Biology subjects

De Martino, F.

Publications and source records attributed to De Martino, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Temporal Progression of Prior Precision, Surprisal, Model Updating and Tone Adaptation in Human Auditory Cortices

Auditory perception emerges from the continuous integration of incoming acoustic signals with statistical predictions, a process complemented by simpler local adaptation. Yet, how all these concurrent mechanisms unfold in time remains underexplored. Using magnetoencephalography (MEG) in participants listening to stochastic tone sequences with periodically shifting inferential statistics, we modelled the temporal, spatial, and spectral dynamics of stimulus-specific repetition suppression, surprisal, model updating, and prior precision. By concurrently tracking these computational parameters within this dynamic auditory environment, we revealed a distinct chronological hierarchy governing predictive and adaptive processing. Prior precision states - reflecting expectations - emerged earliest, followed by rapid and concurrent repetition suppression and surprisal - reflecting local acoustic attenuation and global mismatch detection. These initial dynamics culminated in subsequent, distributed model updates within fronto-parietal networks. Spectrally, these components mapped onto diverging oscillatory fingerprints, ranging from theta-alpha modulation during adaptation to beta suppression during surprisal and broad spectral dampening during precision weighting. Together, these findings demonstrate that the brain navigates uncertain auditory environments through a cascade of neural processes, where fast, local sensory gating and mismatch signals precede the hierarchical revision of internal models.

neuroscience↗

bioRxiv2026

Learned Audiovisual Predictability Modulates Depth-Resolved Hippocampal-Cortical Functional Coupling

Perception reflects the dynamic integration of prior expectations, uncertainty, and incoming sensory evidence, but the neural circuitry supporting these computations remains debated. Current theories implicate coordinated interactions between the hippocampus and neocortex, potentially at the level of hippocampal subfields and cortical layers. Evidence in humans has come primarily from the visual domain, while less is known about how these interactions support learned auditory predictions. Using submillimetre 7T fMRI, we investigated depth-resolved functional coupling between hippocampal subfields and temporal and frontal cortical regions during audiovisual associative prediction. Participants learned deterministic associations between visual cues and auditory syllables, allowing us to compare auditory processing in predictable and unpredictable contexts. Predictability-related connectivity changes showed a spatially structured pattern involving DG/CA3-pSTG and CA1-inferior frontal regions, with significant edge-wise effects resolved across cortical depths. These findings provide fine-grained evidence that learned auditory predictability modulates hippocampal-cortical functional interactions across temporo-frontal systems.

neuroscience↗

bioRxiv2026

Gβγ dually regulates the M current via increased channel surface expression and PIP2 sensitivity

The M-current, generated by voltage gated KV7.2/7.3 channels, sets the threshold for neuronal action potential and acts as a key brake on repetitive firing. The M-current is tightly regulated by signaling molecules, such as calmodulin and phosphatidylinositol-4,5-bisphosphate (PIP2). Here, we show that coexpression of the ubiquitous subunit dimer of heterotrimeric G-proteins, G{beta}{gamma}, with KV7.2/7.3 in Xenopus laevis oocytes doubles maximal M-current. This regulation requires free, prenylated, membrane-associated G{beta}{gamma} and operates via two distinct mechanisms: 1) increasing plasma membrane (PM) channel density and 2) stabilizing KV7.2/7.3-PIP2 coupling. Conversely, G{beta}{gamma} scavengers reduce basal KV7.2/7.3 current and weaken PIP2 coupling. Proximity ligation assays confirm colocalization of G{beta}{gamma} and KV7.2/7.3 in the PM. Peptide array and AlphaFold modeling identify putative interaction sites on the channel cytoplasmic domain. Finally, the disease-causing G{beta}1 variant I80N abolished G{beta}{gamma}-induced M-current potentiation. Together, these findings establish G{beta}{gamma} as a significant physiological regulator and potential site of vulnerability in neuronal M-current function.

physiology↗

bioRxiv2026

Stimulus dependent modulation of perceptual filling-in is predicted by the properties of early visual cortex

