scholarly journals Neural activation patterns and connectivity in visual attention during Number and Non-number processing: An ERP study using the Ishihara pseudoisochromatic plates

2018 ◽  
Vol 17 (3) ◽  
Keyword(s):  
Visual Attention ◽  
Number Processing ◽  
Neural Activation ◽  
Activation Patterns

Neural activation patterns and connectivity in visual attention during Number and Non-number processing: An ERP study using the Ishihara pseudoisochromatic plates

2017 ◽  
pp. 1-20
Author(s):  
Faraj Al-Marri ◽  
Faruque Reza ◽  
Tahamina Begum ◽  
Wan Hazabbah Wan Hitam ◽  
Goh Khean Jin ◽  
...  
Keyword(s):  
Visual Attention ◽  
Number Processing ◽  
Neural Activation ◽  
Activation Patterns

scholarly journals Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0125725 ◽  
Author(s):  
Hiroyuki Akama ◽  
Maki Miyake ◽  
Jaeyoung Jung ◽  
Brian Murphy
Keyword(s):  
Neural Activation ◽  
Activation Patterns

Task-modulated neural activation patterns in chronic stroke patients with aphasia

Aphasiology ◽  
2011 ◽  
Vol 25 (8) ◽  
pp. 927-951 ◽  
Author(s):  
Rajani Sebastian ◽  
Swathi Kiran
Keyword(s):  
Chronic Stroke ◽  
Neural Activation ◽  
Stroke Patients ◽  
Activation Patterns

scholarly journals Different activation patterns for working memory load and visual attention load

2007 ◽  
Vol 1132 ◽  
pp. 158-165 ◽  
Author(s):  
D. Tomasi ◽  
L. Chang ◽  
E.C. Caparelli ◽  
T. Ernst
Keyword(s):  
Working Memory ◽  
Visual Attention ◽  
Memory Load ◽  
Working Memory Load ◽  
Activation Patterns

scholarly journals Shared space, separate processes: Neural activation patterns for auditory description and visual object naming in healthy adults

2013 ◽  
Vol 35 (6) ◽  
pp. 2507-2520 ◽  
Author(s):  
Marla J. Hamberger ◽  
Christian G. Habeck ◽  
Spiro P. Pantazatos ◽  
Alicia C. Williams ◽  
Joy Hirsch
Keyword(s):  
Healthy Adults ◽  
Visual Object ◽  
Object Naming ◽  
Neural Activation ◽  
Activation Patterns ◽  
Shared Space ◽  
Visual Object Naming

scholarly journals The Panoramic ECAP Method: estimating patient-specific patterns of current spread and neural health in cochlear implant users

2020 ◽  
Author(s):  
Charlotte Garcia ◽  
Tobias Goehring ◽  
Stefano Cosentino ◽  
Richard E Turner ◽  
John M. Deeks ◽  
...  
Keyword(s):  
Cochlear Implant ◽  
Electrode Array ◽  
Significant Negative Correlation ◽  
Patient Specific ◽  
Clinical Settings ◽  
Neural Activation ◽  
Activation Patterns ◽  
Current Spread ◽  
Compound Action Potentials ◽  
In Cis

The knowledge of patient-specific neural excitation patterns from cochlear implants can provide important information for optimising efficacy and improving speech perception outcomes. The Panoramic ECAP (or ‘PECAP’) method (Cosentino, et al., 2015) uses forward-masked electrically evoked compound action potentials (ECAPs) to estimate neural activation patterns of cochlear implant (CI) stimulation. The algorithm requires ECAPs be measured for loudness-balanced stimuli from all combinations of probe and masker electrodes, and takes advantage of ECAP amplitudes being a result of the overlapping excitatory areas of both probes and maskers. Here we present an improved version of the PECAP algorithm that imposes biologically realistic constraints on the solution and produces separate estimates of current spread and neural health along the length of the electrode array. The algorithm was evaluated for reliability and accuracy in three ways: (1) computer-simulated current-spread and neural-health scenarios, (2) comparisons to psychophysical correlates of neural health and electrode-modiolus distances in human CI users, and (3) detection of simulated neural ‘dead’ regions (using forward masking) in human CI users. The PECAP algorithm reliably estimated the computer simulated scenarios. A moderate but significant negative correlation between focused thresholds and PECAP’s neural health estimates was found, consistent with previous literature. It also correctly identified simulated dead regions in seven CI users. The revised PECAP algorithm provides an estimate of the electrode-to-neuron interface in CIs that could be used to inform and optimize CI stimulation strategies for individual patients in clinical settings.

scholarly journals Decoding the Information Structure Underlying the Neural Representation of Concepts

2021 ◽  
Author(s):  
Leonardo Fernandino ◽  
Lisa L. Conant ◽  
Colin J. Humphries ◽  
Jeffrey R. Binder
Keyword(s):  
Information Structure ◽  
Conceptual Knowledge ◽  
Neural Representation ◽  
Neural Activation ◽  
Activation Patterns ◽  
Concept Representation ◽  
Native Space ◽  
Distributional Information ◽  
Similarity Structure ◽  
Types Of Information

The nature of the neural code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. Three main types of information have been proposed as candidates for the neural representations of lexical concepts: taxonomic (i.e., information about category membership and inter-category relations), distributional (i.e., information about patterns of word co-occurrence in natural language use), and experiential (i.e., information about sensory-motor, affective, and other features of phenomenal experience engaged during concept acquisition). In two experiments, we investigated the extent to which these three types of information are encoded in the neural activation patterns associated with hundreds of English nouns from a wide variety of conceptual categories. Participants made familiarity judgments on the meaning of written nouns while undergoing functional MRI. A high-resolution, whole-brain activation map was generated for each noun in each participant′s native space. These word-specific activation maps were used to evaluate different representational spaces corresponding to the three types of information described above. In both studies, we found a striking advantage for experience-based models in most brain areas previously associated with concept representation. Partial correlation analyses revealed that only experiential information successfully predicted concept similarity structure when inter-model correlations were taken into account. This pattern of results was found independently for object concepts and event concepts. Our findings indicate that the neural representation of conceptual knowledge primarily encodes information about features of experience, and that - to the extent that it is represented in the brain - taxonomic and distributional information may rely on such an experience-based code.

scholarly journals Functional MRI Neural Activation Patterns in Early and Late Talkers

2010 ◽  
Vol 24 (9) ◽  
pp. 69
Author(s):  
J Gordon Millichap
Keyword(s):  
Functional Mri ◽  
Neural Activation ◽  
Activation Patterns ◽  
Late Talkers

145. Social Cognitive Training Enhances Neural Activation Patterns Associated With Reward Processing in Schizophrenia

2019 ◽  
Vol 85 (10) ◽  
pp. S60-S61
Author(s):  
Karuna Subramaniam ◽  
Bruno Biagianti ◽  
Christine Hooker ◽  
Melissa Fisher ◽  
Srikantan Nagarjan ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Social Cognitive ◽  
Reward Processing ◽  
Neural Activation ◽  
Activation Patterns

scholarly journals Differential Neural Activation Patterns in Patients with Parkinson's Disease and Freezing of Gait in Response to Concurrent Cognitive and Motor Load

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e52602 ◽  
Author(s):  
James M. Shine ◽  
Elie Matar ◽  
Philip B. Ward ◽  
Samuel J. Bolitho ◽  
Mark Pearson ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
Neural Activation ◽  
Activation Patterns ◽  
Motor Load
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