51
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Kaiser D. Spectral brain signatures of aesthetic natural perception in the α and β frequency bands. J Neurophysiol 2022; 128:1501-1505. [PMID: 36259673 DOI: 10.1152/jn.00385.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
During our everyday lives, visual beauty is often conveyed by sustained and dynamic visual stimulation, such as when we walk through an enchanting forest or watch our pets playing. Here, I devised an MEG experiment that mimics such situations: participants viewed 8 s videos of everyday situations and rated their beauty. Using multivariate analysis, I linked aesthetic ratings to 1) sustained MEG broadband responses and 2) spectral MEG responses in the α and β frequency bands. These effects were not accounted for by a set of high- and low-level visual descriptors of the videos, suggesting that they are genuinely related to aesthetic perception. My findings provide the first characterization of spectral brain signatures linked to aesthetic experiences in the real world.NEW & NOTEWORTHY In the real world, aesthetic experiences arise from complex and dynamic inputs. This study shows that such aesthetic experiences are represented in a spectral neural code: cortical α and β activity track our judgments of the aesthetic appearance of natural videos, providing a new starting point for studying the neural correlates of beauty through rhythmic brain activity.
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Affiliation(s)
- Daniel Kaiser
- Mathematical Institute, Department of Mathematics and Computer Science, Physics, Geography, Justus-Liebig-University, Gießen, Germany.,Center for Mind, Brain and Behavior (CMBB), Philipps-University Marburg and Justus-Liebig-University Gießen, Germany
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52
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Feature-space selection with banded ridge regression. Neuroimage 2022; 264:119728. [PMID: 36334814 PMCID: PMC9807218 DOI: 10.1016/j.neuroimage.2022.119728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/05/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different feature spaces, a joint model is fit on multiple feature spaces simultaneously. To adapt regularization strength to each feature space, ridge regression is extended to banded ridge regression, which optimizes a different regularization hyperparameter per feature space. The present paper proposes a method to decompose over feature spaces the variance explained by a banded ridge regression model. It also describes how banded ridge regression performs a feature-space selection, effectively ignoring non-predictive and redundant feature spaces. This feature-space selection leads to better prediction accuracy and to better interpretability. Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms. Finally, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya.
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53
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Representations and decodability of diverse cognitive functions are preserved across the human cortex, cerebellum, and subcortex. Commun Biol 2022; 5:1245. [PMCID: PMC9663596 DOI: 10.1038/s42003-022-04221-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 11/03/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractWhich part of the brain contributes to our complex cognitive processes? Studies have revealed contributions of the cerebellum and subcortex to higher-order cognitive functions; however, it has been unclear whether such functional representations are preserved across the cortex, cerebellum, and subcortex. In this study, we use functional magnetic resonance imaging data with 103 cognitive tasks and construct three voxel-wise encoding and decoding models independently using cortical, cerebellar, and subcortical voxels. Representational similarity analysis reveals that the structure of task representations is preserved across the three brain parts. Principal component analysis visualizes distinct organizations of abstract cognitive functions in each part of the cerebellum and subcortex. More than 90% of the cognitive tasks are decodable from the cerebellum and subcortical activities, even for the novel tasks not included in model training. Furthermore, we show that the cerebellum and subcortex have sufficient information to reconstruct activity in the cerebral cortex.
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54
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Le L, Ambrogioni L, Seeliger K, Güçlütürk Y, van Gerven M, Güçlü U. Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity. Front Neurosci 2022; 16:940972. [DOI: 10.3389/fnins.2022.940972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here, we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance imaging data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction techniques.
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55
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Cavazza J, Ahmed W, Volpi R, Morerio P, Bossi F, Willemse C, Wykowska A, Murino V. Understanding action concepts from videos and brain activity through subjects' consensus. Sci Rep 2022; 12:19073. [PMID: 36351956 PMCID: PMC9646846 DOI: 10.1038/s41598-022-23067-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022] Open
Abstract
In this paper, we investigate brain activity associated with complex visual tasks, showing that electroencephalography (EEG) data can help computer vision in reliably recognizing actions from video footage that is used to stimulate human observers. Notably, we consider not only typical "explicit" video action benchmarks, but also more complex data sequences in which action concepts are only referred to, implicitly. To this end, we consider a challenging action recognition benchmark dataset-Moments in Time-whose video sequences do not explicitly visualize actions, but only implicitly refer to them (e.g., fireworks in the sky as an extreme example of "flying"). We employ such videos as stimuli and involve a large sample of subjects to collect a high-definition, multi-modal EEG and video data, designed for understanding action concepts. We discover an agreement among brain activities of different subjects stimulated by the same video footage. We name it as subjects consensus, and we design a computational pipeline to transfer knowledge from EEG to video, sharply boosting the recognition performance.
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Affiliation(s)
- Jacopo Cavazza
- grid.25786.3e0000 0004 1764 2907Pattern Analysis & Computer Vision (PAVIS), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen 83, 16152 Genova, Italy
| | - Waqar Ahmed
- grid.25786.3e0000 0004 1764 2907Pattern Analysis & Computer Vision (PAVIS), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen 83, 16152 Genova, Italy
| | - Riccardo Volpi
- grid.25786.3e0000 0004 1764 2907Pattern Analysis & Computer Vision (PAVIS), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen 83, 16152 Genova, Italy ,Naver Labs Europe, 6 Chemin de Maupertuis, Meylan, 38240 Grenoble, France
| | - Pietro Morerio
- grid.25786.3e0000 0004 1764 2907Pattern Analysis & Computer Vision (PAVIS), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen 83, 16152 Genova, Italy
| | - Francesco Bossi
- grid.462365.00000 0004 1790 9464IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 55100 Lucca, Italy ,grid.25786.3e0000 0004 1764 2907Social Cognition in Human-Robot Interaction (S4HRI), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen 83, 16152 Genova, Italy
| | - Cesco Willemse
- grid.25786.3e0000 0004 1764 2907Social Cognition in Human-Robot Interaction (S4HRI), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen 83, 16152 Genova, Italy
| | - Agnieszka Wykowska
- grid.25786.3e0000 0004 1764 2907Social Cognition in Human-Robot Interaction (S4HRI), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen 83, 16152 Genova, Italy
| | - Vittorio Murino
- grid.25786.3e0000 0004 1764 2907Pattern Analysis & Computer Vision (PAVIS), Istituto Italiano di Tecnologia (IIT), Via Enrico Melen 83, 16152 Genova, Italy ,grid.5611.30000 0004 1763 1124Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
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56
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Toneva M, Mitchell TM, Wehbe L. Combining computational controls with natural text reveals aspects of meaning composition. NATURE COMPUTATIONAL SCIENCE 2022; 2:745-757. [PMID: 36777107 PMCID: PMC9912822 DOI: 10.1038/s43588-022-00354-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
To study a core component of human intelligence-our ability to combine the meaning of words-neuroscientists have looked to linguistics. However, linguistic theories are insufficient to account for all brain responses reflecting linguistic composition. In contrast, we adopt a data-driven approach to study the composed meaning of words beyond their individual meaning, which we term 'supra-word meaning'. We construct a computational representation for supra-word meaning and study its brain basis through brain recordings from two complementary imaging modalities. Using functional magnetic resonance imaging, we reveal that hubs that are thought to process lexical meaning also maintain supra-word meaning, suggesting a common substrate for lexical and combinatorial semantics. Surprisingly, we cannot detect supra-word meaning in magnetoencephalography, which suggests that composed meaning might be maintained through a different neural mechanism than the synchronized firing of pyramidal cells. This sensitivity difference has implications for past neuroimaging results and future wearable neurotechnology.
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Affiliation(s)
- Mariya Toneva
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.,Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Max Planck Institute for Software Systems, Saarbrücken, Germany
| | - Tom M. Mitchell
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.,Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Leila Wehbe
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.,Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.,Correspondence and requests for materials should be addressed to Leila Wehbe.