Filling-in occurs during the perceptual disappearance of a blank figure presented on a textured background. Current models of perceptual filling-in are based on a two-stage model where the figure boundary weakens after a period of adaptation, followed by the spreading of the background representation into the region representing the figure. This suggests a competition between figure boundary and background representations whereby filling-in is facilitated by a weaker boundary representation and a stronger background representation. Here, we test this interpretation, by using the oblique effect and surround-modulation suppression, which are functional properties of early visual cortex that modulate the expected strengths of the responses to the background texture and to the figure boundary. In a sample of N=58 participants, we found more filling-in with background textures of cardinal compared to oblique orientations (earlier onset time, with more and longer episodes of filling-in per trial), in line with a known, stronger neuronal response for cardinal than for oblique orientation in early visual cortex. We found more filling-in when the main axis of the rectangular figure was iso-oriented rather than cross-oriented with the background texture (more and longer episodes of filling-in per trial, but no change in onset time), in line with a lower response to oriented stimuli when surrounded by iso-oriented flankers compared to cross-oriented flankers. Overall, our results support the two-stage model and suggest the involvement of early visual cortical areas characterized by the oblique effect and orientation-tuned surround-suppression.

neuroscience↗

bioRxiv2026

Feedback to deep layers in human V1 during perceptual filling-in

Visual surface perception is a fundamental aspect of vision, yet its neural implementation remains poorly understood. Troxlers perceptual filling-in paradigm provides a tractable illusion for studying surface perception, in which a peripheral figure becomes perceptually assimilated into the surrounding background after a period of sustained fixation. Although neural correlates of this phenomenon have been reported in early visual cortex, the underlying mechanisms, particularly the contribution of feedback signaling, remain unresolved. Here we use ultra-high-field (7T) layer-fMRI to investigate perceptual filling-in in the human visual cortex. While experimentally controlling perceptual filling-in, we measured GE-BOLD responses in ten participants. Analyses across cortical depth in the independently localized figure representation in primary visual cortex (V1) revealed neural correlates of filling-in in deep cortical layers, which are associated with feedback input. These findings provide evidence that perceptual filling-in and visual surface perception in general are supported by feedback signals to early visual cortex.

neuroscience↗

bioRxiv2026

Distributed representational encoding of food attributes in ventral visual cortex

Despite substantial progress in understanding how visual features of food are processed in the brain, it remains unclear how subjective food properties, such as perceived palatability, perceived caloric content, and perceived health value, are reflected in neural representational structure after accounting for visual similarity. Using functional MRI and representational similarity analysis (RSA), we examined visual, subjective, and categorical food representations in 25 healthy young women viewing 96 food images during an orthogonal color-discrimination task. Univariate analyses revealed reliable activation differences between high- and low-calorie foods in visual and ventral temporal cortex, with additional effects in orbitofrontal regions. Surface-based parcel-wise RSA and hypothesis-driven ROI analyses showed that visual models explained representational structure most strongly in early visual cortex and extended into occipitotemporal regions. Food-related models showed smaller but reliable effects in lateral and ventral occipitotemporal cortex: perceived calorie, perceived health, and objective calorie category were all associated with neural representational geometry in LOTC and VOTC. However, partial RSA and commonality analyses indicated that these effects were largely shared rather than separable. Perceived-calorie structure remained reliable after controlling for visual models, but not after controlling for perceived health. Palatability did not show reliable positive representational correspondence. These findings suggest that food-related information is reflected within occipitotemporal visual representational spaces during incidental viewing, but not as an isolated scalar calorie code. Instead, calorie-related effects appear to reflect a broader food-property axis on which energy density, perceived health, category structure, and learned food knowledge covary.

neuroscience↗

bioRxiv2025

Neural correlates of implicit memory for reoccurring sound patterns

The human brain exhibits remarkable sensitivity to auditory patterns. Previous research has shown that beyond the detection of such patterns, humans form memories for them that are implicit, robust to interference, and persistent over time. Here, we examine the mechanisms underlying implicit memory formation for reoccurring regular tone patterns using UHF fMRI. Prior to scanning, implicit memory was induced through a standard behavioral task where participants detected regular patterns embedded in random tone sequences. Unbeknownst to participants, five regular patterns reoccurred sporadically (every 1.5 min) across trials. Consistent with previous reports, behavioral performance indicated a reaction time advantage to reoccurring patterns, confirming the formation of implicit memory traces. During passive listening in the scanner, regularity engaged the superior temporal gyrus, inferior frontal gyrus, insula, putamen, and hippocampus, a result we interpret as reflecting the higher perceptual precision of regular patterns. Reoccurrence interacted with regularity in the superior temporal gyrus and a frontal cluster, showing higher activity for reoccurring compared to novel regular patterns. The hippocampus showed sensitivity to reoccurrence in both regular and random patterns. Together, these results indicate that the neocortex may selectively amplify reoccurring inputs in line with their enhanced (memory driven) perceptual precision.

