Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

Särkkä, Simo; Solin, Arno; Nummenmaa, Aapo; Vehtari, Aki; Auranen, Toni; Vanni, Simo; Lin, Feng

doi:10.1016/j.neuroimage.2012.01.067

Cited by 135 publications

(94 citation statements)

References 37 publications

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“…Respiratory data was successfully recorded for 18 and cardiac data for 19 subjects. For these subjects, respiratory and cardiac artifacts were modeled and then removed from the fMRI data using the DRIFTER algorithm (Särkkä et al., 2012). Functional datasets were co‐registered to the subject's brain, extracted from T 1 ‐weighted images, and these were then registered to the MNI152 standard space template with 2‐mm resolution.…”

Section: Methodsmentioning

confidence: 99%

Predictive processing increases intelligibility of acoustically distorted speech: Behavioral and neural correlates

et al. 2017

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IntroductionWe examined which brain areas are involved in the comprehension of acoustically distorted speech using an experimental paradigm where the same distorted sentence can be perceived at different levels of intelligibility. This change in intelligibility occurs via a single intervening presentation of the intact version of the sentence, and the effect lasts at least on the order of minutes. Since the acoustic structure of the distorted stimulus is kept fixed and only intelligibility is varied, this allows one to study brain activity related to speech comprehension specifically.MethodsIn a functional magnetic resonance imaging (fMRI) experiment, a stimulus set contained a block of six distorted sentences. This was followed by the intact counterparts of the sentences, after which the sentences were presented in distorted form again. A total of 18 such sets were presented to 20 human subjects.ResultsThe blood oxygenation level dependent (BOLD)‐responses elicited by the distorted sentences which came after the disambiguating, intact sentences were contrasted with the responses to the sentences presented before disambiguation. This revealed increased activity in the bilateral frontal pole, the dorsal anterior cingulate/paracingulate cortex, and the right frontal operculum. Decreased BOLD responses were observed in the posterior insula, Heschl's gyrus, and the posterior superior temporal sulcus.ConclusionsThe brain areas that showed BOLD‐enhancement for increased sentence comprehension have been associated with executive functions and with the mapping of incoming sensory information to representations stored in episodic memory. Thus, the comprehension of acoustically distorted speech may be associated with the engagement of memory‐related subsystems. Further, activity in the primary auditory cortex was modulated by prior experience, possibly in a predictive coding framework. Our results suggest that memory biases the perception of ambiguous sensory information toward interpretations that have the highest probability to be correct based on previous experience.

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Section: Methodsmentioning

confidence: 99%

Predictive processing increases intelligibility of acoustically distorted speech: Behavioral and neural correlates

et al. 2017

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“…The dynamic retrospective filtering of physiological noise (DRIFTER) algorithm, 13 which modeled and removed physiological data that were recorded during experimental time in each subject, was used to filter raw DOT data, allowing accurate dynamical tracking of the variations in the cardiac and respiratory frequencies (Fig. 2).…”

Section: Data Qualitymentioning

confidence: 99%

“…Some physiological signals, such as heart rate or ventilation rate, involved in systemic blood oxygen and cerebral hemodynamics influencing the scalp layer 11 may generate variability, which involves spatial and temporal changes throughout the brain and scalp 12 during DOT neuroimaging experiments. In a previous research, a user-independent procedure consisting of a Bayesian algorithm 13 on raw DOT data was applied to remove physiological noise caused by cardiac and breathing activity.…”

Section: Introductionmentioning

confidence: 99%

Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study

et al. 2017

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, "Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study," Neurophoton. 4(1), 015003 (2017), doi: 10.1117/1.NPh.4.1.015003. Abstract. Diffuse optical tomography (DOT) measures concentration changes in both oxy-and deoxyhemoglobin providing three-dimensional images of local brain activations. A pilot study, which compares both DOT and functional magnetic resonance imaging (fMRI) volumes through t-maps given by canonical statistical parametric mapping (SPM) processing for both data modalities, is presented. The DOT series were processed using a method that is based on a Bayesian filter application on raw DOT data to remove physiological changes and minimum description length application index to select a number of singular values, which reduce the data dimensionality during image reconstruction and adaptation of DOT volume series to normalized standard space. Therefore, statistical analysis is performed with canonical SPM software in the same way as fMRI analysis is done, accepting DOT volumes as if they were fMRI volumes. The results show the reproducibility and ruggedness of the method to process DOT series on group analysis using cognitive paradigms on the prefrontal cortex. Difficulties such as the fact that scalp-brain distances vary between subjects or cerebral activations are difficult to reproduce due to strategies used by the subjects to solve arithmetic problems are considered. T-images given by fMRI and DOT volume series analyzed in SPM show that at the functional level, both DOT and fMRI measures detect the same areas, although DOT provides complementary information to fMRI signals about cerebral activity.

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“…These changes can be modeled using, for example, Fourier series or (quasi-)periodic Gaussian processes (GP). In this work, the latter approach is chosen due to its flexibility and robustness [10], [11].…”

Section: Modelmentioning

confidence: 99%

RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering

Hostettler

Kaltiokallio

Yi̇ği̇tler

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we propose a method for respiratory rate estimation based on the received signal strength of narrowband radio frequency transceivers. We employ a state-space formulation of periodic Gaussian processes to model the observed variations in the signal strength. This is then used in a RaoBlackwellized unscented Kalman filter which exploits the linear substructure of the proposed model and thereby greatly improves computational efficiency. The proposed method is evaluated on measurement data from commercially available off the shelf transceivers. It is found that the proposed method accurately estimates the respiratory rate and provides a systematic way of fusing the measurements of asynchronous frequency channels.

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Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

Cited by 135 publications

References 37 publications

Predictive processing increases intelligibility of acoustically distorted speech: Behavioral and neural correlates

Predictive processing increases intelligibility of acoustically distorted speech: Behavioral and neural correlates

Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study

RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering

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