scholarly journals Modeling Antimicrobial Prescriptions in Scotland: A Spatiotemporal Clustering Approach

Risk Analysis ◽  
2021 ◽  
Author(s):  
Antonia Gieschen ◽  
Jake Ansell ◽  
Raffaella Calabrese ◽  
Belen Martin‐Barragan
Keyword(s):  
Spatiotemporal Clustering ◽  
Clustering Approach

scholarly journals Spatiotemporal dynamics of human attention revealed by intracerebral recording

2021 ◽  
Author(s):  
Tal Seidel Malkinson ◽  
Dimitri J. Bayle ◽  
Alexia Bourgeois ◽  
Katia Lehongre ◽  
Sara Fernandez ◽  
...  
Keyword(s):  
Right Hemisphere ◽  
Brain Activity ◽  
Exogenous Attention ◽  
Neural Basis ◽  
Unexpected Events ◽  
Human Attention ◽  
Manual Responses ◽  
Neural Integration ◽  
Spatiotemporal Clustering ◽  
Clustering Approach

ABSTRACTAttention allows us to rapidly respond to unexpected events, a fundamental capacity for survival. We recorded brain activity from 28 individuals with a total of 1,403 intracortical contacts, while they produced faster manual responses to visual targets preceded by non-predictive attentional cues, which engage exogenous spatial attention. Using a novel spatiotemporal clustering approach, we identified three distinct brain networks: an early visual cluster; an intermediate, predominantly right-hemisphere caudal temporoparietal-prefrontal cluster, sensitive to attentional effects; and a late, predominantly left-hemisphere rostral temporoparietal-prefrontal cluster, sensitive to response-requiring targets. Activity in temporoparietal-prefrontal clusters suggested neural integration of temporally close cues and targets, and was closely related to behavioral responses. These results reveal how cortical networks govern the psychological construct of exogenous attention.One-sentence summaryThe neural basis of human exogenous attention lies in the nexus between perception and action

Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

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