scholarly journals Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

2016 ◽  
Author(s):  
Timothy N. Rubin ◽  
Oluwasanmi Koyejo ◽  
Krzysztof J. Gorgolewski ◽  
Michael N. Jones ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Brain Activity ◽  
Human Cognition ◽  
Brain Images ◽  
Whole Brain ◽  
Context Sensitive ◽  
Cognitive States ◽  
Small Set

AbstractA central goal of cognitive neuroscience is to decode human brain activity--i.e., to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive--i.e., capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a Bayesian decoding framework based on a novel topic model---Generalized Correspondence Latent Dirichlet Allocation---that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text--enabling researchers, for the first time, to generative quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

scholarly journals Post-stroke fatigue is linked to resting state posterior hypoactivity and prefrontal hyperactivity

2021 ◽  
Author(s):  
Georgia Mary Cotter ◽  
Mohamed Salah Khlif ◽  
Laura Bird ◽  
Mark E Howard ◽  
Amy Brodtmann ◽  
...  
Keyword(s):  
Ischaemic Stroke ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Stroke Severity ◽  
Stroke Survivors ◽  
Whole Brain ◽  
Post Stroke

Background and Purpose. Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Methods. This observational study included 63 stroke survivors (22 women; age 30-89 years; mean 67.5 years) from the Cognition And Neocortical Volume After Stroke (CANVAS) study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischaemic stroke. Participants underwent brain imaging 3 months post-stroke, including a 7-minute resting state fMRI echoplanar sequence. We calculated the fractional amplitude of low-frequency fluctuations, a measure of resting state brain activity at the whole-brain level. Results. Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. A generalised linear regression model analysis with age, sex, and stroke severity covariates was conducted to compare resting state brain activity in the 0.01-0.08 Hz range, as well as its subcomponents - slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz) frequency bands between fatigued and non-fatigued participants. We found no significant associations between post-stroke fatigue and ischaemic stroke lesion location or stroke volume. However, in the overall 0.01-0.08 Hz band, participants with post-stroke fatigue demonstrated significantly lower resting-state activity in the calcarine cortex (p<0.001, cluster-corrected pFDR=0.009, k=63) and lingual gyrus (p<0.001, cluster-corrected pFDR=0.025, k=42) and significantly higher activity in the medial prefrontal cortex (p<0.001, cluster-corrected pFDR=0.03, k=45), attributed to slow-4 and slow-5 oscillations, respectively. Conclusions. Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity, reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This first whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.

scholarly journals Exploring the Citywide Human Mobility Patterns of Taxi Trips through a Topic-Modeling Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Xiong ◽  
Kaiqiang Xie ◽  
Lu Ma ◽  
Feng Yuan ◽  
Rui Shen
Keyword(s):  
Power Law ◽  
Traffic Management ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Power Law Distribution ◽  
Modeling Analysis ◽  
Wide Range

Understanding human mobility patterns is of great importance for a wide range of applications from social networks to transportation planning. Toward this end, the spatial-temporal information of a large-scale dataset of taxi trips was collected via GPS, from March 10 to 23, 2014, in Beijing. The data contain trips generated by a great portion of taxi vehicles citywide. We revealed that the geographic displacement of those trips follows the power law distribution and the corresponding travel time follows a mixture of the exponential and power law distribution. To identify human mobility patterns, a topic model with the latent Dirichlet allocation (LDA) algorithm was proposed to infer the sixty-five key topics. By measuring the variation of trip displacement over time, we find that the travel distance in the morning rush hour is much shorter than that in the other time. As for daily patterns, it shows that taxi mobility presents weekly regularity both on weekdays and on weekends. Among different days in the same week, mobility patterns on Tuesday and Wednesday are quite similar. By quantifying the trip distance along time, we find that Topic 44 exhibits dominant patterns, which means distance less than 10 km is predominant no matter what time in a day. The findings could be references for travelers to arrange trips and policymakers to formulate sound traffic management policies.

