The wake human brain constantly samples perceptual information from the environment and integrates them into existing neuronal networks. Neuronal oscillations have been ascribed a key role in the formation of novel memories. The theta rhythm (3-8 Hz) reflects a central executive mechanism, which integrates novel information, reflected in theta-coupled gamma oscillations (> 30 Hz). Alpha oscillations (8-14 Hz) reflect an attentional gating mechanism, which inhibit task irrelevant neuronal processes. In my dissertation I further scrutinized the oscillatory dynamics of memory formation. Study 1 demonstrated that theta-gamma coupling reflects a specific mechanism for associative memory formation. In study 2, I experimentally entrained memory encoding by visual evoked theta-gamma coupling processes, to underline its functional relevance. In two developmental studies, I found that the theta rhythm indexes explicit learning processes in adults and young children (study 3), and that visually entrained theta oscillations are sensitive to the encoding of novel, unexpected events, already in the first year of life (study 4). Throughout these studies alpha oscillations were not sensitive to memory formation processes, but indicated perceptual (study 1) and semantic (study 3) processes. I propose an integrative framework, suggesting that the alpha rhythm reflects activated semantic representations in the neocortex, while theta-gamma coupling reflects an explicit mnemonic control mechanism, which selects, elaborates and integrates activated representations. Specifically, by squeezing real time events onto a faster, neuronal time scale, theta-gamma coding facilitates neuronal plasticity in medio-temporal networks and advances neuronal processes ahead of real time to emulate and guide future behavior.

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Der laterale Hypothalamus (LH) reguliert angeborene Verhaltensweisen. Ob und wie die Koordination von hypothalamischen Neuronengruppen Verhaltensübergänge reguliert, blieb jedoch unbekannt. In dieser Arbeit wurde Optogenetik mit neuronalen Ableitungen in verhaltenden Mäusen kombiniert. LHVgat Neurone erhöhten ihre Aktivitätsrate während Übergängen vom NREM-Schlaf zum Wachzustand. LHVgat Zellen projizieren zum Nucleus reticularis des Thalamus (RTN). Optogenetische Aktivierung von Vgat Ausgängen im RTN führte eine starke, frequenzabhängige Inhibierung von RTN Zellen herbei und replizierte Verhaltenszustands-abhängige Aktivitätsraten in RTN Neuronen. Ableitungen von LH Neuronen während Umgebungserkundung ergaben, dass 65% der LH Neurone ihre Aktivitätsrate erhöhten, wenn das Tier began sich fortzubewegen. 'Top-down’ Vorderhirn Innervation des LH erfolgt größtenteils durch Signale ausgehend vom lateralen Septums (LS). Während spontaner Umgebungserkundung und freiem Zugang zu Futter wiesen der LH und das LS Gamma-Oszillationen (30-90 Hz) auf, welche neuronale Aktivität innerhalb und zwischen diesen beiden Gehirnregionen synchronisierten. Optogenetische Stimulation von Somatostatin-positiven GABAergen Projektionen zum LH mit Gamma-Frequenz förderte die Nahrungssuche und erhöhte die Wahrscheinlichkeit des Betretens der Nahrungszone. Inhibitorische Signale des LS bewirkten eine Unterteilung der LH Neurone: entsprechend ihrer Aktivität im Bezug zur Nahrungsstelle wurden sie während bestimmter Phasen der Gamma-Oszillation aktiviert. Dabei führte optogenetische Stimulation von LS-LH Neuronen mit Gamma-Frequenz keine Veränderung bei der Nahrungsaufnahme selbst herbei. Insgesamt liefert diese Arbeit neue Einsichten über die Funktion der neuronalen Netzwerke des LH, welche durch Signalgebung mit unterschiedlichen Zeitskalen über die Koordination mit vor- und nachgeschalteten neuronalen Netzwerken Übergange zwischen verschiedenen angeborenen Verhaltensweisen regeln. / Lateral hypothalamus (LH) is crucial for regulation of innate behaviors. However, it remained unknown whether and how temporal coordination of hypothalamic neuronal populations regulates behavioral transitions. This work combined optogenetics with neuronal recordings in behaving mice. LHVgat cells were optogenetically identified. LHVgat neurons increased firing rates upon transitions from non-REM (NREM) sleep to wakefulness, and their optogenetic stimulation during NREM sleep induced a fast transition to wakefulness. LHVgat cells project to the reticular thalamic nucleus (RTN). Optogenetic activation of LHVgat terminals in the RTN exerted a strong frequency-dependent inhibition of RTN cells and replicated state-dependent changes in RTN neurons activity. Recordings of LH neurons during exploration revealed that 65% of LH neurons increased their activity upon the onset of locomotion. Top-down forebrain innervation of LH is provided, to a great extent, by inhibitory inputs from the lateral septum (LS). During spontaneous exploration in a free-feeding model, LS and LH displayed prominent gamma oscillations (30-90 Hz) which entrained neuronal activity within and across the two regions. Optogenetic gamma-frequency stimulation of somatostatin-positive GABAergic projections to LH facilitated food-seeking, and increased the probability of entering the food zone. LS inhibitory input enabled separate signaling by LH neurons according to their feeding-related activity, making them fire at distinct phases of the gamma oscillation. In contrast to increased food intake during optogenetic stimulation of LHVgat cells, food intake during gamma-rhythmic LS-LH stimulation was not changed. Overall this works provides new insight into the function of LH circuitry, that employs signalling at different time scales, which, in coordination with upstream and downstream circuits, regulates transitions between innate behaviors.

