201
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Vaessen MJ, Abassi E, Mancini M, Camurri A, de Gelder B. Computational Feature Analysis of Body Movements Reveals Hierarchical Brain Organization. Cereb Cortex 2018; 29:3551-3560. [DOI: 10.1093/cercor/bhy228] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 08/20/2018] [Accepted: 08/21/2018] [Indexed: 11/13/2022] Open
Abstract
Abstract
Social species spend considerable time observing the body movements of others to understand their actions, predict their emotions, watch their games, or enjoy their dance movements. Given the important information obtained from body movements, we still know surprisingly little about the details of brain mechanisms underlying movement perception. In this fMRI study, we investigated the relations between movement features obtained from automated computational analyses of video clips and the corresponding brain activity. Our results show that low-level computational features map to specific brain areas related to early visual- and motion-sensitive regions, while mid-level computational features are related to dynamic aspects of posture encoded in occipital–temporal cortex, posterior superior temporal sulcus and superior parietal lobe. Furthermore, behavioral features obtained from subjective ratings correlated with activity in higher action observation regions. Our computational feature-based analysis suggests that the neural mechanism of movement encoding is organized in the brain not so much by semantic categories than by feature statistics of the body movements.
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Affiliation(s)
- Maarten J Vaessen
- Department of Cognitive Neuroscience, Brain and Emotion Laboratory, Faculty of Psychology and Neuroscience, Maastricht University, EV Maastricht, the Netherlands
| | - Etienne Abassi
- Department of Cognitive Neuroscience, Brain and Emotion Laboratory, Faculty of Psychology and Neuroscience, Maastricht University, EV Maastricht, the Netherlands
| | - Maurizio Mancini
- Department of Informatics, Casa Paganini-InfoMus Research Centre, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genova, Italy
| | - Antonio Camurri
- Department of Informatics, Casa Paganini-InfoMus Research Centre, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genova, Italy
| | - Beatrice de Gelder
- Department of Cognitive Neuroscience, Brain and Emotion Laboratory, Faculty of Psychology and Neuroscience, Maastricht University, EV Maastricht, the Netherlands
- Department of Computer Science, University College London, London, England, United Kingdom
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202
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Ienca M, Haselager P, Emanuel EJ. Brain leaks and consumer neurotechnology. Nat Biotechnol 2018; 36:805-810. [DOI: 10.1038/nbt.4240] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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203
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Varoquaux G. Cross-validation failure: Small sample sizes lead to large error bars. Neuroimage 2018; 180:68-77. [DOI: 10.1016/j.neuroimage.2017.06.061] [Citation(s) in RCA: 211] [Impact Index Per Article: 35.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Revised: 06/19/2017] [Accepted: 06/22/2017] [Indexed: 10/19/2022] Open
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204
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Rommelfanger K, Jeong SJ, Ema A, Fukushi T, Kasai K, Ramos K, Salles A, Singh I, Amadio J, Bi GQ, Boshears PF, Carter A, Devor A, Doya K, Garden H, Illes J, Johnson LSM, Jorgenson L, Jun BO, Lee I, Michie P, Miyakawa T, Nakazawa E, Sakura O, Sarkissian H, Sullivan LS, Uh S, Winickoff D, Wolpe PR, Wu KCC, Yasamura A, Zheng JC. Neuroethics Questions to Guide Ethical Research in the International Brain Initiatives. Neuron 2018; 100:19-36. [DOI: 10.1016/j.neuron.2018.09.021] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 08/28/2018] [Accepted: 09/11/2018] [Indexed: 01/20/2023]
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205
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St-Yves G, Naselaris T. Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images. CONFERENCE PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS 2018; 2018:1054-1061. [PMID: 37333993 PMCID: PMC10276547 DOI: 10.1109/smc.2018.00187] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
We consider the inference problem of reconstructing a visual stimulus from brain activity measurements (e.g. fMRI) that encode this stimulus. Recovering a complete image is complicated by the fact that neural representations are noisy, high-dimensional, and contain incomplete information about image details. Thus, reconstructions of complex images from brain activity require a strong prior. Here we propose to train generative adversarial networks (GANs) to learn a generative model of images that is conditioned on measurements of brain activity. We consider two challenges of this approach: First, given that GANs require far more data to train than is typically collected in an fMRI experiment, how do we obtain enough samples to train a GAN that is conditioned on brain activity? Secondly, how do we ensure that our generated samples are robust against noise present in fMRI data? Our strategy to surmount both of these problems centers around the creation of surrogate brain activity samples that are generated by an encoding model. We find that the generative model thus trained generalizes to real fRMI data measured during perception of images and is able to reconstruct the basic outline of the stimuli.
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Affiliation(s)
- Ghislain St-Yves
- Medical University of South Carolina, Dept. of Neurosciences, 96 Jonathan-Lucas St. CSB 325c, Charleston, SC 29425 USA
| | - Thomas Naselaris
- Medical University of South Carolina, Dept. of Neurosciences, 96 Jonathan-Lucas St. CSB 325h, Charleston, SC 29425 USA
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206
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207
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Ikezoe K, Amano M, Nishimoto S, Fujita I. Mapping stimulus feature selectivity in macaque V1 by two-photon Ca2+ imaging: Encoding-model analysis of fluorescence responses to natural movies. Neuroimage 2018; 180:312-323. [DOI: 10.1016/j.neuroimage.2018.01.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 12/27/2017] [Accepted: 01/06/2018] [Indexed: 11/24/2022] Open
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208
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Dmochowski JP, Ki JJ, DeGuzman P, Sajda P, Parra LC. Extracting multidimensional stimulus-response correlations using hybrid encoding-decoding of neural activity. Neuroimage 2018; 180:134-146. [DOI: 10.1016/j.neuroimage.2017.05.037] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 05/03/2017] [Accepted: 05/17/2017] [Indexed: 10/19/2022] Open
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209
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Decoding Cognitive Processes from Neural Ensembles. Trends Cogn Sci 2018; 22:1091-1102. [PMID: 30279136 DOI: 10.1016/j.tics.2018.09.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 09/04/2018] [Accepted: 09/11/2018] [Indexed: 11/21/2022]
Abstract
An intrinsic difficulty in studying cognitive processes is that they are unobservable states that exist in between observable responses to the sensory environment. Cognitive states must be inferred from indirect behavioral measures. Neuroscience potentially provides the tools necessary to measure cognitive processes directly, but it is challenged on two fronts. First, neuroscientific measures often lack the spatiotemporal resolution to identify the neural computations that underlie a cognitive process. Second, the activity of a single neuron, which is the fundamental building block of neural computation, is too noisy to provide accurate measurements of a cognitive process. In this paper, I examine recent developments in neurophysiological recording and analysis methods that provide a potential solution to these problems.
