Transcranial photobiomodulation with near-infrared light from childhood to elderliness: simulation of dosimetry

Yuan, Yaoshen; Cassano, Paolo; Pias, Matthew; Fang, Qianqian

doi:10.1117/1.nph.7.1.015009

Cited by 34 publications

(24 citation statements)

References 71 publications

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“…shown to be involved in pathological and induced anxiety but also decision making and the processing of rewards 1,43,[45][46]70 . The gamma effects we observed had very similar latencies in the cACC and the dmPFC, whereas no effects were found in the mOFC, which is considered to include the vmPFC in the anatomical parcellations in MEG studies [85][86] . In addition, we found that larger gamma activity across the cACC and dmPFC regions was associated with greater unsigned pwPEs, and trials with larger |ε2| were more likely to be followed by a response shift, more prominently in HTA.…”

Section: Discussionmentioning

confidence: 56%

Oscillations in anterior cingulate and medial prefrontal cortex explain learning alterations in trait anxiety

Hein¹,

Gong²,

Ivanova³

et al. 2022

Preprint

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Recent work suggests that anxiety biases how individuals form beliefs and estimate uncertainty, modulating learning. Yet the precise neural underpinnings of these computational alterations remain undetermined. Here we assess whether oscillatory activity in regions of the overlapping anxiety and decision-making circuitry, such as the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC), is associated with misestimation of uncertainty and altered belief updating in anxiety. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a volatile probabilistic reward-based learning task. A hierarchical Bayesian model revealed that HTA undermines learning through an overestimation of volatility leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. Using convolution modelling for oscillatory responses in the source space, we observed enhanced gamma activity in HTA during the encoding of unsigned precision-weighted prediction errors (pwPE). These effects emerged in the ACC, dorsomedial PFC (dmPFC), and OFC, and they were accompanied by additional suppression of ACC alpha/beta activity with pwPE and precision-weights in HTA. Our study supports the association between altered learning in trait anxiety and faster updating of beliefs through gamma and alpha/beta activity changes across the ACC, dmPFC and OFC.

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Section: Discussionmentioning

confidence: 56%

Oscillations in anterior cingulate and medial prefrontal cortex explain learning alterations in trait anxiety

Hein¹,

Gong²,

Ivanova³

et al. 2022

Preprint

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show abstract

“…Parallelization of diffuse reflectance (DR) MC modeling has led to better understanding of biophotonic phenomena, including optical dosimetry [33], bone injuries [34], and transcranial photobiomodulation [35,36]. In RS, recent work has demonstrated the benefit of a parallelized Raman MC model for simulation of an full Raman spectra [20,23].…”

Section: Discussionmentioning

confidence: 99%

A computationally efficient Monte‐Carlo model for biomedical Raman spectroscopy

Dumont

Fang

Patil

2021

Journal of Biophotonics

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Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through "isoweight," a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique wellsuited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800 − 2000 cm −1 with an approximately 100× increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation.

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“…Prior to LCMV beamforming, the MEG signals were band-pass filtered between 1 and 40 Hz for the alpha and beta range and between 30 and 124 Hz for the gamma range (below the Nyquist rate at 125 Hz). Last, source estimate time courses for individual vertices were obtained for a set of cortical labels corresponding to our ROIs: anterior cingulate cortex (rostral ACC [rACC], caudal ACC [cACC]), medial OFC and lateral OFC, which include the vmPFC according to previous MEG work (Yuan et al, 2020, but see Rushworth et al, 2011 for a debate on the vmPFC delineation), dorsomedial PFC (dmPFC; label superior frontal gyrus, SFG). The representative time course per label was obtained using the “PCA flip” method in MNE-Python.…”

Section: Methodsmentioning

confidence: 99%

“…[rACC], caudal ACC [cACC]), medial OFC and lateral OFC, which include the vmPFC according to previous MEG work (Yuan et al, 2020, but see Rushworth et al, 2011 for a debate on the vmPFC delineation), dorsomedial PFC (dmPFC; label superior frontal gyrus, SFG). The representative time course per label was obtained using the "PCA flip" method in MNE-Python.…”

Section: Source Analysismentioning

confidence: 99%

Changes in oscillations in anterior cingulate and medial prefrontal cortex are associated with altered signatures of Bayesian predictive coding in trait anxiety

Hein

Gong

Ivanova

et al. 2022

Preprint

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Recent advances in the computational understanding of decision-making processes have led to proposals that anxiety biases how individuals form beliefs and estimate uncertainty. The anxiety and decision-making circuitry broadly overlap in regions such as the medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and orbitofrontal cortex (OFC). Changes in activity across these brain areas could help explain how misestimation of uncertainty and altered belief updating can lead to impaired learning in anxiety. To test this prediction, this study built on recent progress in rhythm-based formulations of Bayesian predictive coding to identify sources of oscillatory modulations across the ACC, mPFC, and OFC that are associated with altered learning in subclinical trait anxiety. In a magnetoencephalography (MEG) experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a volatile probabilistic reward-based learning task. We modelled behaviour using a hierarchical Bayesian learning model. Furthermore, we quantified the parametric effects of trial-wise estimates of unsigned precision-weighted prediction errors (pwPEs) and, separately, precision weights and surprise on source-reconstructed MEG time-frequency responses using convolution modelling. We showed that HTA interferes with overall reward-based learning performance associated with more stochastic decisions and more pronounced lose-shift tendencies. These behavioural effects were explained by an overestimation of volatility and faster belief updating in HTA when compared to LTA. On a neural level, we observed enhanced gamma responses and decreased alpha/beta activity in HTA during the encoding of unsigned pwPEs about about stimulus outcomes relative to LTA. These effects emerged primarily in the ACC and dorsomedial PFC (dmPFC), and they were accompanied by additional ACC alpha/beta modulations representing differential encoding of precision weights in each anxiety group. Our study supports the association between subclinical trait anxiety and faster updating of beliefs in a volatile environment through gamma and alpha/beta activity changes in the ACC and dmPFC.

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Transcranial photobiomodulation with near-infrared light from childhood to elderliness: simulation of dosimetry

Cited by 34 publications

References 71 publications

Oscillations in anterior cingulate and medial prefrontal cortex explain learning alterations in trait anxiety

Oscillations in anterior cingulate and medial prefrontal cortex explain learning alterations in trait anxiety

A computationally efficient Monte‐Carlo model for biomedical Raman spectroscopy

Changes in oscillations in anterior cingulate and medial prefrontal cortex are associated with altered signatures of Bayesian predictive coding in trait anxiety

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