520 research outputs found

    Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform

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    Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species

    The emergence of two anti-phase oscillatory neural populations in a computational model of the Parkinsonian globus pallidus.

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    Experiments in rodent models of Parkinson's disease have demonstrated a prominent increase of oscillatory firing patterns in neurons within the Parkinsonian globus pallidus (GP) which may underlie some of the motor symptoms of the disease. There are two main pathways from the cortex to GP: via the striatum and via the subthalamic nucleus (STN), but it is not known how these inputs sculpt the pathological pallidal firing patterns. To study this we developed a novel neural network model of conductance-based spiking pallidal neurons with cortex-modulated input from STN neurons. Our results support the hypothesis that entrainment occurs primarily via the subthalamic pathway. We find that as a result of the interplay between excitatory input from the STN and mutual inhibitory coupling between GP neurons, a homogeneous population of GP neurons demonstrates a self-organizing dynamical behavior where two groups of neurons emerge: one spiking in-phase with the cortical rhythm and the other in anti-phase. This finding mirrors what is seen in recordings from the GP of rodents that have had Parkinsonism induced via brain lesions. Our model also includes downregulation of Hyperpolarization-activated Cyclic Nucleotide-gated (HCN) channels in response to burst firing of GP neurons, since this has been suggested as a possible mechanism for the emergence of Parkinsonian activity. We found that the downregulation of HCN channels provides even better correspondence with experimental data but that it is not essential in order for the two groups of oscillatory neurons to appear. We discuss how the influence of inhibitory striatal input will strengthen our results

    Mean-field analysis of basal ganglia and thalamocortical dynamics

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    When modeling a system as complex as the brain, considerable simplifications are inevitable. The nature of these simplifications depends on the available experimental evidence, and the desired form of model predictions. A focus on the former often inspires models of networks of individual neurons, since properties of single cells are more easily measured than those of entire populations. However, if the goal is to describe the processes responsible for the electroencephalogram (EEG), such models can become unmanageable due to the large numbers of neurons involved. Mean-field models in which assemblies of neurons are represented by their average properties allow activity underlying the EEG to be captured in a tractable manner. The starting point of the results presented here is a recent physiologically-based mean-field model of the corticothalamic system, which includes populations of excitatory and inhibitory cortical neurons, and an excitatory population representing the thalamic relay nuclei, reciprocally connected with the cortex and the inhibitory thalamic reticular nucleus. The average firing rates of these populations depend nonlinearly on their membrane potentials, which are determined by afferent inputs after axonal propagation and dendritic and synaptic delays. It has been found that neuronal activity spreads in an approximately wavelike fashion across the cortex, which is modeled as a two-dimensional surface. On the basis of the literature, the EEG signal is assumed to be roughly proportional to the activity of cortical excitatory neurons, allowing physiological parameters to be extracted by inverse modeling of empirical EEG spectra. One objective of the present work is to characterize the statistical distributions of fitted model parameters in the healthy population. Variability of model parameters within and between individuals is assessed over time scales of minutes to more than a year, and compared with the variability of classical quantitative EEG (qEEG) parameters. These parameters are generally not normally distributed, and transformations toward the normal distribution are often used to facilitate statistical analysis. However, no single optimal transformation exists to render data distributions approximately normal. A uniformly applicable solution that not only yields data following the normal distribution as closely as possible, but also increases test-retest reliability, is described in Chapter 2. Specialized versions of this transformation have been known for some time in the statistical literature, but it has not previously found its way to the empirical sciences. Chapter 3 contains the study of intra-individual and inter-individual variability in model parameters, also providing a comparison of test-retest reliability with that of commonly used EEG spectral measures such as band powers and the frequency of the alpha peak. It is found that the combined model parameters provide a reliable characterization of an individual's EEG spectrum, where some parameters are more informative than others. Classical quantitative EEG measures are found to be somewhat more reproducible than model parameters. However, the latter have the advantage of providing direct connections with the underlying physiology. In addition, model parameters are complementary to classical measures in that they capture more information about spectral structure. Another conclusion from this work was that a few minutes of alert eyes-closed EEG already contain most of the individual variability likely to occur in this state on the scale of years. In Chapter 4, age trends in model parameters are investigated for a large sample of healthy subjects aged 6-86 years. Sex differences in parameter distributions and trends are considered in three age ranges, and related to the relevant literature. We also look at changes in inter-individual variance across age, and find that subjects are in many respects maximally different around adolescence. This study forms the basis for prospective comparisons with age trends in evoked response potentials (ERPs) and alpha peak morphology, besides providing a standard for the assessment of clinical data. It is the first study to report physiologically-based parameters for such a large sample of EEG data. The second main thrust of this work is toward incorporating the thalamocortical system and the basal ganglia in a unified framework. The basal ganglia are a group of gray matter structures reciprocally connected with the thalamus and cortex, both significantly influencing, and influenced by, their activity. Abnormalities in the basal ganglia are associated with various disorders, including schizophrenia, Huntington's disease, and Parkinson's disease. A model of the basal ganglia-thalamocortical system is presented in Chapter 5, and used to investigate changes in average firing rates often measured in parkinsonian patients and animal models of Parkinson's disease. Modeling results support the hypothesis that two pathways through the basal ganglia (the so-called direct and indirect pathways) are differentially affected by the dopamine depletion that is the hallmark of Parkinson's disease. However, alterations in other components of the system are also suggested by matching model predictions to experimental data. The dynamics of the model are explored in detail in Chapter 6. Electrophysiological aspects of Parkinson's disease include frequency reduction of the alpha peak, increased relative power at lower frequencies, and abnormal synchronized fluctuations in firing rates. It is shown that the same parameter variations that reproduce realistic changes in mean firing rates can also account for EEG frequency reduction by increasing the strength of the indirect pathway, which exerts an inhibitory effect on the cortex. Furthermore, even more strongly connected subcircuits in the indirect pathway can sustain limit cycle oscillations around 5 Hz, in accord with oscillations at this frequency often observed in tremulous patients. Additionally, oscillations around 20 Hz that are normally present in corticothalamic circuits can spread to the basal ganglia when both corticothalamic and indirect circuits have large gains. The model also accounts for changes in the responsiveness of the components of the basal ganglia-thalamocortical system, and increased synchronization upon dopamine depletion, which plausibly reflect the loss of specificity of neuronal signaling pathways in the parkinsonian basal ganglia. Thus, a parsimonious explanation is provided for many electrophysiological correlates of Parkinson's disease using a single set of parameter changes with respect to the healthy state. Overall, we conclude that mean-field models of brain electrophysiology possess a versatility that allows them to be usefully applied in a variety of scenarios. Such models allow information about underlying physiology to be extracted from the experimental EEG, complementing traditional measures that may be more statistically robust but do not provide a direct link with physiology. Furthermore, there is ample opportunity for future developments, extending the basic model to encompass different neuronal systems, connections, and mechanisms. The basal ganglia are an important addition, not only leading to unified explanations for many hitherto disparate phenomena, but also contributing to the validation of this form of modeling

