Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex

Preterm infant brain activity is discontinuous; bursts of activity recorded using EEG (electroencephalography), thought to be driven by subcortical regions, display scale free properties and exhibit a complex temporal ordering known as long-range temporal correlations (LRTCs). During brain development, activity-dependent mechanisms are essential for synaptic connectivity formation, and abolishing burst activity in animal models leads to weak disorganised synaptic connectivity. Moreover, synaptic pruning shares similar mechanisms to spike-timing dependent plasticity (STDP), suggesting that the timing of activity may play a critical role in connectivity formation. We investigated, in a computational model of leaky integrate-and-fire neurones, whether the temporal ordering of burst activity within an external driving input could modulate connectivity formation in the network. Connectivity evolved across the course of simulations using an approach analogous to STDP, from networks with initial random connectivity. Small-world connectivity and hub neurones emerged in the network structure—characteristic properties of mature brain networks. Notably, driving the network with an external input which exhibited LRTCs in the temporal ordering of burst activity facilitated the emergence of these network properties, increasing the speed with which they emerged compared with when the network was driven by the same input with the bursts randomly ordered in time. Moreover, the emergence of small-world properties was dependent on the strength of the LRTCs. These results suggest that the temporal ordering of burst activity could play an important role in synaptic connectivity formation and the emergence of small-world topology in the developing brain

Balance Training With a Vibrotactile Biofeedback System Affects the Dynamical Structure of the Center of Pressure Trajectories in Chronic Stroke Patients

Haptic-based vibrotactile biofeedback (BF) is a promising technique to improve rehabilitation of balance in stroke patients. However, the extent to which BF training changes temporal structure of the center of pressure (CoP) trajectories remains unknown. This study aimed to investigate the effect of vibrotactile BF training on the temporal structure of CoP during quiet stance in chronic stroke patients using detrended fluctuation analysis (DFA). Nine chronic stroke patients (age; 81.56 ± 44 months post-stroke) received a balance training regimen using a vibrotactile BF system twice a week over 4 weeks. A Wii Balance board was used to record five 30 s trials of quiet stance pre- and post-training at 50 Hz. DFA revealed presence of two linear scaling regions in CoP indicating presence of fast- and slow-scale fluctuations. Averaged across all trials, fast-scale fluctuations showed persistent dynamics (α = 1.05 ± 0.08 for ML and α = 0.99 ± 0.17 for AP) and slow-scale fluctuations were anti-persistent (α = 0.35 ± 0.05 for ML and α = 0.32 ± 0.05 for AP). The slow-scale dynamics of ML CoP in stroke patients decreased from pre-training to post-BF training (α = 0.40 ± 0.13 vs. 0.31 ± 0.09). These results suggest that the vibrotactile BF training affects postural control strategy used by chronic stroke patients in the ML direction. Results of the DFA are further discussed in the context of balance training using vibrotactile BF and interpreted from the perspective of intermittent control of upright stance

Tightening Up the Control of Treadmill Walking: Effects of Maneuverability Range and Acoustic Pacing on Stride-to-Stride Fluctuations

The correlational structure of stride-to-stride fluctuations differs between healthy and pathological gait. Uncorrelated and anti-persistent stride-to-stride fluctuations are believed to indicate pathology whereas persistence represents healthy functioning. However, this reading can be questioned because the correlational structure changes with task constraints, like acoustic pacing, signifying the tightness of control over particular gait parameters. We tested this “tightness-of-control interpretation” by varying the maneuverability range during treadmill walking (small, intermediate, and large walking areas), with and without acoustic pacing. Stride-speed fluctuations exhibited anti-persistence, suggesting that stride speeds were tightly controlled, with a stronger degree of anti-persistence for smaller walking areas. Constant-speed goal-equivalent-manifold decompositions revealed simultaneous control of stride times and stride lengths, especially for smaller walking areas to limit stride-speed fluctuations. With acoustic pacing, participants followed both constant-speed and constant-stride-time task goals. This was reflected by a strong degree of anti-persistence around the stride-time by stride-length point that uniquely satisfied both goals. Our results strongly support the notion that anti-persistence in stride-to-stride fluctuations reflect the tightness of control over the associated gait parameter, while not tightly regulated gait parameters exhibit statistical persistence. We extend the existing body of knowledge by showing quantitative changes in anti-persistence of already tightly regulated stride-speed fluctuations

