Homozygous Loss of Autism-Risk Gene CNTNAP2 Results in Reduced Local and Long-Range Prefrontal Functional Connectivity

Abstract

Functional connectivity aberrancies, as measured with resting-state functional magnetic resonance imaging (rsfMRI), have been consistently observed in the brain of autism spectrum disorders (ASD) patients. However, the genetic and neurobiological underpinnings of these findings remain unclear. Homozygous mutations in contactin associated protein-like 2 (CNTNAP2), a neurexin-related cell-adhesion protein, are strongly linked to autism and epilepsy. Here we used rsfMRI to show that homozygous mice lacking Cntnap2 exhibit reduced long-range and local functional connectivity in prefrontal and midline brain "connectivity hubs." Long-range rsfMRI connectivity impairments affected heteromodal cortical regions and were prominent between fronto-posterior components of the mouse default-mode network, an effect that was associated with reduced social investigation, a core "autism trait" in mice. Notably, viral tracing revealed reduced frequency of prefrontal-projecting neural clusters in the cingulate cortex of Cntnap2-/- mutants, suggesting a possible contribution of defective mesoscale axonal wiring to the observed functional impairments. Macroscale cortico-cortical white-matter organization appeared to be otherwise preserved in these animals. These findings reveal a key contribution of ASD-associated gene CNTNAP2 in modulating macroscale functional connectivity, and suggest that homozygous loss-of-function mutations in this gene may predispose to neurodevelopmental disorders and autism through a selective dysregulation of connectivity in integrative prefrontal areas.

Loss of function in the autism and learning disabilities associated gene Nf1 disrupts corticocortical and corticostriatal functional connectivity in human and mouse

“…These findings suggest that the reduced long-range functional connectivity of the dorsal attention, frontoparietal and default networks observed here might be related to disruption in myelination. Moreover, our results are in close agreement with other studies that linked reduced posterior-anterior connectivity in the Default network and neurodevelopmental disorders in humans and mice Liska et al, 2018;Pagani et al, 2018;Washington et al, 2014;Yerys et al, 2015).…”

Individual variability in functional connectivity architecture of the mouse brain

“…Future studies can use the individual functional connectome to predict the effect of causal control similar to the way human resting-state fMRI is used to predict individual task fMRI activation pattern 9 , linking the different levels of organization and uncovering sources of individual variation. In addition, rodent models of disease are commonly studied using fcMRI 21,22,61 , which allows direct translation to humans 20,28 , as well as studying the relations between functional connectivity and behavior [23][24][25][26] . Such studies can utilize the approach presented here to follow the trajectory of individual animals during development, aging or after treatment, as well as the CPM, which provide a data-driven alternative to predict behavior based on fcMRI data 32 , instead of following a hypothesis-driven behavioral correlations of functional connections between specific seed regions.…”

“…Such investigation demands adequate sample size that is hard to achieve in studies in non-human primates and may involve genetic manipulations and molecular techniques that are more readily accessible in rodent models, and specifically in mice. Previous fcMRI studies in anesthetized mice demonstrated reproducible resting-state networks 18,19 , applications to mouse models of neuropsychiatric diseases [20][21][22] , and correlations between functional connectivity and behavioral measures [23][24][25][26] . However, characterization of individual differences in functional connectivity is based on repeated data acquisition that can control for measurement instability.…”

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

“…Since its onset in 2011 (Jonckers et al, 2011), mouse rsfMRI has developed in a number of centres and has grown to become a routine method with a number of applications, reviewed in (Chuang and Nasrallah, 2017;Gozzi and Schwarz, 2016;Hoyer et al, 2014;Jonckers et al, 2015Jonckers et al, , 2013Pan et al, 2015). Prominently, mouse rsfMRI has been used to investigate an extensive list of models, including Alzheimer's disease (Grandjean et al, 2014b, Shah et al, 2013, 2016cWiesmann et al, 2016;Zerbi et al, 2014), motor (DeSimone et al, 2016;Li et al, 2017), affective (Grandjean et al, 2016a), autism spectrum Haberl et al, 2015;Liska et al, 2018;Liska and Gozzi, 2016;Michetti et al, 2017;Sforazzini et al, 2016;Zerbi et al, 2018;Zhan et al, 2014), schizophrenia (Errico et al, 2015;Gass et al, 2016), pain (Buehlmann et al, 2018;Komaki et al, 2016), reward (Charbogne et al, 2017;Mechling et al, 2016), and demyelinating disorders (Hübner et al, 2017). Another application of mouse rsfMRI is the elucidation of large-scale functional alterations exerted by pharmacological agents (Razoux et al, 2013;Shah et al, 2016aShah et al, , 2015.…”