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Polimeni JR, Lewis LD. Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response. Prog Neurobiol 2021; 207:102174. [PMID: 34525404 PMCID: PMC8688322 DOI: 10.1016/j.pneurobio.2021.102174] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 07/30/2021] [Accepted: 09/08/2021] [Indexed: 12/20/2022]
Abstract
Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.
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Affiliation(s)
- Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Laura D Lewis
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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2
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Yoo SH, Santosa H, Kim CS, Hong KS. Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study. Front Hum Neurosci 2021; 15:636191. [PMID: 33994978 PMCID: PMC8113416 DOI: 10.3389/fnhum.2021.636191] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/31/2021] [Indexed: 11/13/2022] Open
Abstract
This study aims to decode the hemodynamic responses (HRs) evoked by multiple sound-categories using functional near-infrared spectroscopy (fNIRS). The six different sounds were given as stimuli (English, non-English, annoying, nature, music, and gunshot). The oxy-hemoglobin (HbO) concentration changes are measured in both hemispheres of the auditory cortex while 18 healthy subjects listen to 10-s blocks of six sound-categories. Long short-term memory (LSTM) networks were used as a classifier. The classification accuracy was 20.38 ± 4.63% with six class classification. Though LSTM networks' performance was a little higher than chance levels, it is noteworthy that we could classify the data subject-wise without feature selections.
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Affiliation(s)
- So-Hyeon Yoo
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Hendrik Santosa
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Chang-Seok Kim
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
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3
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Kuntzelman KM, Williams JM, Lim PC, Samal A, Rao PK, Johnson MR. Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox. Front Hum Neurosci 2021; 15:638052. [PMID: 33737872 PMCID: PMC7960649 DOI: 10.3389/fnhum.2021.638052] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/08/2021] [Indexed: 11/17/2022] Open
Abstract
In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.
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Affiliation(s)
- Karl M Kuntzelman
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States.,Office of Technology Development and Coordination, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Jacob M Williams
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Phui Cheng Lim
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States.,Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Ashok Samal
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Prahalada K Rao
- Department of Mechanical and Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Matthew R Johnson
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States.,Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States
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4
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Choupan J, Douglas PK, Gal Y, Cohen MS, Reutens DC, Yang Z. Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy. J Neurosci Methods 2020; 345:108836. [PMID: 32726664 DOI: 10.1016/j.jneumeth.2020.108836] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 06/24/2020] [Accepted: 06/28/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND In fMRI decoding, temporal embedding of spatial features of the brain allows the incorporation of brain activity dynamics into the multivariate pattern classification process, and provides enriched information about stimulus-specific response patterns and potentially improved prediction accuracy. NEW METHOD This study investigates the possibility of enhancing the classification performance by exploring temporal embedding, to identify the optimum combination of spatiotemporal features based on their classification performance. We investigated the importance of spatiotemporal feature selection using a slow event-related design adapted from the classic Haxby study (Haxby et al., 2001). Data were collected using a multiband fMRI sequence with temporal resolution of 0.568 s. COMPARISON WITH EXISTING METHODS A wide range of spatiotemporal observations were created as various combinations of spatiotemporal features. Using both random forest, and support vector machine, classifiers prediction accuracies for these combinations were then compared with the single spatial multivariate pattern approach that uses only a single temporal observation. RESULTS Our findings showed that, on average, spatiotemporal feature selection improved prediction accuracy. Moreover, the random forest algorithm outperformed the support vector machine and benefitted from temporal information to a greater extent. CONCLUSIONS As expected, the most influential temporal durations were found to be around the peak of the hemodynamic response function, a few seconds after the stimuli onset until -4 s after the peak of the hemodynamic response function. The superiority of spatiotemporal feature selection over single time-point spatial approaches invites future work to design optimal approaches that incorporate spatiotemporal dependencies into feature selection for decoding.
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Affiliation(s)
- Jeiran Choupan
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Department of Psychology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, USA; Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Pamela K Douglas
- Center for Cognitive Neuroscience, University of California, Los Angeles, CA, USA; Modeling & Simulation, and Computer Science Departments, UCF, Florida, USA
| | - Yaniv Gal
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Mark S Cohen
- Neuropsychiatric Institute, University of California, Los Angeles, CA, USA; Departments of Psychiatry and Behavioral Sciences, Neurology, Radiological Sciences, Biomedical Physics, Psychology, and Bioengineering, University of California, Los Angeles, CA, USA; California Nanosystems Institute UCLA School of Medicine, Los Angeles, CA, USA
| | - David C Reutens
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Zhengyi Yang
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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5
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Li J, Guo B, Cui L, Huang H, Meng M. Dissociated modulations of multivoxel activation patterns in the ventral and dorsal visual pathways by the temporal dynamics of stimuli. Brain Behav 2020; 10:e01673. [PMID: 32496013 PMCID: PMC7375111 DOI: 10.1002/brb3.1673] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/12/2020] [Accepted: 04/30/2020] [Indexed: 01/29/2023] Open
Abstract
INTRODUCTION Previous studies suggested temporal limitations of visual object identification in the ventral pathway. Moreover, multivoxel pattern analyses (MVPA) of fMRI activation have shown reliable encoding of various object categories including faces and tools in the ventral pathway. By contrast, the dorsal pathway is involved in reaching a target and grasping a tool, and quicker in processing the temporal dynamics of stimulus change. However, little is known about how activation patterns in both pathways may change according to the temporal dynamics of stimulus change. METHODS Here, we measured fMRI responses of two consecutive stimuli with varying interstimulus intervals (ISIs), and we compared how the two visual pathways respond to the dynamics of stimuli by using MVPA and information-based searchlight mapping. RESULTS We found that the temporal dynamics of stimuli modulate responses of the two visual pathways in opposite directions. Specifically, slower temporal dynamics (longer ISIs) led to greater activity and better MVPA results in the ventral pathway. However, faster temporal dynamics (shorter ISIs) led to greater activity and better MVPA results in the dorsal pathway. CONCLUSIONS These results are the first to show how temporal dynamics of stimulus change modulated multivoxel fMRI activation pattern change. And such temporal dynamic response function in different ROIs along the two visual pathways may shed lights on understanding functional relationship and organization of these ROIs.
