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Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, Donohue MR, Foran W, Miller RL, Hendrickson TJ, Malone SM, Kandala S, Feczko E, Miranda-Dominguez O, Graham AM, Earl EA, Perrone AJ, Cordova M, Doyle O, Moore LA, Conan GM, Uriarte J, Snider K, Lynch BJ, Wilgenbusch JC, Pengo T, Tam A, Chen J, Newbold DJ, Zheng A, Seider NA, Van AN, Metoki A, Chauvin RJ, Laumann TO, Greene DJ, Petersen SE, Garavan H, Thompson WK, Nichols TE, Yeo BTT, Barch DM, Luna B, Fair DA, Dosenbach NUF. Reproducible brain-wide association studies require thousands of individuals. Nature 2022; 603:654-660. [PMID: 35296861 PMCID: PMC8991999 DOI: 10.1038/s41586-022-04492-9] [Citation(s) in RCA: 830] [Impact Index Per Article: 415.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/31/2022] [Indexed: 02/01/2023]
Abstract
Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.
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Affiliation(s)
- Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA.
| | - Brenden Tervo-Clemmens
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Meghan Rose Donohue
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - William Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ryland L Miller
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy J Hendrickson
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Stephen M Malone
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Eric A Earl
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Anders J Perrone
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Michaela Cordova
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Lucille A Moore
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Gregory M Conan
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Johnny Uriarte
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Kathy Snider
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Benjamin J Lynch
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - James C Wilgenbusch
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Thomas Pengo
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Angela Tam
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health, Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health, Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Nicole A Seider
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Roselyne J Chauvin
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Steven E Petersen
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Wesley K Thompson
- Division of Biostatistics, University of California San Diego, La Jolla, CA, USA
| | - Thomas E Nichols
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health, Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
| | - Beatriz Luna
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA.
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA.
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA.
- Department of Radiology, Washington University School of Medicine, St Louis, MO, USA.
- Program in Occupational Therapy, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA.
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102
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Alvarez-Gonzalez R, Mendez-Vazquez A. Deep Learning Architecture Reduction for fMRI Data. Brain Sci 2022; 12:brainsci12020235. [PMID: 35203997 PMCID: PMC8870362 DOI: 10.3390/brainsci12020235] [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: 12/16/2021] [Accepted: 01/12/2022] [Indexed: 11/16/2022] Open
Abstract
In recent years, deep learning models have demonstrated an inherently better ability to tackle non-linear classification tasks, due to advances in deep learning architectures. However, much remains to be achieved, especially in designing deep convolutional neural network (CNN) configurations. The number of hyper-parameters that need to be optimized to achieve accuracy in classification problems increases with every layer used, and the selection of kernels in each CNN layer has an impact on the overall CNN performance in the training stage, as well as in the classification process. When a popular classifier fails to perform acceptably in practical applications, it may be due to deficiencies in the algorithm and data processing. Thus, understanding the feature extraction process provides insights to help optimize pre-trained architectures, better generalize the models, and obtain the context of each layer’s features. In this work, we aim to improve feature extraction through the use of a texture amortization map (TAM). An algorithm was developed to obtain characteristics from the filters amortizing the filter’s effect depending on the texture of the neighboring pixels. From the initial algorithm, a novel geometric classification score (GCS) was developed, in order to obtain a measure that indicates the effect of one class on another in a classification problem, in terms of the complexity of the learnability in every layer of the deep learning architecture. For this, we assume that all the data transformations in the inner layers still belong to a Euclidean space. In this scenario, we can evaluate which layers provide the best transformations in a CNN, allowing us to reduce the weights of the deep learning architecture using the geometric hypothesis.
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103
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Du B, Cheng X, Duan Y, Ning H. fMRI Brain Decoding and Its Applications in Brain-Computer Interface: A Survey. Brain Sci 2022; 12:228. [PMID: 35203991 PMCID: PMC8869956 DOI: 10.3390/brainsci12020228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/29/2022] [Accepted: 01/30/2022] [Indexed: 11/25/2022] Open
Abstract
Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain-computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.
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Affiliation(s)
- Bing Du
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; (B.D.); (X.C.)
| | - Xiaomu Cheng
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; (B.D.); (X.C.)
| | - Yiping Duan
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
| | - Huansheng Ning
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; (B.D.); (X.C.)
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104
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Zhang J, Li C, Liu G, Min M, Wang C, Li J, Wang Y, Yan H, Zuo Z, Huang W, Chen H. A CNN-transformer hybrid approach for decoding visual neural activity into text. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106586. [PMID: 34963092 DOI: 10.1016/j.cmpb.2021.106586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/19/2021] [Accepted: 12/12/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Most studies used neural activities evoked by linguistic stimuli such as phrases or sentences to decode the language structure. However, compared to linguistic stimuli, it is more common for the human brain to perceive the outside world through non-linguistic stimuli such as natural images, so only relying on linguistic stimuli cannot fully understand the information perceived by the human brain. To address this, an end-to-end mapping model between visual neural activities evoked by non-linguistic stimuli and visual contents is demanded. METHODS Inspired by the success of the Transformer network in neural machine translation and the convolutional neural network (CNN) in computer vision, here a CNN-Transformer hybrid language decoding model is constructed in an end-to-end fashion to decode functional magnetic resonance imaging (fMRI) signals evoked by natural images into descriptive texts about the visual stimuli. Specifically, this model first encodes a semantic sequence extracted by a two-layer 1D CNN from the multi-time visual neural activity into a multi-level abstract representation, then decodes this representation, step by step, into an English sentence. RESULTS Experimental results show that the decoded texts are semantically consistent with the corresponding ground truth annotations. Additionally, by varying the encoding and decoding layers and modifying the original positional encoding of the Transformer, we found that a specific architecture of the Transformer is required in this work. CONCLUSIONS The study results indicate that the proposed model can decode the visual neural activities evoked by natural images into descriptive text about the visual stimuli in the form of sentences. Hence, it may be considered as a potential computer-aided tool for neuroscientists to understand the neural mechanism of visual information processing in the human brain in the future.
