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 Increased sensitivity to strong perturbations in a whole-brain model of LSD

2021 ◽  
Author(s):  
Beatrice M. Jobst ◽  
Selen Atasoy ◽  
Adrián Ponce-Alvarez ◽  
Ana Sanjuán ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
External Perturbation ◽  
Brain Regions ◽  
Lysergic Acid ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Whole Brain ◽  
Response Diversity ◽  
Brain States ◽  
The Brain

AbstractLysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain’s global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.HighlightsNovel offline perturbational method applied on functional magnetic resonance imaging (fMRI) data under the effect of lysergic acid diethylamide (LSD)Shift of brain’s global working point to more complex dynamics after LSD intakeConsistently longer recovery time after model perturbation under LSD influenceStrongest effects in resting state networks relevant for psychedelic experienceHigher response diversity across brain regions under LSD influence after an external in silico perturbation

scholarly journals Relative Brain Size and Its Relation with the Associative Pallium in Birds

2016 ◽  
Vol 87 (2) ◽  
pp. 69-77 ◽  
Author(s):  
Ferran Sayol ◽  
Louis Lefebvre ◽  
Daniel Sol
Keyword(s):  
Brain Size ◽  
Large Fraction ◽  
Biological Significance ◽  
Size Variation ◽  
Brain Structures ◽  
Brain Regions ◽  
Whole Brain ◽  
Small Areas ◽  
Mosaic Evolution ◽  
The Brain

Despite growing interest in the evolution of enlarged brains, the biological significance of brain size variation remains controversial. Much of the controversy is over the extent to which brain structures have evolved independently of each other (mosaic evolution) or in a coordinated way (concerted evolution). If larger brains have evolved by the increase of different brain regions in different species, it follows that comparisons of the whole brain might be biologically meaningless. Such an argument has been used to criticize comparative attempts to explain the existing variation in whole-brain size among species. Here, we show that pallium areas associated with domain-general cognition represent a large fraction of the entire brain, are disproportionally larger in large-brained birds and accurately predict variation in the whole brain when allometric effects are appropriately accounted for. While this does not question the importance of mosaic evolution, it suggests that examining specialized, small areas of the brain is not very helpful for understanding why some birds have evolved such large brains. Instead, the size of the whole brain reflects consistent variation in associative pallium areas and hence is functionally meaningful for comparative analyses.

scholarly journals Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

2018 ◽  
Author(s):  
Matthieu Gilson ◽  
Nikos E. Kouvaris ◽  
Gustavo Deco ◽  
Jean-François Mangin ◽  
Cyril Poupon ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Theory ◽  
Random Network ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Real Data ◽  
Brain Regions ◽  
Network Activity ◽  
Whole Brain ◽  
The Brain

AbstractNeuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

scholarly journals Large-scale intrinsic connectivity is consistent across varying task demands

2018 ◽  
Author(s):  
Paulina Kieliba ◽  
Sasidhar Madugula ◽  
Nicola Filippini ◽  
Eugene P. Duff ◽  
Tamar R. Makin
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Activation ◽  
Whole Brain ◽  
Activation Patterns ◽  
Intrinsic Connectivity ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

scholarly journals Qualitative and Quantitative Analysis of Regional Cerebral Free Fatty Acids in Rats Using the Stable Isotope Labeling Liquid Chromatography–Mass Spectrometry Method

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 5163
Author(s):  
Ting Hu ◽  
Quanfei Zhu ◽  
Yuning Hu ◽  
Ghulam Mustafa Kamal ◽  
Yuqi Feng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Fatty Acids ◽  
Liquid Chromatography ◽  
Olfactory Bulb ◽  
Free Fatty Acids ◽  
Brain Regions ◽  
Liquid Chromatography Mass Spectrometry ◽  
Whole Brain ◽  
The Relationship ◽  
The Brain

Free fatty acids serve as important bioactive molecules in the brain. They are involved in message transfer in the brain. There are many reports available in the literature regarding the role of cerebral fatty acids in message transfer; however, most of the studies are mainly focused on limited fatty acid species or only a few specific brain regions. To understand the relationship between cerebral functions and free fatty acids, it is necessary to investigate the distribution of the free fatty acids among different regions in the whole brain. In this study, free fatty acids were extracted from different brain regions and analyzed qualitatively and quantitatively using the stable isotopic labeling liquid chromatography–mass spectrometry approach. In total, 1008 potential free fatty acids were detected in the whole brain out of which 38 were found to be commonly present in all brain regions. Among different brain regions, the highest and the smallest amounts of potential free fatty acids were detected in the olfactory bulb and cerebellum, respectively. From a statistical point of view, 4-methyl-2-oxovaleric acid, cis-11, 14-eicosadienoic acid, tridecanoic acid, myristic acid, nonadecanoic acid, and arachidic acid were found to significantly vary among the four different brain regions (olfactory bulb, occipital lobe, hippocampus, and cerebellum). The variation in the composition of free fatty acids among different brain regions may be very important for investigating the relationship between free fatty acids and functions of cerebral regions.

scholarly journals Stress-induced microglial activation occurs through β-adrenergic receptor: noradrenaline as a key neurotransmitter in microglial activation

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Shuei Sugama ◽  
Takato Takenouchi ◽  
Makoto Hashimoto ◽  
Hisayuki Ohata ◽  
Yasuhiro Takenaka ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Microglial Activation ◽  
Acute Stress ◽  
Underlying Mechanism ◽  
Brain Regions ◽  
Laser Scanning Microscopy ◽  
Double Knockout ◽  
Whole Brain ◽  
Β Blocker ◽  
The Brain