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Affiliation(s)
- Jiaxin Li
- School of PsychologySouth China Normal UniversityGuangzhouChina
| | - Bingbing Guo
- School of PsychologySouth China Normal UniversityGuangzhouChina
| | - Lin Cui
- School of PsychologySouth China Normal UniversityGuangzhouChina
| | - Hong Huang
- School of PsychologySouth China Normal UniversityGuangzhouChina
| | - Ming Meng
- School of PsychologySouth China Normal UniversityGuangzhouChina
- Key Laboratory of BrainCognition and Education Sciences (South China Normal University)Ministry of EducationGuangzhouChina
- Center for Studies of Psychological ApplicationSouth China Normal UniversityGuangzhouChina
- Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhouChina
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6
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Neural signals in amygdala predict implicit prejudice toward an ethnic outgroup. Neuroimage 2019; 189:341-352. [DOI: 10.1016/j.neuroimage.2019.01.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Revised: 12/11/2018] [Accepted: 01/08/2019] [Indexed: 11/18/2022] Open
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7
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Seeing patterns in neuroimaging data. PROGRESS IN BRAIN RESEARCH 2018. [PMID: 30514528 DOI: 10.1016/bs.pbr.2018.10.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Functional magnetic resonance imaging research is often associated with images of brains overlaid with patterns of color that indicate significant activity. These images are one of the most salient and recognizable pieces of evidence neuroscientists appeal to as justification for claims about the relationship between cognitive processes and human behavior. The strongest critics of neuroimaging research argue that the technology possesses little, if any, scientific value, in part because of the assumptions implicit in the complex analysis procedures used to transform the data into interpretable data patterns. In this chapter, I shift the focus of this debate away from assumptions implicit in the operation of techniques themselves, and toward the role data analysis techniques play as parts of the process of interpreting neuroimaging data. I propose that data analysis techniques can be conceived of as a lens that brings patterns within the data into focus through its selective transformation. This approach recognizes the double-edged nature of data analysis and interpretation: techniques render data interpretable, but their selection and application is often informed by the methodological and theoretical commitments of researchers using them.
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8
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Gotsopoulos A, Saarimäki H, Glerean E, Jääskeläinen IP, Sams M, Nummenmaa L, Lampinen J. Reproducibility of importance extraction methods in neural network based fMRI classification. Neuroimage 2018; 181:44-54. [PMID: 29964190 DOI: 10.1016/j.neuroimage.2018.06.076] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 06/19/2018] [Accepted: 06/28/2018] [Indexed: 12/12/2022] Open
Abstract
Recent advances in machine learning allow faster training, improved performance and increased interpretability of classification techniques. Consequently, their application in neuroscience is rapidly increasing. While classification approaches have proved useful in functional magnetic resonance imaging (fMRI) studies, there are concerns regarding extraction, reproducibility and visualization of brain regions that contribute most significantly to the classification. We addressed these issues using an fMRI classification scheme based on neural networks and compared a set of methods for extraction of category-related voxel importances in three simulated and two empirical datasets. The simulation data revealed that the proposed scheme successfully detects spatially distributed and overlapping activation patterns upon successful classification. Application of the proposed classification scheme to two previously published empirical fMRI datasets revealed robust importance maps that extensively overlap with univariate maps but also provide complementary information. Our results demonstrate increased statistical power of importance maps compared to univariate approaches for both detection of overlapping patterns and patterns with weak univariate information.
