Inverse correlation of fluctuations of cerebral blood and water concentrations in humans

Abstract Near-infrared spectroscopy (fNIRS) measures concentrations of oxygenated (HbO) and deoxygenated (HbR) hemoglobin in the brain. Recently, we demonstrated its potential also for measuring concentrations of cerebral water (cH₂O). We performed fNIRS measurements during rest to study fluctuations in concentrations of cH₂O, HbO and HbR in 33 well-rested healthy control subjects (HC) and 18 acutely sleep-deprived HC. Resting-state fNIRS signal was filtered in full-band, cardiac, respiratory, low-, and very-low-frequency bands. The sum of HbO and HbR constitutes the regional cerebral blood volume (CBV). CBV and cH₂O concentrations were analyzed via temporal correlation and phase synchrony. Fluctuation in concentrations of cH₂O and CBV was strongly anti-correlated across all frequency bands in both frontal and parietal cortices. Fluctuation in concentrations of cH₂O and CBV showed neither a completely synchronous nor a random phase relationship in both frontal and parietal cortices. Acutely sleep-deprived subjects did not show significant differences in temporal correlation or phase synchrony between fluctuations in cH₂O and CBV concentrations compared with well-rested HC. The reciprocal interrelation between fluctuations in CBV and cH₂O concentrations is consistent with the Munro–Kellie doctrine of constant intracranial volume. This coupling may constitute a functional mechanism underlying glymphatic circulation, which persists despite acutely disturbed sleep patterns

Breath hold effect on cardiovascular brain pulsations:a multimodal magnetic resonance encephalography study

Abstract 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

Sampling rate effects on resting state fMRI metrics

Abstract 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

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

Abstract 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

Altered physiological brain variation in drug‐resistant epilepsy

Abstract Introduction: Functional magnetic resonance imaging (fMRI) combined with simultaneous electroencephalography (EEG‐fMRI) has become a major tool in mapping epilepsy sources. In the absence of detectable epileptiform activity, the resting state fMRI may still detect changes in the blood oxygen level‐dependent signal, suggesting intrinsic alterations in the underlying brain physiology. Methods: In this study, we used coefficient of variation (CV) of critically sampled 10 Hz ultra‐fast fMRI (magnetoencephalography, MREG) signal to compare physiological variance between healthy controls (n = 10) and patients (n = 10) with drug‐resistant epilepsy (DRE). Results: We showed highly significant voxel‐level (p < 0.01, TFCE‐corrected) increase in the physiological variance in DRE patients. At individual level, the elevations range over three standard deviations (σ) above the control mean (μ) CVMREG values solely in DRE patients, enabling patient‐specific mapping of elevated physiological variance. The most apparent differences in group‐level analysis are found on white matter, brainstem, and cerebellum. Respiratory (0.12–0.4 Hz) and very‐low‐frequency (VLF = 0.009–0.1 Hz) signal variances were most affected. Conclusions: The CVMREG increase was not explained by head motion or physiological cardiorespiratory activity, that is, it seems to be linked to intrinsic physiological pulsations. We suggest that intrinsic brain pulsations play a role in DRE and that critically sampled fMRI may provide a powerful tool for their identification

Human NREM Sleep Promotes Brain-Wide Vasomotor and Respiratory Pulsations

Abstract 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

Attachment-specific speech patterns induce dysphoric mood changes in the listener as a function of individual differences in attachment characteristics and psychopathology

OBJECTIVES: Early childhood experiences influence cognitive-emotional development, with insecure attachment predisposing to potential psychopathologies. We investigated whether narratives containing attachment-specific speech patterns shape listeners' emotional responses and social intentions. DESIGN: First, 149 healthy participants listened to three narratives characteristic for secure, insecure-preoccupied, and insecure-dismissing attachment. Following each narrative, the well-being and interpersonal reactivity as a particular aspect of emotional reactivity of the listener were assessed. Likewise, psychopathological aspects of personality were evaluated. A follow-up study compared 10 psychosomatic patients with a current depressive episode and/or personality disorder with distinct depressive symptoms and 10 age- and gender-matched healthy controls. METHODS: Effects of narratives on listeners' mental state were tested with repeated-measures AN(C)OVA. Mediating effects in the listener (attachment characteristics in the context of personality traits) were explored. Narrative effects were compared between patients and controls. RESULTS: Listening to insecure attachment narratives reduced well-being in controls. Nevertheless, tendency for social interaction was highest following the insecure-preoccupied narrative. Importantly, listeners' individual attachment characteristics mediated the relationship between well-being/interpersonal reactivity following the insecure-preoccupied narrative and levels of psychopathology. Furthermore, compared with healthy participants, patients showed higher emotional reactivity following exposure to the insecure-preoccupied narrative, represented by lower well-being and lower estimation of friendliness towards the narrator. CONCLUSIONS: Exposure to attachment-specific speech patterns can result in dysphoric mood changes. Specifically, the insecure-preoccupied narrative influenced the listeners' emotional state, which was further mediated by the individual attachment patterns and psychopathological personality characteristics. This deepens the understanding of interpersonal processes, especially in psychotherapeutic settings. PRACTITIONER POINTS: In clinical populations, insecure-preoccupied attachment has a high prevalence. In this study, listening to a narrative characteristic of insecure-preoccupied speech patterns resulted in reduced well-being in healthy listeners. Patients with depressive symptoms showed a higher emotional reactivity towards the insecure-preoccupied narrative compared to healthy controls. While working on (childhood) traumata, for example, in group therapy or inpatient settings, therapists should raise awareness to possible mood changes through discourse-conveyed attachment characteristics in listeners as a 'side effect'