Bridging structure and function with brain network modeling

High-throughput neuroimaging technology enables rapid acquisition of vast amounts of structural and functional data on multiple spatial and temporal scales. While novel methods to extract information from these data are continuously developed, there is no principled approach for the systematic integration of distinct experimental results into a common theoretical framework, yet. The central result of this dissertation is a biophysically-based framework for brain network modeling that links structural and functional data across scales and modalities and integrates them with dynamical systems theory. Specifically, the publications in this thesis i. introduce an automated pipeline that extracts structural and functional information from multimodal imaging data to construct and constrain brain models, ii. link whole-brain models with empirical EEG-fMRI (simultaneous electroencephalography and functional magnetic resonance imaging) data to integrate neural signals with simulated activity, iii. propose a framework for reverse-engineering neurophysiological dynamics and mechanisms underlying commonly observed features of neural activity, iv. document a software module that makes users acquainted with theory and practice of brain modeling, v. associate aging with structural and functional connectivity and vi. examine how parcellation size and short-range connectivity affect model dynamics. Taken together, these results form a novel approach that enables reverse-engineering of neurophysiological processes and mechanisms on the basis of biophysically-based brain models.Zusammenfassung Hochdurchsatzverfahren zur neuronalen Bildgebung ermöglichen die schnelle Erfassung großer Mengen an strukturellen und funktionellen Daten über verschiedenen räumlichen und zeitlichen Skalen. Obwohl ständig neue Methoden zur Verarbeitung der in diesen Daten enthaltenen Informationen entwickelt werden gibt es bisher kein systematisches Verfahren um experimentelle Ergebnisse in einem gemeinsamen theoretischen Rahmenwerk zu integrieren und zu verknüpfen. Das Hauptergebnis dieser Dissertation ist ein biophysikalisch basiertes Gehirn- Netzwerkmodell das strukturelle und funktionelle Daten über verschiedene Skalen und Modalitäten hinweg verknüpft und mit dynamischer Systemtheorie vereint. Die hier zusammengefassten Publikationen i. stellen eine automatische Software-Pipeline vor die strukturelle und funktionelle Informationen aus multimodalen Bilddaten extrahiert um Gehirnmodelle zu konstruieren und zu parametrisieren, ii. verknüpfen Ganzhi rnmodel le mi t empi r i schen EEG- fMRT ( s imul tane Elektroenzephalographie und funktionelle Magnetresonanztomographie) Daten um neuronale Signale mit simulierter Aktivität zu integrieren, iii. schlagen ein Rahmenwerk vor um neurophysiologische Dynamiken und Mechanismen die häufig beobachteten Eigenschaften neuronaler Aktivität zu Grunde liegen zu rekonstruieren, iv. dokumentieren ein Software-Modul das Benutzer mit Theorie und Praxis der Gehirnmodellierung vertraut macht, v. assoziieren Alterungsprozesse mit struktureller und funktioneller Konnektivität und vi. untersuchen wie Gehirn-Parzellierung und lokale Konnektivität die Modelldynamik beeinflussen. Zusammengenommen ergibt sich ein neuartiges Verfahren das die Rekonstruktion neurophysiologischer Prozesse und Mechanismen ermöglicht und mit dessen Hilfe neuronale Aktivität auf verschiedenen räumlichen und zeitlichen Skalen anhand biophysikalisch basierter Modelle vorhersagt werden kann

Modeling phase synchronization of interacting neuronal populations:from phase reductions to collective behavior of oscillatory neural networks

Synchronous, coherent interaction is key for the functioning of our brain. The coordinated interplay between neurons and neural circuits allows to perceive, process and transmit information in the brain. As such, synchronization phenomena occur across all scales. The coordination of oscillatory activity between cortical regions is hypothesized to underlie the concept of phase synchronization. Accordingly, phase models have found their way into neuroscience. The concepts of neural synchrony and oscillations are introduced in Chapter 1 and linked to phase synchronization phenomena in oscillatory neural networks. Chapter 2 provides the necessary mathematical theory upon which a sound phase description builds. I outline phase reduction techniques to distill the phase dynamics from complex oscillatory networks. In Chapter 3 I apply them to networks of weakly coupled Brusselators and of Wilson-Cowan neural masses. Numerical and analytical approaches are compared against each other and their sensitivity to parameter regions and nonlinear coupling schemes is analysed. In Chapters 4 and 5 I investigate synchronization phenomena of complex phase oscillator networks. First, I study the effects of network-network interactions on the macroscopic dynamics when coupling two symmetric populations of phase oscillators. This setup is compared against a single network of oscillators whose frequencies are distributed according to a symmetric bimodal Lorentzian. Subsequently, I extend the applicability of the Ott-Antonsen ansatz to parameterdependent oscillatory systems. This allows for capturing the collective dynamics of coupled oscillators when additional parameters influence the individual dynamics. Chapter 6 draws the line to experimental data. The phase time series of resting state MEG data display large-scale brain activity at the edge of criticality. After reducing neurophysiological phase models from the underlying dynamics of Wilson-Cowan and Freeman neural masses, they are analyzed with respect to two complementary notions of critical dynamics. A general discussion and an outlook of future work are provided in the final Chapter 7