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210
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Dumoulin SO, Knapen T. How Visual Cortical Organization Is Altered by Ophthalmologic and Neurologic Disorders. Annu Rev Vis Sci 2018; 4:357-379. [DOI: 10.1146/annurev-vision-091517-033948] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Receptive fields are a core property of cortical organization. Modern neuroimaging allows routine access to visual population receptive fields (pRFs), enabling investigations of clinical disorders. Yet how the underlying neural circuitry operates is controversial. The controversy surrounds observations that measurements of pRFs can change in healthy adults as well as in patients with a range of ophthalmological and neurological disorders. The debate relates to the balance between plasticity and stability of the underlying neural circuitry. We propose that to move the debate forward, the field needs to define the implied mechanism. First, we review the pRF changes in both healthy subjects and those with clinical disorders. Then, we propose a computational model that describes how pRFs can change in healthy humans. We assert that we can correctly interpret the pRF changes in clinical disorders only if we establish the capabilities and limitations of pRF dynamics in healthy humans with mechanistic models that provide quantitative predictions.
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Affiliation(s)
- Serge O. Dumoulin
- Spinoza Centre for Neuroimaging, 1105 BK Amsterdam, Netherlands
- Department of Experimental and Applied Psychology, VU University Amsterdam, 1181 BT Amsterdam, Netherlands
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, 3584 CS Utrecht, Netherlands
| | - Tomas Knapen
- Spinoza Centre for Neuroimaging, 1105 BK Amsterdam, Netherlands
- Department of Experimental and Applied Psychology, VU University Amsterdam, 1181 BT Amsterdam, Netherlands
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211
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Natural image reconstruction on the basis of local field potential signals of pigeon optic tectum neurons. Neuroreport 2018; 29:1092-1098. [DOI: 10.1097/wnr.0000000000001077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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212
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Ma Y, Wu H, Zhu M, Ren P, Zheng N, Chen B. Reconstruction of Visual Image From Functional Magnetic Resonance Imaging Using Spiking Neuron Model. IEEE Trans Cogn Dev Syst 2018. [DOI: 10.1109/tcds.2017.2764948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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213
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General Transformations of Object Representations in Human Visual Cortex. J Neurosci 2018; 38:8526-8537. [PMID: 30126975 DOI: 10.1523/jneurosci.2800-17.2018] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 08/07/2018] [Accepted: 08/09/2018] [Indexed: 11/21/2022] Open
Abstract
The brain actively represents incoming information, but these representations are only useful to the extent that they flexibly reflect changes in the environment. How does the brain transform representations across changes, such as in size or viewing angle? We conducted a fMRI experiment and a magnetoencephalography experiment in humans (both sexes) in which participants viewed objects before and after affine viewpoint changes (rotation, translation, enlargement). We used a novel approach, representational transformation analysis, to derive transformation functions that linked the distributed patterns of brain activity evoked by an object before and after an affine change. Crucially, transformations derived from one object could predict a postchange representation for novel objects. These results provide evidence of general operations in the brain that are distinct from neural representations evoked by particular objects and scenes.SIGNIFICANCE STATEMENT The dominant focus in cognitive neuroscience has been on how the brain represents information, but these representations are only useful to the extent that they flexibly reflect changes in the environment. How does the brain transform representations, such as linking two states of an object, for example, before and after an object undergoes a physical change? We used a novel method to derive transformations between the brain activity evoked by an object before and after an affine viewpoint change. We show that transformations derived from one object undergoing a change generalized to a novel object undergoing the same change. This result shows that there are general perceptual operations that transform object representations from one state to another.
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214
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Wen H, Shi J, Chen W, Liu Z. Transferring and generalizing deep-learning-based neural encoding models across subjects. Neuroimage 2018; 176:152-163. [PMID: 29705690 PMCID: PMC5976558 DOI: 10.1016/j.neuroimage.2018.04.053] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Accepted: 04/23/2018] [Indexed: 12/11/2022] Open
Abstract
Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or encoding models), requires measuring cortical responses to large and diverse sets of natural visual stimuli from single subjects. This requirement limits prior studies to few subjects, making it difficult to generalize findings across subjects or for a population. In this study, we developed new methods to transfer and generalize encoding models across subjects. To train encoding models specific to a target subject, the models trained for other subjects were used as the prior models and were refined efficiently using Bayesian inference with a limited amount of data from the target subject. To train encoding models for a population, the models were progressively trained and updated with incremental data from different subjects. For the proof of principle, we applied these methods to functional magnetic resonance imaging (fMRI) data from three subjects watching tens of hours of naturalistic videos, while a deep residual neural network driven by image recognition was used to model visual cortical processing. Results demonstrate that the methods developed herein provide an efficient and effective strategy to establish both subject-specific and population-wide predictive models of cortical representations of high-dimensional and hierarchical visual features.
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Affiliation(s)
- Haiguang Wen
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Junxing Shi
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Wei Chen
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA; School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA.
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215
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Van Uden CE, Nastase SA, Connolly AC, Feilong M, Hansen I, Gobbini MI, Haxby JV. Modeling Semantic Encoding in a Common Neural Representational Space. Front Neurosci 2018; 12:437. [PMID: 30042652 PMCID: PMC6048235 DOI: 10.3389/fnins.2018.00437] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Accepted: 06/11/2018] [Indexed: 12/12/2022] Open
Abstract
Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models.
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Affiliation(s)
- Cara E Van Uden
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Samuel A Nastase
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States.,Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Andrew C Connolly
- Department of Neurology, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Ma Feilong
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Isabella Hansen
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - M Ida Gobbini
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States.,Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale (DIMES), Medical School, University of Bologna, Bologna, Italy
| | - James V Haxby
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
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216
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Zafar R, Kamel N, Naufal M, Malik AS, Dass SC, Ahmad RF, Abdullah JM, Reza F. A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:633-645. [PMID: 29948968 DOI: 10.1007/s13246-018-0656-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Accepted: 06/05/2018] [Indexed: 10/14/2022]
Abstract
Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.
