Biology subjects

Siegel, M.

Publications and source records attributed to Siegel, M..

36 records · Page 2Linked to original sources

bioRxiv2024

Cortico-subcortical dynamics in primate working memory

Working memory is essential for cognition, facilitating the temporary maintenance and manipulation of information to produce goal-directed behavior. While both cortical and subcortical structures are involved, their precise roles and interactions are not fully understood. To investigate this, we simultaneously recorded neural activity from the frontal and parietal cortex, higher-order thalamic nuclei, and core basal ganglia structures during color and spatial working memory tasks in non-human primates. We found widespread yet differential encoding of color and spatial information, marked by area-specific temporal dynamics and modulation according to task demands. Both cortical and subcortical information increased towards working memory-dependent actions, suggesting a task-specific reloading of information. Directed interactions between cortical and subcortical regions were extensive and reciprocal, with dominant directions of information flow, especially from frontoparietal areas. These interactions were dynamically modulated and partially task specific. Our findings provide comprehensive insights into the large-scale circuit dynamics underlying primate working memory and suggest that flexible goal-directed behavior relies on the selective processing of task-relevant memory information within information-specific cortico-subcortical networks.

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bioRxiv2024

Decoding region-level visual functions from invasive EEG monkey data

Decoding vision is an ambitious task as it aims to transform scalar brain activity into dynamic images with refined shapes, colors and movements. In familiar environments, the brain may trigger activity that resembles specific pattern, thereby facilitating decoding. Can an artificial neural network (ANN) decipher such latent patterns? Here, we explore this question using invasive electroencephalography data from monkeys. By decoding multiregion brain activity, ANN effectively captures individual regions functional roles as a consequence of minimizing visual errors. For example, ANN recognizes that regions V4 and LIP are involved in visual color and shape processing while MT predominantly handles visual motion, aligning with regional visual functions evident in the brain. ANN likely reconstructs vision by seizing hidden spike patterns, representing stimuli distinctly in a two-dimensional plane. Furthermore, during the encoding process of transforming visual stimuli into neuronal activity, optimal performance is achieved in regions closely associated with vision processing.

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bioRxiv2024

Spectral waveform analysis dissociates human cortical alpha rhythms

The non-sinusoidal waveform of neuronal oscillations reflects the physiological properties of underlying circuit interactions and may serve as an informative biomarker of healthy and diseased human brain function. However, little is known about which brain rhythms can be dissociated based on their waveform and methods to comprehensively characterize waveforms are missing. Here, we introduce a novel spectral waveform analysis (SWA) that provides a complete waveform description, is noise-resistant, and allows to reconstruct time-domain waveforms. We applied this framework to human magnetoencephalography (MEG) recordings during rest and identified several distinct and previously unknown cortical alpha waveforms that were temporally stable and specific for individual subjects. Our findings suggest at least four distinct alpha rhythms in human sensorimotor, occipital, temporal, and parietal cortex. SWA provides a powerful new framework to characterize the waveform of neural oscillations in the healthy and diseased human brain.

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bioRxiv2024

Predictive learning shapes the representational geometry of the human brain

Predictive coding theories propose that the brain constantly updates its internal models of the world to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link the encoding of prediction errors and optimization of sensory representations remain unclear. Here, we provide direct evidence how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in human participants listening to acoustic sequences with different levels of regularity. Representational similarity analysis revealed how, through learning, the brain aligned its representational geometry to match the statistical structure of the sensory inputs, by clustering the representations of temporally contiguous and predictable stimuli. Crucially, we found that in sensory areas the magnitude of the representational shift correlated with the encoding strength of prediction errors. Furthermore, using partial information decomposition we found that, prediction errors were processed by a synergistic network of high-level associative and sensory areas. Importantly, the strength of synergistic encoding of precition errors predicted the magnitude of representational alignment during learning. Our findings provide evidence that large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas, which may enhance the efficiency of perceptual processing in response to the statistical regularities of the environment.

