An international research collaboration has developed a VR imaging system that can measure a wide range of neural activity in the cortices of mice during active behavior. This enabled them to illuminate the abnormalities in cortical functional network dynamics that are found in autism model mice. Using machine learning, they were also able to highly accurately distinguish between autism model mice and wild-type mice based on the cortical functional network patterns when the mice start or stop running. The results of this research have been published in Cell Reports on March 28. The research group was led by Professor Toru Takumi and Assistant Professor Nobuhiro Nakai (both of the Department of Physiology, Kobe University Graduate School of Medicine), and Masaaki Sato, (Lecturer, Department of Pharmacology, Graduate School of Medicine, Hokkaido University).

Neural decoding is still a challenge and hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatial and temporal structure information, which represents the activation information of brain under external stimuli. %Therefore, the research of decoding stimuli from brain network received extensive more attention. The traditional method extracts brain network features directly from the common machine learning method, then puts these features into the classifier, and realizes to decode external stimuli. However, this method cannot effectively extract the multi-dimensional structural information, which is hidden in the brain network. The tensor researchers show that the tensor decomposition model can fully mine unique spatio-temporal structure characteristics in multi-dimensional structure data. This research proposed a stimulus constrained tensor brain model(STN)which involves the tensor decomposition idea and stimulus category constraint information. The model was verified on the real neuroimaging data sets (MEG and fMRI). The experimental results show that the STN model achieves more than 11.06% and 18.46% on accuracy matrix compared with others methods on two modal data sets. These results imply the superiority of extracting discriminative characteristics about STN model, especially for decoding object stimuli with semantic information.

We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network patterns across disease conditions. We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered neural circuits in individuals with Mild Cognitive Impairment (MCI). In addition to states shared across healthy and individuals with MCI, we discover latent states that are predominantly observed in individuals with MCI. Our model outperforms current state of the art deep learning method on ADNI2 dataset.

Tuning inside the brain is the difference between normal and super smart people, researchers have found. They say general cognitive ability may be the result of a'well-tuned brain network' - and may even be able to develop to tune up the mind of those less intelligent. They found the brains of those with higher intelligence were extremely similar at rest and while carrying out tasks. Researchers say general cognitive ability may be the result of a'well-tuned brain network' - and say they may even be able to develop a way to tune up the mind of those less intelligent. 'Specifically, we found that brain network configuration at rest was already closer to a wide variety of task configurations in intelligent individuals,' the Rutgers University team wrote in The Journal of Neuroscience.

Network analysis is a growing field across many domains, including computer vision, social media marketing, transportation networks, and intelligence analysis. The growing use of digital communication devices and platforms, as well as persistent surveillance sensors, has resulted in explosion of the quantity of data and stretched the abilities of current technologies to process this data and draw meaningful conclusions. Current tools either require significant levels of manual intervention (e.g., to prepare the data, to define patterns, or to draw conclusions from data) or are unable to generalize to new data sources and analysis needs. In this paper, we present automated solutions to two major problems in network analysis: (a) finding patterns in the network data that contains high levels of noise and irrelevant information; and (b) learning repetitive patterns and dependencies between entities and attributes. Our modeling framework represents network data using multi-attributed graphs that can encode various discrete and continuous features and relationships between network entities. The pattern search and learning model is based on probabilistic multi-attributed graph matching, and implemented using distributed message passing algorithms. Our algorithms achieved high accuracy rates in learning and finding patterns in the data, are flexible to new domains and data types, and scale to large datasets using the Map-Reduce framework.