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Affiliation(s)
- Raheel Zafar
- Department of Engineering, National University of Modern Languages, Islamabad, Pakistan
| | - Nidal Kamel
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Perak, Malaysia
| | - Mohamad Naufal
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Perak, Malaysia
| | - Aamir Saeed Malik
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Perak, Malaysia.
| | - Sarat C Dass
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Perak, Malaysia
| | - Rana Fayyaz Ahmad
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Perak, Malaysia
| | - Jafri M Abdullah
- Center for Neuroscience Services and Research, Universiti Sains Malaysia, Kubang Kerian, 16150, Kota Bharu, Kelantan, Malaysia.,Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, 16150, Kota Bharu, Kelantan, Malaysia
| | - Faruque Reza
- Center for Neuroscience Services and Research, Universiti Sains Malaysia, Kubang Kerian, 16150, Kota Bharu, Kelantan, Malaysia.,Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, 16150, Kota Bharu, Kelantan, Malaysia
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217
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Zhang C, Qiao K, Wang L, Tong L, Zeng Y, Yan B. Constraint-Free Natural Image Reconstruction From fMRI Signals Based on Convolutional Neural Network. Front Hum Neurosci 2018; 12:242. [PMID: 29988371 PMCID: PMC6024000 DOI: 10.3389/fnhum.2018.00242] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Accepted: 05/28/2018] [Indexed: 11/30/2022] Open
Abstract
In recent years, research on decoding brain activity based on functional magnetic resonance imaging (fMRI) has made eye-catching achievements. However, constraint-free natural image reconstruction from brain activity remains a challenge, as specifying brain activity for all possible images is impractical. The problem was often simplified by using semantic prior information or just reconstructing simple images, including digitals and letters. Without semantic prior information, we present a novel method to reconstruct natural images from the fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN). First, we extracted the unit output of viewed natural images in each layer of a pre-trained CNN as CNN features. Second, we transformed image reconstruction from fMRI signals into the problem of CNN feature visualization by training a sparse linear regression to map from the fMRI patterns to CNN features. By iteratively optimization to find the matched image, whose CNN unit features become most similar to those predicted from the brain activity, we finally achieved the promising results for the challenging constraint-free natural image reconstruction. The semantic prior information of the stimuli was not used when training decoding model, and any category of images (not constraint by the training set) could be reconstructed theoretically. We found that the reconstructed images resembled the natural stimuli, especially in position and shape. The experimental results suggest that hierarchical visual features may be an effective tool to express the human visual processing.
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Affiliation(s)
- Chi Zhang
- National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China
| | - Kai Qiao
- National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China
| | - Linyuan Wang
- National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China
| | - Li Tong
- National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China
| | - Ying Zeng
- National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China.,Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Yan
- National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China
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218
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Nastase SA, Halchenko YO, Connolly AC, Gobbini MI, Haxby JV. Neural Responses to Naturalistic Clips of Behaving Animals in Two Different Task Contexts. Front Neurosci 2018; 12:316. [PMID: 29867327 PMCID: PMC5962655 DOI: 10.3389/fnins.2018.00316] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 04/24/2018] [Indexed: 12/30/2022] Open
Affiliation(s)
- Samuel A Nastase
- Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States.,Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States
| | - Andrew C Connolly
- Department of Neurology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - M Ida Gobbini
- Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States.,Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale, Medical School, University of Bologna, Bologna, Italy
| | - James V Haxby
- Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States
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219
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Tanaka K, Tanaka M, Kajiwara T, Wang HO. A Practical SSVEP-Based Algorithm for Perceptual Dominance Estimation in Binocular Rivalry. IEEE Trans Cogn Dev Syst 2018. [DOI: 10.1109/tcds.2017.2679224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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220
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Modality-Independent Coding of Scene Categories in Prefrontal Cortex. J Neurosci 2018; 38:5969-5981. [PMID: 29858483 DOI: 10.1523/jneurosci.0272-18.2018] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 05/03/2018] [Accepted: 05/26/2018] [Indexed: 11/21/2022] Open
Abstract
Natural environments convey information through multiple sensory modalities, all of which contribute to people's percepts. Although it has been shown that visual or auditory content of scene categories can be decoded from brain activity, it remains unclear how humans represent scene information beyond a specific sensory modality domain. To address this question, we investigated how categories of scene images and sounds are represented in several brain regions. A group of healthy human subjects (both sexes) participated in the present study, where their brain activity was measured with fMRI while viewing images or listening to sounds of different real-world environments. We found that both visual and auditory scene categories can be decoded not only from modality-specific areas, but also from several brain regions in the temporal, parietal, and prefrontal cortex (PFC). Intriguingly, only in the PFC, but not in any other regions, categories of scene images and sounds appear to be represented in similar activation patterns, suggesting that scene representations in PFC are modality-independent. Furthermore, the error patterns of neural decoders indicate that category-specific neural activity patterns in the middle and superior frontal gyri are tightly linked to categorization behavior. Our findings demonstrate that complex scene information is represented at an abstract level in the PFC, regardless of the sensory modality of the stimulus.SIGNIFICANCE STATEMENT Our experience in daily life includes multiple sensory inputs, such as images, sounds, or scents from the surroundings, which all contribute to our understanding of the environment. Here, for the first time, we investigated where and how in the brain information about the natural environment from multiple senses is merged to form modality-independent representations of scene categories. We show direct decoding of scene categories across sensory modalities from patterns of neural activity in the prefrontal cortex (PFC). We also conclusively tie these neural representations to human categorization behavior by comparing patterns of errors between a neural decoder and behavior. Our findings suggest that PFC is a central hub for integrating sensory information and computing modality-independent representations of scene categories.
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221
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Li C, Xu J, Liu B. Decoding natural images from evoked brain activities using encoding models with invertible mapping. Neural Netw 2018; 105:227-235. [PMID: 29870930 DOI: 10.1016/j.neunet.2018.05.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 04/23/2018] [Accepted: 05/14/2018] [Indexed: 10/16/2022]
Abstract
Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be directly decoded. To solve this problem, we designed a sparse framework-based encoding model that predicted brain activities from a complete feature representation. Moreover, according to the distribution and activation rules of neurons in the primary visual cortex (V1), three key transformations were introduced into the basic feature to improve the model performance. In this setting, the mapping was simple enough that it could be inverted using a closed-form formula. Using this mapping, we designed a hybrid identification method based on the support vector machine (SVM), and tested it on a published functional magnetic resonance imaging (fMRI) dataset. The experiments confirmed the rationality of our encoding model, and the identification accuracies for 2 subjects increased from 92% and 72% to 98% and 92% with the chance level only 0.8%.
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Affiliation(s)
- Chao Li
- School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin 300350, PR China
| | - Junhai Xu
- School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin 300350, PR China
| | - Baolin Liu
- School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin 300350, PR China; State Key Laboratory of Intelligent Technology and Systems, National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, PR China.
