Synchronous Multiscale Neuroimaging Environment for Critically Sampled Physiological Analysis of Brain Function: Hepta-Scan Concept

Functional connectivity of the resting-state networks of the brain is thought to be mediated by very-low-frequency fluctuations (VLFFs <0.1 Hz) in neuronal activity. However, vasomotor waves and cardiorespiratory pulsations influence indirect measures of brain function, such as the functional magnetic resonance imaging blood-oxygen-level-dependent (BOLD) signal. How strongly physiological oscillations correlate with spontaneous BOLD signals is not known, partially due to differences in the data-sampling rates of different methods. Recent ultrafast inverse imaging sequences, including magnetic resonance encephalography (MREG), enable critical sampling of these signals. In this study, we describe a multimodal concept, referred to as Hepta-scan, which incorporates synchronous MREG with scalp electroencephalography, near-infrared spectroscopy, noninvasive blood pressure, and anesthesia monitoring. Our preliminary results support the idea that, in the absence of aliased cardiorespiratory signals, VLFFs in the BOLD signal are affected by vasomotor and electrophysiological sources. Further, MREG signals showed a high correlation coefficient between the ventromedial default mode network (DMNvmpf) and electrophysiological signals, especially in the VLF range. Also, oxy- and deoxyhemoglobin and vasomotor waves were found to correlate with DMNvmpf. Intriguingly, usage of shorter time windows in these correlation measurements produced significantly (p<0.05) higher positive and negative correlation coefficients, suggesting temporal nonstationary behavior between the measurements. Focus on the VLF range strongly increased correlation strength.

[1]  M. Corbetta,et al.  Electrophysiological signatures of resting state networks in the human brain , 2007, Proceedings of the National Academy of Sciences.

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

[3]  K. Linkenkaer-Hansen,et al.  Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws , 2013, Proceedings of the National Academy of Sciences.

[4]  Lian Duan,et al.  Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: A simultaneous recording study , 2012, NeuroImage.

[5]  M. Greicius,et al.  Persistent default‐mode network connectivity during light sedation , 2008, Human brain mapping.

[6]  Xiangyu Long,et al.  Functional segmentation of the brain cortex using high model order group PICA , 2009, Human brain mapping.

[7]  Maxim Zaitsev,et al.  Single shot concentric shells trajectories for ultra fast fMRI , 2012, Magnetic resonance in medicine.

[8]  Valery V. Tuchin,et al.  Dynamics of the brain: Mathematical models and non-invasive experimental studies , 2013 .

[9]  J. Voipio,et al.  Millivolt-scale DC shifts in the human scalp EEG: evidence for a nonneuronal generator. , 2003, Journal of neurophysiology.

[10]  D. Boas,et al.  Resting state functional connectivity of the whole head with near-infrared spectroscopy , 2010, Biomedical optics express.

[11]  A. Villringer,et al.  Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults , 2000, NeuroImage.

[12]  Markus Barth,et al.  An Investigation of RSN Frequency Spectra Using Ultra-Fast Generalized Inverse Imaging , 2013, Front. Hum. Neurosci..

[13]  J. Palva,et al.  Infraslow oscillations modulate excitability and interictal epileptic activity in the human cortex during sleep. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Vesa Kiviniemi,et al.  Separation of physiological very low frequency fluctuation from aliasing by switched sampling interval fMRI scans. , 2005, Magnetic resonance imaging.

[15]  Markus Barth,et al.  Generalized iNverse imaging (GIN): Ultrafast fMRI with physiological noise correction , 2013, Magnetic resonance in medicine.

[16]  Thomas Witzel,et al.  Ultrafast inverse imaging techniques for fMRI , 2012, NeuroImage.

[17]  Abraham Z. Snyder,et al.  Resting-state functional connectivity in the human brain revealed with diffuse optical tomography , 2009, NeuroImage.

[18]  N. Filippini,et al.  Group comparison of resting-state FMRI data using multi-subject ICA and dual regression , 2009, NeuroImage.

[19]  Matti Kinnunen,et al.  Light Propagation in NIR Spectroscopy of the Human Brain , 2014, IEEE Journal of Selected Topics in Quantum Electronics.

