Magnetic Resonance Encephalography Research Articles

Association of body-mass index with physiological brain pulsations across adulthood – a fast fMRI study

Background/objectiveObesity is a risk factor for several brain-related health issues, and high body-mass index (BMI) is associated with an increased risk for several neurological conditions, including cognitive decline and dementia. Cardiovascular, respiratory, and vasomotor brain pulsations have each been shown to drive intracranial cerebrovascular fluid (CSF) flow, which is linked to the brain metabolite efflux that sustains homeostasis. While these three physiological pulsations are demonstrably altered in numerous brain diseases, there is no previous investigation of the association between physiological brain pulsations and BMI.Subjects/methodsWe measured the amplitudes of the physiological brain pulsations using amplitude of low frequency fluctation (ALFF) based method with resting-state functional magnetic resonance imaging via high temporal resolution whole-brain magnetic resonance encephalography (MREG) in 115 healthy subjects. We next undertook multiple linear regression to model the BMI effect voxel-wise whole-brain on very low frequency (VLF), respiration, cardiovascular, and respiratory induced modulation of cardiovascular pulsation amplitudes with age, pulse pressure, and gender as nuisance variables.ResultsIn our study population, BMI was positively associated with the amplitudes of vasomotor, respiratory, and respiratory induced modulations of cardiovascular pulsations (p < 0.05), while negatively associated with the amplitudes of cardiovascular pulsations (p < 0.05).ConclusionsThe findings suggest that BMI is a significant factor in alterations of cardiovascular pulsation of neurofluids. As physiological pulsations are the drivers of CSF flow and subsequent metabolite clearance, these results emphasize the need for further research into the mechanisms through which obesity affects brain clearance.

Physiological instability is linked to mortality in primary central nervous system lymphoma: A case–control fMRI study

Primary central nervous system lymphoma (PCNSL) is an aggressive brain disease where lymphocytes invade along perivascular spaces of arteries and veins. The invasion markedly changes (peri)vascular structures but its effect on physiological brain pulsations has not been previously studied. Using physiological magnetic resonance encephalography (MREGBOLD) scanning, this study aims to quantify the extent to which (peri)vascular PCNSL involvement alters the stability of physiological brain pulsations mediated by cerebral vasculature. Clinical implications and relevance were explored. In this study, 21 PCNSL patients (median 67y; 38% females) and 30 healthy age‐matched controls (median 63y; 73% females) were scanned for MREGBOLD signal during 2018–2021. Motion effects were removed. Voxel‐by‐voxel Coefficient of Variation (CV) maps of MREGBOLD signal was calculated to examine the stability of physiological brain pulsations. Group‐level differences in CV were examined using nonparametric covariate‐adjusted tests. Subject‐level CV alterations were examined against control population Z‐score maps wherein clusters of increased CV values were detected. Spatial distributions of clusters and findings from routine clinical neuroimaging were compared [contrast‐enhanced, diffusion‐weighted, fluid‐attenuated inversion recovery (FLAIR) data]. Whole‐brain mean CV was linked to short‐term mortality with 100% sensitivity and 100% specificity, as all deceased patients revealed higher values (n = 5, median 0.055) than surviving patients (n = 16, median 0.028) (p < .0001). After adjusting for medication, head motion, and age, patients revealed higher CV values (group median 0.035) than healthy controls (group median 0.024) around arterial territories (p ≤ .001). Abnormal clusters (median 1.10 × 105mm3) extended spatially beyond FLAIR lesions (median 0.62 × 105mm3) with differences in volumes (p = .0055).

Human NREM Sleep Promotes Brain-Wide Vasomotor and Respiratory Pulsations.