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222
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Liang Z, Hamada Y, Oba S, Ishii S. Characterization of electroencephalography signals for estimating saliency features in videos. Neural Netw 2018; 105:52-64. [PMID: 29763744 DOI: 10.1016/j.neunet.2018.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Revised: 04/05/2018] [Accepted: 04/18/2018] [Indexed: 11/27/2022]
Abstract
Understanding the functions of the visual system has been one of the major targets in neuroscience for many years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency. The optimal features were extracted from the EEG signals and saliency map which was computed according to an unsupervised saliency model (Tavakoli and Laaksonen, 2017). Subsequently, various unsupervised feature selection/extraction techniques were examined using different supervised regression models. The robustness of the presented model was fully verified by means of ten-fold or nested cross validation procedure, and promising results were achieved in the reconstruction of saliency features based on the selected EEG characteristics. Through the successful demonstration of using EEG characteristics to predict the real-time saliency distribution in natural videos, we suggest the feasibility of quantifying visual content through measuring brain activities (EEG signals) in real environments, which would facilitate the understanding of cortical involvement in the processing of natural visual stimuli and application developments motivated by human visual processing.
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Affiliation(s)
- Zhen Liang
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.
| | - Yasuyuki Hamada
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.
| | - Shigeyuki Oba
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.
| | - Shin Ishii
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan; ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan.
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223
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Exploring collective experience in watching dance through intersubject correlation and functional connectivity of fMRI brain activity. PROGRESS IN BRAIN RESEARCH 2018; 237:373-397. [PMID: 29779744 DOI: 10.1016/bs.pbr.2018.03.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
How the brain contends with naturalistic viewing conditions when it must cope with concurrent streams of diverse sensory inputs and internally generated thoughts is still largely an open question. In this study, we used fMRI to record brain activity while a group of 18 participants watched an edited dance duet accompanied by a soundtrack. After scanning, participants performed a short behavioral task to identify neural correlates of dance segments that could later be recalled. Intersubject correlation (ISC) analysis was used to identify the brain regions correlated among observers, and the results of this ISC map were used to define a set of regions for subsequent analysis of functional connectivity. The resulting network was found to be composed of eight subnetworks and the significance of these subnetworks is discussed. While most subnetworks could be explained by sensory and motor processes, two subnetworks appeared related more to complex cognition. These results inform our understanding of the neural basis of common experience in watching dance and open new directions for the study of complex cognition.
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224
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Botella-Soler V, Deny S, Martius G, Marre O, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. PLoS Comput Biol 2018; 14:e1006057. [PMID: 29746463 PMCID: PMC5944913 DOI: 10.1371/journal.pcbi.1006057] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 02/27/2018] [Indexed: 11/19/2022] Open
Abstract
Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed "pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains.
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Affiliation(s)
| | - Stéphane Deny
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
| | - Georg Martius
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Olivier Marre
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
| | - Gašper Tkačik
- Institute of Science and Technology Austria, Klosterneuburg, Austria
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225
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Nastase SA, Connolly AC, Oosterhof NN, Halchenko YO, Guntupalli JS, Visconti di Oleggio Castello M, Gors J, Gobbini MI, Haxby JV. Attention Selectively Reshapes the Geometry of Distributed Semantic Representation. Cereb Cortex 2018; 27:4277-4291. [PMID: 28591837 PMCID: PMC6248820 DOI: 10.1093/cercor/bhx138] [Citation(s) in RCA: 58] [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] [Received: 07/22/2016] [Accepted: 05/17/2017] [Indexed: 12/30/2022] Open
Abstract
Humans prioritize different semantic qualities of a complex stimulus depending on their
behavioral goals. These semantic features are encoded in distributed neural populations,
yet it is unclear how attention might operate across these distributed representations. To
address this, we presented participants with naturalistic video clips of animals behaving
in their natural environments while the participants attended to either behavior or
taxonomy. We used models of representational geometry to investigate how attentional
allocation affects the distributed neural representation of animal behavior and taxonomy.
Attending to animal behavior transiently increased the discriminability of distributed
population codes for observed actions in anterior intraparietal, pericentral, and ventral
temporal cortices. Attending to animal taxonomy while viewing the same stimuli increased
the discriminability of distributed animal category representations in ventral temporal
cortex. For both tasks, attention selectively enhanced the discriminability of response
patterns along behaviorally relevant dimensions. These findings suggest that behavioral
goals alter how the brain extracts semantic features from the visual world. Attention
effectively disentangles population responses for downstream read-out by sculpting
representational geometry in late-stage perceptual areas.
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Affiliation(s)
- Samuel A. Nastase
- Department of Psychological and Brain Sciences, Dartmouth
College, Hanover, NH 03755,
USA
- Address correspondence to Samuel A. Nastase, 6207 Moore Hall, Hanover, NH
03755, USA.
| | - Andrew C. Connolly
- Department of Psychological and Brain Sciences, Dartmouth
College, Hanover, NH 03755,
USA
- Department of Neurology, Geisel School of Medicine at
Dartmouth, Hanover, NH 03755,
USA
| | - Nikolaas N. Oosterhof
- Center for Mind/Brain Sciences, Università degli Studi
di Trento, 38068 Rovereto,
Italy
| | - Yaroslav O. Halchenko
- Department of Psychological and Brain Sciences, Dartmouth
College, Hanover, NH 03755,
USA
| | - J. Swaroop Guntupalli
- Department of Psychological and Brain Sciences, Dartmouth
College, Hanover, NH 03755,
USA
| | | | - Jason Gors
- Department of Psychological and Brain Sciences, Dartmouth
College, Hanover, NH 03755,
USA
| | - M. Ida Gobbini
- Department of Psychological and Brain Sciences, Dartmouth
College, Hanover, NH 03755,
USA
- Department of Medicina Specialistica, Diagnostica e
Sperimentale (DIMES), Medical School,
University of Bologna, 40126 Bologna,
Italy
| | - James V. Haxby
- Department of Psychological and Brain Sciences, Dartmouth
College, Hanover, NH 03755,
USA
- Center for Mind/Brain Sciences, Università degli Studi
di Trento, 38068 Rovereto,
Italy
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226
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Papale P, Leo A, Cecchetti L, Handjaras G, Kay KN, Pietrini P, Ricciardi E. Foreground-Background Segmentation Revealed during Natural Image Viewing. eNeuro 2018; 5:ENEURO.0075-18.2018. [PMID: 29951579 PMCID: PMC6019392 DOI: 10.1523/eneuro.0075-18.2018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 05/15/2018] [Accepted: 05/15/2018] [Indexed: 11/21/2022] Open
Abstract
One of the major challenges in visual neuroscience is represented by foreground-background segmentation. Data from nonhuman primates show that segmentation leads to two distinct, but associated processes: the enhancement of neural activity during figure processing (i.e., foreground enhancement) and the suppression of background-related activity (i.e., background suppression). To study foreground-background segmentation in ecological conditions, we introduce a novel method based on parametric modulation of low-level image properties followed by application of simple computational image-processing models. By correlating the outcome of this procedure with human fMRI activity, measured during passive viewing of 334 natural images, we produced easily interpretable "correlation images" from visual populations. Results show evidence of foreground enhancement in all tested regions, from V1 to lateral occipital complex (LOC), while background suppression occurs in V4 and LOC only. Correlation images derived from V4 and LOC revealed a preserved spatial resolution of foreground textures, indicating a richer representation of the salient part of natural images, rather than a simplistic model of object shape. Our results indicate that scene segmentation occurs during natural viewing, even when individuals are not required to perform any particular task.
