ATR Neural Information Analysis Laboratories

Kyoto, Japan

ATR Neural Information Analysis Laboratories

Kyoto, Japan

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Nishio A.,National Institute for Physiological science | Shimokawa T.,ATR Neural Information Analysis Laboratories | Goda N.,National Institute for Physiological science | Goda N.,Graduate University for Advanced Studies | And 2 more authors.
Journal of Neuroscience | Year: 2014

There are neurons localized in the lower bank of the superior temporal sulcus (STS) in the inferior temporal (IT) cortex of the monkey that selectively respond to specific ranges of gloss characterized by combinations of three physical reflectance parameters: specular reflectance (ρs), diffuse reflectance (ρd), and spread of specular reflection (α; Nishio et al., 2012). In the present study, we examined how the activities of these gloss-selective IT neurons are related to perceived gloss. In an earlier psychophysical study, Ferwerda et al. (2001) identified a perceptually uniform gloss space defined by two axes where the c-axis corresponds to a nonlinear combination of ρs and ρd and the d-axis corresponds to 1-α. In the present study, we tested the responses of gloss-selective neurons to stimuli in the perceptual gloss space defined by the c-and d-axes. We found that gloss-selective neurons systematically changed their responses in the perceptual gloss space, and the distribution of the tuning directions of the population of gloss-selective neurons is biased toward directions in which perceived gloss increases. We also found that a set of perceptual gloss parameters as well as surface albedo can be well explained by the population activities of gloss-selective neurons, and that these parameters are likely encoded by the gloss-selective neurons in this area of the STS to represent various glosses. These results thus provide evidence that the IT cortex represents perceptual gloss space. © 2014 the authors.

Shimokawa T.,ATR Neural Information Analysis Laboratories | Kosaka T.,ATR Neural Information Analysis Laboratories | Yamashita O.,ATR Neural Information Analysis Laboratories | Hiroe N.,ATR Neural Information Analysis Laboratories | And 3 more authors.
Biomedical Optics Express | Year: 2013

Functional near-infrared spectroscopy (fNIRS) can noninvasively measure hemodynamic responses in the cerebral cortex with a portable apparatus. However, the observation signal in fNIRS measurements is contaminated by the artifact signal from the hemodynamic response in the scalp. In this paper, we propose a method to separate the signals from the cortex and the scalp by estimating both hemodynamic changes by diffuse optical tomography (DOT). In the inverse problem of DOT, we introduce smooth regularization to the hemodynamic change in the scalp and sparse regularization to that in the cortex based on the nature of the hemodynamic responses. These appropriate regularization models, with the spatial information of optical paths of many measurement channels, allow three-dimensional reconstruction of both hemodynamic changes. We validate our proposed method through two-layer phantom experiments and MRI-based head-model simulations. In both experiments, the proposed method simultaneously estimates the superficial smooth activity in the scalp area and the deep localized activity in the cortical area. © 2013 Optical Society of America.

Shimokawa T.,ATR Neural Information Analysis Laboratories | Kosaka T.,ATR Neural Information Analysis Laboratories | Kosaka T.,Nara Institute of Science and Technology | Yamashita O.,ATR Neural Information Analysis Laboratories | And 4 more authors.
Optics Express | Year: 2012

High-density diffuse optical tomography (HD-DOT) is an emerging technique for visualizing the internal state of biological tissues. The large number of overlapping measurement channels due to the use of high-density probe arrays permits the reconstruction of the internal optical properties, even with a reflectance-only measurement. However, accurate three-dimensional reconstruction is still a challenging problem. First, the exponentially decaying sensitivity causes a systematic depth-localization error. Second, the nature of diffusive light makes the image blurred. In this paper, we propose a three-dimensional reconstruction method that overcomes these two problems by introducing sensitivity-normalized regularization and sparsity into the hierarchical Bayesian method. Phantom experiments were performed to validate the proposed method under three conditions of probe interval: 26 mm, 18.4 mm, and 13 mm. We found that two absorbers with distances shorter than the probe interval could be discriminated under the high-density conditions of 18.4-mm and 13-mm intervals. This discrimination ability was possible even if the depths of the two absorbers were different from each other. These results show the high spatial resolution of the proposed method in both depth and horizontal directions. © 2012 Optical Society of America.

