Neural connectivity patterns underlying symbolic number processing indicate mathematical achievement in children

2013 ◽  
Vol 17 (2) ◽  
pp. 187-202 ◽  
Author(s):  
Joonkoo Park ◽  
Rosa Li ◽  
Elizabeth M. Brannon
Keyword(s):  
Number Processing ◽  
Mathematical Achievement ◽  
Neural Connectivity ◽  
Connectivity Patterns ◽  
Symbolic Number

Genetic dyslexia risk variant is related to neural connectivity patterns underlying phonological awareness in children

NeuroImage ◽  
2015 ◽  
Vol 118 ◽  
pp. 414-421 ◽  
Author(s):  
Michael A. Skeide ◽  
Holger Kirsten ◽  
Indra Kraft ◽  
Gesa Schaadt ◽  
Bent Müller ◽  
...  
Keyword(s):  
Phonological Awareness ◽  
Neural Connectivity ◽  
Risk Variant ◽  
Connectivity Patterns

scholarly journals The key role of prefrontal cortex structure and function

2006 ◽  
Vol 29 (1) ◽  
pp. 22-22
Author(s):  
Antonino Raffone ◽  
Gary L. Brase
Keyword(s):  
Prefrontal Cortex ◽  
Species Differences ◽  
Structure And Function ◽  
Primary Role ◽  
Neural Connectivity ◽  
Neuronal Connectivity ◽  
Connectivity Patterns ◽  
Brain Expansion ◽  
And Function

The tension between focusing on species similarities versus species differences (phylogenetic versus adaptationist approaches) recurs in discussions about the nature of neural connectivity and organization following brain expansion. Whereas Striedter suggests a primary role for response inhibition, other possibilities include dense recurrent connectivity loops. Computer simulations and brain imaging technologies are crucial in better understanding actual neuronal connectivity patterns.

scholarly journals A simple generative model of the mouse mesoscale connectome

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sid Henriksen ◽  
Rich Pang ◽  
Mark Wronkiewicz
Keyword(s):  
Mouse Brain ◽  
Network Model ◽  
Generative Model ◽  
Data Sets ◽  
Neural Connectivity ◽  
Developmental Processes ◽  
Technological Advances ◽  
Connectivity Patterns ◽  
Organizational Principles

Recent technological advances now allow for the collection of vast data sets detailing the intricate neural connectivity patterns of various organisms. Oh et al. (2014) recently published the most complete description of the mouse mesoscale connectome acquired to date. Here we give an in-depth characterization of this connectome and propose a generative network model which utilizes two elemental organizational principles: proximal attachment ‒ outgoing connections are more likely to attach to nearby nodes than to distant ones, and source growth ‒ nodes with many outgoing connections are likely to form new outgoing connections. We show that this model captures essential principles governing network organization at the mesoscale level in the mouse brain and is consistent with biologically plausible developmental processes.

The Approximate Number System is not Predictive for Symbolic Number Processing in Kindergarteners

2014 ◽  
Vol 67 (2) ◽  
pp. 271-280 ◽  
Author(s):  
Delphine Sasanguie ◽  
Emmy Defever ◽  
Bieke Maertens ◽  
Bert Reynvoet
Keyword(s):  
Number System ◽  
Number Processing ◽  
Approximate Number System ◽  
Symbolic Number

scholarly journals Highly task-specific and distributed neural connectivity in working memory revealed by single-trial decoding in mice and humans

2021 ◽  
Author(s):  
Daniel Strahnen ◽  
Sampath K.T. Kapanaiah ◽  
Alexei M. Bygrave ◽  
Birgit Liss ◽  
David M. Bannerman ◽  
...  
Keyword(s):  
Working Memory ◽  
Activity Patterns ◽  
Brain Regions ◽  
Cognitive Domain ◽  
Neural Connectivity ◽  
Task Specificity ◽  
Electrophysiological Recordings ◽  
Connectivity Patterns ◽  
Unbiased Manner ◽  
Beta Frequency

