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Duecker, K.

Publications and source records attributed to Duecker, K..

12 recordsLinked to original sources

Next generation Human Neocortical Neurosolver model with biophysically realistic somatic, dendritic and synaptic dynamics

The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling tool for multiscale interpretation of human electroencephalography (EEG) and magnetoencephalography (MEG) signals. HNN's foundation is a pre-tuned canonical neocortical circuit model consisting of excitatory pyramidal neurons and inhibitory interneurons in supra- (L2/3) and infragranular (L5) layers. This model was originally developed based on studies in rodent and non-human primate cortices when dynamics of human neurons were poorly characterized. Yet recent advances in recordings from human tissue now provide unprecedented detail to update the model. Here, we present a significantly updated model for use in the HNN software, with electrophysiological properties and synaptic connections tuned to human tissue. Beyond reproducing biologically realistic somatic dynamics, the pyramidal cells simulate dendritic calcium (Ca2+) spikes in the L5 extratelencephalic-projecting (ET) neurons, and shorter dendritic spikes in the L2/3 pyramidal neurons. We demonstrate how the updated circuitry and new features in the HNN software can be used to disentangle somatic, synaptic, and dendritic contributions to human E-/MEG signatures. The model is distributed with workflows to simulate event-related responses and oscillations, and tutorials demonstrating how membrane potentials, Ca2+ dynamics, and synaptic currents can be recorded and visualized. These enhancements allow extensive model validations based on in vitro and in vivo electrophysiology, voltage and Ca2+ imaging, and provide users with novel insights on the mechanisms underlying E-/MEG signals.

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Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver

SHORT ABSTRACTA key barrier to developing effective drugs for disorders of the central nervous system (CNS) is understanding their impact on neural circuits. This protocol demonstrates how physics-based neural simulations can be used to interpret electrophysiological biomarkers of neurotherapeutics, providing a mechanistically grounded approach to the development of neurotherapeutics. LONG ABSTRACTElectroencephalography (EEG) and electrophysiology methods provide millisecond resolution biomarkers for central nervous system disorders and are used to assess treatment-related effects. However, lack of understanding about the neural mechanisms generating such biomarkers impedes the development of diagnostics and therapeutics based on these signals. The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling software that connects localized EEG biomarkers to their multi-scale neural generators. This protocol demonstrates a hypothesis-driven workflow using HNN to test possible neural mechanisms of neurotherapy-induced EEG biomarkers by optimizing parameters to achieve a fit between simulated and empirical current source waveforms. Corresponding multi-scale cell- and circuit-level activity can then be visualized and quantified, providing validation targets for model predictions in follow up empirical studies. An example is provided which shows how to examine the generating mechanisms of the early event-related potential (ERP) components of an auditory evoked response (P1, N1 and P2) and to assess changes following neural circuit modification due to neurotherapeutic administration. This protocol demonstration enables scientists to design simulation experiments to develop testable predictions on how EEG biomarkers reflect neural circuit mechanisms of example therapeutics. A similar protocol can be applied to study disease mechanisms or other therapies.

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Rhythmic light stimulation elicits multiple concurrent neural responses that separably shape human perception

Rhythmic light stimulation offers solutions to innumerable cognitive and neurological disorders. However, like any neuromodulatory technique, responses to rhythmic light stimulation are highly variable, producing challenges in replicating lab-based studies and translating findings to the clinic. Across three MEG/EEG experiments, we show that this variability can, in part, be attributed to rhythmic light stimulation eliciting multiple, coexisting neural responses which have separable impacts on cognition. Specifically, we find that rhythmic light stimulation produces distinct neural responses at the fundamental (f) and second harmonic (2f) frequencies, and that these responses are differentially shaped by endogenous oscillatory dynamics that vary across participants. Importantly, these responses separably contribute to perception, with harmonic gamma-band responses supporting the representation of stimulus-specific information, and the phase of harmonic alpha-band responses causally contributing to near-threshold visual perception. We reproduce these effects across datasets, paradigms, and oscillatory bands, suggesting that the multiplex oscillatory responses elicited by rhythmic light stimulation are a robust and pervasive phenomenon. We propose that the complexity of neural responses to rhythmic stimulation can explain why there is substantial variability between studies using these techniques, and that understanding these complex responses may help advance neuromodulatory technologies for both fundamental and clinical neuroscience. Supplementary MaterialAll supplementary material can be found at the end of this document.