Callan A.,NICT Multisensory Cognition and Computation Laboratory | Callan D.E.,ATR Neural Information Analysis Laboratories | Ando H.,NICT Multisensory Cognition and Computation Laboratory
NeuroImage | Year: 2013

When we listen to sounds through headphones without utilizing special transforms, sound sources seem to be located inside our heads. The sound sources are said to be lateralized to one side or the other to varying degree. This internal lateralization is different than sound source localization in the natural environment in which the sound is localized distal to the head. We used fMRI to investigate difference in neural responses between lateralization and localization. Individualized binaural recordings were used as externalized auditory stimuli and stereo recordings were used as internalized auditory stimuli. Brain activity was measured while 14 participants performed an active auditory localization task and while 12 participants performed a stimulus type identification task. Irrespective of the task condition, we observed enhanced activity in the bilateral posterior temporal gyri (pSTG) for the externalized stimuli relative to the internalized stimuli. Region of interest analysis indicated that both left and right pSTG were more sensitive to sound sources in contra- than ipsilateral hemifields. Moreover, greater back than front activity was also found in the left pSTG. Compared to impoverished spatial auditory stimuli, realistic spatial auditory stimuli enhance neural responses in the pSTG. This may be why we could observe contralateral hemifield preference in bilateral pSTG that many previous studies have failed to observe. Overall, the results indicate the importance of using ecologically valid stimuli for investigating neural processes in human cortex. © 2012 Elsevier Inc.

Fukushima M.,Nara Institute of Science and Technology | Fukushima M.,ATR Neural Information Analysis Laboratories | Yamashita O.,ATR Neural Information Analysis Laboratories | Yamashita O.,Max Planck Institute for Human Cognitive and Brain Sciences | And 2 more authors.
NeuroImage | Year: 2015

We present an MEG source reconstruction method that simultaneously reconstructs source amplitudes and identifies source interactions across the whole brain. In the proposed method, a full multivariate autoregressive (MAR) model formulates directed interactions (i.e., effective connectivity) between sources. The MAR coefficients (the entries of the MAR matrix) are constrained by the prior knowledge of whole-brain anatomical networks inferred from diffusion MRI. Moreover, to increase the accuracy and robustness of our method, we apply an fMRI prior on the spatial activity patterns and a sparse prior on the MAR coefficients. The observation process of MEG data, the source dynamics, and a series of the priors are combined into a Bayesian framework using a state-space representation. The parameters, such as the source amplitudes and the MAR coefficients, are jointly estimated from a variational Bayesian learning algorithm. By formulating the source dynamics in the context of MEG source reconstruction, and unifying the estimations of source amplitudes and interactions, we can identify the effective connectivity without requiring the selection of regions of interest. Our method is quantitatively and qualitatively evaluated on simulated and experimental data, respectively. Compared with non-dynamic methods, in which the interactions are estimated after source reconstruction with no dynamic constraints, the proposed dynamic method improves most of the performance measures in simulations, and provides better physiological interpretation and inter-subject consistency in real data applications. © 2014 The Authors.

Fukushima M.,Nara Institute of Science and Technology | Yamashita O.,ATR Neural Information Analysis Laboratories | Kanemura A.,ATR Neural Information Analysis Laboratories | Ishii S.,Nara Institute of Science and Technology | And 2 more authors.
IEEE Transactions on Biomedical Engineering | Year: 2012

State-space modeling is a promising approach for current source reconstruction from magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of inverse solutions in a flexible manner. However, state-space model-based source localization research remains underdeveloped; extraction of spatially focal current sources and handling of the high dimensionality of the distributed source model remain problematic. In this study, we propose a novel state-space model-based method that resolves these problems, extending our previous source localization method to include a temporal constraint by state-space modeling. To enable focal current reconstruction, we account for spatially inhomogeneous temporal dynamics by introducing dynamics model parameters that differ for each cortical position. The model parameters and the intensity of the current sources are jointly estimated according to a Bayesian framework. We circumvent the high dimensionality of the problem by assuming prior distributions of the model parameters to reduce the sensitivity to unmodeled components, and by adopting variational Bayesian inference to reduce the computational cost. Through simulation experiments and application to real MEG data, we have confirmed that our proposed method successfully reconstructs focal current activities, which evolve with their temporal dynamics. © 1964-2012 IEEE.

