scholarly journals Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity

2016 ◽  
Author(s):  
Navvab Afrashteh ◽  
Samsoon Inayat ◽  
Mostafa Mohsenvand ◽  
Majid H. Mohajerani

AbstractWide-field optical imaging techniques constitute powerful tools to sample and study mesoscale neuronal activity. The sampled data constitutes a sequence of image frames in which one can perceive the flow of brain activity starting and terminating at source and sink locations respectively. The most common data analyses include qualitative assessment to identify sources and sinks of activity as well as their trajectories. The quantitative analyses is mostly based on computing the temporal variation of the intensity of pixels while a few studies have also reported estimates of wave motion using optical-flow techniques from computer vision. A comprehensive toolbox for the quantitative analyses of mesoscale brain activity data however is still missing. We present a graphical-user-interface based Matlab® toolbox for investigating the spatiotemporal dynamics of mesoscale brain activity using optical-flow analyses. The toolbox includes the implementation of three optical-flow methods namely Horn-Schunck, Combined Local-Global, and Temporospatial algorithms for estimating velocity vector fields of perceived flow in mesoscale brain activity. From the velocity vector fields we determine the locations of sources and sinks as well as the trajectories and temporal velocities of activity flow. Using our toolbox, we compare the efficacy of the three optical-flow methods for determining spatiotemporal dynamics by using simulated data. We also demonstrate the application of optical-flow methods onto sensory-evoked calcium and voltage imaging data. Our results indicate that the combined local-global method we employ, yields results that correlate with the manual assessment. The automated approach permits rapid and effective quantification of mesoscale brain dynamics and may facilitate the study of brain function in response to new experiences or pathology.Conflicts of InterestnoneAuthor contribution statementMHM, MM, NV, and SI designed the study. NA and SI wrote Matlab® code for the toolbox and designed the simulated data. MHM, and NA performed the experiments. NA and SI analyzed the data. SI, NA, and MHM wrote the manuscript.

NeuroImage ◽  
2017 ◽  
Vol 153 ◽  
pp. 58-74 ◽  
Author(s):  
Navvab Afrashteh ◽  
Samsoon Inayat ◽  
Mostafa Mohsenvand ◽  
Majid H. Mohajerani

Author(s):  
Michael Kyweriga ◽  
Jianjun Sun ◽  
Sunny Wang ◽  
Richard Kline ◽  
Majid H. Mohajerani

1977 ◽  
pp. 307-326 ◽  
Author(s):  
S. A. Johnson ◽  
J. F. Greenleaf ◽  
C. R. Hansen ◽  
W. F. Samayoa ◽  
M. Tanaka ◽  
...  

2017 ◽  
Vol 27 (12) ◽  
pp. 5784-5803 ◽  
Author(s):  
Jenq-Wei Yang ◽  
Pierre-Hugues Prouvot ◽  
Vicente Reyes-Puerta ◽  
Maik C Stüttgen ◽  
Albrecht Stroh ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Javier Jimenez-Martin ◽  
Daniil Potapov ◽  
Kay Potapov ◽  
Thomas Knöpfel ◽  
Ruth M. Empson

AbstractCholinergic modulation of brain activity is fundamental for awareness and conscious sensorimotor behaviours, but deciphering the timing and significance of acetylcholine actions for these behaviours is challenging. The widespread nature of cholinergic projections to the cortex means that new insights require access to specific neuronal populations, and on a time-scale that matches behaviourally relevant cholinergic actions. Here, we use fast, voltage imaging of L2/3 cortical pyramidal neurons exclusively expressing the genetically-encoded voltage indicator Butterfly 1.2, in awake, head-fixed mice, receiving sensory stimulation, whilst manipulating the cholinergic system. Altering muscarinic acetylcholine function re-shaped sensory-evoked fast depolarisation and subsequent slow hyperpolarisation of L2/3 pyramidal neurons. A consequence of this re-shaping was disrupted adaptation of the sensory-evoked responses, suggesting a critical role for acetylcholine during sensory discrimination behaviour. Our findings provide new insights into how the cortex processes sensory information and how loss of acetylcholine, for example in Alzheimer’s Disease, disrupts sensory behaviours.


2020 ◽  
Author(s):  
Rui Sun ◽  
Abbas Sohrabpour ◽  
Shuai Ye ◽  
Bin He

AbstractElectroencephalography (EEG) and magnetoencephalography (MEG) are used to measure brain activity, noninvasively, and are useful tools for brain research and clinical management of brain disorders. Tremendous effort has been made in solving the inverse source imaging problem from EEG/MEG measurements. This is a challenging ill-posed problem, since the number of measurements is much smaller than the number of possible sources in the brain. Various methods have been developed to estimate underlying brain sources from noninvasive EEG/MEG as this can offer insight about the underlying brain electrical activity with significantly improved spatial resolution. In this work, we propose a novel data-driven Source Imaging Framework using deep learning neural networks (SIFNet), where (1) a simulation pipeline is designed to model realistic brain activation and EEG/MEG signals to train generalizable neural networks, (2) and a residual convolutional neural network is trained using the simulated data, capable of estimating source distributions from EEG/MEG recordings. The performance of our proposed SIFNet approach is evaluated in a series of computer simulations, which indicates the excellent performance of SIFNet outperforming conventional weighted minimum norm algorithms that were tested in this work. The SIFNet is further tested by analyzing interictal EEG data recorded in a clinical setting from a focal epilepsy patient. The results of this clinical data analysis indicate accurate localization of epileptogenic activity as validated by the epileptogenic zone clinically determined in this patient. In sum, the proposed SIFNet approach promises to offer an alternative solution to the EEG/MEG inverse source imaging problem, shows promising signs of being robust against measurement noise, and is easy to implement, therefore, being translatable to clinical practice.


2002 ◽  
Vol 1235 ◽  
pp. 181-188 ◽  
Author(s):  
M. Tamura ◽  
Y. Hoshi ◽  
M. Nemoto ◽  
C. Sato ◽  
S. Kohri

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