Human local field potentials in motor and non-motor brain areas encode upcoming movement direction.

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

Episodic memory and recognition are influenced by cues' sensory modality: comparing odours, music and faces using virtual reality.

Most everyday experiences are multisensory, and all senses can trigger the conscious re-experience of unique personal events embedded in their specific spatio-temporal context. Yet, little is known about how a cue's sensory modality influences episodic memory, and which step of this process is impacted. This study investigated recognition and episodic memory across olfactory, auditory and visual sensory modalities in a laboratory-ecological task using a non-immersive virtual reality device. At encoding, participants freely and actively explored unique and rich episodes in a three-room house where boxes delivered odours, musical pieces and pictures of face. At retrieval, participants were presented with modality-specific memory cues and were told to 1) recognise encoded cues among distractors and, 2) go to the room and select the box in which they encountered them at encoding. Memory performance and response times revealed that music and faces outperformed odours in recognition memory, but that odours and faces outperformed music in evoking encoding context. Interestingly, correct recognition of music and faces was accompanied by more profound inspirations than correct rejection. By directly comparing memory performance across sensory modalities, our study demonstrated that despite limited recognition, odours are powerful cues to evoke specific episodic memory retrieval.

Decoding the neural dynamics of free choice in humans.

How do we choose a particular action among equally valid alternatives? Nonhuman primate findings have shown that decision-making implicates modulations in unit firing rates and local field potentials (LFPs) across frontal and parietal cortices. Yet the electrophysiological brain mechanisms that underlie free choice in humans remain ill defined. Here, we address this question using rare intracerebral electroencephalography (EEG) recordings in surgical epilepsy patients performing a delayed oculomotor decision task. We find that the temporal dynamics of high-gamma (HG, 60-140 Hz) neural activity in distinct frontal and parietal brain areas robustly discriminate free choice from instructed saccade planning at the level of single trials. Classification analysis was applied to the LFP signals to isolate decision-related activity from sensory and motor planning processes. Compared with instructed saccades, free-choice trials exhibited delayed and longer-lasting HG activity during the delay period. The temporal dynamics of the decision-specific sustained HG activity indexed the unfolding of a deliberation process, rather than memory maintenance. Taken together, these findings provide the first direct electrophysiological evidence in humans for the role of sustained high-frequency neural activation in frontoparietal cortex in mediating the intrinsically driven process of freely choosing among competing behavioral alternatives.

Personal familiarity of music and its cerebral effect on subsequent speech processing.

Despite the obvious personal relevance of some musical pieces, the cerebral mechanisms associated with listening to personally familiar music and its effects on subsequent brain functioning have not been specifically evaluated yet. We measured cerebral correlates with functional magnetic resonance imaging (fMRI) while composers listened to three types of musical excerpts varying in personal familiarity and self (familiar own/composition, familiar other/favorite or unfamiliar other/unknown music) followed by sequences of names of individuals also varying in personal familiarity and self (familiar own/own name, familiar other/close friend and unfamiliar other/unknown name). Listening to music with autobiographical contents (familiar own and/or other) recruited a fronto-parietal network including mainly the dorsolateral prefrontal cortex, the supramarginal/angular gyri and the precuneus. Additionally, while listening to familiar other music (favorite) was associated with the activation of reward and emotion networks (e.g. the striatum), familiar own music (compositions) engaged brain regions underpinning self-reference (e.g. the medial prefrontal cortex) and visuo-motor imagery. The present findings further suggested that familiar music with self-related reference (compositions) leads to an enhanced activation of the autobiographical network during subsequent familiar name processing (as compared to music without self-related reference); among these structures, the precuneus seems to play a central role in personally familiar processing.

NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines.

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html, and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization.

We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuro-oriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal, for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org).

Measuring alterations in oscillatory brain networks in schizophrenia with resting-state MEG: State-of-the-art and methodological challenges.

OBJECTIVE: Neuroimaging studies provide evidence of disturbed resting-state brain networks in Schizophrenia (SZ). However, untangling the neuronal mechanisms that subserve these baseline alterations requires measurement of their electrophysiological underpinnings. This systematic review specifically investigates the contributions of resting-state Magnetoencephalography (MEG) in elucidating abnormal neural organization in SZ patients. METHOD: A systematic literature review of resting-state MEG studies in SZ was conducted. This literature is discussed in relation to findings from resting-state fMRI and EEG, as well as to task-based MEG research in SZ population. Importantly, methodological limitations are considered and recommendations to overcome current limitations are proposed. RESULTS: Resting-state MEG literature in SZ points towards altered local and long-range oscillatory network dynamics in various frequency bands. Critical methodological challenges with respect to experiment design, and data collection and analysis need to be taken into consideration. CONCLUSION: Spontaneous MEG data show that local and global neural organization is altered in SZ patients. MEG is a highly promising tool to fill in knowledge gaps about the neurophysiology of SZ. However, to reach its fullest potential, basic methodological challenges need to be overcome. SIGNIFICANCE: MEG-based resting-state power and connectivity findings could be great assets to clinical and translational research in psychiatry, and SZ in particular.

