Dynamic large-scale network synchronization from perception to action

Sensory-guided actions entail the processing of sensory information, generation of perceptual decisions, and the generation of appropriate actions. Neuronal activity underlying these processes is distributed into sensory, fronto-parietal, and motor brain areas, respectively. How the neuronal processing is coordinated across these brain areas to support functions from perception to action remains unknown. We investigated whether phase synchronization in large-scale networks coordinate these processes. We recorded human cortical activity with magnetoencephalography (MEG) during a task in which weak somatosensory stimuli remained unperceived or were perceived. We then assessed dynamic evolution of phase synchronization in large-scale networks from source-reconstructed MEG data by using advanced analysis approaches combined with graph theory. Here we show that perceiving and reporting of weak somatosensory stimuli is correlated with sustained strengthening of large-scale synchrony concurrently in delta/theta (3-7 Hz) and gamma (40-60 Hz) frequency bands. In a data-driven network localization, we found this synchronization to dynamically connect the task-relevant, that is, the fronto-parietal, sensory, and motor systems. The strength and temporal pattern of interareal synchronization were also correlated with the response times. These data thus show that key brain areas underlying perception, decision-making, and actions are transiently connected by large-scale dynamic phase synchronization in the delta/theta and gamma bands.Peer reviewe

Automatic segmentation of deep intracerebral electrodes in computed tomography scans

Background: Invasive monitoring of brain activity by means of intracerebral electrodes is widely practiced to improve pre-surgical seizure onset zone localization in patients with medically refractory seizures. Stereo-Electroencephalography (SEEG) is mainly used to localize the epileptogenic zone and a precise knowledge of the location of the electrodes is expected to facilitate the recordings interpretation and the planning of resective surgery. However, the localization of intracerebral electrodes on post-implant acquisitions is usually time-consuming (i.e., manual segmentation), it requires advanced 3D visualization tools, and it needs the supervision of trained medical doctors in order to minimize the errors. In this paper we propose an automated segmentation algorithm specifically designed to segment SEEG contacts from a thresholded post-implant Cone-Beam CT volume (0.4 mm, 0.4 mm, 0.8 mm). The algorithm relies on the planned position of target and entry points for each electrode as a first estimation of electrode axis. We implemented the proposed algorithm into DEETO, an open source C++ prototype based on ITK library. Results: We tested our implementation on a cohort of 28 subjects in total. The experimental analysis, carried out over a subset of 12 subjects (35 multilead electrodes; 200 contacts) manually segmented by experts, show that the algorithm: (i) is faster than manual segmentation (i.e., less than 1s/subject versus a few hours) (ii) is reliable, with an error of 0.5 mm +/- 0.06 mm, and (iii) it accurately maps SEEG implants to their anatomical regions improving the interpretability of electrophysiological traces for both clinical and research studies. Moreover, using the 28-subject cohort we show here that the algorithm is also robust (error <0.005 mm) against deep-brain displacements (<12 mm) of the implanted electrode shaft from those planned before surgery. Conclusions: Our method represents, to the best of our knowledge, the first automatic algorithm for the segmentation of SEEG electrodes. The method can be used to accurately identify the neuroanatomical loci of SEEG electrode contacts by a non-expert in a fast and reliable manner.Peer reviewe

The correlation of the neuronal long-range temporal correlations, avalanche dynamics with the behavioral scaling laws and interindividual variability

Spontaneous infra‐slow fluctuations (ISFs) in both neuronal firing rates and membrane potentials are a defining characteristic of fast (&lt;1 Hz) mammalian brain activity both in vitro and in vivo. In human electrophysiological data, ISFs are salient both in direct recordings of scalp potentials and in amplitude dynamics of fast activities. In blood‐oxygenation‐level dependent (BOLD) signals, ISFs are prevalent as well and correlated among specific constellations of cortical and subcortical regions. These correlations define a hierarchical network of brain connectivity and functionally distinct, modular brain systems. While there are numerous examples of narrow‐band oscillations in the aforementioned data in the putative infra‐slow (0.01–0.1 Hz) or slow (0.1–1 Hz) frequency bands, several lines of data also show that ISFs are fractally self‐similar, scale‐free, and long‐range power‐law correlated across time and space. Importantly, a similar scale‐free dynamics characterizes also psychophysical time series. ISFs in fast amplitudes, slow potentials, BOLD signals, and behavioral data are mutually correlated and likely to reflect the same underlying phenomenon; (self‐organized) critical‐state dynamics in human large‐scale brain activity. Conversely, slow fluctuations appear to be the primary manifestation of critical‐state dynamics in the human brain. In this chapter, we briefly overview the physiological background and recording of infra‐slow fluctuations as well as the data analysis approaches for quantifying spatio‐temporal scaling laws in these data. In particular, we discuss neuronal scaling law dynamics from data obtained with magneto‐ and electroencephalography (M/EEG). M/EEG data combined with source reconstruction techniques have revealed spatio‐temporal correlations, scale‐free dynamics, and long‐range temporal correlations in spontaneous and task‐related brain activity. Importantly, both the avalanche dynamics and long‐range temporal correlations are correlated with behavioral scaling laws in confined cortical areas. Non‐invasive M/EEG‐based approaches pave the way for investigating brain‐subsystem‐specific critical‐state dynamics in healthy brains and pathological conditions, and critically, for understanding the putative link between neuronal and behavioral scaling laws

Modular co-organization of functional connectivity and scale-free dynamics in the human brain.

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Critical dynamics of endogenous fluctuations predict cognitive flexibility in the Go/NoGo task

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Whole-Brain Source-Reconstructed MEG-Data Reveal Reduced Long-Range Synchronization in Chronic Schizophrenia.

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