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Affiliation(s)
- Paolo Papale
- Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, 55100 Italy
| | - Andrea Leo
- Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, 55100 Italy
| | - Luca Cecchetti
- Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, 55100 Italy
| | - Giacomo Handjaras
- Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, 55100 Italy
| | - Kendrick N. Kay
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Twin Cities, Minneapolis, MN, 55455
| | - Pietro Pietrini
- Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, 55100 Italy
| | - Emiliano Ricciardi
- Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, 55100 Italy
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227
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Guntupalli JS, Feilong M, Haxby JV. A computational model of shared fine-scale structure in the human connectome. PLoS Comput Biol 2018; 14:e1006120. [PMID: 29664910 PMCID: PMC5922579 DOI: 10.1371/journal.pcbi.1006120] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 04/27/2018] [Accepted: 04/03/2018] [Indexed: 12/20/2022] Open
Abstract
Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study. Resting state fMRI has become a ubiquitous tool for measuring connectivity in normal and diseased brains. Current dominant models of connectivity are based on coarse-scale connectivity among brain regions, ignoring fine-scale structure within those regions. We developed a high-dimensional common model of the human connectome that captures both coarse and fine-scale structure of connectivity shared across brains. We showed that this shared fine-scale structure is related to fine-scale distinctions in representation of information, and our model accounts for substantially more shared variance of connectivity compared to previous models. Our model opens new territory—shared fine-scale structure, a dominant but mostly unexplored component of the human connectome—for analysis and study.
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Affiliation(s)
- J. Swaroop Guntupalli
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States of America
- Vicarious AI, Union City, CA, United States of America
| | - Ma Feilong
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States of America
| | - James V. Haxby
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States of America
- * E-mail:
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228
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Difference in Subjective Accessibility of On Demand Recall of Visual, Taste, and Olfactory Memories. BIOMED RESEARCH INTERNATIONAL 2018; 2018:1630437. [PMID: 29546049 PMCID: PMC5818939 DOI: 10.1155/2018/1630437] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Revised: 11/22/2017] [Accepted: 12/03/2017] [Indexed: 11/18/2022]
Abstract
We present here significant difference in the evocation capability between sensory memories (visual, taste, and olfactory) throughout certain categories of the population. As object for this memory recall we selected French fries that are simple and generally known. From daily life we may intuitively feel that there is much better recall of the visual and auditory memory compared to the taste and olfactory ones. Our results in young (age 12-21 years) mostly females and some males show low capacity for smell and taste memory recall compared to far greater visual memory recall. This situation raises question whether we could train smell and taste memory recall so that it could become similar to visual or auditory ones. In our article we design technique of the volunteers training that could potentially lead to an increase in the capacity of their taste and olfactory memory recollection.
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229
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Pereira F, Lou B, Pritchett B, Ritter S, Gershman SJ, Kanwisher N, Botvinick M, Fedorenko E. Toward a universal decoder of linguistic meaning from brain activation. Nat Commun 2018; 9:963. [PMID: 29511192 PMCID: PMC5840373 DOI: 10.1038/s41467-018-03068-4] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 01/13/2018] [Indexed: 11/09/2022] Open
Abstract
Prior work decoding linguistic meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of meaning relationships between sentences.
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Affiliation(s)
- Francisco Pereira
- Medical Imaging Technologies, Siemens Healthineers, Princeton, NJ 08540, USA.
| | - Bin Lou
- Medical Imaging Technologies, Siemens Healthineers, Princeton, NJ 08540, USA
| | - Brianna Pritchett
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA
| | | | - Samuel J Gershman
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
| | - Nancy Kanwisher
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA
| | - Matthew Botvinick
- DeepMind, London, N1C 4AG, UK
- Gatsby Computational Neuroscience Unit, University College London, London, WC1E 6BT, UK
| | - Evelina Fedorenko
- McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA.
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230
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Polimeni JR, Renvall V, Zaretskaya N, Fischl B. Analysis strategies for high-resolution UHF-fMRI data. Neuroimage 2018; 168:296-320. [PMID: 28461062 PMCID: PMC5664177 DOI: 10.1016/j.neuroimage.2017.04.053] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Revised: 04/21/2017] [Accepted: 04/22/2017] [Indexed: 12/22/2022] Open
Abstract
Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.
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Affiliation(s)
- Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States.
| | - Ville Renvall
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Natalia Zaretskaya
- Centre for Integrative Neuroscience, Department of Psychology, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
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231
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Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization. Sci Rep 2018; 8:3752. [PMID: 29491405 PMCID: PMC5830584 DOI: 10.1038/s41598-018-22160-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 02/19/2018] [Indexed: 11/12/2022] Open
Abstract
The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.
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232
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Zhao S, Han J, Jiang X, Huang H, Liu H, Lv J, Guo L, Liu T. Decoding Auditory Saliency from Brain Activity Patterns during Free Listening to Naturalistic Audio Excerpts. Neuroinformatics 2018; 16:309-324. [DOI: 10.1007/s12021-018-9358-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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233
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The Neural Dynamics of Facial Identity Processing: Insights from EEG-Based Pattern Analysis and Image Reconstruction. eNeuro 2018; 5:eN-NWR-0358-17. [PMID: 29492452 PMCID: PMC5829556 DOI: 10.1523/eneuro.0358-17.2018] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 01/11/2018] [Accepted: 01/12/2018] [Indexed: 11/21/2022] Open
Abstract
Uncovering the neural dynamics of facial identity processing along with its representational basis outlines a major endeavor in the study of visual processing. To this end, here, we record human electroencephalography (EEG) data associated with viewing face stimuli; then, we exploit spatiotemporal EEG information to determine the neural correlates of facial identity representations and to reconstruct the appearance of the corresponding stimuli. Our findings indicate that multiple temporal intervals support: facial identity classification, face space estimation, visual feature extraction and image reconstruction. In particular, we note that both classification and reconstruction accuracy peak in the proximity of the N170 component. Further, aggregate data from a larger interval (50–650 ms after stimulus onset) support robust reconstruction results, consistent with the availability of distinct visual information over time. Thus, theoretically, our findings shed light on the time course of face processing while, methodologically, they demonstrate the feasibility of EEG-based image reconstruction.