The physiological underpinnings of the necessity of sleep remain uncertain. Recent evidence suggests that sleep increases the convection of cerebrospinal fluid (CSF) and promotes the export of interstitial solutes, thus providing a framework to explain why all vertebrate species require sleep. Cardiovascular, respiratory and vasomotor brain pulsations have each been shown to drive CSF flow along perivascular spaces, yet it is unknown how such pulsations may change during sleep in humans. To investigate these pulsation phenomena in relation to sleep, we simultaneously recorded fast fMRI, magnetic resonance encephalography (MREG), and electroencephalography (EEG) signals in a group of healthy volunteers. We quantified sleep-related changes in the signal frequency distributions by spectral entropy analysis and calculated the strength of the physiological (vasomotor, respiratory, and cardiac) brain pulsations by power sum analysis in 15 subjects (age 26.5 ± 4.2 years, 6 females). Finally, we identified spatial similarities between EEG slow oscillation (0.2–2 Hz) power and MREG pulsations. Compared with wakefulness, nonrapid eye movement (NREM) sleep was characterized by reduced spectral entropy and increased brain pulsation intensity. These effects were most pronounced in posterior brain areas for very low-frequency (≤0.1 Hz) vasomotor pulsations but were also evident brain-wide for respiratory pulsations, and to a lesser extent for cardiac brain pulsations. There was increased EEG slow oscillation power in brain regions spatially overlapping with those showing sleep-related MREG pulsation changes. We suggest that reduced spectral entropy and enhanced pulsation intensity are characteristic of NREM sleep. With our findings of increased power of slow oscillation, the present results support the proposition that sleep promotes fluid transport in human brain.SIGNIFICANCE STATEMENT We report that the spectral power of physiological brain pulsation mechanisms driven by vasomotor, respiration, and cardiac rhythms in human brain increase during sleep, extending previous observations of their association with glymphatic brain clearance during sleep in rodents. The magnitudes of increased pulsations follow the rank order of vasomotor greater than respiratory greater than cardiac pulsations, with correspondingly declining spatial extents. Spectral entropy, previously known as vigilance and as an anesthesia metric, decreased during NREM sleep compared with the awake state in very low and respiratory frequencies, indicating reduced signal complexity. An EEG slow oscillation power increase occurring in the early sleep phase (NREM 1–2) spatially overlapped with pulsation changes, indicating reciprocal mechanisms between those measures.

Cardiovascular Pulsatility Increases in Visual Cortex Before Blood Oxygen Level Dependent Response During Stimulus.

The physiological pulsations that drive tissue fluid homeostasis are not well characterized during brain activation. Therefore, we used fast magnetic resonance encephalography (MREG) fMRI to measure full band (0–5 Hz) blood oxygen level-dependent (BOLDFB) signals during a dynamic visual task in 23 subjects. This revealed brain activity in the very low frequency (BOLDVLF) as well as in cardiac and respiratory bands. The cardiovascular hemodynamic envelope (CHe) signal correlated significantly with the visual BOLDVLF response, considered as an independent signal source in the V1-V2 visual cortices. The CHe preceded the canonical BOLDVLF response by an average of 1.3 (± 2.2) s. Physiologically, the observed CHe signal could mark increased regional cardiovascular pulsatility following vasodilation.

P.0491 NREM sleep upregulates human brain pulsation detected by Ultrafast Magnetic Resonance Encephalography

P.0491 NREM sleep upregulates human brain pulsation detected by Ultrafast Magnetic Resonance Encephalography

Spectral analysis of physiological brain pulsations affecting the BOLD signal.

Physiological pulsations have been shown to affect the global blood oxygen level dependent (BOLD) signal in human brain. While these pulsations have previously been regarded as noise, recent studies show their potential as biomarkers of brain pathology. We used the extended 5 Hz spectral range of magnetic resonance encephalography (MREG) data to investigate spatial and frequency distributions of physiological BOLD signal sources. Amplitude spectra of the global image signals revealed cardiorespiratory envelope modulation (CREM) peaks, in addition to the previously known very low frequency (VLF) and cardiorespiratory pulsations. We then proceeded to extend the amplitude of low frequency fluctuations (ALFF) method to each of these pulsations. The respiratory pulsations were spatially dominating over most brain structures. The VLF pulsations overcame the respiratory pulsations in frontal and parietal gray matter, whereas cardiac and CREM pulsations had this effect in central cerebrospinal fluid (CSF) spaces and major blood vessels. A quasi‐periodic pattern (QPP) analysis showed that the CREM pulsations propagated as waves, with a spatiotemporal pattern differing from that of respiratory pulsations, indicating them to be distinct intracranial physiological phenomenon. In conclusion, the respiration has a dominant effect on the global BOLD signal and directly modulates cardiovascular brain pulsations.