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Affiliation(s)
- Jiang Zhang
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Chen Li
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Ganwanming Liu
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Min Min
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Chong Wang
- 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; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jiyi Li
- 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
| | - Yuting Wang
- 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; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hongmei Yan
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Beijing MR Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wei Huang
- 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; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Huafu Chen
- 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; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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105
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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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106
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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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108
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Pramod RT, Arun SP. Improving Machine Vision Using Human Perceptual Representations: The Case of Planar Reflection Symmetry for Object Classification. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:228-241. [PMID: 32750809 PMCID: PMC7611439 DOI: 10.1109/tpami.2020.3008107] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Achieving human-like visual abilities is a holy grail for machine vision, yet precisely how insights from human vision can improve machines has remained unclear. Here, we demonstrate two key conceptual advances: First, we show that most machine vision models are systematically different from human object perception. To do so, we collected a large dataset of perceptual distances between isolated objects in humans and asked whether these perceptual data can be predicted by many common machine vision algorithms. We found that while the best algorithms explain ∼ 70 percent of the variance in the perceptual data, all the algorithms we tested make systematic errors on several types of objects. In particular, machine algorithms underestimated distances between symmetric objects compared to human perception. Second, we show that fixing these systematic biases can lead to substantial gains in classification performance. In particular, augmenting a state-of-the-art convolutional neural network with planar/reflection symmetry scores along multiple axes produced significant improvements in classification accuracy (1-10 percent) across categories. These results show that machine vision can be improved by discovering and fixing systematic differences from human vision.
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109
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Allen EJ, St-Yves G, Wu Y, Breedlove JL, Prince JS, Dowdle LT, Nau M, Caron B, Pestilli F, Charest I, Hutchinson JB, Naselaris T, Kay K. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat Neurosci 2022; 25:116-126. [PMID: 34916659 DOI: 10.1038/s41593-021-00962-x] [Citation(s) in RCA: 95] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 10/12/2021] [Indexed: 11/09/2022]
Abstract
Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.
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Affiliation(s)
- Emily J Allen
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Ghislain St-Yves
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Yihan Wu
- Graduate Program in Cognitive Science, University of Minnesota, Minneapolis, MN, USA
| | - Jesse L Breedlove
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Jacob S Prince
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Logan T Dowdle
- Department of Neuroscience, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
- Department of Neurosurgery, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Matthias Nau
- National Institute of Mental Health (NIMH), Bethesda MD, USA
| | - Brad Caron
- Program in Neuroscience, Indiana University, Bloomington IN, USA
- Program in Vision Science, Indiana University, Bloomington IN, USA
| | - Franco Pestilli
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA
- Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA
| | - Ian Charest
- Center for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
- cerebrUM, Département de Psychologie, Université de Montréal, Montréal QC, Canada
| | | | - Thomas Naselaris
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Kendrick Kay
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
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110
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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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111
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Rakhimberdina Z, Jodelet Q, Liu X, Murata T. Natural Image Reconstruction From fMRI Using Deep Learning: A Survey. Front Neurosci 2021; 15:795488. [PMID: 34987359 PMCID: PMC8722107 DOI: 10.3389/fnins.2021.795488] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 11/23/2021] [Indexed: 11/17/2022] Open
Abstract
With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain. One of the most challenging brain decoding tasks is the accurate reconstruction of the perceived natural images from brain activities measured by functional magnetic resonance imaging (fMRI). In this work, we survey the most recent deep learning methods for natural image reconstruction from fMRI. We examine these methods in terms of architectural design, benchmark datasets, and evaluation metrics and present a fair performance evaluation across standardized evaluation metrics. Finally, we discuss the strengths and limitations of existing studies and present potential future directions.
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Affiliation(s)
- Zarina Rakhimberdina
- Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan
- AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, Tokyo, Japan
| | - Quentin Jodelet
- Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan
- AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, Tokyo, Japan
| | - Xin Liu
- AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, Tokyo, Japan
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
- Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Tsuyoshi Murata
- Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan
- AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, Tokyo, Japan
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112
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Kupers ER, Benson NC, Winawer J. A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex. Neuroimage 2021; 245:118655. [PMID: 34687857 PMCID: PMC8788390 DOI: 10.1016/j.neuroimage.2021.118655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 10/11/2021] [Indexed: 01/23/2023] Open
Abstract
Synchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.
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Affiliation(s)
- Eline R Kupers
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; Department of Psychology, Stanford University, Stanford, CA 94305, United States.
| | - Noah C Benson
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; eSciences Institute, University of Washington, Seattle, WA 98195, United States
| | - Jonathan Winawer
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States
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113
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Moerel M, Yacoub E, Gulban OF, Lage-Castellanos A, De Martino F. Using high spatial resolution fMRI to understand representation in the auditory network. Prog Neurobiol 2021; 207:101887. [PMID: 32745500 PMCID: PMC7854960 DOI: 10.1016/j.pneurobio.2020.101887] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/27/2020] [Accepted: 07/15/2020] [Indexed: 12/23/2022]
Abstract
Following rapid methodological advances, ultra-high field (UHF) functional and anatomical magnetic resonance imaging (MRI) has been repeatedly and successfully used for the investigation of the human auditory system in recent years. Here, we review this work and argue that UHF MRI is uniquely suited to shed light on how sounds are represented throughout the network of auditory brain regions. That is, the provided gain in spatial resolution at UHF can be used to study the functional role of the small subcortical auditory processing stages and details of cortical processing. Further, by combining high spatial resolution with the versatility of MRI contrasts, UHF MRI has the potential to localize the primary auditory cortex in individual hemispheres. This is a prerequisite to study how sound representation in higher-level auditory cortex evolves from that in early (primary) auditory cortex. Finally, the access to independent signals across auditory cortical depths, as afforded by UHF, may reveal the computations that underlie the emergence of an abstract, categorical sound representation based on low-level acoustic feature processing. Efforts on these research topics are underway. Here we discuss promises as well as challenges that come with studying these research questions using UHF MRI, and provide a future outlook.
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Affiliation(s)
- Michelle Moerel
- Maastricht Centre for Systems Biology, Maastricht University, Maastricht, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Maastricht Brain Imaging Center (MBIC), Maastricht, the Netherlands.
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA.
| | - Omer Faruk Gulban
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Maastricht Brain Imaging Center (MBIC), Maastricht, the Netherlands; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA; Brain Innovation B.V., Maastricht, the Netherlands.
| | - Agustin Lage-Castellanos
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Maastricht Brain Imaging Center (MBIC), Maastricht, the Netherlands; Department of NeuroInformatics, Cuban Center for Neuroscience, Cuba.
| | - Federico De Martino
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Maastricht Brain Imaging Center (MBIC), Maastricht, the Netherlands; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA.
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114
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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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115
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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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116
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Gaudry KS, Ayaz H, Bedows A, Celnik P, Eagleman D, Grover P, Illes J, Rao RPN, Robinson JT, Thyagarajan K. Projections and the Potential Societal Impact of the Future of Neurotechnologies. Front Neurosci 2021; 15:658930. [PMID: 34867139 PMCID: PMC8634831 DOI: 10.3389/fnins.2021.658930] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 10/04/2021] [Indexed: 12/17/2022] Open
Abstract
Traditionally, recording from and stimulating the brain with high spatial and temporal resolution required invasive means. However, recently, the technical capabilities of less invasive and non-invasive neuro-interfacing technology have been dramatically improving, and laboratories and funders aim to further improve these capabilities. These technologies can facilitate functions such as multi-person communication, mood regulation and memory recall. We consider a potential future where the less invasive technology is in high demand. Will this demand match that the current-day demand for a smartphone? Here, we draw upon existing research to project which particular neuroethics issues may arise in this potential future and what preparatory steps may be taken to address these issues.
