Functional connectivity and neuronal dynamics: insights from computational methods

International audienceBrain functions rely on flexible communication between microcircuits in distinct cortical regions. The mechanisms underlying flexible information routing are still, however, largely unknown. Here, we hypothesize that the emergence of a multiplicity of possible information routing patterns is due to the richness of the complex dynamics that can be supported by an underlying structural network. Analyses of circuit computational models of interacting brain areas suggest that different dynamical states associated with a given connectome mechanistically implement different information routing patterns between system's components. As a result, a fast, network-wide and self-organized reconfiguration of information routing patterns-and Functional Connectivity networks, seen as their proxy-can be achieved by inducing a transition between the available intrinsic dynamical states. We present here a survey of theoretical and modelling results, as well as of sophisticated metrics of Functional Connectivity which are compliant with the daunting task of characterizing dynamic routing from neural data. Theory: Function follows dynamics, rather than structure Neuronal activity conveys information, but which target should this information be-pushed‖ to, or which source should new information be-pulled‖ from? The problem of dynamic information routing is ubiquitous in a distributed information processing system as the brain. Brain functions in general require the control of distributed networks of interregional communication on fast timescales compliant with behavior, but incompatible with plastic modifications of connectivity tracts (Bressler & Kelso, 2001; Varela et al., 2001). This argument led to notions of connectivity based on information exchange-or more generically, an-interaction‖-between brain regions or neuronal populations, rather than based on the underlying STRUCTURAL CONNECTIVITY (SC, i.e. anatomic). An entire zoo of data-driven metrics has been introduced in the literature and this chapter will review some of them. Notwithstanding, they track simple correlation, or directed causal influence (Friston, 2011) or information transfer (Wibral et al., 2014) between time-series of activity. Thes

Neuronal oscillations, information dynamics, and behaviour: an evolutionary robotics study

Oscillatory neural activity is closely related to cognition and behaviour, with synchronisation mechanisms playing a key role in the integration and functional organization of different cortical areas. Nevertheless, its informational content and relationship with behaviour - and hence cognition - are still to be fully understood. This thesis is concerned with better understanding the role of neuronal oscillations and information dynamics towards the generation of embodied cognitive behaviours and with investigating the efficacy of such systems as practical robot controllers. To this end, we develop a novel model based on the Kuramoto model of coupled phase oscillators and perform three minimally cognitive evolutionary robotics experiments. The analyses focus both on a behavioural level description, investigating the robot’s trajectories, and on a mechanism level description, exploring the variables’ dynamics and the information transfer properties within and between the agent’s body and the environment. The first experiment demonstrates that in an active categorical perception task under normal and inverted vision, networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally, and to adapt to different behavioural conditions. The second experiment relates assembly constitution and phase reorganisation dynamics to performance in supervised and unsupervised learning tasks. We demonstrate that assembly dynamics facilitate the evolutionary process, can account for varying degrees of stimuli modulation of the sensorimotor interactions, and can contribute to solving different tasks leaving aside other plasticity mechanisms. The third experiment explores an associative learning task considering a more realistic connectivity pattern between neurons. We demonstrate that networks with travelling waves as a default solution perform poorly compared to networks that are normally synchronised in the absence of stimuli. Overall, this thesis shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, produce an asymmetric flow of information and can generate minimally cognitive embodied behaviours

Integration of Spiking Neural Networks for Understanding Interval Timing

The ability to perceive the passage of time in the seconds-to-minutes range is a vital and ubiquitous characteristic of life. This ability allows organisms to make behavioral changes based on the temporal contingencies between stimuli and the potential rewards they predict. While the psychophysical manifestations of time perception have been well-characterized, many aspects of its underlying biology are still poorly understood. A major contributor to this is limitations of current in vivo techniques that do not allow for proper assessment of the di signaling over micro-, meso- and macroscopic spatial scales. Alternatively, the integration of biologically inspired artificial neural networks (ANNs) based on the dynamics and cyto-architecture of brain regions associated with time perception can help mitigate these limitations and, in conjunction, provide a powerful tool for progressing research in the field. To this end, this chapter aims to: (1) provide insight into the biological complexity of interval timing, (2) outline limitations in our ability to accurately assess these neural mechanisms in vivo, and (3) demonstrate potential application of ANNs for better understanding the biological underpinnings of temporal processing

Dissociating Alzheimer’s Disease from Amnestic Mild Cognitive Impairment using Time-Frequency Based EEG Neurometrics

This work explores the utility of using magnitude (ERSP), phase angle (ITPC), and cross-frequency coupling (PAC) indices derived from electroencephalogram (EEG) recording using spectral decomposition as unique biomarkers of Alzheimer’s Disease (AD) and amnestic mild cognitive impairment (aMCI), respectively. The experimental protocol was a visual oddball discrimination task conducted during a brief (approximately 20 minute) recording session. Participants were 60 older adults from an outpatient memory clinic diagnosed with either aMCI (n=29; M=73.0; SD=9.32) or AD (n=31; M=78.29; SD=8.28) according to NIA-AA criteria. Results indicate that ITPC values differ significantly between AD and MCI groups. Findings contribute to a growing body of literature seeking to document illness-related abnormalities in time-frequency EEG signatures that may serve as reliable indicators of the pathophysiological processes underlying the cognitive deficits observed in AD and aMCI-afflicted populations

