scholarly journals Large-scale imaging of cortical dynamics during sensory perception and behavior

2016 ◽  
Vol 115 (6) ◽  
pp. 2852-2866 ◽  
Author(s):  
Joseph B. Wekselblatt ◽  
Erik D. Flister ◽  
Denise M. Piscopo ◽  
Cristopher M. Niell
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Dorsal Surface ◽  
Global Scale ◽  
Orientation Discrimination ◽  
Cortical Dynamics ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Cortical Regions ◽  
And Behavior

Sensory-driven behaviors engage a cascade of cortical regions to process sensory input and generate motor output. To investigate the temporal dynamics of neural activity at this global scale, we have improved and integrated tools to perform functional imaging across large areas of cortex using a transgenic mouse expressing the genetically encoded calcium sensor GCaMP6s, together with a head-fixed visual discrimination behavior. This technique allows imaging of activity across the dorsal surface of cortex, with spatial resolution adequate to detect differential activity in local regions at least as small as 100 μm. Imaging during an orientation discrimination task reveals a progression of activity in different cortical regions associated with different phases of the task. After cortex-wide patterns of activity are determined, we demonstrate the ability to select a region that displayed conspicuous responses for two-photon microscopy and find that activity in populations of individual neurons in that region correlates with locomotion in trained mice. We expect that this paradigm will be a useful probe of information flow and network processing in brain-wide circuits involved in many sensory and cognitive processes.

scholarly journals Quantitative analysis of 1300-nm three-photon calcium imaging in the mouse brain

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tianyu Wang ◽  
Chunyan Wu ◽  
Dimitre G Ouzounov ◽  
Wenchao Gu ◽  
Fei Xia ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Large Scale ◽  
Brain Regions ◽  
Excitation Power ◽  
Signal Attenuation ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Tissue Heating ◽  
Photon Excitation ◽  
Deep Brain

1300 nm three-photon calcium imaging has emerged as a useful technique to allow calcium imaging in deep brain regions. Application to large-scale neural activity imaging entails a careful balance between recording fidelity and perturbation to the sample. We calculated and experimentally verified the excitation pulse energy to achieve the minimum photon count required for the detection of calcium transients in GCaMP6s-expressing neurons for 920 nm two-photon and 1320 nm three-photon excitation. By considering the combined effects of in-focus signal attenuation and out-of-focus background generation, we quantified the cross-over depth beyond which three-photon microscopy outpeforms two-photon microscopy in recording fidelity. Brain tissue heating by continuous three-photon imaging was simulated with Monte Carlo method and experimentally validated with immunohistochemistry. Increased immunoreactivity was observed with 150 mW excitation power at 1 and 1.2 mm imaging depths. Our analysis presents a translatable model for the optimization of three-photon calcium imaging based on experimentally tractable parameters.

scholarly journals Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

BME Frontiers ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Waleed Tahir ◽  
Sreekanth Kura ◽  
Jiabei Zhu ◽  
Xiaojun Cheng ◽  
Rafat Damseh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Imaging Systems ◽  
Total Variation Regularization ◽  
Computationally Efficient ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Brain Vasculature ◽  
Vascular Segmentation ◽  
Anatomical Modeling

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

scholarly journals The asynchronous state's relation to large-scale potentials in cortex

2019 ◽  
Vol 122 (6) ◽  
pp. 2206-2219 ◽  
Author(s):  
A. Alishbayli ◽  
J. G. Tichelaar ◽  
U. Gorska ◽  
M. X. Cohen ◽  
B. Englitz
Keyword(s):  
Firing Rate ◽  
Clinical Diagnosis ◽  
Neural Activity ◽  
Large Scale ◽  
Cortical Activity ◽  
Spike Count ◽  
Cortical Dynamics ◽  
Brain States ◽  
Asynchronous State ◽  
Cortical Regions

Understanding the relation between large-scale potentials (M/EEG) and their underlying neural activity can improve the precision of research and clinical diagnosis. Recent insights into cortical dynamics highlighted a state of strongly reduced spike count correlations, termed the asynchronous state (AS). The AS has received considerable attention from experimenters and theorists alike, regarding its implications for cortical dynamics and coding of information. However, how reconcilable are these vanishing correlations in the AS with large-scale potentials such as M/EEG observed in most experiments? Typically the latter are assumed to be based on underlying correlations in activity, in particular between subthreshold potentials. We survey the occurrence of the AS across brain states, regions, and layers and argue for a reconciliation of this seeming disparity: large-scale potentials are either observed, first, at transitions between cortical activity states, which entail transient changes in population firing rate, as well as during the AS, and, second, on the basis of sufficiently large, asynchronous populations that only need to exhibit weak correlations in activity. Cells with no or little spiking activity can contribute to large-scale potentials via their subthreshold currents, while they do not contribute to the estimation of spiking correlations, defining the AS. Furthermore, third, the AS occurs only within particular cortical regions and layers associated with the currently selected modality, allowing for correlations at other times and between other areas and layers.

scholarly journals Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning

2020 ◽  
Author(s):  
Waleed Tahir ◽  
Sreekanth Kura ◽  
Jiabei Zhu ◽  
Xiaojun Cheng ◽  
Rafat Damseh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Imaging Systems ◽  
Total Variation Regularization ◽  
Computationally Efficient ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Brain Vasculature ◽  
Vascular Segmentation ◽  
Anatomical Modeling

