(1996) Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate
Neuron
(2004)
Virtual in vivo interactive dissection of white matter fasciculi in the human brain
NeuroImage
The fornix and mammillary bodies in older adults with Alzheimer's disease, mild cognitive impairment, and cognitive complaints: a volumetric MRI study
Psychiatry Res.
Mini-mental state. A pratical method for grading the cognitive state of patients for the clinician
J. Psychiatr. Res.
Thresholding of statistical maps in functional neuroimaging using False Discovery Rate
NeuroImage
A voxel-based morphometric study of ageing in 465 normal adult human brains
NeuroImage
Average brain models: a convergence study
Comput. Vision Imag. Understand.
Neuroanatomical correlates of selected executive functions in middle-aged and older adults: a prospective MRI study
Neuropsychologia
Effects of age on tissues and regions of the cerebrum and cerebellum
Neurobiol. Aging
Age- and sex-related effects on the neuroanatomy of healthy elderly
NeuroImage
A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis
NeuroImage
The occipitofrontal fascicle in humans: a quantitative, in vivo, DT-MRI study
NeuroImage
The assessment and analysis of handedness
Neuropshychologia
Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging
NeuroImage
Differential aging of the brain: patterns, cognitive correlates and modifiers
Neurosci. Biobehav. Rev.
Neuroanatomical database of normal Japanese brains
Neural. Netw.
Age and gender effects on human brain anatomy: a voxel-based morphometric study in healthy elderly
Neurobiol. Aging
An overlap invariant entropy measure of 3D medical image alignment
Pattern Recognition
Deformation tensor morphometry of semantic dementia with quantitative validation
NeuroImage
Diffusion tensor imaging and aging
Neurosci. Biobehav. Rev.
Effects of age and sex on volumes of the thalamus, pons, and cortex
Neurobiol. Aging
Voxel-based morphometry of human brain with age and cerebrovascular risk factors
Neurobiol. Aging
Age-induced white matter changes in the human brain: a stereological investigation
Neurobiol. Aging
Thalamic volume predict performances on tests of cognitive speed and decreases in healthy aging. A magnetic resonance imaging-based volumetric analysis
Brain Res. Cogn. Brain Res.
Memory and executive function in older adults: relationships with temporal and prefrontal gray matter volumes and white matter hyperintensities
Neuropsychologia
Effects of age on volumes of cortex, white matter and subcortical structures
Neurobiol Aging
Age-related changes in fibre composition of the human corpus callosum: sex differences
Neuroreport
Age-related changes in frontal and temporal lobe volumes in men: a magnetic resonance imaging study
Arch Gen Psychiatry
The basis of anisotropic water diffusion in the nervous system — a technical review
NMR Biomed.
Influence of aging on brain gray and white matter changes assessed by conventional, MT, and DT MRI
Neurology
Myelination of a key relay zone in the hippocampal formation occurs in the human brain during childhood, adolescence, and adulthood
Arch. Gen. Psychiatry
Vulnerability of select neuronal types to Alzheimer's disease
Ann. N. Y. Acad. Sci.
Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers
Radiology
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