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Higher baseline alpha power is associated with faster responses in visual search

Visual search models have long emphasised that task-relevant items must be prioritized for optimal performance. While it is known that search efficiency also benefits from active distractor inhibition, the underlying neuronal mechanisms are debated. Neuronal alpha oscillations (7-14 Hz) have been associated with functional inhibition of cortical excitability, as well as distractor suppression in spatial attention and visual working memory tasks. We therefore hypothesised that alpha oscillations similarly support the deselection of distractors in visual search. Using Magnetoencephalography (MEG), we here show that high alpha power before the onset of a complex search display is associated with faster search performance. Crucially, we used a General Linear Model (GLM) approach to control for confounds between alpha power and task duration, ruling out that this result was merely driven by practice effects paired with increased fatigue over time. In addition to spontaneous oscillatory activity, we quantified the cortical excitability to colours of the search stimuli based on Rapid Invisible Frequency Tagging (RIFT) responses. In contrast to our initial hypothesis, increased pre-search alpha power did not correlate with the RIFT response, providing no direct evidence for feature-specific inhibition of distracting stimuli by alpha. Our findings challenge the traditional view of alpha oscillations reducing visual processing, showing instead that increased occipital alpha power can enhance performance in a visual task. We propose that the increase in alpha power may reflect increased top-down control supporting visual search

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Guided Visual Search is associated with a feature-based priority map in early visual cortex

Visual search models have long emphasised that task-relevant items must be prioritised for optimal performance. While it is known that search efficiency also benefits from active distractor inhibition, the underlying neuronal mechanisms are debated. Here, we used MEG in combination with Rapid Invisible Frequency Tagging (RIFT) to probe how neuronal excitability in early visual cortex is modulated during feature-guided visual search. Participants were instructed to indicate the presence or absence of a letter "T" presented amongst 16 and 32 "L"s. In the guided search condition, participants were informed about the colour of the "T" and could infer the colour of the irrelevant distractors. In the unguided search condition, the target colour was unknown. We found that guided search was associated with enhanced RIFT responses to the target colour, and decreased responses to the distractor colour compared to unguided search. These results conceptually replicated using both a conventional coherence approach, as well as with a General Linear Model approach based on a single-trial measure of the RIFT response. The present findings expand on previous reports based on electrophysiology and fMRI in humans and non-human primates by revealing that feature-guidance in visual search affects neuronal excitability as early as primary visual cortex.

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Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Mendoza-Halliday, Major et al.1 (referred to as "Mendoza-Halliday et al." for brevity), advocate for a local field potential (LFP)-based approach to functional identification of cortical layers during "laminar" (simultaneous recordings from all cortical layers) multielectrode recordings in nonhuman primates (NHPs). They describe a "ubiquitous spectrolaminar motif" in the primate neocortex: 1) 75-150 Hz power peaks in the supragranular layers, 2) 10-19 Hz power peaks in the infragranular layers and 3) the crossing point of their laminar power gradients identifies Layer 4 (L4). Identification of L4 is critical in general, but especially for Mendoza-Halliday et al. as the "motif" discovery is couched within a framework whose central hypothesis is that gamma activity originates in the supragranular layers and reflects feedforward activity, while alpha-beta activity originates in the infragranular layers and reflects feedback activity. In an impressive scientific effort, Mendoza-Halliday et al. analyzed laminar data from 14 cortical areas in 2 prior macaque studies and compared them to marmoset, mouse, and human data to further bolster the canonical nature of the motif. Identification of such canonical principles of brain operation is clearly a topic of broad scientific interest. Similarly, a reliable online method for L4 identification would be of broad scientific value for the rapidly increasing use of laminar recordings using numerous evolving technologies. Despite Mendoza-Halliday et al.s papers strengths, and its potential for scientific impact, a series of concerns that are fundamental to the analysis and interpretation of laminar activity profile data in general, and local field potential (LFP) signals in particular, led us to question its conclusions. Here, we address four key questions: Q1) Is the spectrolaminar motif ubiquitous, i.e. "found everywhere" or "universal"? Q2) Do features of the motif reliably identify Layer (L) 4? Q3) Are Mendoza-Halliday et al.s newly introduced methods (FLIP and vFLIP) reliable? And Q4) Are the proposed biophysical mechanisms underlying the motif well justified? We used new sets of data comprised of stimulus-evoked laminar response profiles from primary and higher-order auditory cortices (A1 and belt cortex), and primary visual cortex (V1) to test these questions. The rationale for using these areas as a test bed for new methods is that, in contrast to higher-order cortical areas, their laminar anatomy and physiology have already been extensively characterized by prior studies, and there is general agreement across laboratories on key matters like L4 identification. In short, we find that Mendoza-Halliday et al.s findings do not generalize well to these cortical areas. Specifically, regarding Q1: Though we can find a spectrolaminar gradient that is qualitatively consistent with Mendoza-Halliday et al., it is quantifiable in only 61-64% of our recordings, indicating that the motif is common but by no means universal (see "Evaluation of the FLIP method [...]" and "Evaluation of vFLIP [...]"). Regarding Q2: The motifs high/low frequency gradient cross point identified L4 in only 29-33% of our recordings. Regarding Q3: FLIP and vFLIP exhibit marked variability across studies, across brain areas, and spuriously detect cortical layer inversions (see "FLIPs fitting process [...]" and "Evaluation of vFLIP [...]"). Regarding Q4: the biophysical modeling findings cited to support Mendoza-Hallidays conclusions (see "Going forward - in silico [...]") do not reproduce the LFP data trends. While our findings are in many respects at odds with those of Mendoza-Halliday et al., the paper already has, and will continue to spark debate and further experimentation. Hopefully this countervailing presentation will lead to robust collegial efforts to define optimal strategies for applying laminar recording methods in future studies.