scholarly journals How the human brain introspects about one’s own episodes of cognitive control

2017 ◽  
Author(s):  
David Soto ◽  
Mona Theodoraki ◽  
Pedro M. Paz-Alonso
Keyword(s):  
Cognitive Control ◽  
Human Brain ◽  
Internal Model ◽  
Large Scale ◽  
Brain Activity ◽  
Temporal Correlation ◽  
Control Networks ◽  
Control Functions ◽  
Cognitive States ◽  
Behavioural Performance

AbstractMetacognition refers to our capacity to reflect upon our experiences, thoughts and actions. Metacognition processes are linked to cognitive control functions that allow keeping our actions on-task. But it is unclear how the human brain builds an internal model of one’s cognition and behaviour. We conducted 2 fMRI experiments in which brain activity was recorded ‘online’ as participants engaged in a memory-guided search task and then later ‘offline’ when participants introspected about their prior experience and cognitive states during performance. In Experiment 1 the memory cues were task-relevant while in Experiment 2 they were irrelevant. Across Experiments, the patterns of brain activity, including frontoparietal regions, were similar during on-task and introspection states. However the connectivity profile amongst frontoparietal areas was distint during introspection and modulated by the relevance of the memory cues. Introspection was also characterized by increased temporal correlation between the default-mode network (DMN), frontoparietal and dorsal attention networks and visual cortex. We suggest that memories of one’s own experience during task performance are encoded in large-scale patterns of brain activity and that coupling between DMN and frontoparietal control networks may be crucial to build an internal model of one’s behavioural performance.

Post-stroke fatigue is associated with resting state posterior hypoactivity and prefrontal hyperactivity

2021 ◽  
pp. 174749302110483
Author(s):  
Georgia Cotter ◽  
Mohamed Salah Khlif ◽  
Laura Bird ◽  
Mark E Howard ◽  
Amy Brodtmann ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Low Frequency ◽  
Stroke Survivors ◽  
Whole Brain ◽  
Post Stroke ◽  
State Study ◽  
Stroke Study ◽  
Patient Health

Background Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Aim This observational study aimed to identify resting state brain activity markers of post-stroke fatigue. Method Sixty-three stroke survivors (22 women; age 30–89 years; mean 67.5 ± 13.4 years) from the Cognition And Neocortical Volume After Stroke study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischemic stroke, underwent brain imaging three months post-stroke, including a 7-minute resting state functional magnetic resonance imaging. We calculated the fractional amplitude of low-frequency fluctuations, which is measured at the whole-brain level and can detect altered spontaneous neural activity of specific regions. Results Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. Fatigued compared to non-fatigued participants demonstrated significantly lower resting-state activity in the calcarine cortex ( p < 0.001, cluster-corrected pFDR = 0.009, k = 63) and lingual gyrus ( p < 0.001, cluster-corrected pFDR = 0.025, k = 42) and significantly higher activity in the medial prefrontal cortex ( p < 0.001, cluster-corrected pFDR = 0.03, k = 45). Conclusions Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.

scholarly journals #EEGManyLabs: Investigating the Replicability of Influential EEG Experiments

2020 ◽  
Author(s):  
Yuri G. Pavlov ◽  
Nika Adamian ◽  
Stefan Appelhoff ◽  
Mahnaz Arvaneh ◽  
Christopher Benwell ◽  
...  
Keyword(s):  
Statistical Power ◽  
Large Scale ◽  
Selection Process ◽  
Brain Activity ◽  
Human Cognition ◽  
Future Research ◽  
Cultural Shift ◽  
Open Access Database ◽  
Search And Selection ◽  
The Relationship

There is growing awareness across the neuroscience community that the replicability of findings on the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardized analysis pipelines. Inspired by efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.

scholarly journals Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

2021 ◽  
Vol 15 ◽  
Author(s):  
Nora Hollenstein ◽  
Cedric Renggli ◽  
Benjamin Glaus ◽  
Maria Barrett ◽  
Marius Troendle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Brain Activity ◽  
Training Data ◽  
Human Cognition ◽  
Special Focus ◽  
Eeg Data

