Good scientific practice in EEG and MEG research: Progress and perspectives.

Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.

Symmetry Processing in the Macaque Visual Cortex.

Symmetry is a highly salient feature of the natural world that is perceived by many species. In humans, the cerebral areas processing symmetry are now well identified from neuroimaging measurements. Macaque could constitute a good animal model to explore the underlying neural mechanisms, but a previous comparative study concluded that functional magnetic resonance imaging responses to mirror symmetry in this species were weaker than those observed in humans. Here, we re-examined symmetry processing in macaques from a broader perspective, using both rotation and reflection symmetry embedded in regular textures. Highly consistent responses to symmetry were found in a large network of areas (notably in areas V3 and V4), in line with what was reported in humans under identical experimental conditions. Our results suggest that the cortical networks that process symmetry in humans and macaques are potentially more similar than previously reported and point toward macaque as a relevant model for understanding symmetry processing.

Modelling binocular disparity processing from statistics in natural scenes.

The statistics of our environment impact not only our behavior, but also the selectivity and connectivity of the early sensory cortices. Over the last fifty years, powerful theories such as efficient coding, sparse coding, and the infomax principle have been proposed to explain the nature of this influence. Numerous computational and theoretical studies have since demonstrated solid, testable evidence in support of these theories, especially in the visual domain. However, most such work has concentrated on monocular, luminance-field descriptions of natural scenes, and studies that systematically focus on binocular processing of realistic visual input have only been conducted over the past two decades. In this review, we discuss the most recent of these binocular computational studies, with particular emphasis on disparity selectivity. We begin with a report of the relevant literature demonstrating concrete evidence for the relationship between natural disparity statistics, neural selectivity, and behavior. This is followed by a discussion of supervised and unsupervised computational studies. For each study, we include a description of the input data, theoretical principles employed in the models, and the contribution of the results in explaining biological data (neural and behavioral). In the discussion, we compare these models to the binocular energy model, and examine their application to the modelling of normal and abnormal development of vision. We conclude with a short description of what we believe are the most important limitations of the current state-of-the-art, and directions for future work which could address these shortcomings and enrich current and future models.

Stereomotion Processing in the Nonhuman Primate Brain.

The cortical areas that process disparity-defined motion-in-depth (i.e., cyclopean stereomotion [CSM]) were characterized with functional magnetic resonance imaging (fMRI) in two awake, behaving macaques. The experimental protocol was similar to previous human neuroimaging studies. We contrasted the responses to dynamic random-dot patterns that continuously changed their binocular disparity over time with those to a control condition that shared the same properties, except that the temporal frames were shuffled. A whole-brain voxel-wise analysis revealed that in all four cortical hemispheres, three areas showed consistent sensitivity to CSM. Two of them were localized respectively in the lower bank of the superior temporal sulcus (CSMSTS) and on the neighboring infero-temporal gyrus (CSMITG). The third area was situated in the posterior parietal cortex (CSMPPC). Additional regions of interest-based analyses within retinotopic areas defined in both animals indicated weaker but significant responses to CSM within the MT cluster (most notably in areas MSTv and FST). Altogether, our results are in agreement with previous findings in both human and macaque and suggest that the cortical areas that process CSM are relatively well preserved between the two primate species.

Processing of Egomotion-Consistent Optic Flow in the Rhesus Macaque Cortex.

The cortical network that processes visual cues to self-motion was characterized with functional magnetic resonance imaging in 3 awake behaving macaques. The experimental protocol was similar to previous human studies in which the responses to a single large optic flow patch were contrasted with responses to an array of 9 similar flow patches. This distinguishes cortical regions where neurons respond to flow in their receptive fields regardless of surrounding motion from those that are sensitive to whether the overall image arises from self-motion. In all 3 animals, significant selectivity for egomotion-consistent flow was found in several areas previously associated with optic flow processing, and notably dorsal middle superior temporal area, ventral intra-parietal area, and VPS. It was also seen in areas 7a (Opt), STPm, FEFsem, FEFsac and in a region of the cingulate sulcus that may be homologous with human area CSv. Selectivity for egomotion-compatible flow was never total but was particularly strong in VPS and putative macaque CSv. Direct comparison of results with the equivalent human studies reveals several commonalities but also some differences.