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Oscillations in an Artificial Neural Network Convert Competing Inputs into a Temporal Code

Deep convolutional neural networks (CNNs) resemble the hierarchically organised neural representations in the primate visual ventral stream. However, these models typically disregard the temporal dynamics experimentally observed in these areas. For instance, alpha oscillations dominate the dynamics of the human visual cortex, yet the computational relevance of oscillations is rarely considered in artificial neural networks (ANNs). We propose an ANN that embraces oscillatory dynamics with the computational purpose of converting simultaneous inputs, presented at two different locations, into a temporal code. The network was trained to classify three individually presented letters. Post-training, we added semi-realistic temporal dynamics to the hidden layer, introducing relaxation dynamics in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, elucidating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activated sequentially, ordered along the phase of the inhibitory oscillations. Our model provides a novel approach for implementing multiplexing in ANNs. It further produces experimentally testable predictions of how the primate visual system handles competing stimuli.

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Alpha oscillations support the efficiency of guided visual search by inhibiting both target and distractor features in early visual cortex

Withdrawal StatementThe authors have withdrawn this manuscript owing to having received feedback that prompted a reconsideration of the findings in light of potential confounds associated with time-on-task effects, as highlighted by Benwell et al. (2019, NeuroImage). Subsequent in-depth analysis has led the authors to reassess the central assertions made in our original manuscript, and the main claims can no longer be supported. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

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The visual cortex produces a signal in the gamma band in response to broadband visual flicker

The aim of this study is to uncover the network dynamics of the human visual cortex by driving it with a broadband random visual flicker. We here applied a broadband flicker (1-720 Hz) while measuring the MEG and then estimated the temporal response function (TRF) between the visual input and the MEG response. This TRF revealed an early response in the 40-60 Hz gamma range as well as in the 8-12 Hz alpha band. While the gamma band response is novel, the latter has been termed the alpha band perceptual echo. The gamma echo preceded the alpha perceptual echo. The dominant frequency of the gamma echo was subject-specific thereby reflecting the individual dynamical properties of the early visual cortex. To understand the neuronal mechanisms generating the gamma echo, we implemented a pyramidal-interneuron gamma (PING) model that produces gamma oscillations in the presence of constant input currents. Applying a broadband input current mimicking the visual stimulation allowed us to estimate TRF between the input current and the population response (akin to the local field potentials). The TRF revealed a gamma echo that was similar to the one we observed in the MEG data. Our results suggest that the visual gamma echo can be explained by the dynamics of the PING model even in the absence of sustained gamma oscillations. Author SummaryThe properties of the neuronal dynamics governing the visual system are highly debated. While some emphasize the neuronal firing rate and evoked activity in response to visual stimuli, others emphasize the oscillatory neuronal dynamics. To investigate the dynamical properties of the visual system, we recorded the magnetoencephalography while stimulating the visual system using a broadband (1-720 Hz) visual flicker. By estimating the temporal response function (similar to cross-correlation) between the visual input and neuronal activity, we demonstrated a clear response in the gamma band that we term the gamma echo. We then constructed a physiologically realistic network model that could generate gamma-band oscillations by a pyramidal-interneuron gamma (PING) mechanism. This model allowed us to account for empirically observed response in the gamma band, and to provide novel insight on the neuronal dynamics governing the early visual system. The stage is now set for further investigating how the gamma echo is modulated by tasks such as spatial attention as well as uncovering how the echo might propagate in the visual hierarchy.