Cardiovascular brain impulses in Alzheimer's disease.

Accumulation of amyloid-β is a key neuropathological feature in brain of Alzheimer’s disease patients. Alterations in cerebral haemodynamics, such as arterial impulse propagation driving the (peri)vascular CSF flux, predict future Alzheimer’s disease progression. We now present a non-invasive method to quantify the three-dimensional propagation of cardiovascular impulses in human brain using ultrafast 10 Hz magnetic resonance encephalography. This technique revealed spatio-temporal abnormalities in impulse propagation in Alzheimer’s disease. The arrival latency and propagation speed both differed in patients with Alzheimer’s disease. Our mapping of arterial territories revealed Alzheimer’s disease-specific modifications, including reversed impulse propagation around the hippocampi and in parietal cortical areas. The findings imply that pervasive abnormality in (peri)vascular CSF impulse propagation compromises vascular impulse propagation and subsequently glymphatic brain clearance of amyloid-β in Alzheimer’s disease.

Respiratory-related brain pulsations are increased in epilepsy-a two-centre functional MRI study.

Resting-state functional MRI has shown potential for detecting changes in cerebral blood oxygen level-dependent signal in patients with epilepsy, even in the absence of epileptiform activity. Furthermore, it has been suggested that coefficient of variation mapping of fast functional MRI signal may provide a powerful tool for the identification of intrinsic brain pulsations in neurological diseases such as dementia, stroke and epilepsy. In this study, we used fast functional MRI sequence (magnetic resonance encephalography) to acquire ten whole-brain images per second. We used the functional MRI data to compare physiological brain pulsations between healthy controls (n = 102) and patients with epilepsy (n = 33) and furthermore to drug-naive seizure patients (n = 9). Analyses were performed by calculating coefficient of variation and spectral power in full band and filtered sub-bands. Brain pulsations in the respiratory-related frequency sub-band (0.11–0.51 Hz) were significantly (P < 0.05) increased in patients with epilepsy, with an increase in both signal variance and power. At the individual level, over 80% of medicated and drug-naive seizure patients exhibited areas of abnormal brain signal power that correlated well with the known clinical diagnosis, while none of the controls showed signs of abnormality with the same threshold. The differences were most apparent in the basal brain structures, respiratory centres of brain stem, midbrain and temporal lobes. Notably, full-band, very low frequency (0.01–0.1 Hz) and cardiovascular (0.8–1.76 Hz) brain pulses showed no differences between groups. This study extends and confirms our previous results of abnormal fast functional MRI signal variance in epilepsy patients. Only respiratory-related brain pulsations were clearly increased with no changes in either physiological cardiorespiratory rates or head motion between the subjects. The regional alterations in brain pulsations suggest that mechanisms driving the cerebrospinal fluid homeostasis may be altered in epilepsy. Magnetic resonance encephalography has both increased sensitivity and high specificity for detecting the increased brain pulsations, particularly in times when other tools for locating epileptogenic areas remain inconclusive.

Time-domain principal component reconstruction (tPCR): A more efficient and stable iterative reconstruction framework for non-Cartesian functional MRI.

To improve the reconstruction efficiency (i.e., computational load) and stability of iterative reconstruction for non-Cartesian fMRI when using high undersampling rates and/or in the presence of strong off-resonance effects. The magnetic resonance encephalography (MREG) sequence with 3D non-Cartesian trajectory and 0.1s repetition time (TR) was applied to acquire fMRI datasets. Different from a conventional time-point-by-time-point sequential reconstruction (SR), the proposed time-domain principal component reconstruction (tPCR) performs three steps: (1) decomposing the k-t-space fMRI datasets into time-domain principal component space using singular value decomposition, (2) reconstructing each principal component with redistributed computation power according to their weights, and (3) combining the reconstructed principal components back to image-t-space. The comparison of reconstruction accuracy was performed by simulation experiments and then verified in real fMRI data. The simulation experiments showed that the proposed tPCR was able to significantly reduce reconstruction errors, and subsequent functional activation errors, relative to SR at identical computational cost. Alternatively, at fixed reconstruction accuracy, computation time was greatly reduced. The improved performance was particularly obvious for L1-norm nonlinear reconstructions relative to L2-norm linear reconstructions and robust to different regularization strength, undersampling rates, and off-resonance effects intensity. By examining activation maps, tPCR was also found to give similar improvements in real fMRI experiments. The proposed proof-of-concept tPCR framework could improve (1) the reconstruction efficiency of iterative reconstruction, and (2) the reconstruction stability especially for nonlinear reconstructions. As a practical consideration, the improved reconstruction speed promotes the application of highly undersampled non-Cartesian fast fMRI.

Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder.

This study investigated whole‐brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting‐state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P‐value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default‐mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large‐scale functional brain networks may contribute to the ASD phenotype. Autism Res 2020, 13: 244–258. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.Lay SummaryAutism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Using an ultra‐fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. We found that ASD individuals had altered information flow patterns across brain regions. Atypical patterns were concentrated in salience, executive, visual, and default‐mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.

Sampling Rate Effects on Resting State fMRI Metrics.

Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning.

3D Multi-Resolution Optical Flow Analysis of Cardiovascular Pulse Propagation in Human Brain.

The brain is cleaned from waste by glymphatic clearance serving a similar purpose as the lymphatic system in the rest of the body. Impairment of the glymphatic brain clearance precedes protein accumulation and reduced cognitive function in Alzheimer's disease (AD). Cardiovascular pulsations are a primary driving force of the glymphatic brain clearance. We developed a method to quantify cardiovascular pulse propagation in the human brain with magnetic resonance encephalography (MREG). We extended a standard optical flow estimation method to three spatial dimensions, with a multi-resolution processing scheme. We added application-specific criteria for discarding inaccurate results. With the proposed method, it is now possible to estimate the propagation of cardiovascular pulse wavefronts from the whole brain MREG data sampled at 10 Hz. The results show that on average the cardiovascular pulse propagates from major arteries via cerebral spinal fluid spaces into all tissue compartments in the brain. We present an example, that cardiovascular pulsations are significantly altered in AD: coefficient of variation and sample entropy of the pulse propagation speed in the lateral ventricles change in AD. These changes are in line with the theory of glymphatic clearance impairment in AD. The proposed non-invasive method can assess a performance indicator related to the glymphatic clearance in the human brain.

Spectral entropy indicates electrophysiological and hemodynamic changes in drug-resistant epilepsy - A multimodal MREG study.

ObjectiveEpilepsy causes measurable irregularity over a range of brain signal frequencies, as well as autonomic nervous system functions that modulate heart and respiratory rate variability. Imaging dynamic neuronal signals utilizing simultaneously acquired ultra-fast 10 Hz magnetic resonance encephalography (MREG), direct current electroencephalography (DC-EEG), and near-infrared spectroscopy (NIRS) can provide a more comprehensive picture of human brain function. Spectral entropy (SE) is a nonlinear method to summarize signal power irregularity over measured frequencies. SE was used as a joint measure to study whether spectral signal irregularity over a range of brain signal frequencies based on synchronous multimodal brain signals could provide new insights in the neural underpinnings of epileptiform activity.MethodsTen patients with focal drug-resistant epilepsy (DRE) and ten healthy controls (HC) were scanned with 10 Hz MREG sequence in combination with EEG, NIRS (measuring oxygenated, deoxygenated, and total hemoglobin: HbO, Hb, and HbT, respectively), and cardiorespiratory signals. After pre-processing, voxelwise SEMREG was estimated from MREG data. Different neurophysiological and physiological subfrequency band signals were further estimated from MREG, DC-EEG, and NIRS: fullband (0–5 Hz, FB), near FB (0.08–5 Hz, NFB), brain pulsations in very-low (0.009–0.08 Hz, VLFP), respiratory (0.12–0.4 Hz, RFP), and cardiac (0.7–1.6 Hz, CFP) frequency bands. Global dynamic fluctuations in MREG and NIRS were analyzed in windows of 2 min with 50% overlap.ResultsRight thalamus, cingulate gyrus, inferior frontal gyrus, and frontal pole showed significantly higher SEMREG in DRE patients compared to HC. In DRE patients, SE of cortical Hb was significantly reduced in FB (p = .045), NFB (p = .017), and CFP (p = .038), while both HbO and HbT were significantly reduced in RFP (p = .038, p = .045, respectively). Dynamic SE of HbT was reduced in DRE patients in RFP during minutes 2 to 6. Fitting to the frontal MREG and NIRS results, DRE patients showed a significant increase in SEEEG in FB in fronto-central and parieto-occipital regions, in VLFP in parieto-central region, accompanied with a significant decrease in RFP in frontal pole and parietal and occipital (O2, Oz) regions.ConclusionThis is the first study to show altered spectral entropy from synchronous MREG, EEG, and NIRS in DRE patients. Higher SEMREG in DRE patients in anterior cingulate gyrus together with SEEEG and SENIRS results in 0.12–0.4 Hz can be linked to altered parasympathetic function and respiratory pulsations in the brain. Higher SEMREG in thalamus in DRE patients is connected to disturbances in anatomical and functional connections in epilepsy. Findings suggest that spectral irregularity of both electrophysiological and hemodynamic signals are altered in specific way depending on the physiological frequency range.

