Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease

Recently, many researchers have used graph theory to study the aberrant brain structures in Alzheimer's disease (AD) and have made great progress. However, the characteristics of the cortical network in Mild Cognitive Impairment (MCI) are still largely unexplored. In this study, the gray matter volumes obtained from magnetic resonance imaging (MRI) for all brain regions except the cerebellum were parcellated into 90 areas using the automated anatomical labeling (AAL) template to construct cortical networks for 98 normal controls (NCs), 113 MCIs and 91 ADs. The measurements of the network properties were calculated for each of the three groups respectively. We found that all three cortical networks exhibited small-world properties and those strong interhemispheric correlations existed between bilaterally homologous regions. Among the three cortical networks, we found the greatest clustering coefficient and the longest absolute path length in AD, which might indicate that the organization of the cortical network was the least optimal in AD. The small-world measures of the MCI network exhibited intermediate values. This finding is logical given that MCI is considered to be the transitional stage between normal aging and AD. Out of all the between-group differences in the clustering coefficient and absolute path length, only the differences between the AD and normal control groups were statistically significant. Compared with the normal controls, the MCI and AD groups retained their hub regions in the frontal lobe but showed a loss of hub regions in the temporal lobe. In addition, altered interregional correlations were detected in the parahippocampus gyrus, medial temporal lobe, cingulum, fusiform, medial frontal lobe, and orbital frontal gyrus in groups with MCI and AD. Similar to previous studies of functional connectivity, we also revealed increased interregional correlations within the local brain lobes and disrupted long distance interregional correlations in groups with MCI and AD.

[1]  Olaf Sporns,et al.  The small world of the cerebral cortex , 2007, Neuroinformatics.

[2]  P. Goldman-Rakic Topography of cognition: parallel distributed networks in primate association cortex. , 1988, Annual review of neuroscience.

[3]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[4]  S. Rombouts,et al.  Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: An fMRI study , 2005, Human brain mapping.

[5]  Brigitte Landeau,et al.  Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: A longitudinal MRI study , 2005, NeuroImage.

[6]  Mark S. Cohen,et al.  Patterns of brain activation in people at risk for Alzheimer's disease. , 2000, The New England journal of medicine.

[7]  Jaime Grutzendler,et al.  Fibrillar amyloid deposition leads to local synaptic abnormalities and breakage of neuronal branches , 2004, Nature Neuroscience.

[8]  Cees Jonker,et al.  Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment , 1999, Journal of Neurology.

[9]  P. Scheltens,et al.  Recommendations for the diagnosis and management of Alzheimer's disease and other disorders associated with dementia: EFNS guideline , 2007, European journal of neurology.

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

[11]  Alan C. Evans,et al.  Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. , 2007, Cerebral cortex.

[12]  Karl J. Friston,et al.  Structural Covariance in the Human Cortex , 2005, The Journal of Neuroscience.

[13]  G. Frisoni,et al.  Detection of grey matter loss in mild Alzheimer's disease with voxel based morphometry , 2002, Journal of neurology, neurosurgery, and psychiatry.

[14]  F. Yetkin,et al.  FMRI of working memory in patients with mild cognitive impairment and probable Alzheimer’s disease , 2005, European Radiology.

[15]  C. Croce,et al.  A microRNA expression signature of human solid tumors defines cancer gene targets , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[16]  J. Cummings,et al.  Mild cognitive impairment (MCI) represents early-stage Alzheimer's disease. , 2005, Journal of Alzheimer's Disease.

[17]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[18]  Martin Suter,et al.  Small World , 2002 .

[19]  S. Black,et al.  Evidence from Functional Neuroimaging of a Compensatory Prefrontal Network in Alzheimer's Disease , 2003, The Journal of Neuroscience.

[20]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[21]  G. Glenner,et al.  Alzheimer's disease: initial report of the purification and characterization of a novel cerebrovascular amyloid protein. , 1984, Biochemical and biophysical research communications.

[22]  A Brun,et al.  Regional pattern of degeneration in Alzheimer's disease: neuronal loss and histopathological grading. A. Brun & E. Englund. Histopathology 1981; 5; 459–564 , 2002, Histopathology.

[23]  H. Braak,et al.  Neuropathological stageing of Alzheimer-related changes , 2004, Acta Neuropathologica.

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

[25]  H. Möller,et al.  Functional connectivity of the fusiform gyrus during a face-matching task in subjects with mild cognitive impairment. , 2006, Brain : a journal of neurology.

[26]  L. Mucke,et al.  A network dysfunction perspective on neurodegenerative diseases , 2006, Nature.

[27]  G. Wenk,et al.  Neuropathologic changes in Alzheimer's disease. , 2003, The Journal of clinical psychiatry.

[28]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[29]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Richard S. J. Frackowiak,et al.  Alzheimer's patients engage an alternative network during a memory task , 2005, Annals of neurology.

[31]  Scott T. Grafton,et al.  Compensatory recruitment of neural resources during overt rehearsal of word lists in Alzheimer's disease. , 1998, Neuropsychology.

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

[33]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[34]  F. Schmitt,et al.  Evidence of increased oxidative damage in subjects with mild cognitive impairment , 2005, Neurology.

[35]  C. Jack,et al.  MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment , 2008, Neurology.

[36]  C. Jack,et al.  Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. , 2004, Archives of neurology.

[37]  Faith M. Gunning-Dixon,et al.  Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume , 2004, Neurobiology of Aging.

[38]  Griselda J. Garrido,et al.  A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease , 2003, Neurobiology of Aging.

[39]  E. Bullmore,et al.  Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia , 2008, The Journal of Neuroscience.

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

[41]  N. Foster,et al.  Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease , 1997, Annals of neurology.

[42]  Benjamin J. Shannon,et al.  Coherent spontaneous activity identifies a hippocampal-parietal memory network. , 2006, Journal of neurophysiology.

[43]  D. Salmon,et al.  Physical basis of cognitive alterations in alzheimer's disease: Synapse loss is the major correlate of cognitive impairment , 1991, Annals of neurology.

[44]  A. Brun,et al.  Regional pattern of degeneration in Alzheimer's disease: neuronal loss and histopathological grading , 1981, Histopathology.

[45]  C. Stam,et al.  Heritability of “small‐world” networks in the brain: A graph theoretical analysis of resting‐state EEG functional connectivity , 2008, Human brain mapping.

[46]  Kuncheng Li,et al.  Altered functional connectivity in early Alzheimer's disease: A resting‐state fMRI study , 2007, Human brain mapping.

[47]  Nick C. Fox,et al.  Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease , 2004, NeuroImage.

[48]  E. Tangalos,et al.  Mild Cognitive Impairment Clinical Characterization and Outcome , 1999 .

[49]  E. Bullmore,et al.  Adaptive reconfiguration of fractal small-world human brain functional networks , 2006, Proceedings of the National Academy of Sciences.

[50]  Alan C. Evans,et al.  Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease , 2008, The Journal of Neuroscience.

[51]  J. Baron,et al.  In Vivo Mapping of Gray Matter Loss with Voxel-Based Morphometry in Mild Alzheimer's Disease , 2001, NeuroImage.

[52]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[53]  J. Baron,et al.  Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment , 2002, Neuroreport.

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

[55]  J. Becker,et al.  Differential cortical atrophy in subgroups of mild cognitive impairment. , 2005, Archives of neurology.