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57
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Hou X, Zhao J, Zhang H. Reconstruction of perceived face images from brain activities based on multi-attribute constraints. Front Neurosci 2022; 16:1015752. [PMID: 36389231 PMCID: PMC9643433 DOI: 10.3389/fnins.2022.1015752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/10/2022] [Indexed: 11/24/2022] Open
Abstract
Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an important application in the field of brain-computer interfaces. Existing methods do not fully consider the multiple facial attributes represented in face images, and their different activity patterns at multiple brain regions are often ignored, which causes the reconstruction performance very poor. In the current study, we propose an algorithmic framework that efficiently combines multiple face-selective brain regions for precise multi-attribute perceived face reconstruction. Our framework consists of three modules: a multi-task deep learning network (MTDLN), which is developed to simultaneously extract the multi-dimensional face features attributed to facial expression, identity and gender from one single face image, a set of linear regressions (LR), which is built to map the relationship between the multi-dimensional face features and the brain signals from multiple brain regions, and a multi-conditional generative adversarial network (mcGAN), which is used to generate the perceived face images constrained by the predicted multi-dimensional face features. We conduct extensive fMRI experiments to evaluate the reconstruction performance of our framework both subjectively and objectively. The results show that, compared with the traditional methods, our proposed framework better characterizes the multi-attribute face features in a face image, better predicts the face features from brain signals, and achieves better reconstruction performance of both seen and unseen face images in both visual effects and quantitative assessment. Moreover, besides the state-of-the-art intra-subject reconstruction performance, our proposed framework can also realize inter-subject face reconstruction to a certain extent.
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Affiliation(s)
- Xiaoyuan Hou
- School of Engineering Medicine, Beihang University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Jing Zhao
- School of Engineering Medicine, Beihang University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hui Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People’s Republic of China, Beihang University, Beijing, China
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58
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Reilly C. Neuromimesis: Picturing the Humanities Picturing the Brain. Front Integr Neurosci 2022; 16:760785. [PMID: 36310715 PMCID: PMC9616043 DOI: 10.3389/fnint.2022.760785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
What do neuroscientific visualizations of mental functioning depict? This article argues that neuroscientific imaging from Santiago Ramón y Cajal’s pen and ink drawings onward falls within the mimetic tradition, that dealing with the artistic representation of reality. Cajal’s iconic images of pyramidal neurons and glial cells surprisingly suggest a non-realist approach to picturing the brain and the mind that opens a new methodological link between humanities and neurosciences. In it, aesthetic works offer a perspective on mimetic practices in neurosciences, providing insight into representational strategies that make otherwise invisible psychic phenomena observable. This approach draws needed attention to the role of metaphor in neuroscientific research. It also reimagines how interdisciplinary scholarship might engage with works of art. While it is a common practice to read humanities objects featuring the brain and/or the mind in terms of their neuroscientific content, films like The Headless Woman (La mujer sin cabeza, dir. Martel, 2008), explored here, show that doing so can easily inhibit interpretations with greater explanatory bearing. Together, Cajal’s images and Martel’s film help elaborate a fresh methodological paradigm—distinct from that of neuropsychoanalysis—that situates aesthetic objects as a long-neglected tool for studying the brain by virtue of (not despite) their imaginative investments.
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59
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Bannert MM, Bartels A. Visual cortex: Big data analysis uncovers food specificity. Curr Biol 2022; 32:R1012-R1015. [PMID: 36220088 DOI: 10.1016/j.cub.2022.08.068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In visual cortex, anatomically distinct patches respond to distinct categories, such as faces or text. New research confirms this parcellation using unsupervised analysis of functional magnetic resonance imaging data obtained from humans viewing tens of thousands of images, discovering one more preference: for food.
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Affiliation(s)
- Michael M Bannert
- Vision and Cognition Lab, Centre for Integrative Neuroscience, Department of Psychology, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Bernstein Center for Computational Neuroscience, Tübingen, Germany
| | - Andreas Bartels
- Vision and Cognition Lab, Centre for Integrative Neuroscience, Department of Psychology, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Bernstein Center for Computational Neuroscience, Tübingen, Germany.
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60
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Zhang YJ, Yu ZF, Liu JK, Huang TJ. Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches. MACHINE INTELLIGENCE RESEARCH 2022. [PMCID: PMC9283560 DOI: 10.1007/s11633-022-1335-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Vision plays a peculiar role in intelligence. Visual information, forming a large part of the sensory information, is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents. Recent advances have led to the development of brain-inspired algorithms and models for machine vision. One of the key components of these methods is the utilization of the computational principles underlying biological neurons. Additionally, advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information. Thus, there is a high demand for mapping out functional models for reading out visual information from neural signals. Here, we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals, from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography (EEG) and functional magnetic resonance imaging recordings of brain signals.
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61
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Kim SG. On the encoding of natural music in computational models and human brains. Front Neurosci 2022; 16:928841. [PMID: 36203808 PMCID: PMC9531138 DOI: 10.3389/fnins.2022.928841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
This article discusses recent developments and advances in the neuroscience of music to understand the nature of musical emotion. In particular, it highlights how system identification techniques and computational models of music have advanced our understanding of how the human brain processes the textures and structures of music and how the processed information evokes emotions. Musical models relate physical properties of stimuli to internal representations called features, and predictive models relate features to neural or behavioral responses and test their predictions against independent unseen data. The new frameworks do not require orthogonalized stimuli in controlled experiments to establish reproducible knowledge, which has opened up a new wave of naturalistic neuroscience. The current review focuses on how this trend has transformed the domain of the neuroscience of music.
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62
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Di X, Zhang Z, Xu T, Biswal BB. Dynamic and stationary brain connectivity during movie watching as revealed by functional MRI. Brain Struct Funct 2022; 227:2299-2312. [PMID: 35767066 PMCID: PMC9420792 DOI: 10.1007/s00429-022-02522-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 06/04/2022] [Indexed: 11/25/2022]
Abstract
Spatially remote brain regions show synchronized activity as typically revealed by correlated functional MRI (fMRI) signals. An emerging line of research has focused on the temporal fluctuations of connectivity; however, its relationships with stationary connectivity have not been clearly illustrated. We examined dynamic and stationary connectivity when the participants watched four different movie clips. We calculated point-by-point multiplication between two regional time series to estimate the time-resolved dynamic connectivity, and estimated the inter-individual consistency of the dynamic connectivity time series. Widespread consistent dynamic connectivity was observed for each movie clip, which also showed differences between the clips. For example, a cartoon movie clip, Wall-E, showed more consistent of dynamic connectivity with the posterior cingulate cortex and supramarginal gyrus, while a court drama clip, A Few Good Men, showed more consistent of dynamic connectivity with the auditory cortex and temporoparietal junction, which might suggest the involvement of specific brain processing for different movie contents. In contrast, the stationary connectivity as measured by the correlations between regional time series was highly similar among the movie clips, and showed fewer statistically significant differences. The patterns of consistent dynamic connectivity could be used to classify different movie clips with higher accuracy than the stationary connectivity and regional activity. These results support the functional significance of dynamic connectivity in reflecting functional brain changes, which could provide more functionally relevant information than stationary connectivity.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, Newark, NJ, 07102, USA.
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, People's Republic of China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, People's Republic of China
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, Newark, NJ, 07102, USA.
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63
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Shahdloo M, Çelik E, Urgen BA, Gallant JL, Çukur T. Task-Dependent Warping of Semantic Representations during Search for Visual Action Categories. J Neurosci 2022; 42:6782-6799. [PMID: 35863889 PMCID: PMC9436022 DOI: 10.1523/jneurosci.1372-21.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 06/29/2022] [Accepted: 07/06/2022] [Indexed: 11/21/2022] Open
Abstract
Object and action perception in cluttered dynamic natural scenes relies on efficient allocation of limited brain resources to prioritize the attended targets over distractors. It has been suggested that during visual search for objects, distributed semantic representation of hundreds of object categories is warped to expand the representation of targets. Yet, little is known about whether and where in the brain visual search for action categories modulates semantic representations. To address this fundamental question, we studied brain activity recorded from five subjects (one female) via functional magnetic resonance imaging while they viewed natural movies and searched for either communication or locomotion actions. We find that attention directed to action categories elicits tuning shifts that warp semantic representations broadly across neocortex and that these shifts interact with intrinsic selectivity of cortical voxels for target actions. These results suggest that attention serves to facilitate task performance during social interactions by dynamically shifting semantic selectivity toward target actions and that tuning shifts are a general feature of conceptual representations in the brain.SIGNIFICANCE STATEMENT The ability to swiftly perceive the actions and intentions of others is a crucial skill for humans that relies on efficient allocation of limited brain resources to prioritize the attended targets over distractors. However, little is known about the nature of high-level semantic representations during natural visual search for action categories. Here, we provide the first evidence showing that attention significantly warps semantic representations by inducing tuning shifts in single cortical voxels, broadly spread across occipitotemporal, parietal, prefrontal, and cingulate cortices. This dynamic attentional mechanism can facilitate action perception by efficiently allocating neural resources to accentuate the representation of task-relevant action categories.