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Affiliation(s)
- Kate S. Gaudry
- Kilpatrick Townsend & Stockton LLP, Washington, DC, United States
| | - Hasan Ayaz
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
- Department of Psychology, College of Arts and Sciences, Drexel University, Philadelphia, PA, United States
- Drexel Solutions Institute, Drexel University, Philadelphia, PA, United States
- Department of Family and Community Health, University of Pennsylvania, Philadelphia, PA, United States
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | | | - Pablo Celnik
- Department of Physical Medicine and Rehabilitation, Johns Hopkins, School of Medicine, Baltimore, MD, United States
| | - David Eagleman
- Department of Psychiatry, Stanford University School of Medicine, Stanford, CA, United States
| | - Pulkit Grover
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, United States
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Judy Illes
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
- Neuroethics Canada, University of British Columbia, Vancouver, BC, Canada
| | - Rajesh P. N. Rao
- Center for Neurotechnology, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, DC, United States
| | - Jacob T. Robinson
- Department of Bioengineering, Rice University, Houston, TX, United States
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
- Applied Physics Program, Rice University, Houston, TX, United States
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States
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117
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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: 46] [Impact Index Per Article: 15.3] [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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118
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Abstract
A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.
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119
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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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120
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Reconstruction of natural images from evoked brain activity with a dictionary-based invertible encoding procedure. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.05.083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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121
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Hansen BC, Greene MR, Field DJ. Dynamic Electrode-to-Image (DETI) mapping reveals the human brain's spatiotemporal code of visual information. PLoS Comput Biol 2021; 17:e1009456. [PMID: 34570753 PMCID: PMC8496831 DOI: 10.1371/journal.pcbi.1009456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 10/07/2021] [Accepted: 09/16/2021] [Indexed: 11/18/2022] Open
Abstract
A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI’s poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world. The visual information that we sample from our environment undergoes a series of neural modifications, with each modification state (or visual code) consisting of a unique distribution of responses across neurons along the visual pathway. However, current noninvasive neuroimaging techniques provide an account of that code that is coarse with respect to time or space. Here, we present dynamic electrode-to-image (DETI) mapping, an analysis technique that capitalizes on the high temporal resolution of EEG to map neural signals to each pixel within a given image to reveal location-specific modifications of the visual code. The DETI technique reveals maps of features that are associated with the neural signal at each pixel and at each time point. DETI mapping shows that real-world scenes undergo a series of nonuniform modifications over both space and time. Specifically, we find that the visual code varies in a location-specific manner, likely reflecting that neural processing prioritizes different features at different image locations over time. DETI mapping therefore offers a potential avenue for future studies to explore how each modification state informs and refines the conceptual meaning of our visual world.
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Affiliation(s)
- Bruce C. Hansen
- Colgate University, Department of Psychological & Brain Sciences, Neuroscience Program, Hamilton New York, United States of America
- * E-mail:
| | - Michelle R. Greene
- Bates College, Neuroscience Program, Lewiston, Maine, United States of America
| | - David J. Field
- Cornell University, Department of Psychology, Ithaca, New York, United States of America
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122
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Wu H, Zhu Z, Wang J, Zheng N, Chen B. An Encoding Framework With Brain Inner State for Natural Image Identification. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.2987352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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123
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Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting. Neuroinformatics 2021; 19:385-392. [PMID: 32935193 PMCID: PMC8233242 DOI: 10.1007/s12021-020-09489-1] [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] [Indexed: 11/27/2022]
Abstract
In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial models and finite impulse response models may also encompass a large number of predictors. In settings where only few of those predictors are expected to be informative, a sparse model fit can be obtained via L1-regularization. However, estimating L1-regularized models requires an iterative fitting procedure, which considerably increases computation time compared to estimating unregularized or L2-regularized models, and complicates the application of L1-regularization on whole-brain data and large sample sizes. Here we provide several functions for estimating L1-regularized models that are optimized for the mass-univariate analysis approach. The package includes a parallel implementation of the coordinate descent algorithm for CPU-only systems and two implementations of the alternating direction method of multipliers algorithm requiring a GPU device. While the core algorithms are implemented in C++/CUDA, data input/output and parameter settings can be conveniently handled via Matlab. The CPU-based implementation is highly memory-efficient and provides considerable speed-up compared to the standard implementation not optimized for the mass-univariate approach. Further acceleration can be achieved on systems equipped with a CUDA-enabled GPU. Using the fastest GPU-based implementation, computation time for whole-brain estimates can be reduced from 9 h to 5 min in an exemplary data setting. Overall, the provided package facilitates the use of L1-regularization for fMRI activation analyses and enables an efficient employment of L1-regularization on whole-brain data and large sample sizes.
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125
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A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream. Brain Sci 2021; 11:brainsci11081004. [PMID: 34439623 PMCID: PMC8391143 DOI: 10.3390/brainsci11081004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/23/2021] [Accepted: 07/26/2021] [Indexed: 11/30/2022] Open
Abstract
Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning objective, but these are limited to the supervised learning method. From the view of unsupervised learning mechanisms, this paper utilized a pre-trained neural network to construct a visual encoding model based on contrastive self-supervised learning for the ventral visual stream measured by functional magnetic resonance imaging (fMRI). We first extracted features using the ResNet50 model pre-trained in contrastive self-supervised learning (ResNet50-CSL model), trained a linear regression model for each voxel, and finally calculated the prediction accuracy of different voxels. Compared with the ResNet50 model pre-trained in a supervised classification task, the ResNet50-CSL model achieved an equal or even relatively better encoding performance in multiple visual cortical areas. Moreover, the ResNet50-CSL model performs hierarchical representation of input visual stimuli, which is similar to the human visual cortex in its hierarchical information processing. Our experimental results suggest that the encoding model based on contrastive self-supervised learning is a strong computational model to compete with supervised models, and contrastive self-supervised learning proves an effective learning method to extract human brain-like representations.
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126
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Mobbs D, Wise T, Suthana N, Guzmán N, Kriegeskorte N, Leibo JZ. Promises and challenges of human computational ethology. Neuron 2021; 109:2224-2238. [PMID: 34143951 PMCID: PMC8769712 DOI: 10.1016/j.neuron.2021.05.021] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/05/2021] [Accepted: 05/17/2021] [Indexed: 12/22/2022]
Abstract
The movements an organism makes provide insights into its internal states and motives. This principle is the foundation of the new field of computational ethology, which links rich automatic measurements of natural behaviors to motivational states and neural activity. Computational ethology has proven transformative for animal behavioral neuroscience. This success raises the question of whether rich automatic measurements of behavior can similarly drive progress in human neuroscience and psychology. New technologies for capturing and analyzing complex behaviors in real and virtual environments enable us to probe the human brain during naturalistic dynamic interactions with the environment that so far were beyond experimental investigation. Inspired by nonhuman computational ethology, we explore how these new tools can be used to test important questions in human neuroscience. We argue that application of this methodology will help human neuroscience and psychology extend limited behavioral measurements such as reaction time and accuracy, permit novel insights into how the human brain produces behavior, and ultimately reduce the growing measurement gap between human and animal neuroscience.