neuroscience↗

bioRxiv2025

Distinct Roles of Deep and Superficial Cortical Layers in Tone Prediction, Comparison, and Adaptation in Human Auditory Cortex

Auditory perception relies on prior sensations both to form expectations about upcoming sounds and to adapt to repeated inputs. Yet, how these processes interact with bottom-up frequency-specific stimulus drive, and how they are organised across cortical depth, remains unclear. Using ultra-high-field functional magnetic resonance imaging (7T fMRI), participants listened to stochastic tone sequences whose repetition structure and predictability varied over time. For each voxel independently, we used computational modelling to estimate responses for stimulus drive, repetition suppression, and expectations. We then partitioned the unique and shared variance of these components to estimate their relative importance in modulating BOLD activity across cortical layers in auditory cortices. Expectation-related signals explained most unique variance in deep (infagranular) layers, in line with accounts that place internal model representations and belief updates in deeper cortical populations. Variance jointly explained by stimulus drive and expectation was instead strongest in superficial layers, consistent with these compartments expressing computations that combine bottom-up input with top-down predictions, such as prediction error signalling. Finally, repetition suppression accounted for variance uniformly across depth, suggesting an adaptation-like mechanism that modulates responsiveness without a clear laminar bias. Together, the data suggest that deep layers contain content-specific predictive models, superficial layers register prediction-input alignment, and repetition suppression provides a depth-invariant, local gain control that complements predictive processing.

neuroscience↗

bioRxiv2025

Unveiling early visual cortical mechanisms in perceptual filling-in: a parametric study of eccentricity and movement dependence

Surface perception relies on interactions between boundary encoding and surface filling-in. In a perceptual filling-in paradigm, a blank figure becomes perceptually replaced by a textured background after prolonged fixation of a point away from the figure. Filling-in begins when neurons representing the figures boundary adapt, allowing background-related activity to spread into the figures retinotopic representation. Adaptation proceeds faster the better the boundary is stabilized in a neurons receptive field (RF). We hypothesized that moving the figure boundary beyond a neurons RF would reduce adaptation and hinder filling-in, with larger movements permitting progressively less filling-in. As RF size increases with eccentricity, we further hypothesized that greater eccentricities would require larger movements to interfere with filling-in. Our results confirmed both predictions. The reduction in filling-in duration with increasing motion range permitted estimating RF size at each eccentricity. The slope of a linear function relating RF size to eccentricity matched values reported in human fMRI studies of V1/V2, suggesting that boundary adaptation involves early visual areas. We also explored whether microsaccade amplitude affects filling-in, but found no supporting evidence. Thus, external figure motion and microsaccades may disrupt adaptation through different mechanisms. These findings provide new insights into neural adaptation processes preceding perceptual filling-in.

neuroscience↗

bioRxiv2025

Predictive acoustical processing in human cortical layers

In our dynamic environments, predictive processing is vital for auditory perception and its associated behaviors. Predictive coding formalizes inferential processes by implementing them as information exchange across cortical layers and areas. With laminar-specific blood oxygenation level dependent we measured responses to a cascading oddball paradigm, to ground predictive auditory processes on the mesoscopic human cortical architecture. We show that the violation of predictions are potentially hierarchically organized and associated with responses in superficial layers of the planum polare and middle layers of the lateral temporal cortex. Moreover, we relate the updating of the brains internal model to changes in deep layers. Using a modeling approach, we derive putative changes in neural dynamics while accounting for draining effects. Our results support the role of temporal cortical architecture in the implementation of predictive coding and highlight the ability of laminar fMRI to investigate mesoscopic processes in a large extent of temporal areas.

neuroscience↗

bioRxiv2025

Neural encoding of biomechanically (im)possible human movements in occipitotemporal cortex

