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IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Velioglu, Burak Vural, Fatos T. Yarman Middle East Tech Univ Comp Engn Ankara Turkey

ISBN: (纸本)9781538607718

Is there a general representation of the information content of human brain, which can be extracted from the functional magnetic resonance imaging (fMRI) data? Is it possible to learn this representation automatically from big data sets by unsupervised learning methods? Is it possible to transfer this representation to learn and decode a set of cognitive states in other fMRI data sets? This study addresses partial answers to the above questions by using transfer learning in deep architectures. First, a hierarchical representation for fMRI data is learned from a large data set in Human Connectome Project (HCP) by a 3-layered stacked denoising autoencoder (SDAE). Then, the learned representations are used to train and recognize the cognitive states recorded by a relatively small data set of one-back repetition detection experiment. Results show that, it is possible to learn a general representation and transfer the learned representation of an fMRI data set to another dataset for brain decoding problem. The learned representation has a better discriminative power compared to the Pearson correlation features. Results also show us that deep neural networks transfer representations better than factor models commonly used in pattern recognition and neuroscience literature.

关键词： Deep Neural Network Transfer Learning Feature Learning brain decoding

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MindBridge: A Cross-Subject brain decoding Framework

MindBridge: A Cross-Subject Brain Decoding Framework

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Shizun Liu, Songhua Tan, Zhenxiong Wang, Xinchao Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350353006

brain decoding, a pivotal field in neuroscience, aims to reconstruct stimuli from acquired brain signals, primarily utilizing functional magnetic resonance imaging (fMRI). Currently, brain decoding is confined to a per-subject-per-model paradigm, limiting its applicability to the same individual for whom the decoding model is trained. This constraint stems from three key challenges: 1) the inherent variability in input dimensions across subjects due to differences in brain size;2) the unique intrinsic neural patterns, influencing how different individuals perceive and process sensory information;3) limited data availability for new subjects in real-world scenarios hampers the performance of decoding models. In this paper, we present a novel approach, MindBridge, that achieves cross-subject brain decoding by employing only one model. Our proposed framework establishes a generic paradigm capable of addressing these challenges by introducing biological-inspired aggregation function and novel cyclic fMRI reconstruction mechanism for subject-invariant representation learning. Notably, by cycle reconstruction of fMRI, MindBridge can enable novel fMRI synthesis, which also can serve as pseudo data augmentation. Within the framework, we also devise a novel reset-tuning method for adapting a pretrained model to a new subject. Experimental results demonstrate MindBridge's ability to reconstruct images for multiple subjects, which is competitive with dedicated subject-specific models. Furthermore, with limited data for a new subject, we achieve a high level of decoding accuracy, surpassing that of subject-specific models. This advancement in cross-subject brain decoding suggests promising directions for wider applications in neuroscience and indicates potential for more efficient utilization of limited fMRI data in real-world scenarios. Project page: https://***/MindBridge

关键词： brain decoding Cross-subject diffusion model

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Modeling Voxel Connectivity for brain decoding 5

Modeling Voxel Connectivity for Brain Decoding

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2015 International Workshop on Pattern Recognition in NeuroImaging PRNI 2015

作者： Onal, Itir Ozay, Mete Vural, Fatos T. Yarman Middle East Tech Univ Dept Comp Engn Ankara Turkey Univ Birmingham Sch Comp Sci Birmingham W Midlands England

ISBN: (纸本)9781467371452

The massively dynamic nature of human brain cannot be represented by considering only a collection of voxel intensity values obtained from fMRI measurements. It has been observed that the degree of connectivity among voxels provide important information for modeling cognitive activities. Moreover, spatially close voxels act together to generate similar BOLD responses to the same stimuli. In this study, we propose a local mesh model, called Local Mesh Model with Temporal Measurements (LMM-TM), to first estimate spatial relationship among a set of voxels using spatial and temporal data measured at each voxel, and then employ the relationship for the construction of a connectivity model for brain decoding. For this purpose, we first construct a local mesh around each voxel (called seed voxel) by connecting it to its spatially nearest neighbors. Then, we represent the BOLD response of each seed voxel in terms of linear combination of the BOLD responses of its p-nearest neighbors. The relationship between a seed voxel and its neighbors is estimated by solving a linear regression problem. The estimated mesh arc weights are used to model local connectivity among the voxels that reside in a spatial neighborhood. Using these weights as features, we train Support Vector Machines and k-Nearest Neighbor classifiers. We test our model on a visual object recognition experiment. In the experimental analysis, we observe that classifiers that employ our features perform better than classifiers that employ raw voxel intensity values, local mesh model weights and features extracted using distance metrics such as Euclidean distance, cosine similarity and Pearson correlation.

