scholarly journals A Precision Functional Atlas of Network Probabilities and Individual-Specific Network Topography

2022 ◽  
Author(s):  
Robert JM Hermosillo ◽  
Lucille A Moore ◽  
Eric J Feczko ◽  
Adam R Pines ◽  
Ally Dworetsky ◽  
...  
Keyword(s):  
Open Source ◽  
Template Matching ◽  
Functional Organization ◽  
Detection Algorithm ◽  
Brain Regions ◽  
Single Subject ◽  
Probabilistic Network ◽  
Community Detection Algorithm ◽  
Online Web

The brain is organized into a broad set of functional neural networks. These networks and their various characteristics have been described and scrutinized through in vivo resting state functional magnetic resonance imaging (rs-fMRI). While the basic properties of networks are generally similar between healthy individuals, there is vast variability in the precise topography across the population. These individual differences are often lost in population studies due to population averaging which assumes topographical uniformity. We leveraged precision brain mapping methods to establish a new open-source, method-flexible set of probabilistic functional network atlases: the Masonic Institute for the Developing Brain (MIDB) Precision Atlas. Using participants from the Adolescent Brain Cognitive Development (ABCD) study, single subject precision maps were generated with two supervised network-matching procedures (template matching and non-negative matrix factorization), as well as an unsupervised community detection algorithm (Infomap). We demonstrate that probabilistic network maps generated for two demographically-matched groups of n~3000 each were nearly identical, both between groups (Pearson r >0.999) and between methods (r=0.96), revealing both regions of high invariance and high variability. Compared to using parcellations based on groups averages, the MIDB Precision Atlases allowed us to derive a set of brain regions that are largely invariant in network topography and provide more reproducible statistical maps of executive function brain-wide associations. We explore an example use case for probabilistic maps, highlighting their potential for use in targeted neuromodulation. The MIDB Precision Atlas is expandable to alternative datasets and methods and is provided open-source with an online web interface to encourage the scientific community to experiment with probabilistic atlases and individual-specific topographies to more precisely relate network phenomenon to functional organization of the human brain.

scholarly journals Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abhishek Uday Patil ◽  
Sejal Ghate ◽  
Deepa Madathil ◽  
Ovid J. L. Tzeng ◽  
Hsu-Wen Huang ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Detection Algorithm ◽  
Brain Regions ◽  
Creative Cognition ◽  
Network Flexibility ◽  
Community Detection Algorithm ◽  
Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

Physiological Properties of Macaque V1 Neurons are Correlated With Extracellular Spike Amplitude, Duration, and Polarity

1999 ◽  
Vol 82 (3) ◽  
pp. 1451-1464 ◽  
Author(s):  
Moshe Gur ◽  
Alexander Beylin ◽  
D. Max Snodderly
Keyword(s):  
Cell Size ◽  
Action Potentials ◽  
Functional Organization ◽  
Brain Regions ◽  
Small Cells ◽  
Cortical Layers ◽  
Stimulus Motion ◽  
V1 Neurons ◽  
Physiological Properties

In the lateral geniculate nucleus (LGN) the large neurons of the magnocellular layers are functionally distinct and anatomically segregated from the small neurons of the parvocellular layers. This segregation of large and small cells is not maintained in the primary visual cortex (V1); instead a heterogeneous mixture of cells occurs, particularly in the output layers. Nevertheless, our results indicate that for the middle and upper layers of V1, cell size remains a predictor of physiological properties. We recorded extracellularly from neurons in V1 of alert monkeys and analyzed the amplitude, duration, and polarity of the action potentials of 199 cells. Of 156 cells that could be assigned to specific cortical layers, 137 (88%) were localized to the middle and upper cortical layers, layer 4 and above. We summarize evidence that the large-amplitude spikes are discharged by large cells, whereas small-amplitude spikes are the action potentials of smaller cells. Large spikes were predominantly negative and of longer duration, whereas small spikes were predominantly positive and briefer. The putative large cells had lower ongoing activity, smaller receptive field activating regions and higher selectivity for stimulus geometry and stimulus motion than the small cells. The contrasting properties of the large and the small cells were illustrated dramatically in simultaneous recordings made from adjacent cells. Our results imply that there may be an anatomic pairing or clustering of small and large cells that could be integral to the functional organization of the cortex. We suggest that the small and the large cells of area V1 have different roles, such that the small cells may shape the properties of the large output cells. If some of the small cells are also output cells, then cell size should be a predictor of the type of information being sent to other brain regions. Because of their high activity and relative ease of stimulation, the small cells also may contribute disproportionately to in vivo images based on metabolic responses such as changes in blood flow.

