Age-related degradation in the central nervous system: assessment with diffusion-tensor imaging and quantitative fiber tracking.

PURPOSE To prospectively quantify differences in age-related changes in the diffusivity parameters and fiber characteristics between association, callosal, and projection fibers. MATERIALS AND METHODS This study was approved by the institutional review board, and informed consent was obtained. Diffusion-tensor imaging data with an isotropic voxel size of 1.9 mm(3) were acquired at 3 T in 38 healthy volunteers (age range, 18-88 years; 18 women). Quantitative fiber tracking was used to calculate fractional anisotropy (FA) and mean diffusivity values, eigenvalues (lambda(1), lambda(2), and lambda(3)), the number of fiber projections, and the number of fiber projections per voxel for three-dimensional reconstructed association, callosal, projection, and total brain fibers. Bivariate linear regression models were used to analyze correlations. Significant differences between correlations were assessed with the Hotelling-Williams test. RESULTS For FA, the strongest degradation in association fibers and no significant changes in projection fibers were observed. The difference in correlation was significant (P = .002). The number of fiber projections and the number of fiber projections per voxel showed strong to moderate negative correlations that were dependent on age (P < .001) in the three fiber structures and total brain fibers, with the exception of the number of fiber projections per voxel in projection fibers, which showed no significant correlation. The decrease in the number of fiber projections was significantly greater (P = .043) in projection fibers than in total brain fibers, whereas the decrease in the number of fiber projections per voxel was significantly weaker (P = .005). Association fibers showed the largest changes per decade of age for FA (-1.13%) and for the number of fiber projections per voxel (-4.7%), whereas callosal fibers showed the largest changes per decade of age for the number of fiber projections (-10.4%). CONCLUSION Quantitative fiber tracking enables identification of differences in diffusivity and fiber characteristics due to normal aging.

[1]  Adolf Pfefferbaum,et al.  Effects of age and sex on volumes of the thalamus, pons, and cortex , 2004, Neurobiology of Aging.

[2]  M. Solaiyappan,et al.  In vivo three‐dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging , 1999, Magnetic resonance in medicine.

[3]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[4]  Susumu Mori,et al.  Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.

[5]  S C Williams,et al.  Non‐invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI , 1999, Magnetic resonance in medicine.

[6]  Talma Hendler,et al.  Characterization of displaced white matter by brain tumors using combined DTI and fMRI , 2006, NeuroImage.

[7]  Frithjof Kruggel,et al.  MRI-based volumetry of head compartments: Normative values of healthy adults , 2006, NeuroImage.

[8]  Haruyasu Yamada,et al.  Normal aging in the central nervous system: quantitative MR diffusion-tensor analysis , 2002, Neurobiology of Aging.

[9]  H. Moser,et al.  Imaging cortical association tracts in the human brain using diffusion‐tensor‐based axonal tracking , 2002, Magnetic resonance in medicine.

[10]  P. Basser,et al.  Toward a quantitative assessment of diffusion anisotropy , 1996, Magnetic resonance in medicine.

[11]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  A. Pfefferbaum,et al.  Selective age-related degradation of anterior callosal fiber bundles quantified in vivo with fiber tracking. , 2006, Cerebral cortex.

[13]  D. Le Bihan,et al.  Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.

[14]  Luis Concha,et al.  Bilateral limbic diffusion abnormalities in unilateral temporal lobe epilepsy , 2005, Annals of neurology.

[15]  D. Head,et al.  Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. , 2004, Cerebral cortex.

[16]  John S. Allen,et al.  Normal neuroanatomical variation due to age: The major lobes and a parcellation of the temporal region , 2005, Neurobiology of Aging.

[17]  Karl J. Friston,et al.  A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains , 2001, NeuroImage.

[18]  A. Dale,et al.  Effects of age on volumes of cortex, white matter and subcortical structures , 2005, Neurobiology of Aging.

[19]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[20]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.

[21]  A. Kassner,et al.  Fiber density index correlates with reduced fractional anisotropy in white matter of patients with glioblastoma. , 2005, AJNR. American journal of neuroradiology.

[22]  M Rovaris,et al.  Influence of aging on brain gray and white matter changes assessed by conventional, MT, and DT MRI , 2006, Neurology.

[23]  C. Poupon,et al.  Regularization of Diffusion-Based Direction Maps for the Tracking of Brain White Matter Fascicles , 2000, NeuroImage.

[24]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[25]  K. Lim,et al.  Age‐related decline in brain white matter anisotropy measured with spatially corrected echo‐planar diffusion tensor imaging , 2000, Magnetic resonance in medicine.

[26]  Scott A. Huettel,et al.  Diffusion tensor imaging of adult age differences in cerebral white matter: relation to response time , 2004, NeuroImage.

[27]  Thanh-Thu T. Tran,et al.  Screening for Early Alzheimer's Disease: Is There Still a Role for the Mini-Mental State Examination? , 2005, Primary care companion to the Journal of clinical psychiatry.

[28]  K. Zou,et al.  Correlation and simple linear regression. , 2003, Radiology.

[29]  Edith V. Sullivan,et al.  Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging , 2005, NeuroImage.

[30]  Roland G. Henry,et al.  Quantitative diffusion tensor MRI fiber tractography of sensorimotor white matter development in premature infants , 2005, NeuroImage.

[31]  Rainer Goebel,et al.  An Efficient Algorithm for Topologically Correct Segmentation of the Cortical Sheet in Anatomical MR Volumes , 2001, NeuroImage.

[32]  Jing Zhang,et al.  Age-related changes of normal adult brain structure: analysed with diffusion tensor imaging. , 2005, Chinese medical journal.

[33]  W. Meier-Ruge,et al.  Age‐Related White Matter Atrophy in the Human Brain , 1992, Annals of the New York Academy of Sciences.

[34]  Hangyi Jiang,et al.  DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking , 2006, Comput. Methods Programs Biomed..

[35]  A. Dale,et al.  Age-related alterations in white matter microstructure measured by diffusion tensor imaging , 2005, Neurobiology of Aging.

[36]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[37]  C. Fennema-Notestine,et al.  Effects of age on tissues and regions of the cerebrum and cerebellum , 2001, Neurobiology of Aging.

[38]  M S Buchsbaum,et al.  Regional and global changes in cerebral diffusion with normal aging. , 2001, AJNR. American journal of neuroradiology.

[39]  Takayuki Obata,et al.  Age-related degeneration of corpus callosum measured with diffusion tensor imaging , 2006, NeuroImage.

[40]  Andrei I Holodny,et al.  Diffusion-tensor MR tractography of somatotopic organization of corticospinal tracts in the internal capsule: initial anatomic results in contradistinction to prior reports. , 2005, Radiology.