In vitro neuronal cultures on MEA: an engineering approach to study physiological and pathological brain networks

Reti neuronali accoppiate a matrici di microelettrodi: un metodo ingegneristico per studiare reti cerebrali in situazioni fisiologiche e patologich

Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity

Burst suppression in the electroencephalogram (EEG) is a well-described phenomenon that occurs during deep anesthesia, as well as in a variety of congenital and acquired brain insults. Classically it is thought of as spatially synchronous, quasi-periodic bursts of high amplitude EEG separated by low amplitude activity. However, its characterization as a “global brain state” has been challenged by recent results obtained with intracranial electrocortigraphy. Not only does it appear that burst suppression activity is highly asynchronous across cortex, but also that it may occur in isolated regions of circumscribed spatial extent. Here we outline a realistic neural field model for burst suppression by adding a slow process of synaptic resource depletion and recovery, which is able to reproduce qualitatively the empirically observed features during general anesthesia at the whole cortex level. Simulations reveal heterogeneous bursting over the model cortex and complex spatiotemporal dynamics during simulated anesthetic action, and provide forward predictions of neuroimaging signals for subsequent empirical comparisons and more detailed characterization. Because burst suppression corresponds to a dynamical end-point of brain activity, theoretically accounting for its spatiotemporal emergence will vitally contribute to efforts aimed at clarifying whether a common physiological trajectory is induced by the actions of general anesthetic agents. We have taken a first step in this direction by showing that a neural field model can qualitatively match recent experimental data that indicate spatial differentiation of burst suppression activity across cortex

Scale-free bursting in human cortex following hypoxia at birth

The human brain is fragile in the face of oxygen deprivation. Even a briefinterruption of metabolic supply at birth challenges an otherwise healthy neonatal cortex, leading to a cascade of homeostatic responses. During recovery from hypoxia, cortical activity exhibits a period of highly irregular electrical fluctuations known as burst suppression. Here we show that these bursts have fractal properties, with power-law scaling of burst sizes across a remarkable 5 orders of magnitude and a scale-free relationship between burst sizes and durations. Although burst waveforms vary greatly, their average shape converges to a simple form that is asymmetric at long time scales. Using a simple computational model, we argue that this asymmetry reflects activity-dependent changes in the excitatory-inhibitory balance of cortical neurons. Bursts become more symmetric following the resumption of normal activity, with a corresponding reorganization of burst scaling relationships. These findings place burst suppression in the broad class of scale-free physical processes termed crackling noise and suggest that the resumption of healthy activity reflects a fundamental reorganization in the relationship between neuronal activity and its underlying metabolic constraints

The coordinating influence of thalamic nucleus reuniens on sleep oscillations in cortical and hippocampal structures – relevance to memory consolidation and sleep structure

Sleep is a fascinating and a bit mysterious behavior. Not only do so called “higher” animals like mammals sleep but also simpler organisms like jellyfish display rhythmic periods of quiescence which are interpreted as sleep. Despite it being almost ubiquitous across the animal kingdom, the function of sleep is still not fully understood. However, we do know that especially the brain is important for the initiation and maintenance of that state and that it is highly active during sleep. There has been a special focus on electric neuro-oscillations where research over the last 90 years has revealed that the brain displays quite distinct oscillatory patterns during sleep and its specific functions are slowly being brought to light, such as memory consolidation and communication between different brain regions. For example, it has been argued that newly formed memories are either stored in the hippocampus or at least dependent on it for reactivation and are later transferred to the neocortex or become independent of the hippocampus while being stabilized in the cortex, with a portion of the thalamus, the nucleus reuniens thalami, being possibly involved in this process as it is an anatomical relay between cortex and hippocampus. The aim of my PhD project was to investigate the coupling of neuro-oscillations between prefrontal cortex, thalamus, and hippocampus in both a descriptive and manipulative way. Namely, we investigated the coupling between prelimbic cortex, nucleus reuniens of the thalamus and the CA1 portion of the hippocampus during unperturbed natural sleep, sleep after sleep deprivation and sleep with increased mnemonic demands after a learning task. Lastly, we optogenetically manipulated nucleus reuniens during sleep to assess its properties as a synchronizing link between prefrontal cortex and hippocampus. We described the coupling of corticothalamic slow waves and spindles with ripples in the hippocampus by quantifying the amount of co-occurrence of the aforementioned events, describing the phase-locking of ripples to slow waves and spindles, and determining which oscillations drives the other. Next we found that spiking behavior of nucleus reuniens is coupled to ripples and cortical slow waves. Lastly, optogenetic manipulation showed that nucleus reuniens is involved in the precise phase-event coupling, in the co-occurrence of the mentioned events, and oscillatory drive between cortex and hippocampus. However, the effects we found on the neuro-oscillatory coupling were not accompanied by a change in memory performance after a learning task

Homeostatic reinforcement learning for integrating reward collection and physiological stability

Efficient regulation of internal homeostasis and defending it against perturbations requires adaptive behavioral strategies. However, the computational principles mediating the interaction between homeostatic and associative learning processes remain undefined. Here we use a definition of primary rewards, as outcomes fulfilling physiological needs, to build a normative theory showing how learning motivated behaviors may be modulated by internal states. Within this framework, we mathematically prove that seeking rewards is equivalent to the fundamental objective of physiological stability, defining the notion of physiological rationality of behavior. We further suggest a formal basis for temporal discounting of rewards by showing that discounting motivates animals to follow the shortest path in the space of physiological variables toward the desired setpoint. We also explain how animals learn to act predictively to preclude prospective homeostatic challenges, and several other behavioral patterns. Finally, we suggest a computational role for interaction between hypothalamus and the brain reward system

Multisite Repetitive Transcranial Magnetic Stimulation: Safety, Feasibility, Tolerability, and Electro-Neurophysiology

The thesis investigated the potential clinical application and measurement of multisite priming repetitive transcranial magnetic stimulation (rTMS) protocols. The findings showed that multisite rTMS protocols are safe and tolerable. Furthermore, the neuro-modulatory effects of rTMS are highly variable but can be characterised using multi-modal techniques