scholarly journals Multimodal gradients across mouse cortex

2018 ◽  
Author(s):  
Ben D. Fulcher ◽  
John D. Murray ◽  
Valerio Zerbi ◽  
Xiao-Jing Wang
Keyword(s):  
Large Scale ◽  
Spatial Dimension ◽  
Brain Organization ◽  
Human Cortex ◽  
Spatial Reference ◽  
Mouse Cortex ◽  
Human Orthologs ◽  
Organizational Principle ◽  
Cell Densities ◽  
Primate Cerebral Cortex

The primate cerebral cortex displays a hierarchy that extends from primary sensorimotor to association areas, supporting increasingly integrated function underpinned by a gradient of heterogeneity in the brain's microcircuits. The extent to which these hierarchical gradients are unique to primate or may reflect a conserved mammalian principle of brain organization remains unknown. Here we report the topographic similarity of large-scale gradients in cytoarchitecture, gene expression, interneuron cell densities, and long-range axonal connectivity, which vary from primary sensory through to prefrontal areas of mouse cortex, highlighting an underappreciated spatial dimension of mouse cortical specialization. Using the T1w:T2w magnetic resonance imaging map as a common spatial reference for comparison across species, we report interspecies agreement in a range of large-scale cortical gradients, including a significant correspondence between gene transcriptional maps in mouse cortex with their human orthologs in human cortex, as well as notable interspecies differences. Our results support the view of systematic structural variation across cortical areas as a core organizational principle that may underlie hierarchical specialization in mammalian brains.

scholarly journals Multimodal gradients across mouse cortex

2019 ◽  
Vol 116 (10) ◽  
pp. 4689-4695 ◽  
Author(s):  
Ben D. Fulcher ◽  
John D. Murray ◽  
Valerio Zerbi ◽  
Xiao-Jing Wang
Keyword(s):  
Large Scale ◽  
Spatial Dimension ◽  
Brain Organization ◽  
Human Cortex ◽  
Spatial Reference ◽  
Mouse Cortex ◽  
Human Orthologs ◽  
Organizational Principle ◽  
Cell Densities ◽  
Primate Cerebral Cortex

The primate cerebral cortex displays a hierarchy that extends from primary sensorimotor to association areas, supporting increasingly integrated function underpinned by a gradient of heterogeneity in the brain’s microcircuits. The extent to which these hierarchical gradients are unique to primate or may reflect a conserved mammalian principle of brain organization remains unknown. Here we report the topographic similarity of large-scale gradients in cytoarchitecture, gene expression, interneuron cell densities, and long-range axonal connectivity, which vary from primary sensory to prefrontal areas of mouse cortex, highlighting an underappreciated spatial dimension of mouse cortical specialization. Using the T1-weighted:T2-weighted (T1w:T2w) magnetic resonance imaging map as a common spatial reference for comparison across species, we report interspecies agreement in a range of large-scale cortical gradients, including a significant correspondence between gene transcriptional maps in mouse cortex with their human orthologs in human cortex, as well as notable interspecies differences. Our results support the view of systematic structural variation across cortical areas as a core organizational principle that may underlie hierarchical specialization in mammalian brains.

scholarly journals Discovering Conserved Properties of Brain Organization Through Multimodal Integration and Interspecies Comparison

2019 ◽  
Vol 13 ◽  
pp. 117906951986204 ◽  
Author(s):  
Ben D Fulcher
Keyword(s):  
Structural Variation ◽  
Brain Organization ◽  
Hierarchical Processing ◽  
Spatial Reference ◽  
Mouse Cortex ◽  
Integrative Studies ◽  
Connectivity Patterns ◽  
Cell Densities ◽  
Primate Cerebral Cortex ◽  
Similar Organization

The primate cerebral cortex is broadly organized along hierarchical processing streams underpinned by corresponding variation in the brain’s microstructure and interareal connectivity patterns. Fulcher et al. recently demonstrated that a similar organization exists in the mouse cortex by combining independent datasets of cytoarchitecture, gene expression, cell densities, and long-range axonal connectivity. Using the T1w:T2w magnetic resonance imaging map as a common spatial reference for data-driven comparison of cortical gradients between mouse and human, we highlighted a common hierarchical expression pattern of numerous brain-related genes, providing new understanding of how systematic structural variation shapes functional specialization in mammalian brains. Reflecting on these findings, here we discuss how open neuroscience datasets, combined with advanced neuroinformatics approaches, will be crucial in the ongoing search for organization principles of brain structure. We explore the promises and challenges of integrative studies and argue that a tighter collaboration between experimental, statistical, and theoretical neuroscientists is needed to drive progress further.

scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura
Keyword(s):  
Molecular Dynamics ◽  
Renormalization Group ◽  
Critical Exponent ◽  
Computational Efficiency ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Coarse Graining ◽  
Spatial Dimension ◽  
Deborah Number

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

scholarly journals Large-scale Dynamics of Perceptual Decision Information across Human Cortex

2020 ◽  
Author(s):  
Niklas Wilming ◽  
Peter R Murphy ◽  
Florent Meyniel ◽  
Tobias H Donner
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Action Plan ◽  
Frequency Channel ◽  
Human Cortex ◽  
Cortical Hierarchy ◽  
Specific Frequency ◽  
Sensory Evidence ◽  
Visual Cortical ◽  
Endogenous Component

AbstractPerceptual decisions entail the accumulation of sensory evidence for a particular choice towards an action plan. An influential framework holds that sensory cortical areas encode the instantaneous sensory evidence and downstream, action-related regions accumulate this evidence. The large-scale distribution of this computation across the cerebral cortex has remained largely elusive. We developed a regionally-specific magnetoencephalography decoding approach to exhaustively map the dynamics of stimulus- and choice-specific signals across the human cortical surface during a visual decision. Comparison with the evidence accumulation dynamics inferred from behavior enabled us to disentangle stimulus-dependent and endogenous components of choice-predictive activity across the visual cortical hierarchy. The endogenous component was present in primary visual cortex, expressed in a low (< 20 Hz) frequency-band, and its time course tracked, with delay, the build-up of choice-predictive activity in (pre-)motor regions. Our results are consistent with choice-specific cortical feedback signaling in a specific frequency channel during decision formation.

scholarly journals Automatic instance segmentation of mitochondria in electron microscopy data

2021 ◽  
Author(s):  
Luke Nightingale ◽  
Joost de Folter ◽  
Helen Spiers ◽  
Amy Strange ◽  
Lucy M Collinson ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Public Repository ◽  
Post Processing ◽  
Human Cortex ◽  
Electron Microscopy Data ◽  
Processing Procedure ◽  
Microscopy Data ◽  
Instance Segmentation

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

scholarly journals Increased burden of deleterious variants in essential genes in autism spectrum disorder

2016 ◽  
Vol 113 (52) ◽  
pp. 15054-15059 ◽  
Author(s):  
Xiao Ji ◽  
Rachel L. Kember ◽  
Christopher D. Brown ◽  
Maja Bućan
Keyword(s):  
Autism Spectrum Disorder ◽  
Large Scale ◽  
De Novo ◽  
Autism Spectrum ◽  
Essential Genes ◽  
Spectrum Disorder ◽  
Model Organisms ◽  
Disease Genes ◽  
Priority List ◽  
Human Orthologs

Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication, and repetitive behavior. It is estimated that hundreds of genes contribute to ASD. We asked if genes with a strong effect on survival and fitness contribute to ASD risk. Human orthologs of genes with an essential role in pre- and postnatal development in the mouse [essential genes (EGs)] are enriched for disease genes and under strong purifying selection relative to human orthologs of mouse genes with a known nonlethal phenotype [nonessential genes (NEGs)]. This intolerance to deleterious mutations, commonly observed haploinsufficiency, and the importance of EGs in development suggest a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. With a comprehensive catalog of 3,915 mammalian EGs, we provide compelling evidence for a stronger contribution of EGs to ASD risk compared with NEGs. By examining the exonic de novo and inherited variants from 1,781 ASD quartet families, we show a significantly higher burden of damaging mutations in EGs in ASD probands compared with their non-ASD siblings. The analysis of EGs in the developing brain identified clusters of coexpressed EGs implicated in ASD. Finally, we suggest a high-priority list of 29 EGs with potential ASD risk as targets for future functional and behavioral studies. Overall, we show that large-scale studies of gene function in model organisms provide a powerful approach for prioritization of genes and pathogenic variants identified by sequencing studies of human disease.

