2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)最新文献

Spatial spreading of representational geometry through source estimation of magnetoencephalography signals 通过脑磁图信号源估计表征几何的空间扩展

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-21 DOI: 10.1109/PRNI.2017.7981509

Masashi Sato, Y. Miyawaki

{"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}

引用次数: 2

Emotional reaction recognition from EEG 基于脑电图的情绪反应识别

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-21 DOI: 10.1109/PRNI.2017.7981501

Kiret Dhindsa, S. Becker

引用次数: 4

Towards a faster randomized parcellation based inference 朝着更快的基于随机分组的推理

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-21 DOI: 10.1109/PRNI.2017.7981503

Andrés Hoyos Idrobo, G. Varoquaux, B. Thirion

引用次数: 1

Multi-output predictions from neuroimaging: assessing reduced-rank linear models 神经成像的多输出预测:评估降秩线性模型

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-21 DOI: 10.1109/PRNI.2017.7981504

M. Rahim, B. Thirion, G. Varoquaux

引用次数: 6

MVPA significance testing when just above chance, and related properties of permutation tests MVPA显著性检验时刚好高于偶然性，与置换检验的相关性质有关

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981498

J. Etzel

引用次数: 13

Adaptive smoothing in fMRI data processing neural networks fMRI数据处理神经网络中的自适应平滑

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981499

A. Vilamala, Kristoffer Hougaard Madsen, L. K. Hansen

引用次数: 0

Modeling the effect of stimulus perturbations on error correlations between brain and behavior 模拟刺激扰动对大脑和行为之间误差相关性的影响

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981497

Heeyoung Choo, Dirk Bernhardt-Walther

引用次数: 1

Automated brain state identification using graph embedding 使用图嵌入的自动大脑状态识别

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981508

Hongyuan You, Adam Liska, Nathan Russell, Payel Das

{"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}

引用次数: 1

Whole brain functional connectivity predicted by indirect structural connections 间接结构连接预测全脑功能连接

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 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

{"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}

引用次数: 9

Manifolds of tool-graspability in the human brain 人类大脑中工具可握性的流形

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) Pub Date : 2017-06-01 DOI: 10.1109/PRNI.2017.7981507

Xixi Wang, Carol A. Jew, F. Lin, Rajeev D. S. Raizada

{"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}

引用次数: 0