Hopfield-Enhanced Deep Neural Networks for Artifact-Resilient Brain State Decoding

The study of brain states, ranging from highly synchronous to asynchronous neuronal patterns like the sleep-wake cycle, is fundamental for assessing the brain's spatiotemporal dynamics and their close connection to behavior. However, the development of new techniques to accurately identify them still remains a challenge, as these are often compromised by the presence of noise, artifacts, and suboptimal recording quality. In this study, we propose a two-stage computational framework combining Hopfield Networks for artifact data preprocessing with Convolutional Neural Networks (CNNs) for classification of brain states in rat neural recordings under different levels of anesthesia. To evaluate the robustness of our framework, we deliberately introduced noise artifacts into the neural recordings. We evaluated our hybrid Hopfield-CNN pipeline by benchmarking it against two comparative models: a standalone CNN handling the same noisy inputs, and another CNN trained and tested on artifact-free data. Performance across various levels of data compression and noise intensities showed that our framework can effectively mitigate artifacts, allowing the model to reach parity with the clean-data CNN at lower noise levels. Although this study mainly benefits small-scale experiments, the findings highlight the necessity for advanced deep learning and Hopfield Network models to improve scalability and robustness in diverse real-world settings

Cobrawap: A pipeline for the analysis of wave activity at different brain states

Plenary talk at "WP2 Meeting: Networks underlying consciousness and cognition" held in Barcelona, Spain, from 19 to 21 June, 2023.Progetto EBRAINS-Italy IR00011, CUP B51E2200015006,Missione 4 - Istruzione e Ricerca, Componente 2, Azione 3.1.1 Funded by EU

Identification and neuromodulation of brain states to promote recovery of consciousness

Experimental and clinical studies of consciousness identify brain states (i.e., transient, relevant features of the brain associated with the state of consciousness) in a non-systematic manner and largely independent from the research into the induction of state changes. In this narrative review with a focus on patients with a disorder of consciousness (DoC), we synthesize advances on the identification of brain states associated with consciousness in animal models and physiological (sleep), pharmacological (anesthesia) and pathological (DoC) states of altered consciousness in human. We show that in reduced consciousness the frequencies in which the brain operates are slowed down and that the pattern of functional communication in the brain 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 in the thalamocortical loops. Next, we show that therapeutic approaches to treat DoC, through pharmacology (e.g., amantadine, zolpidem), and (non-)invasive brain stimulation (e.g., transcranial current stimulation, deep brain stimulation) have shown some effectiveness to promote consciousness recovery. It seems that these deteriorated features of conscious brain states may improve in response to these neuromodulation approaches, yet, targeting often remains non-specific and does not always lead to (behavioral) improvements. Furthermore, in silico model-based approaches allow the development of personalized assessment of the effect of treatment on brain-wide dynamics. Although still in infancy, the fields of brain state identification and neuromodulation of brain states in relation to consciousness are showing fascinating developments that, when united, might propel the development of new and better targeted techniques for DoC. For example, brain states could be identified in a predictive setting, and the theoretical and empirical testing (i.e., in animals, under anesthesia and patients with a DoC) of neuromodulation techniques to promote consciousness could be investigated. This review further helps to identify where challenges and opportunities lay for the maturation of brain state research in the context of states of consciousness. 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. This paper presents interactive figures, supported by the Live Paper initiative of the Human Brain Project, enabling the interaction with data and figures illustrating the concepts in the paper through EBRAINS (go to https://wiki.ebrains.eu/bin/view/Collabs/live-paper-states-altered-consciousness and get started with an EBRAINS account).NA is research fellow, OG is Research Associate, and SL is research director at FRS-FNRS. JA is postdoctoral fellow at the FWO. The study was further supported by the University and University Hospital of Liège, the BIAL Foundation, the Belgian National Funds for Scientific Research (FRS-FNRS), the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the FNRS PDR project (T.0134.21), the ERA-Net FLAG-ERA JTC2021 project ModelDXConsciousness (Human Brain Project Partnering Project), the fund Generet, the King Baudouin Foundation, the Télévie Foundation, the European Space Agency (ESA) and the Belgian Federal Science Policy Office (BELSPO) in the framework of the PRODEX Programme, the Public Utility Foundation 'Université Européenne du Travail', "Fondazione Europea di Ricerca Biomedica", the BIAL Foundation, the Mind Science Foundation, the European Commission, the Fondation Leon Fredericq, the Mind-Care foundation, the DOCMA project (EU-H2020-MSCA–RISE–778234), the National Natural Science Foundation of China (Joint Research Project 81471100) and the European Foundation of Biomedical Research FERB Onlus

Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow wave activity (< 1 Hz), which occurs during unconscious brain states like sleep and general anesthesia, and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow wave characteristics across multiple, openly available ECoG and calcium imaging datasets

Towards an EBRAINS service for brain wave analysis: Cobrawap. Poster presented at CNS2023 - Leipzig

The current variety of data from neuronal recordings, collected with heterogeneous experimental techniques and setups, poses the challenge to consistently compare data across experiments, species, and spatio-temporal scales, and to provide standardized measures/observables for model validation and calibration. We developed Cobrawap (Collaborative Brain Wave Analysis Pipeline) to achieve these goals in the context of brain wave analysis. Aiming at facilitating the usage of this tool for an extended community of users, we are pointing at providing Cobrawap as a service accessible through the EBRAINS web portal, leveraging computational resources from High-Performance Computing (HPC) platforms belonging to the FENIX-ICEI federation