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234
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Pack CC, Theobald JC. Fruit flies are multistable geniuses. PLoS Biol 2018; 16:e2005429. [PMID: 29444072 PMCID: PMC5828447 DOI: 10.1371/journal.pbio.2005429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 02/27/2018] [Indexed: 11/18/2022] Open
Abstract
Our sensory systems have evolved to provide us with information about the external world. Such information is useful only insofar as it leads to actions that enhance fitness, and thus, the link between sensation and action has been thoroughly studied in many species. In insects, for example, specific visual stimuli lead to highly stereotyped responses. In contrast, humans can exhibit a wide range of responses to the same stimulus, as occurs most notably in the phenomenon of multistable perception. On this basis, one might think that humans have a fundamentally different way of generating actions from sensory inputs, but Toepfer et al. show that flies show evidence of multistable perception as well. Specifically, when confronted with a sensory stimulus that can yield different motor responses, flies switch from one response to another with temporal dynamics that are similar to those of humans and other animals. This suggests that the mechanisms that give rise to the rich repertoire of sensory experience in humans have correlates in much simpler nervous systems.
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Affiliation(s)
- Christopher C. Pack
- Department of Neurology & Neurosurgery, McGill University, Montreal, Quebec, Canada
- * E-mail:
| | - Jamie C. Theobald
- Department of Biological Sciences, Florida International University, Miami, Florida, United States of America
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235
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Saxe R. Seeing Other Minds in 3D. Trends Cogn Sci 2018; 22:193-195. [PMID: 29482823 DOI: 10.1016/j.tics.2018.01.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 01/03/2018] [Indexed: 01/20/2023]
Affiliation(s)
- Rebecca Saxe
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 46-4019 MIT, 43 Vassar Street, Cambridge, MA 02139, USA.
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236
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Hoefle S, Engel A, Basilio R, Alluri V, Toiviainen P, Cagy M, Moll J. Identifying musical pieces from fMRI data using encoding and decoding models. Sci Rep 2018; 8:2266. [PMID: 29396524 PMCID: PMC5797093 DOI: 10.1038/s41598-018-20732-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 01/11/2018] [Indexed: 12/04/2022] Open
Abstract
Encoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that first mapped musical features in auditory cortices and then decoded novel musical pieces. We then probed the influence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We further showed that Shannon entropy is a driving factor, boosting accuracy up to 95% for music with highest information content. These findings provide key insights for future decoding and reconstruction algorithms and open new venues for possible clinical applications.
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Affiliation(s)
- Sebastian Hoefle
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil.,Biomedical Engineering Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Annerose Engel
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil.,Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany.,Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Rodrigo Basilio
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Vinoo Alluri
- Finnish Centre for Interdisciplinary Music Research, Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, Finland.,International Institute of Information Technology, Gachibowli, Hyderabad, India
| | - Petri Toiviainen
- Finnish Centre for Interdisciplinary Music Research, Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, Finland
| | - Maurício Cagy
- Biomedical Engineering Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jorge Moll
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil.
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237
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Levin M, Martyniuk CJ. The bioelectric code: An ancient computational medium for dynamic control of growth and form. Biosystems 2018; 164:76-93. [PMID: 28855098 PMCID: PMC10464596 DOI: 10.1016/j.biosystems.2017.08.009] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 08/20/2017] [Accepted: 08/22/2017] [Indexed: 12/19/2022]
Abstract
What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. Cracking the bioelectric code will enable much-improved control over biological patterning, advancing basic evolutionary developmental biology as well as enabling numerous applications in regenerative medicine and synthetic bioengineering.
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Affiliation(s)
- Michael Levin
- Allen Discovery Center at Tufts University, Biology Department, Tufts University, 200 Boston Avenue, Suite 4600 Medford, MA 02155, USA.
| | - Christopher J Martyniuk
- Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida Genetics Institute, Interdisciplinary Program in Biomedical Sciences Neuroscience, College of Veterinary Medicine, University of Florida, Gainesville, FL, 32611, USA
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238
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Hu X, Guo L, Han J, Liu T. Decoding power-spectral profiles from FMRI brain activities during naturalistic auditory experience. Brain Imaging Behav 2018; 11:253-263. [PMID: 26860834 DOI: 10.1007/s11682-016-9515-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Recent studies have demonstrated a close relationship between computational acoustic features and neural brain activities, and have largely advanced our understanding of auditory information processing in the human brain. Along this line, we proposed a multidisciplinary study to examine whether power spectral density (PSD) profiles can be decoded from brain activities during naturalistic auditory experience. The study was performed on a high resolution functional magnetic resonance imaging (fMRI) dataset acquired when participants freely listened to the audio-description of the movie "Forrest Gump". Representative PSD profiles existing in the audio-movie were identified by clustering the audio samples according to their PSD descriptors. Support vector machine (SVM) classifiers were trained to differentiate the representative PSD profiles using corresponding fMRI brain activities. Based on PSD profile decoding, we explored how the neural decodability correlated to power intensity and frequency deviants. Our experimental results demonstrated that PSD profiles can be reliably decoded from brain activities. We also suggested a sigmoidal relationship between the neural decodability and power intensity deviants of PSD profiles. Our study in addition substantiates the feasibility and advantage of naturalistic paradigm for studying neural encoding of complex auditory information.
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Affiliation(s)
- Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
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239
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Haller S, Zanchi D, Rodriguez C, Giannakopoulos P. Brain Structural Imaging in Alzheimer’s Disease. NEUROMETHODS 2018. [DOI: 10.1007/978-1-4939-7674-4_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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240
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Abstract
Psychology moved beyond the stimulus response mapping of behaviorism by adopting an information processing framework. This shift from behavioral to cognitive science was partly inspired by work demonstrating that the concept of information could be defined and quantified (Shannon, 1948). This transition developed further from cognitive science into cognitive neuroscience, in an attempt to measure information in the brain. In the cognitive neurosciences, however, the term information is often used without a clear definition. This paper will argue that, if the formulation proposed by Shannon is applied to modern neuroimaging, then numerous results would be interpreted differently. More specifically, we argue that much modern cognitive neuroscience implicitly focuses on the question of how we can interpret the activations we record in the brain (experimenter-as-receiver), rather than on the core question of how the rest of the brain can interpret those activations (cortex-as-receiver). A clearer focus on whether activations recorded via neuroimaging can actually act as information in the brain would not only change how findings are interpreted but should also change the direction of empirical research in cognitive neuroscience.
