Functional Consequences of Neurite Orientation Dispersion and Density in Humans across the Adult Lifespan

As humans age, a characteristic pattern of widespread neocortical dendritic disruption coupled with compensatory effects in hippocampus and other subcortical structures is shown in postmortem investigations. It is now possible to address age-related effects on gray matter (GM) neuritic organization and density in humans using multishell diffusion-weighted MRI and the neurite-orientation dispersion and density imaging (NODDI) model. In 45 healthy individuals across the adult lifespan (21–84 years), we used a multishell diffusion imaging and the NODDI model to assess the intraneurite volume fraction and neurite orientation-dispersion index (ODI) in GM tissues. We also determined the functional correlates of variations in GM microstructure by obtaining resting-state fMRI and behavioral data. We found a significant age-related deficit in neocortical ODI (most prominently in frontoparietal regions), whereas increased ODI was observed in hippocampus and cerebellum with advancing age. Neocortical ODI outperformed cortical thickness and white matter fractional anisotropy for the prediction of chronological age in the same individuals. Higher GM ODI sampled from resting-state networks with known age-related susceptibility (default mode and visual association networks) was associated with increased functional connectivity of these networks, whereas the task-positive networks tended to show no association or even decreased connectivity. Frontal pole ODI mediated the negative relationship of age with executive function, whereas hippocampal ODI mediated the positive relationship of age with executive function. Our in vivo findings align very closely with the postmortem data and provide evidence for vulnerability and compensatory neural mechanisms of aging in GM microstructure that have functional and cognitive impact in vivo.

[1]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[2]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[3]  Dae-Shik Kim,et al.  A framework to analyze partial volume effect on gray matter mean diffusivity measurements , 2009, NeuroImage.

[4]  David Wechsler,et al.  Wechsler Memory scale. , 2005 .

[5]  B. Anderson,et al.  Age and hemisphere effects on dendritic structure. , 1996, Brain : a journal of neurology.

[6]  Hiroshi Honda,et al.  Partial volume estimation and segmentation of brain tissue based on diffusion tensor MRI. , 2010, Medical physics.

[7]  L. Westlye,et al.  Intracortical Myelin Links with Performance Variability across the Human Lifespan: Results from T1- and T2-Weighted MRI Myelin Mapping and Diffusion Tensor Imaging , 2013, The Journal of Neuroscience.

[8]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[9]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[10]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[11]  Dennis A. Turner,et al.  Increased dendritic extent in hippocampal CA1 neurons from aged F344 rats , 1996, Neurobiology of Aging.

[12]  B. Jacobs,et al.  Life‐span dendritic and spine changes in areas 10 and 18 of human cortex: A quantitative golgi study , 1997, The Journal of comparative neurology.

[13]  Christina M. Weaver,et al.  Dendritic spine changes associated with normal aging , 2013, Neuroscience.

[14]  Hui Zhang,et al.  Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy☆ , 2014, Epilepsy Research.

[15]  Paul D. Coleman,et al.  Age-related dendritic growth in dentate gyrus of human brain is followed by regression in the ‘oldest old’ , 1985, Brain Research.

[16]  A. Scheibel,et al.  A quantitative dendritic analysis of wernicke's area in humans. I. Lifespan changes , 1993, The Journal of comparative neurology.

[17]  P. Penzes,et al.  Dendritic spine pathology in neuropsychiatric disorders , 2011, Nature Neuroscience.

[18]  Kristopher J Preacher,et al.  Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models , 2008, Behavior research methods.

[19]  Timothy Edward John Behrens,et al.  Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI , 2012, NeuroImage.

[20]  G. Busatto,et al.  Resting-state functional connectivity in normal brain aging , 2013, Neuroscience & Biobehavioral Reviews.

[21]  Brian B. Avants,et al.  An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data , 2011, Neuroinformatics.

[22]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[23]  G. Shepherd.  The Microcircuit Concept Applied to Cortical Evolution: from Three-Layer to Six-Layer Cortex , 2011, Front. Neuroanat..

[24]  E. Westman,et al.  Regional vulnerability of hippocampal subfields to aging measured by structural and diffusion MRI , 2014, Hippocampus.

[25]  B. Reisberg,et al.  Retrogenesis: clinical, physiologic, and pathologic mechanisms in brain aging, Alzheimer’s and other dementing processes , 1999, European Archives of Psychiatry and Clinical Neuroscience.

