30 research outputs found
Analyses at microscopic, mesoscopic, and mean-field scales
Die AktivitĂ€t des Hippocampus im Tiefschlaf ist geprĂ€gt durch sharp wave-ripple Komplexe (SPW-R): kurze (50â100 ms) Phasen mit erhöhter neuronaler AktivitĂ€t, moduliert durch eine schnelle âRippleâ-Oszillation (140â220 Hz). SPW-R werden mit GedĂ€chtniskonsolidierung in Verbindung gebracht, aber ihr Ursprung ist unklar. Sowohl exzitatorische als auch inhibitorische Neuronpopulationen könnten die Oszillation generieren.
Diese Arbeit analysiert Ripple-Oszillationen in inhibitorischen Netzwerkmodellen auf mikro-, meso- und makroskopischer Ebene und zeigt auf, wie die Ripple-Dynamik von exzitatorischem Input, inhibitorischer KopplungsstÀrke und dem Rauschmodell abhÀngt.
Zuerst wird ein stark getriebenes Interneuron-Netzwerk mit starker, verzögerter Kopplung analysiert. Es wird eine Theorie entwickelt, die die Drift-bedingte Feuerdynamik im Mean-field Grenzfall beschreibt. Die Ripple-Frequenz und die Dynamik der Membranpotentiale werden analytisch als Funktion des Inputs und der Netzwerkparameter angenÀhert. Die Theorie erklÀrt, warum die Ripple-Frequenz im Verlauf eines SPW-R-Ereignisses sinkt (intra-ripple frequency accommodation, IFA). Weiterhin zeigt eine numerische Analyse, dass ein alternatives Modell, basierend auf einem transienten Störungseffekt in einer schwach gekoppelten Interneuron-Population, unter biologisch plausiblen Annahmen keine IFA erzeugen kann. IFA kann somit zur Modellauswahl beitragen und deutet auf starke, verzögerte inhibitorische Kopplung als plausiblen Mechanismus hin.
SchlieĂlich wird die Anwendbarkeit eines kĂŒrzlich entwickelten mesoskopischen Ansatzes fĂŒr die effiziente Simulation von Ripples in endlich groĂen Netzwerken geprĂŒft. Dabei wird das Rauschen nicht im Input der Neurone beschrieben, sondern als stochastisches Feuern entsprechend einer Hazard-Rate. Es wird untersucht, wie die Wahl des Hazards die dynamische SuszeptibilitĂ€t einzelner Neurone, und damit die Ripple-Dynamik in rekurrenten Interneuron-Netzwerken beeinflusst.Hippocampal activity during sleep or rest is characterized by sharp wave-ripples (SPW-Rs): transient (50â100 ms) periods of elevated neuronal activity modulated by a fast oscillation â the ripple (140â220 Hz). SPW-Rs have been linked to memory consolidation, but their generation mechanism remains unclear. Multiple potential mechanisms have been proposed, relying on excitation and/or inhibition as the main pacemaker.
This thesis analyzes ripple oscillations in inhibitory network models at micro-, meso-, and macroscopic scales and elucidates how the ripple dynamics depends on the excitatory drive, inhibitory coupling strength, and the noise model.
First, an interneuron network under strong drive and strong coupling with delay is analyzed. A theory is developed that captures the drift-mediated spiking dynamics in the mean-field limit. The ripple frequency as well as the underlying dynamics of the membrane potential distribution are approximated analytically as a function of the external drive and network parameters. The theory explains why the ripple frequency decreases over the course of an event (intra-ripple frequency accommodation, IFA). Furthermore, numerical analysis shows that an alternative inhibitory ripple model, based on a transient ringing effect in a weakly coupled interneuron population, cannot account for IFA under biologically realistic assumptions. IFA can thus guide model selection and provides new support for strong, delayed inhibitory coupling as a mechanism for ripple generation.