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Affiliation(s)
- Mo Shahdloo
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX3 9DU, United Kingdom
- National Magnetic Resonance Research Centre, Bilkent University, 06800 Ankara, Turkey
- Departments of Electrical and Electronics Engineering and
| | - Emin Çelik
- National Magnetic Resonance Research Centre, Bilkent University, 06800 Ankara, Turkey
- Neuroscience Program, Aysel Sabuncu Brain Research Centre, Bilkent University, 06800 Ankara, Turkey
| | - Burcu A Urgen
- National Magnetic Resonance Research Centre, Bilkent University, 06800 Ankara, Turkey
- Psychology, Bilkent University, 06800 Ankara, Turkey
- Neuroscience Program, Aysel Sabuncu Brain Research Centre, Bilkent University, 06800 Ankara, Turkey
| | - Jack L Gallant
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720
| | - Tolga Çukur
- National Magnetic Resonance Research Centre, Bilkent University, 06800 Ankara, Turkey
- Departments of Electrical and Electronics Engineering and
- Neuroscience Program, Aysel Sabuncu Brain Research Centre, Bilkent University, 06800 Ankara, Turkey
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720
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Bandettini PA, Gonzalez-Castillo J, Handwerker D, Taylor P, Chen G, Thomas A. The challenge of BWAs: Unknown unknowns in feature space and variance. MED 2022; 3:526-531. [PMID: 35963233 DOI: 10.1016/j.medj.2022.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The recent paper by Marek et al.1 has shown that, to capture brain-wide associations using fMRI and MRI measures, thousands of individuals are required. These results can be potentially misunderstood to imply that MRI or fMRI lack sensitivity or specificity. This commentary discusses the demonstrated sensitivity of fMRI and focuses on methodology that may allow improvements in BWA studies. While individual variation may be an ultimate constraint, refinements in acquisition, population selection, and processing may bring about higher correlations.
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Affiliation(s)
- Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA; Functional MRI Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA.
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA
| | - Dan Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA
| | - Paul Taylor
- Scientific and Statistical Computing Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA
| | - Gang Chen
- Scientific and Statistical Computing Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA
| | - Adam Thomas
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD 20817, USA
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65
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Gaziv G, Beliy R, Granot N, Hoogi A, Strappini F, Golan T, Irani M. Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity. Neuroimage 2022; 254:119121. [PMID: 35342004 PMCID: PMC9133799 DOI: 10.1016/j.neuroimage.2022.119121] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 01/19/2022] [Accepted: 03/19/2022] [Indexed: 11/24/2022] Open
Abstract
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We present a novel self-supervised approach that goes well beyond the scarce paired data, for achieving both: (i) state-of-the art fMRI-to-image reconstruction, and (ii) first-ever large-scale semantic classification from fMRI responses. By imposing cycle consistency between a pair of deep neural networks (from image-to-fMRI & from fMRI-to-image), we train our image reconstruction network on a large number of "unpaired" natural images (images without fMRI recordings) from many novel semantic categories. This enables to adapt our reconstruction network to a very rich semantic coverage without requiring any explicit semantic supervision. Specifically, we find that combining our self-supervised training with high-level perceptual losses, gives rise to new reconstruction & classification capabilities. In particular, this perceptual training enables to classify well fMRIs of never-before-seen semantic classes, without requiring any class labels during training. This gives rise to: (i) Unprecedented image-reconstruction from fMRI of never-before-seen images (evaluated by image metrics and human testing), and (ii) Large-scale semantic classification of categories that were never-before-seen during network training. Such large-scale (1000-way) semantic classification from fMRI recordings has never been demonstrated before. Finally, we provide evidence for the biological consistency of our learned model.
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Affiliation(s)
- Guy Gaziv
- Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
| | - Roman Beliy
- Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
| | - Niv Granot
- Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
| | - Assaf Hoogi
- Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
| | | | - Tal Golan
- Zuckerman Institute, Columbia University, New York, NY USA
| | - Michal Irani
- Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
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Kamps FS, Richardson H, Murty NAR, Kanwisher N, Saxe R. Using child-friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years. Hum Brain Mapp 2022; 43:2782-2800. [PMID: 35274789 PMCID: PMC9120553 DOI: 10.1002/hbm.25815] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/11/2022] [Accepted: 02/13/2022] [Indexed: 01/21/2023] Open
Abstract
Scanning young children while they watch short, engaging, commercially-produced movies has emerged as a promising approach for increasing data retention and quality. Movie stimuli also evoke a richer variety of cognitive processes than traditional experiments, allowing the study of multiple aspects of brain development simultaneously. However, because these stimuli are uncontrolled, it is unclear how effectively distinct profiles of brain activity can be distinguished from the resulting data. Here we develop an approach for identifying multiple distinct subject-specific Regions of Interest (ssROIs) using fMRI data collected during movie-viewing. We focused on the test case of higher-level visual regions selective for faces, scenes, and objects. Adults (N = 13) were scanned while viewing a 5.6-min child-friendly movie, as well as a traditional localizer experiment with blocks of faces, scenes, and objects. We found that just 2.7 min of movie data could identify subject-specific face, scene, and object regions. While successful, movie-defined ssROIS still showed weaker domain selectivity than traditional ssROIs. Having validated our approach in adults, we then used the same methods on movie data collected from 3 to 12-year-old children (N = 122). Movie response timecourses in 3-year-old children's face, scene, and object regions were already significantly and specifically predicted by timecourses from the corresponding regions in adults. We also found evidence of continued developmental change, particularly in the face-selective posterior superior temporal sulcus. Taken together, our results reveal both early maturity and functional change in face, scene, and object regions, and more broadly highlight the promise of short, child-friendly movies for developmental cognitive neuroscience.
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Affiliation(s)
- Frederik S. Kamps
- Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Hilary Richardson
- School of Philosophy, Psychology and Language SciencesUniversity of EdinburghEdinburghUK
| | - N. Apurva Ratan Murty
- Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Nancy Kanwisher
- Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Rebecca Saxe
- Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
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67
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Zhang Y, Farrugia N, Bellec P. Deep learning models of cognitive processes constrained by human brain connectomes. Med Image Anal 2022; 80:102507. [DOI: 10.1016/j.media.2022.102507] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/13/2022] [Accepted: 05/31/2022] [Indexed: 01/02/2023]
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68
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Dima DC, Tomita TM, Honey CJ, Isik L. Social-affective features drive human representations of observed actions. eLife 2022; 11:75027. [PMID: 35608254 PMCID: PMC9159752 DOI: 10.7554/elife.75027] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 05/24/2022] [Indexed: 11/13/2022] Open
Abstract
Humans observe actions performed by others in many different visual and social settings. What features do we extract and attend when we view such complex scenes, and how are they processed in the brain? To answer these questions, we curated two large-scale sets of naturalistic videos of everyday actions and estimated their perceived similarity in two behavioral experiments. We normed and quantified a large range of visual, action-related, and social-affective features across the stimulus sets. Using a cross-validated variance partitioning analysis, we found that social-affective features predicted similarity judgments better than, and independently of, visual and action features in both behavioral experiments. Next, we conducted an electroencephalography experiment, which revealed a sustained correlation between neural responses to videos and their behavioral similarity. Visual, action, and social-affective features predicted neural patterns at early, intermediate, and late stages, respectively, during this behaviorally relevant time window. Together, these findings show that social-affective features are important for perceiving naturalistic actions and are extracted at the final stage of a temporal gradient in the brain.
