scholarly journals An adult brain atlas reveals broad neuroanatomical changes in independently evolved populations of Mexican cavefish

2019 ◽  
Author(s):  
Cody Loomis ◽  
Robert Peuß ◽  
James Jaggard ◽  
Yongfu Wang ◽  
Sean McKinney ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Brain Structures ◽  
Brain Regions ◽  
Adult Brain ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Behavioral Regulation ◽  
Surface Population ◽  
Volumetric Reconstruction ◽  
And Behavior

AbstractA shift in environmental conditions impacts the evolution of complex developmental and behavioral traits. The Mexican cavefish, Astyanax mexicanus, is a powerful model for examining the evolution of development, physiology, and behavior because multiple cavefish populations can be compared to an extant and ancestral-like surface population of the same species. Many behaviors have diverged in cave populations of A. mexicanus, and previous studies have shown that cavefish have a loss of sleep, reduced stress, an absence of social behaviors, and hyperphagia. Despite these findings, surprisingly little is known about the changes in neuroanatomy that underlie these behavioral phenotypes. Here, we use serial sectioning to generate a brain atlas of surface fish and three independent cavefish populations. Volumetric reconstruction of serial-sectioned brains confirms convergent evolution on reduced optic tectum volume in all cavefish populations tested. In addition, we quantified volumes of specific neuroanatomical loci within several brain regions, which have previously been implicated in behavioral regulation, including the hypothalamus, thalamus, and habenula. These analyses reveal an expansion of the hypothalamus across all three cavefish populations relative to surface fish, as well as subnuclei-specific differences within the thalamus and habenulae. Taken together, these analyses support the notion that changes in environmental conditions are accompanied by neuroanatomical changes in brain structures associated with behavior. This atlas provides a resource for comparative neuroanatomy of additional brain regions and the opportunity to associate brain anatomy with evolved changes in behavior.

scholarly journals The Antennal Pathway of Dragonfly Nymphs, from Sensilla to the Brain

Insects ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 886
Author(s):  
Silvana Piersanti ◽  
Manuela Rebora ◽  
Gianandrea Salerno ◽  
Sylvia Anton
Keyword(s):  
Brain Structure ◽  
Antennal Lobe ◽  
Sensory Information ◽  
Brain Structures ◽  
Brain Regions ◽  
Adult Brain ◽  
Published Data ◽  
Antennal Sensilla ◽  
Aquatic Life ◽  
The Brain

Dragonflies are hemimetabolous insects, switching from an aquatic life style as nymphs to aerial life as adults, confronted to different environmental cues. How sensory structures on the antennae and the brain regions processing the incoming information are adapted to the reception of fundamentally different sensory cues has not been investigated in hemimetabolous insects. Here we describe the antennal sensilla, the general brain structure, and the antennal sensory pathways in the last six nymphal instars of Libellula depressa, in comparison with earlier published data from adults, using scanning electron microscopy, and antennal receptor neuron and antennal lobe output neuron mass-tracing with tetramethylrhodamin. Brain structure was visualized with an anti-synapsin antibody. Differently from adults, the nymphal antennal flagellum harbors many mechanoreceptive sensilla, one olfactory, and two thermo-hygroreceptive sensilla at all investigated instars. The nymphal brain is very similar to the adult brain throughout development, despite the considerable differences in antennal sensilla and habitat. Like in adults, nymphal brains contain mushroom bodies lacking calyces and small aglomerular antennal lobes. Antennal fibers innervate the antennal lobe similar to adult brains and the gnathal ganglion more prominently than in adults. Similar brain structures are thus used in L. depressa nymphs and adults to process diverging sensory information.

scholarly journals Accurate localization of linear probe electrodes across multiple brains

2020 ◽  
Author(s):  
Liu D Liu ◽  
Susu Chen ◽  
Michael N Economo ◽  
Nuo Li ◽  
Karel Svoboda
Keyword(s):  
Ground Truth ◽  
Brain Structures ◽  
Brain Regions ◽  
Brain Atlas ◽  
3 Dimensional ◽  
Accurate Localization ◽  
Large Numbers ◽  
Linear Probe ◽  
Electrode Localization ◽  
Recording Electrodes