Finally, a recently proposed mesoscopic integration scheme is tested as a potential tool for the efficient numerical simulation of ripple dynamics in networks of finite size. This approach requires a switch of the noise model, from noisy input to stochastic output spiking mediated by a hazard function. It is demonstrated how the choice of a hazard function affects the linear response of single neurons and therefore the ripple dynamics in a recurrent interneuron network
Metabolic and Blood Flow Properties of Functional Brain Networks Using Human Multimodal Neuroimaging
The brain has a high energetic cost to support neuronal activity, requiring both oxygen and glucose supply from the cerebral vascular system. Additionally, the brain functions through complex patterns of interconnectivity between neuronal assemblies giving rise to functional network architectures that can be investigated across multiple spatial scales. Different brain regions have different roles and importance within these network architectures, with some regions exhibiting more global importance by being involved in cross-network communication while other being predominantly involved in local connections. There are indications that regions exhibiting a more global role in inter networks connectivity are characterized by a higher and more efficient metabolic profile, leading to differences in metabolic properties when compared to more locally connected regions. Understanding the link between oxygen/glucose metabolism and functional features of brain network architectures, across different spatial scales, is of primary importance.
This thesis consists of three original studies combining human brain resting-state multimodal neuroimaging and transcriptional data to investigate the glucose/oxygen metabolic costs of brain functional connectivity. We quantified glucose metabolism from positron emission tomography, and oxygen metabolism and functional connectivity from magnetic resonance imaging. In the first study, we highlight how the oxygen/glucose metabolism of brain regions can non-linearly relate to their functional hubness, within the resting-state networks of the brain across a nested hierarchy. We found that an increase in oxygen/glucose metabolism is associated with a non-linear increase in functional hubness where increase rates are both network- and scale-dependent. In the second study, we show specific transcriptional signatures that characterize the oxygen/glucose metabolic costs of regions involved in network global versus local centrality. This study highlights the different metabolic profiles of local and global regions, with gene expression related to oxidative metabolism and synaptic pathways being enriched in association with spatial patterns in common with resting blood flow and metabolism (oxygen and glucose) and globally-connected regions. In the third study, we demonstrate that there are oxygen/glucose metabolic costs to the functional integration and segregation of resting-state networks. We highlight that the metabolic costs of functional integration could reflect the hierarchical organization of the brain from unimodal to transmodal regions
Microscopy Conference 2021 (MC 2021) - Proceedings
Das Dokument enthÀlt die Kurzfassungen der BeitrÀge aller Teilnehmer an der Mikroskopiekonferenz "MC 2021"
THE THALAMIC RETICULAR NUCLEUS: A MULTIFACETED GUARDIAN
Interactions between the cortex and the thalamus are essential for major brain functions such as sensory information processing and integration, sleep and wake regulation and cognitive processes. The thalamic reticular nucleus (TRN) is strategically positioned within the thalamocortical circuit and has a strong inhibitory control over the thalamus. It can act on a global scale, such as suppressing the flow of sensory information from the thalamus to the cortex during sleep. The TRN also acts locally on the activity of single cells or small cell groups. To reconcile both of these global and local aspects of TRN functions, we studied the cellular, synaptic and functional heterogeneity of the TRN, with a focus on the comparison between the classical sensory TRN and the less well-described limbic TRN.
In study 1, using anatomical tracing and cellular electrophysiology, we identified the dorsal presubiculum (dPreS), the retrosplenial cortex (RSC) and the anterior thalamic nuclei (ATN) as part of a novel thalamo-cortical circuit involving the limbic TRN in mice. The dPreS, RSC and ATN are three key structures for spatial navigation. dPreS/RSC excitatory glutamatergic synapses formed on TRN and ATN are part of a feedforward circuit through which TRN-mediated inhibition generates large burst-mediated inhibitory synaptic currents. The PreS/RSC afferents to the TRN showed driver-like characteristics, which is unprecedented for corticoreticular synapses and expands the scope of the TRN heterogeneity to the nature of its synaptic afferents. We further investigated the role of the limbic TRN in the control of head-direction neurons that were previously described to be located in the anterodorsal thalamus. The width of the tuning curve of head-direction neurons in the thalamus was broadened upon chemogenetic silencing of the TRN, revealing a novel form of internal sensory gating by the TRN. About half of the head-direction neurons showed action potential discharge patterns consistent with feedforward inhibitory responses upon light activation of dPreS/RSC. These data suggest that the limbic TRN sharpens the tuning of thalamic head-direction neurons under dPreS/RSC control. Finally, we investigated the potential function of the limbic TRN in the hidden version of the Morris watermaze. We discovered that chemogenetic silencing of the limbic TRN biased the search patterns towards allocentric strategies and generated perseverance to previously learned escape positions, suggesting an impairment of the egocentric system in which the head-direction system plays a critical role.