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No evidence for entrainment: endogenous gamma oscillations and responses to rhythmic visual stimulation coexist in visual cortex

Over the past decades, a plethora of studies have linked cortical gamma oscillations ([~]30-100 Hz) to neuro-computational mechanisms. Their functional relevance, however, is still passionately debated. Here, we asked if endogenous gamma oscillations in the human brain can be entrained by a rhythmic photic drive >50 Hz. A noninvasive modulation of endogenous brain rhythms allows conclusions about their causal involvement in neurocognition. To this end, we systematically investigated oscillatory responses to a rapid sinusoidal flicker in the absence and presence of endogenous gamma oscillations using magnetoencephalography (MEG) in combination with a high-frequency projector. The photic drive produced a robust response over visual cortex to stimulation frequencies of up to 80 Hz. Strong, endogenous gamma oscillations were induced using moving grating stimuli as repeatedly done in previous research. When superimposing the flicker and the gratings, there was no evidence for phase or frequency entrainment of the endogenous gamma oscillations by the photic drive. Unexpectedly, we did not observe an amplification of the flicker response around participants individual gamma frequencies; rather, the magnitude of the response decreased monotonically with increasing frequency. Source reconstruction suggests that the flicker response and the gamma oscillations were produced by separate, coexistent generators in visual cortex. The presented findings challenge the notion that cortical gamma oscillations can be entrained by rhythmic visual stimulation. Instead, the mechanism generating endogenous gamma oscillations seems to be resilient to external perturbation. Significance StatementWe aimed to investigate to what extent ongoing, high-frequency oscillations in the gamma band (30-100 Hz) in the human brain can be entrained by a visual flicker. Gamma oscillations have long been suggested to coordinate neuronal firing and enable inter-regional communication. Our results demonstrate that rhythmic visual stimulation cannot hijack the dynamics of ongoing gamma oscillations; rather, the flicker response and the endogenous gamma oscillations coexist in different visual areas. Therefore, while a visual flicker evokes a strong neuronal response even at high frequencies in the gamma-band, it does not entrain endogenous gamma oscillations in visual cortex. This has important implications for interpreting studies investigating the causal and neuroprotective effects of rhythmic sensory stimulation in the gamma band.

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Integrating electric field modelling and neuroimaging to explain variability of low intensity tES effects

Understanding variability of transcranial electrical stimulation (tES) effects is one of the major challenges in the brain stimulation community. Promising candidates to explain this variability are individual anatomy and the resulting differences of electric fields inside the brain. We integrated individual simulations of electric fields during tES with source-localization to predict variability of transcranial alternating current stimulation (tACS) aftereffects on -oscillations. In two experiments, participants received 20 minutes of either -tACS (1 mA) or sham stimulation. Magnetoencephalogram was recorded for 10 minutes before and after stimulation. tACS caused a larger power increase in the -band as compared to sham. The variability of this effect was significantly predicted by measures derived from individual electric field modelling. Our results directly link electric field variability to variability of tACS outcomes, stressing the importance of individualizing stimulation protocols and providing a novel approach to analyze tACS effects in terms of dose-response relationships.

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Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

Background: Just as substance use disorders (SUDs), gambling disorder (GD) is characterized by an increase in cue-dependent decision-making (similar to Pavlovian-to-instrumental transfer, PIT). PIT, as studied in SUDs and healthy subjects, is associated with altered communication between Nucleus Accumbens (NAcc), amygdala, and orbitofrontal cortex (OFC). These neural differences are, however, poorly understood. For example, it is unclear whether they are due to the physiological effects of substance abuse, or rather related to learning processes and/or other etiological factors like innate traits associated with addiction. We have thus investigated whether network activation patterns during a PIT task are also altered in GD, an addictive disorder not involving substance abuse. We have specifically studied which neural PIT patterns were best at distinguishing GD from HC subjects, all to improve our understanding of the neural signatures of GD and of addiction-related PIT in general. Methods: 30 GD and 30 HC subjects completed an affective decision-making task in a functional magnetic resonance imaging (fMRI) scanner. Gambling associated and other emotional cues were shown in the background during the task, allowing us to record multivariate neural PIT signatures focusing on a network of NAcc, amygdala and OFC. We built and tested a classifier based on these multivariate neural PIT signatures using cross-validated elastic net regression. Results and Discussion: As expected, GD subjects showed stronger PIT than HC subjects because they showed stronger increase in gamble acceptance when gambling cues were presented in the background. Classification based on neural PIT signatures yielded a significant AUC-ROC (0.70, p = 0.013). When inspecting the features of the classifier, we observed that GD showed stronger PIT-related functional connectivity between NAcc and amygdala elicited by gambling background cues, as well as between amygdala and OFC elicited by negative and positive cues. Conclusion: We propose that HC and GD subjects are distinguishable by PIT-related neural signatures including amygdala-NAcc-OFC functional connectivity. Our findings suggest that neural PIT alterations in addictive disorders might not depend on the physiological effect of a substance of abuse, but on related learning processes or even innate neural traits, also found in behavioral addictions.

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