Pub Date : 2017-06-21DOI: 10.1109/PRNI.2017.7981509
Masashi Sato, Y. Miyawaki
To clarify where and when information is represented in the human brain, close investigation of brain activity at high spatiotemporal resolution is important. However, no current neuroimaging method is able to achieve such high spatiotemporal resolution. One attempt to extract necessary information from measured data under the limitation is combination of magnetoencephalography (MEG) source estimation and multivariate pattern analysis (MVPA). This combination may allow accurate localization of informative brain areas in fine time steps. However, because MEG source estimation is underdetermined, the source cortical current from a particular brain area can spread to other brain areas. In addition, information represented by the source cortical current may spread, too. Therefore, we should evaluate the accuracy of the localization of informative brain areas when combining MEG source estimation and MVPA. In this study, we used representational similarity analysis (RSA) as one of major methods of MVPA to investigate whether its result was influenced by the spreading of the cortical current through MEG source estimation. We found that relationship of the distance between brain activity patterns for multiple experimental conditions, or representational geometry, spread to brain areas where information about the experimental conditions was not represented as difference in brain activity patterns. These results suggest that we should be aware of the spreading of representational geometry through MEG source estimation, which may yield false positive interpretation about the localization of informative brain areas. Finally, we demonstrated that the possibility of mislocalization of informative brain areas can be reduced by weighting results of RSA with the reliability of the representational dissimilarity matrices.
{"title":"Spatial spreading of representational geometry through source estimation of magnetoencephalography signals","authors":"Masashi Sato, Y. Miyawaki","doi":"10.1109/PRNI.2017.7981509","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981509","url":null,"abstract":"To clarify where and when information is represented in the human brain, close investigation of brain activity at high spatiotemporal resolution is important. However, no current neuroimaging method is able to achieve such high spatiotemporal resolution. One attempt to extract necessary information from measured data under the limitation is combination of magnetoencephalography (MEG) source estimation and multivariate pattern analysis (MVPA). This combination may allow accurate localization of informative brain areas in fine time steps. However, because MEG source estimation is underdetermined, the source cortical current from a particular brain area can spread to other brain areas. In addition, information represented by the source cortical current may spread, too. Therefore, we should evaluate the accuracy of the localization of informative brain areas when combining MEG source estimation and MVPA. In this study, we used representational similarity analysis (RSA) as one of major methods of MVPA to investigate whether its result was influenced by the spreading of the cortical current through MEG source estimation. We found that relationship of the distance between brain activity patterns for multiple experimental conditions, or representational geometry, spread to brain areas where information about the experimental conditions was not represented as difference in brain activity patterns. These results suggest that we should be aware of the spreading of representational geometry through MEG source estimation, which may yield false positive interpretation about the localization of informative brain areas. Finally, we demonstrated that the possibility of mislocalization of informative brain areas can be reduced by weighting results of RSA with the reliability of the representational dissimilarity matrices.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"686 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116185655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-21DOI: 10.1109/PRNI.2017.7981501
Kiret Dhindsa, S. Becker
In this study we explore the application of pattern recognition models for recognizing emotional reactions elicited by videos from electroencephalography (EEG). We show that both the presence and magnitude of each emotion can be predicted above chance levels with up to 88% accuracy. Furthermore, we show that there are differences in classifiability for different emotions and participants, but whether a participant’s data can be classified with respect to different emotions can itself be predicted from their EEG. Index Terms– Emotion recognition, electroenecephalography (EEG), pattern recognition, classification, regression, individual differences, affective computing applied.
