Neuronal Morphology
neuronal morphology
Low intensity rTMS: age dependent effects, and mechanisms underlying neural plasticity
Neuroplasticity is essential for the establishment and strengthening of neural circuits. Repetitive transcranial magnetic stimulation (rTMS) is commonly used to modulate cortical excitability and shows promise in the treatment of some neurological disorders. Low intensity magnetic stimulation (LI-rTMS), which does not directly elicit action potentials in the stimulated neurons, have also shown some therapeutic effects, and it is important to determine the biological mechanisms underlying the effects of these low intensity magnetic fields, such as would occur in the regions surrounding the central high-intensity focus of rTMS. Our team has used a focal low-intensity (10mT) magnetic stimulation approach to address some of these questions and to identify cellular mechanisms. I will present several studies from our laboratory, addressing (1) effects of LIrTMS on neuronal activity and excitability ; and (2) neuronal morphology and post-lesion repair. The ensemble of our results indicate that the effects of LI-rTMS depend upon the stimulation pattern, the age of the animal, and the presence of cellular magnetoreceptors.
Imaging neuronal morphology and activity pattern in developing cerebral cortex layer 4
Establishment of precise neuronal connectivity in the neocortex relies on activity-dependent circuit reorganization during postnatal development. In the mouse somatosensory cortex layer 4, barrels are arranged in one-to-one correspondence to whiskers on the face. Thalamocortical axon termini are clustered in the center of each barrel. The layer 4 spiny stellate neurons are located around the barrel edge, extend their dendrites primarily toward the barrel center, and make synapses with thalamocortical axons corresponding to a single whisker. These organized circuits are established during the first postnatal week through activity-dependent refinement processes. However, activity pattern regulating the circuit formation is still elusive. Using two-photon calcium imaging in living neonatal mice, we found that layer 4 neurons within the same barrel fire synchronously in the absence of peripheral stimulation, creating a ''patchwork'' pattern of spontaneous activity corresponding to the barrel map. We also found that disruption of GluN1, an obligatory subunit of the N-methyl-D-aspartate (NMDA) receptor, in a sparse population of layer 4 neurons reduced activity correlation between GluN1 knockout neuron pairs within a barrel. Our results provide evidence for the involvement of layer 4 neuron NMDA receptors in spatial organization of the spontaneous firing activity of layer 4 neurons in the neonatal barrel cortex. In the talk I will introduce our strategy to analyze the role of NMDA receptor-dependent correlated activity in the layer 4 circuit formation.
Dorothy J Killam Lecture: Cell Type Classification and Circuit Mapping in the Mouse Brain
To understand the function of the brain and how its dysfunction leads to brain diseases, it is essential to have a deep understanding of the cell type composition of the brain, how the cell types are connected with each other and what their roles are in circuit function. At the Allen Institute, we have built multiple platforms, including single-cell transcriptomics, single and multi-patching electrophysiology, 3D reconstruction of neuronal morphology, high throughput brain-wide connectivity mapping, and large-scale neuronal activity imaging, to characterize the transcriptomic, physiological, morphological, and connectional properties of different types of neurons in a standardized way, towards a taxonomy of cell types and a description of their wiring diagram for the mouse brain, with a focus on the visual cortico-thalamic system. Building such knowledge base lays the foundation towards the understanding of the computational mechanisms of brain circuit function.
Neuronal morphology imposes a tradeoff between stability, accuracy and efficiency of synaptic scaling
Synaptic scaling is a homeostatic normalization mechanism that preserves relative synaptic strengths by adjusting them with a common factor. This multiplicative change is believed to be critical, since synaptic strengths are involved in learning and memory retention. Further, this homeostatic process is thought to be crucial for neuronal stability, playing a stabilizing role in otherwise runaway Hebbian plasticity [1-3]. Synaptic scaling requires a mechanism to sense total neuron activity and globally adjust synapses to achieve some activity set-point [4]. This process is relatively slow, which places limits on its ability to stabilize network activity [5]. Here we show that this slow response is inevitable in realistic neuronal morphologies. Furthermore, we reveal that global scaling can in fact be a source of instability unless responsiveness or scaling accuracy are sacrificed." "A neuron with tens of thousands of synapses must regulate its own excitability to compensate for changes in input. The time requirement for global feedback can introduce critical phase lags in a neuron’s response to perturbation. The severity of phase lag increases with neuron size. Further, a more expansive morphology worsens cell responsiveness and scaling accuracy, especially in distal regions of the neuron. Local pools of reserve receptors improve efficiency, potentiation, and scaling, but this comes at a cost. Trafficking large quantities of receptors requires time, exacerbating the phase lag and instability. Local homeostatic feedback mitigates instability, but this too comes at the cost of reducing scaling accuracy." "Realization of the phase lag instability requires a unified model of synaptic scaling, regulation, and transport. We present such a model with global and local feedback in realistic neuron morphologies (Fig. 1). This combined model shows that neurons face a tradeoff between stability, accuracy, and efficiency. Global feedback is required for synaptic scaling but favors either system stability or efficiency. Large receptor pools improve scaling accuracy in large morphologies but worsen both stability and efficiency. Local feedback improves the stability-efficiency tradeoff at the cost of scaling accuracy. This project introduces unexplored constraints on neuron size, morphology, and synaptic scaling that are weakened by an interplay between global and local feedback.
The effect of the autism-associated A749G CACNA1D (Cav1.3) mutation on neuronal morphology
FENS Forum 2024
Influence of antibiotic-induced dysbiosis on neuronal morphology of mouse entorhinal inhibitory interneurons
FENS Forum 2024
Neuronal morphology impacts optogenetic stimulation precision
FENS Forum 2024