Periodic Reporting for period 5 - TrueBrainConnect (Advancing the non-invasive assessment of brain communication in neurological disease)
Okres sprawozdawczy: 2024-05-01 do 2024-12-31
Demographic changes across Europe are resulting in a steep increase in the numbers of elderly citizens; this, in turn, is accompanied by rising numbers of people afflicted by aging-associated neurodegenerative disorders such as dementia (including Alzheimer’s disease, AD) and Parkinson’s disease (PD). Functional brain connectivity analysis may substantially contribute to understanding these diseases, and others, by localizing and quantifying disruptions of normal brain communication. Ultimately, brain connectivity patterns can serve as biomarkers for diagnoses and inform treatments.
Magneto- and electroencephalography (M/EEG) are techniques to measure brain activity and play a key role in the study of human functional brain connectivity as they have high temporal resolution, are non-invasive, and are applicable to healthy subjects as well as patients in early disease stages.
In this context, the goals of TrueBrainConnect were two-fold. First, the project aimed to characterize impaired brain communication in aging-associated neurological disorders using non-invasive electromagneticrecordings. Second, in order to achieve this, a novel methodology for functional brain connectivity analysis needed to be developed that overcomes limitations of existing approaches. This led to the following concrete objectives pursued by the project.
1. To develop source reconstruction methods that can accurately reconstruct and localize interacting sources and automatically adjust to noise characteristics.
2. To develop robust time series connectivity metrics for non-linear types of brain interaction.
3. To develop interpretable models to robustly predict external variables from brain connectivity data.
4. To provide a comprehensive validation of functional brain connectivity estimation methodologies under a wide range of conditions.
5. To provide a better understanding of brain communication in age-related neurological disorders.
6. To disseminate the project results in open access publications and as open source software.
The project met these goals, achieving significant scientific advances. The developed methods provided novel insights into the mechanisms by which different treatments in PD restore healthy brain communication patterns in PD. These results will ultimately be used to inform and improve future PD treatments. More generally, the developed approach will open up new research directions in clinical and cognitive neuroscience.
Magneto- and electroencephalography (M/EEG) are techniques to measure brain activity and play a key role in the study of human functional brain connectivity as they have high temporal resolution, are non-invasive, and are applicable to healthy subjects as well as patients in early disease stages.
In this context, the goals of TrueBrainConnect were two-fold. First, the project aimed to characterize impaired brain communication in aging-associated neurological disorders using non-invasive electromagneticrecordings. Second, in order to achieve this, a novel methodology for functional brain connectivity analysis needed to be developed that overcomes limitations of existing approaches. This led to the following concrete objectives pursued by the project.
1. To develop source reconstruction methods that can accurately reconstruct and localize interacting sources and automatically adjust to noise characteristics.
2. To develop robust time series connectivity metrics for non-linear types of brain interaction.
3. To develop interpretable models to robustly predict external variables from brain connectivity data.
4. To provide a comprehensive validation of functional brain connectivity estimation methodologies under a wide range of conditions.
5. To provide a better understanding of brain communication in age-related neurological disorders.
6. To disseminate the project results in open access publications and as open source software.
The project met these goals, achieving significant scientific advances. The developed methods provided novel insights into the mechanisms by which different treatments in PD restore healthy brain communication patterns in PD. These results will ultimately be used to inform and improve future PD treatments. More generally, the developed approach will open up new research directions in clinical and cognitive neuroscience.
Accurate localization of brain activity recorded by M/EEG measurements to their generating brain structures is a prerequisite for investigating brain functional connectivity. The project team achieved to develop algorithms that perform substantially better than existing ones under low-noise conditions and are furthermore able to automatically learn the noise level and correlation structure from the data, further contributing to improved localization. The project team further developed robust measures of non-linear brain interactions. Novel methods were developed for the estimation of phase-amplitude coupling (PAC) as well as the estimation of transmission delays under mixed noise. PAC is a widely hypothesized mechanism of brain communication by which the amplitude of a fast oscillation is tied to the phase of a slow oscillation. Being able to estimate transmission delays between regions, on the other hand, is critical to identify mental and clinical state dependent deviations from the brains normal signal transmission pathways. The novel methods are theoretically and practically shown to be insensitive to inevitable mixed noise sources such physiological and technical artifacts, leading to dramatic improvements in estimation quality.
The developed source connectivity analysis pipelines were extensively validated. Best practice pipelines ere identified, confirming the superiority of the newly developed methods. These pipelines were integrated into open-source toolboxes for Matlab and Python, including ROIConnect, PyBispectra and the MNE multivariate connectivity toolbox.
