Abstract
A leading theory holds that neurodevelopmental brain disorders arise from imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this one-dimensional model is rich enough to capture the multiple neural circuit alterations underlying brain disorders. Here, we combined computational simulations with analysis of in vivo two-photon Ca2+ imaging data from somatosensory cortex of Fmr1 knock-out (KO) mice, a model of Fragile-X Syndrome, to test the E/I imbalance theory. We found that: (1) The E/I imbalance model cannot account for joint alterations in the observed neural firing rates and correlations; (2) Neural circuit function is vastly more sensitive to changes in some cellular components over others; (3) The direction of circuit alterations in Fmr1 KO mice changes across development. These findings suggest that the basic E/I imbalance model should be updated to higher dimensional models that can better capture the multidimensional computational functions of neural circuits.
Highlights
The nervous system shows complex organization at many spatial scales: from genes and molecules, to cells and synapses, to neural circuits
The one-dimensional excitatory and inhibitory (E/I) imbalance model has been widely used for interpreting neural circuit changes observed in animal models of diverse brain disorders (Bateup et al, 2011; Dani et al, 2005; Gibson et al, 2008; Kehrer et al, 2008; Wallace et al, 2012)
In the case of Fragile-X syndrome, the hyperexcitability prediction of the E/I imbalance model is consistent with many of the symptoms of the disease and the known pathogenic defects implicated in Fmr1 KO mice
Summary
The nervous system shows complex organization at many spatial scales: from genes and molecules, to cells and synapses, to neural circuits. Even for the most successful drugs, we have little understanding of how pharmaceutical actions at the molecular level percolate up the organizational ladder to affect behavior and cognition This classic bottom-up approach may even be further confounded if phenotypic heterogeneity in disorders such as autism turn out not to reflect a unique cellular pathology, but rather ‘a perturbation of the network properties that emerge when neurons interact’ (Belmonte et al, 2004). These considerations imply that a more promising level of analysis might be at the level of neural circuits, since the explanatory gap between circuits and behavior is smaller than the gap between molecules and behavior. This circuitlevel viewpoint argues for a reverse-engineering approach to tackling brain disorders: rather than
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