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.

scholarly journals Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

2017 ◽  
Vol 13 (10) ◽  
pp. e1005649 ◽  
Author(s):  
Timothy N. Rubin ◽  
Oluwasanmi Koyejo ◽  
Krzysztof J. Gorgolewski ◽  
Michael N. Jones ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Human Cognition ◽  
Anatomical Atlas

Context-Aware Expert Finding in Tag Based Knowledge Sharing Communities

2012 ◽  
Vol 3 (1) ◽  
pp. 48-63 ◽  
Author(s):  
Hengshu Zhu ◽  
Enhong Chen ◽  
Huanhuan Cao ◽  
Jilei Tian
Keyword(s):  
Knowledge Sharing ◽  
Information Needs ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Rapid Development ◽  
Development Trend ◽  
Semantic Relationship ◽  
Real World Data ◽  
Data Set

With the rapid development of online Knowledge Sharing Communities (KSCs), the problem of finding experts becomes increasingly important for knowledge propagation and putting crowd wisdom to work. A recent development trend of KSCs is to allow users to add text tags for annotating their posts, which are more accurate than traditional category information. However, how to leverage these user-generated tags for finding experts is still underdeveloped. To this end, this paper develops a novel approach for finding experts in tag based KSCs by leveraging tag context and the semantic relationship between tags. Specifically, the extracted prior knowledge and user profiles are first used for enriching the query tags to infer tag context, which represents the user’s latent information needs. Specifically, two different approaches for addressing the problem of tag sparseness in authority ranking are proposed. The first is a memory-based collaborative filtering approach, which leverages non-negative matrix factorization (NMF) to find similar users for alleviating tag sparseness. The second approach is based on Latent Dirichlet Allocation (LDA) topic model, which can further capture the latent semantic relationship between tags. A large-scale real-world data set is collected from a tag based Chinese commercial Q&A web site. Experimental results show that the proposed method outperforms several baseline methods with a significant margin.

scholarly journals Cross-Modality Coherent Registration of Whole Mouse Brains

2021 ◽  
Author(s):  
Hanchuan Peng ◽  
Lei Qu ◽  
Yuanyuan Li ◽  
Peng Xie ◽  
Lijuan Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Neuron ◽  
Cell Types ◽  
Brain Anatomy ◽  
Neuron Morphology ◽  
Brain Images ◽  
Whole Brain ◽  
Preparation Methods ◽  
Registration Method ◽  
3D Images

Abstract Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types.

scholarly journals An evolutionary analysis of new energy and industry policy tools in China based on large-scale policy topic modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252502
Author(s):  
Qiqing Wang ◽  
Cunbin Li
Keyword(s):  
Topic Modeling ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Policy Tools ◽  
Evolutionary Analysis ◽  
Energy Policies ◽  
New Energy ◽  
Industry Policy ◽  
And Control

This study investigates the evolution of provincial new energy policies and industries of China using a topic modeling approach. To this end, six out of 31 provinces in China are first selected as research samples, central and provincial new energy policies in the period of 2010 to 2019 are collected to establish a text corpus with 23, 674 documents. Then, the policy corpus is fed to two different topic models, one is the Latent Dirichlet Allocation for modeling static policy topics, another is the Dynamic Topic Model for extracting topics over time. Finally, the obtained topics are mapped into policy tools for comparisons. The dynamic policy topics are further analyzed with the panel data from provincial new energy industries. The results show that the provincial new energy policies moved to different tracks after about 2014 due to the regional conditions such as the economy and CO2 emission intensity. Underdeveloped provinces tend to use environment-oriented tools to regulate and control CO2 emissions, while developed regions employ the more balanced policy mix for improving new energy vehicles and other industries. Widespread hysteretic effects are revealed during the correlation analysis of the policy topics and new energy capacity.

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