{"title":"Emotional reaction recognition from EEG","authors":"Kiret Dhindsa, S. Becker","doi":"10.1109/PRNI.2017.7981501","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981501","url":null,"abstract":"In this study we explore the application of pattern recognition models for recognizing emotional reactions elicited by videos from electroencephalography (EEG). We show that both the presence and magnitude of each emotion can be predicted above chance levels with up to 88% accuracy. Furthermore, we show that there are differences in classifiability for different emotions and participants, but whether a participant’s data can be classified with respect to different emotions can itself be predicted from their EEG. Index Terms– Emotion recognition, electroenecephalography (EEG), pattern recognition, classification, regression, individual differences, affective computing applied.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117246685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-21DOI: 10.1109/PRNI.2017.7981503
Andrés Hoyos Idrobo, G. Varoquaux, B. Thirion
In neuroimaging, multi-subject statistical analysis is an essential step, as it makes it possible to draw conclusions for the population under study. However, the lack of power in neuroimaging studies combined with the lack of stability and sensitivity of voxel-based methods may lead to non-reproducible results. A method designed to tackle this problem is Randomized Parcellation-Based Inference (RPBI), which has shown good empirical performance. Nevertheless, the use of an agglomerative clustering algorithm proposed in the initial RPBI formulation to build the parcellations entails a large computation cost. In this paper, we explore two strategies to speedup RPBI: Firstly, we use a fast clustering algorithm called Recursive Nearest Agglomeration (ReNA), to find the parcellations. Secondly, we consider the aggregation of p-values over multiple parcellations to avoid a permutation test. We evaluate their the computation time, as well as their recovery performance. As a main conclusion, we advocate the use of (permuted) RPBI with ReNA, as it yields very fast models, while keeping the performance of slower methods.
{"title":"Towards a faster randomized parcellation based inference","authors":"Andrés Hoyos Idrobo, G. Varoquaux, B. Thirion","doi":"10.1109/PRNI.2017.7981503","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981503","url":null,"abstract":"In neuroimaging, multi-subject statistical analysis is an essential step, as it makes it possible to draw conclusions for the population under study. However, the lack of power in neuroimaging studies combined with the lack of stability and sensitivity of voxel-based methods may lead to non-reproducible results. A method designed to tackle this problem is Randomized Parcellation-Based Inference (RPBI), which has shown good empirical performance. Nevertheless, the use of an agglomerative clustering algorithm proposed in the initial RPBI formulation to build the parcellations entails a large computation cost. In this paper, we explore two strategies to speedup RPBI: Firstly, we use a fast clustering algorithm called Recursive Nearest Agglomeration (ReNA), to find the parcellations. Secondly, we consider the aggregation of p-values over multiple parcellations to avoid a permutation test. We evaluate their the computation time, as well as their recovery performance. As a main conclusion, we advocate the use of (permuted) RPBI with ReNA, as it yields very fast models, while keeping the performance of slower methods.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128448090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-21DOI: 10.1109/PRNI.2017.7981504
M. Rahim, B. Thirion, G. Varoquaux
Typical neuroimaging studies analyze associations between physiological or behavioral traits and brain structure or function. Some rely on predicting these scores from neuroimaging data. To explain association between brain features and multiple traits, reduced-rank regression (RRR) models are often used, such as canonical correlation analysis (CCA) and partial least squares (PLS). These methods estimate latent variables, or canonical modes, that maximize the covariations between neuroimaging features and behavioral scores. Here, we investigate theoretically and empirically the extent to which reduced-rank models predict out-of-sample clinical scores from functional connectivity. Experiments on a schizophrenia dataset show that i) significant correlations between canonical modes do not necessarily mean accurate generalization on unseen data, and ii) better accuracy is achieved when taking into account regularized covariance between scores.