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bioRxiv2024

Novel murine closed-loop auditory stimulation paradigm elicits macrostructural sleep benefits in neurodegeneration

Boosting slow-wave activity (SWA) by modulating slow-waves through closed-loop auditory stimulation (CLAS) might provide a powerful nonpharmacological tool to investigate the link between sleep and neurodegeneration. Nevertheless, CLAS in this context was not yet explored. Here, we established mouse CLAS (mCLAS)-mediated SWA enhancement and explored its effects onto sleep deficits in neurodegeneration, by targeting the up-phase of slow-waves in mouse models of Alzheimers (AD, Tg2576) and Parkinsons disease (PD, M83). We found that tracking a 2Hz component of slow-waves leads to highest precision of NREM sleep detection in mice, and that its combination with a 30{degrees} up-phase-target produces a significant SWA 15-30% increase from baseline in WTAD and TGAD mice versus a MOCK group. Conversely, combining 2Hz with a 40{degrees} phase target yields a significant increase ranging 30-35% in WTPD and TGPD mice. Interestingly, these phase-target-triggered SWA increases are not genotype dependent but strain specific. Sleep alterations that may contribute to disease progression and burden were described in AD and PD lines. Notably, pathological sleep traits where rescued by mCLAS, which elicited a 14% decrease of pathologically heightened NREM sleep fragmentation in TGAD mice, accompanied by a steep decrease in microarousal events during both light and dark periods. Overall, our results indicate that model-tailored phase-targeting is key to modulate SWA through mCLAS, prompting the acute alleviation of key neurodegeneration-associated sleep phenotypes and potentiating sleep regulation and consolidation. Further experiments assessing the long-term effect of mCLAS in neurodegeneration may majorly impact the establishment of sleep-based therapies.

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bioRxiv2023

Adaptive stretching of representations across brain regions and deep learning model layers

Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to optimize performance by stretching visual representations along task-relevant dimensions? We considered a task that required monkeys to selectively attend on a trial-by-trial basis to one of two dimensions (color or motion direction) to make a decision. Except for V4 (color bound) and MT (motion bound), the brain radically re-configured itself to stretch representations along task-relevant dimensions in lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, the model displayed task-relevant stretching as a consequence of error minimization, indicating that stretching is an adaptive strategy.

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bioRxiv2023

Discrimination of finger movements by magnetomyography with optically pumped magnetometers

Optically pumped magnetometers (OPM) are quantum sensors that offer new possibilities to measure biomagnetic signals. In magnetomyography (MMG), compared to the current standard surface electromyography (EMG), OPM sensors offer the advantage of contactless measurements of muscle activity. However, little is known about the relative performance of OPM-MMG and EMG, e.g. in their ability to detect and classify finger movements. To address this, we recorded simultaneous OPM-MMG and EMG of finger flexor muscles for the discrimination of individual finger movements. Using a deep learning model for movement classification, we found that both sensor modalities were able to discriminate finger movements with above 89% accuracy. Furthermore, model predictions for the two sensor modalities showed high agreement in movement detection (85% agreement; Cohens kappa: 0.45). Our findings show that OPM sensors can be employed for reliable, contactless discrimination of finger movements and incentivize future applications of OPM in magnetomyography.

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bioRxiv2022

Neural representations of the content and production of human vocalization

Speech, as the spoken form of language, is fundamental for human communication. The phenomenon of covert inner speech implies a functional independence of speech content and motor production. However, it remains unclear how a flexible mapping between speech content and production is achieved on the neural level. To address this, we recorded magnetoencephalography (MEG) in humans performing a rule-based vocalization task. On each trial, vocalization content (one of two vowels) and production form (overt or covert) were instructed independently. Using multivariate pattern analysis, we found robust neural information about vocalization content and production, mostly originating from speech areas of the left hemisphere. Production signals dynamically transformed upon presentation of the content cue, whereas content signals remained largely stable throughout the trial. In sum, our results show dissociable neural representations of vocalization content and production in the human brain and provide new insights into the neural dynamics underlying human vocalization.

