Cardiovascular brain impulses in Alzheimer’s disease

Rajna, Zalan; Mattila, Heli; Huotari, Niko; Tuovinen, Timo; Krüger, Johanna; Holst, Sebastian C.; Korhonen, Vesa; Remes, Anne M.; Seppänen, Tapio; Hennig, Jürgen; Kiviniemi, Vesa

doi:10.1093/brain/awab144

Cited by 45 publications

(42 citation statements)

References 46 publications

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“…The pulsatile and propagating nature of the cardiac and respiratory waves and as well as the modulatory CREM pulsation along brain and CSF structures suggest that they are distinct physiological phenomena (Birn et al, 2006 ; Huotari et al, 2019 ; Kiviniemi et al, 2016 ;Windischberger et al, 2002 ; Wise et al, 2004 ). The arterial pressure impulse becomes absorbed into a convective force, pushing blood within the vasculature and CSF along paravascular spaces (Mestre et al, 2018 ; Rajna et al, 2021 ). The cardiovascular pulses dominate around arteries, where respiratory modulation is of small but detectable magnitude (Berger, 1901 ; Mestre et al, 2018 ; Santisakultarm et al, 2012 ).…”

Section: Discussionmentioning

confidence: 99%

“…The cardiorespiratory pulsations have been shown to drive blood and also cerebrospinal fluid (CSF) flow inside the brain, and measures of these pulsations thus contain valuable information on flow dynamics (Dreha‐Kulaczewski et al, 2015 ; Kiviniemi et al, 2016 ). For example, there is a significant alteration in the variance of cardiovascular pulsation in the brain of Alzheimer' disease patients (Rajna et al, 2019 , 2021 ; Tuovinen et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Spectral analysis of physiological brain pulsations affecting the BOLD signal

Raitamaa

Huotari

Korhonen

et al. 2021

Human Brain Mapping

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral analysis of physiological brain pulsations affecting the BOLD signal

Raitamaa

Huotari

Korhonen

et al. 2021

Human Brain Mapping

Self Cite

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“…It would be crucial to understand the time-course of the sequence of events in the “G-Ls pathology” and the threshold of possible reversibility in any of its steps to plot new therapeutic strategies. To this end, the possibility of using dynamic magnetic resonance imaging to track G-Ls, in small laboratory animals (Xue et al, 2020 ) and humans (Rajna et al, 2021 ), at various steps, might prove instrumental in achieving this goal.…”

Section: Discussionmentioning

confidence: 99%

“…Primary noxae are the genetic aquaporin-4 channelopathies (Rainey-Smith et al, 2018 ) or auto-antibodies that directly damage aquaporin-4 (Papadopoulos and Verkman, 2013 ). Secondary noxae are the derangements of the intra-extra cranial hydrodynamic at various levels leading to CSF circulation alterations (Wilson, 2016 ; Tuovinen et al, 2020 ; Rajna et al, 2021 ). CSF flow waves, hemodynamic, and neuronal electrical activity oscillations connect in sleep (Fultz et al, 2019 ).…”

Section: Opinionmentioning

confidence: 99%

The “Glymphatic-Lymphatic System Pathology” and a New Categorization of Neurodegenerative Disorders

Gallina

Nicoletti

Scollato³

et al. 2021

Front. Neurosci.

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“…Various MRI techniques have been proposed to measure intracranial CPWs (e.g. phase-contrast, magnetic resonance encephalography, gradient-recalled-echo MRI) as these waveforms are considered a useful biomarker in certain cerebrovascular diseases (Bianciardi et al, 2016;Rajna et al, 2021;Wagshul et al, 2011;Whittaker et al, 2021), and are often studied in order to understand the role of intracranial cardiac pulsatility in the glymphatic activity (Fultz et al, 2019;Kiviniemi et al, 2016). With this in mind, here we sought to investigate whether the CPM applied to BOLD fMRI data may provide an alternative technique for measuring intracranial pulsatility.…”

Section: Cardiac Pulsatility Model (Cpm)mentioning

confidence: 99%

Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

Kassinopoulos

Mitsis

2020

Preprint

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The blood oxygenation level-dependent (BOLD) contrast mechanism allows someone to non-invasively probe changes in deoxyhemoglobin content. As such, it is commonly used in fMRI to study brain activity since levels of 10 deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed through the years to correct for the associated confounds. This study sought to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. RETROICOR, a technique commonly used to model fMRI fluctuations induced by cardiac pulsatility was compared with a new technique proposed here, 15 named cardiac pulsatility model (CPM), that is based on convolution filtering. Further, this study investigated whether the variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations as well as with systemic low frequency oscillations (SLFOs) present in the global signal (i.e. mean fMRI timeseries averaged across all voxels in gray matter). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain significantly more variance in fMRI compared to RETROICOR, 20 particularly for subjects that presented high variance in heart rate during the scan. The amplitude of the fMRI pulserelated fluctuations did not seem to covary with PPG-Amp. That said, PPG-Amp explained significant variance in the GS that did not seem to be attributed to variations in heart rate or breathing patterns. In conclusion, our results suggest that the techniques proposed here can model high-frequency fluctuations due to pulsation as well as lowfrequency physiological fluctuations more accurately than model-based techniques commonly employed in fMRI 25 studies.

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Cardiovascular brain impulses in Alzheimer’s disease

Cited by 45 publications

References 46 publications

Spectral analysis of physiological brain pulsations affecting the BOLD signal

Spectral analysis of physiological brain pulsations affecting the BOLD signal

The “Glymphatic-Lymphatic System Pathology” and a New Categorization of Neurodegenerative Disorders

Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

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