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Affiliation(s)
- Diana C Dima
- Department of Cognitive Science, Johns Hopkins University, Baltimore, United States
| | - Tyler M Tomita
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, United States
| | - Christopher J Honey
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, United States
| | - Leyla Isik
- Department of Cognitive Science, Johns Hopkins University, Baltimore, United States
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69
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Fujiwara Y, Ushiba J. Deep Residual Convolutional Neural Networks for Brain-Computer Interface to Visualize Neural Processing of Hand Movements in the Human Brain. Front Comput Neurosci 2022; 16:882290. [PMID: 35669388 PMCID: PMC9165810 DOI: 10.3389/fncom.2022.882290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/19/2022] [Indexed: 11/29/2022] Open
Abstract
Concomitant with the development of deep learning, brain-computer interface (BCI) decoding technology has been rapidly evolving. Convolutional neural networks (CNNs), which are generally used as electroencephalography (EEG) classification models, are often deployed in BCI prototypes to improve the estimation accuracy of a participant's brain activity. However, because most BCI models are trained, validated, and tested via within-subject cross-validation and there is no corresponding generalization model, their applicability to unknown participants is not guaranteed. In this study, to facilitate the generalization of BCI model performance to unknown participants, we trained a model comprising multiple layers of residual CNNs and visualized the reasons for BCI classification to reveal the location and timing of neural activities that contribute to classification. Specifically, to develop a BCI that can distinguish between rest, left-hand movement, and right-hand movement tasks with high accuracy, we created multilayers of CNNs, inserted residual networks into the multilayers, and used a larger dataset than in previous studies. The constructed model was analyzed with gradient-class activation mapping (Grad-CAM). We evaluated the developed model via subject cross-validation and found that it achieved significantly improved accuracy (85.69 ± 1.10%) compared with conventional models or without residual networks. Grad-CAM analysis of the classification of cases in which our model produced correct answers showed localized activity near the premotor cortex. These results confirm the effectiveness of inserting residual networks into CNNs for tuning BCI. Further, they suggest that recording EEG signals over the premotor cortex and some other areas contributes to high classification accuracy.
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Affiliation(s)
- Yosuke Fujiwara
- Graduate School of Science and Technology, Keio University, Yokohama, Japan
- Information Services International-Dentsu, Ltd., Tokyo, Japan
| | - Junichi Ushiba
- Faculty of Science and Technology, Keio University, Yokohama, Japan
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70
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Di X, Biswal BB. Principal component analysis reveals multiple consistent responses to naturalistic stimuli in children and adults. Hum Brain Mapp 2022; 43:3332-3345. [PMID: 35586919 PMCID: PMC9248318 DOI: 10.1002/hbm.25568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 05/27/2021] [Accepted: 06/07/2021] [Indexed: 12/12/2022] Open
Abstract
Functional MRI (fMRI) study of naturalistic conditions, for example, movie watching, usually focuses on shared responses across subjects. However, individual differences have been attracting increasing attention in search of group differences or associations with behavioral outcomes. Individual differences are typically studied by directly modeling the pair‐wise intersubject correlation matrix or projecting the relations onto a single dimension. We contend that it is critical to examine whether there are one or more consistent responses underlying the whole sample, because multiple components, if exist, may undermine the intersubject relations using the previous methods. We propose to use principal component analysis (PCA) to examine the heterogeneity of brain responses across subjects and project the individual variability into higher dimensions. By analyzing an fMRI dataset of children and adults watching a cartoon movie, we showed evidence of two consistent responses in the supramarginal gyrus and other regions. While the first components in many regions represented a response pattern mostly in older children and adults, the second components mainly represented the younger children. The second components in the supramarginal network resembled a delayed version of the first PCs for 4 s (2 TR), indicating slower responses in the younger children than the older children and adults. The analyses highlight the importance of identifying multiple consistent responses in responses to naturalistic stimuli. This PCA‐based approach could be complementary to the commonly used intersubject correlation to analyze movie‐watching data.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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71
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Xu Q, Shen J, Ran X, Tang H, Pan G, Liu JK. Robust Transcoding Sensory Information With Neural Spikes. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:1935-1946. [PMID: 34665741 DOI: 10.1109/tnnls.2021.3107449] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies only aim at dealing with the continuous signal of neural systems, while lacking a unique feature of biological neurons, termed spike, which is the fundamental information unit for neural computation as well as a building block for brain-machine interface. Aiming at these limitations, we propose a transcoding framework to encode multi-modal sensory information into neural spikes and then reconstruct stimuli from spikes. Sensory information can be compressed into 10% in terms of neural spikes, yet re-extract 100% of information by reconstruction. Our framework can not only feasibly and accurately reconstruct dynamical visual and auditory scenes, but also rebuild the stimulus patterns from functional magnetic resonance imaging (fMRI) brain activities. More importantly, it has a superb ability of noise immunity for various types of artificial noises and background signals. The proposed framework provides efficient ways to perform multimodal feature representation and reconstruction in a high-throughput fashion, with potential usage for efficient neuromorphic computing in a noisy environment.
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72
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Zheng H, Yao L, Long Z. Reconstruction of 3D Images from Human Activity by a Compound Reconstruction Model. Cognit Comput 2022. [DOI: 10.1007/s12559-022-09992-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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73
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Zhang Y, Bu T, Zhang J, Tang S, Yu Z, Liu JK, Huang T. Decoding Pixel-Level Image Features from Two-Photon Calcium Signals of Macaque Visual Cortex. Neural Comput 2022; 34:1369-1397. [PMID: 35534008 DOI: 10.1162/neco_a_01498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/20/2021] [Indexed: 11/04/2022]
Abstract
Images of visual scenes comprise essential features important for visual cognition of the brain. The complexity of visual features lies at different levels, from simple artificial patterns to natural images with different scenes. It has been a focus of using stimulus images to predict neural responses. However, it remains unclear how to extract features from neuronal responses. Here we address this question by leveraging two-photon calcium neural data recorded from the visual cortex of awake macaque monkeys. With stimuli including various categories of artificial patterns and diverse scenes of natural images, we employed a deep neural network decoder inspired by image segmentation technique. Consistent with the notation of sparse coding for natural images, a few neurons with stronger responses dominated the decoding performance, whereas decoding of ar tificial patterns needs a large number of neurons. When natural images using the model pretrained on artificial patterns are decoded, salient features of natural scenes can be extracted, as well as the conventional category information. Altogether, our results give a new perspective on studying neural encoding principles using reverse-engineering decoding strategies.
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Affiliation(s)
- Yijun Zhang
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240.,Department of Computer Science and Technology, Peking University, Peking 100871, P.R.C.
| | - Tong Bu
- Department of Computer Science and Technology, Peking University, Beijing 100871, P.R.C.
| | - Jiyuan Zhang
- Department of Computer Science and Technology, Peking University, Beijing 100871, P.R.C.
| | - Shiming Tang
- School of Life Sciences and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, P.R.C.
| | - Zhaofei Yu
- Department of Computer Science and Technology and In stitute for Artificial Intelligence, Peking University, Beijing 100871, P.R.C.
| | - Jian K Liu
- School of Computing, University of Leeds, Leeds LS2 9JT, U.K.
| | - Tiejun Huang
- Department of Computer Science and Technology and Institute for Artificial Intelligence, Peking University, Beijing 100871, P.R.C.,Beijing Academy of Artificial Intelligence, Beijing 100190, P.R.C.