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Affiliation(s)
- Dean Mobbs
- Department of Humanities and Social Sciences, 1200 E. California Blvd., HSS 228-77, Pasadena, CA 91125, USA; Computation and Neural Systems Program at the California Institute of Technology, 1200 E. California Blvd., HSS 228-77, Pasadena, CA 91125, USA.
| | - Toby Wise
- Department of Humanities and Social Sciences, 1200 E. California Blvd., HSS 228-77, Pasadena, CA 91125, USA; Wellcome Centre for Human Neuroimaging, University College London, London, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA; Departments of Neurosurgery, Psychology, and Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Guzmán
- Computation and Neural Systems Program at the California Institute of Technology, 1200 E. California Blvd., HSS 228-77, Pasadena, CA 91125, USA
| | - Nikolaus Kriegeskorte
- Department of Psychology, Columbia University, New York, NY, USA; Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
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127
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Abstract
Selectivity for many basic properties of visual stimuli, such as orientation, is thought to be organized at the scale of cortical columns, making it difficult or impossible to measure directly with noninvasive human neuroscience measurement. However, computational analyses of neuroimaging data have shown that selectivity for orientation can be recovered by considering the pattern of response across a region of cortex. This suggests that computational analyses can reveal representation encoded at a finer spatial scale than is implied by the spatial resolution limits of measurement techniques. This potentially opens up the possibility to study a much wider range of neural phenomena that are otherwise inaccessible through noninvasive measurement. However, as we review in this article, a large body of evidence suggests an alternative hypothesis to this superresolution account: that orientation information is available at the spatial scale of cortical maps and thus easily measurable at the spatial resolution of standard techniques. In fact, a population model shows that this orientation information need not even come from single-unit selectivity for orientation tuning, but instead can result from population selectivity for spatial frequency. Thus, a categorical error of interpretation can result whereby orientation selectivity can be confused with spatial frequency selectivity. This is similarly problematic for the interpretation of results from numerous studies of more complex representations and cognitive functions that have built upon the computational techniques used to reveal stimulus orientation. We suggest in this review that these interpretational ambiguities can be avoided by treating computational analyses as models of the neural processes that give rise to measurement. Building upon the modeling tradition in vision science using considerations of whether population models meet a set of core criteria is important for creating the foundation for a cumulative and replicable approach to making valid inferences from human neuroscience measurements. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Justin L Gardner
- Department of Psychology, Stanford University, Stanford, California 94305, USA;
| | - Elisha P Merriam
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
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128
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Leahy J, Kim SG, Wan J, Overath T. An Analytical Framework of Tonal and Rhythmic Hierarchy in Natural Music Using the Multivariate Temporal Response Function. Front Neurosci 2021; 15:665767. [PMID: 34335154 PMCID: PMC8322238 DOI: 10.3389/fnins.2021.665767] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Abstract
Even without formal training, humans experience a wide range of emotions in response to changes in musical features, such as tonality and rhythm, during music listening. While many studies have investigated how isolated elements of tonal and rhythmic properties are processed in the human brain, it remains unclear whether these findings with such controlled stimuli are generalizable to complex stimuli in the real world. In the current study, we present an analytical framework of a linearized encoding analysis based on a set of music information retrieval features to investigate the rapid cortical encoding of tonal and rhythmic hierarchies in natural music. We applied this framework to a public domain EEG dataset (OpenMIIR) to deconvolve overlapping EEG responses to various musical features in continuous music. In particular, the proposed framework investigated the EEG encoding of the following features: tonal stability, key clarity, beat, and meter. This analysis revealed a differential spatiotemporal neural encoding of beat and meter, but not of tonal stability and key clarity. The results demonstrate that this framework can uncover associations of ongoing brain activity with relevant musical features, which could be further extended to other relevant measures such as time-resolved emotional responses in future studies.
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Affiliation(s)
- Jasmine Leahy
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States
| | - Seung-Goo Kim
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States
| | - Jie Wan
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States.,Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Tobias Overath
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States.,Duke Institute for Brain Sciences, Duke University, Durham, NC, United States.,Center for Cognitive Neuroscience, Duke University, Durham, NC, United States
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129
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Svanera M, Morgan AT, Petro LS, Muckli L. A self-supervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes. J Vis 2021; 21:5. [PMID: 34259828 PMCID: PMC8288063 DOI: 10.1167/jov.21.7.5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 05/14/2021] [Indexed: 11/24/2022] Open
Abstract
The promise of artificial intelligence in understanding biological vision relies on the comparison of computational models with brain data with the goal of capturing functional principles of visual information processing. Convolutional neural networks (CNN) have successfully matched the transformations in hierarchical processing occurring along the brain's feedforward visual pathway, extending into ventral temporal cortex. However, we are still to learn if CNNs can successfully describe feedback processes in early visual cortex. Here, we investigated similarities between human early visual cortex and a CNN with encoder/decoder architecture, trained with self-supervised learning to fill occlusions and reconstruct an unseen image. Using representational similarity analysis (RSA), we compared 3T functional magnetic resonance imaging (fMRI) data from a nonstimulated patch of early visual cortex in human participants viewing partially occluded images, with the different CNN layer activations from the same images. Results show that our self-supervised image-completion network outperforms a classical object-recognition supervised network (VGG16) in terms of similarity to fMRI data. This work provides additional evidence that optimal models of the visual system might come from less feedforward architectures trained with less supervision. We also find that CNN decoder pathway activations are more similar to brain processing compared to encoder activations, suggesting an integration of mid- and low/middle-level features in early visual cortex. Challenging an artificial intelligence model to learn natural image representations via self-supervised learning and comparing them with brain data can help us to constrain our understanding of information processing, such as neuronal predictive coding.
