The copy-and-shift mechanism modelled in the insect central complex facilitates multimodal navigation, providing a general computation model explaining flexible navigation behaviours.

Combined cell labelling with a bi-cistronic reporter-gene vector and gold nanorods enables short- and long-term cell tracking in vivo via multimodal imaging (multispectral optoacoustic tomography, bioluminescence, fluorescence) with high spatial resolution.

Anoctamin/TMEM16 and SLC12 co-transporter genes selectively function in regulating cold nociception via excitatory chloride currents and can promote neuropathic sensitization phenotypes, including behavioral sensitization and neuronal hyperexcitability in Drosophila multimodal sensory neurons.

SPOT-MAS (screening for the presence of tumor by methylation and size) is an affordable and effective multi-cancer early detection test analyzing multiple features of circulating tumor DNA.

THINGS-data reflects three large-scale neuroimaging and behavioral datasets of object processing in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1854 objects.

The multimodal linked independent component analysis promotes a more integrative understanding of transdiagnostic mechanisms of psychopathology.

Building on previous work (Huang et al., 2013), it is shown that single granule cells receive excitatory synaptic input driven by up to three separate sensory modalities in vivo, which demonstrate that individual neurons can contribute to multisensory integration at the input layer of the cerebellar cortex.

Oligo-conjugated antibodies are a powerful tool but require thorough optimization to reduce background signal, increase sensitivity, and reduce sequencing costs.

The macaque monkey intraparietal sulcus encompasses 17 cyto- and receptorarchitectonically distinct areas, which can be grouped into three clusters based on (dis)similarities of their molecular structure.

Machine learning-based models of brain age using distinct MRI modalities (structural MRI and functional connectivity) capture complementary signals related to healthy age differences and capture unique patterns related to Alzheimer disease, including stages of disease progression, biomarkers, and cognition.