The mechanisms of tinnitus: perspectives from human functional neuroimaging

In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural substrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power

Complementarity of Spike- and Rate-Based Dynamics of Neural Systems

Relationships between spiking-neuron and rate-based approaches to the dynamics of neural assemblies are explored by analyzing a model system that can be treated by both methods, with the rate-based method further averaged over multiple neurons to give a neural-field approach. The system consists of a chain of neurons, each with simple spiking dynamics that has a known rate-based equivalent. The neurons are linked by propagating activity that is described in terms of a spatial interaction strength with temporal delays that reflect distances between neurons; feedback via a separate delay loop is also included because such loops also exist in real brains. These interactions are described using a spatiotemporal coupling function that can carry either spikes or rates to provide coupling between neurons. Numerical simulation of corresponding spike- and rate-based methods with these compatible couplings then allows direct comparison between the dynamics arising from these approaches. The rate-based dynamics can reproduce two different forms of oscillation that are present in the spike-based model: spiking rates of individual neurons and network-induced modulations of spiking rate that occur if network interactions are sufficiently strong. Depending on conditions either mode of oscillation can dominate the spike-based dynamics and in some situations, particularly when the ratio of the frequencies of these two modes is integer or half-integer, the two can both be present and interact with each other

Orexin Receptor Activation Generates Gamma Band Input to Cholinergic and Serotonergic Arousal System Neurons and Drives an Intrinsic Ca(2+)-Dependent Resonance in LDT and PPT Cholinergic Neurons

A hallmark of the waking state is a shift in EEG power to higher frequencies with epochs of synchronized intracortical gamma activity (30-60 Hz) - a process associated with high-level cognitive functions. The ascending arousal system, including cholinergic laterodorsal (LDT) and pedunculopontine (PPT) tegmental neurons and serotonergic dorsal raphe (DR) neurons, promotes this state. Recently, this system has been proposed as a gamma wave generator, in part, because some neurons produce high-threshold, Ca(2+)-dependent oscillations at gamma frequencies. However, it is not known whether arousal-related inputs to these neurons generate such oscillations, or whether such oscillations are ever transmitted to neuronal targets. Since key arousal input arises from hypothalamic orexin (hypocretin) neurons, we investigated whether the unusually noisy, depolarizing orexin current could provide significant gamma input to cholinergic and serotonergic neurons, and whether such input could drive Ca(2+)-dependent oscillations. Whole-cell recordings in brain slices were obtained from mice expressing Cre-induced fluorescence in cholinergic LDT and PPT, and serotonergic DR neurons. After first quantifying reporter expression accuracy in cholinergic and serotonergic neurons, we found that the orexin current produced significant high frequency, including gamma, input to both cholinergic and serotonergic neurons. Then, by using a dynamic clamp, we found that adding a noisy orexin conductance to cholinergic neurons induced a Ca(2+)-dependent resonance that peaked in the theta and alpha frequency range (4-14 Hz) and extended up to 100 Hz. We propose that this orexin current noise and the Ca(2+) dependent resonance work synergistically to boost the encoding of high-frequency synaptic inputs into action potentials and to help ensure cholinergic neurons fire during EEG activation. This activity could reinforce thalamocortical states supporting arousal, REM sleep, and intracortical gamma

Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role

The lateral geniculate nucleus (LGN) has often been treated in the past as a linear filter that adds little to retinal processing of visual inputs. Here we review anatomical, neurophysiological, brain imaging, and modeling studies that have in recent years built up a much more complex view of LGN . These include effects related to nonlinear dendritic processing, cortical feedback, synchrony and oscillations across LGN populations, as well as involvement of LGN in higher level cognitive processing. Although recent studies have provided valuable insights into early visual processing including the role of LGN, a unified model of LGN responses to real-world objects has not yet been developed. In the light of recent data, we suggest that the role of LGN deserves more careful consideration in developing models of high-level visual processing

The (un)conscious mouse as a model for human brain functions: key principles of anesthesia and their impact on translational neuroimaging

In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca(2+) imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species

