Comparative Connectomics.

We introduce comparative connectomics, the quantitative study of cross-species commonalities and variations in brain network topology that aims to discover general principles of network architecture of nervous systems and the identification of species-specific features of brain connectivity. By comparing connectomes derived from simple to more advanced species, we identify two conserved themes of wiring: the tendency to organize network topology into communities that serve specialized functionality and the general drive to enable high topological integration by means of investment of neural resources in short communication paths, hubs, and rich clubs. Within the space of wiring possibilities that conform to these common principles, we argue that differences in connectome organization between closely related species support adaptations in cognition and behavior.We thank Lianne Scholtens, Jim Rilling, Tom Schoenemann for discussions and comments. MPvdH was supported by a VENI (# 451-12-001) grant from the Netherlands Organization for Scientific Research (NWO) and a Fellowship of MQ.This is the author accepted manuscript. The final version is available from Elsevier via https://doi.org/10.1016/j.tics.2016.03.00

Perspectives on the Neuroscience of Cognition and Consciousness

The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness

Of circuits and brains. The origin and diversification of neural architectures

Nervous systems are complex cellular structures that allow animals to interact with their environment, which includes both the external and the internal milieu. The astonishing diversity of nervous system architectures present in all animal clades has prompted the idea that selective forces must have shaped them over evolutionary time. In most cases, neurons seem to coalesce into specific (centralized) structures that function as "central processing units" (CPU): "brains." Why did neural systems adopt this physical configuration? When did it first happen? What are the physiological, computational, and/or structural advantages of concentrating many neurons in a specific place within the body? Here we examine the concept of nervous system centralization and factors that might have contributed to the evolutionary success of this centralization strategy. In particular, we suggest a putative scenario for the evolution of neural system centralization that incorporates different strands of evidence. This scenario is based on some premises: (1) Receptors originated before neurons (sensors before transmitters) and there were deployed in the first organisms in an asymmetric fashion (deposited randomly in the outer layer); (2) Receptors were segregated in a preferential position in response to an anisotropic environment, (3) Neurons were born in association with this receptors and used to transmit signals distally; (4) Energetics preferentially selected the localization of neurons, and synapsis, close to the receptors (to minimize wire use, for instance); (5) The presence of condensed areas of neurons could have stimulated the proliferation of more receptors in the vicinity, increasing the repertoire of signals processed in an specific body domain (i.e., head) plus contributing to amplify the computational power of the neuronal aggregate; (6) The proliferation of receptors would have induced the proliferation of more neurons in the aggregate, with a further increase in its computational power (hence, diversifying the behavioral repertoire). These last two steps of proliferation and aggregation could have been sustained through a feedback loop, reiterated many times, generating distinct topologies in different lineages. Our main aim in this paper is to examine the brain as both a biological and a physical or computational device

Integrated Multi-Omics Maps of Lower-Grade Gliomas

Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It “portrays” the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers

Topological specification of connections between prefrontal cortex and hypothalamus in rhesus monkey

The hypothalamus is a subcortical brain region whose limits and constituent nuclei lack consensus. The hypothalamus has been linked to emotion and different states of stress, providing critical feedback about the internal environment to the prefrontal cortex, a region known for executive function within the cortex of humans. An understanding of the developmental origin of the hypothalamus can provide a basis for defining which limits and nuclei are ontologically hypothalamic, and which are not, as well as a framework for understanding its connectional relationship with other brain regions. The Prosomeric Model (Rubenstein et al. 1994; Puelles and Rubenstein 2003; Nieuwenhuys and Puelles 2016; Puelles 2018) explains the embryological development of the central nervous system (CNS) shared by all vertebrates as a Bauplan. As a primary event, the early neural plate is patterned by intersecting longitudinal plates and transverse segments, forming a mosaic of progenitor units. The hypothalamus is specified by three prosomeres [hp1, hp2, and the acroterminal domain (At)] of the secondary prosencephalon with corresponding alar and basal plate parts, which develop apart from the diencephalon. Mounting evidence suggests that progenitor units within alar plate and basal plate parts of hp1 and hp2 give rise to distinct hypothalamic nuclei, which preserve their relative invariant positioning (topology) in the adult brain. Nonetheless, the principles of the Prosomeric Model have not been applied to the hypothalamus of adult primates. The Structural Model (Barbas 1986; Barbas and Rempel-Clower 1997) highlights the variation of laminar structure in the grey matter of the prefrontal cortex as a basis for predicting specific cortico-cortical connections. The areas of the prefrontal cortex vary along a spectrum by number of layers, laminar definition, and cellularity of those layers. The systematic laminar patterns of different areas of the prefrontal cortex seem to be associated with differential rates of development or maturation. A topographical analysis of bidirectional projections between the prefrontal cortex and the hypothalamus was previously applied using the Structural Model (Rempel-Clower and Barbas 1998). The authors found the prefrontal cortex has highly specific projections to the hypothalamus, originating mostly from limbic orbital and medial prefrontal areas, which have lower laminar definition than other prefrontal areas. In addition, the hypothalamus has relatively specific patterns of projection to the prefrontal cortex. We previously lacked an organizing principle to examine the specific pattern of connections between the hypothalamus and prefrontal cortex in adult rhesus monkey. In the present study, hypothalamic nuclei in the rhesus monkey (Macaca mulatta) were parcellated using classic architectonic boundaries and stains. The topological relations of hypothalamic nuclei and adjacent hypothalamic landmarks were then analyzed with homology across rodent and primate species to trace the origin of adult hypothalamic nuclei to the alar or basal plate components of hp1 and hp2. A novel atlas of the hypothalamus of the adult rhesus monkey was generated with developmental ontologies for each hypothalamic nucleus. This atlas was then applied to a topological analysis of the strength and pattern of connections between the hypothalamus and prefrontal cortex in the adult rhesus monkey. The result is a systematic reinterpretation of the adult hypothalamus of the rhesus monkey whose prosomeric ontology was used to study connections and neuraxial pathways linking the hypothalamus and prefrontal cortex. The convergence of the Prosomeric and Structural Models provides a framework through development to explain the structural patterns found in the adult primate cortex and hypothalamus, and the likely consequences of their disruption

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.Comment: 59 pages, 5 figures, review articl

Progress toward an understanding of cortical computation

The additional data, perspectives, questions, and criticisms contributed by the commentaries strengthen our view that local cortical processors coordinate their activity with the context in which it occurs using contextual fields and synchronized population codes. We therefore predict that whereas the specialization of function has been the keynote of this century the coordination of function will be the keynote of the next