Technical Reports

A multiplane confocal microscope provides high-contrast volumetric imaging at kilohertz rates. This system enables imaging of densely expressed genetically encoded voltage indicators with cellular resolution in the mouse brain in vivo and in vitro.

Sebenius et al. present Morphometric INverse Divergence (MIND), a robust MRI-based metric of similarity between brain areas that reflects biological factors that define cortical network architecture, such as gene expression and axonal connectivity.

Bonheur et al. developed a subcellular-distribution-based bidirectional neural activity reporter that operates on the timescale of minutes, and they demonstrate increases and decreases of neural activity associated with social behaviors in Drosophila.

The Neuro-stack is a wearable platform for recording from single neurons in freely moving humans. It also allows for personalized stimulation during real-time decoding of neural activity.

The authors developed flexible, unfolded mesh electronics for implantation in multiple brain regions of mice. The probes show minimal immune response and electrode drift, enabling stable recording of single-unit action potentials from the same neurons during the adult life of animals.

Zhu et al. develop a deep learning method to precisely infer single-trial neural dynamics from calcium imaging with subframe temporal resolution, which shows improvement over the state-of-the-art methods in capturing high-frequency dynamics and predicting behavior.

The authors report a functional class of channelrhodopsins that are highly selective for K⁺ over Na⁺. These light-gated channels, named ‘kalium channelrhodopsins’, enable robust inhibition of mouse cortical neurons with millisecond precision.

The authors present a circuit tracing method, Trans-Seq, which determines the targets of a given neuron type through anterograde tracing combined with single-cell RNA sequencing. Applying Trans-Seq to retinotectal synapses, the authors find a selective connection assembled by Nephronectin.

This study introduces genetically encoded imaging probes that convert intracellular calcium signaling into hemodynamic fMRI responses. The authors show how the probes can be used to map information flow in reward-related brain circuitry in rats.

Neuropixels probes were used to simultaneously record from more than 200 cortical neurons in human participants during neurosurgical procedures. The approach could reveal insights underlying human cognition and pathology.

Viewing behavior is a key variable of interest but also a confound in fMRI studies. This paper presents a deep learning framework to decode gaze position from the magnetic resonance signal of the eyeballs, which enables eye tracking in fMRI data without a camera.

This paper explores neural network and cellular complexity within human cortical and subcortical fusion organoids. The platform is used to model network dysfunction associated with Rett syndrome and to identify new therapeutic candidates.

The authors introduce advanced technology for controlled wireless light delivery in optogenetics applications with real-time user programming capacity. The utility of the platform is highlighted by induction of neural synchrony to modify social behavior in mice.

Sun et al. present BARseq2, a high-throughput method combining in situ sequencing of endogenous mRNAs with barcode-based axonal projection mapping, and apply it to identify cadherins that correlate with similar projections in two cortical areas.

Liu et al. present a flexible, insertable and transparent microelectrode (FITM) array termed Neuro-FITM. Multimodal recordings with Neuro-FITM reveal diverse and selective large-scale cortical activation patterns associated with hippocampal sharp-wave ripples.

The authors develop a genetically encoded GPCR-based sensor to image serotonin dynamics in behaving animals with high specificity, sensitivity and spatiotemporal resolution.

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.

This work develops PSID, a dynamic modeling method to dissociate and prioritize neural dynamics relevant to a given behavior.

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.

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.