Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec)最新文献_第2页

Insights into cognition from network science analyses of human brain functional connectivity: Working memory as a test case 从人脑功能连通性的网络科学分析中洞察认知:工作记忆作为一个测试案例

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2019-01-01 DOI: 10.1016/B978-0-12-813838-0.00002-9

D. Dagenbach

引用次数: 6

Overlapping and dynamic networks of the emotional brain 情感大脑的重叠和动态网络

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2019-01-01 DOI: 10.1016/B978-0-12-813838-0.00003-0

L. Pessoa

引用次数: 2

Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra. 通过fMRI时间过程和光谱的形状分析测量大脑连通性。

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2017-09-01 Epub Date: 2017-09-02 DOI: 10.1007/978-3-319-67159-8_15

David S Lee, Amber Leaver, Katherine L Narr, Roger P Woods, Shantanu H Joshi

引用次数: 2

Topological Distances Between Brain Networks. 脑网络之间的拓扑距离。

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2017-09-01 Epub Date: 2017-09-02 DOI: 10.1007/978-3-319-67159-8_19

Moo K Chung, Hyekyoung Lee, Victor Solo, Richard J Davidson, Seth D Pollak

引用次数: 42

Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference. 利用拓扑启发的统计推断重新审视自闭症背后的脑网络结构异常。

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2017-09-01 Epub Date: 2017-09-02 DOI: 10.1007/978-3-319-67159-8_12

Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P Thomas Fletcher, Bei Wang

{"title":"Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference.","authors":"Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P Thomas Fletcher, Bei Wang","doi":"10.1007/978-3-319-67159-8_12","DOIUrl":"https://doi.org/10.1007/978-3-319-67159-8_12","url":null,"abstract":"A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance MRI (scMRI) is a technique that maps brain regions with covarying gray matter density across subjects. It provides a way to probe the anatomical structures underlying intrinsic connectivity networks (ICNs) through the analysis of the gray matter signal covariance. In this paper, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender- and IQ-matched controls. Specifically, we investigate topological differences in gray matter structures captured by structural covariance networks (SCNs) derived from three ICNs strongly implicated in autism, namely, the salience network (SN), the default mode network (DMN) and the executive control network (ECN). By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using scMRI (Zielinski et al. 2012).","PeriodicalId":92190,"journal":{"name":"Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-67159-8_12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36421126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network. 利用高阶脑功能连接网络预测意识水平和恢复结果。

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2017-01-01 Epub Date: 2017-09-02 DOI: 10.1007/978-3-319-67159-8_3

Xiuyi Jia, Han Zhang, Ehsan Adeli, Dinggang Shen

{"title":"Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network.","authors":"Xiuyi Jia, Han Zhang, Ehsan Adeli, Dinggang Shen","doi":"10.1007/978-3-319-67159-8_3","DOIUrl":"https://doi.org/10.1007/978-3-319-67159-8_3","url":null,"abstract":"Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson's correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region's low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.","PeriodicalId":92190,"journal":{"name":"Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-67159-8_3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36604211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment. 构建多频高阶功能连接网络诊断轻度认知障碍。

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2017-01-01 Epub Date: 2017-09-02 DOI: 10.1007/978-3-319-67159-8_2

Yu Zhang, Han Zhang, Xiaobo Chen, Dinggang Shen

{"title":"Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment.","authors":"Yu Zhang, Han Zhang, Xiaobo Chen, Dinggang Shen","doi":"10.1007/978-3-319-67159-8_2","DOIUrl":"https://doi.org/10.1007/978-3-319-67159-8_2","url":null,"abstract":"Human brain functional connectivity (FC) networks, estimated based on resting-state functional magnetic resonance imaging (rs-fMRI), has become a promising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy, characterizing more complex interactions among low-order FC sub-networks that involve multiple brain regions. However, both LON and HON are usually constructed within a fixed and relatively wide frequency band, which may fail in capturing (sensitive) frequency-specific FC changes caused by pathological attacks. To address this issue, we propose a novel \"multi-frequency HON construction\" method. Specifically, we construct not only multiple frequency-specific HONs (intra-spectrum HONs), but also a series of cross-frequency interaction-based HONs (inter-spectrum HONs) based on the low-order FC sub-networks constructed at different frequency bands. Both types of these HONs, together with the frequency-specific LONs, are used for the complex network analysis-based feature extraction, followed by sparse regression-based feature selection and the classification between mild cognitive impairment (MCI) patients and normal aging subjects using a support vector machine. Compared with the previous methods, our proposed method achieves the best diagnosis accuracy in early diagnosis of Alzheimer's disease.","PeriodicalId":92190,"journal":{"name":"Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-67159-8_2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36604210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Topological Network Analysis of Electroencephalographic Power Maps. 脑电波功率图的拓扑网络分析

Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) Pub Date : 2017-01-01 Epub Date: 2017-09-02 DOI: 10.1007/978-3-319-67159-8_16

Yuan Wang, Moo K Chung, Daniela Dentico, Antoine Lutz, Richard Davidson

{"title":"Topological Network Analysis of Electroencephalographic Power Maps.","authors":"Yuan Wang, Moo K Chung, Daniela Dentico, Antoine Lutz, Richard Davidson","doi":"10.1007/978-3-319-67159-8_16","DOIUrl":"10.1007/978-3-319-67159-8_16","url":null,"abstract":"Meditation practice as a non-pharmacological intervention to provide health related benefits has generated much neuroscientific interest in its effects on brain activity. Electroencephalogram (EEG), an imaging modality known for its inexpensive procedure and excellent temporal resolution, is often utilized to investigate the neuroplastic effects of meditation under various experimental conditions. In these studies, EEG signals are routinely mapped on a topographic layout of channels to visualize variations in spectral powers within certain frequency ranges. Topological data analysis (TDA) of the topographic power maps modeled as graphs can provide different insight to EEG signals than standard statistical methods. A highly effective TDA technique is persistent homology, which reveals topological characteristics of a power map by tracking feature changes throughout a filtration process on the graph structure of the map. In this paper, we propose a novel inference procedure based on filtrations induced by sublevel sets of the power maps of high-density EEG signals. We apply the pipeline to simulated and real data, where we compare the persistent homological features of topographic maps of spectral powers in high-frequency bands of EEG signals recorded on long-term meditators and meditation-naive practitioners.","PeriodicalId":92190,"journal":{"name":"Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5922271/pdf/nihms875270.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36055385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0