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74
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Katayama R, Yoshida W, Ishii S. Confidence modulates the decodability of scene prediction during partially-observable maze exploration in humans. Commun Biol 2022; 5:367. [PMID: 35440615 PMCID: PMC9018866 DOI: 10.1038/s42003-022-03314-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 03/23/2022] [Indexed: 11/23/2022] Open
Abstract
Prediction ability often involves some degree of uncertainty-a key determinant of confidence. Here, we sought to assess whether predictions are decodable in partially-observable environments where one's state is uncertain, and whether this information is sensitive to confidence produced by such uncertainty. We used functional magnetic resonance imaging-based, partially-observable maze navigation tasks in which subjects predicted upcoming scenes and reported their confidence regarding these predictions. Using a multi-voxel pattern analysis, we successfully decoded both scene predictions and subjective confidence from activities in the localized parietal and prefrontal regions. We also assessed confidence in their beliefs about where they were in the maze. Importantly, prediction decodability varied according to subjective scene confidence in the superior parietal lobule and state confidence estimated by the behavioral model in the inferior parietal lobule. These results demonstrate that prediction in uncertain environments depends on the prefrontal-parietal network within which prediction and confidence interact.
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Affiliation(s)
- Risa Katayama
- Graduate School of Informatics, Kyoto University, Kyoto, Kyoto, 606-8501, Japan.
| | - Wako Yoshida
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, OX3 9DU, UK
- Department of Neural Computation for Decision-making, Advanced Telecommunications Research Institute International, Soraku-gun, Kyoto, 619-0288, Japan
| | - Shin Ishii
- Graduate School of Informatics, Kyoto University, Kyoto, Kyoto, 606-8501, Japan
- Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute International, Soraku-gun, Kyoto, 619-0288, Japan
- International Research Center for Neurointelligence, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
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75
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Hu G, Li H, Zhao W, Hao Y, Bai Z, Nickerson LD, Cong F. Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data. Neuroimage 2022; 255:119193. [PMID: 35398543 DOI: 10.1016/j.neuroimage.2022.119193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 02/23/2022] [Accepted: 04/06/2022] [Indexed: 11/19/2022] Open
Abstract
The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. TCA then reveals spatially and temporally shared components, i.e., evoked networks with the naturalistic stimuli, their time courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with the adaptive TCA algorithm, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of the TCA algorithm. We demonstrated the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also applied the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are evoked by naturalistic movie viewing.
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Affiliation(s)
- Guoqiang Hu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
| | - Huanjie Li
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Wei Zhao
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Yuxing Hao
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Zonglei Bai
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | - Lisa D Nickerson
- Brain Imaging Center, Mclean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, Dalian, China; Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.
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76
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Farisco M, Pennartz C, Annen J, Cecconi B, Evers K. Indicators and criteria of consciousness: ethical implications for the care of behaviourally unresponsive patients. BMC Med Ethics 2022; 23:30. [PMID: 35313885 PMCID: PMC8935680 DOI: 10.1186/s12910-022-00770-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 03/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background Assessing consciousness in other subjects, particularly in non-verbal and behaviourally disabled subjects (e.g., patients with disorders of consciousness), is notoriously challenging but increasingly urgent. The high rate of misdiagnosis among disorders of consciousness raises the need for new perspectives in order to inspire new technical and clinical approaches.
Main body We take as a starting point a recently introduced list of operational indicators of consciousness that facilitates its recognition in challenging cases like non-human animals and Artificial Intelligence to explore their relevance to disorders of consciousness and their potential ethical impact on the diagnosis and healthcare of relevant patients. Indicators of consciousness mean particular capacities that can be deduced from observing the behaviour or cognitive performance of the subject in question (or from neural correlates of such performance) and that do not define a hard threshold in deciding about the presence of consciousness, but can be used to infer a graded measure based on the consistency amongst the different indicators. The indicators of consciousness under consideration offer a potential useful strategy for identifying and assessing residual consciousness in patients with disorders of consciousness, setting the theoretical stage for an operationalization and quantification of relevant brain activity. Conclusions Our heuristic analysis supports the conclusion that the application of the identified indicators of consciousness to its disorders will likely inspire new strategies for assessing three very urgent issues: the misdiagnosis of disorders of consciousness; the need for a gold standard in detecting consciousness and diagnosing its disorders; and the need for a refined taxonomy of disorders of consciousness.
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Affiliation(s)
- Michele Farisco
- Centre for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden. .,Science and Society Unit, Biogem, Biology and Molecular Genetics Research Institute, Ariano Irpino, AV, Italy.
| | - Cyriel Pennartz
- Department of Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.,Research Priority Area, Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands
| | - Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of Liege, Liege, Belgium.,Centre du Cerveau, University Hospital of Liege, Liege, Belgium
| | - Benedetta Cecconi
- Coma Science Group, GIGA-Consciousness, University of Liege, Liege, Belgium.,Centre du Cerveau, University Hospital of Liege, Liege, Belgium
| | - Kathinka Evers
- Centre for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden
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77
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Higgins C, Vidaurre D, Kolling N, Liu Y, Behrens T, Woolrich M. Spatiotemporally resolved multivariate pattern analysis for M/EEG. Hum Brain Mapp 2022; 43:3062-3085. [PMID: 35302683 PMCID: PMC9188977 DOI: 10.1002/hbm.25835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/14/2022] [Accepted: 02/27/2022] [Indexed: 11/15/2022] Open
Abstract
An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. While electroencephalography (EEG) and magnetoencephalography (MEG) offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. We instead propose the use of a generative encoding model framework that simultaneously infers the multivariate spatial patterns of activity and the variable timing at which these patterns emerge on individual trials. An encoding model inversion maps from these parameters to the equivalent decoding model, allowing predictions to be made about unseen test data in the same way as in standard decoding methodology. These SpatioTemporally Resolved MVPA (STRM) models can be flexibly applied to a wide variety of experimental paradigms, including classification and regression tasks. We show that these models provide insightful maps of the activity driving predictive accuracy metrics; demonstrate behaviourally meaningful variation in the timing of pattern emergence on individual trials; and achieve predictive accuracies that are either equivalent or surpass those achieved by more widely used methods. This provides a new avenue for investigating the brain's representational dynamics and could ultimately support more flexible experimental designs in the future.
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Affiliation(s)
- Cameron Higgins
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.,Department of Psychiatry, University of Oxford, Oxford, UK
| | - Diego Vidaurre
- Department of Psychiatry, University of Oxford, Oxford, UK.,Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Nils Kolling
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Yunzhe Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Chinese Institute for Brain Research, Beijing, China.,Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Tim Behrens
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.,Department of Psychiatry, University of Oxford, Oxford, UK
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78
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Voluntary control of semantic neural representations by imagery with conflicting visual stimulation. Commun Biol 2022; 5:214. [PMID: 35304588 PMCID: PMC8933408 DOI: 10.1038/s42003-022-03137-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 02/08/2022] [Indexed: 12/04/2022] Open
Abstract
Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery changes neural representations when imagined and perceived images semantically conflict. We hypothesized that imagining an image would activate a neural representation during its perception even while watching a conflicting image. To test this hypothesis, we developed a closed-loop system to show images inferred from electrocorticograms using a visual semantic space. The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories. Moreover, modulation of the inferred vectors by mental imagery depended asymmetrically on the perceived and imagined categories. Shared neural representation between mental imagery and perception was still activated by the imagery under semantically conflicting perceptions depending on the semantic category. In this study, intracranial EEG recordings show that neural representations of imagined images can still be present in humans even when they are shown conflicting images.
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79
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Abstract
The THINGS database is a freely available stimulus set that has the potential to facilitate the generation of theory that bridges multiple areas within cognitive neuroscience. The database consists of 26,107 high quality digital photos that are sorted into 1,854 concepts. While a valuable resource, relatively few technical details relevant to the design of studies in cognitive neuroscience have been described. We present an analysis of two key low-level properties of THINGS images, luminance and luminance contrast. These image statistics are known to influence common physiological and neural correlates of perceptual and cognitive processes. In general, we found that the distributions of luminance and contrast are in close agreement with the statistics of natural images reported previously. However, we found that image concepts are separable in their luminance and contrast: we show that luminance and contrast alone are sufficient to classify images into their concepts with above chance accuracy. We describe how these factors may confound studies using the THINGS images, and suggest simple controls that can be implemented a priori or post-hoc. We discuss the importance of using such natural images as stimuli in psychological research.
