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Fries, P.

Publications and source records attributed to Fries, P..

At least 19 recordsLinked to original sources

Surprising effects of stimulus repetition on neuronal firing rates and gamma-band synchronization in awake macaque V1

Stimulus repetition is abundant, because the environment is redundant and/or because it is redundantly sampled. This offers an opportunity to optimize the processing of repeated stimuli. Indeed, stimulus repetition leads to classically described neuronal response decreases, and to more recently described neuronal gamma synchronization increases (sometimes preceded by decreases for a few trials). Here, we used a full-screen colored background (FSCB) and a flashed black bar, while recording multi-unit activity (MUA) and local field potentials (LFP) from area V1 of an awake macaque monkey. We found that the FSCB repetition induced neuronal response increases (sometimes preceded by decreases for a few trials) and gamma synchronization decreases (preceded by increases for a few trials). These effects are largely opposite to the dominant previous findings. Intriguingly, these surprising effects reversed when we isolated the responses to the flashed black bar. We discuss these findings, considering differences to previous studies with regards to the subject of the study, the stimuli and the task. We notice that in studies reporting classical results for gamma, sometimes in combination with firing rates, the stimuli were typically (partly) predictive of the reward. Here, we found non-classical results for the FSCB that was not reward predictive, and classical results for the black bar that was reward predictive. Whether this has revealed a general effect of reward predictive versus non-predictive stimuli will require further investigation with stimuli and task designs tailored specifically for this question. SignificanceNatural visual experience often entails repetitions of the same stimulus. This allows optimization of the brains processing of those stimuli. Indeed, repeated stimuli typically induce decreasing firing rates and increasing gamma-band synchronization in many visual areas. However, in this study in awake macaque primary visual cortex, we report surprising opposite effects: under some conditions, stimulus repetition can result in neuronal firing rate increases and gamma-band synchronization decreases. We argue that these effects might relate to the fact that the repeated stimulus was not predictive of a reward. In fact, when we isolated the responses to a second stimulus that was added later in each trial and that was predictive of reward, the effects reverted again to the typical pattern.

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Repetition-related gamma plasticity in macaque V1 and V2 is highly stimulus specific and robust to stimulus set size

When a visual stimulus is repeated, the cortex has the opportunity to adjust its processing. Indeed, repeated stimuli induce reduced neuronal spike rates and increased neuronal gamma-band synchronization. Previous studies found the repetition-related gamma increase to occur both in human and non-human primates, for artificial and natural stimuli, to persist for minutes and to not transfer between strongly differing stimuli. Here, we further investigated the repetition-related effects using laminar recordings of multi-unit activity and local field potentials from awake macaque areas V1 and V2. We find that the effects on spike rate and gamma occur in all laminar compartments of V1 and V2. We quantify the degree of stimulus specificity with oriented gratings and find that the repetition-related gamma increase does not transfer to gratings differing by merely 10 {degrees}, the smallest difference tested. Furthermore, we find that the repetition-related effects are robust to stimulus set size, occurring both when one stimulus was repeated and when eighteen different interleaved stimuli were repeated. Finally, we show that alpha-beta activity increases and remains elevated when a stimulus is repeated, and decreases sharply when an unexpected stimulus is presented. These results suggest that repetition-related plasticity leads to changes in spike rates and rhythmic neuronal synchronization in different frequency bands that adjust the cortical processing of repeated stimuli.

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On variability in local field potentials

Neuronal coding and decoding would be compromised if neuronal responses were highly variable. Intriguingly, neuronal spike counts (SCs) show a reduction in across-trial variance (ATV) in response to sensory stimulation, when SC variance is normalized by SC mean, that is, when using the Fano factor 1. Inspired by this seminal finding, ATV has also been studied in electroencephalography (EEG) signals, revealing effects of various stimulus and cognitive factors as well as disease states. Here, we empirically show that outside of evoked potentials, the ATV of the EEG or local field potential (LFP) is very highly correlated to the intra-trial variance (ITV), which corresponds to the well-known power metric. We propose that the LFP power, rather than the raw LFP signal, should be considered with regard to putative changes of its variability. We quantify LFP power variability as the standard deviation of the logarithm of the power ratio between an active and a baseline condition, normalized by the mean of that log(power ratio), that is the coefficient of variation (CV) of the log(power ratio). This CV(log(power ratio)) is reduced for gamma and alpha power when they are enhanced by stimulation, and it is enhanced for alpha power when it is reduced by stimulation. This suggests a potential inverse relation between changes in band-limited power and the corresponding CV. We propose that the CV(log(power ratio)) is a useful metric that can be computed for numerous existing and future LFP, EEG or MEG datasets, which will provide insights into those signals frequency-specific variability and how they might be used for neuronal coding and decoding.

