Periodic Reporting for period 1 - MBMsforDOC (Model-based biomarkers for the characterization and quantification of global states of consciousness)
Periodo di rendicontazione: 2021-09-01 al 2023-08-31
Studying the brain mechanisms behind consciousness is a major challenge for neuroscience and medicine. Yet so far, there is no such thing as a unique biomarker that can precisely define the state of consciousness of a disorders of consciousness
(DOC) patient. All the biomarkers proposed so far are theory-based but empirically defined (EBM; empirical biomarkers): the thresholds that separate categories are set in a data-driven way. Thus, the practical implications in the clinic are related to detecting signatures of consciousness for patients with disorders of consciousness, impacting the diagnosis and prognosis of the patients.
In this project, we propose a novel approach using model-based biomarkers (MBM). This new family of biomarkers (MBMs) will not only complement the EBMs but will mainly naturally address the knowledge gaps associated with the understanding of the underlying causal mechanisms behind the different states of consciousness. The modelling of the structural and functional connectivity will be combined with novel, systematic perturbational approaches that can provide new insights into the human brain’s ability to integrate and segregate information over time. In particular, with this approach we will address the hypothesis that MBMs provide functional fingerprinting of conscious states and insights into the underlying necessary and sufficient brain networks as well as their neural
mechanisms. We developed computational whole-brain models based on single-patient neuroimaging data. We will extract MBM from the adjusted model parameters and from in-silico simulations. We will test the utility of these biomarkers for the diagnosis of patients with chronic DOC. Then, we will contrast the MBM with a set of previously developed EBM. Finally, we will analyze the diagnostic and prognostic capacity of these biomarkers in DOC patients in both chronic and acute stages.
(DOC) patient. All the biomarkers proposed so far are theory-based but empirically defined (EBM; empirical biomarkers): the thresholds that separate categories are set in a data-driven way. Thus, the practical implications in the clinic are related to detecting signatures of consciousness for patients with disorders of consciousness, impacting the diagnosis and prognosis of the patients.
In this project, we propose a novel approach using model-based biomarkers (MBM). This new family of biomarkers (MBMs) will not only complement the EBMs but will mainly naturally address the knowledge gaps associated with the understanding of the underlying causal mechanisms behind the different states of consciousness. The modelling of the structural and functional connectivity will be combined with novel, systematic perturbational approaches that can provide new insights into the human brain’s ability to integrate and segregate information over time. In particular, with this approach we will address the hypothesis that MBMs provide functional fingerprinting of conscious states and insights into the underlying necessary and sufficient brain networks as well as their neural
mechanisms. We developed computational whole-brain models based on single-patient neuroimaging data. We will extract MBM from the adjusted model parameters and from in-silico simulations. We will test the utility of these biomarkers for the diagnosis of patients with chronic DOC. Then, we will contrast the MBM with a set of previously developed EBM. Finally, we will analyze the diagnostic and prognostic capacity of these biomarkers in DOC patients in both chronic and acute stages.
The work performed can be organized according to the three specific objectives of the project:
1) Implement whole brain models of the brain activity of each single patient and extract from these models physiologically relevant parameters and test them as putative biomarkers of the patients’ state of consciousness (MBMs):
- The successful implementation of subject modeling and the extraction of MBM from the complete cohort. Whole-brain model compounded by coupled non-linear oscillators. Different model parameter's optimization strategies were developed to fit the model to empirical data. (Fig1)
2) Benchmark the capacity of an automated machine-learning classifier combining these two types of biomarkers; testing whether synergies between EBMs and MBMs can be exploited and better prediction accuracy is obtained compared with the use of EBMs only.
- We obtained a combined set of model-based biomarkers (MBMs) and empirical biomarkers (EMBs) along the selected data set. The MBMs were based not only in the model parameters but also on markers derived from the inclusion of the non-equilibrium dynamics quantification of different states of consciousness. (Fig. 2)
- We are still working to find the best combination of EEG empirical biomarkers with fMRI model and empirical markers that better perform in classification tasks. In particular, we are progressing in dealing with the difficulties that carry the combination of modalities.
3) The objective is to use MBMs to better understand the physiopathology underlying disorders of consciousness and also to generate perturbations in silico that can provide insights into the prognosis of clinical treatments.
We selected specific perturbations that represent a different and non-in vivo obtained source of information to provide insight into the diagnostic and prognosis of acute DOC patients. We developed the full framework applied to DOC patients using deep learning algorithms and the whole-brain models developed in the previous objectives. The result is that we created a prototype of computational open-source software that includes the generation of in silico perturbation that can be used as a clinical guideline.
Dissemination activities
3 international conferences in the poster sessions: The association for the scientific study of consciousness (ASSC) conference (July 2023, New York, USA), Organization for Human Brain Mapping, a general (OHBM) conference (July 2023, Montreal, Canada) and Barcelona Computational, Cognitive and Systems Neuroscience Community (Barccsyn) conference (May 2023, Barcelona, Spain). I was also invited as a speaker in 2 international Workshops: IBD, the interpretable Brain Data Workshop (June 2023, Stockholm, Sweden), and Bernstein Computational Neuroscience Satellite Workshop: Whole brain dynamics: Modeling and applications (Sep 2023, Berlin, Germany). (This last workshop was developed after the two-year project, but I was invited before that time and as the results obtained during these two years). I also disseminated and communicated project activities and results via Twitter from my profile: @ysanz6
Outreach activities:
1) GIGA consciousness seminar. The seminar from GIGA consciousness group from University of Liege, Belgium. (Sept 2021, on line).