    Cortical beta burst dynamics are altered in Parkinson's disease but normalized by deep brain stimulation

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    Exaggerated subthalamic beta oscillatory activity and increased beta range cortico-subthalamic synchrony have crystallized as the electrophysiological hallmarks of Parkinson's disease. Beta oscillatory activity is not tonic but occurs in 'bursts' of transient amplitude increases. In Parkinson's disease, the characteristics of these bursts are altered especially in the basal ganglia. However, beta oscillatory dynamics at the cortical level and how they compare with healthy brain activity is less well studied. We used magnetoencephalography (MEG) to study sensorimotor cortical beta bursting and its modulation by subthalamic deep brain stimulation in Parkinson's disease patients and age-matched healthy controls. We show that the changes in beta bursting amplitude and duration typical of Parkinson's disease can also be observed in the sensorimotor cortex, and that they are modulated by chronic subthalamic deep brain stimulation, which, in turn, is reflected in improved motor function at the behavioural level. In addition to the changes in individual beta bursts, their timing relative to each other was altered in patients compared to controls: bursts were more clustered in untreated Parkinson's disease, occurring in 'bursts of bursts', and re-burst probability was higher for longer compared to shorter bursts. During active deep brain stimulation, the beta bursting in patients resembled healthy controls' data. In summary, both individual bursts' characteristics and burst patterning are affected in Parkinson's disease, and subthalamic deep brain stimulation normalizes some of these changes to resemble healthy controls' beta bursting activity, suggesting a non-invasive biomarker for patient and treatment follow-up.Peer reviewe