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A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation

Brain function relies on coordinated activity across a wide range of spatial and temporal scales. The activity of single neurons depends on their unique pattern of local and long-range connectivity and thus on large-scale patterns of distributed activity across brain-wide networks. Understanding integrated, whole brain function requires new tools capable of recording from anatomically connected populations in distributed brain areas to bridge local and global dynamics. Here, we present high-density, micro-electrocorticography arrays that facilitate multi-scale studies of brain activity. The hybrid arrays integrate the desirable features of silicone elastomers and polyimide films. The silicone elastomer superstructure provides optical transparency and permits repeated penetration with rigid intracortical arrays. The polyimide films enable fine feature definition through photolithography. This combination facilitates high-throughput functional mapping of brain areas to define functionally characterized targets and permits targeted insertion of depth electrodes through the same array for dense local sampling. We demonstrate their suitability for functional mapping of cortical regions in rats, cats and marmosets and the benefit of the resulting functional maps for targeting functionally defined populations for multi-area laminar recordings. Finally, we demonstrate the utility of the hybrid {micro}ECoG for local optogenetic stimulation and its use in investigating cortico-cortical interactions through feedforward optogenetic stimulation. Together, these capabilities make the hybrid {micro}ECoG a compelling tool for systems neuroscience.

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Stimulus-repetition effects on macaque V1 and V4 microcircuits explain gamma-synchronization increase

Under natural conditions, animals repeatedly encounter the same visual scenes, objects or patterns repeatedly. These repetitions constitute statistical regularities, which the brain captures in an internal model through learning. A signature of such learning in primate visual areas V1 and V4 is the gradual strengthening of gamma synchronization. We used a V1-V4 Dynamic Causal Model (DCM) to explain visually induced responses in early and late epochs from a sequence of several hundred grating presentations. The DCM reproduced the empirical increase in local and inter-areal gamma synchronization, revealing specific intrinsic connectivity effects that could explain the phenomenon. In a sensitivity analysis, the isolated modulation of several connection strengths induced increased gamma. Comparison of alternative models showed that empirical gamma increases are better explained by (1) repetition effects in both V1 and V4 intrinsic connectivity (alone or together with extrinsic) than in extrinsic connectivity alone, and (2) repetition effects on V1 and V4 population input rather than output gain. The best input gain model included effects in V1 granular and superficial excitatory populations and in V4 granular and deep excitatory populations. Our findings are consistent with gamma reflecting bottom-up signal precision, which increases with repetition and, therefore, with predictability and learning. HighlightsO_LIWe model learning effects in macaque visual cortex using Dynamic Causal Modeling. C_LIO_LIMicrocircuit-level changes explain the repetition-induced gamma increases. C_LIO_LIThe best models include changes 1) within V1 and V4 and 2) in neuronal input gain. C_LIO_LIGamma may reflect bottom-up signal precision. C_LI

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Spectral estimation at the edge

Cognitive functions depend on neuronal communication, which is subserved by the synchronization of neuronal rhythms. Rhythms are characterized by their frequency, power and phase. If the phase of a rhythm just preceding an input is predictive of the neuronal or behavioral response to the input, this provides strong evidence for a functional role of the rhythm. Yet, this requires estimating the phase of a rhythm at the edge of the epoch. This is challenging, because any phase estimation that is spectrally specific requires a finite window length often combined with tapers that de-emphasize the signal close to the edge. To overcome this, we propose a method that builds on previously described approaches based on autoregressive modeling of the data and corresponding extrapolation beyond the edge. In contrast to related previous approaches, the modeling is based on the broadband signals, avoiding filtering-related group delays, and the extrapolation is performed multiple times, allowing averaging and thereby the reduction of extrapolation noise. The new method provided more accurate phase estimation at the edge for most simulated datasets, and for an empirical dataset from awake macaque area V4. We propose that the enhanced phase estimation accuracy at the edge might help to investigate the functional roles of brain rhythms and potentially also to improve phase-specific stimulation for clinical applications.