Frequency preference and reliability of signal integration

Die Eigenschaften einzelner Nervenzellen sind von grundlegender Bedeutung für die Verarbeitung von Informationen im Nervensystem. Neuronen antworten auf Eingangsreize durch Veränderung der elektrischen Spannung über die Zellmembran. Die Spannungsantwort wird dabei durch die Dynamik der Ionenkanäle in der Zellmembran bestimmt. In dieser Arbeit untersuche ich anhand von leitfähigkeits-basierten Modellneuronen den Einfluss von Ionenkanälen auf zwei Aspekte der Signalverarbeitung: die Frequenz-Selektivität sowie die Zuverlässigkeit und zeitliche Präzision von Aktionspotentialen. Zunächst werden die zell-intrinsischen Mechanismen identifiziert, welche the Frequenz-Selektivität und die Zuverlässigkeit bestimmen. Weiterhin wird untersucht, wie Ionenkanäle diese Mechanismen modulieren können, um die Integration von Signalen zu optimieren. Im ersten Teil der Arbeit wird demonstriert, dass der Mechanismus der unterschwelligen Resonanz, so wie er bisher für periodische Signale beobachtet wurde, auch auf nicht-periodische Signale anwendbar ist und sich ebenfalls in den Feuerraten niederschlägt. Im zweiten Teil wird gezeigt, dass zeitliche Präzision und Zuverlässigkeit von Aktionspotentialen mit der Stimulusfrequenz variieren und dass, in Abhängigkeit davon, ob das Stimulusmittel über- oder unterhalb der Feuerschwelle liegt, zwei Stimulusregime unterschieden werden müssen. In beiden Regimen existiert eine bevorzugte Stimulusfrequenz, welche durch die Gesamtleitfähigkeit und die Dynamik spezifischer Ionenkanäle moduliert werden kann. Im dritten Teil wird belegt, dass Ionenkanäle die Zuverlässigkeit auch direkt über eine Veränderung der Sensitivität einer Zelle gegenüber neuronalem Rauschen bestimmen können. Die Ergebnisse der Arbeit lassen auf eine wichtige Rolle der dynamischen Regulierung der Ionenkanäle für die Frequenz-Selektivität und die zeitliche Präzision und Zuverlässigkeit der Spannungsantworten schließen.The properties of individual neurons are of fundamental importance for the processing of information in the nervous system. The generation of voltage responses to input signals, in particular, depends on the properties of ion channels in the cell membrane. Within this thesis, I employ conductance-based model neurons to investigate the effect of ionic conductances and their dynamics on two aspects of signal processing: frequency-selectivity and temporal precision and reliability of spikes. First, the cell-intrinsic mechanisms that determine frequency selectivity and spike timing reliability are identified on the basis of conductance-based model neurons. Second, it is analyzed how ionic conductances can serve to modulate these mechanisms in order to optimize signal integration. In the first part, the frequency selectivity of subthreshold response amplitudes previously observed for periodic stimuli is proven to extend to nonperiodic stimuli and to translate into firing rates. In the second part, it is demonstrated that spike timing reliability is frequency-selective and that two different stimulus regimes have to be distinguished, depending on whether the stimulus mean is below or above threshold. In both cases, resonance effects determine the most reliable stimulus frequency. It is shown that this frequency preference can be modulated by the peak conductance and dynamics of specific ion channels. In the third part, evidence is provided that ionic conductances determine spike timing reliability beyond changes in the preferred frequency. It is demonstrated that ionic conductances also exert a direct influence on the sensitivity of the timing of spikes to neuronal noise. The findings suggest an important role for dynamic neuromodulation of ion channels with regard to frequency selectivity and spike timing reliability

Transcranial Electric Stimulation Entrains Cortical Neuronal Populations in Rats

Low intensity electric fields have been suggested to affect the ongoing neuronal activity in vitro and in human studies. However, the physiological mechanism of how weak electrical fields affect and interact with intact brain activity is not well understood. We performed in vivo extracellular and intracellular recordings from the neocortex and hippocampus of anesthetized rats and extracellular recordings in behaving rats. Electric fields were generated by sinusoid patterns at slow frequency (0.8, 1.25 or 1.7 Hz) via electrodes placed on the surface of the skull or the dura. Transcranial electric stimulation (TES) reliably entrained neurons in widespread cortical areas, including the hippocampus. The percentage of TES phase-locked neurons increased with stimulus intensity and depended on the behavioral state of the animal. TES-induced voltage gradient, as low as 1 mV/mm at the recording sites, was sufficient to phase-bias neuronal spiking. Intracellular recordings showed that both spiking and subthreshold activity were under the combined influence of TES forced fields and network activity. We suggest that TES in chronic preparations may be used for experimental and therapeutic control of brain activity

Connecting the Brain to Itself through an Emulation.

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions