Sleeping and wakefulness are two completely different states that define our daily activities. Long-term research has shown that sleep can be detected through neuronal activities that last only milliseconds, opening new possibilities for studying brain waves that govern consciousness.

These activity patterns have been discovered for the first time and enable a better understanding of the basic brain waves. Additionally, researchers have found that small regions of the brain can instantly "flicker" awake while the rest of the brain remains asleep, and vice versa. These findings were published in the journal Nature Neuroscience, as a result of the collaboration between the laboratories of Keith Hengen from Washington University and David Haussler from the University of California. The research was conducted by doctoral students David Parks and Aidan Schneider.

Over four years, Parks and Schneider trained a neural network to study brain wave patterns and discovered high-frequency patterns that challenge long-standing beliefs about the neurological basis of sleep and wakefulness. Scientists have found that models can distinguish sleep and wakefulness from just milliseconds of brain activity, which was surprising given previous knowledge.

"We see information at an unprecedented level of detail," said Haussler. This research reveals new ways of understanding brain activities during sleep and wakefulness, including local activity patterns that cannot be explained by traditional, slow waves.

Impact on understanding sleep
Neuroscientists study the brain by recording electrical signals of brain activity, known as electrophysiological data. In Hengen's laboratory in St. Louis, freely behaving animals were equipped with lightweight headsets that recorded brain activity from 10 different regions of the brain for months, creating petabytes of data. David Parks fed this data into an artificial neural network that can find complex patterns, differentiating sleep and wakefulness data and discovering patterns that human observation might miss.

According to the research, models can distinguish sleep and wakefulness from milliseconds of data, showing that they did not use slow waves to learn the differences between these states. These findings challenge traditional beliefs and show that there is much more complexity in brain processes than previously thought.

New research opportunities
Further research revealed another surprising phenomenon - "brain flickers." During the research, scientists observed that for a fraction of a second, one part of the brain can be awake while the rest is asleep, and vice versa. These flickers could have significant implications for understanding the function of sleep and how it affects behavior during wakefulness and sleep.

Furthermore, scientists have discovered that certain parts of the brain enhance their internal rhythms during the transition from wakefulness to sleep, creating low-frequency delta waves. These findings open new perspectives for sleep research and its role in health and disease, offering potential new methods for treating sleep disorders and improving sleep quality. Integrating EEG with fMRI and PET scans could provide a more comprehensive insight into brain activity during sleep, while artificial intelligence can help analyze large amounts of data and detect patterns that are invisible to human researchers.

A study from the University of California, San Francisco, which used magnetoencephalography (MEG) to monitor brain activity during the transition from wakefulness to sleep, found that there is a shift in the flow of information within the brain. Activity shifts from the back of the brain, which is responsible for processing external information, to the front, which processes internal information. This shift is accompanied by an increase in excitation in the cortex compared to inhibition, reflecting this shift in the flow of information.

Application of research in clinical practice
New findings on local sleep and wakefulness patterns can help develop new therapies for neurological diseases and sleep disorders. Integrating this knowledge with existing methods can lead to more precise and effective treatments, reducing side effects and improving patients' quality of life. Further research will continue to shed light on the complexity of brain functions during sleep, paving the way for new innovations in medical practice.

Advancements in understanding the genetics and molecular basis of sleep can also offer new approaches to personalized sleep medicine. Identifying specific genes and molecular pathways that regulate the phenomena of local sleep can lead to individualized sleep optimization plans. Future interventions may include personalized plans based on specific sleep patterns, genetic makeup, and individual lifestyle factors, providing tailored recommendations for improving sleep quality.

Integrating EEG with fMRI and PET scans could provide a more comprehensive insight into brain activity during sleep. These integrated approaches allow for more detailed analysis and mapping of brain regions involved in local sleep, which can result in a better understanding of the interactions between different parts of the brain during sleep. Additionally, artificial intelligence and machine learning can play a crucial role in analyzing large amounts of sleep data, enabling the identification of patterns and correlations that are invisible to human researchers.

Conclusion
This research provides a deeper understanding of the complex dynamics of brain networks, opening new possibilities for improving sleep and treating sleep disorders, and enhancing overall well-being. Integrating advanced technologies, such as wearable technology and smart home systems, can provide real-time feedback and interventions to improve sleep quality. The development of new pharmacological agents that target specific aspects of sleep can also offer more effective treatments for sleep disorders with fewer side effects.

Source: UNIVERSITY OF CALIFORNIA