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Riemannian Geometry for the Classification of Brain States with fNIRS

BackgroundFunctional near-infrared spectroscopy (fNIRS) has recently gained momentum as a reliable and accurate tool for assessing brain states. This increase in popularity is due to its robustness to movement, non-invasive nature, portability, and user-friendly application. However, compared to functional magnetic resonance imaging (fMRI), fNIRS is less sensitive to deeper brain activity and offers less coverage. Additionally, due to fewer advancements in method development, the performance of fNIRS-based brain-state classification still lags behind more prevalent methods like fMRI. MethodsWe introduce a novel classification approach grounded in Riemannian geometry for the classification of kernel matrices, leveraging the temporal and spatial channel relationships and inherent duality of fNIRS signals--more specifically, oxygenated and deoxygenated hemoglobin. For the Riemannian geometry-based models, we compared different kernel matrix estimators and two classifiers: Riemannian Support Vector Classifier and Tangent Space Logistic Regression. These were benchmarked against four models employing traditional feature extraction methods. Our approach was tested in two brain-state classification scenarios based on the same fNIRS dataset: an 8-choice classification, which includes seven established plus an individually selected imagery task, and a 2-choice classification of all possible 28 2-task combinations. ResultsThe novel approach achieved a mean 8-choice classification accuracy of 65%, significantly surpassing the mean accuracy of 42% obtained with traditional methods. Additionally, the best-performing model achieved an average accuracy of 96% for 2-choice classification across all possible 28 task combinations, compared to 78% with traditional models. ConclusionTo our knowledge, we are the first to demonstrate that the proposed Riemannian geometry-based classification approach is both powerful and viable for fNIRS data, considerably increasing the accuracy in binary and multi-class classification of brain activation patterns.

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Would you agree if N is three?On statistical inference for small N.

Non-human primate studies traditionally use two or three animals. We previously used standard statistics to argue for using either one animal, for an inference about that sample, or five or more animals, for a useful inference about the population. A recently proposed framework argued for testing three animals and accepting the outcome found in the majority as the outcome that is most representative for the population. The proposal tests this framework under various assumptions about the true probability of the representative outcome in the population, i.e. its typicality. On this basis, it argues that the framework is valid across a wide range of typicalities. Here, we show (1) that the error rate of the framework depends strongly on the typicality of the representative outcome, (2) that an acceptable error rate requires this typicality to be very high (87% for a single type of outlier), which actually renders empirical testing beyond a single animal obsolete, (3) that moving from one to three animals decreases error rates mainly for typicality values of 70-90%, and much less for both lower and higher values. Furthermore, we use conjunction analysis to demonstrate that two out of three animals with a given outcome only allow to infer a lower bound to typicality of 9%, which is of limited value. Thus, the use of two or three animals does not allow a useful inference about the population, and if this option is nevertheless chosen, the inferred lower bound of typicality should be reported.

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Systems Neuroscience Computing in Python (SyNCoPy): A Python Package for Large-scale Analysis of Electrophysiological Data

We introduce an open-source Python package for the analysis of large-scale electrophysiological data called SyNCoPy, for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g. time-lock analysis), frequency (e.g. power spectrum), and connectivity (e.g. coherence) domains. It enables user-friendly data analysis on both laptop-based and high-performance computing systems. SyNCoPy is designed to facilitate trial-parallel workflows (parallel processing of trials) making it an ideal tool for large-scale analysis of electrophysiological data. Based on parallel processing of trials, the software can support very large-scale datasets via innovative out-of-core computation techniques. It also provides seamless interoperability with other standard software packages through a range of file format importers and exporters and open file formats. The naming of the user functions closely follows the well-established FieldTrip framework, which is an open-source Matlab toolbox for advanced analysis of electrophysiological data.

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Top-down modulation of visual cortical stimulus encoding and gamma independent of firing rates

Neurons in primary visual cortex integrate sensory input with signals reflecting the animals internal state to support flexible behavior. Internal variables, such as expectation, attention, or current goals, are imposed in a top-down manner via extensive feedback projections from higher-order areas. We optogenetically activated a high-order visual area, area 21a, in the lightly anesthetized cat (OptoTD), while recording from neuronal populations in V1. OptoTD induced strong, up to several fold, changes in gamma-band synchronization together with much smaller changes in firing rate, and the two effects showed no correlation. OptoTD effects showed specificity for the features of the simultaneously presented visual stimuli. OptoTD-induced changes in gamma synchronization, but not firing rates, were predictive of simultaneous changes in the amount of encoded stimulus information. Our findings suggest that one important role of top-down signals is to modulate synchronization and the information encoded by populations of sensory neurons.

