arXiv subjects

Naoki Tani

Publications and source records attributed to Naoki Tani.

4 recordsLinked to original sources

arXiv2025

Wirelessly transmitted subthalamic nucleus signals predict endogenous pain levels in Parkinson's disease patients

Parkinson disease (PD) patients experience pain fluctuations that significantly reduce their quality of life. Despite the vast knowledge of the subthalamic nucleus (STN) role in PD, the STN biomarkers for pain fluctuations and the relationship between bilateral subthalamic nucleus (STN) activities and pain occurrence are still less understood. This observational study used data-driven methods by collecting annotated pain followed by a series of corresponding binary pain ratings and wirelessly transmitted STN signals, then leveraging the explainable machine learning algorithm to predict binary pain levels and sort the feature influence. The binary pain levels could be predicted among annotated pain reports corresponding to PD-related pain characteristics. The STN activity from both sides could impact pain prediction, with gamma and beta bands in the contralateral STN and delta and theta bands in the ipsilateral STN showing a prominent role. This study emphasizes the role of bilateral STN biomarkers on endogenous pain fluctuations.

q-bio.NC↗

arXiv2025

Singular Spectrum Analysis of Time-series Data from Time-dependent density-functional theory in Real-time

This paper introduces a spectral analysis of time-seires data derived from real-time time-dependent density functional theory (TDDFT) using Singular Spectrum Analysis (SSA). TDDFT is a robust method for obtaining molecular excited states and optical spectra by tracking the time evolution of dynamical dipole moments. However, the spectral resolution can be compromised when Fourier transformation's total time duration is insufficient. SSA enabled the extraction of specific oscillation components from the time-series data, facilitating the generation of higher-precision spectra. Even with relatively short time-series dataset, the predictive extension of SSA yielded high-resolution spectra, demonstrating substantial agreement with results obtained through conventional methods. The efficacy of this approach was validated for several small molecules, including ethylene, benzene, and others. SSA's ability to conduct detailed spectral anasysis in specific energy regions enhance spectral resolution and facilitates the clarification of oscillation components within these regions. Real-time TDDFT combined with SSA provides a new analytical method for analyzing the optical properties of molecules, significantly improving the accuracy of the analysis of emission and absorption spectra analysis. This method is expected to have various applications.

physics.comp-ph↗

arXiv2024

Brain-aligning of semantic vectors improves neural decoding of visual stimuli

The development of algorithms to accurately decode neural information has long been a research focus in the field of neuroscience. Brain decoding typically involves training machine learning models to map neural data onto a preestablished vector representation of stimulus features. These vectors are usually derived from image- and/or text-based feature spaces. Nonetheless, the intrinsic characteristics of these vectors might fundamentally differ from those that are encoded by the brain, limiting the ability of decoders to accurately learn this mapping. To address this issue, we propose a framework, called brain-aligning of semantic vectors, that fine-tunes pretrained feature vectors to better align with the structure of neural representations of visual stimuli in the brain. We trained this model with functional magnetic resonance imaging (fMRI) and then performed zero-shot brain decoding on fMRI, magnetoencephalography (MEG), and electrocorticography (ECoG) data. fMRI-based brain-aligned vectors improved decoding performance across all three neuroimaging datasets when accuracy was determined by calculating the correlation coefficients between true and predicted vectors. Additionally, when decoding accuracy was determined via stimulus identification, this accuracy increased in specific category types; improvements varied depending on the original vector space that was used for brain-alignment, and consistent improvements were observed across all neuroimaging modalities.

q-bio.NC↗

arXiv2021

Voluntary control of semantic neural representations by imagery with conflicting visual stimulation

Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery changes neural representations when imagined and perceived images semantically conflict. We hypothesized that imagining an image would activate a neural representation during its perception even while watching a conflicting image. To test this hypothesis, we developed a closed-loop system to show images inferred from electrocorticograms using a visual semantic space. The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories. Moreover, modulation of the inferred vectors by mental imagery depended on both the image category and time from the initiation of imagery. The closed-loop control of the semantic vectors revealed an asymmetrical interaction between visual perception and imagery.

q-bio.NC↗