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brain decoding of Viewed Image Categories via Semi-Supervised Multi-View Bayesian Generative Model

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 5769-5781页

作者： Akamatsu, Yusuke Harakawa, Ryosuke Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan Nagaoka Univ Technol Dept Elect Elect & Informat Engn Nagaoka Niigata 9402188 Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

brain decoding has shown that viewed image categories can be estimated from evoked functional magnetic resonance imaging (fMRI) activity. Recent studies attempted to estimate viewed image categories that were not used for training previously. Nevertheless, the estimation performance is limited since it is difficult to collect a large amount of fMRI data for training. This paper presents a method to accurately estimate viewed image categories not used for training via a semi-supervised multi-view Bayesian generative model. Our model focuses on the relationship between fMRI activity and multiple modalities, i.e., visual features extracted from viewed images and semantic features obtained from viewed image categories. Furthermore, in order to accurately estimate image categories not used for training, our semi-supervised framework incorporates visual and semantic features obtained from additional image categories in addition to image categories of training data. The estimation performance of the proposed model outperforms existing state-of-the-art models in the brain decoding field and achieves more than 95% identification accuracy. The results also have shown that the incorporation of additional image category information is remarkably effective when the number of training samples is small. Our semi-supervised framework is significant for the brain decoding field where brain activity patterns are insufficient but visual stimuli are sufficient.

关键词： brain decoding functional magnetic resonance imaging (fMRI) generative model bayesian inference multi-view learning semi-supervised learning

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brain decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model

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SENSORS 2022年第16期22卷 999-1009页

作者： Higashi, Takaaki Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Kita Ku N-14W-9 Sapporo Hokkaido 0600814 Japan Hokkaido Univ Fac Informat Sci & Technol Kita Ku N-14W-9 Sapporo Hokkaido 0600814 Japan

brain decoding is a process of decoding human cognitive contents from brain activities. However, improving the accuracy of brain decoding remains difficult due to the unique characteristics of the brain, such as the small sample size and high dimensionality of brain activities. Therefore, this paper proposes a method that effectively uses multi-subject brain activities to improve brain decoding accuracy. Specifically, we distinguish between the shared information common to multi-subject brain activities and the individual information based on each subject's brain activities, and both types of information are used to decode human visual cognition. Both types of information are extracted as features belonging to a latent space using a probabilistic generative model. In the experiment, an publicly available dataset and five subjects were used, and the estimation accuracy was validated on the basis of a confidence score ranging from 0 to 1, and a large value indicates superiority. The proposed method achieved a confidence score of 0.867 for the best subject and an average of 0.813 for the five subjects, which was the best compared to other methods. The experimental results show that the proposed method can accurately decode visual cognition compared with other existing methods in which the shared information is not distinguished from the individual information.

关键词： brain decoding functional magnetic resonance imaging (fMRI) multiple subjects visual features generative model

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brain decoding: Opportunities and challenges for pattern recognition

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PATTERN RECOGNITION 2012年第6期45卷 2033-2034页

作者： De Ville, Dimitri Van Lee, Seong-Whan Univ Geneva Dept Radiol & Med Informat CH-1211 Geneva 4 Switzerland Ecole Polytech Fed Lausanne Inst Bioengn CH-1015 Lausanne Switzerland Korea Univ Dept Brain & Cognit Engn Seoul South Korea

The neuroimaging community heavily relies on statistical inference to explain measured brain activity given the experimental paradigm. Undeniably, this method has led to many results, but it is limited by the richness of the generative models that are deployed, typically in a mass-univariate way. Such an approach is suboptimal given the high-dimensional and complex spatiotemporal correlation structure of neuroimaging data. Over the recent years, techniques from pattern recognition have brought new insights into where and how information is stored in the brain by prediction of the stimulus or state from the data. Pattern recognition is intrinsically multivariate and the underlying models are data-driven. Moreover, the predictive setting is more powerful for many applications, including clinical diagnosis and brain-computer interfacing. This special issue features a number of papers that identify and tackle remaining challenges in this field. The specific problems at hand constitute opportunities for future research in pattern recognition and neurosciences. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： brain decoding Neuroimaging Functional magnetic resonance imaging Electroencephalography brain-computer interface

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Memory recall: Retrieval-Augmented mind reconstruction for brain decoding

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Information Fusion 2025年 123卷

作者： Yuxiao Zhao Guohua Dong Lei Zhu Xiaomin Ying School of Information Science and Engineering Shandong Normal University Jinan 250358 China Center for Computational Biology Beijing Institute of Basic Medical Sciences Beijing 100850 China School of Computer Science and Technology Tongji University Shanghai 200092 China