Forming Canonical 8D Data Clouds to Explore the Transient Dynamics of Physical Stiffened Plates: A Machine Learning Approach for Complex Mechanical Structures

2020 ◽  
Author(s):  
Ioannis T. Georgiou
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Complex Structure ◽  
Spatial Dimension ◽  
Physical Structure ◽  
Transient Dynamics ◽  
Alloy Plate ◽  
Considerable Uncertainty ◽  
Acceleration Signal ◽  
The Impact

Abstract This work presents a data-driven explorative study of the physics of the dynamics of a physical structure of complicated geometry. The geometric complexity of the physical system renders the typical single sensor acceleration signal quite complicated for a physics interpretation. We need the spatial dimension to resolve the single sensory signal over its entire time horizon. Thus we are introducing the spatial dimension by the canonical eight-dimensional data cloud (Canonical 8D-Data Cloud) concept to build methods to explore the impact-induced free dynamics of physical complex mechanical structures. The complex structure in this study is a large scale aluminum alloy plate stiffened by a frame made of T-section beams. The Canonical 8D-Data Cloud is identified with the simultaneous acceleration measurements by eight piezoelectric sensors equally spaced and attached on the periphery of a circular material curve drawn on the uniform surface of the stiffened plate. The Data Cloud approach leads to a systematic exploration-discovery-quantification of uncertainty in this physical complex structure. It is found that considerable uncertainty is stemming from the sensitivity of transient dynamics on the parameters of space-time localized force pulses, the latter being used as a means to diagnose the presence of structural anomalies. The Data Cloud approach leads to aspects of machine learning such as reduced dynamics analytics of big sensory data by means of heavenly machine-assisted computations to carry out the unparalleled data reduction analysis enabled by the Advanced Proper Orthogonal Decomposition Transform. Emphasized is the connection between the characteristic geometric features of high-dimensional datasets as a whole, the Data Cloud, and the modal physics of the dynamics.

A New Business Dimension

2001 ◽  
pp. 75-91
Author(s):  
Andre L. Brandao
Keyword(s):  
Antenna Arrays ◽  
Large Scale ◽  
Digital Signal ◽  
Spatial Dimension ◽  
Link Quality ◽  
Radio Link ◽  
Wireless Internet ◽  
Space Division Multiple Access ◽  
New Business ◽  
Market Opportunities

Space division multiple access (SDMA) is a promising technique useful for increasing capacity, reducing interference and improving overall wireless communication link quality. With a large-scale penetration expected for wireless Internet, the radio link will require significant reduction in cost and increase in capacity, benefits that the proper exploitation of the spatial dimension can offer. Market opportunities with SDMA are significant, as a number of companies have been recently formed to bring products based on this new concept to the wireless marketplace. The approach to SDMA is broad, ranging from "switched-beam techniques" to "adaptive antennas." Basically the technique employs antenna arrays and digital signal processing to achieve the necessary increases incapacity and quality needed in the wireless world.

scholarly journals Organization of Propagated Intrinsic Brain Activity in Individual Humans

2019 ◽  
Vol 30 (3) ◽  
pp. 1716-1734 ◽  
Author(s):  
Ryan V Raut ◽  
Anish Mitra ◽  
Scott Marek ◽  
Mario Ortega ◽  
Abraham Z Snyder ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Organization ◽  
Individual Level ◽  
Slow Activity ◽  
Lag Structure

Abstract Spontaneous infra-slow (&lt;0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

scholarly journals The Functional Foundations of Episodic Memory Remain Stable Throughout the Lifespan

2020 ◽  
Author(s):  
Didac Vidal-Piñeiro ◽  
Markus H Sneve ◽  
Inge K Amlien ◽  
Håkon Grydeland ◽  
Athanasia M Mowinckel ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Episodic Memory ◽  
Cognitive Abilities ◽  
Large Scale ◽  
Grey Matter ◽  
Late Life ◽  
Older Age ◽  
Memory Encoding ◽  
Brain Organization ◽  
Life Phenomenon

Abstract It has been suggested that specific forms of cognition in older age rely largely on late-life specific mechanisms. Here instead, we tested using task-fMRI (n = 540, age 6–82 years) whether the functional foundations of successful episodic memory encoding adhere to a principle of lifespan continuity, shaped by developmental, structural, and evolutionary influences. We clustered regions of the cerebral cortex according to the shape of the lifespan trajectory of memory activity in each region so that regions showing the same pattern were clustered together. The results revealed that lifespan trajectories of memory encoding function showed a continuity through life but no evidence of age-specific mechanisms such as compensatory patterns. Encoding activity was related to general cognitive abilities and variations of grey matter as captured by a multi-modal independent component analysis, variables reflecting core aspects of cognitive and structural change throughout the lifespan. Furthermore, memory encoding activity aligned to fundamental aspects of brain organization, such as large-scale connectivity and evolutionary cortical expansion gradients. Altogether, we provide novel support for a perspective on memory aging in which maintenance and decay of episodic memory in older age needs to be understood from a comprehensive life-long perspective rather than as a late-life phenomenon only.

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