AbstractRecently developed silicon probes have large numbers of recording electrodes on long linear shanks. Specifically, Neuropixels probes have 960 recording electrodes distributed over 9.6 mm shanks. Because of their length, Neuropixels probe recordings in rodents naturally span multiple brain areas. Typical studies collate recordings across several recording sessions and animals. Neurons recorded in different sessions and animals have to be aligned to each other and to a standardized brain coordinate system. Here we report a workflow for accurate localization of individual electrodes in standardized coordinates and aligned across individual brains. This workflow relies on imaging brains with fluorescent probe tracks and warping 3-dimensional image stacks to standardized brain atlases. Electrophysiological features are then used to anchor particular electrodes along the reconstructed tracks to specific locations in the brain atlas and therefore to specific brain structures. We performed ground-truth experiments, in which motor cortex outputs are labelled with ChR2 and a fluorescence protein. Recording from brain regions targeted by these outputs reveals better than 100 μm accuracy for electrode localization.

scholarly journals Spatiotemporal ontogeny of brain wiring

2019 ◽  
Vol 5 (6) ◽  
pp. eaav9694 ◽  
Author(s):  
A. Goulas ◽  
R. F. Betzel ◽  
C. C. Hilgetag
Keyword(s):  
Computational Modeling ◽  
Network Topology ◽  
Brain Regions ◽  
Adult Brain ◽  
Axonal Guidance ◽  
Global Network ◽  
Dependent Plasticity ◽  
Guidance Cues ◽  
And Behavior ◽  
Activity Dependent

The wiring of vertebrate and invertebrate brains provides the anatomical skeleton for cognition and behavior. Connections among brain regions are characterized by heterogeneous strength that is parsimoniously described by the wiring cost and homophily principles. Moreover, brains exhibit a characteristic global network topology, including modules and hubs. However, the mechanisms resulting in the observed interregional wiring principles and network topology of brains are unknown. Here, with the aid of computational modeling, we demonstrate that a mechanism based on heterochronous and spatially ordered neurodevelopmental gradients, without the involvement of activity-dependent plasticity or axonal guidance cues, can reconstruct a large part of the wiring principles (on average, 83%) and global network topology (on average, 80%) of diverse adult brain connectomes, including fly and human connectomes. In sum, space and time are key components of a parsimonious, plausible neurodevelopmental mechanism of brain wiring with a potential universal scope, encompassing vertebrate and invertebrate brains.

scholarly journals fMRI reveals neural activity overlap between adult and infant pain

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Sezgi Goksan ◽  
Caroline Hartley ◽  
Faith Emery ◽  
Naomi Cockrill ◽  
Ravi Poorun ◽  
...  
Keyword(s):  
Brain Activity ◽  
Management Strategies ◽  
Newborn Infants ◽  
Brain Structures ◽  
Brain Regions ◽  
Adult Brain ◽  
Noxious Stimulation ◽  
Pain Experience ◽  
Infant Pain ◽  
Affective Components

Limited understanding of infant pain has led to its lack of recognition in clinical practice. While the network of brain regions that encode the affective and sensory aspects of adult pain are well described, the brain structures involved in infant nociceptive processing are less well known, meaning little can be inferred about the nature of the infant pain experience. Using fMRI we identified the network of brain regions that are active following acute noxious stimulation in newborn infants, and compared the activity to that observed in adults. Significant infant brain activity was observed in 18 of the 20 active adult brain regions but not in the infant amygdala or orbitofrontal cortex. Brain regions that encode sensory and affective components of pain are active in infants, suggesting that the infant pain experience closely resembles that seen in adults. This highlights the importance of developing effective pain management strategies in this vulnerable population.

scholarly journals Early Life Epidemiology of Alzheimer's Disease - A Critical Review