In study 2, we combined opto-tagging of TRN sectors with in vitro electrophysiological recordings and discovered that the limbic TRN neurons produced less repetitive burst firing than their sensory counterpart. The burst discharge of sensory TRN neurons is known to generate sleep spindles that propagate to the cortex, that are a marker of sleep quality and that correlate with memory consolidation. Consistently, local field potential recordings in the prefrontal cortex that is related to the less bursty limbic TRN revealed smaller amplitude and slower sleep spindles compared to sensory ones, making the heterogeneity of the TRN a critical player in local sleep rhythms.
This thesis summarizes elements supporting the heterogeneity of the TRN, in particular between the sensory and the limbic TRN. It also provides a novel function for the limbic TRN in the spatial navigation system
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Development and Application of a Synthetic Near Infrared Fluorescent Probe for Imaging Modulatory Neurotransmitters
Dopamine neurotransmission plays critical roles in brain function in both health anddisease and aberrations in dopamine neurotransmission are implicated in severalpsychiatric and neurological disorders, including schizophrenia, depression, anxiety, andParkinsonâs disease. Until recently, measuring the dynamics of dopamine and otherneurotransmitters of this class could not be achieved at spatiotemporal resolutionsnecessary to study how dopamine regulates the plasticity and function of neurons and neuralcircuits, and how dysfunctions in this regulation lead to disease. Probes that satisfy criticalattributes in spatiotemporal resolution and chemical selectivity are needed to facilitateinvestigations of dopamine neurochemistry.To address this need, this dissertation describes the synthesis and implementation ofan ultrasensitive near-infrared âturn-onâ nanosensor (nIRCat) for the catecholamineneuromodulators dopamine and norepinephrine. To guide probe development, we presentresults from a computational model that offers insight into the spatiotemporal dynamics ofdopamine in the striatum, a subcortical structure that is enriched in dopamine. With thismodel, we elucidated the kinetic requirements for a prototypical optical indicator as well asoptimal imaging frame rates needed for measuring dopamine neurochemical dynamics.Stochastic modeling of dopamine dynamics, driven by kinetic phenomena of vesicularrelease, diffusion and clearance, provide a platform to evaluate dopaminergic volumetransmission arising from a single terminal or ensemble terminal activity. With this work,we illustrate that only probes with kinetic parameters in a particular range are feasible fordopamine imaging at spatiotemporal scales likely to be encountered in brain tissue.In two subsequent chapters, we describe the development and in vitrocharacterization of nIRCats, synthesized from functionalized single wall carbon nanotubes(SWCNT) that fluoresce in the near infrared range of the spectrum. We show that nIRCatsexhibit maximal relative change in fluorescence intensity (ÎF/F0) of up to 35-fold inresponse to catecholamines and have optimal dynamic range that span physiologicalconcentrations of their target brain analytes. Through a combination of experimental andmolecular dynamics approaches, we elucidate the photophysical principles and intermolecularinteractions that govern the molecular recognition and fluorescence modulation of nIRCats by dopamine.Finally, we demonstrate that nIRCat can be used to measure electrically andoptogenetically evoked release of dopamine in striatal brain slices, revealing hotspots ofactivity with a median size of 2 ÎŒm, and exhibiting a log-normal size distribution that extendsup to 10 ÎŒm. Moreover, nIRCats are shown to be compatible with dopamine pharmacologyand permit studies of how receptor-targeting drugs modulate evoked dopamine release. Ourresults suggest nIRCats may uniquely support similar explorations of processes that regulatedopamine neuromodulation at the level of individual synapses, and exploration of the effectsof receptor agonists and antagonists that are commonly used as psychiatric drugs andpsychoactive molecules that modulate the release and clearance profiles of dopamine. Weconclude that nIRCats and other nanosensors of this class can serve as versatile syntheticoptical tools to monitor interneuronal chemical signaling in the brain extracellular space atspatial and temporal scales pertinent to the encoded information
