ATR Neural Information Analysis Laboratories

Kyoto, Japan

ATR Neural Information Analysis Laboratories

Kyoto, Japan
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Takeda Y.,ATR Neural Information Analysis Laboratories | Hiroe N.,ATR Neural Information Analysis Laboratories | Yamashita O.,ATR Neural Information Analysis Laboratories | Sato M.-A.,ATR Neural Information Analysis Laboratories
NeuroImage | Year: 2016

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. © 2016.


Morioka H.,ATR Neural Information Analysis Laboratories | Morioka H.,Kyoto University | Kanemura A.,ATR Neural Information Analysis Laboratories | Morimoto S.,ATR Neural Information Analysis Laboratories | And 5 more authors.
NeuroImage | Year: 2014

For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance. NIRS has an inherent time delay as it measures blood flow, which therefore detracts from practical real-time BMI utility. To try to improve real environment EEG-NIRS-based BMIs, we propose here a novel methodology in which the subjects' mental states are decoded from cortical currents estimated from EEG, with the help of information from NIRS. Using a Variational Bayesian Multimodal EncephaloGraphy (VBMEG) methodology, we incorporated a novel form of NIRS-based prior to capture event related desynchronization from isolated current sources on the cortical surface. Then, we applied a Bayesian logistic regression technique to decode subjects' mental states from further sparsified current sources. Applying our methodology to a spatial attention task, we found our EEG-NIRS-based decoder exhibited significant performance improvement over decoding methods based on EEG sensor signals alone. The advancement of our methodology, decoding from current sources sparsely isolated on the cortex, was also supported by neuroscientific considerations; intraparietal sulcus, a region known to be involved in spatial attention, was a key responsible region in our task. These results suggest that our methodology is not only a practical option for EEG-NIRS-based BMI applications, but also a potential tool to investigate brain activity in non-laboratory and naturalistic environments. © 2013 Elsevier Inc.


Callan D.E.,ATR Neural Information Analysis Laboratories | Terzibas C.,ATR Neural Information Analysis Laboratories | Cassel D.B.,ATR Neural Information Analysis Laboratories | Callan A.,NICT Multimodal Communication Group | And 2 more authors.
NeuroImage | Year: 2013

In this fMRI study we investigate neural processes related to the action observation network using a complex perceptual-motor task in pilots and non-pilots. The task involved landing a glider (using aileron, elevator, rudder, and dive brake) as close to a target as possible, passively observing a replay of one's own previous trial, passively observing a replay of an expert's trial, and a baseline do nothing condition. The objective of this study is to investigate two types of motor simulation processes used during observation of action: imitation based motor simulation and error-feedback based motor simulation. It has been proposed that the computational neurocircuitry of the cortex is well suited for unsupervised imitation based learning, whereas, the cerebellum is well suited for error-feedback based learning. Consistent with predictions, pilots (to a greater extent than non-pilots) showed significant differential activity when observing an expert landing the glider in brain regions involved with imitation based motor simulation (including premotor cortex PMC, inferior frontal gyrus IFG, anterior insula, parietal cortex, superior temporal gyrus, and middle temporal MT area) than when observing one's own previous trial which showed significant differential activity in the cerebellum (only for pilots) thought to be concerned with error-feedback based motor simulation. While there was some differential brain activity for pilots in regions involved with both Execution and Observation of the flying task (potential Mirror System sites including IFG, PMC, superior parietal lobule) the majority was adjacent to these areas (Observation Only Sites) (predominantly in PMC, IFG, and inferior parietal loblule). These regions showing greater activity for observation than for action may be involved with processes related to motor-based representational transforms that are not necessary when actually carrying out the task. © 2013.


De Souza A.C.S.,Federal University of Säo João del Rei | Yehia H.C.,Federal University of Minas Gerais | Sato M.-A.,ATR Neural Information Analysis Laboratories | Callan D.,ATR Neural Information Analysis Laboratories
BMC Neuroscience | Year: 2013

Background: There is an accumulating body of evidence indicating that neuronal functional specificity to basic sensory stimulation is mutable and subject to experience. Although fMRI experiments have investigated changes in brain activity after relative to before perceptual learning, brain activity during perceptual learning has not been explored. This work investigated brain activity related to auditory frequency discrimination learning using a variational Bayesian approach for source localization, during simultaneous EEG and fMRI recording. We investigated whether the practice effects are determined solely by activity in stimulus-driven mechanisms or whether high-level attentional mechanisms, which are linked to the perceptual task, control the learning process.Results: The results of fMRI analyses revealed significant attention and learning related activity in left and right superior temporal gyrus STG as well as the left inferior frontal gyrus IFG. Current source localization of simultaneously recorded EEG data was estimated using a variational Bayesian method. Analysis of current localized to the left inferior frontal gyrus and the right superior temporal gyrus revealed gamma band activity correlated with behavioral performance.Conclusions: Rapid improvement in task performance is accompanied by plastic changes in the sensory cortex as well as superior areas gated by selective attention. Together the fMRI and EEG results suggest that gamma band activity in the right STG and left IFG plays an important role during perceptual learning. © 2013 Souza et al.; licensee BioMed Central Ltd.


Yoshimura N.,Tokyo Institute of Technology | Yoshimura N.,National Institute of Neuroscience | DaSalla C.S.,National Institute of Neuroscience | Hanakawa T.,National Institute of Neuroscience | And 5 more authors.
NeuroImage | Year: 2012

The ability to reconstruct muscle activity time series from electroencephalography (EEG) may lead to drastic improvements in brain-machine interfaces (BMIs) by providing a means for realistic continuous reproduction of dexterous movements in human beings. However, it is considered difficult to isolate signals related to individual muscle activities from EEG because EEG sensors record a mixture of signals originating from many cortical regions. Here, we challenge this assumption by reconstructing agonist and antagonist muscle activities (i.e. filtered electromyography (EMG) signals) from EEG cortical currents estimated using a hierarchical Bayesian EEG inverse method. Results of 5 volunteer subjects performing isometric right wrist flexion and extension tasks showed that individual muscle activity time series, as well as muscle activities at different force levels, were well reconstructed using EEG cortical currents and with significantly higher accuracy than when directly reconstructing from EEG sensor signals. Moreover, spatial distribution of weight values for reconstruction models revealed that highly contributing cortical sources to flexion and extension tasks were mutually exclusive, even though they were mapped onto the same cortical region. These results suggest that EEG sensor signals were reasonably isolated into cortical currents using the applied method and provide the first evidence that agonist and antagonist muscle activity time series can be reconstructed using EEG cortical currents. © 2011 Elsevier Inc.


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 | 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.


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.

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