2015 ◽  
Vol 45 (4) ◽  
pp. 237-254 ◽  
Author(s):  
Alon Seifan ◽  
Matthew Schelke ◽  
Yaa Obeng-Aduasare ◽  
Richard Isaacson
Keyword(s):  
Risk Factors ◽  
Early Life ◽  
Later Life ◽  
Underlying Mechanism ◽  
Perinatal Period ◽  
Brain Regions ◽  
Adult Brain ◽  
Brain Anatomy ◽  
Infancy And Childhood ◽  
Childhood Socioeconomic Status

Background: As adult brain structure is primarily established in early life, genetic and environmental exposures in infancy and childhood influence the risk for Alzheimer disease (AD). In this systematic review, we identified several early life risk factors and discussed the evidence and underlying mechanism for each. Summary: Early risk factors for AD may alter brain anatomy, causing vulnerability to AD-related dementia later in life. In the perinatal period, both genes and learning disabilities have been associated with the development of distinct AD phenotypes. During early childhood, education and intellect, as well as body growth, may predispose to AD through alterations in cognitive and brain reserve, though the specific mediators of neural injury are disputed. Childhood socioeconomic status (SES) may predispose to AD by influencing adult SES and cognition. Association of these risk factors with underlying AD pathology (rather than just clinical diagnosis) has not been sufficiently examined. Key Messages: Factors that impede or alter brain growth during early life could render certain brain regions or networks selectively vulnerable to the onset, accumulation or spread of AD-related pathology during later life. Careful life-course epidemiology could provide clues as to why the brain systematically degenerates during AD.

scholarly journals Pendidikan Anak Usia Dini (PAUD) Dalam Pendekatan Psikologi Anak

2017 ◽  
Vol 1 (2) ◽  
pp. 73-81
Author(s):  
Nabil Nabil
Keyword(s):  
Brain Development ◽  
Golden Age ◽  
Brain Structures ◽  
Brain Regions ◽  
Adult Brain ◽  
Sensitive Period ◽  
Early Age ◽  
Conceptual Foundation ◽  
Maria Montessori ◽  
Daily Behavior

In the point of psychology, the phenomenon of early childhood education is a necessity. Because the development of the human brain at an early age (0 to 6 years) accelerates to 80% of the overall adult brain. Often the early age is often referred to as the golden age (golden age) in the development of human history. Maria Montessori said that this period is a sensitive period in which children easily receive stimuli from their environment. It is during this sensitive period that physical and psychological functions are matured so that the child is ready to respond and realize all the developmental tasks that are expected to appear in his daily behavior patterns.The conceptual foundation that underlies the importance of PAUD is the discovery of experts about child development, especially in the field of neuroscience and psychology. Growth and development of children can not be released by the development of brain structures. According to Wittrock there are three areas of brain development that experience rapid increase at an early age, namely the growth of dendritic fibers, the complexity of synaptic relationships, and division of nerve cells. The three brain regions are very important to develop from an early age, because at this age the three brain regions experience maximum development, which is 80% of the overall adult brain development. After children aged 6 years and older until adulthood, their development does not exceed 20%.

scholarly journals Spatiotemporal ontogeny of brain wiring

2018 ◽  
Author(s):  
Alexandros Goulas ◽  
Richard F. Betzel ◽  
Claus C. Hilgetag
Keyword(s):  
Network Topology ◽  
Short Path ◽  
Brain Regions ◽  
Adult Brain ◽  
Axonal Guidance ◽  
Global Network ◽  
Path Lengths ◽  
And Behavior ◽  
Activity Dependent ◽  
The Brain