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Affiliation(s)
- William J Harrison
- Queensland Brain Institute and School of Psychology, 1974The University of Queensland
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80
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Hatanaka G, Inagaki M, Takeuchi RF, Nishimoto S, Ikezoe K, Fujita I. Processing of visual statistics of naturalistic videos in macaque visual areas V1 and V4. Brain Struct Funct 2022; 227:1385-1403. [PMID: 35286478 PMCID: PMC9046337 DOI: 10.1007/s00429-022-02468-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 02/02/2022] [Indexed: 11/25/2022]
Abstract
Natural scenes are characterized by diverse image statistics, including various parameters of the luminance histogram, outputs of Gabor-like filters, and pairwise correlations between the filter outputs of different positions, orientations, and scales (Portilla–Simoncelli statistics). Some of these statistics capture the response properties of visual neurons. However, it remains unclear to what extent such statistics can explain neural responses to natural scenes and how neurons that are tuned to these statistics are distributed across the cortex. Using two-photon calcium imaging and an encoding-model approach, we addressed these issues in macaque visual areas V1 and V4. For each imaged neuron, we constructed an encoding model to mimic its responses to naturalistic videos. By extracting Portilla–Simoncelli statistics through outputs of both filters and filter correlations, and by computing an optimally weighted sum of these outputs, the model successfully reproduced responses in a subpopulation of neurons. We evaluated the selectivities of these neurons by quantifying the contributions of each statistic to visual responses. Neurons whose responses were mainly determined by Gabor-like filter outputs (low-level statistics) were abundant at most imaging sites in V1. In V4, the relative contribution of higher order statistics, such as cross-scale correlation, was increased. Preferred image statistics varied markedly across V4 sites, and the response similarity of two neurons at individual imaging sites gradually declined with increasing cortical distance. The results indicate that natural scene analysis progresses from V1 to V4, and neurons sharing preferred image statistics are locally clustered in V4.
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Affiliation(s)
- Gaku Hatanaka
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Mikio Inagaki
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, 565-0871, Japan
- Center for Information and Neural Networks, Osaka University and National Institute of Information and Communications Technology, Suita, Osaka, 565-0871, Japan
| | - Ryosuke F Takeuchi
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Shinji Nishimoto
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, 565-0871, Japan
- Center for Information and Neural Networks, Osaka University and National Institute of Information and Communications Technology, Suita, Osaka, 565-0871, Japan
| | - Koji Ikezoe
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, 565-0871, Japan
- Center for Information and Neural Networks, Osaka University and National Institute of Information and Communications Technology, Suita, Osaka, 565-0871, Japan
- Faculty of Medicine, University of Yamanashi, Chuo, Yamanashi, 409-3898, Japan
| | - Ichiro Fujita
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, 565-0871, Japan.
- Center for Information and Neural Networks, Osaka University and National Institute of Information and Communications Technology, Suita, Osaka, 565-0871, Japan.
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81
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Qiao K, Chen J, Wang L, Zhang C, Tong L, Yan B. Reconstructing natural images from human fMRI by alternating encoding and decoding with shared autoencoder regularization. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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82
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Abstract
AbstractThis paper analyses recent calls for so called “neurorights”, suggested novel human rights whose adoption is allegedly required because of advances in neuroscience, exemplified by a proposal of the Neurorights Initiative. Advances in neuroscience and technology are indeed impressive and pose a range of challenges for the law, and some novel applications give grounds for human rights concerns. But whether addressing these concerns requires adopting novel human rights, and whether the proposed neurorights are suitable candidates, are a different matter. This paper argues that the proposed rights, as individuals and a class, should not be adopted and lobbying on their behalf should stop. The proposal tends to promote rights inflationism, is tainted by neuroexceptionalism and neuroessentialism, and lacks grounding in relevant scholarship. None of the proposed individual rights passes quality criteria debated in the field. While understandable from a moral perspective, the proposal is fundamentally flawed from a legal perspective. Rather than conjuring up novel human rights, existing rights should be further developed in face of changing societal circumstances and technological possibilities.
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83
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Ito T, Yang GR, Laurent P, Schultz DH, Cole MW. Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior. Nat Commun 2022; 13:673. [PMID: 35115530 PMCID: PMC8814166 DOI: 10.1038/s41467-022-28323-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 01/17/2022] [Indexed: 11/09/2022] Open
Abstract
The human ability to adaptively implement a wide variety of tasks is thought to emerge from the dynamic transformation of cognitive information. We hypothesized that these transformations are implemented via conjunctive activations in "conjunction hubs"-brain regions that selectively integrate sensory, cognitive, and motor activations. We used recent advances in using functional connectivity to map the flow of activity between brain regions to construct a task-performing neural network model from fMRI data during a cognitive control task. We verified the importance of conjunction hubs in cognitive computations by simulating neural activity flow over this empirically-estimated functional connectivity model. These empirically-specified simulations produced above-chance task performance (motor responses) by integrating sensory and task rule activations in conjunction hubs. These findings reveal the role of conjunction hubs in supporting flexible cognitive computations, while demonstrating the feasibility of using empirically-estimated neural network models to gain insight into cognitive computations in the human brain.
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Affiliation(s)
- Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA. .,Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, USA. .,Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
| | - Guangyu Robert Yang
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | | | - Douglas H Schultz
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
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84
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Du C, Du C, Huang L, Wang H, He H. Structured Neural Decoding With Multitask Transfer Learning of Deep Neural Network Representations. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:600-614. [PMID: 33074832 DOI: 10.1109/tnnls.2020.3028167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The reconstruction of visual information from human brain activity is a very important research topic in brain decoding. Existing methods ignore the structural information underlying the brain activities and the visual features, which severely limits their performance and interpretability. Here, we propose a hierarchically structured neural decoding framework by using multitask transfer learning of deep neural network (DNN) representations and a matrix-variate Gaussian prior. Our framework consists of two stages, Voxel2Unit and Unit2Pixel. In Voxel2Unit, we decode the functional magnetic resonance imaging (fMRI) data to the intermediate features of a pretrained convolutional neural network (CNN). In Unit2Pixel, we further invert the predicted CNN features back to the visual images. Matrix-variate Gaussian prior allows us to take into account the structures between feature dimensions and between regression tasks, which are useful for improving decoding effectiveness and interpretability. This is in contrast with the existing single-output regression models that usually ignore these structures. We conduct extensive experiments on two real-world fMRI data sets, and the results show that our method can predict CNN features more accurately and reconstruct the perceived natural images and faces with higher quality.
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85
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Wang C, Yan H, Huang W, Li J, Wang Y, Fan YS, Sheng W, Liu T, Li R, Chen H. Reconstructing Rapid Natural Vision with fMRI-Conditional Video Generative Adversarial Network. Cereb Cortex 2022; 32:4502-4511. [PMID: 35078227 DOI: 10.1093/cercor/bhab498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/24/2021] [Accepted: 12/03/2021] [Indexed: 11/14/2022] Open
Abstract
Recent functional magnetic resonance imaging (fMRI) studies have made significant progress in reconstructing perceived visual content, which advanced our understanding of the visual mechanism. However, reconstructing dynamic natural vision remains a challenge because of the limitation of the temporal resolution of fMRI. Here, we developed a novel fMRI-conditional video generative adversarial network (f-CVGAN) to reconstruct rapid video stimuli from evoked fMRI responses. In this model, we employed a generator to produce spatiotemporal reconstructions and employed two separate discriminators (spatial and temporal discriminators) for the assessment. We trained and tested the f-CVGAN on two publicly available video-fMRI datasets, and the model produced pixel-level reconstructions of 8 perceived video frames from each fMRI volume. Experimental results showed that the reconstructed videos were fMRI-related and captured important spatial and temporal information of the original stimuli. Moreover, we visualized the cortical importance map and found that the visual cortex is extensively involved in the reconstruction, whereas the low-level visual areas (V1/V2/V3/V4) showed the largest contribution. Our work suggests that slow blood oxygen level-dependent signals describe neural representations of the fast perceptual process that can be decoded in practice.