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Affiliation(s)
- Athanasios Gotsopoulos
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland.
| | - Heini Saarimäki
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Enrico Glerean
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Turku PET Centre and Department of Psychology, University of Turku, Turku, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland; Helsinki Institute for Information Technology, Aalto University, Finland
| | - Iiro P Jääskeläinen
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging, School of Science, Aalto University, Espoo, Finland
| | - Mikko Sams
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Lauri Nummenmaa
- Turku PET Centre and Department of Psychology, University of Turku, Turku, Finland
| | - Jouko Lampinen
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland
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9
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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: 1.1] [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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10
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Izuma K, Shibata K, Matsumoto K, Adolphs R. Neural predictors of evaluative attitudes toward celebrities. Soc Cogn Affect Neurosci 2017; 12:382-390. [PMID: 27651542 PMCID: PMC5390710 DOI: 10.1093/scan/nsw135] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Accepted: 09/09/2016] [Indexed: 11/13/2022] Open
Abstract
Our attitudes toward others influence a wide range of everyday behaviors and have been the most extensively studied concept in the history of social psychology. Yet they remain difficult to measure reliably and objectively, since both explicit and implicit measures are typically confounded by other psychological processes. We here address the feasibility of decoding incidental attitudes based on brain activations. Participants were presented with pictures of members of a Japanese idol group inside an functional magnetic resonance imaging scanner while performing an unrelated detection task, and subsequently (outside the scanner) performed an incentive-compatible choice task that revealed their attitude toward each celebrity. We used a real-world election scheme that exists for this idol group, which confirmed both strongly negative and strongly positive attitudes toward specific individuals. Whole-brain multivariate analyses (searchlight-based support vector regression) showed that activation patterns in the anterior striatum predicted each participant’s revealed attitudes (choice behavior) using leave-one-out (as well as 4-fold) cross-validation across participants. In contrast, attitude extremity (unsigned magnitude) could be decoded from a distinct region in the posterior striatum. The findings demonstrate dissociable striatal representations of valenced attitude and attitude extremity and constitute a first step toward an objective and process-pure neural measure of attitudes.
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Affiliation(s)
- Keise Izuma
- Department of Psychology, University of York, Heslington, York YO10 5DD, UK.,Division of Humanities and Social Sciences, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA.,Brain Science Institute, Tamagawa University, 6-1-1, Tamagawa-gakuen, Machida, Tokyo 194-8610, Japan
| | - Kazuhisa Shibata
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, 190 Thayer Street, Providence, RI 02912, USA.,Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
| | - Kenji Matsumoto
- Brain Science Institute, Tamagawa University, 6-1-1, Tamagawa-gakuen, Machida, Tokyo 194-8610, Japan
| | - Ralph Adolphs
- Division of Humanities and Social Sciences, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
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11
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Mill RD, Ito T, Cole MW. From connectome to cognition: The search for mechanism in human functional brain networks. Neuroimage 2017; 160:124-139. [PMID: 28131891 DOI: 10.1016/j.neuroimage.2017.01.060] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 12/17/2016] [Accepted: 01/25/2017] [Indexed: 11/30/2022] Open
Abstract
Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for "network mechanisms", which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or "effective" connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain "stable" across task domains and more "flexible" components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.
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Affiliation(s)
- Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA.
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA
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12
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Schlegel A, Konuthula D, Alexander P, Blackwood E, Tse PU. Fundamentally Distributed Information Processing Integrates the Motor Network into the Mental Workspace during Mental Rotation. J Cogn Neurosci 2016; 28:1139-51. [PMID: 27054403 DOI: 10.1162/jocn_a_00965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The manipulation of mental representations in the human brain appears to share similarities with the physical manipulation of real-world objects. In particular, some neuroimaging studies have found increased activity in motor regions during mental rotation, suggesting that mental and physical operations may involve overlapping neural populations. Does the motor network contribute information processing to mental rotation? If so, does it play a similar computational role in both mental and manual rotation, and how does it communicate with the wider network of areas involved in the mental workspace? Here we used multivariate methods and fMRI to study 24 participants as they mentally rotated 3-D objects or manually rotated their hands in one of four directions. We find that information processing related to mental rotations is distributed widely among many cortical and subcortical regions, that the motor network becomes tightly integrated into a wider mental workspace network during mental rotation, and that motor network activity during mental rotation only partially resembles that involved in manual rotation. Additionally, these findings provide evidence that the mental workspace is organized as a distributed core network that dynamically recruits specialized subnetworks for specific tasks as needed.
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13
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Haxby JV, Connolly AC, Guntupalli JS. Decoding neural representational spaces using multivariate pattern analysis. Annu Rev Neurosci 2014; 37:435-56. [PMID: 25002277 DOI: 10.1146/annurev-neuro-062012-170325] [Citation(s) in RCA: 442] [Impact Index Per Article: 40.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A major challenge for systems neuroscience is to break the neural code. Computational algorithms for encoding information into neural activity and extracting information from measured activity afford understanding of how percepts, memories, thought, and knowledge are represented in patterns of brain activity. The past decade and a half has seen significant advances in the development of methods for decoding human neural activity, such as multivariate pattern classification, representational similarity analysis, hyperalignment, and stimulus-model-based encoding and decoding. This article reviews these advances and integrates neural decoding methods into a common framework organized around the concept of high-dimensional representational spaces.
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Affiliation(s)
- James V Haxby
- Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, Hanover, New Hampshire 03755; , ,
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14
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Distinguishing multi-voxel patterns and mean activation: Why, how, and what does it tell us? COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2013; 13:667-73. [DOI: 10.3758/s13415-013-0186-2] [Citation(s) in RCA: 94] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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