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241
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Abstract
How is temporal information processed in human visual cortex? Visual input is relayed to V1 through segregated transient and sustained channels in the retina and lateral geniculate nucleus (LGN). However, there is intense debate as to how sustained and transient temporal channels contribute to visual processing beyond V1. The prevailing view associates transient processing predominately with motion-sensitive regions and sustained processing with ventral stream regions, while the opposing view suggests that both temporal channels contribute to neural processing beyond V1. Using fMRI, we measured cortical responses to time-varying stimuli and then implemented a two temporal channel-encoding model to evaluate the contributions of each channel. Different from the general linear model of fMRI that predicts responses directly from the stimulus, the encoding approach first models neural responses to the stimulus from which fMRI responses are derived. This encoding approach not only predicts cortical responses to time-varying stimuli from milliseconds to seconds but also, reveals differential contributions of temporal channels across visual cortex. Consistent with the prevailing view, motion-sensitive regions and adjacent lateral occipitotemporal regions are dominated by transient responses. However, ventral occipitotemporal regions are driven by both sustained and transient channels, with transient responses exceeding the sustained. These findings propose a rethinking of temporal processing in the ventral stream and suggest that transient processing may contribute to rapid extraction of the content of the visual input. Importantly, our encoding approach has vast implications, because it can be applied with fMRI to decipher neural computations in millisecond resolution in any part of the brain.
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242
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Chang KH, Thomas JM, Boynton GM, Fine I. Reconstructing Tone Sequences from Functional Magnetic Resonance Imaging Blood-Oxygen Level Dependent Responses within Human Primary Auditory Cortex. Front Psychol 2017; 8:1983. [PMID: 29184522 PMCID: PMC5694557 DOI: 10.3389/fpsyg.2017.01983] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 10/30/2017] [Indexed: 01/12/2023] Open
Abstract
Here we show that, using functional magnetic resonance imaging (fMRI) blood-oxygen level dependent (BOLD) responses in human primary auditory cortex, it is possible to reconstruct the sequence of tones that a person has been listening to over time. First, we characterized the tonotopic organization of each subject’s auditory cortex by measuring auditory responses to randomized pure tone stimuli and modeling the frequency tuning of each fMRI voxel as a Gaussian in log frequency space. Then, we tested our model by examining its ability to work in reverse. Auditory responses were re-collected in the same subjects, except this time they listened to sequences of frequencies taken from simple songs (e.g., “Somewhere Over the Rainbow”). By finding the frequency that minimized the difference between the model’s prediction of BOLD responses and actual BOLD responses, we were able to reconstruct tone sequences, with mean frequency estimation errors of half an octave or less, and little evidence of systematic biases.
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Affiliation(s)
- Kelly H Chang
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Jessica M Thomas
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Geoffrey M Boynton
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Ione Fine
- Department of Psychology, University of Washington, Seattle, WA, United States
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243
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Robust inter-subject audiovisual decoding in functional magnetic resonance imaging using high-dimensional regression. Neuroimage 2017; 163:244-263. [DOI: 10.1016/j.neuroimage.2017.09.032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Revised: 09/14/2017] [Accepted: 09/17/2017] [Indexed: 11/23/2022] Open
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244
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Kraft CJ, Giordano J. Integrating Brain Science and Law: Neuroscientific Evidence and Legal Perspectives on Protecting Individual Liberties. Front Neurosci 2017; 11:621. [PMID: 29167633 PMCID: PMC5682320 DOI: 10.3389/fnins.2017.00621] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 10/24/2017] [Indexed: 12/02/2022] Open
Abstract
Advances in neuroscientific techniques have found increasingly broader applications, including in legal neuroscience (or “neurolaw”), where experts in the brain sciences are called to testify in the courtroom. But does the incursion of neuroscience into the legal sphere constitute a threat to individual liberties? And what legal protections are there against such threats? In this paper, we outline individual rights as they interact with neuroscientific methods. We then proceed to examine the current uses of neuroscientific evidence, and ultimately determine whether the rights of the individual are endangered by such approaches. Based on our analysis, we conclude that while federal evidence rules constitute a substantial hurdle for the use of neuroscientific evidence, more ethical safeguards are needed to protect against future violations of fundamental rights. Finally, we assert that it will be increasingly imperative for the legal and neuroscientific communities to work together to better define the limits, capabilities, and intended direction of neuroscientific methods applicable for use in law.
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Affiliation(s)
- Calvin J Kraft
- Program of Liberal Studies, Neuroscience and Behavior, University of Notre Dame, Notre Dame, IN, United States.,Departments of Neurology and Biochemistry, Pellegrino Center for Clinical Bioethics, Georgetown University Medical Center, Washington, DC, United States
| | - James Giordano
- Departments of Neurology and Biochemistry, Pellegrino Center for Clinical Bioethics, Georgetown University Medical Center, Washington, DC, United States
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245
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Consciousness Regained: Disentangling Mechanisms, Brain Systems, and Behavioral Responses. J Neurosci 2017; 37:10882-10893. [PMID: 29118218 DOI: 10.1523/jneurosci.1838-17.2017] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 09/26/2017] [Accepted: 09/27/2017] [Indexed: 11/21/2022] Open
Abstract
How consciousness (experience) arises from and relates to material brain processes (the "mind-body problem") has been pondered by thinkers for centuries, and is regarded as among the deepest unsolved problems in science, with wide-ranging theoretical, clinical, and ethical implications. Until the last few decades, this was largely seen as a philosophical topic, but not widely accepted in mainstream neuroscience. Since the 1980s, however, novel methods and theoretical advances have yielded remarkable results, opening up the field for scientific and clinical progress. Since a seminal paper by Crick and Koch (1998) claimed that a science of consciousness should first search for its neural correlates (NCC), a variety of correlates have been suggested, including both content-specific NCCs, determining particular phenomenal components within an experience, and the full NCC, the neural substrates supporting entire conscious experiences. In this review, we present recent progress on theoretical, experimental, and clinical issues. Specifically, we (1) review methodological advances that are important for dissociating conscious experience from related enabling and executive functions, (2) suggest how critically reconsidering the role of the frontal cortex may further delineate NCCs, (3) advocate the need for general, objective, brain-based measures of the capacity for consciousness that are independent of sensory processing and executive functions, and (4) show how animal studies can reveal population and network phenomena of relevance for understanding mechanisms of consciousness.