关键词： fMRI voxel connectivity brain decoding object recognition classification

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A Spatial Deep Network Architecture For brain decoding

A Spatial Deep Network Architecture For Brain Decoding

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作者： Haroun Habeeb University of Illinois – Urbana-Champaign

学位级别：硕士

We propose the Fixed Grouping Layer (FGL); a novel feedforward layer designed to incorporate structured smoothness in a deep learning model. FGL achieves this goal by connecting nodes across layers based on spatial similarity. The inductive bias of structured smoothness implemented by FGL is motivated by applications such as brain image decoding, i.e., predicting behavior based on brain images, where scientific prior knowledge suggests that brain responses conditioned on behaviour are smoothed. Experimental results on simulated and real data is provided. Our proposed model architecture performs better than conventional neural network architectures.

关键词： Machine Learning brain decoding Deep Learning

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brain decoding of spontaneous thought: Predictive modeling of selfrelevance and valence using personal narratives

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2024年第14期121卷 e2401959121页

作者： Kim, Hong Ji Lux, Byeol Kim Lee, Eunjin Finn, Emily S. Woo, Choong- Wan Inst Basic Sci Ctr Neurosci Imaging Res Suwon 16419 South Korea Sungkyunkwan Univ Dept Biomed Engn Suwon 16419 South Korea Sungkyunkwan Univ Dept Intelligent Precis Healthcare Convergence Suwon 16419 South Korea Dartmouth Coll Dept Psychol & Brain Sci Hanover NH 03755 USA Life Inspired Neural Network Predict & Optimizat R Suwon 16419 South Korea

The contents and dynamics of spontaneous thought are important factors for personality traits and mental health. However, assessing spontaneous thoughts is challenging due to their unconstrained nature, and directing participants' attention to report their thoughts may fundamentally alter them. Here, we aimed to decode two key content dimensions of spontaneous thought-self- relevance and valence-directly from brain activity. To train functional MRI-based predictive models, we used individually generated personal stories as stimuli in a story- reading task to mimic narrative - like spontaneous thoughts (n = 49). We then tested these models on multiple test datasets (total n = 199). The default mode, ventral attention, and frontoparietal networks played key roles in the predictions, with the anterior insula and midcingulate cortex contributing to self- relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex contributing to valence prediction. Overall, this study presents brain models of internal thoughts and emotions, highlighting the potential for the brain decoding of spontaneous thought.

关键词： personal story spontaneous thought functional magnetic resonance imaging brain decoding affective neuroscience

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brain decoding OF FMRI CONNECTIVITY GRAPHS USING DECISION TREE ENSEMBLES

BRAIN DECODING OF FMRI CONNECTIVITY GRAPHS USING DECISION TR...

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IEEE International Symposium on Biomedical Imaging

作者： Jonas Richiardi Hamdi Eryilmaz Sophie Schwartz Patrik Vuilleumier Dimitri Van De Ville Medical Image Processing Lab Ecole Polytechnique Federale de Lausanne Laboratory of Neurology and Imaging of Cognition Universite de Geneve

ISBN: (纸本)9781424441259

Functional connectivity analysis of fMRI data can reveal synchronized activity between anatomically distinct brain regions. Here, we exploit the characteristic connectivity graphs of task and resting epochs to perform classification between these conditions. Our approach is based on ensembles of decision trees, which combine powerful discriminative ability with interpretability of results. This makes it possible to extract discriminative graphs that represent a subset of the connections that distinguish best between the experimental conditions. Our experimental results also show that the method can be applied for group-level brain decoding.

关键词： FMRI brain decoding Functional connectivity Graphs Decision tree

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Detecting Connected Consciousness During Propofol-Induced Anesthesia Using EEG Based brain decoding

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2021年第6期31卷 2150021页

作者： Wang, Yubo Wan, Chenghao Zhang, Yun Zhou, Yu Wang, Haidong Yan, Fei Song, Dawei Du, Ruini Wang, Qiang Huang, Liyu Xidian Univ Sch Life Sci & Technol Xian Peoples R China Xi An Jiao Tong Univ Affiliated Hosp 1 Dept Anesthesiol Xian Peoples R China Xi An Jiao Tong Univ Affiliated Hosp 1 Ctr Brain Sci Xian Peoples R China

