Impact of physiological noise in characterizing the functional MRI default-mode network in Alzheimer’s disease

The functional connectivity of the default-mode network (DMN) monitored by functional magnetic resonance imaging (fMRI) in Alzheimer's disease (AD) patients has been found weaker than that in healthy participants. Since breathing and heart beating can cause fluctuations in the fMRI signal, these physiological activities may affect the fMRI data differently between AD patients and healthy participants. We collected resting-state fMRI data from AD patients and age-matched healthy participants. With concurrent cardiac and respiratory recordings, we estimated both physiological responses phase-locked and non-phase-locked to heart beating and breathing. We found that the cardiac and respiratory physiological responses in AD patients were 3.00 ± 0.51 s and 3.96 ± 0.52 s later (both p < 0.0001) than those in healthy participants, respectively. After correcting the physiological noise in the resting-state fMRI data by population-specific physiological response functions, the DMN estimated by seed-correlation was more localized to the seed region. The DMN difference between AD patients and healthy controls became insignificant after suppressing physiological noise. Our results indicate the importance of controlling physiological noise in the resting-state fMRI analysis to obtain clinically related characterizations in AD.

[1]  P. Hedera,et al.  Differential degeneration of the cerebral microvasculature in Alzheimer's disease. , 1995, Neuroreport.

[2]  Catie Chang,et al.  Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test–retest reliability and effect of fMRI sampling rate , 2015, NeuroImage.

[3]  E. Engelhardt,et al.  Alzheimer disease neuropathology: understanding autonomic dysfunction , 2008, Dementia & neuropsychologia.

[4]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

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

[6]  M. Weiner,et al.  Reduced hippocampal functional connectivity in Alzheimer disease. , 2007, Archives of neurology.

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

[8]  Mary E. Meyerand,et al.  The Influence of Physiological Noise Correction on Test-Retest Reliability of Resting-State Functional Connectivity , 2014, Brain Connect..

[9]  Olaf Wolkenhauer,et al.  Mathematical analysis of the influence of brain metabolism on the BOLD signal in Alzheimer’s disease , 2017, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[10]  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.

[11]  C. Iadecola,et al.  Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. , 2006, Journal of applied physiology.

[12]  D. Head,et al.  Amyloid Plaques Disrupt Resting State Default Mode Network Connectivity in Cognitively Normal Elderly , 2010, Biological Psychiatry.

[13]  Stephen D. Mayhew,et al.  Brainstem functional magnetic resonance imaging: Disentangling signal from physiological noise , 2008, Journal of magnetic resonance imaging : JMRI.

[14]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[15]  Stephen C. Strother,et al.  The association between cerebrovascular reactivity and resting-state fMRI functional connectivity in healthy adults: The influence of basal carbon dioxide , 2016, NeuroImage.

[16]  Darren R Gitelman,et al.  Hemodynamic response changes in cerebrovascular disease: implications for functional MR imaging. , 2002, AJNR. American journal of neuroradiology.

[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]  V. Fischer,et al.  Altered angioarchitecture in selected areas of brains with Alzheimer's disease , 2004, Acta Neuropathologica.

[19]  G. Glover,et al.  Physiological noise in oxygenation‐sensitive magnetic resonance imaging , 2001, Magnetic resonance in medicine.

[20]  Lawrence L. Wald,et al.  Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects , 2017, NeuroImage.

[21]  Catie Chang,et al.  Effects of model-based physiological noise correction on default mode network anti-correlations and correlations , 2009, NeuroImage.

[22]  Zhifeng Liang,et al.  Mapping resting-state brain networks in conscious animals , 2010, Journal of Neuroscience Methods.

[23]  Cornelis J. Stam,et al.  Delayed rather than decreased BOLD response as a marker for early Alzheimer's disease , 2005, NeuroImage.

[24]  C. Rosazza,et al.  Resting-state brain networks: literature review and clinical applications , 2011, Neurological Sciences.

[25]  B. Zlokovic.  Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders , 2011, Nature Reviews Neuroscience.

[26]  Peter A. Bandettini,et al.  The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration , 2008, NeuroImage.

[27]  Guorong Wu,et al.  A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data , 2012, Medical Image Anal..

[28]  S. Stone-Elander,et al.  Brain Activation Induced by the Perceptual Maze Test: A PET Study of Cognitive Performance , 1995, NeuroImage.

[29]  M. Fukunaga,et al.  Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. , 2009, Magnetic resonance imaging.

[30]  Stephen M. Smith,et al.  Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data , 2010, Front. Syst. Neurosci..

[31]  Olivia K. Faull,et al.  Physiological Noise in Brainstem fMRI , 2013, Front. Hum. Neurosci..

[32]  Jaemin Shin,et al.  Retrospective Correction of Physiological Noise: Impact on Sensitivity, Specificity, and Reproducibility of Resting-State Functional Connectivity in a Reading Network Model , 2018, Brain Connect..

[33]  Mahsa Alizadeh Shalchy,et al.  Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event-related fMRI , 2017, Healthcare technology letters.

[34]  Kevin Murphy,et al.  Is fMRI “noise” really noise? Resting state nuisance regressors remove variance with network structure , 2015, NeuroImage.

[35]  Lawrence L. Wald,et al.  Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters , 2005, NeuroImage.

[36]  M. Raichle,et al.  Searching for a baseline: Functional imaging and the resting human brain , 2001, Nature Reviews Neuroscience.

[37]  M Hutchinson,et al.  Task-specific deactivation patterns in functional magnetic resonance imaging. , 1999, Magnetic resonance imaging.

[38]  Tianzi Jiang,et al.  Changes in hippocampal connectivity in the early stages of Alzheimer's disease: Evidence from resting state fMRI , 2006, NeuroImage.

[39]  Daniel S. Margulies,et al.  Mapping the functional connectivity of anterior cingulate cortex , 2007, NeuroImage.

[40]  Oliver Speck,et al.  The impact of physiological noise correction on fMRI at 7 T , 2011, NeuroImage.

[41]  O. Tervonen,et al.  Correction of low-frequency physiological noise from the resting state BOLD fMRI—Effect on ICA default mode analysis at 1.5T , 2010, Journal of Neuroscience Methods.

[42]  Catie Chang,et al.  Influence of heart rate on the BOLD signal: The cardiac response function , 2009, NeuroImage.

[43]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[44]  Peter A. Bandettini,et al.  Integration of motion correction and physiological noise regression in fMRI , 2008, NeuroImage.

[45]  Salvatore Mazza,et al.  Primary cerebral blood flow deficiency and Alzheimer's disease: shadows and lights. , 2011, Journal of Alzheimer's disease : JAD.

[46]  Michal Mikl,et al.  Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation , 2018, Human brain mapping.

[47]  Jan Kassubek,et al.  Functional Connectivity Within the Default Mode Network Is Associated With Saccadic Accuracy in Parkinson's Disease: A Resting-State fMRI and Videooculographic Study , 2013, Brain Connect..

[48]  P T Fox,et al.  Detection of the brain response during a cognitive task using perfusion‐based event‐related functional MRI , 2000, Neuroreport.

[49]  M. Raichle,et al.  Disease and the brain's dark energy , 2010, Nature Reviews Neurology.

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