Periodic Reporting for period 4 - BrainDyn (Tracking information flow in the brain: A unified and general framework for dynamic communication in brain networks)
Periodo di rendicontazione: 2021-08-01 al 2023-02-28
The brain is constituted of a set of specialized neural networks that must communicate with each other so that cognition emerges.
This project tests a theory concerning the flexible communication between brain areas based on neuronal rhythms.
It postulates that slow internal oscillations (<20 cycles per second) establish or prevent communication between brain areas whereas gamma oscillations (> 30 cycles per second) represent local neural processing of sensory information.
To test the model, a variety of methods were used.
The theoretical approach and methodological developments used in this research will serve as a basis for future basic and clinical research directly relevant to society. Indeed, several brain-related troubles (e.g. chronic pain or schizophrenia) have been associated with altered brain communication.
The overall objectives are to better understand brain communication and to develop brain stimulation therapies based on the results obtained in this project.
This project tests a theory concerning the flexible communication between brain areas based on neuronal rhythms.
It postulates that slow internal oscillations (<20 cycles per second) establish or prevent communication between brain areas whereas gamma oscillations (> 30 cycles per second) represent local neural processing of sensory information.
To test the model, a variety of methods were used.
The theoretical approach and methodological developments used in this research will serve as a basis for future basic and clinical research directly relevant to society. Indeed, several brain-related troubles (e.g. chronic pain or schizophrenia) have been associated with altered brain communication.
The overall objectives are to better understand brain communication and to develop brain stimulation therapies based on the results obtained in this project.
Since the beginning of the project, we have published the framework behind the project. The paper is already highly cited. A second theoretical paper going further, based on our results, on the role of brain fluctuations in visual processing in a naturalistic environment will be published soon.
We have also run an experiment using functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) combined with 52 subjects.
As predicted by the framework, we found that fast oscillations (i.e. gamma) and two slow oscillations (here in the alpha band, between 8 and 14Hz) are associated with specific layers of the cortex (which contains around 6 layers) and are involved in specific processing of the stimuli presented to the participants. The paper has been submitted to a journal for peer review. Another paper is in preparation regarding the link between the communication between brain regions as observed with fMRI and the oscillations at different frequencies. In addition, a new statistical tool (which allows one to determine whether the results observed can be generalized to the entire population) had to be developed and will be published soon.
We have also analyzed data obtained in Monkey revealing similar results as well as the interaction between the oscillations at different frequencies in the different layers of the cortex.
We have performed the fMRI and Magnetoencephalography (MEG) experiments in 45 participants aimed at determining whether the slow oscillations control the flow of information in the visual network. We used attention (participants were asked to attend to different parts of the visual field without moving their eyes) and prediction (each stimulus presented was predicting the features of the next one with a high probability) tasks.
We were able to track where the attention spotlight (where participants attended without moving their eyes) of participants and we showed that this attention spotlight was moving around at a slow rhythm. We also obtained results showing our ability to decode the content of the predictions regarding the next stimulus of participants. Several papers containing these results will be submitted for peer review soon.
We also found results showing the importance of alpha oscillations (8-14Hz) in preventing the transfer of irrelevant information in brain networks as predicted by our framework. These results will fuel the current very hot debate about the link between alpha oscillations and functional inhibition.
Finally, we obtained groundbreaking results using spiking neural networks performing attention tasks as our participants. Implementing alpha oscillations in these networks improve their performance. In addition, we observed that alpha oscillations emerge naturally in these networks when we reproduce the connectivity observed in the visual network of mammals' brains.
We have also run an experiment using functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) combined with 52 subjects.
As predicted by the framework, we found that fast oscillations (i.e. gamma) and two slow oscillations (here in the alpha band, between 8 and 14Hz) are associated with specific layers of the cortex (which contains around 6 layers) and are involved in specific processing of the stimuli presented to the participants. The paper has been submitted to a journal for peer review. Another paper is in preparation regarding the link between the communication between brain regions as observed with fMRI and the oscillations at different frequencies. In addition, a new statistical tool (which allows one to determine whether the results observed can be generalized to the entire population) had to be developed and will be published soon.
We have also analyzed data obtained in Monkey revealing similar results as well as the interaction between the oscillations at different frequencies in the different layers of the cortex.
We have performed the fMRI and Magnetoencephalography (MEG) experiments in 45 participants aimed at determining whether the slow oscillations control the flow of information in the visual network. We used attention (participants were asked to attend to different parts of the visual field without moving their eyes) and prediction (each stimulus presented was predicting the features of the next one with a high probability) tasks.
We were able to track where the attention spotlight (where participants attended without moving their eyes) of participants and we showed that this attention spotlight was moving around at a slow rhythm. We also obtained results showing our ability to decode the content of the predictions regarding the next stimulus of participants. Several papers containing these results will be submitted for peer review soon.
We also found results showing the importance of alpha oscillations (8-14Hz) in preventing the transfer of irrelevant information in brain networks as predicted by our framework. These results will fuel the current very hot debate about the link between alpha oscillations and functional inhibition.
Finally, we obtained groundbreaking results using spiking neural networks performing attention tasks as our participants. Implementing alpha oscillations in these networks improve their performance. In addition, we observed that alpha oscillations emerge naturally in these networks when we reproduce the connectivity observed in the visual network of mammals' brains.
Alpha oscillations are widely considered to have low spatial specificity in the brain in our community. The hypotheses regarding the cognitive processes that these oscillations can be associated with are partially based on that idea. The feature specificity (very high spatial specificity) of a specific frequency of alpha oscillations in superficial layers is, therefore, a ground-breaking result and was only predicted by our framework.
Likewise, the differential relationship between slow oscillations in superficial and deep layers and fast oscillations was predicted by the framework and has never been shown before.
The tracking of the rhythm of attention has never been observed in humans and will allow to have a clear overview of the role of each node of the attention network in implementing attention in humans.
If confirmed, the results of the spiking neural networks will be of major importance to understanding the anatomical basis and the general role of alpha oscillations. A major question in the literature is "Why do we observe specifically this frequency?", we might provide the first stone of the answer.
We now expect to show, with the final analysis of the high-resolution MEG results (based on the fMRI data), that superficial slow oscillations are associated with setting up the specific communication between layers (associated with fast oscillations) controlling the flow of information, while deep alpha oscillations would be associated with broader cognitive processes such as inhibition of areas or visual integration. This is a key question in Neuroscience.
Likewise, the differential relationship between slow oscillations in superficial and deep layers and fast oscillations was predicted by the framework and has never been shown before.
The tracking of the rhythm of attention has never been observed in humans and will allow to have a clear overview of the role of each node of the attention network in implementing attention in humans.
If confirmed, the results of the spiking neural networks will be of major importance to understanding the anatomical basis and the general role of alpha oscillations. A major question in the literature is "Why do we observe specifically this frequency?", we might provide the first stone of the answer.
We now expect to show, with the final analysis of the high-resolution MEG results (based on the fMRI data), that superficial slow oscillations are associated with setting up the specific communication between layers (associated with fast oscillations) controlling the flow of information, while deep alpha oscillations would be associated with broader cognitive processes such as inhibition of areas or visual integration. This is a key question in Neuroscience.