AbstractThe wiring of the brain provides the anatomical skeleton for cognition and behavior. Connections among brain regions have a diverse and characteristic strength. This strength heterogeneity is captured by the wiring cost and homophily principles. Moreover, brains have a characteristic global network topology, including modularity and short path lengths. However, the mechanisms underlying the inter-regional wiring principles and global network topology of brains are unknown. Here, we address this issue by modeling the ontogeny of brain connectomes. We demonstrate that spatially embedded and heterochronous neurogenetic gradients, without the need of axonal-guidance molecules or activity-dependent plasticity, can reconstruct the wiring principles and shape the global network topology observed in adult brain connectomes. Thus, two fundamental dimensions, that is, space and time, are key components of a plausible neurodevelopmental mechanism with a universal scope, encompassing vertebrate and invertebrate brains.

scholarly journals Exploiting Gene Expression Profiles for the Automated Prediction of Connectivity between Brain Regions

2019 ◽  
Vol 20 (8) ◽  
pp. 2035 ◽  
Author(s):  
Ilaria Roberti ◽  
Marta Lovino ◽  
Santa Di Cataldo ◽  
Elisa Ficarra ◽  
Gianvito Urgese
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Expression Profiles ◽  
Brain Area ◽  
Gene Expression Profiles ◽  
Brain Structures ◽  
Brain Regions ◽  
Brain Atlas ◽  
Data Driven Approach ◽  
Prediction Problems

The brain comprises a complex system of neurons interconnected by an intricate network of anatomical links. While recent studies demonstrated the correlation between anatomical connectivity patterns and gene expression of neurons, using transcriptomic information to automatically predict such patterns is still an open challenge. In this work, we present a completely data-driven approach relying on machine learning (i.e., neural networks) to learn the anatomical connection directly from a training set of gene expression data. To do so, we combined gene expression and connectivity data from the Allen Mouse Brain Atlas to generate thousands of gene expression profile pairs from different brain regions. To each pair, we assigned a label describing the physical connection between the corresponding brain regions. Then, we exploited these data to train neural networks, designed to predict brain area connectivity. We assessed our solution on two prediction problems (with three and two connectivity class categories) involving cortical and cerebellum regions. As demonstrated by our results, we distinguish between connected and unconnected regions with 85% prediction accuracy and good balance of precision and recall. In our future work we may extend the analysis to more complex brain structures and consider RNA-Seq data as additional input to our model.

scholarly journals Mapping the Self: A network approach for understanding psychological and neural representations of self-concept structure

2020 ◽  
Author(s):  
Jacob Elder ◽  
Ki Man Bernice Man Bernice Cheung;Cheung ◽  
Tyler Davis ◽  
Brent Hughes
Keyword(s):  
Brain Activity ◽  
Sense Of Self ◽  
Brain Structures ◽  
Brain Regions ◽  
The Self ◽  
Self Concept ◽  
Activation Patterns ◽  
Self Reflection ◽  
Similarity Relationships ◽  
And Behavior

How people self-reflect and maintain a coherent sense of self is a central question that spans from early philosophy to modern psychology and neuroscience. Neurobiological approaches to self-concept representation have focused on localizing brain structures that are involved in self-reflection, such as the medial prefrontal cortex (mPFC). This research has largely approached the self as a unitary construct, without a formal representational theory of how self-perceptions cohere and depend upon one another. We develop a network-based approach, which suggests that the self-concept is represented as a web of interrelated traits. Leveraging this trait network to inform two behavioral and functional magnetic resonance imaging (fMRI) studies, we show how network features predict positivity and coherence in self-evaluations and activation in brain regions involved in self-referential cognition. Specifically, we find that network-based measures that preserve network stability (i.e. their outdegree centrality) are associated with more favorable and consistent self-evaluations and decreases in mPFC activation. Further, individuals low in self-esteem and high in depressive symptoms are less sensitive to central, stability preserving traits, suggesting that network-defined self-concept coherence may play a role in maintaining positive and consistent self-views. In addition, similarity relationships amongst traits in the network explain both consistency in self-evaluations as well as similar activation patterns in semantic- and self-processing regions. Together, our model and findings present the first computational theory of how individuals holistically represent an interconnected self-concept that joins individual differences, brain activity, and behavior.

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