Long-term test-retest reliability of resting-state networks in healthy elderly subjects and with amnestic mild cognitive impairment patients.

The investigation of cerebral resting-state networks (RSNs) by functional magnetic resonance imaging (fMRI) is a promising tool for the early diagnosis and follow-up of neuropsychiatric and neurodegenerative disorders like Alzheimer's disease (AD). In this context, the determination of inter-session reliability of these networks is crucial. However, data on network reliability in healthy elderly subjects is rare and does not exist for patients with amnestic mild cognitive impairment (aMCI), a prodromal stage of AD. Therefore, the aim of this study was to investigate the long-term test-retest reliability of RSNs in both groups. Twelve healthy controls (HC) and 13 aMCI patients underwent resting-state fMRI and neuropsychological testing (CERAD test battery) twice, at baseline and after 13-16 months. Resting-state fMRI data was decomposed into independent components using independent component analysis. Inter-session test-retest reliability of the resulting RSNs was determined by calculating voxel-wise intra-class correlation coefficients. Overall test-retest reliability of corresponding RSNs was moderate to high in both groups, but significantly higher in the HC group compared to the aMCI group (p < 0.001), while the cognitive performance within the CERAD test battery remained stable over time in either group. Most reliable RSNs derived from the HC group and were associated with sensory and motor as well as higher order cognitive and the default-mode function. Particularly low reliability was found in basal frontal regions, which are known to be prone to susceptibility-induced noise. We conclude that stable RSNs may represent healthy aging, whereas decreased RSN reliability may indicate progressive neuro-functional alterations before the actual manifestation of clinical symptoms.

[1]  G. Ratcliff,et al.  Cognitive tests that best discriminate between presymptomatic AD and those who remain nondemented , 2000, Neurology.

[2]  Martijn P. van den Heuvel,et al.  Motor Network Degeneration in Amyotrophic Lateral Sclerosis: A Structural and Functional Connectivity Study , 2010, PloS one.

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

[4]  A. Monsch,et al.  Normal ranges of neuropsychological tests for the diagnosis of Alzheimer's disease. , 2000, Studies in health technology and informatics.

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

[6]  Justin L. Vincent,et al.  Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Wang Zhan,et al.  Group independent component analysis reveals consistent resting-state networks across multiple sessions , 2008, Brain Research.

[8]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[9]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[11]  Justin L. Vincent,et al.  Intrinsic functional architecture in the anaesthetized monkey brain , 2007, Nature.

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

[13]  A. McKinney,et al.  Resting Brain Connectivity: Changes during the Progress of Alzheimer Disease , 2011 .

[14]  K. Shulman Clock‐drawing: is it the ideal cognitive screening test? , 2000, International journal of geriatric psychiatry.

[15]  Kuncheng Li,et al.  Changes of functional connectivity of the motor network in the resting state in Parkinson's disease , 2009, Neuroscience Letters.

[16]  Katherine E. Prater,et al.  Functional connectivity tracks clinical deterioration in Alzheimer's disease , 2012, Neurobiology of Aging.

[17]  Scott T. Grafton,et al.  Wandering Minds: The Default Network and Stimulus-Independent Thought , 2007, Science.

[18]  M. Boly,et al.  Consciousness and cerebral baseline activity fluctuations , 2008, Human brain mapping.

[19]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[20]  Simon B. Eickhoff,et al.  One-year test–retest reliability of intrinsic connectivity network fMRI in older adults , 2012, NeuroImage.

[21]  R. Deichmann,et al.  Optimized EPI for fMRI studies of the orbitofrontal cortex: compensation of susceptibility-induced gradients in the readout direction , 2007, Magnetic Resonance Materials in Physics, Biology and Medicine.

[22]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[23]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[24]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[25]  M. Filippi,et al.  Default-mode network dysfunction and cognitive impairment in progressive MS , 2010, Neurology.

[26]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[27]  C. McDougle,et al.  Structural and functional magnetic resonance imaging of autism spectrum disorders , 2011, Brain Research.

[28]  O. Dietrich,et al.  Test–retest reproducibility of the default‐mode network in healthy individuals , 2009, Human brain mapping.

[29]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[30]  H. Abdi The Bonferonni and Šidák Corrections for Multiple Comparisons , 2006 .

[31]  J. Morris,et al.  Current concepts in mild cognitive impairment. , 2001, Archives of neurology.

[32]  Yaakov Stern,et al.  Exploring the neural basis of cognitive reserve in aging. , 2012, Biochimica et biophysica acta.

[33]  P. Bandettini,et al.  The effect of respiration variations on independent component analysis results of resting state functional connectivity , 2008, Human brain mapping.

[34]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[35]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[36]  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.

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

[38]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[39]  E. Crone,et al.  Neural evidence for dissociable components of task-switching. , 2006, Cerebral cortex.

[40]  C Caltagirone,et al.  Regional grey matter loss and brain disconnection across Alzheimer disease evolution. , 2011, Current medicinal chemistry.

[41]  James K. Kroger,et al.  Recruitment of anterior dorsolateral prefrontal cortex in human reasoning: a parametric study of relational complexity. , 2002, Cerebral cortex.

[42]  K. Miller,et al.  Direct electrophysiological measurement of human default network areas , 2009, Proceedings of the National Academy of Sciences.

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

[44]  A. Braun,et al.  Decoupling of the brain's default mode network during deep sleep , 2009, Proceedings of the National Academy of Sciences.

[45]  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.

[46]  V. Calhoun,et al.  Selective changes of resting-state networks in individuals at risk for Alzheimer's disease , 2007, Proceedings of the National Academy of Sciences.

[47]  David Bartrés-Faz,et al.  Structural and Functional Imaging Correlates of Cognitive and Brain Reserve Hypotheses in Healthy and Pathological Aging , 2011, Brain Topography.

[48]  N. Volkow,et al.  Aging and Functional Brain Networks , 2011, Molecular Psychiatry.

[49]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[50]  D. Keeser,et al.  Functional and Structural MR Imaging in Neuropsychiatric Disorders, Part 1: Imaging Techniques and Their Application in Mild Cognitive Impairment and Alzheimer Disease , 2012, American Journal of Neuroradiology.

[51]  G. Shulman,et al.  Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Jessica A. Turner,et al.  Behavioral Interpretations of Intrinsic Connectivity Networks , 2011, Journal of Cognitive Neuroscience.

[53]  C. Dickey,et al.  Investigation of Long-Term Reproducibility of Intrinsic Connectivity Network Mapping: A Resting-State fMRI Study , 2012, American Journal of Neuroradiology.

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