Reduced Functional Integration and Segregation of Distributed Neural Systems Underlying Social and Emotional Information Processing in Autism Spectrum Disorders

Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, 660 Charles E Young Drive South, Los Angeles, CA 90095-7085, USA.
Cerebral Cortex (Impact Factor: 8.67). 07/2011; 22(5):1025-37. DOI: 10.1093/cercor/bhr171
Source: PubMed


A growing body of evidence suggests that autism spectrum disorders (ASDs) are related to altered communication between brain regions. Here, we present findings showing that ASD is characterized by a pattern of reduced functional integration as well as reduced segregation of large-scale brain networks. Twenty-three children with ASD and 25 typically developing matched controls underwent functional magnetic resonance imaging while passively viewing emotional face expressions. We examined whole-brain functional connectivity of two brain structures previously implicated in emotional face processing in autism: the amygdala bilaterally and the right pars opercularis of the inferior frontal gyrus (rIFGpo). In the ASD group, we observed reduced functional integration (i.e., less long-range connectivity) between amygdala and secondary visual areas, as well as reduced segregation between amygdala and dorsolateral prefrontal cortex. For the rIFGpo seed, we observed reduced functional integration with parietal cortex and increased integration with right frontal cortex as well as right nucleus accumbens. Finally, we observed reduced segregation between rIFGpo and the ventromedial prefrontal cortex. We propose that a systems-level approach-whereby the integration and segregation of large-scale brain networks in ASD is examined in relation to typical development-may provide a more detailed characterization of the neural basis of ASD.

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Available from: Susan Y Bookheimer, Feb 21, 2014
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    • "The reduced variability within this window may explain the lack of significant correlations between connectivity profiles and intelligence. This suggests that these connections may act as moderately stable backbones for brain functioning [van den Heuvel et al., 2012], whose alterations might be significantly captured in the case of severe pathological conditions affecting cognition [Bassett et al., 2012; Di et al., 2013; Rudie et al., 2012; Santarnecchi et al., 2012; Tijms et al., 2013]. One may hypothesize that a combination of Q1 and Q4 connections accounts for the largest variability in IQ levels. "
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    ABSTRACT: Brain network topology provides valuable information on healthy and pathological brain functioning. Novel approaches for brain network analysis have shown an association between topological properties and cognitive functioning. Under the assumption that "stronger is better", the exploration of brain properties has generally focused on the connectivity patterns of the most strongly correlated regions, whereas the role of weaker brain connections has remained obscure for years. Here, we assessed whether the different strength of connections between brain regions may explain individual differences in intelligence. We analyzed-functional connectivity at rest in ninety-eight healthy individuals of different age, and correlated several connectivity measures with full scale, verbal, and performance Intelligent Quotients (IQs). Our results showed that the variance in IQ levels was mostly explained by the distributed communication efficiency of brain networks built using moderately weak, long-distance connections, with only a smaller contribution of stronger connections. The variability in individual IQs was associated with the global efficiency of a pool of regions in the prefrontal lobes, hippocampus, temporal pole, and postcentral gyrus. These findings challenge the traditional view of a prominent role of strong functional brain connections in brain topology, and highlight the importance of both strong and weak connections in determining the functional architecture responsible for human intelligence variability. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Full-text · Article · Sep 2014 · Human Brain Mapping
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    • "These studies suggest that network differentiation and specialization, which are emerging features of development in the typical brain [Johnson , 2011], may be impaired in ASD. Two of these [Rudie et al., 2012; Shih et al., 2011] reported reduced network differentiation in ASD, using seed-based fcMRI analyses. Concordant findings for both functional and anatomical network organization from graph theory were more recently reported by Rudie et al. [2013]. "
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    ABSTRACT: Growing evidence suggests that Autism Spectrum Disorder (ASD) involves abnormalities of multiple functional networks. Neuroimaging studies of ASD have therefore increasingly focused on connectivity. Many functional connectivity (fcMRI) studies have reported network underconnectivity in children and adults with ASD. However, there are notable inconsistencies, with some studies reporting overconnectivity. A previous literature survey suggested that a few methodological factors play a crucial role in differential fcMRI outcomes. Using three ASD data sets (two task-related, one resting state) from 54 ASD and 51 typically developing (TD) participants (ages 9-18 years), we examined the impact of four methodological factors: type of pipeline (co-activation vs. intrinsic analysis, related to temporal filtering and removal of task-related effects), seed selection, field of view (whole brain vs. limited ROIs), and dataset. Significant effects were found for type of pipeline, field of view, and dataset. Notably, for each dataset results ranging from robust underconnectivity to robust overconnectivity were detected, depending on the type of pipeline, with intrinsic fcMRI analyses (low bandpass filter and task regressor) predominantly yielding overconnectivity in ASD, but co-activation analyses (no low bandpass filter or task removal) mostly generating underconnectivity findings. These results suggest that methodological variables have dramatic impact on group differences reported in fcMRI studies. Improved awareness of their implications appears indispensible in fcMRI studies when inferences about "underconnectivity" or "overconnectivity" in ASD are made. In the absence of a gold standard for functional connectivity, the combination of different methodological approaches promises a more comprehensive understanding of connectivity in ASD. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals,Inc.
    Full-text · Article · Aug 2014 · Human Brain Mapping
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    • "Another potential confound in our data is the fact that most of the autistic children in our study were on various psychoactive medications. It is common practice in autism research to perform neuroimaging studies on medicated children [Rudie et al., 2011; Scott-Van Zeeland et al., 2010]. Nevertheless, psychoactive medications may enhance, reduce, or not alter DMN functional connectivity. "
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    ABSTRACT: Two hypotheses of autism spectrum disorder (ASD) propose that this condition is characterized by deficits in Theory of Mind and by hypoconnectivity between remote cortical regions with hyperconnectivity locally. The default mode network (DMN) is a set of remote, functionally connected cortical nodes less active during executive tasks than at rest and is implicated in Theory of Mind, episodic memory, and other self-reflective processes. We show that children with ASD have reduced connectivity between DMN nodes and increased local connectivity within DMN nodes and the visual and motor resting-state networks. We show that, like the trajectory of synaptogenesis, internodal DMN functional connectivity increased as a quadratic function of age in typically developing children, peaking between, 11 and 13 years. In children with ASD, these long-distance connections fail to develop during adolescence. These findings support the "developmental disconnection model" of ASD, provide a possible mechanistic explanation for the Theory-of-Mind hypothesis of ASD, and show that the window for effectively treating ASD could be wider than previously thought. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
    Full-text · Article · Apr 2014 · Human Brain Mapping
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