Auditory cortex modelled as a dynamical network of oscillators: Understanding event-related fields and their adaptation

Adaptation, the reduction of neuronal responses by repetitive stimulation, is a ubiquitous feature of auditory cortex (AC). It is not clear what causes adaptation, but short-term synaptic depression (STSD) is a potential candidate for the underlying mechanism. We examined this hypothesis via a computational model based on AC anatomy, which includes serially connected core, belt, and parabelt areas. The model replicates the event-related field (ERF) of the magnetoencephalogram as well as ERF adaptation. The model dynamics are described by excitatory and inhibitory state variables of cell populations, with the excitatory connections modulated by STSD. We analysed the system dynamics by linearizing the firing rates and solving the STSD equation using time-scale separation. This allows for characterization of AC dynamics as a superposition of damped harmonic oscillators, so-called normal modes. We show that repetition suppression of the N1m is due to a mixture of causes, with stimulus repetition modifying both the amplitudes and the frequencies of the normal modes. In this view, adaptation results from a complete reorganization of AC dynamics rather than a reduction of activity in discrete sources. Further, both the network structure and the balance between excitation and inhibition contribute significantly to the rate with which AC recovers from adaptation. This lifetime of adaptation is longer in the belt and parabelt than in the core area, despite the time constants of STSD being spatially constant. Finally, we critically evaluate the use of a single exponential function to describe recovery from adaptation

Ein realistisches neuronales Massenmodell des Kortex mit angeordeten Layer-Verbindungen und synaptischer Plastizität

In the electroencephalography (EEG) and magnetoencephalography (MEG) studies of brain cognition functions and cortical networks, dynamic causal modeling (DCM) provides an useful tool to explore the effective coupling among brain regions. DCM is a computational method that enables the best brain models as well as parameters to be identified from the observed EEG/MEG data. One main challenge of DCM is how to construct a reasonably realistic model that can capture the important microscopic generative mechanisms of brain functions, at the same time can predict those macroscopic effects like observable oscillations or evoked responses in EEG/MEG. Such a model will allow for the integration of data from different sources, both microscopic (i.e. anatomical and physiological features of neurons) and macroscopic (i.e. measurable brain activity), as well as enabling us to test hypotheses and quantify microscopic dynamics for given macroscopic observations. In order to achieve a more biological plausible model for DCM, this thesis contributes to the development of a biologically realistic local cortical circuit model (LCCM), based on neural masses that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: 1. activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and 2. realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the processes of auditory as well as somatosensory neural response adaptation of repetitive stimulation. The model parameters were specified using Bayesian inference. Our evaluation demonstrated that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain functions. The incorporation of these features into a neural mass model makes it applicable to modeling the macroscopic data like EEG/MEG, which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.Neuronale Massenmodelle sind sparsam hinsichtlich der verwendeten Parameter und biologisch plausible in ihrer Struktur. Sie sind gut geeignet für die Modellierung der Kortikalen Aktivität, die durch extrakranielle Messungen wie Elektroenzephalographie (EEG) oder Magnetoenzephalographie (MEG) erfasst werden. Die in bisherigen Studien verwendeten Modelle machen jedoch starke Annahmen und Vereinfachungen. So wird zum Beispiel die synaptische Plastizität, wichtig für Gehirnfunktionen wie Gedächtnis und Lernen, bisher nicht repräsentiert. Weiterhin wird die Vielfalt aller kortikalen Neuronen häufig nur durch drei verschiedene Populationen berücksichtigt. Um die lokale Informationsverarbeitung besser zu verstehen ist es außerdem notwendig, die Organisation der Neuronen und ihre synaptischen Verbindungen hinsichtlich der Laminas des Kortexes detailliert darzustellen. Das Forschungsvorhaben dieser Doktorarbeit ist es, ein solches neuronales Massenmodell mit synaptischer Plastizität und detaillierten synaptischen Verbindungen zu konstruieren und dessen Simulationen mit klinisch relevanten Messungen (Habituation von auditorisch und somatosensorisch evozierter Aktivität) zu vergleichen. Insbesondere wird gezeigt, dass das Modell eine Möglichkeit bietet, den Informationsfluss zwischen verschiedenen kortikalen Laminas und den Grad der Plastizität in verschiedenen Verbindungen zu ermitteln. Die Studie ist relevant für die Erforschung von Erkrankungen des Gehirns, die auf der Pathologie der neuronalen Konnektivität beruhen, zum Beispiel im Falle einer Alzheimererkrankung. Da das entwickelte Modell die kognitiven Prozessen des Gehirns zur Generation von EEG/MEG-Daten erklärt, ist der wissenschaftliche Beitrag dieser Studie nicht nur für Entwickler neuronaler Massenmodelle relevant, sondern auch für ein breites Feld von Neurowissenschaftlern

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 130, July 1974

This special bibliography lists 291 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1974

Great expectations: Is there evidence for predictive coding in auditory cortex?

Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. Whilst proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modelling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organised - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfil its expectations

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 129, June 1974

This special bibliography lists 280 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1974

Novel measure of olfactory bulb function in health and disease

Present neuroimaging techniques are capable of recording the neural activity from all over the brain but the olfactory bulb (OB). The OB is the first olfactory processing stage of the central nervous system and the site of insult in several neurological disorders, particularly Parkinson’s disease (PD). It has been suggested that the OB has a pivotal role in the olfactory system anal-ogous to primary visual cortex (V1) and thalamus in the visual system. However, due to the existing technical limitations, there has not been any non-invasive technique that can reliably measure the OB function in humans, consequently limiting its functional recording to one in-tracranial study dating back to the 60s. Initially in Study I, a non-invasive method of measuring the function of human OB is devel-oped, so-called electrobulbogram (EBG). In line with previous animal literature as well as the only intracranial study in human OB, it was demonstrated that gamma oscillations on the EBG electrodes occurred shortly after the odor onset. Subsequently, applying source recon-struction analysis provided evidence that observed oscillations were localized to the OB. Ad-ditionally, the OB recording with the EBG method showed a test-retest reliability comparable with visual event related potentials. Notably, the detected gamma oscillations were demon-strated to be insensitive to habituation, the OB’s marked characteristic which has previously been demonstrated in rodents. Last, but not least, assessing the EBG response in an individual who did not have the bilateral OB indicated that the lack of OB results in disappearance of gamma oscillations in the EBG electrodes. Given that Study I determined the possibility of reliably measuring the function of the OB using the EBG, in Study II, I assessed the functional role of OB’s oscillations in the pro-cessing of the odor valence. Odor valence has been suggested to be linked to approach–avoidance responses and therefore, processing of odor valence is thought to be one of the core aspects of odor processing in the olfactory system. Consequently, using combined EBG and EEG recording, OB activity was reconstructed on the source level during processing of odors with different valences. Gamma and beta oscillations were found to be related to va-lence perception in the human OB. Moreover, the early beta oscillations were associated with negative but not positive odors, where these beta oscillations can be linked to preparatory neural responses in the motor cortex. Subsequently, in a separate experiment, negative odors were demonstrated to trigger a whole-body motor avoidance response in the time window overlapping with the valence processes in the OB. These negative odor-elicited motor re-sponses were measured by a force plate as a leaning backward motion. Altogether, the results from Study II indicated that the human OB processes odor valence sequentially in the gamma and beta frequency bands, where the early processing of negative odors in the OB might be facilitating rapid approach-avoidance behaviors. To further evaluate the functional role of the OB in odor processing, in Study III, OB’s communication with its immediate recipient, namely piriform cortex (PC), was assessed. These two areas are critical nodes of the olfactory system which communicate with each other through neural oscillations. The activity of the OB and the PC were reconstructed using a combination of EBG, EEG, and source reconstruction techniques. Subsequently, the cross spectrogram of the OB and the PC was assessed as a measure of functional connectivity where temporal evolution from fast to slow oscillations in the OB–PC connectivity was found during the one second odor processing. Furthermore, the spectrally resolved Granger causal-ity analysis suggested that the afferent connection form the OB to the PC occurred in the gamma and beta bands whereas the efferent connection from the PC to the OB was concen-trated in the theta and delta bands. Notably, odor identity could be deciphered from the low gamma oscillatory pattern in the OB–PC connectivity as early as 100ms after the odor onset. Hence, findings from this study elucidate on our understanding of the bidirectional infor-mation flow in the human olfactory system. Olfactory dysfunction, due to neurodegeneration in the OB, commonly appears several years earlier than the occurrence of the PD-related characteristic motor symptoms. Consequently, a functional measure of the OB may serve as a potential early biomarker of PD. In Study IV, OB function was assessed in PD to answer whether the EBG method can be used to dissociate individuals with a PD diagnosis from healthy age-matched controls. The spectrogram of the EBG signals indicated that there were different values in gamma, beta, and theta for PDs compared with healthy controls. Specifically, six components were found in the EBG re-sponse during early and late time points which together dissociate PDs from controls with a 90% sensitivity and a 100% specificity. Furthermore, these components were linked to med-ication, disease duration and severity, as well as clinical odor identification performance. Overall, these findings support the notion that EBG has a diagnostic value and can be further developed to serve as an early biomarker for PD. In the last study, Study V, the prevalence of COVID-19 was determined using odor intensity ratings as an indication of olfactory dysfunction. Using a large sample data (n = 2440) from a Swedish population, odor intensity ratings of common household items over time were found to be closely associated with prevalence prediction of COVID-19 in the Stockholm region over the same time-period (r = -.83). Impairment in odor intensity rating was further correlated with the number of reported COVID-19 symptoms. Relatedly, individuals who progressed from having no symptoms to having at least one symptom had a marked decline in their odor intensity ratings. The results from this study, given the relatively large sample size, provided a concrete basis for the future studies to further assess the potential association between the deficits in the OB function and olfactory dysfunction in COVID-19. In conclusion, our proposed method for non-invasive measurement of the OB function was shown to provide a reliable recording with a potential as a diagnostic tool for PD. Combining EBG and EEG allowed for reconstruction of the OB signal at the source level, where specific oscillations were found to be critical for odor valence processing and rapid avoidance re-sponse. Moreover, oscillations in different frequency bands were found to be critical for the OB reciprocal communications and transfer of odor identity information to higher order ol-factory subsystems. Finally, COVID-19 was found to be associated with a decline in olfactory acuity which might originate from damage to the patient’s OB. In conclusion, the results from the studies within this thesis provide a new perspective on the functional role of oscillations in the human OB