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Heterogeneity of layer 4 in visual areas of rhesus macaque cortex

Non-human primates like rhesus macaques are pivotal models for decoding human visual cortex physiology and disorders. We introduce BrainSPACE, an innovative pipeline for rapid and precise brain tissue banking, to sample visual cortex areas: V1, V2, V4, MT, and TEO. Applying snRNA-seq to V1 and V4 (95,071 nuclei), we uncovered conserved GABAergic neuron profiles but stark area-specific diversity in principal neurons, featuring seven unique layer 4 subtypes in V1 and one in V4. Complementary smFISH validated transcriptional gradients across these areas, aligning with ventral and dorsal stream hierarchies. Gene ontology analyses highlighted plasticity-related pathways in unique layer 4 subtypes, with genes like NTNG1 and NLGN1 linked to neurodevelopmental disorders such as autism and schizophrenia. Our insights bridge molecular architecture to visual processing, offering an interactive atlas for community use. By revealing how layer 4 heterogeneity drives hierarchical specialization, our work advances primate brain mapping and informs therapeutic strategies for vision-related pathologies.

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Primate Saccade Rhythmicity

Rhythmic sampling is a hallmark of many sensory systems in many diverse organisms. In primate vision, rapid foveating eye movements continuously scan the visual environment at a rate of 2-6 Hz, but the statistics of inter-saccadic interval distributions could equally well be described by non-rhythmic stochastic processes as by a (putatively variable) rhythmic oscillator. This raises the fundamental question whether primate visual scanning actually differs from the numerous examples of rhythmic sampling strategies exhibited across species and sensory modalities. Here, using experiments in humans, macaques, and a marmoset, as well as statistical approaches inspired by studies of temporal structure in neuronal spiking patterns, we show that primate saccade generation is unambiguously rooted in a rhythmic source. This finding was remarkably consistent across the three primate species despite their significant evolutionary distance. We find that saccade rate undergoes smooth, slow fluctuations. Accounting for those rate fluctuations was crucial for revealing the true degree of saccade rhythmicity. Thus, exact saccade timing is determined by an interaction between the slow saccade-rate fluctuations with the more temporally-local rhythmic generator. This demonstration of rhythmicity in overt oculomotor sampling behavior provides a link to the fundamental rhythmic nature of many central perceptual and cognitive processes, and places primate saccadic sampling into the large family of rhythmic active sampling behaviors.

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Eccentricity-Dependent Saccadic Reaction Time: The Roles of Foveal Magnification and Attentional Orienting

A hallmark of primate vision is the emphasis on foveal processing, accompanied by frequent saccades that bring the fovea to salient parts of the scene, or to newly appearing stimuli. A saccade to a new stimulus is one of the most fundamental sensory-motor transformations. In macaque monkeys, we show that foveal magnification is not only the reason for saccades, but it also affects the dynamics of saccade initiation. In a task where the monkeys made saccades to peripheral target onsets, saccadic reaction time (SRT) increased with target eccentricity. Notably, we effectively eliminated this increase by scaling the target size according to the foveal magnification factor in the superior colliculus. We repeated the comparison between non-scaled and scaled targets, while changing the task to a delayed saccade task. In this task, the target was presented long before the saccade, and the saccade was triggered by foveal fixation offset rather than target onset, such that target onset long before fixation offset was essentially irrelevant for SRT. In this task, we found that SRT increased with target eccentricity, with similar rate for both non-scaled and scaled targets. Furthermore, this increase survived the addition of a salient distracting flash resetting attention to the foveal. The results obtained with the delayed saccades task are consistent with an attentional scan from the fovea to the target, a recently hypothesized general mechanism of attention.

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In vivo magnetic recording of single-neuron action potentials

Measuring fast neuronal signals is the domain of electrophysiology and magnetophysiology. While electrophysiology is easier to perform, magnetophysiology avoids tissue-based distortions and measures a signal with directional information. At the macroscale, magnetoencephalography (MEG) is established, and at the mesoscale, visually evoked magnetic fields have been reported. At the microscale however, while benefits of recording magnetic counterparts of electric spikes would be numerous, they are also highly challenging in vivo. Here, we combine magnetic and electric recordings of neuronal action potentials in anesthetized rats using miniaturized giant magneto-resistance (GMR) sensors. We reveal the magnetic signature of action potentials of well-isolated single units. The recorded magnetic signals showed a distinct waveform and considerable signal strength. This demonstration of in vivo magnetic action potentials opens a wide field of possibilities to profit from the combined power of magnetic and electric recordings and thus to significantly advance the understanding of neuronal circuits. Significance statementElectrophysiological tools allow the measurement of single-neuron action potentials with high temporal resolution. Magnetophysiological measurements would add valuable information, but are particularly hard to achieve for single neurons. Established technology for non-invasive magnetic brain signal measurements can currently not be used inside living tissue. Here, we demonstrate that miniaturized magnetic sensors based on giant magneto-resistance enable the measurement of the magnetic counterpart of single-neuron action potentials in vivo. This proof-of-principle shows a way towards integrating magnetic and electric recordings in future experiments and thus to profit from the complementary information measured by the two modalities.