{"title":"Multi-output predictions from neuroimaging: assessing reduced-rank linear models","authors":"M. Rahim, B. Thirion, G. Varoquaux","doi":"10.1109/PRNI.2017.7981504","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981504","url":null,"abstract":"Typical neuroimaging studies analyze associations between physiological or behavioral traits and brain structure or function. Some rely on predicting these scores from neuroimaging data. To explain association between brain features and multiple traits, reduced-rank regression (RRR) models are often used, such as canonical correlation analysis (CCA) and partial least squares (PLS). These methods estimate latent variables, or canonical modes, that maximize the covariations between neuroimaging features and behavioral scores. Here, we investigate theoretically and empirically the extent to which reduced-rank models predict out-of-sample clinical scores from functional connectivity. Experiments on a schizophrenia dataset show that i) significant correlations between canonical modes do not necessarily mean accurate generalization on unseen data, and ii) better accuracy is achieved when taking into account regularized covariance between scores.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"285 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132518791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-01DOI: 10.1109/PRNI.2017.7981498
J. Etzel
Parametric statistical tests (e.g., t-tests) can sometimes return highly significant results in cases that would be considered uninformative, such as when the individuals’ accuracies are just above chance. This paper demonstrates that permutation tests can produce the expected non-significant results in these datasets. The properties of null distributions underlying this difference in significance are illustrated: their relative insensitivity to dataset information content, but sensitivity to dataset characteristics such as number of participants, examples, and runs.
{"title":"MVPA significance testing when just above chance, and related properties of permutation tests","authors":"J. Etzel","doi":"10.1109/PRNI.2017.7981498","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981498","url":null,"abstract":"Parametric statistical tests (e.g., t-tests) can sometimes return highly significant results in cases that would be considered uninformative, such as when the individuals’ accuracies are just above chance. This paper demonstrates that permutation tests can produce the expected non-significant results in these datasets. The properties of null distributions underlying this difference in significance are illustrated: their relative insensitivity to dataset information content, but sensitivity to dataset characteristics such as number of participants, examples, and runs.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125892336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-01DOI: 10.1109/PRNI.2017.7981499
A. Vilamala, Kristoffer Hougaard Madsen, L. K. Hansen
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent of new tools for deep learning, recent work has proposed to turn these pipelines into end-to-end learning networks. This change of paradigm offers new avenues to improvement as it allows for a global optimisation. The current work aims at benefitting from this paradigm shift by defining a smoothing step as a layer in these networks able to adaptively modulate the degree of smoothing required by each brain volume to better accomplish a given data analysis task. The viability is evaluated on real fMRI data where subjects did alternate between left and right finger tapping tasks.
{"title":"Adaptive smoothing in fMRI data processing neural networks","authors":"A. Vilamala, Kristoffer Hougaard Madsen, L. K. Hansen","doi":"10.1109/PRNI.2017.7981499","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981499","url":null,"abstract":"Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent of new tools for deep learning, recent work has proposed to turn these pipelines into end-to-end learning networks. This change of paradigm offers new avenues to improvement as it allows for a global optimisation. The current work aims at benefitting from this paradigm shift by defining a smoothing step as a layer in these networks able to adaptively modulate the degree of smoothing required by each brain volume to better accomplish a given data analysis task. The viability is evaluated on real fMRI data where subjects did alternate between left and right finger tapping tasks.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130813446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-01DOI: 10.1109/PRNI.2017.7981497
Heeyoung Choo, Dirk Bernhardt-Walther
Over the last decade, machine learning algorithms have proven to be useful tools for exploring neural representations of percepts and concepts in the brain. An important but often neglected next step is it to relate neural representations to human behavior. Here, we introduce a novel approach to definitively linking neural representations to structural properties of stimuli as well as human behavior by analyzing patterns of classification errors using linear mixed-effects (LME) models. An LME model includes a priori predictive models of matching of error patterns between neural decoding and human behavior as fixed effects as well as random effects to account for subject variability. Finally, we demonstrate the viability of this approach using data from a set of fMRI and behavioral experiments testing the influence of visual properties on the neural representation of categories of real-world visual scenes.