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Affiliation(s)
- Michele Svanera
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, UK
| | - Andrew T Morgan
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, UK
| | - Lucy S Petro
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, UK
| | - Lars Muckli
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, UK
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130
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Welbourne LE, Jonnalagadda A, Giesbrecht B, Eckstein MP. The transverse occipital sulcus and intraparietal sulcus show neural selectivity to object-scene size relationships. Commun Biol 2021; 4:768. [PMID: 34158579 PMCID: PMC8219818 DOI: 10.1038/s42003-021-02294-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 05/26/2021] [Indexed: 02/05/2023] Open
Abstract
To optimize visual search, humans attend to objects with the expected size of the sought target relative to its surrounding scene (object-scene scale consistency). We investigate how the human brain responds to variations in object-scene scale consistency. We use functional magnetic resonance imaging and a voxel-wise feature encoding model to estimate tuning to different object/scene properties. We find that regions involved in scene processing (transverse occipital sulcus) and spatial attention (intraparietal sulcus) have the strongest responsiveness and selectivity to object-scene scale consistency: reduced activity to mis-scaled objects (either unusually smaller or larger). The findings show how and where the brain incorporates object-scene size relationships in the processing of scenes. The response properties of these brain areas might explain why during visual search humans often miss objects that are salient but at atypical sizes relative to the surrounding scene.
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Affiliation(s)
- Lauren E Welbourne
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, USA.
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, USA.
- York NeuroImaging Centre, Department of Psychology, University of York, York, UK.
| | - Aditya Jonnalagadda
- Electrical and Computer Engineering, University of California, Santa Barbara, USA
| | - Barry Giesbrecht
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, USA
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, USA
| | - Miguel P Eckstein
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, USA.
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, USA.
- Electrical and Computer Engineering, University of California, Santa Barbara, USA.
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, USA.
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131
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Shahbazi M, Shirali A, Aghajan H, Nili H. Using distance on the Riemannian manifold to compare representations in brain and in models. Neuroimage 2021; 239:118271. [PMID: 34157410 DOI: 10.1016/j.neuroimage.2021.118271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/10/2021] [Accepted: 06/14/2021] [Indexed: 11/28/2022] Open
Abstract
Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner product and correlation matrix. These representational matrices reside on the manifold of positive semidefinite matrices, called the Riemannian manifold. We hypothesize that representational similarities would be more accurately quantified by considering the underlying manifold of the representational matrices. Thus, we introduce the distance on the Riemannian manifold as a metric for comparing representations. Analyzing simulated and real fMRI data and considering a wide range of metrics, we show that the Riemannian distance is least susceptible to sampling bias, results in larger intra-subject reliability, and affords searchlight mapping with high sensitivity and specificity. Furthermore, we show that the Riemannian distance can be used for measuring multi-dimensional connectivity. This measure captures both univariate and multivariate connectivity and is also more sensitive to nonlinear regional interactions compared to the state-of-the-art measures. Applying our proposed metric to neural network representations of natural images, we demonstrate that it also possesses outstanding performance in quantifying similarity in models. Taken together, our results lend credence to the proposition that RSA should consider the manifold of the representational matrices to summarize response patterns in the brain and in models.
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Affiliation(s)
- Mahdiyar Shahbazi
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Ali Shirali
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Hamid Aghajan
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Hamed Nili
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom.
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132
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Knights E, Mansfield C, Tonin D, Saada J, Smith FW, Rossit S. Hand-Selective Visual Regions Represent How to Grasp 3D Tools: Brain Decoding during Real Actions. J Neurosci 2021; 41:5263-5273. [PMID: 33972399 PMCID: PMC8211542 DOI: 10.1523/jneurosci.0083-21.2021] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/23/2021] [Accepted: 03/29/2021] [Indexed: 02/02/2023] Open
Abstract
Most neuroimaging experiments that investigate how tools and their actions are represented in the brain use visual paradigms where tools or hands are displayed as 2D images and no real movements are performed. These studies discovered selective visual responses in occipitotemporal and parietal cortices for viewing pictures of hands or tools, which are assumed to reflect action processing, but this has rarely been directly investigated. Here, we examined the responses of independently visually defined category-selective brain areas when participants grasped 3D tools (N = 20; 9 females). Using real-action fMRI and multivoxel pattern analysis, we found that grasp typicality representations (i.e., whether a tool is grasped appropriately for use) were decodable from hand-selective areas in occipitotemporal and parietal cortices, but not from tool-, object-, or body-selective areas, even if partially overlapping. Importantly, these effects were exclusive for actions with tools, but not for biomechanically matched actions with control nontools. In addition, grasp typicality decoding was significantly higher in hand than tool-selective parietal regions. Notably, grasp typicality representations were automatically evoked even when there was no requirement for tool use and participants were naive to object category (tool vs nontools). Finding a specificity for typical tool grasping in hand-selective, rather than tool-selective, regions challenges the long-standing assumption that activation for viewing tool images reflects sensorimotor processing linked to tool manipulation. Instead, our results show that typicality representations for tool grasping are automatically evoked in visual regions specialized for representing the human hand, the primary tool of the brain for interacting with the world.
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Affiliation(s)
- Ethan Knights
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - Courtney Mansfield
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
| | - Diana Tonin
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
| | - Janak Saada
- Department of Radiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich NR4 7UY, United Kingdom
| | - Fraser W Smith
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
| | - Stéphanie Rossit
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
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133
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Mell MM, St-Yves G, Naselaris T. Voxel-to-voxel predictive models reveal unexpected structure in unexplained variance. Neuroimage 2021; 238:118266. [PMID: 34129949 DOI: 10.1016/j.neuroimage.2021.118266] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 06/08/2021] [Accepted: 06/11/2021] [Indexed: 11/19/2022] Open
Abstract
Encoding models based on deep convolutional neural networks (DCNN) predict BOLD responses to natural scenes in the human visual system more accurately than many other currently available models. However, DCNN-based encoding models fail to predict a significant amount of variance in the activity of most voxels in all visual areas. This failure could reflect limitations in the data (e.g., a noise ceiling), or could reflect limitations of the DCNN as a model of computation in the brain. Understanding the source and structure of the unexplained variance could therefore provide helpful clues for improving models of brain computation. Here, we characterize the structure of the variance that DCNN-based encoding models cannot explain. Using a publicly available dataset of BOLD responses to natural scenes, we determined if the source of unexplained variance was shared across voxels, individual brains, retinotopic locations, and hierarchically distant visual brain areas. We answered these questions using voxel-to-voxel (vox2vox) models that predict activity in a target voxel given activity in a population of source voxels. We found that simple linear vox2vox models increased within-subject prediction accuracy over DCNN-based models for any pair of source/target visual areas, clearly demonstrating that the source of unexplained variance is widely shared within and across visual brain areas. However, vox2vox models were not more accurate than DCNN-based encoding models when source and target voxels came from different brains, demonstrating that the source of unexplained variance was not shared across brains. Importantly, control analyses demonstrated that the source of unexplained variance was not encoded in the mean activity of source voxels, or the activity of voxels in white matter. Interestingly, the weights of vox2vox models revealed preferential connection of target voxel activity to source voxels with adjacent receptive fields, even when source and target voxels were in different functional brain areas. Finally, we found that the prediction accuracy of the vox2vox models decayed with hierarchical distance between the source and target voxels but showed detailed patterns of dependence on hierarchical relationships that we did not observe in DCNNs. Given these results, we argue that the structured variance unexplained by DCNN-based encoding models is unlikely to be entirely caused by non-neural artifacts (e.g., spatially correlated measurement noise) or a failure of DCNNs to approximate the features encoded in brain activity; rather, our results point to a need for brain models that provide both mechanistic and computational explanations for structured ongoing activity in the brain. Keywords: fMRI, encoding models, deep neural networks, functional connectivity.