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Enhanced Behavioral Performance through Interareal Gamma and Beta Synchronization

Cognitive functioning requires coordination between brain areas. Between visual areas, feedforward gamma synchronization improves behavioral performance. Here, we investigate whether similar principles hold across brain regions and frequency bands, using simultaneous local field potential recordings from 15 areas during performance of a selective attention task. Short behavioral reaction times (RTs), an index of efficient interareal communication, occurred when occipital areas V1, V2, V4, DP showed gamma synchronization, and fronto-central areas S1, 5, F1, F2, F4 showed beta synchronization. For both area clusters and corresponding frequency bands, deviations from the typically observed phase relations increased RTs. Across clusters and frequency bands, good phase relations occurred in a correlated manner specifically when they processed the behaviorally relevant stimulus. Furthermore, the fronto- central cluster exerted a beta-band influence onto the occipital cluster whose strength predicted short RTs. These results suggest that local gamma and beta synchronization and their inter-regional coordination jointly improve behavioral performance.

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Predictive coding during action observation - an Electrocorticographic study

Predictive coding is a theoretical framework that has received much attention for its ability to generate testable hypotheses on how multiple brain regions integrate information during cognitive functions. Given relatively large sensorimotor delays, during social interactions, predicting the behavior of others is crucial to enable joint actions or provide competitive advantages. The action observation network (AON) has been extensively studied, but how information is integrated across its main nodes remains poorly understood. Here we leverage the high spatial and temporal resolution of intracranial Electrocorticography (ECoG), to characterize how the key nodes of the AON - including precentral, supramarginal and visual areas - exchange information. We found more top-down beta oscillation from precentral to supramarginal contacts during the observation of predictable actions while more bottom-up gamma oscillation from visual to supramarginal contacts were measured for unpredictable actions. These results, in line with predictive coding, provide critical evidence towards an understanding of how nodes of the AON integrate information to process the actions of others.

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Performance modulations phase-locked to action depend on internal state

Several studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulations of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participants internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at {approx}17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a "miss" showed an action-related modulation at {approx}17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at {approx}17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.

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Modular, cement-free, customized headpost and connector-chamber implants for macaques

BackgroundNeurophysiological studies with awake macaques typically require chronic cranial implants. Headpost and connector-chamber implants are used to allow head stabilization and to house connectors of chronically implanted electrodes, respectively. New MethodWe present long- lasting, modular, cement-free headpost implants made of titanium that consist of two pieces: a baseplate and a top part. The baseplate is implanted first, covered by muscle and skin and allowed to heal and osseointegrate for several weeks to months. The percutaneous part is added in a second, brief surgery. Using a punch tool, a perfectly round skin cut is achieved providing a tight fit around the implant without any sutures. We describe the design, planning and production of manually bent and CNC-milled baseplates. We also developed a remote headposting technique that increases handling safety. Finally, we present a modular, footless connector chamber that is implanted in a similar two- step approach and achieves a minimized footprint on the skull. ResultsTwelve adult male macaques were successfully implanted with a headpost and one with the connector chamber. To date, we report no implant failure, great headpost stability and implant condition, in four cases even more than 9 years post-implantation. Comparison with Existing MethodsThe methods presented here build on several related previous methods and provide additional refinements to further increase implant longevity and handling safety. ConclusionsOptimized implants can remain stable and healthy for at least 9 years and thereby exceed the typical experiment durations. This minimizes implant-related complications and corrective surgeries and thereby significantly improves animal welfare. HighlightsO_LILong-lasting titanium implants for non-human primates C_LIO_LIRefined implantation techniques that reduce post-operative complications C_LIO_LIMinimized, footless connector chamber to house connectors of chronic arrays C_LIO_LITwelve adult male macaques were implanted with long-lasting headpost implants C_LIO_LIHeadpost implants so far without failure and with longevity up to > 9 years C_LI

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