Computational Neuroanatomy: Overview

2020 ◽  
pp. 1-3
Author(s):  
David Mayerich ◽  
Yoonsuck Choe
Keyword(s):  
Computational Neuroanatomy

scholarly journals Computational neuroanatomy: ontology-based representation of neural components and connectivity

2009 ◽  
Vol 10 (S2) ◽  
Author(s):  
Daniel L Rubin ◽  
Ion-Florin Talos ◽  
Michael Halle ◽  
Mark A Musen ◽  
Ron Kikinis
Keyword(s):  
Computational Neuroanatomy

scholarly journals A Revised Computational Neuroanatomy for Motor Control

2020 ◽  
Vol 32 (10) ◽  
pp. 1823-1836 ◽  
Author(s):  
Shlomi Haar ◽  
Opher Donchin
Keyword(s):  
Motor Control ◽  
Posterior Parietal Cortex ◽  
Motor Behavior ◽  
Premotor Cortex ◽  
Computational Neuroanatomy ◽  
Posterior Parietal ◽  
Optimal Information ◽  
Parietal Cortices ◽  
Recent Claim ◽  
New Framework

We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical–subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes that each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and BG. These subcortical areas are thus engaged in domain-appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modeled? We suggest that one fundamental division is between modeling of task and body whereas another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices.

Patterns of brain atrophy on magnetic resonance imaging and the boundary between ageing and Alzheimer's disease

2009 ◽  
Vol 19 (4) ◽  
pp. 295-307 ◽  
Author(s):  
Mike O'Sullivan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Practical Knowledge ◽  
Resonance Imaging ◽  
Early Assessment ◽  
Computational Neuroanatomy ◽  
Advantages And Disadvantages ◽  
Normal Ageing

SummaryClinicians are increasingly faced with the problem of interpreting subtle, early cognitive symptoms. Enhanced awareness of Alzheimer's disease (AD) and available treatments has led to a growing demand for early assessment. Although it is known that a proportion of individuals with mild cognitive impairment will progress to dementia in following years, our ability to identify these individuals and predict individual cognitive trajectories is limited. The emergence of disease-modifying treatments would make these problems more acute. In this review, the potential role of magnetic resonance imaging (MRI) in aiding the clinician in early diagnosis of AD will be considered. The changes in grey matter structure that accompany ‘normal’ ageing will be described briefly, before moving on to studies that have attempted to distinguish the onset of disease from this background of structural change. Volumetric methods range from measurements of single key structures, such as the hippocampus, to methods based on computational neuroanatomy, which evaluate subtle structural alterations across the whole brain simultaneously. Computational methods are rapidly evolving and already perform as well as radiologists in distinguishing AD from normal ageing at an individual level. This article aims to provide a practical knowledge of how and why these methods work, point out the main advantages and disadvantages and sketch out outstanding issues and possible future directions.

scholarly journals Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy

PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e53361 ◽  
Author(s):  
Stephen J. Sawiak ◽  
Nigel I. Wood ◽  
T. Adrian Carpenter ◽  
A. Jennifer Morton
Keyword(s):  
High Resolution ◽  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Mouse Models ◽  
Computational Neuroanatomy ◽  
High Resolution Mri

scholarly journals Cross-species cortical alignment identifies different types of anatomical reorganization in the primate temporal lobe

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Nicole Eichert ◽  
Emma C Robinson ◽  
Katherine L Bryant ◽  
Saad Jbabdi ◽  
Mark Jenkinson ◽  
...  
Keyword(s):  
Temporal Lobe ◽  
Parietal Cortex ◽  
Connectivity Pattern ◽  
Arcuate Fasciculus ◽  
Computational Neuroanatomy ◽  
Brain Organization ◽  
Cortical Organization ◽  
Connectivity Patterns ◽  
Different Types ◽  
Brain Architecture

Evolutionary adaptations of temporo-parietal cortex are considered to be a critical specialization of the human brain. Cortical adaptations, however, can affect different aspects of brain architecture, including local expansion of the cortical sheet or changes in connectivity between cortical areas. We distinguish different types of changes in brain architecture using a computational neuroanatomy approach. We investigate the extent to which between-species alignment, based on cortical myelin, can predict changes in connectivity patterns across macaque, chimpanzee, and human. We show that expansion and relocation of brain areas can predict terminations of several white matter tracts in temporo-parietal cortex, including the middle and superior longitudinal fasciculus, but not the arcuate fasciculus. This demonstrates that the arcuate fasciculus underwent additional evolutionary modifications affecting the temporal lobe connectivity pattern. This approach can flexibly be extended to include other features of cortical organization and other species, allowing direct tests of comparative hypotheses of brain organization.

Computational Neuroanatomy

10.1142/8036 ◽  
2011 ◽  
Author(s):  
Moo K Chung
Keyword(s):  
Computational Neuroanatomy
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