Neural oscillatory signatures of auditory and audiovisual illusions

Questions of the relationship between human perception and brain activity can be approached from different perspectives: in the first, the brain is mainly regarded as a recipient and processor of sensory data. The corresponding research objective is to establish mappings of neural activity patterns and external stimuli. Alternatively, the brain can be regarded as a self-organized dynamical system, whose constantly changing state affects how incoming sensory signals are processed and perceived. The research reported in this thesis can chiefly be located in the second framework, and investigates the relationship between oscillatory brain activity and the perception of ambiguous stimuli. Oscillations are here considered as a mechanism for the formation of transient neural assemblies, which allows efficient information transfer. While the relevance of activity in distinct frequency bands for auditory and audiovisual perception is well established, different functional architectures of sensory integration can be derived from the literature. This dissertation therefore aims to further clarify the role of oscillatory activity in the integration of sensory signals towards unified perceptual objects, using illusion paradigms as tools of study. In study 1, we investigate the role of low frequency power modulations and phase alignment in auditory object formation. We provide evidence that auditory restoration is associated with a power reduction, while the registration of an additional object is reflected by an increase in phase locking. In study 2, we analyze oscillatory power as a predictor of auditory influence on visual perception in the sound-induced flash illusion. We find that increased beta-/ gamma-band power over occipitotemporal electrodes shortly before stimulus onset predicts the illusion, suggesting a facilitation of processing in polymodal circuits. In study 3, we address the question of whether visual influence on auditory perception in the ventriloquist illusion is reflected in primary sensory or higher-order areas. We establish an association between reduced theta-band power in mediofrontal areas and the occurrence of illusion, which indicates a top-down influence on sensory decision-making. These findings broaden our understanding of the functional relevance of neural oscillations by showing that different processing modes, which are reflected in specific spatiotemporal activity patterns, operate in different instances of sensory integration.Fragen nach dem Zusammenhang zwischen menschlicher Wahrnehmung und Hirnaktivität können aus verschiedenen Perspektiven adressiert werden: in der einen wird das Gehirn hauptsächlich als Empfänger und Verarbeiter von sensorischen Daten angesehen. Das entsprechende Forschungsziel wäre eine Zuordnung von neuronalen Aktivitätsmustern zu externen Reizen. Dieser Sichtweise gegenüber steht ein Ansatz, der das Gehirn als selbstorganisiertes dynamisches System begreift, dessen sich ständig verändernder Zustand die Verarbeitung und Wahrnehmung von sensorischen Signalen beeinflusst. Die Arbeiten, die in dieser Dissertation zusammengefasst sind, können vor allem in der zweitgenannten Forschungsrichtung verortet werden, und untersuchen den Zusammenhang zwischen oszillatorischer Hirnaktivität und der Wahrnehmung von mehrdeutigen Stimuli. Oszillationen werden hier als ein Mechanismus für die Formation von transienten neuronalen Zusammenschlüssen angesehen, der effizienten Informationstransfer ermöglicht. Obwohl die Relevanz von Aktivität in verschiedenen Frequenzbändern für auditorische und audiovisuelle Wahrnehmung gut belegt ist, können verschiedene funktionelle Architekturen der sensorischen Integration aus der Literatur abgeleitet werden. Das Ziel dieser Dissertation ist deshalb eine Präzisierung der Rolle oszillatorischer Aktivität bei der Integration von sensorischen Signalen zu einheitlichen Wahrnehmungsobjekten mittels der Nutzung von Illusionsparadigmen. In der ersten Studie untersuchen wir die Rolle von Leistung und Phasenanpassung in niedrigen Frequenzbändern bei der Formation von auditorischen Objekten. Wir zeigen, dass die Wiederherstellung von Tönen mit einer Reduktion der Leistung zusammenhängt, während die Registrierung eines zusätzlichen Objekts durch einen erhöhten Phasenangleich widergespiegelt wird. In der zweiten Studie analysieren wir oszillatorische Leistung als Prädiktor von auditorischem Einfluss auf visuelle Wahrnehmung in der sound-induced flash illusion. Wir stellen fest, dass erhöhte Beta-/Gamma-Band Leistung über occipitotemporalen Elektroden kurz vor der Reizdarbietung das Auftreten der Illusion vorhersagt, was auf eine Begünstigung der Verarbeitung in polymodalen Arealen hinweist. In der dritten Studie widmen wir uns der Frage, ob ein visueller Einfluss auf auditorische Wahrnehmung in der ventriloquist illusion sich in primären sensorischen oder übergeordneten Arealen widerspiegelt. Wir weisen einen Zusammenhang von reduzierter Theta-Band Leistung in mediofrontalen Arealen und dem Auftreten der Illusion nach, was einen top-down Einfluss auf sensorische Entscheidungsprozesse anzeigt. Diese Befunde erweitern unser Verständnis der funktionellen Bedeutung neuronaler Oszillationen, indem sie aufzeigen, dass verschiedene Verarbeitungsmodi, die sich in spezifischen räumlich-zeitlichen Aktivitätsmustern spiegeln, in verschiedenen Phänomenen von sensorischer Integration wirksam sind

Brain dynamics sustaining rapid rule extraction from speech

Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization mechanisms between neuronal assemblies might have a key role during language learning. In particular, studying these dynamics may help uncover whether different oscillatory patterns sustain more item-based learning of words and rule-based learning from speech input. Therefore, we tracked the modulation of oscillatory neural activity during the initial exposure to an artificial language, which contained embedded rules. We analyzed both spectral power variations, as a measure of local neuronal ensemble synchronization, as well as phase coherence patterns, as an index of the long-range coordination of these local groups of neurons. Synchronized activity in the gamma band (2040 Hz), previously reported to be related to the engagement of selective attention, showed a clear dissociation of local power and phase coherence between distant regions. In this frequency range, local synchrony characterized the subjects who were focused on word identification and was accompanied by increased coherence in the theta band (48 Hz). Only those subjects who were able to learn the embedded rules showed increased gamma band phase coherence between frontal, temporal, and parietal regions