
Hyperconnectivity and Slow Synapses during Early Development of Medial Prefrontal Cortex in a Mouse Model for Mental Retardation and Autism
Author(s) -
Guilherme Testa-Silva,
Alex Loebel,
Michèle Giugliano,
De Kock,
Huibert D. Mansvelder,
Rhian M. Meredith
Publication year - 2012
Publication title -
cerebral cortex
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.694
H-Index - 250
eISSN - 1460-2199
pISSN - 1047-3211
DOI - 10.1093/cercor/bhr224
Subject(s) - neuroscience , prefrontal cortex , autism , excitatory postsynaptic potential , synapse , inhibitory postsynaptic potential , psychology , cognition , psychiatry
Neuronal theories of neurodevelopmental disorders (NDDs) of autism and mental retardation propose that abnormal connectivity underlies deficits in attentional processing. We tested this theory by studying unitary synaptic connections between layer 5 pyramidal neurons within medial prefrontal cortex (mPFC) networks in the Fmr1-KO mouse model for mental retardation and autism. In line with predictions from neurocognitive theory, we found that neighboring pyramidal neurons were hyperconnected during a critical period in early mPFC development. Surprisingly, excitatory synaptic connections between Fmr1-KO pyramidal neurons were significantly slower and failed to recover from short-term depression as quickly as wild type (WT) synapses. By 4-5 weeks of mPFC development, connectivity rates were identical for both KO and WT pyramidal neurons and synapse dynamics changed from depressing to facilitating responses with similar properties in both groups. We propose that the early alteration in connectivity and synaptic recovery are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low-frequency stimulation, which may explain deficits in integration and early information processing in attentional neuronal networks in NDDs.