Levodopa medication and deep brain stimulation (DBS) are effective treatments that substantially reduce symptoms in PD, but their mechanisms of action has not been well understood. We, therefore, investigated functional connectivity between cortical and subcortical regions of the motor network under medication and stimulation on/off conditions. The observed results suggest that medication and stimulation possess shared but also distinct mechanisms of action, differentially activating different cortico-basagal-ganglia pathways. Further work characterized functional during gait using the developed FC analysis pipelines and marks an important step towards adaptive DBS that can switch stimulation parameters depending on the movement pattern (e.g. walking vs. standing). These insights will inform future, improved, treatments, for example using adaptive stimulation.
The project employed machine learning (ML) methods to extract specific brain signals, for example those encoding movement intention, with high signal-to-noise ratio from the multivariate M/EEG data. A scientific focus was then put on the question of how to correctly interpret such multivariate models in neurophysiological terms. To this end, the project group showed that highly popular XAI methods can lead to substantial misunderstandings about the relationship between input features of an ML model (e.g. functional properties of brain areas) and the predicted target variable (e.g. the presence of a disorder), thus promoting incorrect conclusions about brain functioning and generally limiting the utility of such methods for purposes such as model or data validation, counterfactual reasoning or scientific discovery. Further project work devised controlled benchmark data to validate the performance of XAI algorithms in linear and non-linear settings. All benchmarks and metrics for explanation peformance developed within the project have been made openly available and will enable future studies to avoid misinterpretations of multivariate machine learning models.
The developed source connectivity analysis pipelines were extensively validated. Best practice pipelines ere identified, confirming the superiority of the newly developed methods. These pipelines were integrated into open-source toolboxes for Matlab and Python, including ROIConnect, PyBispectra and the MNE multivariate connectivity toolbox.
Levodopa medication and deep brain stimulation (DBS) are effective treatments that substantially reduce symptoms in PD, but their mechanisms of action has not been well understood. We, therefore, investigated functional connectivity between cortical and subcortical regions of the motor network under medication and stimulation on/off conditions. The observed results suggest that medication and stimulation possess shared but also distinct mechanisms of action, differentially activating different cortico-basagal-ganglia pathways. Further work characterized functional during gait using the developed FC analysis pipelines and marks an important step towards adaptive DBS that can switch stimulation parameters depending on the movement pattern (e.g. walking vs. standing). These insights will inform future, improved, treatments, for example using adaptive stimulation.
The project employed machine learning (ML) methods to extract specific brain signals, for example those encoding movement intention, with high signal-to-noise ratio from the multivariate M/EEG data. A scientific focus was then put on the question of how to correctly interpret such multivariate models in neurophysiological terms. To this end, the project group showed that highly popular XAI methods can lead to substantial misunderstandings about the relationship between input features of an ML model (e.g. functional properties of brain areas) and the predicted target variable (e.g. the presence of a disorder), thus promoting incorrect conclusions about brain functioning and generally limiting the utility of such methods for purposes such as model or data validation, counterfactual reasoning or scientific discovery. Further project work devised controlled benchmark data to validate the performance of XAI algorithms in linear and non-linear settings. All benchmarks and metrics for explanation peformance developed within the project have been made openly available and will enable future studies to avoid misinterpretations of multivariate machine learning models.
The project has substantially advanced the state-of-the-art in M/EEG data analysis, which in turn led to novel insights about neurological disorders. From a methodological perspective, the proposed source reconstruction methods are applicable to lower signal-to-noise ratio settings than prior works, enabling more accurate source imaging of M/EEG sources in experimental paradigms in which no averaging can be done. Moreover, by estimating and accounting for essential properties of the noise jointly with the source reconstruction, these methods obviate sometimes infeasible baseline measurements or error-prone heuristics to estimate noise levels and related parameters. Similarly, the project developed the first robust measures for important types of brain communication. With these measures, phase-amplitude interactions and transmission delays between brain sites can now for the first time be measured with minimal bias introduced by inevitable superimposed mixed noise.
From a clinical perspective, the project has built on these methodological advances to significantly extend our understanding of age-related disorders on brain communication patterns, and has elucidated previously unknown therapeutic mechanisms on brain functional networks in PD.
From a clinical perspective, the project has built on these methodological advances to significantly extend our understanding of age-related disorders on brain communication patterns, and has elucidated previously unknown therapeutic mechanisms on brain functional networks in PD.