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Gowers, R.

Publications and source records attributed to Gowers, R..

2 recordsLinked to original sources

Spatial and morphological organization of mitochondria across a connectome

Neuronal function depends critically on the cell biological organization of mitochondria, which regulate calcium signals and produce energy, among other roles. However, little is known about how mitochondria are organized within the circuits of neurons that make up each brain. To uncover the systematic rules that govern mitochondria shape and position in a connectome, we analyzed the morphological and spatial organization of more than 100,000 mitochondria in over 1,000 visual projection neurons in the Drosophila connectome. We found that mitochondrial shape and size differ systematically between cell types, and are distinct enough between cell types to serve as an identifying fingerprint. Moreover, we derived three quantitative rules that describe how mitochondria are positioned within neurons relative to synapses and other subcellular features: (1) they are positioned with a precision of 2-3 microns; (2) their relative preference for pre- and postsynaptic sites and other subcellular features differs between axons and dendrites; (3) their positions were specialized to different cell types. These organizing rules correlated with functional and anatomical properties of the cells, including visual responses and input connectivity. We also find that, in the flys olfactory associative learning circuits, mitochondria are enriched at presynapses to particular postsynaptic cells by accumulating in functional sub-compartments of axons. Overall, our findings reveal a robust set of organizing principles for mitochondria within and between cells, uncovering cell biology that maps onto the organization of the connectome and adding new dimensions for understanding circuit function in the connectome.

neuroscience↗

Dendritic morphology affects the neuronal excitability type and the network state

The biophysical properties of neurons not only affect how information is processed within cells, they can also impact the dynamical states of the network. Specifically, the cellular dynamics of action-potential generation have shown relevance for setting the (de)synchronisation state of the network. The dynamics of tonically spiking neurons typically fall into one of three qualitatively distinct types that arise from distinct mathematical bifurcations of voltage dynamics at the onset of spiking. Accordingly, changes in ion channel composition or even external factors, like temperature, have been demonstrated to switch network behaviour via changes in the spike onset bifurcation and hence its associated dynamical type. A thus far less addressed modulator of neuronal dynamics is cellular morphology. Based on simplified and anatomically realistic mathematical neuron models, we show here that the extent of dendritic arborisation has an influence on the neuronal dynamical spiking type and therefore on the (de)synchronisation state of the network. Specifically, larger dendritic trees prime neuronal dynamics for in-phase-synchronised or splayed-out activity in weakly coupled networks, in contrast to cells with otherwise identical properties yet smaller dendrites. Our biophysical insights hold for generic multicompartmental classes of spiking neuron models (from ball-and-stick-type to anatomically reconstructed models) and establish a direct mechanistic link between neuronal morphology and the susceptibility of neural tissue to synchronisation in health and disease. Significance StatementCellular morphology varies widely across different cell types and brain areas. In this study, we provide a mechanistic link between neuronal morphology and the dynamics of electrical activity arising at the network level. Based on mathematical modelling, we demonstrate that modifications of the size of dendritic arbours alone suffice to switch the behaviour of otherwise identical networks from synchronised to asynchronous activity. Specifically, neurons with larger dendritic trees tend to produce more stable phase relations of spiking across neurons. Given the generality of the approach, we provide a novel, morphology-based hypothesis that explains the differential sensitivity of tissue to epilepsy in different brain areas and assigns relevance to cellular morphology in healthy network computation.

neuroscience↗