Volume 23 Issue 12, December 2020

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.

A new technique developed by Garcia-Marques and colleagues uses CRISPR–Cas9 editing to activate an ordered sequence of fluorescent markers in stem cells and their progeny. These tools represent a new way to probe the spatial and temporal patterns of cell lineage progression.

Network neuroscientists envision the brain as a network of nodes (regions) linked via edges (connections). A long-held assumption is that node-centric interactions are the primary phenomena of interest. Faskowitz et al. introduce a novel edge-centric framework with the potential to usher in a new era of discovery in connectomics research.

The Organization for Human Brain Mapping presents its best practices report for reproducible EEG and MEG research, highlighting issues and main recommendations in this Perspective.

In this Primer article, Bijsterbosch and colleagues provide an accessible discussion of the challenges faced in analytical representations of functional brain organization and provide clear recommendations to unite a fractionated field.

Chiaradia and Lancaster review applications and limitations of brain organoids, placing them in context with other technologies and describing how these methods are heavily informed by in vivo development.

This Review discusses two high-throughput techniques—massively parallel reporter assays (MPRAs) and CRISPR screens—focusing on their potential to validate non-coding genetic risk variants in human stem cell models of complex brain disorders.

This review summarizes advances in electrical, optical and microfluidic neural interfaces with characteristics that suggest near-term potential for broad deployment to the neuroscience community.

Behavioral quantification is changing neuroscience. Pereira et al. provide an overview of the latest advances in motion tracking and behavior prediction and discuss how these methods are used to understand the brain in ways not previously possible.

Learning to suppress maladaptive behaviors is critical for good mental health. Kim et al. show that mice can be taught to suppress previously acquired motor responses by selective and properly timed stimulation of the cerebello-olivary pathway.

Chun et al. find that a severe model of reactive astrocytes overproduces hydrogen peroxide, leading to the development of Alzheimer’s disease-like pathologies, including neurodegeneration, tauopathy and memory impairment.

Alzheimer’s disease is often considered a disease of neurons. This study reveals that astrocytes are also impaired by the disease and that these cells contribute more to memory deterioration than previously thought.

Uhlmann et al. show that the preclinical phase of Alzheimer’s disease may in fact be a relatively late manifestation of a much earlier pathogenic and targetable process of seed formation and propagation.

Using cryo-electron tomography to detect individual GABA_A receptors in hippocampal synapses, we discovered a hierarchical and mesophasic organization of inhibitory postsynaptic density proteins that enables efficient synaptic transmission.

When people are isolated, they crave social interactions. Midbrain craving regions were activated by food in hungry people, and by social interactions in people mandated to be isolated.

The authors show that a coordinated epigenetic priming event during memory encoding and consolidation facilitates promoter–enhancer interactions that are vital for the unique transcriptional output of reactivated engram neurons.

Garcia-Marques et al. present CLADES, an innovative approach to study neuronal lineages based on CRISPR. Inspired by synthetic biology, CLADES relies on a system of genetic switches to activate and inactivate reporter genes in a predetermined order.

This study describes a series of new gene-regulatory sequences that restrict expression of viral transgenes to specific interneuron subtypes, allowing for selective monitoring and manipulation of their activity from mice to humans.

Kuan, Phelps, et al. used synchrotron X-ray imaging and deep learning to map dense neuronal wiring in fly and mouse tissue, enabling examination of individual cells and connectivity in circuits governing motor control and perceptual decision-making.

The authors present an edge-centric model of brain connectivity. Edge networks are stable across datasets, and their structure can be modulated by sensory input. When clustered, edge networks yield pervasively overlapping functional modules.

A method for parameterizing electrophysiological neural power spectra into periodic and aperiodic components is introduced, addressing limitations of common approaches. The method is validated in simulation and demonstrated on real data applications.