Connected consciousness refers to the state when external stimuli can enter into the stream of our consciousness experience. Emerging evidence suggests that although patients may not respond behaviorally to external stimuli during anesthesia, they may be aware of their surroundings. In this work, we investigated whether EEG based brain decoding could be used for detecting connected consciousness in the absence of behavioral responses during propofol infusion. A total of 14 subjects participated in our experiment. Subjects were asked to discriminate two types of auditory stimuli with a finger press during an ultraslow propofol infusion. We trained an EEG based brain decoding model using data collected in the awakened state using the same auditory stimuli and tested the model on data collected during the propofol infusion. The model provided a correct classification rate (CCR) of 78.74 +/- 11.15% when subjects were able to respond to the stimuli during the propofol infusion. The CCR dropped to 67.29 +/- 8.21% when subjects ceased responding and further decreased to 58.11 +/- 8.15% when we increased the propofol concentration by another 0.2 mu g/ml. After terminating the propofol infusion, we observed that the CCR rebounded to 65.31 +/- 7.11% before the subjects regained consciousness. With the classification results, we provided evidence that loss of consciousness is a gradual process and may progress from full consciousness to connected consciousness and then to disconnected consciousness.

关键词： EEG connected consciousness brain decoding propofol

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Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model

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JOURNAL OF NEURAL ENGINEERING 2024年第4期21卷 046001-046001页

作者： Ferrante, Matteo Boccato, Tommaso Passamonti, Luca Toschi, Nicola Univ Roma Tor Vergata Dept Biomed & Prevent Rome Italy CNR Ist Bioimmagini & Fisiol Mol Milan Italy MGH Martinos Ctr Biomed Imaging Boston MA USA Harvard Med Sch Boston MA USA

Objective. brain decoding is a field of computational neuroscience that aims to infer mental states or internal representations of perceptual inputs from measurable brain activity. This study proposes a novel approach to brain decoding that relies on semantic and contextual similarity. Approach. We use several functional magnetic resonance imaging (fMRI) datasets of natural images as stimuli and create a deep learning decoding pipeline inspired by the bottom-up and top-down processes in human vision. Our pipeline includes a linear brain-to-feature model that maps fMRI activity to semantic visual stimuli features. We assume that the brain projects visual information onto a space that is homeomorphic to the latent space of last layer of a pretrained neural network, which summarizes and highlights similarities and differences between concepts. These features are categorized in the latent space using a nearest-neighbor strategy, and the results are used to retrieve images or condition a generative latent diffusion model to create novel images. Main results. We demonstrate semantic classification and image retrieval on three different fMRI datasets: Generic Object decoding (vision perception and imagination), BOLD5000, and NSD. In all cases, a simple mapping between fMRI and a deep semantic representation of the visual stimulus resulted in meaningful classification and retrieved or generated images. We assessed quality using quantitative metrics and a human evaluation experiment that reproduces the multiplicity of conscious and unconscious criteria that humans use to evaluate image similarity. Our method achieved correct evaluation in over 80% of the test set. Significance. Our study proposes a novel approach to brain decoding that relies on semantic and contextual similarity. The results demonstrate that measurable neural correlates can be linearly mapped onto the latent space of a neural network to synthesize images that match the original content. These findings have

关键词： fMRI latent brain decoding vision semantic

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Hierarchical Neural Representation of Dreamed Objects Revealed by brain decoding with Deep Neural Network Features

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2017年 11卷 4页

作者： Horikawa, Tomoyasu Kamitani, Yukiyasu Adv Telecommun Res Inst ATR Computat Neurosci Labs Kyoto Japan Kyoto Univ Grad Sch Informat Kyoto Japan

Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid-to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

关键词： dream brain decoding deep neural networks hierarchical neural representations functional magnetic resonance imaging

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On the benefits of self-taught learning for brain decoding

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GIGASCIENCE 2023年第12期12卷 giad029页

作者： Germani, Elodie Fromont, Elisa Maumet, Camille Univ Rennes Inria CNRS InsermIRISA UMR 6074Empenn ERL U 1228 F-35000 Rennes France Univ Rennes IUF CNRS IRISA UMR 6074Inria F-35000 Rennes France

Context: We study the benefits of using a large public neuroimaging database composed of functional magnetic resonance imaging (fMRI) statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. Results: We show that such a self-taught learning process always improves the performance of the classifiers, but the magnitude of the benefits strongly depends on the number of samples available both for pretraining and fine-tuning the models and on the complexity of the targeted downstream task. Conclusion: The pretrained model improves the classification performance and displays more generalizable features, less sensitive to individual differences.

关键词： self-taught learning brain decoding autoencoder convolutional neural network deep learning

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