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Approximately half of the associations identified between attention-deficit/hyperactivity disorder and brain morphology in two large independent studies of adolescents are likely due to confounding bias by socioeconomic and parental behavioral factors.

MRI is sensitive to the increase in axonal caliber due to pathology and was used to detect widespread increase in the average axonal caliber in multiple sclerosis brains with short disease duration only.

The slope of the power spectrum of subthalamic local field potentials tracks pathological states in Parkinson’s disease and likely complements beta activity as a feedback marker for adaptive deep brain stimulation.

The combination of deep learning with whole-brain computational models reveals the low-dimensional representation of neurodegenerative diseases, which emerges from a highly multidimensional brain, providing valuable insight into pathological states' diagnostic, prognosis, and treatment response.

The NeuroML model description language, with its extensive software ecosystem, supports researchers in the development of FAIR, data-driven, biologically detailed models of neural systems.

A new method that uses the cellular neuroanatomy of neuronal connectivity to quickly and robustly reconstruct neuronal circuits in Drosophila from large electron microscopy volumes.

The three main types of inhibitory neurons in mouse primary visual cortex respond differently to locomotion in darkness and during visual stimulation, revealing context-dependent responses to changes in behavioral state.

Nitric oxide is a type-selective neuromodulator affecting the temporal, but not the spatial response kinetics of a specific subset of mouse retinal ganglion cells.

A comprehensive meta-analysis of the neuroimaging literature reveals that the lateral prefrontal cortex of humans is mainly organized along its rostrocaudal axis according to a unimodal-to-transmodal pattern of network connectivity and a concrete-to-abstract axis of functional associations.

A novel machine learning approach improves quantitative comparisons of mouse and human brains using spatial transcriptomics data sets.