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Objective. Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks. Approach. In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks. Main results. The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics. Significance. We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.

International audience; Evidence from language, visual and sensorimotor learning suggests that training early in life is more effective. The present work explores the hypothesis that learning during sensitive periods involves distinct brain networks in addition to those involved when learning later in life. Expert pianists were tested who started their musical training early (

Brain awake surgery with cognitive monitoring for tumor removal has become a standard of treatment for functional purpose. Yet, little attention has been given to patients’ interpretation and awareness of their own responses to selected cognitive tasks during direct electrostimulation (DES). We aim to report disruptions of self-evaluative processing evoked by DES during awake surgery. We further investigate cortico-subcortical structures involved in self-assessment process and report the use of an intraoperative self-assessment tool, the self-confidence index (SCI). Seventy-two patients who had undergone awake brain tumor resections were selected. Inclusion criteria were the occurrence of a DES-induced disruption of an ongoing task followed by patient’s failure to remember or criticize these impairments, or a dissociation between patient’s responses to an ongoing task and patient’s SCI. Disruptions of self-evaluation were frequently associated with semantic disorders and critical sites were mostly found along the left/right ventral semantic streams. Disconnectome analyses generated from a tractography-based atlas confirmed the high probability of the inferior fronto-occipital fasciculus to be transitory ‘disconnected’. These findings suggest that white matters pathways belonging to the ventral semantic stream may be critically involved in human self-evaluative processing. Finally, the authors discuss the implementation of the SCI task during multimodal intraoperative monitoring.

The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, ‘hyperscanning’ setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such ‘inter-brain connectivity analysis’, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.

Objective The structural and functional organization of brain networks subserving basic daily activities (i.e. language, visuo-spatial cognition, movement, semantics, etc.) are not completely understood to date. Here, we report the first probabilistic cortical and subcortical atlas of critical structures mediating human brain functions based on direct electrical stimulation (DES), a well-validated tool for the exploration of cerebral processing and for performing safe surgical interventions in eloquent areas. Methods We collected 1162 cortical and 659 subcortical DES responses during testing of 16 functional domains in 256 patients undergoing awake surgery. Spatial coordinates for each functional response were calculated, and probability distributions for the entire patient cohort were mapped onto a standardized three-dimensional brain template using a multinomial statistical analysis. In addition, matching analyses were performed against prior established anatomy-based cortical and white matter (WM) atlases. Results The probabilistic maps for each functional domain were provided. The topographical analysis demonstrated a wide spatial distribution of cortical functional responses, while subcortical responses were more restricted, localizing to known WM pathways. These DES-derived data showed reliable matching with existing cortical and WM atlases as well as recent neuroimaging and neurophysiological data. Conclusions We present the first integrated and comprehensive cortical-subcortical atlas of structures essential for humans’ neural functions based on highly-specific DES mapping during real-time neuropsychological testing. This novel atlas can serve as a complementary tool for neuroscientists, along with data obtained from other modalities, to improve and refine our understanding of the functional anatomy of critical brain networks.

International audience; Background: Whilst modern awake intraoperative mapping has been widely accepted and implemented in the last decades in neuro-oncology, sparse reports have been published on the safety and efficiency of this approach in epilepsy surgery.Method: This article reports four cases with different locations of epileptogenic zones as examples of possible safe and efficient resections.Result: The results of the resections on seizure control were Engel 1 (no disabling seizures) in all cases and no patient experienced significant neurological deficits.Discussion: The discussion focuses on aspects of the future of epilepsy surgery in a hodotopical paradigm.

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility., Author summary Neuroscience is experiencing a rapid growth of data streams characterizing composition, connectivity, and activity of brain networks in ever increasing details. Data-driven modeling will be essential to integrate these multimodal and complex data into predictive simulations to advance our understanding of brain function and mechanisms. To enable efficient development and sharing of such large-scale models utilizing diverse data types, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is already supported by several popular tools for model building, simulations, and visualization. It is free and open for everyone to use and build upon and will enable increased efficiency, reproducibility, and scientific exchange in the community.

International audience; Neurosurgeons have been directly involved with brain mapping efforts for well over a century. Direct access to the functioning human brain during surgery for intrinsic brain tumors, epilepsy, and functional neurosurgical procedures both requires individual functional brain mapping and presents an opportunity to precisely map human brain function. Several papers in this issue of Neurosurgical Focus extend our current understanding of cerebral organization with detailed examination of specific regions. Others introduce new technical nuances for intraoperative brain mapping. A third group of papers focuses on noninvasive brain mapping techniques that have emerged and been refined over the last several years.

The computational role of primary visual cortex (V1) in low-level perception remains largely debated. A dominant view assumes the prevalence of higher cortical areas and top-down processes in binding information across the visual field. Here, we investigated the role of long-distance intracortical connections in form and motion processing by measuring, with intracellular recordings, their synaptic impact on neurons in area 17 (V1) of the anesthetized cat. By systematically mapping synaptic responses to stimuli presented in the nonspiking surround of V1 receptive fields, we provide the first quantitative characterization of the lateral functional connectivity kernel of V1 neurons. Our results revealed at the population level two structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. First, subthreshold responses to oriented stimuli flashed in isolation in the nonspiking surround exhibited a geometric organization around the preferred orientation axis mirroring the psychophysical “association field” for collinear contour perception. Second, apparent motion stimuli, for which horizontal and feedforward synaptic inputs summed in-phase, evoked dominantly facilitatory nonlinear interactions, specifically during centripetal collinear activation along the preferred orientation axis, at saccadic-like speeds. This spatiotemporal integration property, which could constitute the neural correlate of a human perceptual bias in speed detection, suggests that local (orientation) and global (motion) information is already linked within V1. We propose the existence of a “dynamic association field” in V1 neurons, whose spatial extent and anisotropy are transiently updated and reshaped as a function of changes in the retinal flow statistics imposed during natural oculomotor exploration.SIGNIFICANCE STATEMENTThe computational role of primary visual cortex in low-level perception remains debated. The expression of this “pop-out” perception is often assumed to require attention-related processes, such as top-down feedback from higher cortical areas. Using intracellular techniques in the anesthetized cat and novel analysis methods, we reveal unexpected structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. These structural-functional biases provide a substrate, within V1, for contour detection and, more unexpectedly, global motion flow sensitivity at saccadic speed, even in the absence of attentional processes. We argue for the concept of a “dynamic association field” in V1 neurons, whose spatial extent and anisotropy changes with retinal flow statistics, and more generally for a renewed focus on intracortical computation.