{"title":"Modeling the effect of stimulus perturbations on error correlations between brain and behavior","authors":"Heeyoung Choo, Dirk Bernhardt-Walther","doi":"10.1109/PRNI.2017.7981497","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981497","url":null,"abstract":"Over the last decade, machine learning algorithms have proven to be useful tools for exploring neural representations of percepts and concepts in the brain. An important but often neglected next step is it to relate neural representations to human behavior. Here, we introduce a novel approach to definitively linking neural representations to structural properties of stimuli as well as human behavior by analyzing patterns of classification errors using linear mixed-effects (LME) models. An LME model includes a priori predictive models of matching of error patterns between neural decoding and human behavior as fixed effects as well as random effects to account for subject variability. Finally, we demonstrate the viability of this approach using data from a set of fMRI and behavioral experiments testing the influence of visual properties on the neural representation of categories of real-world visual scenes.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132618409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-01DOI: 10.1109/PRNI.2017.7981508
Hongyuan You, Adam Liska, Nathan Russell, Payel Das
The functional activation pattern within the human brain is known to change at varying time-scales. This existence of and dynamics between inherently different brain functional states are found to be related to human learning, behavior, and development, and, are therefore of high importance. Yet, tools to automatically identify such cognitive states are limited. In this study, we consider high-dimensional functional connectome data constructed from BOLD fMRI over short time-intervals as a graph, each time-point as a node, and the similarity between two time-points as the edge between those two nodes. We apply graph embedding techniques to automatically extract clusters of time-points, which represent canonical brain states. Application of graph embedding technique to BOLD fMRI time-series of a population comprised of autistic and neurotypical subjects demonstrates that two-layer embedding by preserving the higherorder similarity between different time-points is crucial toward successful identification of low-dimensional brain functional states. Finally, the present study reveals inherent existence of two brain meta-states within human brain.
{"title":"Automated brain state identification using graph embedding","authors":"Hongyuan You, Adam Liska, Nathan Russell, Payel Das","doi":"10.1109/PRNI.2017.7981508","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981508","url":null,"abstract":"The functional activation pattern within the human brain is known to change at varying time-scales. This existence of and dynamics between inherently different brain functional states are found to be related to human learning, behavior, and development, and, are therefore of high importance. Yet, tools to automatically identify such cognitive states are limited. In this study, we consider high-dimensional functional connectome data constructed from BOLD fMRI over short time-intervals as a graph, each time-point as a node, and the similarity between two time-points as the edge between those two nodes. We apply graph embedding techniques to automatically extract clusters of time-points, which represent canonical brain states. Application of graph embedding technique to BOLD fMRI time-series of a population comprised of autistic and neurotypical subjects demonstrates that two-layer embedding by preserving the higherorder similarity between different time-points is crucial toward successful identification of low-dimensional brain functional states. Finally, the present study reveals inherent existence of two brain meta-states within human brain.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117104086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-01DOI: 10.1109/PRNI.2017.7981496
R. Roge, Karen Sandø Ambrosen, K. J. Albers, Casper T. Eriksen, M. Liptrot, Mikkel N. Schmidt, Kristoffer Hougaard Madsen, Morten Mørup
Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain, they emerge from the same underlying brain organization, and functional communication is presumably mediated by structural connections. In this paper, we assess the structure-function relationship by evaluating how well functional connectivity can be predicted from structural graphs. Using high-resolution whole brain networks generated with varying density, we contrast the performance of several non-parametric link predictors that measure structural communication flow. While functional connectivity is not well predicted directly by structural connections, we show that superior predictions can be achieved by taking indirect structural pathways into account. In particular, we find that the length of the shortest structural path between brain regions is a good predictor of functional connectivity in sparse networks (density less than one percent), and that this improvement comes from integrating indirect pathways comprising up to three steps. Our results support the existence of important indirect relationships between structure and function, extending beyond the immediate direct structural connections that are typically investigated.