Reconstructing visual stimuli from functional magnetic resonance imaging (fMRI) is a complex challenge in neuroscience. Most existing approaches rely on mapping neural signals to pretrained models to generate latent variables, which are then used to reconstruct images via a diffusion model. However, this multi-step process can result in the loss of crucial semantic details, limiting reconstruction accuracy. In this paper, we introduce a novel brain decoding framework, called Memory Recall (MR), inspired by bionic brain mechanisms. MR mimics the human visual perception process, where the brain retrieves stored visual experiences to compensate for incomplete visual cues. Initially, low- and high-level visual cues are extracted using spatial mapping techniques based on VAE and CLIP, replicating the brain’s ability to interpret degraded stimuli. A visual experience database is then created to retrieve complementary information that enriches these high-level representations, simulating the brain’s memory retrieval process. Finally, an Attentive Visual Signal Fusion Network (AVSFN) with a novel attention scoring mechanism integrates the retrieved information, enhancing the generative model’s performance and emulating the brain’s refinement of visual perception. Experimental results show that MR outperforms state-of-the-art models across multiple evaluation metrics and subjective assessments. Moreover, our results provide new evidence supporting a well-known psychological conclusion that the basic information capacity of short-term memory is four items, further demonstrating the informativeness and interpretability of our model.

关键词： Functional magnetic resonance imaging (fMRI) brain decoding Visual experiences Visual stimulus reconstruction

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brain decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks 1

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21st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM) / International Workshop on Computational Diffusion MRI (CDMRI)

作者： Li, Hongming Fan, Yong Univ Penn Dept Radiol Perelman Sch Med Ctr Biomed Image Comp & Analyt Philadelphia PA 19104 USA

ISBN: (数字)9783030009311

ISBN: (纸本)9783030009311;9783030009304

decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies. Promising performance has been achieved using brain functional connectivity or brain activation signatures for a variety of brain decoding tasks. However, most of existing studies have built decoding models upon features extracted from imaging data at individual time points or temporal windows with a fixed interval, which might not be optimal across different cognitive processes due to varying temporal durations and dependency of different cognitive processes. In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, functional profiles extracted from task functional imaging data based on their corresponding subject-specific intrinsic functional networks are used as features to build brain decoding models, and LSTM RNNs are adopted to learn decoding mappings between functional profiles and brain states. We evaluate the proposed method using task fMRI data from the HCP dataset, and experimental results have demonstrated that the proposed method could effectively distinguish brain states under different task events and obtain higher accuracy than conventional decoding models.

关键词： brain decoding Recurrent neural networks Long short-term memory

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Cross recurrence quantifiers as new connectivity measures for structure learning of Bayesian networks in brain decoding

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CHAOS SOLITONS & FRACTALS 2019年 123卷 263-274页

作者： Yargholi, E. Hossein-Zadeh, G-A Univ Tehran Univ Coll Engn Sch Elect & Comp Engn POB 14395-515 Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci POB 19395-5746 Tehran Iran

Bayesian networks were efficiently applied for brain decoding along with connectivity information used in structure learning of Bayesian networks. The modified structure learning proposed expands the application of Bayesian networks in brain-decoding. There are various structure learning methods which usually require large training samples. As the simplest method, tree structure learning commonly uses measures like correlation or mutual information to quantify dependencies in a network. Correlation captures the linear relations and mutual information detects linear and nonlinear dependencies while it needs more data to estimate the distribution of the variables. In current study, these measures were substituted with cross recurrence quantifiers which efficiently describes nonlinear behaviors of biological systems and effectively detect linear and nonlinear interactions without requiring long length of data. As novel applications, we used cross recurrence quantifiers for structure learning of Bayesian networks and functional connectivity analysis of fMRI data. To compare the performance of cross recurrence quantifiers with conventional indices in learning the structure of Bayesian networks, we used them in retrieving known structures. Moreover, the new method was applied for extracting fMRI brain connections in Bayesian networks devised for decoding-reconstruction of hand written digits. Objective and subjective assessment of results showed that Bayesian networks which used correlation or mutual information yielded significantly inferior accuracy in comparison to Bayesian networks which employed cross recurrence quantifiers. Therefore, cross recurrence quantifiers could be efficient measures in structure learning of Bayesian networks or functional connectivity analysis of fMRI data. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： brain decoding brain connectivity Cross recurrence quantifier Bayesian network Structure learning

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A New Representation of fMRI Signal by a Set of Local Meshes for brain decoding

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2017年第4期3卷 683-694页

作者： Onal, Itir Ozay, Mete Mizrak, Eda Oztekin, Ilke Vural, Fatos T. Yarman Middle East Tech Univ Dept Comp Engn TR-06800 Ankara Turkey Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808577 Japan Koc Univ Dept Psychol TR-34450 Istanbul Turkey

How neurons influence each other's firing depends on the strength of synaptic connections among them. Motivated by the highly interconnected structure of the brain, in this study, we propose a computational model to estimate the relationships among voxels and employ them as features for cognitive state classification. We represent the sequence of functional Magnetic Resonance Imaging (fMRI) measurements recorded during a cognitive stimulus by a set of local meshes. Then, we represent the corresponding cognitive state by the edge weights of these meshes each of which is estimated assuming a regularized linear relationship among voxel time series in a predefined locality. The estimated mesh edge weights provide a better representation of information in the brain for cognitive state or task classification. We examine the representative power of ourmesh edge weights on visual recognition and emotional memory retrieval experiments by training a support vector machine classifier. Also, we use mesh edge weights as feature vectors of inter-subject classification onHuman Connectome Project task fMRI dataset, and test their performance. We observe that mesh edge weights perform better than the popular fMRI features, such as, raw voxel intensity values, pairwise correlations, features extracted using PCA and ICA, for classifying the cognitive states.

