Neural Information Analysis Laboratories

Kyoto, Japan

Neural Information Analysis Laboratories

Kyoto, Japan
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Yamashita O.,Neural Information Analysis Laboratories | Yamashita O.,Brain Functional Imaging Technologies Group | Shimokawa T.,Neural Information Analysis Laboratories | Aisu R.,Neural Information Analysis Laboratories | And 4 more authors.
NeuroImage | Year: 2016

Diffuse optical tomography (DOT) is an emerging technology for improving the spatial resolution and spatial specificity of conventional multi-channel near-infrared spectroscopy (NIRS) by the use of high-density measurements and an image reconstruction algorithm. We recently proposed a hierarchical Bayesian DOT algorithm that allows for accurate simultaneous reconstruction of scalp and cortical hemodynamic changes, and verified its performance with a phantom experiment, a computer simulation, and experimental data from one human subject. We extend our previous human case study to a multi-subject, multi-task study, to demonstrate the validity of the algorithm on a wider population and varied task conditions. We measured brain activity during three graded tasks (hand movement, index finger movement, and no-movement), in 12 subjects, using high-density NIRS and functional magnetic resonance imaging (fMRI), acquired in different sessions. The reconstruction performance of our algorithm, and the current gold-standard method for DOT image reconstruction, were evaluated using the blood-oxygenation-level-dependent (BOLD) signals of the fMRI as a reference. In comparison with the BOLD signals, our method achieved a median localization error of 6 and 8. mm, and a spatial-pattern similarity of 0.6 and 0.4 for the hand and finger tasks, respectively. It also did not reconstruct any activity in the no-movement task. Compared with the current gold-standard method, the new method showed fewer false positives, which resulted in improved spatial-pattern similarity, although the localization errors of the main activity clusters were comparable. © 2016 .


Hubbard A.L.,University of California at Los Angeles | Hubbard A.L.,Carnegie Mellon University | Hubbard A.L.,Neural Information Analysis Laboratories | Mcnealy K.,University of California at Los Angeles | And 4 more authors.
Brain and Behavior | Year: 2012

The presence of gesture during speech has been shown to impact perception, comprehension, learning, and memory in normal adults and typically developing children. In neurotypical individuals, the impact of viewing co-speech gestures representing an object and/or action (i.e., iconic gesture) or speech rhythm (i.e., beat gesture) has also been observed at the neural level. Yet, despite growing evidence of delayed gesture development in children with autism spectrum disorders (ASD), few studies have examined how the brain processes multimodal communicative cues occurring during everyday communication in individuals with ASD. Here, we used a previously validated functional magnetic resonance imaging (fMRI) paradigm to examine the neural processing of co-speech beat gesture in children with ASD and matched controls. Consistent with prior observations in adults, typically developing children showed increased responses in right superior temporal gyrus and sulcus while listening to speech accompanied by beat gesture. Children with ASD, however, exhibited no significant modulatory effects in secondary auditory cortices for the presence of co-speech beat gesture. Rather, relative to their typically developing counterparts, children with ASD showed significantly greater activity in visual cortex while listening to speech accompanied by beat gesture. Importantly, the severity of their socio-communicative impairments correlated with activity in this region, such that the more impaired children demonstrated the greatest activity in visual areas while viewing co-speech beat gesture. These findings suggest that although the typically developing brain recognizes beat gesture as communicative and successfully integrates it with co-occurring speech, information from multiple sensory modalities is not effectively integrated during social communication in the autistic brain. © 2012 The Authors. Published by Wiley Periodicals, Inc.


Yamashita O.,Neural Information Analysis Laboratories | Yamashita O.,Brain Functional Imaging Technologies Group | Shimokawa T.,Neural Information Analysis Laboratories | Kosaka T.,Neural Information Analysis Laboratories | And 3 more authors.
Journal of Advanced Computational Intelligence and Intelligent Informatics | Year: 2014

Diffuse optical tomography (DOT) is an emerging technology for improving the spatial resolution of conventional multi-channel near infrared spectroscopy (NIRS). The hemodynamics changes in two distinct anatomical layers, the scalp and the cortex, are known as the main contributor of NIRS measurement. Although any DOT algorithm has the ability to reconstruct scalp and cortical hemodynamics changes in their respective layers, no DOT algorithm has used a model characterizing the distinct nature of scalp and cortical hemodynamics changes to achieve accurate separation. Previously, we have proposed a hierarchical Bayesian model for DOT in which distinct prior distributions for the scalp and the cortical hemodynamics changes are assumed and then verified the reconstruction performance with a phantom experiment and a computer simulation of a real human head model (Shimokawa et al. 2013, Biomedical Optical Express). Here, we investigate the reconstruction accuracy of the proposed algorithm using human experimental data for the first time. We measured the brain activities of a single subject during a finger extension task with NIRS and fMRI. Our DOT reconstruction was compared with the fMRI localization results. Consequently, a remarkable consistency between fMRI and our DOT reconstruction was observed both in the spatial and temporal patterns. By extending the advantages of NIRS such as low running cost and portability with our DOT method, it might be possible to advance brain research in a real environment, which cannot be done with fMRI.


PubMed | Nara Institute of Science and Technology, Neural Information Analysis Laboratories, Brain Functional Imaging Technologies Group and Shimadzu Corporation
Type: | Journal: NeuroImage | Year: 2016

Diffuse optical tomography (DOT) is an emerging technology for improving the spatial resolution and spatial specificity of conventional multi-channel near-infrared spectroscopy (NIRS) by the use of high-density measurements and an image reconstruction algorithm. We recently proposed a hierarchical Bayesian DOT algorithm that allows for accurate simultaneous reconstruction of scalp and cortical hemodynamic changes, and verified its performance with a phantom experiment, a computer simulation, and experimental data from one human subject. We extend our previous human case study to a multi-subject, multi-task study, to demonstrate the validity of the algorithm on a wider population and varied task conditions. We measured brain activity during three graded tasks (hand movement, index finger movement, and no-movement), in 12 subjects, using high-density NIRS and functional magnetic resonance imaging (fMRI), acquired in different sessions. The reconstruction performance of our algorithm, and the current gold-standard method for DOT image reconstruction, were evaluated using the blood-oxygenation-level-dependent (BOLD) signals of the fMRI as a reference. In comparison with the BOLD signals, our method achieved a median localization error of 6 and 8mm, and a spatial-pattern similarity of 0.6 and 0.4 for the hand and finger tasks, respectively. It also did not reconstruct any activity in the no-movement task. Compared with the current gold-standard method, the new method showed fewer false positives, which resulted in improved spatial-pattern similarity, although the localization errors of the main activity clusters were comparable.


Yamashita O.,Neural Information Analysis Laboratories | Shimokawa T.,Neural Information Analysis Laboratories | Kosaka T.,Neural Information Analysis Laboratories | Sato M.-A.,Neural Information Analysis Laboratories
6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012 | Year: 2012

Diffuse optical tomography (DOT) is emerging technology to improve spatial resolution of conventional multichannel near infrared spectroscopy (NIRS). Although the scalp blood flow heavily contaminates the cerebral blood flow, all of previously proposed DOT algorithms fail to provide a way to segregate these two components. Here we propose a hierarchical Bayesian model and DOT reconstruction algorithm to segregate the cerebral blood flow from the scalp blood flow. The key idea of our method is that the different prior distributions for the scalp and cerebral blood flow are assumed based on observations that spatial distribution of scalp blood flow is broad whereas that of the cerebral blood flow is focal. Our DOT results were compared with fMRI data using human experimental data. © 2012 IEEE.

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