Measuring integrated information from the decoding perspective

Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, $\Phi$. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the sum of the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure $\Phi$ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system and is realized when the amount of information generated independently by its parts equals to 0. Here we derive the novel practical measure $\Phi^*$ by introducing a concept of mismatched decoding developed from information theory. We show that $\Phi^*$ is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression $\Phi^*$ under the Gaussian assumption, which makes it readily applicable to experimental data

Long-Term Asynchronous Decoding of Arm Motion Using Electrocorticographic Signals in Monkeys

Brain–machine interfaces (BMIs) employ the electrical activity generated by cortical neurons directly for controlling external devices and have been conceived as a means for restoring human cognitive or sensory-motor functions. The dominant approach in BMI research has been to decode motor variables based on single-unit activity (SUA). Unfortunately, this approach suffers from poor long-term stability and daily recalibration is normally required to maintain reliable performance. A possible alternative is BMIs based on electrocorticograms (ECoGs), which measure population activity and may provide more durable and stable recording. However, the level of long-term stability that ECoG-based decoding can offer remains unclear. Here we propose a novel ECoG-based decoding paradigm and show that we have successfully decoded hand positions and arm joint angles during an asynchronous food-reaching task in monkeys when explicit cues prompting the onset of movement were not required. Performance using our ECoG-based decoder was comparable to existing SUA-based systems while evincing far superior stability and durability. In addition, the same decoder could be used for months without any drift in accuracy or recalibration. These results were achieved by incorporating the spatio-spectro-temporal integration of activity across multiple cortical areas to compensate for the lower fidelity of ECoG signals. These results show the feasibility of high-performance, chronic and versatile ECoG-based neuroprosthetic devices for real-life applications. This new method provides a stable platform for investigating cortical correlates for understanding motor control, sensory perception, and high-level cognitive processes

Higher-Order Partial Least Squares (HOPLS) : a generalized multi-linear regression method

A new generalized multilinear regression model, termed the Higher-Order Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) Y from a tensor X through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both X and Y. Instead of decomposing X and Y individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram (ECoG) signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.Fil: Zhao, Qibin . RIKEN Brain Science Institute; JapónFil: Caiafa, Cesar Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto Argentino de Radioastronomia (i); ArgentinaFil: Mandic, Danilo P. . Imperial College Of Science And Technology; Reino UnidoFil: Chao, Zenas C. . RIKEN Brain Science Institute; JapónFil: Nagasaka, Yasuo . RIKEN Brain Science Institute; JapónFil: Fujii, Naotaka. RIKEN Brain Science Institute; JapónFil: Zhang, Liqing. Shanghai Jiao Tong University; ChinaFil: Cichocki, Andrzej. RIKEN Brain Science Institute; Japó

Multidimensional Recording (MDR) and Data Sharing: An Ecological Open Research and Educational Platform for Neuroscience

Primate neurophysiology has revealed various neural mechanisms at the single-cell level and population level. However, because recording techniques have not been updated for several decades, the types of experimental design that can be applied in the emerging field of social neuroscience are limited, in particular those involving interactions within a realistic social environment. To address these limitations and allow more freedom in experimental design to understand dynamic adaptive neural functions, multidimensional recording (MDR) was developed. MDR obtains behavioral, neural, eye position, and other biological data simultaneously by using integrated multiple recording systems. MDR gives a wide degree of freedom in experimental design because the level of behavioral restraint is adjustable depending on the experimental requirements while still maintaining the signal quality. The biggest advantage of MDR is that it can provide a stable neural signal at higher temporal resolution at the network level from multiple subjects for months, which no other method can provide. Conventional event-related analysis of MDR data shows results consistent with previous findings, whereas new methods of analysis can reveal network mechanisms that could not have been investigated previously. MDR data are now shared in the public server Neurotycho.org. These recording and sharing methods support an ecological system that is open to everyone and will be a valuable and powerful research/educational platform for understanding the dynamic mechanisms of neural networks

Dynamic Social Adaptation of Motion-Related Neurons in Primate Parietal Cortex

Social brain function, which allows us to adapt our behavior to social context, is poorly understood at the single-cell level due largely to technical limitations. But the questions involved are vital: How do neurons recognize and modulate their activity in response to social context? To probe the mechanisms involved, we developed a novel recording technique, called multi-dimensional recording, and applied it simultaneously in the left parietal cortices of two monkeys while they shared a common social space. When the monkeys sat near each other but did not interact, each monkey's parietal activity showed robust response preference to action by his own right arm and almost no response to action by the other's arm. But the preference was broken if social conflict emerged between the monkeys—specifically, if both were able to reach for the same food item placed on the table between them. Under these circumstances, parietal neurons started to show complex combinatorial responses to motion of self and other. Parietal cortex adapted its response properties in the social context by discarding and recruiting different neural populations. Our results suggest that parietal neurons can recognize social events in the environment linked with current social context and form part of a larger social brain network

Prescribing patterns of low doses of antipsychotic medications in older Asian patients with schizophrenia, 2001-2009

Background: This study examined the use of low doses of antipsychotic medications (300mg/day CPZeq or less) in older Asian patients with schizophrenia and its demographic and clinical correlates. Methods: Information on hospitalized patients with schizophrenia, aged 55 years or older, was extracted from the database of the Research on Asian Psychotropic Prescription Patterns (REAP) study (2001-2009). Data on 1,452 patients in eight Asian countries and territories including China, Hong Kong, Japan, Korea, Singapore, Taiwan, India, and Malaysia were analyzed. Sociodemographic and clinical characteristics and antipsychotic prescriptions were recorded using a standardized protocol and data collection procedure. Results: The prescription frequency for low doses of antipsychotic medications was 40.9% in the pooled sample. Multiple logistic regression analysis of the whole sample showed that patients on low doses of antipsychotic medications were more likely to be female, have an older age, a shorter length of illness, and less positive symptoms. Of patients in the six countries and territories that participated in all the surveys between 2001 and 2009, those in Japan were less likely to receive low doses of antipsychotics. Conclusion: Low doses of antipsychotic medications were only applied in less than half of older Asian patients with schizophreni

Adjunctive benzodiazepine treatment of hospitalized schizophrenia patients in Asia from 2001 to 2008

10.1017/S146114571000163XThe International Journal of Neuropsychopharmacology146735-74

るいそうに関する実態調査と今後の対策 : プロジェクトチームの結成(予報)

平成10年ごろから国民栄養調査において20歳代女性のやせ傾向が問題になっている。そこで、やせ体型の学生が自分自身が持つ健康上の問題点を理解し、現在および将来の健康な女性を目指して和洋女子大学を卒業するまでに正常体格になるように支援するためのプロジェクトチームが平成17年に結成されたので紹介する。まず、このプロジェクトを遂行するにあたって予備的な問題点を検討するために、予備研究を行った。その結果、やせ体型を示す若年女性に、月経異常、骨密度の低下が認められた。一般に、やせ体型は生活習慣病ハイリスクの低体重児を産む傾向、平均余命の低下などの問題点を抱えている。これらの問題を検討するには体組成の違いを考慮する必要があると考えられる。今後、やせ体型改善対策プロジェクトを通して、食事や運動などの指導による本格的な介入を続け、対象者が健康的なライフスタイルを身につけることによりQOLを高めると同時に、先に述べた各種の健康障害や将来の低体重児の出産率を低下させることが期待される。また、この研究成果は学生に対する健康教育と健康管理に活用できると考える