We present a new 3D digital brain atlas of the non-human primate, common marmoset monkey (Callithrix jacchus), with MRI and coregistered Nissl histology data. To the best of our knowledge this is the first comprehensive digital 3D brain atlas of the common marmoset having normalized multi-modal data, cortical and sub-cortical segmentation, and in a common file format (NIfTI). The atlas can be registered to new data, is useful for connectomics, functional studies, simulation and as a reference. The atlas was based on previously published work but we provide several critical improvements to make this release valuable for researchers. Nissl histology images were processed to remove illumination and shape artifacts and then normalized to the MRI data. Brain region segmentation is provided for both hemispheres. The data is in the NIfTI format making it easy to integrate into neuroscience pipelines, whereas the previous atlas was in an inaccessible file format. We also provide cortical, mid-cortical and white matter boundary segmentations useful for visualization and analysis.

We describe our connectomics pipeline for processing tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the anterograde tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data was mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, each brain was cytoarchitectonically annotated and used for making an individual 3D atlas. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze tracer segmentation and brain registration accuracy. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.

Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain’s global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan’s Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.

The primate prefrontal cortex (PFC) has greatly expanded to evolve specialized architecture, but its roles in top-down brain control remain enigmatic. Based on connectomics mapping of the marmoset PFC, we characterized two contrasting features of corticocortical and corticostriatal projections. One is the "focalness" of projections, exemplified by multiple columnar axonal terminations in the cortical layers and the other is the "widespreadness" of weaker projections, whose patterns consisted of several common motifs representing the framework of PFC connectivity. We clarified the topographic rules of distribution for these features, which should constrain how PFC neurons can coordinate to control the target regions as populations. These features are observed only primitively in rodents and are considered critical in understanding the roles of the PFC in neuropsychiatric disorders.

We respond to the commentaries by Hodgson and Lewis-Williams by clarifying the novelty of our theory. We argue that whenever Turing instabilities of neural activity play a role in generating visual hallucinations, they do more than shape the geometric patterns. Their relatively autonomous self-organization is a source of intrinsic value related to their selfmaintenance as a pattern of activity, and they would also thereby decouple ''higher-level'' stages of neural processing from external stimulation, thus facilitating a more abstract mode of cognition. These additional features of our proposal support Hodgson and Lewis-Williams in their respective theories about the very first origins of human artistic activity. We also evaluate the critical literature regarding the possibility of ritualized enaction of altered states of consciousness (ASC) in early prehistory. We conclude that ASC were indeed possible, and suggest that they were likely involved in facilitating the social development of more symbolic forms of life and mind.

It has been argued that the worldwide prevalence of certain types of geometric visual patterns found in prehistoric art can be best explained by the common experience of these patterns as geometric hallucinations during altered states of consciousness induced by shamanic ritual practices. And in turn the worldwide prevalence of these types of hallucinations has been explained by appealing to humanity's shared neurobiological embodiment. Moreover, it has been proposed that neural network activity can exhibit similar types of spatiotemporal patterns, especially those caused by Turing instabilities under disinhibited, non-ordinary conditions. Altered states of consciousness thus provide a suitable pivot point from which to investigate the complex relationships between symbolic material culture, first-person experience, and neurobiology. We critique prominent theories of these relationships. Drawing inspiration from neurophenomenology, we sketch the beginnings of an alternative, enactive approach centered on the concepts of sense-making, value, and sensorimotor decoupling.

International audienceThe accurate prediction of solute transport through soils is a necessity to counter the worldwide degradation of aquifers. Dye tracers are widely used to visualize active flow paths in cross-sections of soil, but methods previously proposed to map concentrations have been very costly, demanding, or of coarse resolution and not always applicable in dark allophanic soils. We have developed a cheap and fairly easy experimental procedure and used multiple regression to map dye concentrations in two dimensions. We tested the method using the fluorescent dye, pyranine, in intact cores of an allophanic soil. The method requires a calibration step, which we made using eight dye concentrations. The main difficulty was to mix the soil homogeneously with the dye and to pack it evenly before acquisition of the images. The pyranine was infiltrated in soil cores under unsaturated conditions: its distribution on the vertical core faces was highly heterogeneous with fingered penetration. The maps of dye concentration obtained from each core section achieved fine spatial resolution (e.g. 0.25 mm2 per pixel) and satisfactory dye concentration localization and estimation. We could achieve better spatial resolution by sectioning the soil cores at finer intervals, and estimate the dye concentration more accurately by improving the correction for illumination variations

The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains.