Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3‐Tesla whole‐brain diffusion‐weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative–50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole‐brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the “rich club” – a network property where high‐degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low‐degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step‐wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline. Hum Brain Mapp 36:3087–3103, 2015. © 2015 Wiley Periodicals, Inc.

[1]  Michael Weiner,et al.  Disrupted Brain Connectivity in Alzheimer's Disease: Effects of Network Thresholding , 2013, CDMRI/MMBC@MICCAI.

[2]  Paul M. Thompson,et al.  Communication of brain network core connections altered in behavioral variant frontotemporal dementia but possibly preserved in early-onset Alzheimer's disease , 2015, Medical Imaging.

[3]  P. Visser,et al.  New MRI markers for Alzheimer's disease: a meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements. , 2012, Journal of Alzheimer's disease : JAD.

[4]  N. Jahanshad,et al.  Common Alzheimer's Disease Risk Variant Within the CLU Gene Affects White Matter Microstructure in Young Adults , 2011, The Journal of Neuroscience.

[5]  Derek K. Jones,et al.  Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review , 2002 .

[6]  Joaquín Goñi,et al.  Abnormal rich club organization and functional brain dynamics in schizophrenia. , 2013, JAMA psychiatry.

[7]  Alex R. Smith,et al.  Sex differences in the structural connectome of the human brain , 2013, Proceedings of the National Academy of Sciences.

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

[9]  H. Jacobs,et al.  Alzheimer's disease: the downside of a highly evolved parietal lobe? , 2013, Journal of Alzheimer's disease : JAD.

[10]  G. Sapiro,et al.  Reconstruction of the orientation distribution function in single‐ and multiple‐shell q‐ball imaging within constant solid angle , 2010, Magnetic resonance in medicine.

[11]  Paul M. Thompson,et al.  Disrupted Brain Networks in the Aging HIV+ Population , 2012, Brain Connect..

[12]  Paul M. Thompson,et al.  Connectomics Sheds New Light on Alzheimer’s Disease , 2013, Biological Psychiatry.

[13]  H. Braak,et al.  Development of Alzheimer-related neurofibrillary changes in the neocortex inversely recapitulates cortical myelogenesis , 1996, Acta Neuropathologica.

[14]  John M Stern,et al.  Connectomics and epilepsy. , 2013, Current opinion in neurology.

[15]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[16]  Jesse A. Brown,et al.  Brain network local interconnectivity loss in aging APOE-4 allele carriers , 2011, Proceedings of the National Academy of Sciences.

[17]  Essa Yacoub,et al.  A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography , 2011, Medical Image Anal..

[18]  Paul M. Thompson,et al.  Understanding scanner upgrade effects on brain integrity & connectivity measures , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[19]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[20]  Michael Weiner,et al.  Breakdown of Brain Connectivity Between Normal Aging and Alzheimer's Disease: A Structural k-Core Network Analysis , 2013, Brain Connect..

[21]  Alessandro Vespignani,et al.  Large scale networks fingerprinting and visualization using the k-core decomposition , 2005, NIPS.

[22]  Paul M. Thompson,et al.  A Dynamical Clustering Model of Brain Connectivity Inspired by the N-Body Problem , 2013, MBIA.

[23]  Self-Concept Variables Sex Differences in , 2016 .

[24]  Kiralee M. Hayashi,et al.  Dynamics of Gray Matter Loss in Alzheimer's Disease , 2003, The Journal of Neuroscience.

[25]  Yong He,et al.  Disrupted Functional Brain Connectome in Individuals at Risk for Alzheimer's Disease , 2013, Biological Psychiatry.

[26]  Paul M. Thompson,et al.  Voxelwise Spectral Diffusional Connectivity and Its Applications to Alzheimer's Disease and Intelligence Prediction , 2013, MICCAI.

[27]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[28]  Christoph Palm,et al.  A novel approach to the human connectome: Ultra-high resolution mapping of fiber tracts in the brain , 2011, NeuroImage.

[29]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[30]  Paul M. Thompson,et al.  Evaluation of diffusion imaging protocols for the Alzheimer's disease Neuroimaging Initiative , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[31]  Paul M. Thompson,et al.  Tractography density and network measures in Alzheimer'S disease , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[32]  Michael Weiner,et al.  Small world network measures predict white matter degeneration in patients with early-stage mild cognitive impairment , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[33]  Vladimir S Fonov,et al.  White Matter Abnormalities and Structural Hippocampal Disconnections in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease , 2013, PloS one.

[34]  Paul M. Thompson,et al.  Automated multi-atlas labeling of the fornix and its integrity in alzheimer's disease , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[35]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[36]  O. Sporns,et al.  Identification and Classification of Hubs in Brain Networks , 2007, PloS one.

[37]  Paul M. Thompson,et al.  Diffusion tensor imaging in seven minutes: Determining trade-offs between spatial and directional resolution , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[38]  Paul M. Thompson,et al.  Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics , 2014, NeuroImage.

[39]  D. Long Networks of the Brain , 2011 .

[40]  Alexander Leemans,et al.  Disruption of cerebral networks and cognitive impairment in Alzheimer disease , 2013, Neurology.

[41]  Nick C Fox,et al.  EFNS task force: the use of neuroimaging in the diagnosis of dementia , 2012, European journal of neurology.

[42]  Michael Weiner,et al.  Connectivity Network Breakdown Predicts Imminent Volumetric Atrophy in Early Mild Cognitive Impairment , 2012, MBIA.

[43]  Christophe Lenglet,et al.  Sex differences in the human connectome: 4-Tesla high angular resolution diffusion imaging (HARDI) tractography in 234 young adult twins , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[44]  O. Sporns,et al.  High-cost, high-capacity backbone for global brain communication , 2012, Proceedings of the National Academy of Sciences.

[45]  Essa Yacoub,et al.  Magnetic Resonance Field Strength Effects on Diffusion Measures and Brain Connectivity Networks , 2013, Brain Connect..

[46]  Michael Weiner,et al.  Spectral graph theory and graph energy metrics show evidence for the alzheimer's disease disconnection syndrome in APOE-4 risk gene carriers , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[47]  Neda Jahanshad,et al.  Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease. , 2014, Mathematics and visualization.

[48]  Paul M. Thompson,et al.  Labeling white matter tracts in hardi by fusing multiple tract atlases with applications to genetics , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[49]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[50]  M. Coleman Axon degeneration mechanisms: commonality amid diversity , 2005, Nature Reviews Neuroscience.

[51]  E. Bullmore,et al.  The hubs of the human connectome are generally implicated in the anatomy of brain disorders , 2014, Brain : a journal of neurology.

[52]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[53]  CSF Biomarker and PIB-PET–Derived Beta-Amyloid Signature Predicts Metabolic, Gray Matter, and Cognitive Changes in Nondemented Subjects , 2012 .

[54]  Owen Carmichael,et al.  Update on the Magnetic Resonance Imaging core of the Alzheimer's Disease Neuroimaging Initiative , 2010, Alzheimer's & Dementia.

[55]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[56]  Clifford R Jack,et al.  Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease. , 2014, Computational diffusion MRI : MICCAI Workshop, Boston, MA, USA, September 2014. CDMRI (Workshop).

[57]  C. Jack,et al.  Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging☆ , 2013, NeuroImage: Clinical.

[58]  Gorka Zamora-López,et al.  Cortical Hubs Form a Module for Multisensory Integration on Top of the Hierarchy of Cortical Networks , 2009, Front. Neuroinform..