Hata S.,Kyoto University | Shimokawa T.,ATR Neural Information Analysis Laboratories | Arai K.,RIKEN | Nakao H.,Kyoto University | Nakao H.,Japan Science and Technology Agency
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2010

Nonlinear oscillators can mutually synchronize when they are driven by common external impulses. Two important scenarios are (i) synchronization resulting from phase locking of each oscillator to regular periodic impulses and (ii) noise-induced synchronization caused by the Poisson random impulses, but their difference has not been fully quantified. Here, we analyze a pair of uncoupled oscillators subject to common random impulses with gamma-distributed intervals, which can be smoothly interpolated between the regular periodic and the random Poisson impulses. Their dynamics are characterized by phase distributions, frequency detuning, Lyapunov exponents, and information-theoretic measures, which clearly reveal the differences between the two synchronization scenarios. © 2010 The American Physical Society.

Callan D.E.,ATR Neural Information Analysis Laboratories | Gamez M.,ATR Neural Information Analysis Laboratories | Cassel D.B.,ATR Neural Information Analysis Laboratories | Terzibas C.,ATR Neural Information Analysis Laboratories | And 3 more authors.
PLoS ONE | Year: 2012

Brain regions involved with processing dynamic visuomotor representational transformation are investigated using fMRI. The perceptual-motor task involved flying (or observing) a plane through a simulated Red Bull Air Race course in first person and third person chase perspective. The third person perspective is akin to remote operation of a vehicle. The ability for humans to remotely operate vehicles likely has its roots in neural processes related to imitation in which visuomotor transformation is necessary to interpret the action goals in an egocentric manner suitable for execution. In this experiment for 3 rd person perspective the visuomotor transformation is dynamically changing in accordance to the orientation of the plane. It was predicted that 3 rd person remote flying, over 1 st, would utilize brain regions composing the 'Mirror Neuron' system that is thought to be intimately involved with imitation for both execution and observation tasks. Consistent with this prediction differential brain activity was present for 3 rd person over 1 st person perspectives for both execution and observation tasks in left ventral premotor cortex, right dorsal premotor cortex, and inferior parietal lobule bilaterally (Mirror Neuron System) (Behaviorally: 1 st>3 rd). These regions additionally showed greater activity for flying (execution) over watching (observation) conditions. Even though visual and motor aspects of the tasks were controlled for, differential activity was also found in brain regions involved with tool use, motion perception, and body perspective including left cerebellum, temporo-occipital regions, lateral occipital cortex, medial temporal region, and extrastriate body area. This experiment successfully demonstrates that a complex perceptual motor real-world task can be utilized to investigate visuomotor processing. This approach (Aviation Cerebral Experimental Sciences ACES) focusing on direct application to lab and field is in contrast to standard methodology in which tasks and conditions are reduced to their simplest forms that are remote from daily life experience. © 2012 Callan et al.

Kanemura A.,Kyoto University | Kanemura A.,ATR Neural Information Analysis Laboratories | Maeda S.-I.,Kyoto University | Ishii S.,Kyoto University
IEEE Transactions on Image Processing | Year: 2010

We propose a framework for expanding a given image using an interpolator that is trained in advance with training data, based on sparse Bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones. © 2010 IEEE.

Singh A.K.,ATR Neural Information Analysis Laboratories | Asoh H.,ATR Neural Information Analysis Laboratories | Takeda Y.,Japan National Institute of Advanced Industrial Science and Technology | Phillips S.,Japan National Institute of Advanced Industrial Science and Technology
PLoS ONE | Year: 2015

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries. © 2015 Singh et al.

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