Synchronized oscillations of ensembles of neurons in the brain underlie human cognition and behaviors. Neuronal network oscillations can be described by the physics of coupled dynamical systems. This dissertation examines the dynamic network activities in two distinct neurocognitive networks, the salience network (SN) and the ventral temporal cortex-dorsolateral prefrontal cortex (VTC-DLPFC) network, during perceptual decision-making (PDM). The key nodes of the SN include the right anterior insula (rAI), left anterior insula (lAI), and dorsal anterior cingulate cortex (dACC) in the brain. When and how a sensory signal enters and organizes within the SN before reaching the central executive network including the prefrontal cortex has been a mystery. Second, prior studies also report that perception of visual objects (face and house) involves a network of the VTC—the fusiform face area (FFA) and para-hippocampal place area (PPA)—and the DLPFC. How sensory information enters and organizes within the VTC-DLPFC network is not well understood, in milliseconds time-scale of human’s perception and decision-making. We used clear and noisy face/house image categorization tasks and scalp electroencephalography (EEG) recordings to study the dynamics of these networks. We demonstrated that beta (13–30 Hz) oscillation bound the SN, became most active around 100 ms after the stimulus onset, the rAI acted as a main outflow hub within the SN, and the SN activities were negatively correlated with the difficult tasks. We also uncovered that the VTC-DLPFC network activities were mediated by beta (13-30 Hz) and gamma (30-100 Hz) oscillations. Beta activities were enhanced in the time frame 125-250 ms after stimulus onset, the VTC acted as main outflow hub, and network activities were negatively correlated with the difficult tasks. In contrast, gamma activities were elevated in the time frame 0-125 ms, the DLPFC acted as a main outflow hub, and network activities—specifically the FFA-PPA pair—were positively correlated with the difficult tasks. These findings significantly enhance our understanding of how sensory information enters and organizes within the SN and the VTC-DLPFC network, respectively in PDM.

Many phenomena in the BAEP are difficult to explain on the basis of the accepted hypothesis of its origin (after Jewett, 1970). The alternative mechanism of origin to which these phenomena point is summation of oscillations. Therefore, simulation of the BAEP by a mathematical model consisting of the addition of four sine waves was tested. The model did simulate a normal BAEP as well variations in the waveform produced by reversing click polarity. This simulation gives further clues to the origin of the BAEP. The four sine waves begin simultaneously; corresponding BAEP oscillations must, therefore, originate from a single structure. These oscillations begin in less than half a millisecond after the click. This suggests that the structure from which they arise is outside the brainstem. This alternative mechanism indicates that wave latencies do not reflect nervous conduction between discrete nuclei, and interpretation of BAEP abnormality need to be reconsidered. It also implies that mathematical frequency analysis is more appropriate, but this could be applied only when these methods have been perfected. Meanwhile, through visual analysis and recognition of oscillations, abnormality can be detected and described in terms that may have physiological significance.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-03-09T12:11:00Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_LeonardoDallaPorta.pdf: 4244662 bytes, checksum: 214ab17f2ee3583441af553e0a0a7931 (MD5) / Made available in DSpace on 2017-03-09T12:11:00Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_LeonardoDallaPorta.pdf: 4244662 bytes, checksum: 214ab17f2ee3583441af553e0a0a7931 (MD5) Previous issue date: 2016-08-26 / FACEPE / Avalanches neuronais, assim como oscilações e sincronização, são padrõesde atividade espontânea observados em redes neuronais. O conceito de avalanches neuronais foi concebido na última década.EssepadrãodeatividadetemdistribuiçõesdetamanhosP(s)eduraçõesP(d)invariantes por escala, i.e., obedecem relações do tipo lei de potênciaP(s)∼s−τ, com expoenteτ≃3/2, e P(d)∼d−τt, com expoenteτt ≃2, respectivamente. Essas propriedades são compatíveis com a ideia de que o cérebro opera em um regime crítico. A partir dessas constatações, muitos estudos teóricos e experimentais reportaram os potenciais benefícios de um cérebro operando na criticalidade, como por exemplo a máxima sensibilidade aos estímulos sensoriais, máxima capacidade de informação e transmissão e uma ótima capacidade computacional. Modelos da classe de universalidade de percolação direcionada (DP) têm sido amplamente utilizados para explicar a estatística invariante por escala das avalanches neuronais. Porém estes modelos não levam em consideração a dinâmica dos neurônios inibitórios e, além disso, como apresentam uma transição de fase entre um estado absorvente e uma fase ativa, torna-se difícil conciliar o modelo com correlações temporais de longo alcance que são observadas experimentalmente