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Parto-Dezfouli, M.

Publications and source records attributed to Parto-Dezfouli, M..

4 recordsLinked to original sources

Prefrontal working memory signal primarily controls phase-coded information within extrastriate cortex

In order to understand how prefrontal cortex provides the benefits of working memory (WM) for visual processing we examined the influence of WM on the representation of visual signals in V4 neurons in two macaque monkeys. We found that WM induces strong {beta} oscillations in V4 and that the timing of action potentials relative to this oscillation reflects sensory information-i.e., a phase coding of visual information. Pharmacologically inactivating the Frontal Eye Field part of prefrontal cortex, we confirmed the necessity of prefrontal signals for the WM-driven boost in phase coding of visual information. Indeed, changes in the average firing rate of V4 neurons were correlated with WM-induced oscillatory changes. We present a network model to describe how WM signals can recruit sensory areas by inducing oscillations within these areas and discuss the implications of these findings for a sensory recruitment theory of WM through coherence.

animal behavior and cognition↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