scholarly journals A population MRI brain template and analysis tools for the macaque

2017 ◽  
Author(s):  
Jakob Seidlitz ◽  
Caleb Sponheim ◽  
Daniel Glen ◽  
Frank Q. Ye ◽  
Kadharbatcha S. Saleem ◽  
...  
Keyword(s):  
Data Analysis ◽  
Brain Regions ◽  
Single Subject ◽  
Tissue Segmentation ◽  
Brain Extraction ◽  
Surface Reconstructions ◽  
Morphometric Feature ◽  
Feature Estimation ◽  
Brain Template

AbstractThe use of standard anatomical templates is common in human neuroimaging, as it facilitates data analysis and comparison across subjects and studies. For non-human primates, previous in vivo templates have lacked sufficient contrast to reliably validate known anatomical brain regions and have not provided tools for automated single-subject processing. Here we present the “National Institute of Mental Health Macaque Template”, or NMT for short. The NMT is a high-resolution in vivo MRI template of the average macaque brain generated from 31 subjects, as well as a neuroimaging tool for improved data analysis and visualization. From the NMT volume, we generated maps of tissue segmentation and cortical thickness. Surface reconstructions and transformations to previously published digital brain atlases are also provided. We further provide an analysis pipeline using the NMT that automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and morphometric feature estimation for anatomical scans of individual subjects. The NMT and associated tools thus provide a common platform for precise single-subject data analysis and for characterizations of neuroimaging results across subjects and studies.

scholarly journals Connectomic consistency: a systematic stability analysis of structural and functional connectivity

2019 ◽  
Author(s):  
Yusuf Osmanlıoğlu ◽  
Jacob A. Alappatt ◽  
Drew Parker ◽  
Ragini Verma
Keyword(s):  
Network Topology ◽  
Graph Matching ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Single Subject ◽  
Brain Organization ◽  
Single Person ◽  
Indispensable Tool ◽  
Pruning Techniques

AbstractConnectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated for different modalities such as using diffusion MRI to capture structural organization of the brain or using functional MRI to elaborate brain’s functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a proper understanding on how much variation should be expected on the two modalities, both between people cross-sectionally and within a single person longitudinally. Notably, connectomes of both modalities were shown to have significant differences depending on how they are generated. In this study, for both modalities, we systematically analyzed consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions or in terms of overall network topology. We present a comprehensive study of consistency in structural and resting-state functional connectomes both for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally. We compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. We evaluated consistency both at the levels of individual edges using correlation and at the level of network topology via graph matching accuracy. We also examined consistency of connectomes that are generated using most commonly applied communication schemes. Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated. Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.

scholarly journals Growth Factor Therapy for Parkinson’s Disease: Alternative Delivery Systems

2021 ◽  
pp. 1-7
Author(s):  
Sarah Jarrin ◽  
Abrar Hakami ◽  
Ben Newland ◽  
Eilís Dowd
Keyword(s):  
Parkinson’S Disease ◽  
Gene Therapy ◽  
Parkinson's Disease ◽  
Growth Factors ◽  
Growth Factor ◽  
Ex Vivo ◽  
Brain Regions ◽  
Delivery Systems ◽  
Alternative Delivery