"What-Where-Which" Episodic Retrieval Requires Conscious Recollection and Is Promoted by Semantic Knowledge.

Episodic memory is defined as the conscious retrieval of specific past events. Whether accurate episodic retrieval requires a recollective experience or if a feeling of knowing is sufficient remains unresolved. We recently devised an ecological approach to investigate the controlled cued-retrieval of episodes composed of unnamable odors (What) located spatially (Where) within a visual context (Which context). By combining the Remember/Know procedure with our laboratory-ecological approach in an original way, the present study demonstrated that the accurate odor-evoked retrieval of complex and multimodal episodes overwhelmingly required conscious recollection. A feeling of knowing, even when associated with a high level of confidence, was not sufficient to generate accurate episodic retrieval. Interestingly, we demonstrated that the recollection of accurate episodic memories was promoted by odor retrieval-cue familiarity and describability. In conclusion, our study suggested that semantic knowledge about retrieval-cues increased the recollection which is the state of awareness required for the accurate retrieval of complex episodic memories.

Lateralization of olfactory processing: differential impact of right and left temporal lobe epilepsies.

Olfactory processes were reported to be lateralized. The purpose of this study was to further explore this phenomenon and investigate the effect of the hemispheric localization of epileptogenic foci on olfactory deficits in patients with temporal lobe epilepsy (TLE). Olfactory functioning was assessed in 61 patients and 60 healthy control (HC) subjects. The patients and HC subjects were asked to rate the intensity, pleasantness, familiarity, and edibility of 12 common odorants and then identify them. Stimulations were delivered monorhinally in the nostril ipsilateral to the epileptogenic focus in TLE and arbitrarily in either the left or the right nostril in the HC subjects. The results demonstrated that regardless of the side of stimulation, patients with TLE had reduced performance in all olfactory tasks compared with the HC subjects. With regard to the side of the epileptogenic focus, patients with left TLE judged odors as less pleasant and had more difficulty with identification than patients with right TLE, underlining a privileged role of the left hemisphere in the emotional and semantic processing of odors. Finally, irrespective of group, a tendency towards a right-nostril advantage for judging odor familiarity was found in agreement with a prominent role of the right hemisphere in odor memory processing.

A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches.

Odors are powerful cues that trigger episodic memories. However, in light of the amount of behavioral data describing the characteristics of episodic odor memory, the paucity of information available on the neural substrates of this function is startling. Furthermore, the diversity of experimental paradigms complicates the identification of a generic episodic odor memory network. We conduct a systematic review of the literature depicting the current state of the neural correlates of episodic odor memory in healthy humans by placing a focus on the experimental approaches. Functional neuroimaging data are introduced by a brief characterization of the memory processes investigated. We present and discuss laboratory-based approaches, such as odor recognition and odor associative memory, and autobiographical approaches, such as the evaluation of odor familiarity and odor-evoked autobiographical memory. We then suggest the development of new laboratory-ecological approaches allowing for the controlled encoding and retrieval of specific multidimensional events that could open up new prospects for the comprehension of episodic odor memory and its neural underpinnings. While large conceptual differences distinguish experimental approaches, the overview of the functional neuroimaging findings suggests relatively stable neural correlates of episodic odor memory.

A unique memory process modulated by emotion underpins successful odor recognition and episodic retrieval in humans.

We behaviorally explore the link between olfaction, emotion and memory by testing the hypothesis that the emotion carried by odors facilitates the memory of specific unique events. To investigate this idea, we used a novel behavioral approach inspired by a paradigm developed by our team to study episodic memory in a controlled and as ecological as possible way in humans. The participants freely explored three unique and rich laboratory episodes; each episode consisted of three unfamiliar odors (What) positioned at three specific locations (Where) within a visual context (Which context). During the retrieval test, which occurred 24-72 h after the encoding, odors were used to trigger the retrieval of the complex episodes. The participants were proficient in recognizing the target odors among distractors and retrieving the visuospatial context in which they were encountered. The episodic nature of the task generated high and stable memory performances, which were accompanied by faster responses and slower and deeper breathing. Successful odor recognition and episodic memory were not related to differences in odor investigation at encoding. However, memory performances were influenced by the emotional content of the odors, regardless of odor valence, with both pleasant and unpleasant odors generating higher recognition and episodic retrieval than neutral odors. Finally, the present study also suggested that when the binding between the odors and the spatio-contextual features of the episode was successful, the odor recognition and the episodic retrieval collapsed into a unique memory process that began as soon as the participants smelled the odors.