    Physiology of rodent olfactory bulb interneurons

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    The sense of olfaction is a central gateway of perceiving and evaluating an animal’s environment filled with volatile chemicals. It affects individual and social behavior in an evaluative way, i.e. by helping to find food sources, warning from dangers like toxins or predators or influencing mating choice. Already the first central station for vertebrate olfactory processing, the olfactory bulb (OB), is astonishingly complex. Its structure features several horizontal layers of signal transformation that includes a large variety of local interneurons (INs). Most of these cells are subject to adult neurogenesis, which rejuvenates and remodels the circuitry throughout life. One of those interneuron subtypes, the granule cell (GC), poses the most numerous cell type of the olfactory bulb. As the major synaptic connection of the bulb, linking different glomerular units, it participates in numerous reported tasks like odor discrimination or memory formation. Many of those capacities are attributable to the function of peculiar spines with long necks and enormous bulbar heads called gemmules. They accommodate pre- and postsynaptic specializations of the reciprocal synapse with mitral cells (MCs) that are topographically and functionally linked and feature many modes of signal integration and transmission. As of yet, the mechanistic underpinnings of activation and neurotransmitter release are not yet resolved in great detail. This gave rise to the first project of this thesis, which focusses on the detailed granule cell gemmule physiology during local glutamatergic activation. With the help of two-photon glutamate uncaging and concomitant calcium imaging, the spine could be selectively stimulated and its physiological dynamics tested. By the use of different pharmacological agents, we could verify the importance of voltage gated sodium channels (Nav) for local signal amplification and the involvement of NMDA and high voltage activated calcium channels (HVACCs) in the calcium elevation during local stimulation, which is important for γ-aminobutyric acid (GABA) release from the spine. The superthreshold depolarizing signal and strong calcium elevation during local input are exclusively restricted to the spine, which affirms the chemical and electrical isolation of gemmules from the rest of the cell. In this study we thereby confirmed the theoretical prediction of active computation within single spines in our system, emphasizing the functional importance of morphological compartmentalization for the cell’s physiology. The second largest population of interneurons in the olfactory bulb is located in the glomerular layer (GL) of the olfactory bulb and subsumes a plethora of different cell types, categorized in terms of molecular characteristics (mostly neurotransmitter), morphology and function. Among those, dopaminergic (DAergic) juxtaglomerular cells (JGCs) form a subpopulation, which the second part of this thesis is focused on. Innervated by the first or second synapse in the olfactory pathway, these cells exert strong influence in very early stages of olfactory signaling. The gating and transformation of inputs locally and very importantly also laterally over large distances originate from several factors. This cell grouping usually expresses two neurotransmitters at the same time, GABA and dopamine (DA), and encompass many different morphologies and synaptic arrangements with other cell types. Utilizing dopamine transporter (DAT) based staining methods in three animal populations differing in age and species, this study revealed a larger diversity of dopaminergic cell types in the glomerular layer. New ‘uniglomerular’ and a ‘clasping’ cell types were discriminated, showing distinct dendritic formations and glomerulus innervations, which was assessed with a new morphometric tool kit. The clasping cell type features dendritic specializations, densely clasping around single cell bodies. These morphological traits occur in higher abundance and complexity specifically among adult animals and could be structures of neurotransmitter output since they show strong calcium influx upon soma depolarization. Comparisons of the three animal populations showed age- and/or species-dependent changes in the subtype composition of dopaminergic JGCs. Concordant with recent research, the inclusion of age-dependent comparisons in bulbar studies turned out to be of great significance

    Human Reinforcement Learning: Insights from intracranial recordings and stimulation

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    Reinforcement learning is the process by which individuals alter their decisions to maximize positive outcomes, and minimize negative outcomes. It is a cognitive process that is widely used in our daily lives and is often disrupted during psychiatric disease. Thus, a major goal of neuroscience is to characterize the neural underpinnings of reinforcement learning. Whereas animal studies have utilized invasive physiological methods to characterize several neural mechanisms that underlie reinforcement learning, human studies have largely relied on non-invasive techniques that have reduced physiological precision. Although ethical limitations preclude the use of invasive physiological methods in healthy human populations, patient populations undergoing certain neurosurgical interventions offer a rare opportunity to directly assay neural activity from the brain during human reinforcement learning. This dissertation presents early findings from this research effort

    Hetereogeneity in Neuronal Intrinsic Properties: A Possible Mechanism for Hub-Like Properties of the Rat Anterior Cingulate Cortex during Network Activity.

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    The anterior cingulate cortex (ACC) is vital for a range of brain functions requiring cognitive control and has highly divergent inputs and outputs, thus manifesting as a hub in connectomic analyses. Studies show diverse functional interactions within the ACC are associated with network oscillations in the β (20-30 Hz) and γ (30-80 Hz) frequency range. Oscillations permit dynamic routing of information within cortex, a function that depends on bandpass filter-like behavior to selectively respond to specific inputs. However, a putative hub region such as ACC needs to be able to combine inputs from multiple sources rather than select a single input at the expense of others. To address this potential functional dichotomy, we modeled local ACC network dynamics in the rat in vitro. Modal peak oscillation frequencies in the β- and γ-frequency band corresponded to GABAAergic synaptic kinetics as seen in other regions; however, the intrinsic properties of ACC principal neurons were highly diverse. Computational modeling predicted that this neuronal response diversity broadened the bandwidth for filtering rhythmic inputs and supported combination-rather than selection-of different frequencies within the canonical γ and β electroencephalograph bands. These findings suggest that oscillating neuronal populations can support either response selection (routing) or combination, depending on the interplay between the kinetics of synaptic inhibition and the degree of heterogeneity of principal cell intrinsic conductances.Wellcome Trus
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