Dynamic Control of Network Level Information Processing through Cholinergic Modulation
Acetylcholine (ACh) release is a prominent neurochemical marker of arousal state
within the brain. Changes in ACh are associated with changes in neural activity and
information processing, though its exact role and the mechanisms through which it
acts are unknown. Here I show that the dynamic changes in ACh levels that are
associated with arousal state control informational processing functions of networks
through its effects on the degree of Spike-Frequency Adaptation (SFA), an activity
dependent decrease in excitability, synchronizability, and neuronal resonance displayed
by single cells. Using numerical modeling I develop mechanistic explanations
for how control of these properties shift network activity from a stable high frequency
spiking pattern to a traveling wave of activity. This transition mimics the change
in brain dynamics seen between high ACh states, such as waking and Rapid Eye
Movement (REM) sleep, and low ACh states such as Non-REM (NREM) sleep. A
corresponding, and related, transition in network level memory recall is also occurs
as ACh modulates neuronal SFA. When ACh is at its highest levels (waking) all
memories are stably recalled, as ACh is decreased (REM) in the model weakly encoded
memories destabilize while strong memories remain stable. In levels of ACh
that match Slow Wave Sleep (SWS), no encoded memories are stably recalled. This
results from a competition between SFA and excitatory input strength and provides
a mechanism for neural networks to control the representation of underlying synaptic
information. Finally I show that during the low ACh conditions, oscillatory conditions
allow for external inputs to be properly stored in and recalled from synaptic weights. Taken together this work demonstrates that dynamic neuromodulation is
critical for the regulation of information processing tasks in neural networks. These
results suggest that ACh is capable of switching networks between two distinct information
processing modes. Rate coding of information is facilitated during high
ACh conditions and phase coding of information is facilitated during low ACh conditions.
Finally I propose that ACh levels control whether a network is in one of
three functional states: (High ACh; Active waking) optimized for encoding of new
information or the stable representation of relevant memories, (Mid ACh; resting
state or REM) optimized for encoding connections between currently stored memories
or searching the catalog of stored memories, and (Low ACh; NREM) optimized
for renormalization of synaptic strength and memory consolidation. This work provides
a mechanistic insight into the role of dynamic changes in ACh levels for the
encoding, consolidation, and maintenance of memories within the brain.PHDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147503/1/roachjp_1.pd
Synchronization of electrically coupled resonate-and-fire neurons
Electrical coupling between neurons is broadly present across brain areas and
is typically assumed to synchronize network activity. However, intrinsic
properties of the coupled cells can complicate this simple picture. Many cell
types with strong electrical coupling have been shown to exhibit resonant
properties, and the subthreshold fluctuations arising from resonance are
transmitted through electrical synapses in addition to action potentials. Using
the theory of weakly coupled oscillators, we explore the effect of both
subthreshold and spike-mediated coupling on synchrony in small networks of
electrically coupled resonate-and-fire neurons, a hybrid neuron model with
linear subthreshold dynamics and discrete post-spike reset. We calculate the
phase response curve using an extension of the adjoint method that accounts for
the discontinuity in the dynamics. We find that both spikes and resonant
subthreshold fluctuations can jointly promote synchronization. The subthreshold
contribution is strongest when the voltage exhibits a significant post-spike
elevation in voltage, or plateau. Additionally, we show that the geometry of
trajectories approaching the spiking threshold causes a "reset-induced shear"
effect that can oppose synchrony in the presence of network asymmetry, despite
having no effect on the phase-locking of symmetrically coupled pairs