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Affiliation(s)
- Maggie Mae Mell
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Ghislain St-Yves
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Thomas Naselaris
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
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134
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CUI YIBO, ZHANG CHI, WANG LINYUAN, YAN BIN, TONG LI. DENSE-GWP: AN IMPROVED PRIMARY VISUAL ENCODING MODEL BASED ON DENSE GABOR FEATURES. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421400170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain visual encoding models based on functional magnetic resonance imaging are growing increasingly popular. The Gabor wavelet pyramid model (GWP) is a classic example, exhibiting a good prediction performance for the primary visual cortex (V1, V2, and V3). However, the local variations in the visual stimulation are quite convoluted in terms of spatial frequency, orientation, and position, posing a challenge for visual encoding models. Whether the GWP model can thoroughly extract informative and effective features from visual stimulus remains unclear. To this end, this paper proposes a dense GWP visual encoding model by ameliorating the composition of the Gabor wavelet basis from three aspects: spatial frequency, orientation, and position. The improved model named Dense-GWP model could extract denser features from the image stimulus. A regularization optimization algorithm was used to select informative and effective features, which were crucial for predicting voxel activity in the region of interest. Extensive experimental results showed that the Dense-GWP model exhibits an improved prediction performance and can therefore help further understand the human visual perception mechanism.
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Affiliation(s)
- YIBO CUI
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force, Information Engineering University, Zhengzhou 450001, P. R. China
| | - CHI ZHANG
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force, Information Engineering University, Zhengzhou 450001, P. R. China
| | - LINYUAN WANG
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force, Information Engineering University, Zhengzhou 450001, P. R. China
| | - BIN YAN
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force, Information Engineering University, Zhengzhou 450001, P. R. China
| | - LI TONG
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force, Information Engineering University, Zhengzhou 450001, P. R. China
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135
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Cai Y, Hofstetter S, van der Zwaag W, Zuiderbaan W, Dumoulin SO. Individualized cognitive neuroscience needs 7T: Comparing numerosity maps at 3T and 7T MRI. Neuroimage 2021; 237:118184. [PMID: 34023448 DOI: 10.1016/j.neuroimage.2021.118184] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 05/14/2021] [Accepted: 05/16/2021] [Indexed: 02/06/2023] Open
Abstract
The field of cognitive neuroscience is weighing evidence about whether to move from the current standard field strength of 3 Tesla (3T) to ultra-high field (UHF) of 7T and above. The present study contributes to the evidence by comparing a computational cognitive neuroscience paradigm at 3T and 7T. The goal was to evaluate the practical effects, i.e. model predictive power, of field strength on a numerosity task using accessible pre-processing and analysis tools. Previously, using 7T functional magnetic resonance imaging and biologically-inspired analyses, i.e. population receptive field modelling, we discovered topographical organization of numerosity-selective neural populations in human parietal cortex. Here we show that these topographic maps are also detectable at 3T. However, averaging of many more functional runs was required at 3T to reliably reconstruct numerosity maps. On average, one 7T run had about four times the model predictive power of one 3T run. We believe that this amount of scanning would have made the initial discovery of the numerosity maps on 3T highly infeasible in practice. Therefore, we suggest that the higher signal-to-noise ratio and signal sensitivity of UHF MRI is necessary to build mechanistic models of the organization and function of our cognitive abilities in individual participants.
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Affiliation(s)
- Yuxuan Cai
- Spinoza Centre for Neuroimaging, Amsterdam, Netherlands; Experimental and Applied Psychology, VU University Amsterdam, Amsterdam, Netherlands.
| | | | | | | | - Serge O Dumoulin
- Spinoza Centre for Neuroimaging, Amsterdam, Netherlands; Experimental and Applied Psychology, VU University Amsterdam, Amsterdam, Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands.
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136
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Zhang T, Gao JS, Çukur T, Gallant JL. Voxel-Based State Space Modeling Recovers Task-Related Cognitive States in Naturalistic fMRI Experiments. Front Neurosci 2021; 14:565976. [PMID: 34045937 PMCID: PMC8145286 DOI: 10.3389/fnins.2020.565976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 09/24/2020] [Indexed: 11/23/2022] Open
Abstract
Complex natural tasks likely recruit many different functional brain networks, but it is difficult to predict how such tasks will be represented across cortical areas and networks. Previous electrophysiology studies suggest that task variables are represented in a low-dimensional subspace within the activity space of neural populations. Here we develop a voxel-based state space modeling method for recovering task-related state spaces from human fMRI data. We apply this method to data acquired in a controlled visual attention task and a video game task. We find that each task induces distinct brain states that can be embedded in a low-dimensional state space that reflects task parameters, and that attention increases state separation in the task-related subspace. Our results demonstrate that the state space framework offers a powerful approach for modeling human brain activity elicited by complex natural tasks.
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Affiliation(s)
- Tianjiao Zhang
- Program in Bioengineering, University of California, Berkeley, Berkeley, CA, United States
| | - James S Gao
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Tolga Çukur
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.,National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey.,Neuroscience Program, Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
| | - Jack L Gallant
- Program in Bioengineering, University of California, Berkeley, Berkeley, CA, United States.,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States.,Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
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137
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Zhou W, Kholiqov O, Zhu J, Zhao M, Zimmermann LL, Martin RM, Lyeth BG, Srinivasan VJ. Functional interferometric diffusing wave spectroscopy of the human brain. SCIENCE ADVANCES 2021; 7:eabe0150. [PMID: 33980479 PMCID: PMC8115931 DOI: 10.1126/sciadv.abe0150] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 03/23/2021] [Indexed: 05/18/2023]
Abstract
Cerebral blood flow (CBF) is essential for brain function, and CBF-related signals can inform us about brain activity. Yet currently, high-end medical instrumentation is needed to perform a CBF measurement in adult humans. Here, we describe functional interferometric diffusing wave spectroscopy (fiDWS), which introduces and collects near-infrared light via the scalp, using inexpensive detector arrays to rapidly monitor coherent light fluctuations that encode brain blood flow index (BFI), a surrogate for CBF. Compared to other functional optical approaches, fiDWS measures BFI faster and deeper while also providing continuous wave absorption signals. Achieving clear pulsatile BFI waveforms at source-collector separations of 3.5 cm, we confirm that optical BFI, not absorption, shows a graded hypercapnic response consistent with human cerebrovascular physiology, and that BFI has a better contrast-to-noise ratio than absorption during brain activation. By providing high-throughput measurements of optical BFI at low cost, fiDWS will expand access to CBF.
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Affiliation(s)
- Wenjun Zhou
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
| | - Oybek Kholiqov
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
| | - Jun Zhu
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
| | - Mingjun Zhao
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
| | - Lara L Zimmermann
- Department of Neurological Surgery, University of California, Davis, Sacramento, CA, USA
| | - Ryan M Martin
- Department of Neurological Surgery, University of California, Davis, Sacramento, CA, USA
| | - Bruce G Lyeth
- Department of Neurological Surgery, University of California, Davis, Sacramento, CA, USA
| | - Vivek J Srinivasan
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA.
- Department of Ophthalmology and Vision Science, University of California, Davis, Sacramento, CA, USA
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Tech4Health Institute, NYU Langone Health, New York, NY, USA
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138
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Khosla M, Ngo GH, Jamison K, Kuceyeski A, Sabuncu MR. Cortical response to naturalistic stimuli is largely predictable with deep neural networks. SCIENCE ADVANCES 2021; 7:7/22/eabe7547. [PMID: 34049888 PMCID: PMC8163078 DOI: 10.1126/sciadv.abe7547] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 04/12/2021] [Indexed: 05/08/2023]
Abstract
Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.
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Affiliation(s)
- Meenakshi Khosla
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Gia H Ngo
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
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139
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Relationships between expertise and distinctiveness: Abnormal medical images lead to enhanced memory performance only in experts. Mem Cognit 2021; 49:1067-1081. [PMID: 33855674 DOI: 10.3758/s13421-021-01160-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/21/2021] [Indexed: 11/08/2022]
Abstract
Memories are encoded in a manner that depends on our knowledge and expectations ("schemas"). Consistent with this, expertise tends to improve memory: Experts have elaborated schemas in their domains of expertise, allowing them to efficiently represent information in this domain (e.g., chess experts have enhanced memory for realistic chess layouts). On the other hand, in most situations, people tend to remember abnormal or surprising items best-those that are also rare or out-of-the-ordinary occurrences (e.g., surprising-but not random-chess board configurations). This occurs, in part, because such images are distinctive relative to other images. In the current work, we ask how these factors interact in a particularly interesting case-the domain of radiology, where experts actively search for abnormalities. Abnormality in mammograms is typically focal but can be perceived in the global "gist" of the image. We ask whether, relative to novices, expert radiologists show improved memory for mammograms. We also test for any additional advantage for abnormal mammograms that can be thought of as unexpected or rare stimuli in screening. We find that experts have enhanced memory for focally abnormal images relative to normal images. However, radiologists showed no memory benefit for images of the breast that were not focally abnormal, but were only abnormal in their gist. Our results speak to the role of schemas and abnormality in expertise; the necessity for spatially localized abnormalities versus abnormalities in the gist in enhancing memory; and the nature of memory and decision-making in radiologists.
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140
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Xu Y, Vaziri-Pashkam M. Limits to visual representational correspondence between convolutional neural networks and the human brain. Nat Commun 2021; 12:2065. [PMID: 33824315 PMCID: PMC8024324 DOI: 10.1038/s41467-021-22244-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 03/05/2021] [Indexed: 02/01/2023] Open
Abstract
Convolutional neural networks (CNNs) are increasingly used to model human vision due to their high object categorization capabilities and general correspondence with human brain responses. Here we evaluate the performance of 14 different CNNs compared with human fMRI responses to natural and artificial images using representational similarity analysis. Despite the presence of some CNN-brain correspondence and CNNs' impressive ability to fully capture lower level visual representation of real-world objects, we show that CNNs do not fully capture higher level visual representations of real-world objects, nor those of artificial objects, either at lower or higher levels of visual representations. The latter is particularly critical, as the processing of both real-world and artificial visual stimuli engages the same neural circuits. We report similar results regardless of differences in CNN architecture, training, or the presence of recurrent processing. This indicates some fundamental differences exist in how the brain and CNNs represent visual information.
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Affiliation(s)
- Yaoda Xu
- Psychology Department, Yale University, New Haven, CT, USA.
| | - Maryam Vaziri-Pashkam
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
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141
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Liu C, Kang Y, Zhang L, Zhang J. Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach. IEEE J Biomed Health Inform 2021; 25:1139-1150. [PMID: 32750957 DOI: 10.1109/jbhi.2020.3008731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recent advances in the development of multivariate analysis methods have led to the application of multivariate pattern analysis (MVPA) to investigate the interactions between brain regions using graph theory (functional connectivity, FC) and decode visual categories from functional magnetic resonance imaging (fMRI) data from a continuous multicategory paradigm. To estimate stable FC patterns from fMRI data, previous studies required long periods in the order of several minutes, in comparison to the human brain that categories visual stimuli within hundreds of milliseconds. Constructing short-time dynamic FC patterns in the order of milliseconds and decoding visual categories is a relatively novel concept. In this study, we developed a multivariate decoding algorithm based on FC patterns and applied it to magnetoencephalography (MEG) data. MEG data were recorded from participants presented with image stimuli in four categories (faces, scenes, animals and tools). MEG data from 17 participants demonstrate that short-time dynamic FC patterns yield brain activity patterns that can be used to decode visual categories with high accuracy. Our results show that FC patterns change over the time window, and FC patterns extracted in the time window of 0∼200 ms after the stimulus onset were most stable. Further, the categorizing accuracy peaked (the mean binary accuracy is above 78.6% at individual level) in the FC patterns estimated within the 0∼200 ms interval. These findings elucidate the underlying connectivity information during visual category processing on a relatively smaller time scale and demonstrate that the contribution of FC patterns to categorization fluctuates over time.
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142
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Pezzulo G, LaPalme J, Durant F, Levin M. Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration. Philos Trans R Soc Lond B Biol Sci 2021; 376:20190765. [PMID: 33550952 PMCID: PMC7935058 DOI: 10.1098/rstb.2019.0765] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2020] [Indexed: 02/06/2023] Open
Abstract
Nervous systems' computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states-in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo. This article is part of the theme issue 'Basal cognition: multicellularity, neurons and the cognitive lens'.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Joshua LaPalme
- Allen Discovery Center, Tufts University, Medford, MA, USA
| | - Fallon Durant
- Allen Discovery Center, Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA, USA
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143
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Tuckute G, Hansen ST, Kjaer TW, Hansen LK. Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback. Neural Comput 2021; 33:967-1004. [PMID: 33513324 DOI: 10.1162/neco_a_01363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/16/2020] [Indexed: 11/04/2022]
Abstract
Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback. During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance = 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction p=7.23e-4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities. We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.
