neuronwide

Neuronwide refers to analyses, measurements, or interventions that encompass an entire neuron, from soma through the dendritic arbor and axon, rather than focusing on a single subcellular region. It aims to characterize how distributed inputs are integrated and how signals propagate across compartments to shape neuronal output and plasticity. The term has gained usage as imaging, recording, and computational tools allow sampling of activity across multiple compartments in the same neuron.

Origins and scope:

Neuronwide concepts have emerged with advances in high-resolution imaging, voltage- and calcium-sensitive indicators, and multi-site or whole-neuron recording techniques. These approaches enable simultaneous or temporally aligned measurements across soma, proximal and distal dendrites, and axonal segments, enabling study of compartmental coupling, backpropagating action potentials, and intracellular signaling cascades. Neuronwide analysis complements compartmental studies and network-scale investigations by focusing on the single-cell level.

Applications:

Neuronwide methods support investigations of synaptic integration, dendritic computation, event propagation, and activity-dependent plasticity within individual neurons. In computational models, neuronwide morphologies are used to simulate how distributed synaptic inputs generate output and how intracellular signaling coordinates responses. The neuronwide perspective is also used in pathology research to examine how alterations in signaling across compartments contribute to disease.

Methods:

Techniques include multi-site electrophysiology adapted for full-cell recordings, two-photon or voltage imaging across dendrites, patch-clamp recordings from multiple compartments, targeted optogenetic stimulation, and computational reconstructions of complete neuronal activity.

Challenges:

Key challenges include technical complexity, data volume, sampling bias, and distinguishing causation from correlation across compartments. Interpreting neuronwide data requires careful modeling of signal propagation delays and measurement artifacts.