Optogenetic Methods to Investigate Brain Alterations in Preclinical Models

Brondi, Marco; Bruzzone, Matteo; Lodovichi, Claudia; Maschio, Marco Dal

doi:10.3390/cells11111848

Cited by 7 publications

(4 citation statements)

References 255 publications

(279 reference statements)

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“…Thus, despite there are still some experimental issues to solve [9] it can be envisioned that the GEVIs, or a similar technique, will allow to record spikes and PSP signals at the population level. Interestingly, spiking activities and PSP events could also be obtained by combining different optical recording techniques [10], e.g., calcium (i.e., spikes) together with neurotransmitter (i.e., synaptic events) imaging recordings. As anticipated, a fundamental step of the proposed framework consists of the detection of spiking and PSP events.…”

Section: Introductionmentioning

confidence: 99%

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

Nieus

Borgonovo²,

Diwakar³

et al. 2022

Front. Appl. Math. Stat.

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In neuroscience, the structural connectivity matrix of synaptic weights between neurons is one of the critical factors that determine the overall function of a network of neurons. The mechanisms of signal transduction have been intensively studied at different time and spatial scales and both the cellular and molecular levels. While a better understanding and knowledge of some basic processes of information handling by neurons has been achieved, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor the electrical activity of a large number of neurons in real time. The analysis of the data related to the activities of individual neurons can become a very valuable tool for the study of the dynamics and architecture of neural networks. In particular, advances in optical imaging techniques allow us to record up to thousands of neurons nowadays. However, most of the efforts have been focused on calcium signals, that lack relevant aspects of cell activity. In recent years, progresses in the field of genetically encoded voltage indicators have shown that imaging signals could be well suited to record spiking and synaptic events from a large population of neurons. Here, we present a methodology to infer the connectivity of a population of neurons from their voltage traces. At first, putative synaptic events were detected. Then, a multi-class logistic regression was used to fit the putative events to the spiking activities and a penalization term was allowed to regulate the sparseness of the inferred network. The proposed Multi-Class Logistic Regression with L1 penalization (MCLRL) was benchmarked against data obtained from in silico network simulations. MCLRL properly inferred the connectivity of all tested networks, as indicated by the Matthew correlation coefficient (MCC). Importantly, MCLRL was accomplished to reconstruct the connectivity among subgroups of neurons sampled from the network. The robustness of MCLRL to noise was also assessed and the performances remained high (MCC>0.95) even in extremely high noise conditions (>95% noisy events). Finally, we devised a procedure to determine the optimal MCLRL regularization term, which allows us to envision its application to experimental data.

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Section: Introductionmentioning

confidence: 99%

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

Nieus

Borgonovo²,

Diwakar³

et al. 2022

Front. Appl. Math. Stat.

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“…This real-time feedback is typically obtained through the continuous monitoring of brain signals, such as via electroencephalography (EEG) or electrocorticography (ECoG), which provide valuable information about the brain’s electrical activity [ 128 ]. The primary advantage of closed-loop stimulation is its ability to adapt to the patient’s physiological state and dynamically intervene at the earliest signs of abnormal brain activity, such as pre-seizure or prodromal patterns [ 129 ]. By detecting these patterns in real time, closed-loop systems can promptly deliver targeted stimulation precisely when and where it is needed, effectively preventing the progression of seizures before they fully manifest [ 125 , 130 ].…”

Section: Closed-loop Stimulationmentioning

confidence: 99%

A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management

Ghosh,

Sinha,

Ghosh

et al. 2023

Brain Sciences

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Epilepsy is a complex neurological disorder affecting millions worldwide, with a substantial number of patients facing drug-resistant epilepsy. This comprehensive review explores innovative therapies for epilepsy management, focusing on their principles, clinical evidence, and potential applications. Traditional antiseizure medications (ASMs) form the cornerstone of epilepsy treatment, but their limitations necessitate alternative approaches. The review delves into cutting-edge therapies such as responsive neurostimulation (RNS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS), highlighting their mechanisms of action and promising clinical outcomes. Additionally, the potential of gene therapies and optogenetics in epilepsy research is discussed, revealing groundbreaking findings that shed light on seizure mechanisms. Insights into cannabidiol (CBD) and the ketogenic diet as adjunctive therapies further broaden the spectrum of epilepsy management. Challenges in achieving seizure control with traditional therapies, including treatment resistance and individual variability, are addressed. The importance of staying updated with emerging trends in epilepsy management is emphasized, along with the hope for improved therapeutic options. Future research directions, such as combining therapies, AI applications, and non-invasive optogenetics, hold promise for personalized and effective epilepsy treatment. As the field advances, collaboration among researchers of natural and synthetic biochemistry, clinicians from different streams and various forms of medicine, and patients will drive progress toward better seizure control and a higher quality of life for individuals living with epilepsy.

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“…The understanding of the brain mechanisms takes advantage of the continuous refinement of molecular, hardware, and software approaches. These methods allow reconstructing, in suitable model organisms, the brain dynamics from synapse level to brain-wide scales [1][2][3][4][5] . Current optical and electrophysiological methods represent invaluable tools for studying the neuronal dynamics associated with sensory information, the fine-tuning of motor programs, and spatiotemporal patterns characterizing brain activity [6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

A method for extracting an approximated connectome from libraries of single cell reconstructions

Manjunatha

Bruzzone

Nicoletti

et al. 2023

Preprint

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Understanding brain mechanisms is a currently open challenge. There are several methods available to characterize the functional aspects. However, the information that one can retrieve about the organization of the wiring between the cells across the brain that could support the network organization and the circuit mechanism remains limited. This appears to be a general problem, even when the cell anatomy is well described and potentially accessible at scales up to the synaptic level across the whole brain. Typically, such high-resolution analyses focus on the reconstruction of a limited portion of the brain or of a specific circuit, while missing the general organization principles of the neuronal networks within the brain, the network components, and internetwork connection schemes. To extend the investigation on these aspects, we present a theoretical and computational framework using modularity analysis to identify from connectome datasets the brain-wide circuit organization and the underlying cell modules based on synaptic connectivity patterns. We applied this approach on an Electron Microscopy (EM) dataset with about 25-thousand annotated cell reconstructions that incorporate synaptic information from the hemibrain of adultDrosophila melanogaster. The analysis provided a representation of the connectome organized in a hierarchical structure and characterized by several cell modules with precise and topographically organized connection patterns. We then tested this approach on a “synthetic” connectome, one obtained from a light microscopy dataset of more than three thousand neuronal skeletonizations from the zebrafish larvae brain, where a cell-to-cell proximity rule was used to assign synaptic contacts. Despite this approximation, the developed approach revealed reported connectivity patterns and functional connection schemes. In conclusion, we show that our framework is scalable and amenable for different types of datasets, independently from the particular anatomical reconstruction method adopted, to reveal the neuronal architectures supporting the brain mechanisms.

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Optogenetic Methods to Investigate Brain Alterations in Preclinical Models

Cited by 7 publications

References 255 publications

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management

A method for extracting an approximated connectome from libraries of single cell reconstructions

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