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Affiliation(s)
- Chong Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hongmei Yan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jiyi Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuting Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yun-Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Tao Liu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
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86
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Cortical traveling waves reflect state-dependent hierarchical sequencing of local regions in the human connectome network. Sci Rep 2022; 12:334. [PMID: 35013416 PMCID: PMC8748796 DOI: 10.1038/s41598-021-04169-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/14/2021] [Indexed: 11/08/2022] Open
Abstract
Recent human studies using electrocorticography have demonstrated that alpha and theta band oscillations form traveling waves on the cortical surface. According to neural synchronization theories, the cortical traveling waves may group local cortical regions and sequence them by phase synchronization; however these contributions have not yet been assessed. This study aimed to evaluate the functional contributions of traveling waves using connectome-based network modeling. In the simulation, we observed stable traveling waves on the entire cortical surface wherein the topographical pattern of these phases was substantially correlated with the empirically obtained resting-state networks, and local radial waves also appeared within the size of the empirical networks (< 50 mm). Importantly, individual regions in the entire network were instantaneously sequenced by their internal frequencies, and regions with higher intrinsic frequency were seen in the earlier phases of the traveling waves. Based on the communication-through-coherence theory, this phase configuration produced a hierarchical organization of each region by unidirectional communication between the arbitrarily paired regions. In conclusion, cortical traveling waves reflect the intrinsic frequency-dependent hierarchical sequencing of local regions, global traveling waves sequence the set of large-scale cortical networks, and local traveling waves sequence local regions within individual cortical networks.
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87
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88
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Dado T, Güçlütürk Y, Ambrogioni L, Ras G, Bosch S, van Gerven M, Güçlü U. Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space. Sci Rep 2022; 12:141. [PMID: 34997012 PMCID: PMC8741893 DOI: 10.1038/s41598-021-03938-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 11/16/2021] [Indexed: 11/24/2022] Open
Abstract
Neural decoding can be conceptualized as the problem of mapping brain responses back to sensory stimuli via a feature space. We introduce (i) a novel experimental paradigm that uses well-controlled yet highly naturalistic stimuli with a priori known feature representations and (ii) an implementation thereof for HYPerrealistic reconstruction of PERception (HYPER) of faces from brain recordings. To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by acquiring fMRI data as participants perceive face images synthesized by the generator network of a GAN. We show that the latent vectors used for generation effectively capture the same defining stimulus properties as the fMRI measurements. As such, these latents (conditioned on the GAN) are used as the in-between feature representations underlying the perceived images that can be predicted in neural decoding for (re-)generation of the originally perceived stimuli, leading to the most accurate reconstructions of perception to date.
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Affiliation(s)
- Thirza Dado
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
| | - Yağmur Güçlütürk
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Luca Ambrogioni
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Gabriëlle Ras
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Sander Bosch
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Marcel van Gerven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Umut Güçlü
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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89
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NeuroVision: perceived image regeneration using cProGAN. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06774-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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90
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Nenning KH, Langs G. Machine learning in neuroimaging: from research to clinical practice. RADIOLOGIE (HEIDELBERG, GERMANY) 2022; 62:1-10. [PMID: 36044070 PMCID: PMC9732070 DOI: 10.1007/s00117-022-01051-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 12/14/2022]
Abstract
Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.
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Affiliation(s)
- Karl-Heinz Nenning
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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91
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Functional selectivity for social interaction perception in the human superior temporal sulcus during natural viewing. Neuroimage 2021; 245:118741. [PMID: 34800663 DOI: 10.1016/j.neuroimage.2021.118741] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/15/2021] [Accepted: 11/16/2021] [Indexed: 11/22/2022] Open
Abstract
Recognizing others' social interactions is a crucial human ability. Using simple stimuli, previous studies have shown that social interactions are selectively processed in the superior temporal sulcus (STS), but prior work with movies has suggested that social interactions are processed in the medial prefrontal cortex (mPFC), part of the theory of mind network. It remains unknown to what extent social interaction selectivity is observed in real world stimuli when controlling for other covarying perceptual and social information, such as faces, voices, and theory of mind. The current study utilizes a functional magnetic resonance imaging (fMRI) movie paradigm and advanced machine learning methods to uncover the brain mechanisms uniquely underlying naturalistic social interaction perception. We analyzed two publicly available fMRI datasets, collected while both male and female human participants (n = 17 and 18) watched two different commercial movies in the MRI scanner. By performing voxel-wise encoding and variance partitioning analyses, we found that broad social-affective features predict neural responses in social brain regions, including the STS and mPFC. However, only the STS showed robust and unique selectivity specifically to social interactions, independent from other covarying features. This selectivity was observed across two separate fMRI datasets. These findings suggest that naturalistic social interaction perception recruits dedicated neural circuity in the STS, separate from the theory of mind network, and is a critical dimension of human social understanding.
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92
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Wakita S, Orima T, Motoyoshi I. Photorealistic Reconstruction of Visual Texture From EEG Signals. Front Comput Neurosci 2021; 15:754587. [PMID: 34867251 PMCID: PMC8640460 DOI: 10.3389/fncom.2021.754587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Recent advances in brain decoding have made it possible to classify image categories based on neural activity. Increasing numbers of studies have further attempted to reconstruct the image itself. However, because images of objects and scenes inherently involve spatial layout information, the reconstruction usually requires retinotopically organized neural data with high spatial resolution, such as fMRI signals. In contrast, spatial layout does not matter in the perception of "texture," which is known to be represented as spatially global image statistics in the visual cortex. This property of "texture" enables us to reconstruct the perceived image from EEG signals, which have a low spatial resolution. Here, we propose an MVAE-based approach for reconstructing texture images from visual evoked potentials measured from observers viewing natural textures such as the textures of various surfaces and object ensembles. This approach allowed us to reconstruct images that perceptually resemble the original textures with a photographic appearance. The present approach can be used as a method for decoding the highly detailed "impression" of sensory stimuli from brain activity.
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Affiliation(s)
- Suguru Wakita
- Department of Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Taiki Orima
- Department of Life Sciences, The University of Tokyo, Tokyo, Japan.,Japan Society for the Promotion of Science, Tokyo, Japan
| | - Isamu Motoyoshi
- Department of Life Sciences, The University of Tokyo, Tokyo, Japan
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93
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Wang H, Huang L, Du C, Li D, Wang B, He H. Neural Encoding for Human Visual Cortex With Deep Neural Networks Learning “What” and “Where”. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.3007761] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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94
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Polimeni JR, Lewis LD. Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response. Prog Neurobiol 2021; 207:102174. [PMID: 34525404 PMCID: PMC8688322 DOI: 10.1016/j.pneurobio.2021.102174] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 07/30/2021] [Accepted: 09/08/2021] [Indexed: 12/20/2022]
Abstract
Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.
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Affiliation(s)
- Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Laura D Lewis
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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95
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Gămănuţ R, Shimaoka D. Anatomical and functional connectomes underlying hierarchical visual processing in mouse visual system. Brain Struct Funct 2021; 227:1297-1315. [PMID: 34846596 DOI: 10.1007/s00429-021-02415-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 10/08/2021] [Indexed: 10/19/2022]
Abstract
Over the last 10 years, there has been a surge in interest in the rodent visual system resulting from the discovery of visual processing functions shared with primates V1, and of a complex anatomical structure in the extrastriate visual cortex. This surprisingly intricate visual system was elucidated by recent investigations using rapidly growing genetic tools primarily available in the mouse. Here, we examine the structural and functional connections of visual areas that have been identified in mice mostly during the past decade, and the impact of these findings on our understanding of brain functions associated with vision. Special attention is paid to structure-function relationships arising from the hierarchical organization, which is a prominent feature of the primate visual system. Recent evidence supports the existence of a hierarchical organization in rodents that contains levels that are poorly resolved relative to those observed in primates. This shallowness of the hierarchy indicates that the mouse visual system incorporates abundant non-hierarchical processing. Thus, the mouse visual system provides a unique opportunity to study non-hierarchical processing and its relation to hierarchical processing.