Modeling biophysical and neural circuit bases for core cognitive abilities evident in neuroimaging patterns: hippocampal mismatch, mismatch negativity, repetition positivity, and alpha suppression of distractors

This dissertation develops computational models to address outstanding problems in the domain of expectation-related cognitive processes and their neuroimaging markers in functional MRI or EEG. The new models reveal a way to unite diverse phenomena within a common framework focused on dynamic neural encoding shifts, which can arise from robust interactive effects of M-currents and chloride currents in pyramidal neurons. By specifying efficient, biologically realistic circuits that achieve predictive coding (e.g., Friston, 2005), these models bridge among neuronal biophysics, systems neuroscience, and theories of cognition. Chapter one surveys data types and neural processes to be examined, and outlines the Dynamically Labeled Predictive Coding (DLPC) framework developed during the research. Chapter two models hippocampal prediction and mismatch, using the DLPC framework. Chapter three presents extensions to the model that allow its application for modeling neocortical EEG genesis. Simulations of this extended model illustrate how dynamic encoding shifts can produce Mismatch Negativity (MMN) phenomena, including pharmacological effects on MMN reported for humans or animals. Chapters four and five describe new modeling studies of possible neural bases for alpha-induced information suppression, a phenomenon associated with active ignoring of stimuli. Two models explore the hypothesis that in simple rate-based circuits, information suppression might be a robust effect of neural saturation states arising near peaks of resonant alpha oscillations. A new proposal is also introduced for how the basal ganglia may control onset and offset of alpha-induced information suppression. Although these rate models could reproduce many experimental findings, they fell short of reproducing a key electrophysiological finding: phase-dependent reduction in spiking activity correlated with power in the alpha frequency band. Therefore, chapter five also specifies how a DLPC model, adapted from the neocortical model developed in chapter three, can provide an expectation-based model of alpha-induced information suppression that exhibits phase-dependent spike reduction during alpha-band oscillations. The model thus can explain experimental findings that were not reproduced by the rate models. The final chapter summarizes main theses, results, and basic research implications, then suggests future directions, including expanded models of neocortical mismatch, applications to artificial neural networks, and the introduction of reward circuitry

The Neural Dynamics of Somatosensory Processing and Adaptation Across Childhood: a High-Density Electrical Mapping Study

Young children are often hyperreactive to somatosensory inputs hardly noticed by adults, as exemplified by irritation to seams or labels in clothing. The neurodevelopmental mechanisms underlying changes in sensory reactivity are not well understood. Based on the idea that neurodevelopmental changes in somatosensory processing and/or changes in sensory adaptation might underlie developmental differences in somatosensory reactivity, high-density electroencephalography was used to examine how the nervous system responds and adapts to repeated vibrotactile stimulation over childhood. Participants aged 6–18 yr old were presented with 50-ms vibrotactile stimuli to the right wrist over the median nerve at 5 blocked interstimulus intervals (ranging from 7 to 1 per second). Somatosensory evoked potentials (SEPs) revealed three major phases of activation within the first 200 ms, with scalp topographies suggestive of neural generators in contralateral somatosensory cortex. Although overall SEPs were highly similar for younger, middle, and older age groups (6.1–9.8, 10.0 –12.9, and 13.0 –17.8 yr old), there were significant age-related amplitude differences in initial and later phases of the SEP. In contrast, robust adaptation effects for fast vs. slow presentation rates were observed that did not differ as a function of age. A greater amplitude response in the later portion of the SEP was observed for the youngest group and may be related to developmental changes in responsivity to somatosensory stimuli. These data suggest the protracted development of the somatosensory system over childhood, whereas adaptation, as assayed in this study, is largely in place by 7 years of age