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246
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Mandelkow H, de Zwart J, Duyn J. Effects of spatial fMRI resolution on the classification of naturalistic movies. Neuroimage 2017; 162:45-55. [PMID: 28842385 PMCID: PMC9881349 DOI: 10.1016/j.neuroimage.2017.08.053] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 08/14/2017] [Accepted: 08/20/2017] [Indexed: 01/31/2023] Open
Abstract
Studies involving multivariate pattern analysis (MVPA) of BOLD fMRI data generally attribute the success of the information-theoretic approach to BOLD signal contrast on the fine spatial scale of millimeters facilitating the classification or decoding of perceptual stimuli. However, to date MVPA studies that have actually explored fMRI resolutions at less than 2 mm voxel size are rare and limited to small sets of unnatural stimuli (like visual gratings) as well as specific sub-regions of the brain, notably the primary somatosensory cortices. To investigate what spatial scale best supports high information extraction under more general conditions this study combined naturalistic movie stimuli with high-resolution fMRI at 7 T and linear discriminant analysis (LDA) of global and local BOLD signal patterns. Contrary to predictions, LDA and similar classifiers reached a maximum in classification accuracy (CA) at a smoothed resolution close to 3 mm, well above the 1.2 mm voxel size of the fMRI acquisition. Maximal CAs around 90% were contingent upon global fMRI signal patterns comprising 4 k-16 k of the most reactive voxels distributed sparsely throughout the occipital and ventro-temporal cortices. A Searchlight analysis of local fMRI patterns largely confirmed the global results, but also revealed a small subset of brain regions in early visual cortex showing limited increases in CA with higher resolution. Principal component analysis of the global and local fMRI signal patterns suggested that reproducible neuronal contributions were spatially auto-correlated and smooth, while other components of higher spatial frequency were likely related to physiological noise and responsible for the reduced CA at higher resolution. Systematic differences between experiments and subjects suggested that higher CA was significantly correlated with more consistent behavior revealed by eye tracking. Thus, the optimal resolution of fMRI data for MVPA was mainly limited by physiological noise of high spatial frequency as well as behavioral (in-)consistency.
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247
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Ito T, Kulkarni KR, Schultz DH, Mill RD, Chen RH, Solomyak LI, Cole MW. Cognitive task information is transferred between brain regions via resting-state network topology. Nat Commun 2017; 8:1027. [PMID: 29044112 PMCID: PMC5715061 DOI: 10.1038/s41467-017-01000-w] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 08/10/2017] [Indexed: 11/17/2022] Open
Abstract
Resting-state network connectivity has been associated with a variety of cognitive abilities, yet it remains unclear how these connectivity properties might contribute to the neurocognitive computations underlying these abilities. We developed a new approach—information transfer mapping—to test the hypothesis that resting-state functional network topology describes the computational mappings between brain regions that carry cognitive task information. Here, we report that the transfer of diverse, task-rule information in distributed brain regions can be predicted based on estimated activity flow through resting-state network connections. Further, we find that these task-rule information transfers are coordinated by global hub regions within cognitive control networks. Activity flow over resting-state connections thus provides a large-scale network mechanism for cognitive task information transfer and global information coordination in the human brain, demonstrating the cognitive relevance of resting-state network topology. Resting-state functional connections have been associated with cognitive abilities but it is unclear how these connections contribute to cognition. Here Ito et al present a new approach, information transfer mapping, showing that task-relevant information can be predicted by estimated activity flow through resting-state networks.
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Affiliation(s)
- Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA. .,Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, NJ, 07102, USA.
| | - Kaustubh R Kulkarni
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Douglas H Schultz
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Richard H Chen
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA.,Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, NJ, 07102, USA
| | - Levi I Solomyak
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
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248
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Ruffini G. An algorithmic information theory of consciousness. Neurosci Conscious 2017; 2017:nix019. [PMID: 30042851 PMCID: PMC6007168 DOI: 10.1093/nc/nix019] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 07/24/2017] [Accepted: 07/27/2017] [Indexed: 11/13/2022] Open
Abstract
Providing objective metrics of conscious state is of great interest across multiple research and clinical fields-from neurology to artificial intelligence. Here we approach this challenge by proposing plausible mechanisms for the phenomenon of structured experience. In earlier work, we argued that the experience we call reality is a mental construct derived from information compression. Here we show that algorithmic information theory provides a natural framework to study and quantify consciousness from neurophysiological or neuroimaging data, given the premise that the primary role of the brain is information processing. We take as an axiom that "there is consciousness" and focus on the requirements for structured experience: we hypothesize that the existence and use of compressive models by cognitive systems, e.g. in biological recurrent neural networks, enables and provides the structure to phenomenal experience. Self-awareness is seen to arise naturally (as part of a better model) in cognitive systems interacting bidirectionally with the external world. Furthermore, we argue that by running such models to track data, brains can give rise to apparently complex (entropic but hierarchically organized) data. We compare this theory, named KT for its basis on the mathematical theory of Kolmogorov complexity, to other information-centric theories of consciousness. We then describe methods to study the complexity of the brain's output streams or of brain state as correlates of conscious state: we review methods such as (i) probing the brain through its input streams (e.g. event-related potentials in oddball paradigms or mutual algorithmic information between world and brain), (ii) analyzing spontaneous brain state, (iii) perturbing the brain by non-invasive transcranial stimulation, and (iv) quantifying behavior (e.g. eye movements or body sway).
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Affiliation(s)
- Giulio Ruffini
- Starlab Barcelona, Avda. Tibidabo 47bis, 08035 Barcelona, Spain and Neuroelectrics Corporation, 210 Broadway, Cambridge, MA 02139, USA
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249
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Boly M, Massimini M, Tsuchiya N, Postle BR, Koch C, Tononi G. Are the Neural Correlates of Consciousness in the Front or in the Back of the Cerebral Cortex? Clinical and Neuroimaging Evidence. J Neurosci 2017; 37:9603-9613. [PMID: 28978697 PMCID: PMC5628406 DOI: 10.1523/jneurosci.3218-16.2017] [Citation(s) in RCA: 245] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Revised: 06/28/2017] [Accepted: 06/30/2017] [Indexed: 01/14/2023] Open
Abstract
The role of the frontal cortex in consciousness remains a matter of debate. In this Perspective, we will critically review the clinical and neuroimaging evidence for the involvement of the front versus the back of the cortex in specifying conscious contents and discuss promising research avenues.Dual Perspectives Companion Paper: Should a Few Null Findings Falsify Prefrontal Theories of Conscious Perception?, by Brian Odegaard, Robert T. Knight, and Hakwan Lau.
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Affiliation(s)
- Melanie Boly
- Department of Neurology, University of Wisconsin, Madison, Wisconsin 53705,
- Department of Psychiatry, University of Wisconsin, Madison, Wisconsin 53719
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan 20157, Italy
- Instituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan 20148, Italy
| | - Naotsugu Tsuchiya
- School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, 3800 Victoria, Australia
- Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Melbourne, 3800 Victoria, Australia
| | - Bradley R Postle
- Department of Psychiatry, University of Wisconsin, Madison, Wisconsin 53719
- Department of Psychology, University of Wisconsin, Madison, Wisconsin 53705, and
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington 98109
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin, Madison, Wisconsin 53719,
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250
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Barsalou LW. What does semantic tiling of the cortex tell us about semantics? Neuropsychologia 2017; 105:18-38. [DOI: 10.1016/j.neuropsychologia.2017.04.011] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Revised: 04/05/2017] [Accepted: 04/06/2017] [Indexed: 11/30/2022]
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