Towards an EBRAINS service for brain wave analysis: Cobrawap

The current variety of data from neuronal recordings, collected with heterogeneous experimental techniques and setups, poses the challenge to consistently compare data across experiments, species, and spatio-temporal scales, and to provide standardized metrics for model validation and calibration. In the context of brain wave analysis, Cobrawap (Collaborative Brain Wave Analysis Pipeline) is a successful tool, built to achieve these goals. Aiming at a wider diffusion and at facilitating the usage of this tool for an extended community of users, we are pointing at providing Cobrawap as a service accessible through the EBRAINS web portal, leveraging computational resources from High-Performance Computing (HPC) platforms belonging to the FENIX-ICEI federation

Thalamic Foxp2 regulates output connectivity and sensory‑motor impairments in a model of Huntington’s Disease

Here, we demonstrate in a HD mouse model a clear and early thalamo-striatal aberrant connectivity associated with a reduction of thalamic Foxp2 levels. Recovering thalamic Foxp2 levels in the mouse rescued motor coordination and sensory skills concomitant with an amelioration of neuropathological features and with a repair of the structural and functional connectivity through a restoration of neurotransmitter release. In addition, reduction of thalamic Foxp2 levels in wild type mice induced HD-like phenotypes

Multi-scale, multi-species, multi-methodology experiments, analysis tools and simulation models of Brain States and Complexity in SP3-UseCase002

The general goal of SP3-UseCase002 is to offer to external users, through EBRAINS Knowledge Graph, an integrated environment, dedicated to the topic of cortical slow wave activity (SWA) [1,2] in spontaneous and perturbed mode, and to sleep/awake transitions, measures of complexity (like PCI, perturbational complexity index) and the cognitive effects of sleep in thalamo-cortical systems. The offering includes multi-scale multi-species experimental data, simulation models, simulation results, and analysis tools. The analysis tools are designed to be applicable to both experimental data and simulation results since, for a fair comparison and accurate validation of the models, the outcome of data-driven biologically-plausible simulations [3] should be subjected to the same analysis tools used for the data. We note that the variety of the experimental techniques for data acquisition and the diversity of subjects and species involved (due to large biological variability, but also to brain states, physiological/pathological conditions, drug doses and data taking setups) make challenging the building of reliable and generalizable data analysis tools aimed at identifying common observables when comparing the outcome of different experiments acquired with different experimental modalities, and at obtaining reproducible results.SP3-UseCase002 integrates the results of WP3.2 (aka WaveScalES, focusing on sleep, anaesthesia and transition to wakefulness, KR3.2) and WP3.4 (aka ConsciousBrain, focusing on neural correlates and measure of consciousness in physiological and pathological brains, KR3.4).The analysis pipeline developed by WaveScalES [4,5], when applied to experimental data, enables the extraction of key spatio-temporal characteristics from slow waves acquired with multiple experimental methodologies (using micro-ECoG arrays and wide-field Calcium imaging techniques, to be extended to hd-EEG and stereo-EEG), at local and multi-areal spatial resolution. The platform also includes simulation models of SWA and AW-like cortical activity at biologically-plausible neural and synaptic densities [6] and simulation models demonstrating the effects of interactions among sleep and memories and the changes in cognitive performances of thalamo-cortical models passing through wakefulness-sleep-wakefulness cycles [7]. When applied to simulation results, similar features should be extracted, to enable a quantitative comparison between simulation and experimental data, fostering a better calibration of simulations.Concerning the ConsciousBrain research, the measure is based on a perturbational approach (i.e. perturbing the brain with an exogenous input and gauging the derived spatiotemporal dynamics). The proposed analysis pipeline calculates several complexity indices on multi-scale experimental data that includes TMS-EEG data in healthy humans and patients with disorder of consciousness, intracerebral recordings in epileptic patients undergoing presurgical evaluation as well as spikes and LFP signals in rats/mice. The Perturbational Complexity Index based on Lempel and Ziv algorithm (PCIlz)[8] correlates with the level of consciousness and has been validated using TMS-EEG data collected from a large cohort of healthy subjects and patients affected by disorder of consciousness [9]; the Perturbational Complexity Index based on State Transitions (PCIst)[10] is faster than PCIlz, it does not depend on source modelling algorithms and can be applied on data different from scalp EEG. In addition, a revisited version of PCIlz, calibrated on TMS/EEG and extracellular signals from cerebellar brain slices, will also be included.Here, we present the status of the implementation of the Use Case, with some preliminary results and conclusions.References 1. Steriade (1993), Journal of Neuroscience. 2. Sanchez-Vives, M.V. et al (2017), Neuron. 3. Capone C et al., Cerebral cortex (2019): 29, 1. 4. De Bonis, G. et al. (2019) Front. Syst. Neurosci, 13, 70. 5. Celotto M, et al (2018), arXiv:1811.11687 6. Pastorelli, E. et al. (2019) Front. Syst. Neurosci 13, 33. 7. Capone C., et al (2019), Sci. Rep. 9, 8990 8. Casali et al Science Tr. Med, 2013 9. Casarotto et al Ann. of Neurol, 2016 10. Comolatti et al. Brain Stim, 201