2) Computational Domain meeting. Meeting for the whole ICM focused on disseminating the computational approach that was developed in the institute. (Nov 2021, Paris, France)
3) Newspaper articles highlighting two investigations in collaboration with Dr. Deco
1) Implement whole brain models of the brain activity of each single patient and extract from these models physiologically relevant parameters and test them as putative biomarkers of the patients’ state of consciousness (MBMs):
- The successful implementation of subject modeling and the extraction of MBM from the complete cohort. Whole-brain model compounded by coupled non-linear oscillators. Different model parameter's optimization strategies were developed to fit the model to empirical data. (Fig1)
2) Benchmark the capacity of an automated machine-learning classifier combining these two types of biomarkers; testing whether synergies between EBMs and MBMs can be exploited and better prediction accuracy is obtained compared with the use of EBMs only.
- We obtained a combined set of model-based biomarkers (MBMs) and empirical biomarkers (EMBs) along the selected data set. The MBMs were based not only in the model parameters but also on markers derived from the inclusion of the non-equilibrium dynamics quantification of different states of consciousness. (Fig. 2)
- We are still working to find the best combination of EEG empirical biomarkers with fMRI model and empirical markers that better perform in classification tasks. In particular, we are progressing in dealing with the difficulties that carry the combination of modalities.
3) The objective is to use MBMs to better understand the physiopathology underlying disorders of consciousness and also to generate perturbations in silico that can provide insights into the prognosis of clinical treatments.
We selected specific perturbations that represent a different and non-in vivo obtained source of information to provide insight into the diagnostic and prognosis of acute DOC patients. We developed the full framework applied to DOC patients using deep learning algorithms and the whole-brain models developed in the previous objectives. The result is that we created a prototype of computational open-source software that includes the generation of in silico perturbation that can be used as a clinical guideline.
Dissemination activities
3 international conferences in the poster sessions: The association for the scientific study of consciousness (ASSC) conference (July 2023, New York, USA), Organization for Human Brain Mapping, a general (OHBM) conference (July 2023, Montreal, Canada) and Barcelona Computational, Cognitive and Systems Neuroscience Community (Barccsyn) conference (May 2023, Barcelona, Spain). I was also invited as a speaker in 2 international Workshops: IBD, the interpretable Brain Data Workshop (June 2023, Stockholm, Sweden), and Bernstein Computational Neuroscience Satellite Workshop: Whole brain dynamics: Modeling and applications (Sep 2023, Berlin, Germany). (This last workshop was developed after the two-year project, but I was invited before that time and as the results obtained during these two years). I also disseminated and communicated project activities and results via Twitter from my profile: @ysanz6
Outreach activities:
1) GIGA consciousness seminar. The seminar from GIGA consciousness group from University of Liege, Belgium. (Sept 2021, on line).
2) Computational Domain meeting. Meeting for the whole ICM focused on disseminating the computational approach that was developed in the institute. (Nov 2021, Paris, France)
3) Newspaper articles highlighting two investigations in collaboration with Dr. Deco
We developed different theoretically grounded frameworks that allow us to extract novel empirical and model biomarkers from DOC neuroimaging data.
We found that brain dynamics can be captured by the mathematical formalism of the turbulence developed on fluid dynamics. From this perspective, we developed empirical and model biomarkers based on the fact that the information transmission changes in the turbulence regime according to different states of consciousness.
Then, we focus our analysis on the level of non-equilibrium that brain dynamics exhibit. In brief, this framework is based on the fact that from the cellular level to the macroscopic scale, living organisms as dissipative systems require the violation of their detailed balance, i.e. metabolic enzymatic reactions, in order to survive. Consequently, our framework is based on temporal asymmetry as a measure of non-equilibrium. By means of statistical physics, it was discovered that temporal asymmetries establish an arrow of time useful for assessing the reversibility in human brain time series. We found that this irreversibility of the signal is a powerful biomarker to characterize states of consciousness.
We also developed a combined framework that includes machine learning techniques and computational modeling that allow us to create a tool to visualize in silico perturbations as simple trajectories into a 2D dimensional space. By assessing these trajectories, it is possible to obtain insights about which kind or perturbation may induce transitions from a pathological state of consciousness toward a healthy state.
The social impact of this project is focusing on turning innovative knowledge and technologies into practical applications, by providing new tools with the potential for concrete impact on the quality of life of patients suffering from DOC, their family members, and on healthcare system workload and cost.
We found that brain dynamics can be captured by the mathematical formalism of the turbulence developed on fluid dynamics. From this perspective, we developed empirical and model biomarkers based on the fact that the information transmission changes in the turbulence regime according to different states of consciousness.
Then, we focus our analysis on the level of non-equilibrium that brain dynamics exhibit. In brief, this framework is based on the fact that from the cellular level to the macroscopic scale, living organisms as dissipative systems require the violation of their detailed balance, i.e. metabolic enzymatic reactions, in order to survive. Consequently, our framework is based on temporal asymmetry as a measure of non-equilibrium. By means of statistical physics, it was discovered that temporal asymmetries establish an arrow of time useful for assessing the reversibility in human brain time series. We found that this irreversibility of the signal is a powerful biomarker to characterize states of consciousness.
We also developed a combined framework that includes machine learning techniques and computational modeling that allow us to create a tool to visualize in silico perturbations as simple trajectories into a 2D dimensional space. By assessing these trajectories, it is possible to obtain insights about which kind or perturbation may induce transitions from a pathological state of consciousness toward a healthy state.
The social impact of this project is focusing on turning innovative knowledge and technologies into practical applications, by providing new tools with the potential for concrete impact on the quality of life of patients suffering from DOC, their family members, and on healthcare system workload and cost.