scholarly journals Combining human MEG and fMRI data reveals the spatio-temporal dynamics of animacy and real-world object size

2017 ◽  
Vol 17 (10) ◽  
pp. 574
Author(s):  
Seyed-Mahdi Khaligh-Razavi ◽  
Radoslaw Cichy ◽  
Dimitrios Pantazis ◽  
Aude Oliva
Keyword(s):  
Real World ◽  
Temporal Dynamics ◽  
Fmri Data ◽  
Object Size ◽  
Spatio Temporal

scholarly journals Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

2017 ◽  
Vol 24 (4) ◽  
pp. 599-611 ◽  
Author(s):  
Ankit Agarwal ◽  
Norbert Marwan ◽  
Maheswaran Rathinasamy ◽  
Bruno Merz ◽  
Jürgen Kurths
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Real World ◽  
Temporal Dynamics ◽  
Multiple Scales ◽  
Temporal Patterns ◽  
Multi Scale ◽  
Main Drawback ◽  
Spatio Temporal ◽  
Linear Interactions

Abstract. The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-)processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.

Tracking the Spatiotemporal Neural Dynamics of Real-world Object Size and Animacy in the Human Brain

2018 ◽  
Vol 30 (11) ◽  
pp. 1559-1576 ◽  
Author(s):  
Seyed-Mahdi Khaligh-Razavi ◽  
Radoslaw Martin Cichy ◽  
Dimitrios Pantazis ◽  
Aude Oliva
Keyword(s):  
Human Brain ◽  
Real World ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Temporal Cortex ◽  
Neural Dynamics ◽  
Control Analysis ◽  
Object Size ◽  
Parahippocampal Cortex ◽  
Early Visual Cortex

Animacy and real-world size are properties that describe any object and thus bring basic order into our perception of the visual world. Here, we investigated how the human brain processes real-world size and animacy. For this, we applied representational similarity to fMRI and MEG data to yield a view of brain activity with high spatial and temporal resolutions, respectively. Analysis of fMRI data revealed that a distributed and partly overlapping set of cortical regions extending from occipital to ventral and medial temporal cortex represented animacy and real-world size. Within this set, parahippocampal cortex stood out as the region representing animacy and size stronger than most other regions. Further analysis of the detailed representational format revealed differences among regions involved in processing animacy. Analysis of MEG data revealed overlapping temporal dynamics of animacy and real-world size processing starting at around 150 msec and provided the first neuromagnetic signature of real-world object size processing. Finally, to investigate the neural dynamics of size and animacy processing simultaneously in space and time, we combined MEG and fMRI with a novel extension of MEG–fMRI fusion by representational similarity. This analysis revealed partly overlapping and distributed spatiotemporal dynamics, with parahippocampal cortex singled out as a region that represented size and animacy persistently when other regions did not. Furthermore, the analysis highlighted the role of early visual cortex in representing real-world size. A control analysis revealed that the neural dynamics of processing animacy and size were distinct from the neural dynamics of processing low-level visual features. Together, our results provide a detailed spatiotemporal view of animacy and size processing in the human brain.

A Similarity Based Representation for Identifying Healthcare Anomalous Activities

2020 ◽  
Vol 10 (4) ◽  
pp. 787-794
Author(s):  
K. Deepak ◽  
Mohamed Yacin Sikkandar ◽  
S. Siddharth ◽  
S. Chandrakala
Keyword(s):  
Real World ◽  
Patient Monitoring ◽  
Temporal Dynamics ◽  
State Of The Art ◽  
Recognition Task ◽  
View Point ◽  
Benchmark Data ◽  
Flow Displacement ◽  
Patient Monitoring System ◽  
Spatio Temporal

A Vision Based Patient Monitoring system focuses on detecting abnormal activities of a patient. In real-world, factors like occlusion and view point variations make the activity recognition task challenging. This work proposes a similarity-based representation for healthcare activities including abnormal patient activities such as coughing, sneezing, vomiting, falling, etc. Global and depth-based representations such as histogram of optical flow, displacement between skeletal sequences and relative position of skeletal joints are used to represent the spatio-temporal dynamics of activities. A benchmark data namely "NTU RGB + D Action Recognition dataset" is used for testing the performance of the proposed approach. A comparison of the proposed methodology against other state-of-the-art approaches has proved the discrimination of the proposed approach.

scholarly journals A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in resting-state functional MRI Data

