Biology subjects

Piantoni, G.

Publications and source records attributed to Piantoni, G..

5 recordsLinked to original sources

bioRxiv2025

Neuronal or vascular receptive fields? On the relation between BOLD-fMRI population receptive field (pRF) estimates and cortical vascularization.

This study investigates the contribution of different vascular compartments on population receptive field (pRF) size estimates within the early visual cortex (V1, V2, and V3) using BOLD-fMRI. We employed T2*-weighted gradient-echo (GE) and T2-weighted spin-echo (SE) sequences at 7 Tesla (7T) and a multi-band GE sequence at 3T to explore how different vessel sensitivities across these sequences influence pRF modeling. Our results confirm the expected pRF size increase across eccentricity and visual areas but found no significant differences in pRF size estimates across MR sequences, voxel sizes, or field strengths. BOLD signal amplitudes were influenced by MR sequence, with largest signal changes observed for 7TGE, and amplitude increases were noted across cortical depth for GE sequences but not for SE sequences. Contrary to our hypotheses, pRF size estimates were not noticeably affected by the local vascularization, suggesting that pRFs primarily reflect neuronal activity rather than vascular compartment characteristics. Our study highlights the robust nature of pRF size estimates across various fMRI conditions and points toward the decoupling of pRF properties from vascular factors.

neuroscience↗

bioRxiv2023

Spatial Tuning of Alpha Oscillations in Human Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWNeuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz) but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention. Significance StatementThe alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention. Grant support: NIH R01 MH111417 (Petridou, Winawer, Ramsey, Devinsky); JSPS Overseas Research Fellowship (Yuasa) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

neuroscience↗

bioRxiv2021

Temporal dynamics of neural responses in human visual cortex

Neural responses to visual stimuli exhibit complex temporal dynamics, including sub-additive temporal summation, response reduction with repeated or sustained stimuli (adaptation), and slower dynamics at low contrast. These phenomena are often studied independently. Here, we demonstrate these phenomena within the same experiment and model the underlying neural computations with a single computational model. We extracted time-varying responses from electrocorticographic (ECoG) recordings from patients presented with stimuli that varied in contrast, duration, and inter-stimulus interval (ISI). Aggregating data across patients yielded 98 electrodes with robust visual responses, covering both earlier (V1-V3) and higher-order (V3a/b, LO, TO, IPS) retinotopic maps. In all regions, the temporal dynamics of neural responses exhibit several non-linear features: peak response amplitude saturates with high contrast and longer stimulus durations; the response to a second stimulus is suppressed for short ISIs and recovers for longer ISIs; response latency decreases with increasing contrast. These features are accurately captured by a computational model comprised of a small set of canonical neuronal operations: linear filtering, rectification, exponentiation, and a delayed divisive normalization. We find that an increased normalization term captures both contrast- and adaptation-related response reductions, suggesting potentially shared underlying mechanisms. We additionally demonstrate both changes and invariance in temporal response dynamics between earlier and higher-order visual areas. Together, our results reveal the presence of a wide range of temporal and contrast-dependent neuronal dynamics in the human visual cortex, and demonstrate that a simple model captures these dynamics at millisecond resolution. Significance StatementSensory inputs and neural responses change continuously over time. It is especially challenging to understand a system that has both dynamic inputs and outputs. Here we use a computational modeling approach that specifies computations to convert a time-varying input stimulus to a neural response time course, and use this to predict neural activity measured in the human visual cortex. We show that this computational model predicts a wide variety of complex neural response shapes that we induced experimentally by manipulating the duration, repetition and contrast of visual stimuli. By comparing data and model predictions, we uncover systematic properties of temporal dynamics of neural signals, allowing us to better understand how the brain processes dynamic sensory information.

neuroscience↗

bioRxiv2021

Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film

Intracranial human recordings are a valuable and rare resource that the whole neuroscience community can benefit from. Making such data available to the neuroscience community not only helps tackle the reproducibility issues in science, it also helps make more use of this valuable data. The latter is especially true for data collected using naturalistic tasks. Here, we describe a dataset collected from a large group of human subjects while they watched a short audiovisual film. The dataset is characterized by several unique features. First, it combines a large amount of intracranial data from 51 intracranial electroencephalography (iEEG) participants, who all did the same task. Second, the intracranial data are accompanied by fMRI recordings acquired for the same task in 30 functional magnetic resonance imaging (fMRI) participants. Third, the data were acquired using a rich audiovisual stimulus, for which we provide detailed speech and video annotations. This multimodal dataset can be used to address questions about neural mechanisms of multimodal perception and language comprehension as well as the nature of the neural signal acquired during the same task across brain recording modalities.

neuroscience↗

bioRxiv2017

Integrated analysis of anatomical and electrophysiological human intracranial data

The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated, taking under an hour to complete, and readily adjusted to iEEG data sets with other characteristics.

neuroscience↗