An Investigation of RSN Frequency Spectra Using Ultra-Fast Generalized Inverse Imaging

Boyacıoğlu, Rasim; Beckmann, Christian F.; Barth, Markus

doi:10.3389/fnhum.2013.00156

Cited by 33 publications

(35 citation statements)

References 26 publications

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“…MREG signal (Boyacioglu et al, 2013b). The autocorrelation corrections in the fifth order reduce statistical power of the signal and, partially due to this reason, our dual-regression-based results have higher correlation as well.…”

Section: Eeg Versus Mregmentioning

confidence: 72%

“…This can be taken account by using all the components in dual regression where the fsl_glm function ''multiple regression'' divides the overlapping variance to partial regression coefficients and uses these in calculation of subject-specific components. The dual-regression-derived time signal was used since the time domain signal can be analyzed as such without any further need of corrections for auto-correlation effects (Boyacioglu et al, 2013b). The set of subject-specific dualregressed timeseries, one per group-level DMN vmpf spatial map, were used in our correlation analysis.…”

Section: Figmentioning

confidence: 99%

See 1 more Smart Citation

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

Korhonen¹,

Hiltunen²,

Myllylä

et al. 2014

Brain Connectivity

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Functional connectivity of the resting-state networks of the brain is thought to be mediated by very-lowfrequency 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 (DMN vmpf ) and electrophysiological signals, especially in the VLF range. Also, oxy-and deoxyhemoglobin and vasomotor waves were found to correlate with DMN vmpf . 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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Section: Eeg Versus Mregmentioning

confidence: 72%

Section: Figmentioning

confidence: 99%

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

Korhonen¹,

Hiltunen²,

Myllylä

et al. 2014

Brain Connectivity

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show abstract

“…These early investigations (Boubela et al, 2013;Boyacioglu et al, 2013;Chen and Glover, 2015;Gohel and Biswal, 2015;Lee et al, 2013;Lin et al, 2015;Niazy et al, 2011;Wu et al, 2008), although with disparate acquisition protocols, converge on similar conclusions that spontaneous activity persists at frequencies well above the typical upper limit of 0.1 Hz and shares partially overlapped functional information across frequencies. These intriguing findings have offered new biomarker opportunities for clinical and neuroscience research and have provoked emerging efforts to reexamine the frequency dependence of brain functional behaviors across broad mental states (Lin et al, 2015;Yuan et al, 2014), age (Smith-Collins et al, 2015), and clinical populations (Morgan et al, 2015;Sours et al, 2015;Wang et al, 2015).…”

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confidence: 72%

Nuisance Regression of High-Frequency Functional Magnetic Resonance Imaging Data: Denoising Can Be Noisy

2017

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Recently, emerging studies have demonstrated the existence of brain resting-state spontaneous activity at frequencies higher than the conventional 0.1 Hz. A few groups utilizing accelerated acquisitions have reported persisting signals beyond 1 Hz, which seems too high to be accommodated by the sluggish hemodynamic process underpinning blood oxygen level-dependent contrasts (the upper limit of the canonical model is *0.3 Hz). It is thus questionable whether the observed high-frequency (HF) functional connectivity originates from alternative mechanisms (e.g., inflow effects, proton density changes in or near activated neural tissue) or rather is artificially introduced by improper preprocessing operations. In this study, we examined the influence of a common preprocessing step-whole-band linear nuisance regression (WB-LNR)-on resting-state functional connectivity (RSFC) and demonstrated through both simulation and analysis of real dataset that WB-LNR can introduce spurious network structures into the HF bands of functional magnetic resonance imaging (fMRI) signals. Findings of present study call into question whether published observations on HF-RSFC are partly attributable to improper data preprocessing instead of actual neural activities.

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“…The capacity of ICA to find common patterns of activation in huge cohorts of subjects is demonstrated by the parallel computing approach described by Kalcher et al (2012) and the use of ICA with cutting edge MR methods are presented by the groups of Stefan Posse [Echo Volume Imaging (Posse et al, 2013)], Markus Barth [EEG-fMRI (Meyer et al, 2013) and Ultra-Fast Generalized Inverse Imaging (Boyacioglu et al, 2013)], and Jorge Jovicich [realtime fMRI (Soldati et al, 2013a,b)]. …”

mentioning

confidence: 99%

“…In addition to using frequency signatures to identify noise, the frequencies of signal fluctuations during rest have been studied using temporal ICA (Boubela et al, 2013) and in ultra-fast generalized imaging (Boyacioglu et al, 2013), while Di et al (2013) examine the influence of amplitude ICA of fMRI studies: new approaches and cutting edge applications…”

mentioning

confidence: 99%

ICA of fMRI studies: new approaches and cutting edge applications

2014

Frontiers Research Topics

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on resting-state connectivity and Balsters et al. (2013) assess the correlation between BOLD spectral power and working memory performance.The ICA applications featured in this Research Topic range from clinical resting-state studies with patients suffering from schizophrenia (Manoliu et al., 2013;Sui et al., 2013) and neurological patients performing chin and hand motor tasks (Robinson et al., 2013) to the investigation of processing streams using chemosensory stimuli (Frasnelli et al., 2012). Combined methodological approaches are used to study belief decision making with fMRI and EEG (Douglas et al., 2013), to discriminate schizophrenia using data from fMRI, DTI, and sMRI (Sui et al., 2013), to identify amyotrophic lateral sclerosis diseased brains (Welsh et al., 2013) and to examine the microvascular specificity of the BOLD effect at 3 and 7 T using SWI (Geissler et al., 2013).We hope this collection of original research articles illustrates the extent to which ICA is becoming an increasingly flexible and potent analysis method -particularly through innovations such as real-time ICA, temporal ICA, and parallel processing implementations -and that the capacity of ICA to isolate the underlying signal sources in fMRI data is being enhanced by multimodal and ultra-fast imaging. These innovations are leading to an increase in the utility of ICA and the richness of information it can provide in both basic research work and clinical applications.

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An Investigation of RSN Frequency Spectra Using Ultra-Fast Generalized Inverse Imaging

Cited by 33 publications

References 26 publications

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

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

Nuisance Regression of High-Frequency Functional Magnetic Resonance Imaging Data: Denoising Can Be Noisy

ICA of fMRI studies: new approaches and cutting edge applications

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