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Affiliation(s)
- Greta Tuckute
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark, and Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, U.S.A.,
| | - Sofie Therese Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, 4000 Roskilde, Denmark, and Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark,
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
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144
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Slivkoff S, Gallant JL. Design of complex neuroscience experiments using mixed-integer linear programming. Neuron 2021; 109:1433-1448. [PMID: 33689687 DOI: 10.1016/j.neuron.2021.02.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 02/05/2021] [Accepted: 02/16/2021] [Indexed: 11/29/2022]
Abstract
Over the past few decades, neuroscience experiments have become increasingly complex and naturalistic. Experimental design has in turn become more challenging, as experiments must conform to an ever-increasing diversity of design constraints. In this article, we demonstrate how this design process can be greatly assisted using an optimization tool known as mixed-integer linear programming (MILP). MILP provides a rich framework for incorporating many types of real-world design constraints into a neuroscience experiment. We introduce the mathematical foundations of MILP, compare MILP to other experimental design techniques, and provide four case studies of how MILP can be used to solve complex experimental design challenges.
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Affiliation(s)
- Storm Slivkoff
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jack L Gallant
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
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145
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Cross L, Cockburn J, Yue Y, O'Doherty JP. Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments. Neuron 2021; 109:724-738.e7. [PMID: 33326755 PMCID: PMC7897245 DOI: 10.1016/j.neuron.2020.11.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/15/2020] [Accepted: 11/17/2020] [Indexed: 11/21/2022]
Abstract
Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations. However, it is unknown how the brain compactly represents the current state of the environment to guide this process. The deep Q-network (DQN) achieves this by capturing highly nonlinear mappings from multivariate inputs to the values of potential actions. We deployed DQN as a model of brain activity and behavior in participants playing three Atari video games during fMRI. Hidden layers of DQN exhibited a striking resemblance to voxel activity in a distributed sensorimotor network, extending throughout the dorsal visual pathway into posterior parietal cortex. Neural state-space representations emerged from nonlinear transformations of the pixel space bridging perception to action and reward. These transformations reshape axes to reflect relevant high-level features and strip away information about task-irrelevant sensory features. Our findings shed light on the neural encoding of task representations for decision-making in real-world situations.
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Affiliation(s)
- Logan Cross
- Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Jeff Cockburn
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Yisong Yue
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - John P O'Doherty
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
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146
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Cui Y, Qiao K, Zhang C, Wang L, Yan B, Tong L. GaborNet Visual Encoding: A Lightweight Region-Based Visual Encoding Model With Good Expressiveness and Biological Interpretability. Front Neurosci 2021; 15:614182. [PMID: 33613179 PMCID: PMC7893978 DOI: 10.3389/fnins.2021.614182] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/04/2021] [Indexed: 11/17/2022] Open
Abstract
Computational visual encoding models play a key role in understanding the stimulus-response characteristics of neuronal populations in the brain visual cortex. However, building such models typically faces challenges in the effective construction of non-linear feature spaces to fit the neuronal responses. In this work, we propose the GaborNet visual encoding (GaborNet-VE) model, a novel end-to-end encoding model for the visual ventral stream. This model comprises a Gabor convolutional layer, two regular convolutional layers, and a fully connected layer. The key design principle for the GaborNet-VE model is to replace regular convolutional kernels in the first convolutional layer with Gabor kernels with learnable parameters. One GaborNet-VE model efficiently and simultaneously encodes all voxels in one region of interest of functional magnetic resonance imaging data. The experimental results show that the proposed model achieves state-of-the-art prediction performance for the primary visual cortex. Moreover, the visualizations demonstrate the regularity of the region of interest fitting to the visual features and the estimated receptive fields. These results suggest that the lightweight region-based GaborNet-VE model based on combining handcrafted and deep learning features exhibits good expressiveness and biological interpretability.
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Affiliation(s)
| | | | | | | | | | - Li Tong
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
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147
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Li D, Du C, Wang S, Wang H, He H. Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.09.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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148
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Barron HC, Mars RB, Dupret D, Lerch JP, Sampaio-Baptista C. Cross-species neuroscience: closing the explanatory gap. Philos Trans R Soc Lond B Biol Sci 2021; 376:20190633. [PMID: 33190601 PMCID: PMC7116399 DOI: 10.1098/rstb.2019.0633] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2020] [Indexed: 12/17/2022] Open
Abstract
Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit-level manipulations with exquisite spatio-temporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains, this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.
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Affiliation(s)
- Helen C. Barron
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Jason P. Lerch
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, CanadaM5G 1L7
| | - Cassandra Sampaio-Baptista
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
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149
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Huang S, Sun L, Yousefnezhad M, Wang M, Zhang D. Temporal Information Guided Generative Adversarial Networks for Stimuli Image Reconstruction from Human Brain Activities. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3098743] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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150
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Nakai T, Koide-Majima N, Nishimoto S. Correspondence of categorical and feature-based representations of music in the human brain. Brain Behav 2021; 11:e01936. [PMID: 33164348 PMCID: PMC7821620 DOI: 10.1002/brb3.1936] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/24/2020] [Accepted: 10/21/2020] [Indexed: 01/11/2023] Open
Abstract
INTRODUCTION Humans tend to categorize auditory stimuli into discrete classes, such as animal species, language, musical instrument, and music genre. Of these, music genre is a frequently used dimension of human music preference and is determined based on the categorization of complex auditory stimuli. Neuroimaging studies have reported that the superior temporal gyrus (STG) is involved in response to general music-related features. However, there is considerable uncertainty over how discrete music categories are represented in the brain and which acoustic features are more suited for explaining such representations. METHODS We used a total of 540 music clips to examine comprehensive cortical representations and the functional organization of music genre categories. For this purpose, we applied a voxel-wise modeling approach to music-evoked brain activity measured using functional magnetic resonance imaging. In addition, we introduced a novel technique for feature-brain similarity analysis and assessed how discrete music categories are represented based on the cortical response pattern to acoustic features. RESULTS Our findings indicated distinct cortical organizations for different music genres in the bilateral STG, and they revealed representational relationships between different music genres. On comparing different acoustic feature models, we found that these representations of music genres could be explained largely by a biologically plausible spectro-temporal modulation-transfer function model. CONCLUSION Our findings have elucidated the quantitative representation of music genres in the human cortex, indicating the possibility of modeling this categorization of complex auditory stimuli based on brain activity.
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Affiliation(s)
- Tomoya Nakai
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan.,Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
| | - Naoko Koide-Majima
- Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.,AI Science Research and Development Promotion Center, National Institute of Information and Communications Technology, Suita, Japan
| | - Shinji Nishimoto
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan.,Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.,Graduate School of Medicine, Osaka University, Suita, Japan
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