AbstractWorking memory (WM), the capacity to briefly and intentionally maintain mental items, is key to successful goal-directed behaviour and impaired in a range of psychiatric disorders. To date, several brain regions, connections, and types of neural activity have been correlatively associated with WM performance. However, no unifying framework to integrate these findings exits, as the degree of their species- and task-specificity remains unclear. Here, we investigate WM correlates in three task paradigms each in mice and humans, with simultaneous multi-site electrophysiological recordings. We developed a machine learning-based approach to decode WM-mediated choices in individual trials across subjects from hundreds of electrophysiological measures of neural connectivity with up to 90% prediction accuracy. Relying on predictive power as indicator of correlates of psychological functions, we unveiled a large number of task phase-specific WM-related connectivity from analysis of predictor weights in an unbiased manner. Only a few common connectivity patterns emerged across tasks. In rodents, these were thalamus-prefrontal cortex delta- and beta-frequency connectivity during memory encoding and maintenance, respectively, and hippocampal-prefrontal delta- and theta-range coupling during retrieval, in rodents. In humans, task-independent WM correlates were exclusively in the gamma-band. Mostly, however, the predictive activity patterns were unexpectedly specific to each task and always widely distributed across brain regions. Our results suggest that individual tasks cannot be used to uncover generic physiological correlates of the psychological construct termed WM and call for a new conceptualization of this cognitive domain in translational psychiatry.

scholarly journals Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

2020 ◽  
Author(s):  
Abigail Dickinson ◽  
Manjari Daniel ◽  
Andrew Marin ◽  
Bilwaj Gaonkar ◽  
Mirella Dapretto ◽  
...  
Keyword(s):  
Early Infancy ◽  
Neural Connectivity ◽  
Autism Symptoms ◽  
Connectivity Patterns

scholarly journals Disentangling the Mechanisms of Symbolic Number Processing in Adults’ Mathematics and Arithmetic Achievement

2019 ◽  
Vol 43 (1) ◽  
Author(s):  
Josetxu Orrantia ◽  
David Muñez ◽  
Laura Matilla ◽  
Rosario Sanchez ◽  
Sara San Romualdo ◽  
...  
Keyword(s):  
Number Processing ◽  
Symbolic Number

scholarly journals Symbols are special: An fMRI adaptation study of symbolic, nonsymbolic and non-numerical magnitude processing in the human brain

2019 ◽  
Author(s):  
H Moriah Sokolowski ◽  
Zachary Hawes ◽  
Lien Peters ◽  
Daniel Ansari
Keyword(s):  
Human Brain ◽  
Processing System ◽  
Number Processing ◽  
Numerical Magnitude ◽  
Physical Size ◽  
Parietal Lobes ◽  
Magnitude Processing ◽  
The Right ◽  
Fmri Adaptation ◽  
Symbolic Number

Humans have the unique ability to represent and manipulate symbols. It is widely believed that this ability is rooted in an evolutionarily ancient system used to process nonsymbolic quantities in the human brain. In the current study, we used an fMRI adaptation paradigm to isolate the representations of symbols, quantities, and physical size in forty-five human adults. Results indicate that the neural correlates supporting symbolic number processing are entirely distinct from those supporting nonsymbolic magnitude processing. At the univariate level, symbolic number processing is associated with activation in the left inferior parietal lobule, whereas the processing of nonsymbolic magnitudes (both quantity and physical size), relates to activation in the right intraparietal sulcus. At the multivariate level, normalized patterns of activation for symbolic number processing exhibit a dissimilar pattern of activation compared to nonsymbolic magnitude processing in both the left and right parietal lobes. Additionally, the patterns of activation that associate with quantity and physical size are practically indistinguishable from one another. These findings challenge the longstanding belief that the culturally acquired ability to conceptualize symbolic numbers is rooted in an evolutionarily ancient system for nonsymbolic magnitude processing. Moreover, these data reveal that the system used to process nonsymbolic numbers may actually be a general magnitude processing system used to process numerical and non-numerical magnitudes. These findings highlight the need for the field to shift away from exploring how symbols are grounded in analog nonsymbolic representations, and toward more complex questions related to the neural consequences of learning symbolic numbers.

Symbolic and non-symbolic number processing: an fMRI study

NeuroImage ◽  
2001 ◽  
Vol 13 (6) ◽  
pp. 459
Author(s):  
M. Piazza ◽  
C.J. Price ◽  
A. Mechelli ◽  
B. Butterworth
Keyword(s):  
Number Processing ◽  
Fmri Study ◽  
Symbolic Number
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