A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Gutzen, Robin; De Bonis, Giulia; De Luca, Chiara; Pastorelli, Elena; Capone, Cristiano; Allegra Mascaro, Anna Letizia; Resta, Francesco; Manasanch, Arnau; Pavone, Francesco Saverio; Sanchez-Vives, Maria V.; Mattia, Maurizio; Grün, Sonja; Paolucci, Pier Stanislao; Denker, Michael

doi:10.1016/j.crmeth.2023.100681

Cited by 3 publications

(2 citation statements)

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“…A particularity of Snakemake as a WMS is that it orchestrates the execution of different steps that can take the form of custom Python scripts, instead of modular and specific workflow elements such as the ones provided by a WMS such as LONI Pipeline ( MacKenzie-Graham et al, 2008 ). This is attractive when working with electrophysiology data as different aspects of the analysis process (as mentioned in Section 1) can be considered yet providing modular and reusable elements ( Gutzen et al, 2024 ). The Snakemake WMS is based on binding input and output files as dependencies to each script executed in sequence.…”

Section: Discussionmentioning

confidence: 99%

Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture

Köhler,

Ulianych,

Grün

et al. 2024

eNeuro

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Scientific research demands reproducibility and transparency, particularly in data-intensive fields like electrophysiology. Electrophysiology data is typically analyzed using scripts that generate output files, including figures. Handling these results poses several challenges due to the complexity and iterative nature of the analysis process. These stem from the difficulty to discern the analysis steps, parameters, and data flow from the results, making knowledge transfer and findability challenging in collaborative settings. Provenance information tracks data lineage and processes applied to it, and provenance capture during the execution of an analysis script can address those challenges. We present Alpaca (Automated Lightweight Provenance Capture), a tool that captures fine-grained provenance information with minimal user intervention when running data analysis pipelines implemented in Python scripts. Alpaca records inputs, outputs, and function parameters and structures information according to the W3C PROV standard. We demonstrate the tool using a realistic use case involving multichannel local field potential recordings of a neurophysiological experiment, highlighting how the tool makes result details known in a standardized manner in order to address the challenges of the analysis process. Ultimately, using Alpaca will help to represent results according to the FAIR principles, which will improve research reproducibility and facilitate sharing the results of data analyses.Significance StatementSharing electrophysiology data analysis results is challenging, especially in collaborative environments. The results can be understood and interpreted only with the accurate description of the individual analysis steps, the parameters and the data flow, which can be achieved by storing the results together with detailed provenance information. We implemented the Alpaca toolbox to capture provenance during the execution of Python scripts, a typical implementation in pipelines that analyze electrophysiology datasets. Alpaca provides an easy and lightweight solution to record the relevant details of the analysis, facilitating sharing the results.

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Section: Discussionmentioning

confidence: 99%

Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture

Köhler,

Ulianych,

Grün

et al. 2024

eNeuro

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“…To this aim, we applied the group Independent Component Analysis (group ICA) to wide-field cortical imaging recordings. Wide-field imaging has been used to study brain states and slow-oscillation propagation across the entire dorsal cortical mantle (Mitra et al, 2018;Rosenthal et al, 2021;Gutzen et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

Group ICA of wide-field calcium imaging data reveals the retrosplenial cortex as a major contributor to cortical activity during anesthesia

Scaglione,

Resta,

Goretti

et al. 2024

Front. Cell. Neurosci.

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Large-scale cortical dynamics play a crucial role in many cognitive functions such as goal-directed behaviors, motor learning and sensory processing. It is well established that brain states including wakefulness, sleep, and anesthesia modulate neuronal firing and synchronization both within and across different brain regions. However, how the brain state affects cortical activity at the mesoscale level is less understood. This work aimed to identify the cortical regions engaged in different brain states. To this end, we employed group ICA (Independent Component Analysis) to wide-field imaging recordings of cortical activity in mice during different anesthesia levels and the awake state. Thanks to this approach we identified independent components (ICs) representing elements of the cortical networks that are common across subjects under decreasing levels of anesthesia toward the awake state. We found that ICs related to the retrosplenial cortices exhibited a pronounced dependence on brain state, being most prevalent in deeper anesthesia levels and diminishing during the transition to the awake state. Analyzing the occurrence of the ICs we found that activity in deeper anesthesia states was characterized by a strong correlation between the retrosplenial components and this correlation decreases when transitioning toward wakefulness. Overall these results indicate that during deeper anesthesia states coactivation of the posterior-medial cortices is predominant over other connectivity patterns, whereas a richer repertoire of dynamics is expressed in lighter anesthesia levels and the awake state.

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Brain state identification and neuromodulation to promote recovery of consciousness

van der Lande,

Casas-Torremocha,

Manasanch

et al. 2024

Brain Communications

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Experimental and clinical studies of consciousness identify brain states (i.e., quasi-stable functional cerebral organization) in a non-systematic manner and largely independent from the research into brain state modulation. In this narrative review we synthesize advances on the identification of brain states associated with consciousness in animal models and physiological (sleep), pharmacological (anesthesia) and pathological (disorders of consciousness) states of altered consciousness in humans. We show that in reduced consciousness the frequencies in which the brain operates are slowed down and that the pattern of functional communication is sparser, less efficient, and less complex. The results also highlight damaged resting state networks, in particular the default mode network, decreased connectivity in long-range connections and especially in the thalamocortical loops. Next, we show that therapeutic approaches to treat disorders of consciousness, through pharmacology (e.g., amantadine, zolpidem), and (non-) invasive brain stimulation (e.g., transcranial direct current stimulation, deep brain stimulation) have shown partial effectiveness to promote consciousness recovery. Although some features of conscious brain states may improve in response to neuromodulation, targeting often remains non-specific and does not always lead to (behavioral) improvements. The fields of brain state identification and neuromodulation of brain states in relation to consciousness are showing fascinating developments that, when integrated, might propel the development of new and better targeted techniques for disorders of consciousness. We here propose a therapeutic framework for identification and modulation of brain states to facilitate the interaction between the two fields. We propose that brain states should be identified in a predictive setting, followed by theoretical and empirical testing (i.e., in animal models, under anesthesia and patients with a disorder of consciousness) of neuromodulation techniques to promote consciousness in line with such predictions. This framework further helps to identify where challenges and opportunities lay for the maturation of brain state research in the context of states of consciousness. It will become apparent that one angle of opportunity is provided through the addition of computational modelling. Finally, it aids in recognizing possibilities and obstacles for the clinical translation of these diagnostic techniques and neuromodulation treatment options across both the multi-modal and multi-species approaches outlined throughout the review.

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A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Cited by 3 publications

References 92 publications

Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture

Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture

Group ICA of wide-field calcium imaging data reveals the retrosplenial cortex as a major contributor to cortical activity during anesthesia

Brain state identification and neuromodulation to promote recovery of consciousness

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