{"title":"Whole brain functional connectivity predicted by indirect structural connections","authors":"R. Roge, Karen Sandø Ambrosen, K. J. Albers, Casper T. Eriksen, M. Liptrot, Mikkel N. Schmidt, Kristoffer Hougaard Madsen, Morten Mørup","doi":"10.1109/PRNI.2017.7981496","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981496","url":null,"abstract":"Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain, they emerge from the same underlying brain organization, and functional communication is presumably mediated by structural connections. In this paper, we assess the structure-function relationship by evaluating how well functional connectivity can be predicted from structural graphs. Using high-resolution whole brain networks generated with varying density, we contrast the performance of several non-parametric link predictors that measure structural communication flow. While functional connectivity is not well predicted directly by structural connections, we show that superior predictions can be achieved by taking indirect structural pathways into account. In particular, we find that the length of the shortest structural path between brain regions is a good predictor of functional connectivity in sparse networks (density less than one percent), and that this improvement comes from integrating indirect pathways comprising up to three steps. Our results support the existence of important indirect relationships between structure and function, extending beyond the immediate direct structural connections that are typically investigated.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124246575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-01DOI: 10.1109/PRNI.2017.7981507
Xixi Wang, Carol A. Jew, F. Lin, Rajeev D. S. Raizada
Neural representations for object recognition are difficult to construct because vision operates in highdimensional space. This study aims to develop low-dimensional neural representations (“manifolds”) that could contain either rotation or viewpoint information. In our experiments, four rotating tools were used as visual stimuli and brain activity was recorded using functional magnetic resonance imaging. We selected voxels whose signal changes were temporally correlated with the rotation task period and we proposed using principal component analysis to construct low-dimensional manifolds for these selected voxels. We hypothesized that manifolds for these voxels will be “loop-shaped” based on the rotation design featured in the experiment. Our results revealed two types of manifolds: voxels from lower-level visual areas (i.e. occipital pole, occipital fusiform gyrus) showed smooth “two-loops” shaped manifolds, which suggested that they treated those stimuli as rotating bars and they were sensitive to rotations instead of details of objects; voxels from higher-level visual areas didn’t show obvious shaped manifolds, but higher-level visual areas (i.e. inferior temporal gyrus, middle temporal gyrus) were able to predict objects’ category with accuracies above 0.53 for four-class classification. Our experiments demonstrated that for lower-level visual areas, the proposed manifolds structures could represent neural activities when participants were visualizing rotating tools. The proposed representation structures can shed light on rotation angle decoding. However, the manifolds structures may not be suitable for higher-level visual areas. Future studies should further differentiate the roles of the manifolds structures in lower-level vs. higher-level visual areas.
{"title":"Manifolds of tool-graspability in the human brain","authors":"Xixi Wang, Carol A. Jew, F. Lin, Rajeev D. S. Raizada","doi":"10.1109/PRNI.2017.7981507","DOIUrl":"https://doi.org/10.1109/PRNI.2017.7981507","url":null,"abstract":"Neural representations for object recognition are difficult to construct because vision operates in highdimensional space. This study aims to develop low-dimensional neural representations (“manifolds”) that could contain either rotation or viewpoint information. In our experiments, four rotating tools were used as visual stimuli and brain activity was recorded using functional magnetic resonance imaging. We selected voxels whose signal changes were temporally correlated with the rotation task period and we proposed using principal component analysis to construct low-dimensional manifolds for these selected voxels. We hypothesized that manifolds for these voxels will be “loop-shaped” based on the rotation design featured in the experiment. Our results revealed two types of manifolds: voxels from lower-level visual areas (i.e. occipital pole, occipital fusiform gyrus) showed smooth “two-loops” shaped manifolds, which suggested that they treated those stimuli as rotating bars and they were sensitive to rotations instead of details of objects; voxels from higher-level visual areas didn’t show obvious shaped manifolds, but higher-level visual areas (i.e. inferior temporal gyrus, middle temporal gyrus) were able to predict objects’ category with accuracies above 0.53 for four-class classification. Our experiments demonstrated that for lower-level visual areas, the proposed manifolds structures could represent neural activities when participants were visualizing rotating tools. The proposed representation structures can shed light on rotation angle decoding. However, the manifolds structures may not be suitable for higher-level visual areas. Future studies should further differentiate the roles of the manifolds structures in lower-level vs. higher-level visual areas.","PeriodicalId":429199,"journal":{"name":"2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117279652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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