|Center for Sleep and Consciousness|
Our laboratories focus on two neurobiological problems – the mechanisms and functions of sleep and the neural substrates of consciousness. Both problems have considerable medical implications, especially for psychiatric disorders such as depression and schizophrenia.
MECHANISMS AND FUNCTIONS OF SLEEP
Sleep is a pervasive, universal, and fundamental behavior: it occupies a third of our life; it is present in every animal species where it has been studied; even partial deprivation of sleep has serious consequences on cognition, mood, and health. All available evidence indicates that sleep is “of the brain, by the brain, and for the brain”. However, despites decades of intensive research, the mechanisms and functions of sleep remain unclear.
Our work has been informed by the conviction that the key to sleep is to be found at the intersection between the cellular and the system’s level. This is why our laboratories use a combination of different approaches to try to understand what sleep is for, from fly genetics to computer simulations.
The molecular/genetic approach includes genome-wide expression profiling studies, in flies, rodents and humans, to identify the genes whose expression changes in the brain in sleep relative to spontaneous wakefulness and sleep deprivation. This approach also exploits the power of Drosophila genetics. Fruit flies sleep and need sleep in much the same way as we and other mammals do. This finding has opened the way to the genetic dissection of sleep using mutant screening to identify flies that need little sleep and/or are resistant to the effects of sleep deprivation.
The efforts of many years have converged on a new hypothesis about the functions of sleep - the synaptic homeostasis hypothesis, which claims that that sleep maintains synaptic homeostasis. In essence, sleep is the price we have to pay for plasticity, and its function would be the homeostatic regulation of the total synaptic weight impinging on neurons (Tononi and Cirelli, 2003, 2006)
The synaptic homeostasis hypothesis claims that plastic processes during wakefulness result in a net increase in synaptic strength in many brain circuits; during sleep, synaptic strength is globally downscaled to a baseline level that is energetically sustainable and beneficial for memory and performance. In addition to accounting for available evidence, the synaptic homeostasis hypothesis makes several surprising predictions. For example, it predicts that it should be possible to induce sleep locally, that to do so requires learning, not just use, and that such local sleep should have a performance-enhancing effect. We are testing several of the predictions of the hypothesis using animal models and high-density EEG in humans. The hypothesis is also tested using a synthetic approach, through large-scale computational simulations of the thalamo-cortical system.
THE NEURAL SUBSTRATES OF CONSCIOUSNESS
Understanding how brain activity gives rise to conscious experience has important implications for neuroscience, psychology, and psychiatry. Dr. Tononi has worked on this problem since the beginning of his scientific career (it was the topic of his MD dissertation) and he and his laboratory have approached consciousness in several complementary ways.
Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why do certain parts of the brain, such as the thalamocortical system, contribute directly to consciousness, and other parts, such as the cerebellum, do not? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition?
To understand what consciousness is at the fundamental level, how it can be measured in a principled manner, and how and why certain parts of the brain are capable of generating it, a theoretical approach is required. The information integration theory of consciousness constitutes such an approach (Tononi, 2004). According to it, consciousness corresponds to a system’s capacity to integrate information. This is indicated by two key phenomenological properties of consciousness: differentiation - the availability of a very large number of conscious experiences; and integration - the unity of each such experience. The theory claims that the quantity of consciousness is determined by the capacity to integrate information, which can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be exchanged across the minimum information bipartition of a complex (its informational weakest link). A complex is a subset of elements with Φ>0 and no inclusive subset of higher Φ. The theory also claims that the quality of consciousness is determined by the effective information matrix of a complex, which specifies all informational relationships among its elements. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.
The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. For example, the theory explains the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness. Among its implications are the graded nature of consciousness, its presence in infants and animals, and the possibility of building conscious artifacts.
Several approaches are been used to test some of the predictions of the theory, including information integration analysis of different kinds of networks, computer simulations of large-scale neural architectures, and experiments using transcranial magnetic stimulation in connection with high-density EEG recordings.
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