101
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Yu Q, Erhardt EB, Sui J, Du Y, He H, Hjelm D, Cetin MS, Rachakonda S, Miller RL, Pearlson G, Calhoun VD. Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia. Neuroimage 2014; 107:345-355. [PMID: 25514514 DOI: 10.1016/j.neuroimage.2014.12.020] [Citation(s) in RCA: 150] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Revised: 11/12/2014] [Accepted: 12/07/2014] [Indexed: 01/08/2023] Open
Abstract
Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia, which may underscore the abnormal brain performance in this mental illness.
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Affiliation(s)
- Qingbao Yu
- The Mind Research Network, Albuquerque, NM 87106, USA.
| | - Erik B Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87113, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM 87106, USA; School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
| | - Hao He
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA
| | - Devon Hjelm
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Computer Science, University of New Mexico, Albuquerque, NM 87106, USA
| | - Mustafa S Cetin
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Computer Science, University of New Mexico, Albuquerque, NM 87106, USA
| | | | | | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA; Department of Psychiatry, Yale University, New Haven, CT 06520, USA; Department of Neurobiology, Yale University, New Haven, CT 06520, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA; Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA; Department of Psychiatry, Yale University, New Haven, CT 06520, USA.
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102
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Liang Z, Liu X, Zhang N. Dynamic resting state functional connectivity in awake and anesthetized rodents. Neuroimage 2014; 104:89-99. [PMID: 25315787 DOI: 10.1016/j.neuroimage.2014.10.013] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Revised: 09/27/2014] [Accepted: 10/04/2014] [Indexed: 01/01/2023] Open
Abstract
Since its introduction, resting-state functional magnetic resonance imaging (rsfMRI) has been a powerful tool for investigating functional neural networks in both normal and pathological conditions. When measuring resting-state functional connectivity (RSFC), most rsfMRI approaches do not consider its temporal variations and thus only provide the averaged RSFC over the scan time. Recently, there has been a surge of interest to investigate the dynamic characteristics of RSFC in humans, and promising results have been yielded. However, our knowledge regarding the dynamic RSFC in animals remains sparse. In the present study we utilized the single-volume co-activation method to systematically study the dynamic properties of RSFC within the networks of infralimbic cortex (IL) and primary somatosensory cortex (S1) in both awake and anesthetized rats. Our data showed that both IL and S1 networks could be decomposed into several spatially reproducible but temporally changing co-activation patterns (CAPs), suggesting that dynamic RSFC was indeed a characteristic feature in rodents. In addition, we demonstrated that anesthesia profoundly impacted the dynamic RSFC of neural circuits subserving cognitive and emotional functions but had less effects on sensorimotor systems. Finally, we examined the temporal characteristics of each CAP, and found that individual CAPs exhibited consistent temporal evolution patterns. Together, these results suggest that dynamic RSFC might be a general phenomenon in vertebrate animals. In addition, this study has paved the way for further understanding the alterations of dynamic RSFC in animal models of brain disorders.
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Affiliation(s)
- Zhifeng Liang
- Department of Biomedical Engineering, The Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Xiao Liu
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
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103
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Korhonen V, Hiltunen T, Myllylä T, Wang X, Kantola J, Nikkinen J, Zang YF, LeVan P, Kiviniemi V. Synchronous multiscale neuroimaging environment for critically sampled physiological analysis of brain function: hepta-scan concept. Brain Connect 2014; 4:677-89. [PMID: 25131996 DOI: 10.1089/brain.2014.0258] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
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.
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Affiliation(s)
- Vesa Korhonen
- 1 Department of Diagnostic Radiology, Institute of Diagnostics , Medical Research Center of Oulu, Oulu, Finland
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105
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Thompson GJ, Pan WJ, Billings JCW, Grooms JK, Shakil S, Jaeger D, Keilholz SD. Phase-amplitude coupling and infraslow (<1 Hz) frequencies in the rat brain: relationship to resting state fMRI. Front Integr Neurosci 2014; 8:41. [PMID: 24904325 PMCID: PMC4034045 DOI: 10.3389/fnint.2014.00041] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 04/25/2014] [Indexed: 11/17/2022] Open
Abstract
Resting state functional magnetic resonance imaging (fMRI) can identify network alterations that occur in complex psychiatric diseases and behaviors, but its interpretation is difficult because the neural basis of the infraslow BOLD fluctuations is poorly understood. Previous results link dynamic activity during the resting state to both infraslow frequencies in local field potentials (LFP) (<1 Hz) and band-limited power in higher frequency LFP (>1 Hz). To investigate the relationship between these frequencies, LFPs were recorded from rats under two anesthetics: isoflurane and dexmedetomidine. Signal phases were calculated from low-frequency LFP and compared to signal amplitudes from high-frequency LFP to determine if modulation existed between the two frequency bands (phase-amplitude coupling). Isoflurane showed significant, consistent phase-amplitude coupling at nearly all pairs of frequencies, likely due to the burst-suppression pattern of activity that it induces. However, no consistent phase-amplitude coupling was observed in rats that were anesthetized with dexmedetomidine. fMRI-LFP correlations under isoflurane using high frequency LFP were reduced when the low frequency LFP's influence was accounted for, but not vice-versa, or in any condition under dexmedetomidine. The lack of consistent phase-amplitude coupling under dexmedetomidine and lack of shared variance between high frequency and low frequency LFP as it relates to fMRI suggests that high and low frequency neural electrical signals may contribute differently, possibly even independently, to resting state fMRI. This finding suggests that researchers take care in interpreting the neural basis of resting state fMRI, as multiple dynamic factors in the underlying electrophysiology could be driving any particular observation.
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Affiliation(s)
- Garth J Thompson
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
| | - Wen-Ju Pan
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
| | - Jacob C W Billings
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
| | - Joshua K Grooms
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
| | - Sadia Shakil
- Department of Electrical and Computer Engineering, Georgia Institute of Technology Atlanta, GA, USA
| | - Dieter Jaeger
- Computational Neuroscience Lab, Department of Biology, Emory University Atlanta, GA, USA
| | - Shella D Keilholz
- Magnetic Resonance Imaging of Neural Dynamics Lab, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University Atlanta, GA, USA
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