AbstractObjective and Impact StatementSegmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis.IntroductionVascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems, or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems, and is able to segment large-scale angiograms.MethodsWe employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and a total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in-vivo angiograms from mouse brains of dimensions up to 808×808×702 μm.ResultsTo demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope, and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art.ConclusionOur work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

scholarly journals Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

BME Frontiers ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Waleed Tahir ◽  
Sreekanth Kura ◽  
Jiabei Zhu ◽  
Xiaojun Cheng ◽  
Rafat Damseh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Imaging Systems ◽  
Total Variation Regularization ◽  
Computationally Efficient ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Brain Vasculature ◽  
Vascular Segmentation ◽  
Anatomical Modeling

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

scholarly journals Task-Modulated Corticocortical Synchrony in the Cognitive-Motor Network Supporting Handwriting

2019 ◽  
Vol 30 (3) ◽  
pp. 1871-1886
Author(s):  
Timo Saarinen ◽  
Jan Kujala ◽  
Hannu Laaksonen ◽  
Antti Jalava ◽  
Riitta Salmelin
Keyword(s):  
Large Scale ◽  
Phase Synchronization ◽  
Task Demands ◽  
Low Frequencies ◽  
Simple Loop ◽  
Cortical Dynamics ◽  
Motor Network ◽  
Neural Processes ◽  
Hand Coordination ◽  
Cortical Regions

Abstract Both motor and cognitive aspects of behavior depend on dynamic, accurately timed neural processes in large-scale brain networks. Here, we studied synchronous interplay between cortical regions during production of cognitive-motor sequences in humans. Specifically, variants of handwriting that differed in motor variability, linguistic content, and memorization of movement cues were contrasted to unveil functional sensitivity of corticocortical connections. Data-driven magnetoencephalography mapping (n = 10) uncovered modulation of mostly left-hemispheric corticocortical interactions, as quantified by relative changes in phase synchronization. At low frequencies (~2–13 Hz), enhanced frontoparietal synchrony was related to regular handwriting, whereas premotor cortical regions synchronized for simple loop production and temporo-occipital areas for a writing task substituting normal script with loop patterns. At the beta-to-gamma band (~13–45 Hz), enhanced synchrony was observed for regular handwriting in the central and frontoparietal regions, including connections between the sensorimotor and supplementary motor cortices and between the parietal and dorsal premotor/precentral cortices. Interpreted within a modular framework, these modulations of synchrony mainly highlighted interactions of the putative pericentral subsystem of hand coordination and the frontoparietal subsystem mediating working memory operations. As part of cortical dynamics, interregional phase synchrony varies depending on task demands in production of cognitive-motor sequences.

scholarly journals Excitatory-Inhibitory Homeostasis and Diaschisis: Tying the Local and Global Scales in the Post-stroke Cortex

2022 ◽  
Vol 15 ◽  
Author(s):  
Francisco Páscoa dos Santos ◽  
Paul F. M. J. Verschure
Keyword(s):  
Cortical Neurons ◽  
Large Scale ◽  
Essential Feature ◽  
Global Scale ◽  
Cortical Reorganization ◽  
Stroke Recovery ◽  
Focal Lesions ◽  
Post Stroke ◽  
Macro Scale ◽  
Cortical Regions

Maintaining a balance between excitatory and inhibitory activity is an essential feature of neural networks of the neocortex. In the face of perturbations in the levels of excitation to cortical neurons, synapses adjust to maintain excitatory-inhibitory (EI) balance. In this review, we summarize research on this EI homeostasis in the neocortex, using stroke as our case study, and in particular the loss of excitation to distant cortical regions after focal lesions. Widespread changes following a localized lesion, a phenomenon known as diaschisis, are not only related to excitability, but also observed with respect to functional connectivity. Here, we highlight the main findings regarding the evolution of excitability and functional cortical networks during the process of post-stroke recovery, and how both are related to functional recovery. We show that cortical reorganization at a global scale can be explained from the perspective of EI homeostasis. Indeed, recovery of functional networks is paralleled by increases in excitability across the cortex. These adaptive changes likely result from plasticity mechanisms such as synaptic scaling and are linked to EI homeostasis, providing a possible target for future therapeutic strategies in the process of rehabilitation. In addition, we address the difficulty of simultaneously studying these multiscale processes by presenting recent advances in large-scale modeling of the human cortex in the contexts of stroke and EI homeostasis, suggesting computational modeling as a powerful tool to tie the meso- and macro-scale processes of recovery in stroke patients.

scholarly journals Bridging scales in scattering tissues via multifocal two-photon microscopy

2020 ◽  
Author(s):  
David Chen ◽  
Fabian Segovia-Miranda ◽  
Noreen Walker ◽  
Jose I. Valenzuela ◽  
Marino Zerial ◽  
...  
Keyword(s):  
High Resolution ◽  
High Speed ◽  
Large Scale ◽  
Fluorescent Proteins ◽  
Morphological Changes ◽  
Optical Techniques ◽  
Tissue Clearing ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Subcellular Resolution

Imaging biological systems at subcellular resolution and across scales is essential to under-standing how cells form tissues, organs, and organisms. However, existing large-scale optical techniques often require harsh tissue-clearing methods that cause significant morphological changes, compromise the integrity of cell membranes, and reduce the signal of fluorescent proteins. Here, we demonstrate multifocal two-photon microscopy that enables imaging mesoscopic scattering samples in their native tissue environment at high resolution and high speed.

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