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bioRxiv2021

Altered cortical phase- and amplitude-coupling in Multiple Sclerosis

Distinguishing groups of subjects or experimental conditions in a high-dimensional feature space is a common goal in modern neuroimaging studies. Successful classification depends on the selection of relevant features as not every neuronal signal component or parameter is informative about the research question at hand. Here, we developed a novel unsupervised multistage analysis approach that combines dimensionality reduction, bootstrap aggregating and multivariate classification to select relevant neuronal features. We tested the approach by identifying changes of brain-wide electrophysiological coupling in Multiple Sclerosis. Multiple Sclerosis is a demyelinating disease of the central nervous system that can result in cognitive decline and physical disability. However, related changes in large-scale brain interactions remain poorly understood and corresponding non-invasive biomarkers are sparse. We thus compared brain-wide phase- and amplitude-coupling of frequency specific neuronal activity in relapsing-remitting Multiple Sclerosis patients (n = 17) and healthy controls (n = 17) using magnetoencephalography. Our analysis approach allowed us to identify systematic and non-redundant changes of both phase- and amplitude-coupling in the diseased brain. Changes included both, increased and decreased neuronal coupling in wide-spread, bilateral neuronal networks across a broad range of frequencies. These changes allowed to successfully classify patients and controls with an accuracy of 84%. Furthermore, classification confidence predicted behavioral scores of disease severity. In sum, our results unravel systematic changes of large-scale phase- and amplitude coupling in Multiple Sclerosis. Furthermore, our results establish a new analysis approach to efficiently contrast high-dimensional neuroimaging data between experimental groups or conditions. HighlightsO_LIA novel multistage approach to analyze high-dimensional neuronal coupling data. C_LIO_LIApplication to MEG recordings in relapsing-remitting Multiple Sclerosis patients. C_LIO_LIIdentification of altered phase- and amplitude-coupling in Multiple Sclerosis. C_LIO_LIClassification of patients and controls with 84% accuracy. C_LIO_LIClassification confidence predicts behavioral scores of disease severity. C_LI

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bioRxiv2020

Distinct cortical correlation structures of fractal and oscillatory neuronal activity

Electrophysiological population signals contain oscillatory and fractal (1/frequency) components. So far research has largely focused on oscillatory activity and only recently interest in fractal population activity has gained momentum. Accordingly, while the cortical correlation structure of oscillatory population activity has been characterized, little is known about the correlation of fractal neuronal activity. To address this, we investigated fractal neuronal population activity in the human brain using resting-state magnetoencephalography (MEG). We combined source-analysis, signal orthogonalization and irregular-resampling auto-spectral analysis (IRASA) to systematically characterize the cortical distribution and correlation of fractal neuronal activity. We found that fractal population activity is robustly correlated across the cortex and that this correlation is spatially well structured. Furthermore, we found that the cortical correlation structure of fractal activity is similar but distinct from the correlation structure of oscillatory neuronal activity. Anterior cortical regions showed the strongest differences between oscillatory and fractal correlation patterns. Our results suggest that correlations of fractal population activity serve as robust markers of cortical network interactions. Furthermore, our results show that fractal and oscillatory signal components provide non-redundant information about large-scale neuronal correlations. This may reflect at least partly distinct neuronal mechanisms underlying and reflected by oscillatory and fractal neuronal population activity.

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bioRxiv2020

Abstract neuronal choice signals during embodied decisions

Humans can make abstract choices independent of motor actions. However, in laboratory tasks, choices are typically reported with an associated action. Consequentially, knowledge about the neural representation of abstract choices is sparse, and choices are often thought to evolve as motor intentions. Here, we show that in the human brain, perceptual choices are represented in an abstract, motor-independent manner, even when they are directly linked to an action. We measured MEG signals while participants made choices with known or unknown motor response mapping. Using multivariate decoding, we quantified stimulus, perceptual choice and motor response information with distinct cortical distributions. Choice representations were invariant to whether the response mapping was known during stimulus presentation, and they occupied distinct representational spaces from both stimulus and motor signals. Furthermore, their strength predicted decision confidence and accuracy, as expected from an internal decision variable. Our results uncover abstract neural choice signals that generalize to action-linked decisions, suggesting a general role of an abstract choice stage in human decision-making.