Abstract Background The involvement of microglia in neuroinflammatory responses has been extensively demonstrated. Recent animal studies have shown that exposure to either acute or chronic stress induces robust microglial activation in the brain. In the present study, we investigated the underlying mechanism of brain microglial activation by acute stress. Methods We first looked at the spatial distribution of the noradrenaline (NA)-synthesizing enzyme, DBH (dopamine β-hydroxylase), in comparison with NA receptors—β1, β2, and β3 adrenergic receptors (β1-AR, β2-AR, and β3-AR)—after which we examined the effects of the β-blocker propranolol and α-blockers prazosin and yohimbine on stress-induced microglial activation. Finally, we compared stress-induced microglial activation between wild-type (WT) mice and double-knockout (DKO) mice lacking β1-AR and β2-AR. Results The results demonstrated that (1) microglial activation occurred in most studied brain regions, including the hippocampus (HC), thalamus (TM), and hypothalamus (HT); (2) within these three brain regions, the NA-synthesizing enzyme DBH was densely stained in the neuronal fibers; (3) β1-AR and β2-AR, but not β3-AR, are detected in the whole brain, and β1-AR and β2-AR are co-localized with microglial cells, as observed by laser scanning microscopy; (4) β-blocker treatment inhibited microglial activation in terms of morphology and count through the whole brain; α-blockers did not show such effect; (5) unlike WT mice, DKO mice exhibited substantial inhibition of stress-induced microglial activation in the brain. Conclusions We demonstrate that neurons/microglia may interact with NA via β1-AR and β2-AR.

scholarly journals Wiring diagram of the oxytocin system in the mouse brain

2020 ◽  
Author(s):  
Seoyoung Son ◽  
Steffy B. Manjila ◽  
Kyra T. Newmaster ◽  
Yuan-ting Wu ◽  
Daniel J. Vanselow ◽  
...  
Keyword(s):  
Neural Circuits ◽  
Physiological Responses ◽  
Brain Regions ◽  
Receptor Expression ◽  
Wiring Diagram ◽  
Whole Brain ◽  
Functional Studies ◽  
Quantitative Correlation ◽  
Anatomical Arrangement ◽  
The Brain

AbstractIn the brain, oxytocin (OT) neurons make direct connections with discreet regions to regulate social behavior and diverse physiological responses. Obtaining an integrated neuroanatomical understanding of pleiotropic OT functions requires comprehensive wiring diagram of OT neurons. Here, we have created a whole-brain map of distribution and anatomical connections of hypothalamic OT neurons, and their relationship with OT receptor (OTR) expression. We used our brain-wide quantitative mapping at cellular resolution combined with a 2D flatmap to provide an intuitive understanding of the spatial arrangements of OT neurons. Then, we utilized knock-in Ot-Cre mice injected with Cre dependent retrograde monosynaptic rabies viruses and anterograde adeno associated virus to interrogate input-output patterns. We find that brain regions with cognitive functions such as the thalamus are reciprocally connected, while areas associated with physiological functions such as the hindbrain receive unidirectional outputs. Lastly, comparison between OT output and OTR expression showed no significant quantitative correlation, suggesting that OT transmission mostly occurs through indirect pathways. In summary, our OT wiring diagram provides structural and quantitative insights of distinct behavioral functions of OT neurons in the brain.Significance StatementOxytocin (OT) neurons in the brain play an important role in socio-physiological responses. Impairment of OT signaling has been implicated in many neurodevelopmental disorders. To understand diverse OT functions in the context of discreet neural circuits, it is imperative to understand the anatomical arrangement of OT neurons across the whole brain in significant detail. Here, we have established a comprehensive brain-wide wiring diagram of OT neurons. Our anatomical and connectivity map of OT neurons includes brain-wide cell distribution, synaptic inputs, axonal outputs, and their relationships with the oxytocin receptor expression. This whole brain structural perspective of the OT system provides a foundation for understanding the diversity of neural circuits modulated by OT and will guide future circuit-based OT functional studies.

scholarly journals Focused attention meditation changes the boundary and configuration of functional networks in the brain

2019 ◽  
Author(s):  
Shogo Kajimura ◽  
Naoki Masuda ◽  
Johnny King Lau ◽  
Kou Murayama
Keyword(s):  
Single Case ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Health It ◽  
Community Size ◽  
Whole Brain ◽  
Motor Network ◽  
Functional Brain Networks ◽  
The Brain

AbstractResearch has shown that meditation not only improves our cognitive and motivational functioning (e.g., attention, mental health), it influences the way how our brain networks [e.g., default mode network (DMN), fronto-parietal network (FPN), and sensory-motor network (SMN)] function and operate. However, surprisingly little attention has been paid to the possibility that meditation alters the structure (composition) of these functional brain networks. Here, using a single-case experimental design with longitudinal intensive data, we examined the effect of mediation practice on intra-individual changes in the composition of whole-brain networks. The results showed that meditation (1) changed the community size (with a number of regions in the FPN being merged into the DMN after meditation), (2) changed the brain regions composing the SMN community without changing its size, and (3) led to instability in the community allegiance of the regions in the FPN. These results suggest that, in addition to altering specific functional connectivity, meditation leads to reconfiguration of whole-brain network structure. The reconfiguration of community structure in the brain provides fruitful information about the neural mechanisms of meditation.

scholarly journals The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches

2021 ◽  
Vol 15 ◽  
Author(s):  
Sierra Simpson ◽  
Yueyi Chen ◽  
Emma Wellmeyer ◽  
Lauren C. Smith ◽  
Brianna Aragon Montes ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Computational Neuroscience ◽  
Brain Function ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
Whole Brain ◽  
Model Studies ◽  
Wide Range ◽  
Network Approaches ◽  
The Brain

A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.

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