em diferentes escalas espaciais. Neste contexto, um novo modelo computacional (CROs, do original em inglês Critical Oscillations) surgiu na literatura (Poil et al., J. Neurosci.,32 9817, 2012), incluindo neurônios inibitórios e buscando conciliar correlações temporais com avalanches neuronais. Neste modelo não há uma fase absorvente, e uma suposta transição de fases ocorre entre uma fase ativa e outra com oscilações coletivas. Devido à ausência de uma fase absorvente, avalanchesneuronaissãodefinidascomparando-seaatividadeinstantâneadaredecomumlimiar que depende da mediana da atividade total. Justamente na linha crítica do espaço de parâmetros, quandoháumabalançoentreexcitaçãoeinibiçãoneuronal,avalanchesneuronaisinvariantespor escala são observadas juntamente com correlações temporais de longo alcance (ruído 1/f). No presente trabalho, um estudo mais profundo a respeito dos resultados reportados para o modelo CROs foi realizado. As oscilações neuronais mostraram-se robustas para diferentes tamanhos de rede, e observamos que a dinâmica local reflete a dinâmica oscilatória global da rede. Correlações temporais de longo alcance foram observadas (num intervalo de escalas temporais) através da técnica deDetrendedFluctuationAnalysis, sendo robustas perante modificações no tamanho da rede. O resultado foi confirmado pela análise direta do espectro, que apresentou decaimento do tipo 1/f numa determinada faixa de frequências. O diagrama de fases do modelo mostrou-se robusto em relação ao tamanho da rede, mantendo-se o alcance das interações locais. Entretanto,osresultadosmostraram-sefortementedependentesdolimiarutilizadoparadetecção dasavalanchesneuronais.Porfim,mostramosquedistribuiçõesdeduraçõesdeavalanchessãodo tipo lei de potência, com expoenteτt ≃2. Este resultado é inédito e o valor encontrado coincide com o expoente crítico da classe de universalidade de DP na dimensão crítica superior. Em conjunto, nossos resultados fornecem mais evidências de que o modelo CROs de fato apresenta uma transição de fases. / Neuronal avalanches, as well as waves and synchronization, are types of spontaneous activity experimentally observed in neuronal networks. The concept of neuronal avalanches was conceivedinthepastdecade.ThispatternofactivityhasdistributionsofsizeP(s)anddurationP(d) which are scale invariant, i.e., follow power-law relationsP(s)∼s−τ, with exponentτ≃3/2, and P(d)∼ d−τd, with exponentτt ≃ 2, respectively. These properties are compatible with the idea that the brain operates in a critical regime. From these findings, many theoretical and experimental studies have reported the potential benefits of a brain operating at criticality, such as maximum sensitivity to sensory stimuli, maximum information capacity and transmission and an optimal computational capabilities. Models belonging to the directed percolation universality class (DP) have been widely used to explain the scale invariant statistic of neuronal avalanches. However,these modelsdo not take into account the dynamics ofinhibitory neuronsand, since as they present a phase transition between an absorbing state and an active phase, it is difficult to reconcile the model with long-range temporal correlations that are observed experimentally at different spatial scales. In this context, a new computational model (CROs, Critical Oscillations) appeared in the literature (Poil et al., J. Neurosci.,32 9817, 2012), including inhibitory neurons and seeking to reconcile temporal correlations with neuronal avalanches. In this model there is no absorbing phase, and a supposed phase transition occurs between an active phase and another with collective oscillations. Due to the lack of an absorbing phase, neuronal avalanches are defined comparing by the instant network activity with a threshold that depends of the total activity median. Precisely at the critical line in parameter space, when a balance between neuronal excitation and inhibition occurs, scale invariant neuronal avalanches are observed with long-range temporal correlations (1/f-like noise). In the present work, a deeper study about the resultsreportedfortheCROsmodelwasperformed.Neuronaloscillationshavebeenshowntobe robust to increasing network sizes, and it was observed that local dynamic reflects the oscillatory global dynamic of the network. Long-range temporal correlations were observed (in a range of time scales) via Detrended Fluctuation Analysis, being robust against changes in network size. The result was confirmed by direct analysis of the spectrum, which showed a decay like 1/f in a given frequency band. The phase diagram of the model was robust with respect to the network size, as long as the range of local interactions was kept. However, the results were dependent of the thresholdused to detect neuronal avalanches.Finally,we have shown thatthe distributions of avalanches duration follows a power-law with exponentτt ≃2. This result is unprecedented and the value obtainedcoincides with the criticalexponent of the DP universality class in the upper criticaldimension.Together,ourresultsprovidefurtherevidencethatinfacttheCROsmodel presents aphasetransition.

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