Understanding how the human brain processes body movements is essential for clarifying the mechanisms underlying social cognition and interaction. This study investigates the encoding of biomechanically possible and impossible body movements in occipitotemporal cortex using ultra-high field 7Tesla fMRI. By predicting the response of single voxels to impossible/possible movements using a computational modelling approach, our findings demonstrate that a combination of postural, biomechanical, and categorical features significantly predicts neural responses in the ventral visual cortex, particularly within the extrastriate body area (EBA), underscoring the brains sensitivity to biomechanical plausibility. Lastly, these findings highlight the functional heterogeneity of EBA, with specific regions (middle/superior occipital gyri) focusing on detailed biomechanical features and anterior regions (lateral occipital sulcus and inferior temporal gyrus) integrating more abstract, categorical information.

neuroscience↗

bioRxiv2024

Spanning spatial scales with functional imaging in the human brain; initial experiences at 10.5 Tesla

One of the most important frontiers in the efforts to improve functional imaging of brain activity (fMRI) is the recent push to increase the magnetic fields for human imaging beyond 10 Tesla. Having established safety, large gains in signal-to-noise-ratio (SNR), and novel radiofrequency arrays that capture the higher SNR, here we demonstrate major gains in the spatial resolution, sensitivity, and functional contrast of BOLD (Blood Oxygenation Level Dependent) based human fMRI at 10.5 Tesla. Using image reconstruction methods developed to minimize blurring, we also demonstrate that ultrahigh resolutions achieved at 10.5 Tesla suppress large-vein confounds in gradient-recalled-echo BOLD fMRI, thus improving the fidelity of functional signals relative to neuronal activity and yielding accurate cortical depth-profiles for layer-specific activation. Together, these multiplicative benefits deliver much needed increases in precision and resolution for meso-scale fMRI applications and illustrate the transformative potential of human functional imaging at magnetic fields that exceed 10 Tesla.

neuroscience↗

bioRxiv2024

Dissociable dynamic effects of expectation during statistical learning.

The brain is thought to generate internal predictions, based on previous statistical regularities in the environment, to optimise behaviour. Predictive processing has been repeatedly demonstrated and seemingly explains expectation suppression (ES), or the attenuation of neural activity in response to expected stimuli. However, the mechanisms behind ES are unclear and various models of the mechanisms supporting ES have been suggested with conflicting evidence. Sharpening models propose that expectations suppress neurons that are not tuned to the expected stimulus, increasing the signal-to-noise ratio for expected stimuli. In contrast, dampening models posit that expectations suppress neurons that are tuned to the expected stimuli, increasing the relative response amplitude for unexpected stimuli. Previous studies have used decoding analyses to examine these effects, with increases in decoding accuracy interpreted in terms of sharpening and decreases related to dampening. The opposing process theory (OPT) has suggested that both processes may occur at different time points, namely that initial sharpening is followed by later dampening of the neural representations of the expected stimulus as learning progresses. In this study we aim to test this theory and shed light on the dynamics of expectation effects, both at single trial level and over time. Thirty-one participants completed a statistical learning task consisting of paired scene categories whereby a "leading" image from one category is quickly followed by a "trailing" image from a different category. Multivariate EEG analyses focussed on decoding stimulus information related to the trailing image category. Within-trial, decoding analyses showed that stimulus expectation increased decoding accuracy at early latencies and decreased decoding accuracy at later latencies, in line with OPT. However, across trials, stimulus expectation decreased decoding accuracy in initial trials and increased decoding accuracy in later trials. We theorise that these dissociable dynamics of expectation effects within and across trials can be explained in the context of hierarchical learning mechanisms. Our single trial results provide evidence for the OPT, while our results over time suggest that sharpening and dampening effects emerge at different stages of learning in the visual domain.

neuroscience↗

bioRxiv2024

Unbiased estimation of the coefficient of determination in linear models: an application to fMRI encoding model comparison

Neuroscientific investigation has greatly benefited from the combination of functional Magnetic Resonance Imaging (fMRI) with linearized encoding, which allows to validate and compare computational models of neural activity based on neuroimaging data. In linearized encoding, a multidimensional feature space, usually obtained from a computational model applied to the stimuli, is related to the measured brain activity. This is often done by mapping such space to a dataset (training data, or in-sample), and validating the mapping on a separate dataset (test data, or out-of-sample), to avoid overfitting. When comparing models, the one with the highest explained variance on the test data, as indicated by the coefficient of determination (R2), is the one that better reflects the neural computations performed by the brain. An implicit assumption underlying this procedure is that the out-of-sample R2 is an unbiased estimator of the explanatory power of a computational model in the population of stimuli, and can therefore be safely used to compare models. In this work, we show that this is not the case, as the out-of-sample R2 has a negative bias, related to the amount of overfitting in the training data. This phenomenon has dramatic implications for model comparison when models of different dimensionalities are compared. To this aim, we develop an analytical framework that allows us to evaluate and correct biases in both in- and out-of-sample R2, with and without L2 regularization. Our proposed approach yields unbiased estimators of the population R2, thus enabling a valid model comparison. We validate it through illustrative simulations and with an application to a large public fMRI dataset.