Despite decades of research and billions in global investment, there remains no preventative or curative treatment for any neurodegenerative condition, including Parkinson’s disease (PD). Arguably, the most promising approach for neuroprotection and neurorestoration in PD is using growth factors which can promote the growth and survival of degenerating neurons. However, although neurotrophin therapy may seem like the ideal approach for neurodegenerative disease, the use of growth factors as drugs presents major challenges because of their protein structure which creates serious hurdles related to accessing the brain and specific targeting of affected brain regions. To address these challenges, several different delivery systems have been developed, and two major approaches—direct infusion of the growth factor protein into the target brain region and in vivo gene therapy—have progressed to clinical trials in patients with PD. In addition to these clinically evaluated approaches, a range of other delivery methods are in various degrees of development, each with their own unique potential. This review will give a short overview of some of these alternative delivery systems, with a focus on ex vivo gene therapy and biomaterial-aided protein and gene delivery, and will provide some perspectives on their potential for clinical development and translation.

scholarly journals Cortical neurons exhibit diverse myelination patterns that scale between mouse brain regions and regenerate after demyelination

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Cody L. Call ◽  
Dwight E. Bergles
Keyword(s):  
Cortical Neurons ◽  
Regional Differences ◽  
Brain Regions ◽  
Axon Diameter ◽  
Neuronal Identity ◽  
Cortical Areas ◽  
Myelin Sheaths ◽  
Parvalbumin Interneurons ◽  
Selection Of

ABSTRACTAxons in the cerebral cortex show a broad range of myelin coverage. Oligodendrocytes establish this pattern by selecting a cohort of axons for myelination; however, the distribution of myelin on distinct neurons and extent of internode replacement after demyelination remain to be defined. Here we show that myelination patterns of seven distinct neuron subtypes in somatosensory cortex are influenced by both axon diameter and neuronal identity. Preference for myelination of parvalbumin interneurons was preserved between cortical areas with varying myelin density, suggesting that regional differences in myelin abundance arises through local control of oligodendrogenesis. By imaging loss and regeneration of myelin sheaths in vivo we show that myelin distribution on individual axons was altered but overall myelin content on distinct neuron subtypes was restored. Our findings suggest that local changes in myelination are tolerated, allowing regenerated oligodendrocytes to restore myelin content on distinct neurons through opportunistic selection of axons.

scholarly journals The pharmacokinetics of [18F]UCB-H revisited in the healthy non-human primate brain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sébastien Goutal ◽  
Martine Guillermier ◽  
Guillaume Becker ◽  
Mylène Gaudin ◽  
Yann Bramoullé ◽  
...  
Keyword(s):  
Slow Component ◽  
Specific Binding ◽  
Compartment Model ◽  
Brain Regions ◽  
Volume Of Distribution ◽  
Pharmacokinetic Data ◽  
Limited Information ◽  
Positron Emission ◽  
Human Primate

Abstract Background Positron Emission Tomography (PET) imaging of the Synaptic Vesicle glycoprotein (SV) 2A is a new tool to quantify synaptic density. [18F]UCB-H was one of the first promising SV2A-ligands to be labelled and used in vivo in rodent and human, while limited information on its pharmacokinetic properties is available in the non-human primate. Here, we evaluate the reliability of the three most commonly used modelling approaches for [18F]UCB-H in the non-human cynomolgus primate, adding the coupled fit of the non-displaceable distribution volume (VND) as an alternative approach to improve unstable fit. The results are discussed in the light of the current state of SV2A PET ligands. Results [18F]UCB-H pharmacokinetic data was optimally fitted with a two-compartment model (2TCM), although the model did not always converge (large total volume of distribution (VT) or large uncertainty of the estimate). 2TCM with coupled fit K1/k2 across brain regions stabilized the quantification, and confirmed a lower specific signal of [18F]UCB-H compared to the newest SV2A-ligands. However, the measures of VND and the influx parameter (K1) are similar to what has been reported for other SV2A ligands. These data were reinforced by displacement studies using [19F]UCB-H, demonstrating only 50% displacement of the total [18F]UCB-H signal at maximal occupancy of SV2A. As previously demonstrated in clinical studies, the graphical method of Logan provided a more robust estimate of VT with only a small bias compared to 2TCM. Conclusions Modeling issues with a 2TCM due to a slow component have previously been reported for other SV2A ligands with low specific binding, or after blocking of specific binding. As all SV2A ligands share chemical structural similarities, we hypothesize that this slow binding component is common for all SV2A ligands, but only hampers quantification when specific binding is low.

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