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bioRxiv2020

Dissociating harmonic and non-harmonic phase-amplitude coupling in the human brain

Phase-amplitude coupling (PAC) has been hypothesized to coordinate cross-frequency interactions of neuronal activity in the brain. However, little is known about the distribution of PAC across the human brain and the frequencies involved. Furthermore, it remains unclear to what extend PAC may reflect spurious cross-frequency coupling induced by physiological artifacts or rhythmic non-sinusoidal signals with higher harmonics. Here, we combined MEG, source-reconstruction and different measures of cross-frequency coupling to systematically characterize PAC across the resting human brain. We show that cross-frequency measures of phase-amplitude, phase-phase, and amplitude-amplitude coupling are all sensitive to signals with higher harmonics. In conjunction, these measures allow to distinguish harmonic and non-harmonic PAC. Based on these insights, we found no evidence for non-harmonic PAC in resting-state MEG. Instead, we found cortically and spectrally wide-spread PAC driven by harmonic signals. Furthermore, we show how physiological artifacts and spectral leakage cause spurious PAC across wide frequency ranges. Our result clarify how different measures of cross-frequency interactions can be combined to characterize PAC and cast doubt on the presence of prominent non-harmonic phase-amplitude coupling in human resting-state MEG.

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bioRxiv2019

Motion Coherence and Luminance Contrast Interact in Driving Visual Gamma-Band Activity

Synchronized neuronal population activity in the gamma-frequency range (> 30 Hz) correlates with the bottom-up drive of various visual features. It has been hypothesized that gamma-band synchronization enhances the gain of neuronal representations, yet evidence remains sparse. We tested a critical prediction of the gain hypothesis, which is that features that drive synchronized gamma-band activity interact super-linearly. To test this prediction, we employed whole-head magnetencephalography (MEG) in human subjects and investigated if the strength of visual motion (motion coherence) and luminance contrast interact in driving gamma-band activity in visual cortex. We found that gamma-band activity (64 to 128 Hz) monotonically increased with coherence and contrast while lower frequency activity (8 to 32 Hz) decreased with both features. Furthermore, as predicted for a gain mechanism, we found a multiplicative interaction between motion coherence and contrast in their joint drive of gamma-band activity. The lower frequency activity did not show such an interaction. Our findings provide evidence, that gamma-band activity acts as a cortical gain mechanism that nonlinearly combines the bottom-up drive of different visual features in support of visually guided behavior.

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bioRxiv2019

Task-evoked activity quenches neural correlations and variability in large-scale brain systems

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

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bioRxiv2019

Extracellular spike waveform dissociates four functionally distinct cell classes in primate cortex

Understanding the function of different neuronal cell types is key to understanding brain function. However, cell type diversity is typically overlooked in electrophysiological studies in awake behaving animals. Here, we show that four functionally distinct cell classes can be robustly identified from extracellular recordings in several cortical regions of awake behaving monkeys. We recorded extracellular spiking activity from dorsolateral prefrontal cortex (dlPFC), the frontal eye field (FEF), and the lateral intraparietal area of macaque monkeys during a visuomotor decision-making task. We employed unsupervised clustering of spike waveforms, which robustly dissociated four distinct cell classes across all three brain regions. The four cell classes were functionally distinct. They showed different baseline firing statistics, visual response dynamics, and coding of visual information. While cell class-specific baseline statistics were consistent across brain regions, response dynamics and information coding were regionally specific. Our results identify four waveform-based cell classes in primate cortex. This opens a new window to dissect and study the cell-type specific function of cortical circuits.