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Affiliation(s)
- Răzvan Gămănuţ
- Department of Physiology, Monash University, Melbourne, Australia
| | - Daisuke Shimaoka
- Department of Physiology, Monash University, Melbourne, Australia.
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96
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Ravishankar S, Toneva M, Wehbe L. Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling. Front Comput Neurosci 2021; 15:737324. [PMID: 34858157 PMCID: PMC8632362 DOI: 10.3389/fncom.2021.737324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 08/23/2021] [Indexed: 11/13/2022] Open
Abstract
A pervasive challenge in brain imaging is the presence of noise that hinders investigation of underlying neural processes, with Magnetoencephalography (MEG) in particular having very low Signal-to-Noise Ratio (SNR). The established strategy to increase MEG's SNR involves averaging multiple repetitions of data corresponding to the same stimulus. However, repetition of stimulus can be undesirable, because underlying neural activity has been shown to change across trials, and repeating stimuli limits the breadth of the stimulus space experienced by subjects. In particular, the rising popularity of naturalistic studies with a single viewing of a movie or story necessitates the discovery of new approaches to increase SNR. We introduce a simple framework to reduce noise in single-trial MEG data by leveraging correlations in neural responses across subjects as they experience the same stimulus. We demonstrate its use in a naturalistic reading comprehension task with 8 subjects, with MEG data collected while they read the same story a single time. We find that our procedure results in data with reduced noise and allows for better discovery of neural phenomena. As proof-of-concept, we show that the N400m's correlation with word surprisal, an established finding in literature, is far more clearly observed in the denoised data than the original data. The denoised data also shows higher decoding and encoding accuracy than the original data, indicating that the neural signals associated with reading are either preserved or enhanced after the denoising procedure.
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Affiliation(s)
| | - Mariya Toneva
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, United States
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Leila Wehbe
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, United States
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, United States
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97
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Popham SF, Huth AG, Bilenko NY, Deniz F, Gao JS, Nunez-Elizalde AO, Gallant JL. Visual and linguistic semantic representations are aligned at the border of human visual cortex. Nat Neurosci 2021; 24:1628-1636. [PMID: 34711960 DOI: 10.1038/s41593-021-00921-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 08/11/2021] [Indexed: 11/09/2022]
Abstract
Semantic information in the human brain is organized into multiple networks, but the fine-grain relationships between them are poorly understood. In this study, we compared semantic maps obtained from two functional magnetic resonance imaging experiments in the same participants: one that used silent movies as stimuli and another that used narrative stories. Movies evoked activity from a network of modality-specific, semantically selective areas in visual cortex. Stories evoked activity from another network of semantically selective areas immediately anterior to visual cortex. Remarkably, the pattern of semantic selectivity in these two distinct networks corresponded along the boundary of visual cortex: for visual categories represented posterior to the boundary, the same categories were represented linguistically on the anterior side. These results suggest that these two networks are smoothly joined to form one contiguous map.
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Affiliation(s)
- Sara F Popham
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Alexander G Huth
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.,Departments of Neuroscience & Computer Science, The University of Texas at Austin, Austin TX, USA
| | - Natalia Y Bilenko
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Fatma Deniz
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.,Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
| | - James S Gao
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Anwar O Nunez-Elizalde
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Jack L Gallant
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA. .,Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
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98
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Palazzo S, Spampinato C, Kavasidis I, Giordano D, Schmidt J, Shah M. Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021; 43:3833-3849. [PMID: 32750768 DOI: 10.1109/tpami.2020.2995909] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations. We then carry out image classification and saliency detection on the learned manifold. Performance analyses show that our approach satisfactorily decodes visual information from neural signals. This, in turn, can be used to effectively supervise the training of deep learning models, as demonstrated by the high performance of image classification and saliency detection on out-of-training classes. The obtained results show that the learned brain-visual features lead to improved performance and simultaneously bring deep models more in line with cognitive neuroscience work related to visual perception and attention.
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99
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Generalizable EEG Encoding Models with Naturalistic Audiovisual Stimuli. J Neurosci 2021; 41:8946-8962. [PMID: 34503996 DOI: 10.1523/jneurosci.2891-20.2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 08/24/2021] [Accepted: 08/29/2021] [Indexed: 11/21/2022] Open
Abstract
In natural conversations, listeners must attend to what others are saying while ignoring extraneous background sounds. Recent studies have used encoding models to predict electroencephalography (EEG) responses to speech in noise-free listening situations, sometimes referred to as "speech tracking." Researchers have analyzed how speech tracking changes with different types of background noise. It is unclear, however, whether neural responses from acoustically rich, naturalistic environments with and without background noise can be generalized to more controlled stimuli. If encoding models for acoustically rich, naturalistic stimuli are generalizable to other tasks, this could aid in data collection from populations of individuals who may not tolerate listening to more controlled and less engaging stimuli for long periods of time. We recorded noninvasive scalp EEG while 17 human participants (8 male/9 female) listened to speech without noise and audiovisual speech stimuli containing overlapping speakers and background sounds. We fit multivariate temporal receptive field encoding models to predict EEG responses to pitch, the acoustic envelope, phonological features, and visual cues in both stimulus conditions. Our results suggested that neural responses to naturalistic stimuli were generalizable to more controlled datasets. EEG responses to speech in isolation were predicted accurately using phonological features alone, while responses to speech in a rich acoustic background were more accurate when including both phonological and acoustic features. Our findings suggest that naturalistic audiovisual stimuli can be used to measure receptive fields that are comparable and generalizable to more controlled audio-only stimuli.SIGNIFICANCE STATEMENT Understanding spoken language in natural environments requires listeners to parse acoustic and linguistic information in the presence of other distracting stimuli. However, most studies of auditory processing rely on highly controlled stimuli with no background noise, or with background noise inserted at specific times. Here, we compare models where EEG data are predicted based on a combination of acoustic, phonetic, and visual features in highly disparate stimuli-sentences from a speech corpus and speech embedded within movie trailers. We show that modeling neural responses to highly noisy, audiovisual movies can uncover tuning for acoustic and phonetic information that generalizes to simpler stimuli typically used in sensory neuroscience experiments.
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Ki JJ, Dmochowski JP, Touryan J, Parra LC. Neural responses to natural visual motion are spatially selective across the visual field, with selectivity differing across brain areas and task. Eur J Neurosci 2021; 54:7609-7625. [PMID: 34679237 PMCID: PMC9298375 DOI: 10.1111/ejn.15503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 09/16/2021] [Accepted: 10/07/2021] [Indexed: 11/28/2022]
Abstract
It is well established that neural responses to visual stimuli are enhanced at select locations in the visual field. Although spatial selectivity and the effects of spatial attention are well understood for discrete tasks (e.g. visual cueing), little is known for naturalistic experience that involves continuous dynamic visual stimuli (e.g. driving). Here, we assess the strength of neural responses across the visual space during a kart‐race game. Given the varying relevance of visual location in this task, we hypothesized that the strength of neural responses to movement will vary across the visual field, and it would differ between active play and passive viewing. To test this, we measure the correlation strength of scalp‐evoked potentials with optical flow magnitude at individual locations on the screen. We find that neural responses are strongly correlated at task‐relevant locations in visual space, extending beyond the focus of overt attention. Although the driver's gaze is directed upon the heading direction at the centre of the screen, neural responses were robust at the peripheral areas (e.g. roads and surrounding buildings). Importantly, neural responses to visual movement are broadly distributed across the scalp, with visual spatial selectivity differing across electrode locations. Moreover, during active gameplay, neural responses are enhanced at select locations in the visual space. Conventionally, spatial selectivity of neural response has been interpreted as an attentional gain mechanism. In the present study, the data suggest that different brain areas focus attention on different portions of the visual field that are task‐relevant, beyond the focus of overt attention.
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Affiliation(s)
- Jason J Ki
- Department of Biomedical Engineering, City College of the City University of New York, New York, New York, USA
| | - Jacek P Dmochowski
- Department of Biomedical Engineering, City College of the City University of New York, New York, New York, USA
| | | | - Lucas C Parra
- Department of Biomedical Engineering, City College of the City University of New York, New York, New York, USA
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