关键词： brain decoding classification functional magnetic resonance imaging (fMRI) voxel connectivity

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A Novel brain decoding Method: A Correlation Network Framework for Revealing brain Connections

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2019年第1期11卷 95-106页

作者： Yu, Siyu Zheng, Nanning Ma, Yongqiang Wu, Hao Chen, Badong Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China

brain decoding is a hot spot in cognitive science, which focuses on reconstructing perceptual images from brain activities. Analyzing the correlations of collected data from human brain activities and representing activity patterns are two key problems in brain decoding based on functional magnetic resonance imaging signals. However, existing correlation analysis methods mainly focus on the strength information of voxel, which reveals functional connectivity in the cerebral cortex. They tend to neglect the structural information that implies the intra-cortical or intrinsic connections;that is, structural connectivity. Hence, the effective connectivity inferred by these methods is relatively unilateral. Therefore, we propose in this paper a correlation network (CorrNet) framework that could he flexibly combined with diverse pattern representation models. In the CorrNet framework, the topological correlation is introduced to reveal structural information. Rich correlations can be obtained, which contribute to specifying the underlying effective connectivity. We also combine the CorrNet framework with a linear support vector machine and a dynamic evolving spike neuron network for pattern representation separately, thus provide a novel method for decoding cognitive activity patterns. Experimental results verify the reliability and robustness of our CorrNet framework, and demonstrate that the new method can achieve significant improvement in brain decoding over comparable methods.

关键词： brain decoding connection correlation network (CorrNet) framework functional magnetic resonance imaging (fMRI) pattern representation

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brain decoding via Graph Kernels

Brain Decoding via Graph Kernels

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3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： Vega-Pons, Sandro Avesani, Paolo Fdn Bruno Kessler NeuroInformat Lab NILab Trento Italy

ISBN: (纸本)9780769550619

An emergent trend in data analysis of functional brain recordings is based on multivariate pattern recognition. Unlike univariate approaches, it is designed as a prediction task by decoding the brain state. fMRI brain decoding is a challenging classification problem due to the noisy, redundant and spatio-temporal correlated data, where there are generally much more features than samples. The use of a classifier requires that raw data is mapped into n-dimensional real vectors where the structural information of the data is not taken into account. Alternative methods propose a different data representation based on a graph encoding. While graphs provide a more powerful representation, machine learning algorithms for this type of encoding become computationally intensive. The contribution of this paper is the introduction of a graph kernel with a lower computational complexity that allows taking advantage from both the representative power of graphs and the discrimination power of kernel-based classifiers such as Support Vector Machines. We provide experimental results for a discrimination task between faces and houses on a fMRI dataset. We also investigate on synthetic data, how the brain decoding task differs according to the different encodings: vectorial and graph-based. A remarkable feature of the graph approach is its capability to handle data from different subjects, without the need of any intersubject alignment. An intersubject decoding experiment is also performed for the faces versus houses problem.

关键词： brain decoding connectivity graphs graph kernels

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brain decoding for brain Mapping: Definition, Heuristic Quantification, and Improvement of Interpretability in Group MEG decoding

Brain Decoding for Brain Mapping: Definition, Heuristic Quan...

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作者： Seyed Mostafa Kia University of Trento

学位级别：博士

In the last century, a huge multi–disciplinary scientific endeavor is de- voted to answer the historical questions in understanding the brain func- tions. Among the statistical methods used for this purpose, brain decoding provides a tool to predict the mental state of a human subject based on the recorded brain signal. brain decoding is widely applied in the contexts of brain–computer interfacing, medical diagnosis, and multivariate hypothesis testing on neuroimaging data. In the latest case, linear classifiers are gen- erally employed to discriminate between experimental conditions. Then, the derived weights are visualized in the form of brain maps to further study the spatio–temporal patterns of the underlying neurophysiological activity. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal–to–noise ratio, across–subject variability, and the high dimension- ality of the neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of differ- ent brain decoding methods. In this thesis, as the primary contribution, we propose a theoretical definition of interpretability in linear brain decoding; we show that the interpretability of multivariate brain maps can be decom- posed into their reproducibility and representativeness. As an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. We propose to combine the approximated interpretability and the generalization performance of the model into a new multi–objective criterion for model selection.

关键词： brain decoding brain Mapping Neuroimaging Machine Learning Magnetoencephalography Interpretability Reproducibility

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