[20]  Davide Contini,et al.  Study of neurovascular and autonomic response in a divided attention test by means of EEG, ECG and NIRS signals , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  René J. Huster,et al.  Methods for Simultaneous EEG-fMRI: An Introductory Review , 2012, The Journal of Neuroscience.

[22]  Norihiro Sadato,et al.  A NIRS–fMRI study of resting state network , 2012, NeuroImage.

[23]  J. Matias Palva,et al.  Infra-Slow EEG Fluctuations Are Correlated with Resting-State Network Dynamics in fMRI , 2014, The Journal of Neuroscience.

[24]  Jürgen Hennig,et al.  Tracking dynamic resting-state networks at higher frequencies using MR-encephalography , 2013, NeuroImage.

[25]  David A. Boas,et al.  The utility of near-infrared spectroscopy in the regression of low-frequency physiological noise from functional magnetic resonance imaging data , 2012, NeuroImage.

[26]  H. Sorvoja Noninvasive blood pressure pulse detection and blood pressure determination , 2006 .

[27]  J. Duyn,et al.  Time-varying functional network information extracted from brief instances of spontaneous brain activity , 2013, Proceedings of the National Academy of Sciences.

[28]  Elizabeth M C Hillman,et al.  Optical brain imaging in vivo: techniques and applications from animal to man. , 2007, Journal of biomedical optics.

[29]  Vesa Kiviniemi,et al.  A Sliding Time-Window ICA Reveals Spatial Variability of the Default Mode Network in Time , 2011, Brain Connect..

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

[31]  Bharat B. Biswal,et al.  Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy. , 2010, Journal of biomedical optics.

[32]  Helmut Laufs,et al.  A personalized history of EEG–fMRI integration , 2012, NeuroImage.

[33]  Yu-Feng Zang,et al.  Altered resting‐state activity in seasonal affective disorder , 2014, Human brain mapping.

[34]  A. Kleinschmidt,et al.  Intrinsic Connectivity Networks, Alpha Oscillations, and Tonic Alertness: A Simultaneous Electroencephalography/Functional Magnetic Resonance Imaging Study , 2010, The Journal of Neuroscience.

[35]  Waqas Majeed,et al.  Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans , 2011, NeuroImage.

[36]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[37]  Biyu J. He,et al.  The Temporal Structures and Functional Significance of Scale-free Brain Activity , 2010, Neuron.

[38]  Gabriel Curio,et al.  Monochromatic Ultra-Slow (~0.1Hz) Oscillations in the human electroencephalogram and their relation to hemodynamics , 2014, NeuroImage.

[39]  Jürgen Hennig,et al.  Single shot whole brain imaging using spherical stack of spirals trajectories , 2013, NeuroImage.

[40]  Louis Lemieux,et al.  Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction , 1998, NeuroImage.

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

[42]  E. Gratton,et al.  Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging. , 2001, Medical physics.

[43]  A. Kleinschmidt,et al.  Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Olli Silven,et al.  On applicability of PCA, voxel-wise variance normalization and dimensionality assumptions for sliding temporal window sICA in resting-state fMRI. , 2013, Magnetic resonance imaging.

[45]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[46]  Vesa Korhonen,et al.  Optical sensing of a pulsating liquid in a brain-mimicking phantom , 2013, European Conference on Biomedical Optics.

[47]  David A. Boas,et al.  A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans , 2006, NeuroImage.

[48]  D. Leopold,et al.  Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest , 2008, Human brain mapping.

[49]  E.N. Brown,et al.  Interaction between heart rate variability and respiration in preterm infants , 2008, 2008 Computers in Cardiology.

[50]  J. Mandeville,et al.  The Accuracy of Near Infrared Spectroscopy and Imaging during Focal Changes in Cerebral Hemodynamics , 2001, NeuroImage.

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

[52]  Irene Tracey,et al.  Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal , 2004, NeuroImage.

[53]  Hans-Jochen Heinze,et al.  Association between heart rate variability and fluctuations in resting-state functional connectivity , 2013, NeuroImage.

[54]  Hellmuth Obrig,et al.  Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy , 2003, NeuroImage.

[55]  A. Patzak,et al.  Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method , 2011, European Journal of Applied Physiology.

[56]  Bharat Biswal,et al.  Slow vasomotor fluctuation in fMRI of anesthetized child brain , 2000, Magnetic resonance in medicine.

[57]  Dieter Jaeger,et al.  Quasi-periodic patterns (QPP): Large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity , 2014, NeuroImage.

[58]  Risto Myllylä,et al.  APPLICATION OF LASERS AND LASER-OPTICAL METHODS IN LIFE SCIENCES Non-invasive, MRI-compatible fibreoptic device for functional near-IR reflectometry of human brain , 2011 .

[59]  Yevgeniy B. Sirotin,et al.  Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity. , 2009, Nature.

[60]  Hannu Sorvoja,et al.  Human Heart Pulse Wave Responses Measured Simultaneously at Several Sensor Placements by Two MR-Compatible Fibre Optic Methods , 2012, J. Sensors.

[61]  Yunjie Tong,et al.  Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks , 2013, NeuroImage.

[62]  David A. Boas,et al.  Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters , 2003, NeuroImage.

[63]  Aapo Hyvärinen,et al.  Independent component analysis of nondeterministic fMRI signal sources , 2003, NeuroImage.

[64]  Vesa Kiviniemi,et al.  Endogenous brain fluctuations and diagnostic imaging , 2008, Human brain mapping.

[65]  A. Steptoe,et al.  Pulse wave velocity as a measure of blood pressure change. , 1976, Psychophysiology.

[66]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

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

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

[69]  Teemu Myllylä,et al.  Multimodal biomedical measurement methods to study brain functions simultaneously with functional magnetic resonance imaging , 2014 .

[70]  Robert Turner,et al.  A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI , 2000, NeuroImage.

[71]  S Vanhatalo,et al.  Scalp-recorded slow EEG responses generated in response to hemodynamic changes in the human brain , 2003, Clinical Neurophysiology.

[72]  James R Laguardia,et al.  Interhemispheric synchrony of slow oscillations of cortical blood volume and cytochrome aa3 redox state in unanesthetized rabbits , 1997, Brain Research.

[73]  Bruce R. Rosen,et al.  Spatio-temporal characteristics of low-frequency BOLD signal fluctuations in isoflurane-anesthetized rat brain , 2008, NeuroImage.

[74]  Yunjie Tong,et al.  An improved method for mapping cerebrovascular reserve using concurrent fMRI and near-infrared spectroscopy with Regressor Interpolation at Progressive Time Delays (RIPTiDe) , 2011, NeuroImage.

[75]  Christa Neuper,et al.  Does conscious intention to perform a motor act depend on slow prefrontal (de)oxyhemoglobin oscillations in the resting brain? , 2012, Neuroscience Letters.

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

[77]  Yunjie Tong,et al.  Concurrent fNIRS and fMRI processing allows independent visualization of the propagation of pressure waves and bulk blood flow in the cerebral vasculature , 2012, NeuroImage.

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

[79]  Marco Ferrari,et al.  A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application , 2012, NeuroImage.

[80]  Hannu Sorvoja,et al.  Instrumentation and method for measuring NIR light absorbed in tissue during MR imaging in medical NIRS measurements , 2011, European Conference on Biomedical Optics.

[81]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[82]  Dieter Jaeger,et al.  Infraslow LFP correlates to resting-state fMRI BOLD signals , 2013, NeuroImage.

[83]  Yunjie Tong,et al.  Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain , 2010, NeuroImage.

[84]  C. Tallon-Baudry,et al.  Spontaneous fluctuations in neural responses to heartbeats predict visual detection , 2014, Nature Neuroscience.

[85]  M. Schölvinck,et al.  Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.

[86]  T. Koenig,et al.  Topographic Electrophysiological Signatures of fMRI Resting State Networks , 2010, PloS one.

[87]  Vesa Kiviniemi,et al.  Fibre optic sensor for non‐invasive monitoring of blood pressure during MRI scanning , 2011, Journal of biophotonics.