Heart Rate and Respiration Affect the Functional Connectivity of Default Mode Network in Resting-State Functional Magnetic Resonance Imaging

Yoshikawa, Akira; Masaoka, Yuri; Yoshida, Masaki; Koiwa, Nobuyoshi; Honma, Motoyasu; Watanabe, Kentaro; Kubota, Satomi; Iizuka, Natsuko; Ida, Masahiro; Izumizaki, Masahiko

doi:10.3389/fnins.2020.00631

Cited by 25 publications

(22 citation statements)

References 55 publications

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“…In our analysis of the haemodynamic data, we removed heart rate and respiration rate variation as noise, as removing heart rate and respiration variation improves strength of connectivity estimates but does not influence the network structure ( Yoshikawa et al., 2020 ). Also, our previous research has shown only modest correlations between experienced emotion ratings and heart rate and respiration rate ( Nummenmaa et al., 2014b ), and a recent meta-analysis has shown that autonomic nervous system variation alone does not distinguish between emotion categories ( Siegel et al., 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Classification of emotion categories based on functional connectivity patterns of the human brain

et al. 2022

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Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity patterns differ across emotion categories. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.

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Section: Discussionmentioning

confidence: 99%

Classification of emotion categories based on functional connectivity patterns of the human brain

et al. 2022

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“… Yoshikawa et al (2020) examined the influence of respiration and pulse on functional connectivity by measuring respiratory flow with a nasal mask and pulse rate with an infrared sensor. Connectivity of the default-mode network (and related areas) was compared between three models including: (1) no physiological noise regressors, (2) a cardiac output regressor, and (3) both cardiac output and respiration regressors.…”

Section: Challenges To Chemosensory Connectome Neuroimaging Studiesmentioning

confidence: 99%

“…A double line shows a significant difference between the uncorrected model and both of the models with corrected time series (panel A vs B and C ), while a single unbroken line indicates the difference between the uncorrected model and model corrected for cardiac noise (panel A vs B ) and a single broken line indicates the difference between the uncorrected model and the model corrected for both types of noise (panel A vs C ). Reproduced from Yoshikawa et al (2020) .…”

Section: Challenges To Chemosensory Connectome Neuroimaging Studiesmentioning

confidence: 99%

Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges

Veldhuizen

Cecchetto

Fjældstad

et al. 2022

Front. Syst. Neurosci.

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Ecological chemosensory stimuli almost always evoke responses in more than one sensory system. Moreover, any sensory processing takes place along a hierarchy of brain regions. So far, the field of chemosensory neuroimaging is dominated by studies that examine the role of brain regions in isolation. However, to completely understand neural processing of chemosensation, we must also examine interactions between regions. In general, the use of connectivity methods has increased in the neuroimaging field, providing important insights to physical sensory processing, such as vision, audition, and touch. A similar trend has been observed in chemosensory neuroimaging, however, these established techniques have largely not been rigorously applied to imaging studies on the chemical senses, leaving network insights overlooked. In this article, we first highlight some recent work in chemosensory connectomics and we summarize different connectomics techniques. Then, we outline specific challenges for chemosensory connectome neuroimaging studies. Finally, we review best practices from the general connectomics and neuroimaging fields. We recommend future studies to develop or use the following methods we perceive as key to improve chemosensory connectomics: (1) optimized study designs, (2) reporting guidelines, (3) consensus on brain parcellations, (4) consortium research, and (5) data sharing.

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“…The images need to be carefully preprocessed in order to filter out the noise while conserving the signal of interest (Fassbender et al, 2017; Johnstone et al, 2006; Lund et al, 2005; Oakes et al, 2005). Noise can emerge from different sources including motion, in particular head motion, MRI scanner artifacts, or physiological noise arising from normal cardio-respiratory activity (Brooks et al, 2013; Fassbender et al, 2017; Lund et al, 2005; Network et al, 2013; Yoshikawa et al, 2020). In the case of task-related MRI sequences, the task itself can add noise to the data (Fassbender et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In the case of task-related MRI sequences, the task itself can add noise to the data (Fassbender et al, 2017). This additional noise can be due to the inherent nature of the task participants are asked to perform, such as any movement participants would have to complete in the task, or to any physical or physiological reaction to the performed task, in addition to noise introduced by equipment used during the task (Brooks et al, 2013; Epstein et al, 2007; Kasper et al, 2017; Yoshikawa et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Not all noise-reduction methods for fMRI preprocessing are created equal

Hoeppli

Garenfeld

Mortensen

et al. 2022

Preprint

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Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high-pass filtering. However, these standard steps do not remove many sources of noise. Thus, noise-reduction techniques such as CompCor and FIX have been developed to further improve the ability to draw meaningful conclusions from the data. The ability of these techniques to minimize noise while conserving signals of interest has been tested almost exclusively in resting-state fMRI datasets and, only rarely, in task-related fMRI datasets. Application of noise-reduction techniques to task-related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. However, little remains known about the impact of different noise reduction techniques on the retention of signal, particularly during tasks that may be associated with systemic physiological changes. In this paper, we compared two noise-reduction techniques, i.e. FIX and CompCor, in an fMRI dataset including noxious heat stimulation and non-noxious auditory stimulation. Results show that preprocessing including FIX noise-reduction technique conserves significantly more signal than a preprocessing protocol including CompCor noise-reduction technique in both noxious heat and non-noxious auditory stimulations, while removing only slightly less noise. These results suggest that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise.

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Heart Rate and Respiration Affect the Functional Connectivity of Default Mode Network in Resting-State Functional Magnetic Resonance Imaging

Cited by 25 publications

References 55 publications

Classification of emotion categories based on functional connectivity patterns of the human brain

Classification of emotion categories based on functional connectivity patterns of the human brain

Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges

Not all noise-reduction methods for fMRI preprocessing are created equal

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