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bioRxiv2019

Monkey EEG links neuronal color and motion information across species and scales

It remains challenging to relate EEG and MEG to underlying circuit processes and comparable experiments on both spatial scales are rare. To close this gap between invasive and non-invasive electrophysiology we developed and recorded human-comparable EEG in macaque monkeys during visual stimulation with colored dynamic random dot patterns. Furthermore, we performed simultaneous microelectrode recordings from 6 areas of macaque cortex and human MEG. Motion direction and color information were accessible in all signal types. Tuning of the non-invasive signals was similar to V4 and IT, but not to dorsal and frontal areas. Thus, MEG and EEG were dominated by early visual and ventral stream sources. Source level analysis revealed corresponding information and latency gradients across the cortex. We show how information-based methods and monkey EEG can identify analogous properties of visual processing in signals spanning spatial scales from single units to MEG - a valuable framework for relating human and animal studies.

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bioRxiv2018

Dissociated cortical phase- and amplitude-coupling patterns in the human brain

Coupling of neuronal oscillations may reflect and facilitate the communication between neuronal populations. Two primary neuronal coupling modes have been described: phase-coupling and amplitude-coupling. Theoretically, both coupling modes are independent, but so far, their neuronal relationship remains unclear. Here, we combined MEG, source-reconstruction and simulations to systematically compare cortical phase-coupling and amplitude-coupling patterns in the human brain. Importantly, we took into account a critical bias of amplitude-coupling measures due to phase-coupling. We found differences between both coupling modes across a broad frequency range and most of the cortex. Furthermore, by combining empirical measurements and simulations we ruled out that these results were caused by methodological biases, but instead reflected genuine neuronal amplitude coupling. Overall, our results suggest that cortical phase- and amplitude-coupling patterns are non-redundant, which may reflect at least partly distinct neuronal mechanisms. Furthermore, our findings highlight and clarify the compound nature of amplitude coupling measures.\n\nHighlightsO_LISystematic comparison of cortical phase- and amplitude-coupling patterns\nC_LIO_LIDemonstration of genuine amplitude coupling independent of phase coupling bias\nC_LIO_LIAmplitude- and phase coupling patterns differ across many cortical regions and frequencies\nC_LI

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bioRxiv2017

Gradual progression from sensory to task-related processing in cerebral cortex

Somewhere along the cortical hierarchy, behaviorally relevant information is distilled from raw sensory inputs. We examined how this transformation progresses along multiple levels of the hierarchy by comparing neural representations in visual, temporal, parietal, and frontal cortices in monkeys categorizing across three visual domains (shape, motion direction, color). Representations in visual areas MT and V4 were tightly linked to external sensory inputs. In contrast, lateral prefrontal cortex (PFC) largely represented the abstracted behavioral relevance of stimuli (task rule, motion category, color category). Intermediate-level areas--posterior inferotemporal (PIT), lateral intraparietal (LIP), and frontal eye fields (FEF)--exhibited mixed representations. While the distribution of sensory information across areas aligned well with classical functional divisions--MT carried stronger motion information, V4 and PIT carried stronger color and shape information--categorical abstraction did not, suggesting these areas may participate in different networks for stimulus-driven and cognitive functions. Paralleling these representational differences, the dimensionality of neural population activity decreased progressively from sensory to intermediate to frontal cortex. This shows how raw sensory representations are transformed into behaviorally relevant abstractions and suggests that the dimensionality of neural activity in higher cortical regions may be specific to their current task.\n\nSignificance statementThe earliest stages of processing in cerebral cortex reflect a relatively faithful copy of sensory inputs, but intelligent behavior requires abstracting behaviorally relevant concepts and categories. We examined how this transformation progresses through multiple levels of the cortical hierarchy by comparing neural representations in six cortical areas in monkeys categorizing across three visual domains. We found that categorical abstraction occurred in a gradual fashion across the cortical hierarchy and reached an apex in prefrontal cortex. Categorical coding did not respect classical models of large-scale cortical organization. The dimensionality of neural population activity was reduced in parallel with these representational changes. Our results shed light on how raw sensory inputs are transformed into behaviorally relevant abstractions.

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