neuroscience↗

bioRxiv2024

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC

Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise - the dominant contributing noise component in high resolution fMRI. NORDIC PCA is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. As investigating auditory functional responses poses unique challenges, we anticipated that the benefit of this technique would be especially pronounced. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we also observed a reduction in the average response amplitude (percent signal), which may suggest that a small amount of signal was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.

neuroscience↗

bioRxiv2022

Voxelwise encoding models of body stimuli reveal a representational gradient from low-level visual features to postural features in extrastriate body area.

Previous research has focused on the role of the extrastriate body area (EBA) in category-specific body representation, but the specific features that are represented in this area are not well understood. This study used ultra-high field fMRI and banded ridge regression to investigate the coding of body images by comparing the performance of three encoding models in predicting brain activity in ventral visual cortex and specifically the EBA. Our results suggest that EBA represents body stimuli based on a combination of low-level visual features and postural features. Author SummaryHistorically, research on body representation in the brain has focused on category-specific representation, using fMRI to investigate the most posterior body selective region, the extrastriate body area (EBA). However, the role of this area in body perception is still not well understood. This study aims to clarify the role of EBA, in coding information about body images. Using ultra-high field neuroimaging (fMRI) and advanced encoding techniques we tested different computational hypotheses to understand how body images are represented in EBA. Our results suggest that EBA represents bodies using a combination of low-level properties and postural information extracted from the stimulus.

neuroscience↗

bioRxiv2022

Selective attention reduces responses to relevant sounds in human auditory cortex

Selective attention enables the preferential processing of relevant stimulus aspects. Invasive animal studies have shown that attending a sound feature rapidly modifies neuronal tuning throughout the auditory cortex. Human neuroimaging studies have reported enhanced auditory cortical responses with selective attention. To date, it remains unclear how the results obtained with functional magnetic resonance imaging (fMRI) in humans relate to the electrophysiological findings in animal models. Here we aim to close the gap between animal and human research by combining a selective attention task similar in design to those used in animal electrophysiology with high spatial resolution ultra-high field fMRI at 7 Tesla. Specifically, human participants perform a detection task, while the probability of target occurrence varies with sound frequency. Contrary to previous fMRI studies, we show that selective attention reduces responses to the attended frequencies in those neuronal populations preferring the attended frequency. Through population receptive field (pRF) mapping, we furthermore show that these response reductions are at least partially driven by frequency-induced pRF narrowing. The difference between our results to those of previous fMRI studies supports the notion that the influence of selective attention on auditory cortex is diverse and may depend on context, task, and auditory processing stage.

neuroscience↗

bioRxiv2022

Cerebral blood volume sensitive layer-fMRI in the human auditory cortex at 7 Tesla: Challenges and capabilities

The development of ultra high field (UHF) fMRI signal readout strategies and contrasts has led to the possibility of imaging the human brain in vivo and non-invasively at increasingly higher spatial resolutions of cortical layers and columns. One emergent layer-fMRI acquisition method with increasing popularity is the cerebral blood volume (CBV) sensitive sequence named vascular space occupancy (VASO). This approach has been shown to be mostly sensitive to locally-specific changes of laminar microvasculature, without unwanted biases of trans-laminar draining veins. Until now, however, VASO has not been applied in the technically challenging cortical area of the primary auditory cortex. Here, we developed a VASO imaging protocol for auditory neuroscientific applications. We describe the main challenges we encountered and the solutions we have adopted to mitigate them. With our optimized protocol, we investigate laminar responses to sounds. Finally, as proof of concept for future investigations, we map the topographic representation of frequency preference (tonotopy) in the auditory cortex. HighlightsO_LILayer fMRI VASO in the auditory cortex is challenging due to its physiology C_LIO_LIAfter protocol optimization we show the applicability of VASO to the auditory cortex C_LIO_LITopographic maps obtained with VASO respect the large-scale tonotopic organization that has previously been shown with BOLD fMRI data. C_LI

neuroscience↗