scholarly journals Hierarchical Computational Anatomy: Unifying the Molecular to Tissue Continuum via Measure Representations of the Brain

2021 ◽  
Author(s):  
Michael I Miller ◽  
Daniel Jacob Tward ◽  
Alain Trouve
Keyword(s):  
Scale Space ◽  
Generalized Functions ◽  
Geodesic Flows ◽  
Computational Anatomy ◽  
Empirical Probability ◽  
Basic Particle ◽  
Classical Formalism ◽  
Copy And Paste ◽  
The Brain ◽  
Scale Data

This paper presents a unified representation of the brain based on mathematical functional measures integrating the molecular and cellular scale descriptions with continuum tissue scale descriptions. We present a fine-to-coarse recipe for traversing the brain as a hierarchy of measures projecting functional description into stable empirical probability laws that unifies scale-space aggregation. The representation uses measure norms for mapping the brain across scales from different measurement technologies. Brainspace is constructed as a metric space with metric comparison between brains provided by a hierarchy of Hamiltonian geodesic flows of diffeomorphisms connecting the molecular and continuum tissue scales. The diffeomorphisms act on the brain measures via the 3D varifold action representing "copy and paste" so that basic particle quantities that are conserved biologically are combined with greater multiplicity and not geometrically distorted. Two applications are examined, the first histological and tissue scale data in the human brain for studying Alzheimer's disease, and the second the RNA and cell signatures of dense spatial transcriptomics mapped to the meso-scales of brain atlases. The representation unifies the classical formalism of computational anatomy for representing continuum tissue scale with non-classical generalized functions appropriate for molecular particle scales.

scholarly journals FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

2021 ◽  
Vol 15 ◽  
Author(s):  
Tinashe M. Tapera ◽  
Matthew Cieslak ◽  
Max Bertolero ◽  
Azeez Adebimpe ◽  
Geoffrey K. Aguirre ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Curation ◽  
Imaging Data ◽  
Large Scale Data ◽  
Neuroscience Research ◽  
Neuroimaging Data ◽  
Brain Imaging Data ◽  
Database Platform ◽  
The Brain ◽  
Scale Data

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is the de facto standard for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are limited on Flywheel. To address these challenges, we developed “FlywheelTools,” a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

scholarly journals Genetics and psychophysiology of ADHD and autism

2020 ◽  
Author(s):  
Elena I. Nikolaeva ◽  
Keyword(s):  
Gene Expression ◽  
Attention Deficit Hyperactivity Disorder ◽  
Dna Sequences ◽  
Somatic Mosaicism ◽  
Autism Spectrum ◽  
Hyperactivity Disorder ◽  
Common Causes ◽  
Target Sites ◽  
Copy And Paste ◽  
The Brain

The paper discusses the brain mechanisms of autism and attention deficit hyperactivity disorder. It is shown that these disorders are associated with different genetic causes that create certain psychophysiological mechanisms. Nevertheless, their diagnosis is interrelated. Moreover, a child is often first diagnosed with ADHD, and then the diagnosis is changed to “autism spectrum disease”. Among the most common causes of the disease is the behavior of retrotransposons. Retrotransposons (also called transposons via intermediate RNA) are genetic elements that can amplify themselves in the genome. These DNA sequences use a “copy and paste” mechanism, whereby they are first transcribed into RNA and then converted back to identical DNA sequences via reverse transcription, and these sequences are then inserted into the genome at target sites. In humans, retro elements take up 42 % of the DNA. The conclusion is made that for the formation of an individual profile of gene expression in the neuron, the most important is the phenomenon of somatic mosaicism, due to the process of L1 retrotransposition, in addition to the classical described mechanisms of differentiation. The number of such events and their localization is significant as they are likely to contribute to the development of both autism and ADHD.

scholarly journals A null model of the mouse whole-neocortex micro-connectome

2019 ◽  
Author(s):  
Michael W Reimann ◽  
Michael Gevaert ◽  
Ying Shi ◽  
Huanxiang Lu ◽  
Henry Markram ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Null Model ◽  
Brain Regions ◽  
Whole Brain ◽  
Micro Scale ◽  
Connectivity Patterns ◽  
Experimental Findings ◽  
The Brain ◽  
Scale Data ◽  
Meso Scale

1AbstractConnectomics, the study of the structure of networks of synaptically connected neurons, is one of the most important frontiers of neuroscience. Great advances are being made on the level of macro- and meso-scale connectomics, that is the study of how and which populations of neurons are wired together by tracing axons of anatomically and genetically defined neurons throughout the brain. Similarly, the use of electron-microscopy and statistical connectome models has improved our understanding of micro-connectomics, that is the study of connectivity patterns between individual neurons. We have combined these two complementary views of connectomics to build a first draft statistical model of the neuron-to-neuron micro-connectome of a whole mouse neocortex. We combined available data on region-to-region connectivity and individual whole-brain axon reconstructions to model in addition to the meso-scale trends also the innervation of individual neurons by individual axons, within and across regions. This process revealed a novel targeting principle that allowed us to predict the innervation logic of individual axons from meso-scale data. The resulting micro-connectome of 10 million neurons and 88 billion synapses recreates biological trends of targeting on the macro-meso- and micro-scale, i.e. targeting of brain regions, domains and layers within a brain region down to individual neurons. This openly accessible connectome can serve as a powerful null model to compare experimental findings to and as a substrate for whole-brain simulations of detailed neural networks.

scholarly journals Scan Once, Analyse Many: Using large open-access neuroimaging datasets to understand the brain

2020 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Large Scale ◽  
Use Cases ◽  
Imaging Data ◽  
Replication Sample ◽  
Methods Development ◽  
Large Scale Data ◽  
Secondary Analyses ◽  
Brain Imaging Data ◽  
The Brain ◽  
Scale Data

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility--both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

scholarly journals The map in the brain: Distributed cortical representations of large-scale space

2010 ◽  
Vol 7 (9) ◽  
pp. 1048-1048
Author(s):  
A. M. Feiler ◽  
R. A. Epstein ◽  
G. K. Aguirre
Keyword(s):  
Large Scale ◽  
Scale Space ◽  
Cortical Representations ◽  
The Brain

scholarly journals LinkRbrain: Multi-scale data integrator of the brain

2015 ◽  
Vol 241 ◽  
pp. 44-52 ◽  
Author(s):  
Salma Mesmoudi ◽  
Mathieu Rodic ◽  
Claudia Cioli ◽  
Jean-Philippe Cointet ◽  
Tal Yarkoni ◽  
...  
Keyword(s):  
Multi Scale ◽  
The Brain ◽  
Scale Data

scholarly journals Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

2021 ◽  
Author(s):  
Christopher R. Madan
Keyword(s):  
Large Scale ◽  
Use Cases ◽  
Imaging Data ◽  
Replication Sample ◽  
Methods Development ◽  
Large Scale Data ◽  
Secondary Analyses ◽  
Brain Imaging Data ◽  
The Brain ◽  
Scale Data

AbstractWe are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility–both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

Sign in / Sign up

Export Citation Format

Share Document