2020 ◽  
Author(s):  
Tiago Azevedo ◽  
Alexander Campbell ◽  
Rafael Romero-Garcia ◽  
Luca Passamonti ◽  
Richard A.I. Bethlehem ◽  
...  
Keyword(s):  
Neural Network ◽  
Resting State ◽  
Network Architecture ◽  
Temporal Dynamics ◽  
Feature Learning ◽  
Blood Oxygen Level Dependent ◽  
Fmri Data ◽  
Neural Network Architecture ◽  
Pairwise Correlation ◽  
Spatio Temporal

AbstractResting-state functional magnetic resonance imaging (rs-fMRI) has been successfully employed to understand the organisation of the human brain. For rs-fMRI analysis, the brain is typically parcellated into regions of interest (ROIs) and modelled as a graph where each ROI is a node and pairwise correlation between ROI blood-oxygen-level-dependent (BOLD) time series are edges. Recently, graph neural networks (GNNs) have seen a surge in popularity due to their successes in modelling unstructured relational data. The latest developments with GNNs, however, have not yet been fully exploited for the analysis of rs-fMRI data, particularly with regards to its spatio-temporal dynamics. Herein we present a novel deep neural network architecture, combining both GNNs and temporal convolutional networks (TCNs), which is able to learn from the spatial and temporal components of rs-fMRI data in an end-to-end fashion. In particular, this corresponds to intra-feature learning (i.e., learning temporal dynamics with TCNs) as well as inter-feature learning (i.e., leveraging spatial interactions between ROIs with GNNs). We evaluate our model with an ablation study using 35,159 samples from the UK Biobank rs-fMRI database. We also demonstrate explainability features of our architecture which map to realistic neurobiological insights. We hope our model could lay the groundwork for future deep learning architectures focused on leveraging the inherently and inextricably spatio-temporal nature of rs-fMRI data.

Temporal dynamics of real-world emotion are more strongly linked to prediction error than outcome.

2020 ◽  
Vol 149 (9) ◽  
pp. 1755-1766 ◽  
Author(s):  
William J. Villano ◽  
A. Ross Otto ◽  
C. E. Chiemeka Ezie ◽  
Roderick Gillis ◽  
Aaron S. Heller
Keyword(s):  
Real World ◽  
Prediction Error ◽  
Temporal Dynamics

Spatial and temporal dynamics of Pacific capelin Mallotus catervarius in the Gulf of Alaska: implications for ecosystem-based fisheries management

2020 ◽  
Vol 637 ◽  
pp. 117-140 ◽  
Author(s):  
DW McGowan ◽  
ED Goldstein ◽  
ML Arimitsu ◽  
AL Deary ◽  
O Ormseth ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Marine Ecosystem ◽  
Gulf Of Alaska ◽  
Data Series ◽  
Limited Information ◽  
Important Species ◽  
Multiple Data ◽  
Spatial And Temporal Dynamics ◽  
Spawning Areas ◽  
Spatio Temporal

Pacific capelin Mallotus catervarius are planktivorous small pelagic fish that serve an intermediate trophic role in marine food webs. Due to the lack of a directed fishery or monitoring of capelin in the Northeast Pacific, limited information is available on their distribution and abundance, and how spatio-temporal fluctuations in capelin density affect their availability as prey. To provide information on life history, spatial patterns, and population dynamics of capelin in the Gulf of Alaska (GOA), we modeled distributions of spawning habitat and larval dispersal, and synthesized spatially indexed data from multiple independent sources from 1996 to 2016. Potential capelin spawning areas were broadly distributed across the GOA. Models of larval drift show the GOA’s advective circulation patterns disperse capelin larvae over the continental shelf and upper slope, indicating potential connections between spawning areas and observed offshore distributions that are influenced by the location and timing of spawning. Spatial overlap in composite distributions of larval and age-1+ fish was used to identify core areas where capelin consistently occur and concentrate. Capelin primarily occupy shelf waters near the Kodiak Archipelago, and are patchily distributed across the GOA shelf and inshore waters. Interannual variations in abundance along with spatio-temporal differences in density indicate that the availability of capelin to predators and monitoring surveys is highly variable in the GOA. We demonstrate that the limitations of individual data series can be compensated for by integrating multiple data sources to monitor fluctuations in distributions and abundance trends of an ecologically important species across a large marine ecosystem.

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