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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. Source


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. Source


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. Source


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. Source


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. Source

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