Breath hold effect on cardiovascular brain pulsations – A multimodal magnetic resonance encephalography study

Ultra-fast functional magnetic resonance encephalography (MREG) enables separate assessment of cardiovascular, respiratory, and vasomotor waves from brain pulsations without temporal aliasing. We examined effects of breath hold- (BH) related changes on cardiovascular brain pulsations using MREG to study the physiological nature of cerebrovascular reactivity. We used alternating 32 s BH and 88 s resting normoventilation (NV) to change brain pulsations during MREG combined with simultaneously measured respiration, continuous non-invasive blood pressure, and cortical near-infrared spectroscopy (NIRS) in healthy volunteers. Changes in classical resting-state network BOLD-like signal and cortical blood oxygenation were reproduced based on MREG and NIRS signals. Cardiovascular pulsation amplitudes of MREG signal from anterior cerebral artery, oxygenated hemoglobin concentration in frontal cortex, and blood pressure decreased after BH. MREG cardiovascular pulse amplitudes in cortical areas and sagittal sinus increased, while cerebrospinal fluid and white matter remained unchanged. Respiratory centers in the brainstem – hypothalamus – thalamus – amygdala network showed strongest increases in cardiovascular pulsation amplitude. The spatial propagation of averaged cardiovascular impulses altered as a function of successive BH runs. The spread of cardiovascular pulse cycles exhibited a decreasing spatial similarity over time. MREG portrayed spatiotemporally accurate respiratory network activity and cardiovascular pulsation dynamics related to BH challenges at an unpreceded high temporal resolution.

Back Cover: Assessment of the dynamics of human glymphatic system by near‐infrared spectroscopy (J. Biophotonics 8/2018)

A functional near‐infrared spectroscopy (fNIRS) based approach to non‐invasive monitoring of the (g)lymphatic system in the human brain. The cover figure shows depth sensitivity of fNIRS, particularly at the wavelength of 980 nm, for sensing dynamic variations of water and cerebrospinal fluid (CSF) volume, e.g. in the subarachnoid space. Observed fluctuations in water content correlated with magnetic resonance encephalography (MREG) signals during visual task, further, are assumed to be connected with the glymphatic circulation.Further details can be found in the article by Teemu Myllylä, Markus Harju, Vesa Korhonen, et al. (e201870154) image

Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network.

Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting-state functional connectivity is temporally variable in human brain. Combined full-band electroencephalography-fMRI (fbEEG-fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood-oxygen-level-dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG-fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting-state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short-time-window comparisons of infra-slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG-BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG-fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG-BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on-average low correlations between infra-slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.

Assessment of the dynamics of human glymphatic system by near-infrared spectroscopy.

