To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

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Cerebral ischemia is one of the leading causes of mortality and disability in infants and adults and its timely diagnosis is essential for an efficient treatment. We present a methodology for fast detection and real-time monitoring of fluctuations of calcium ions associated with focal ischemia using a molecular functional MRI approach. We used a dinuclear paramagnetic gadolinium(III) complex chelate that changes MR image contrast through its reversible interaction with extracellular calcium ions, while applying a remote transient middle cerebral artery occlusion as a model for ischemic stroke. Our method sensitively recognizes the onset and follows the dynamics of the ischemic core and penumbra with submillimeter spatial and second-scale temporal resolution, thus paving the way for noninvasive monitoring and development of targeted treatment strategies for cerebral ischemia.

We present a condition for delay-independent stability of a class of nonlinear positive systems.This result applies to systems that are not necessarily monotone and extends recent work on cooperative nonlinear systems.

Neuropsychiatric disorders are the third leading cause of global disease burden. Current pharmacological treatment for these disorders is inadequate, with often insufficient efficacy and undesirable side effects. One reason for this is that the links between molecular drug action and neurobehavioral drug effects are elusive. We use a big data approach from the neurotransmitter response patterns of 258 different neuropsychiatric drugs in rats to address this question. Data from experiments comprising 110,674 rats are presented in the Syphad database [www.syphad.org]. Chemoinformatics analyses of the neurotransmitter responses suggest a mismatch between the current classification of neuropsychiatric drugs and spatiotemporal neurostransmitter response patterns at the systems level. In contrast, predicted drug–target interactions reflect more appropriately brain region related neurotransmitter response. In conclusion the neurobiological mechanism of neuropsychiatric drugs are not well reflected by their current classification or their chemical similarity, but can be better captured by molecular drug–target interactions.