[26]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[27]  N. Voets,et al.  Structural substrates for resting network disruption in temporal lobe epilepsy. , 2012, Brain : a journal of neurology.

[28]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[29]  D. Pandya,et al.  The cerebrocerebellar system. , 1997, International review of neurobiology.

[30]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[31]  J. Morrison,et al.  Age-related dendritic and spine changes in corticocortically projecting neurons in macaque monkeys. , 2003, Cerebral cortex.

[32]  Lars T. Westlye,et al.  Network-specific effects of age and in-scanner subject motion: A resting-state fMRI study of 238 healthy adults , 2012, NeuroImage.

[33]  de Brabander,et al.  Layer‐specific dendritic regression of pyramidal cells with ageing in the human prefrontal cortex , 1998, The European journal of neuroscience.

[34]  N. Spruston.  Pyramidal neurons: dendritic structure and synaptic integration , 2008, Nature Reviews Neuroscience.

[35]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[36]  M. Andersson,et al.  Longitudinal evidence for diminished frontal cortex function in aging , 2010, Proceedings of the National Academy of Sciences.

[37]  Paul M. Thompson,et al.  Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics , 2014, NeuroImage.

[38]  Floyd E. Bloom,et al.  Senescent microstructural changes in rat cerebellum , 1984, Brain Research.

[39]  Timothy Edward John Behrens,et al.  The CONNECT project: Combining macro- and micro-structure , 2013, NeuroImage.

[40]  Leif Østergaard,et al.  Modeling dendrite density from magnetic resonance diffusion measurements , 2007, NeuroImage.

[41]  P. Coleman,et al.  Dendritic growth in the aged human brain and failure of growth in senile dementia. , 1979, Science.

[42]  R. Freeman,et al.  Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity , 2007, Nature Neuroscience.

[43]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[44]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

[45]  D. Hillman,et al.  Dying-back of Purkinje cell dendrites with synapse loss in aging rats , 1999, Journal of neurocytology.

[46]  Joseph V. Hajnal,et al.  Development of cortical microstructure in the preterm human brain , 2013, Proceedings of the National Academy of Sciences.

[47]  P. Matthews,et al.  Distinct patterns of brain activity in young carriers of the APOE e4 allele , 2009, NeuroImage.

[48]  I. Akiguchi,et al.  Age-related changes of pyramidal cell basal dendrites in layers III and V of human motor cortex: A quantitative Golgi study , 2004, Acta Neuropathologica.

[49]  M E Shenton,et al.  Gray matter alterations in early aging: A diffusion magnetic resonance imaging study , 2014, Human brain mapping.

[50]  Kwanghun Chung,et al.  Light microscopy mapping of connections in the intact brain , 2013, Trends in Cognitive Sciences.

[51]  A. Scheibel,et al.  A quantitative dendritic analysis of wernicke's area in humans. II. Gender, hemispheric, and environmental factors , 1993, The Journal of comparative neurology.

[52]  Danielle S Bassett,et al.  Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.

[53]  Thomas E. Nichols,et al.  Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.

[54]  N. Intrator,et al.  Free water elimination and mapping from diffusion MRI , 2009, Magnetic resonance in medicine.

[55]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[56]  Cheryl L. Dahle,et al.  Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. , 2005, Cerebral cortex.

[57]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

[58]  Lei Guo,et al.  Brain tissue segmentation based on DTI data , 2007, NeuroImage.

[59]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[60]  Carl-Fredrik Westin,et al.  Excessive Extracellular Volume Reveals a Neurodegenerative Pattern in Schizophrenia Onset , 2012, The Journal of Neuroscience.

[61]  M. Sahraian,et al.  The human cerebellum: a review of physiologic neuroanatomy. , 2014, Neurologic clinics.

[62]  M. Schlossberg.  The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation. , 1986 .

[63]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[64]  J. Morrison,et al.  Differential effects of aging on dendritic spines in visual cortex and prefrontal cortex of the rhesus monkey , 2014, Neuroscience.

[65]  Sati Mazumdar,et al.  Rating chronic medical illness burden in geropsychiatric practice and research: Application of the Cumulative Illness Rating Scale , 1992, Psychiatry Research.

[66]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.