Modular structure of functional networks in olfactory memory.

Graph theory enables the study of systems by describing those systems as a set of nodes and edges. Graph theory has been widely applied to characterize the overall structure of data sets in the social, technological, and biological sciences, including neuroscience. Modular structure decomposition enables the definition of sub-networks whose components are gathered in the same module and work together closely, while working weakly with components from other modules. This processing is of interest for studying memory, a cognitive process that is widely distributed. We propose a new method to identify modular structure in task-related functional magnetic resonance imaging (fMRI) networks. The modular structure was obtained directly from correlation coefficients and thus retained information about both signs and weights. The method was applied to functional data acquired during a yes-no odor recognition memory task performed by young and elderly adults. Four response categories were explored: correct (Hit) and incorrect (False alarm, FA) recognition and correct and incorrect rejection. We extracted time series data for 36 areas as a function of response categories and age groups and calculated condition-based weighted correlation matrices. Overall, condition-based modular partitions were more homogeneous in young than elderly subjects. Using partition similarity-based statistics and a posteriori statistical analyses, we demonstrated that several areas, including the hippocampus, caudate nucleus, and anterior cingulate gyrus, belonged to the same module more frequently during Hit than during all other conditions. Modularity values were negatively correlated with memory scores in the Hit condition and positively correlated with bias scores (liberal/conservative attitude) in the Hit and FA conditions. We further demonstrated that the proportion of positive and negative links between areas of different modules (i.e., the proportion of correlated and anti-correlated areas) accounted for most of the observed differences in signed modularity. Taken together, our results provided some evidence that the neural networks involved in odor recognition memory are organized into modules and that these modular partitions are linked to behavioral performance and individual strategies.

The impact of expertise in olfaction.

Olfactory expertise remains poorly understood, most likely because experts in odor, such as perfumers, sommeliers, and oenologists, are much rarer than experts in other modalities, such as musicians or sportsmen. In this review, we address the specificities of odor expertise in both odor experts and in a priori untrained individuals who have undergone specific olfactory training in the frame of an experiment, such as repeated exposure to odors or associative learning. Until the 21st century, only the behavioral effects of olfactory training of untrained control individuals had been reported, revealing an improvement of olfactory performance in terms of sensitivity, discrimination, memory, and identification. Behavioral studies of odor experts have been scarce, with inconsistent or inconclusive results. Recently, the development of cerebral imaging techniques has enabled the identification of brain areas and neural networks involved in odor processing, revealing functional and structural modifications as a function of experience. The behavioral approach to odor expertise has also evolved. Researchers have particularly focused on odor mental imagery, which is characteristic of odor experts, because this ability is absent in the average person but is part of a perfumer's professional practice. This review summarizes behavioral, functional, and structural findings on odor expertise. These data are compared with those obtained using animals subjected to prolonged olfactory exposure or to olfactory-enriched environments and are discussed in the context of functional and structural plasticity.

A novel experimental approach to episodic memory in humans based on the privileged access of odors to memories.

Episodic memory is defined as the conscious recollection of a personal event (What) in its spatial (Where) and contextual (Which context) environment. In existing approaches, human episodic memory is either explored separately from real-life situations or is not fully controlled. In this study, we propose an intermediate approach, inspired by animal studies, that permits the control of the encoding and recall phases, while still being ecologically valid. As odors are known to be especially evocative reminders, we explored the memory of olfactory episodes. During trial-unique encoding, participants freely explored three episodes, one episode per day, each composed of three unnamable odors (What) that were positioned at specific locations on a board (Where) within a visual context (Which context). On the fourth day, both old and new odors were presented, and when an odor was recognized, the participants had to remember both its spatial location and the visual context in which it occurred. In Experiment 1, the participants were highly proficient at recognizing odors, and they recall the spatio-contextual environment associated with these odors in approximately half of the trials. To adapt the recall procedure to the constraints of fMRI, we conducted Experiment 2 demonstrating that trial repetition did not disturb the memory process. Thus, we first validated our protocol, which investigates the memory of olfactory episodes in a fully controlled way that is as close as possible to real-life situations. Then, we demonstrated the adaptability of our protocol for the future exploration of the neural networks implicated in episodic recall.