Fluctuations in brain water content has attracted increasing interest, particularly as regards studies of the glymphatic system, which is connected with the complex organization of dural lymphatic vessels, responsible for cleaning tissue. Disturbances of glymphatic circulation are associated with several brain disorders, including dementia. This article introduces an approach to noninvasive measurement of water dynamics in the human brain utilizing near-infrared spectroscopy (NIRS). We demonstrate the possibility to sense dynamic variations of water content between the skull and grey matter, for instance, in the subarachnoid space. Measured fluctuations in water content, especially in the cerebrospinal fluid (CSF), are assumed to be correlated with the dynamics of glymphatic circulation. The sampling volume for the NIRS optode was estimated by Monte Carlo modelling for the wavelengths of 660, 740, 830 and 980 nm. In addition, using combinations of these wavelengths, this article presents the calculation models for quantifying water and haemodynamics. The presented NIRS technique allows long-term functional brain monitoring, including sleeping time. Furthermore, it is used in combination with different magnetic neuroimaging techniques, particularly magnetic resonance encephalography. Using the combined setup, we report the preliminary results on the interaction between CSF and blood oxygen level-dependent fluctuations.

Subject‐level reliability analysis of fast fMRI with application to epilepsy

PurposeRecent studies have applied the new magnetic resonance encephalography (MREG) sequence to the study of interictal epileptic discharges (IEDs) in the electroencephalogram (EEG) of epileptic patients. However, there are no criteria to quantitatively evaluate different processing methods, to properly use the new sequence.MethodsWe evaluated different processing steps of this new sequence under the common generalized linear model (GLM) framework by assessing the reliability of results. A bootstrap sampling technique was first used to generate multiple replicated data sets; a GLM with different processing steps was then applied to obtain activation maps, and the reliability of these maps was assessed.ResultsWe applied our analysis in an event‐related GLM related to IEDs. A higher reliability was achieved by using a GLM with head motion confound regressor with 24 components rather than the usual 6, with an autoregressive model of order 5 and with a canonical hemodynamic response function (HRF) rather than variable latency or patient‐specific HRFs. Comparison of activation with IED field also favored the canonical HRF, consistent with the reliability analysis.ConclusionThe reliability analysis helps to optimize the processing methods for this fast fMRI sequence, in a context in which we do not know the ground truth of activation areas. Magn Reson Med 78:1–1, 2017. © 2016 International Society for Magnetic Resonance in Medicine

Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data

This study investigated lag structure in the resting-state fMRI by applying a novel independent component (ICA) method to magnetic resonance encephalography (MREG) data. Briefly, the spatial ICA (sICA) was used for defining the frontal and back nodes of the default mode network (DMN), and the temporal ICA (tICA), which is enabled by the high temporal resolution of MREG (TR=100ms), was used to separate both neuronal and physiological components of these two spatial map regions. Subsequently, lag structure was investigated between the frontal (DMNvmpf) and posterior (DMNpcc) DMN nodes using both conventional method with all-time points and a sliding-window approach.A rigorous noise exclusion criterion was applied for tICs to remove physiological pulsations, motion and system artefacts. All the de-noised tICs were used to calculate the null-distributions both for expected lag variability over time and over subjects. Lag analysis was done for the three highest correlating denoised tICA pairs.Mean time lag of 0.6s (± 0.5 std) and mean absolute correlation of 0.69 (± 0.08) between the highest correlating tICA pairs of DMN nodes was observed throughout the whole analyzed period. In dynamic 2min window analysis, there was large variability over subjects as ranging between 1–10sec. Directionality varied between these highly correlating sources an average 28.8% of the possible number of direction changes.The null models show highly consistent correlation and lag structure between DMN nodes both in continuous and dynamic analysis. The mean time lag of a null-model over time between all denoised DMN nodes was 0.0s and, thus the probability of having either DMNpcc or DMNvmpf as a preceding component is near equal. All the lag values of highest correlating tICA pairs over subjects lie within the standard deviation range of a null-model in whole time window analysis, supporting the earlier findings that there is a consistent temporal lag structure across groups of individuals. However, in dynamic analysis, there are lag values exceeding the threshold of significance of a null-model meaning that there might be biologically meaningful variation in this measure. Taken together the variability in lag and the presence of high activity peaks during strong connectivity indicate that individual avalanches may play an important role in defining dynamic independence in resting state connectivity within networks.

Enhanced subject-specific resting-state network detection and extraction with fast fMRI.

Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc.