Amygdalofrontal Functional Disconnectivity and Aggression in Schizophrenia

Division of Clinical Research, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA.
Schizophrenia Bulletin (Impact Factor: 8.45). 04/2009; 36(5):1020-8. DOI: 10.1093/schbul/sbp012
Source: PubMed


A significant proportion of patients with schizophrenia demonstrate abnormalities in dorsal prefrontal regions including the dorsolateral prefrontal and dorsal anterior cingulate cortices. However, it is less clear to what extent abnormalities are exhibited in ventral prefrontal and limbic regions, despite their involvement in social cognitive dysfunction and aggression, which represent problem domains for patients with schizophrenia. Previously, we found that reduced white matter integrity in right inferior frontal regions was associated with higher levels of aggression. Here, we used resting-state functional magnetic resonance imaging to examine amygdala/ventral prefrontal cortex (vPFC) functional connectivity (FC) and its relation to aggression in schizophrenia. Twenty-one healthy controls and 25 patients with schizophrenia or schizoaffective disorder participated. Aggression was measured using the Buss Perry Aggression Questionnaire. Regions of interest were placed in the amygdala based on previously published work. A voxelwise FC analysis was performed in which the mean time series across voxels for this bilateral amygdala seed was entered as a predictor in a multiple regression model with motion parameters and global, cerebrospinal fluid, and white matter signals as covariates. Patients showed significant reductions in FC between amygdala and vPFC regions. Moreover, in patients, the strength of this connection showed a significant inverse relationship with aggression, such that lower FC was associated with higher levels of self-rated aggression. Similar results were obtained for 2 other measures--Life History of Aggression and total arrests. These results suggest that amygdala/vPFC FC is compromised in schizophrenia and that this compromise is associated with aggression.

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Available from: Matthew Hoptman, Oct 03, 2015
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    • "Methodologically, seed-based functional connectivity analysis (Biswal et al. 1995) allowed us to examine the resting state and task-related networks originating from a priori-defined brain regions based on the group activation peaks during speech production. Seed-based analysis was chosen over independent component analysis (ICA) because the latter is a fully data-driven approach not requiring an a priori choice of a seed region and yielding network components not restricted to specified brain regions (e.g., seeds) or behaviors (e.g., the left fronto-parietal component in resting-state data may encompasses both memory and language networks) (Hoptman et al. 2010; Smith et al. 2009). We hypothesized that individual functional networks from different brain regions controlling speech production would form a shared common network, which would have a pattern topographically similar to the underlying shared resting-state network (RSN) (Biswal et al. 1995; Smith et al. 2009). "
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    ABSTRACT: Speech production is one of the most complex human behaviors. While brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based inter-regional correlation analysis with graph theoretical analysis of functional MRI data during resting and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech network integration and segregation. While networks were bilaterally distributed, inter-regional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL and cerebellum in the formation of speech production network. Copyright © 2014, Journal of Neurophysiology.
    Journal of Neurophysiology 02/2015; 113(7):jn.00964.2014. DOI:10.1152/jn.00964.2014 · 2.89 Impact Factor
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    • "Schizophrenia is a severe neuropsychiatric disorder that represents the 18th leading cause of years lived with disability globally (Whiteford et al., 2013) and has an estimated point prevalence of 0.5% to 1.0% (Tandon et al., 2008). Functional and structural disconnectivity are among the most reproducible neurophysiological abnormalities associated with schizophrenia (Burns et al., 2003; Whalley et al., 2005; Liang et al., 2006; Begre and Koenig, 2008; Konrad and Winterer, 2008; Hoptman et al., 2010; Qiu et al., 2010; Whitford et al., 2011; Shi et al., 2012a,b; Curčić-Blake et al., 2013; Rane et al., 2013; Straube et al., 2013; Tepest et al., 2013) (recently reviewed by Schmitt et al. (2011)). "
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    ABSTRACT: Background: Neuroinflammation and white matter pathology have each been independently associated with schizophrenia, and experimental studies have revealed mechanisms by which the two can interact in vitro, but whether these abnormalities simultaneously co-occur in people with schizophrenia remains unclear. Method: We searched MEDLINE, EMBASE, PsycINFO and Web of Science from inception through 12 January 2014 for studies reporting human data on the relationship between microglial or astroglial activation, or cytokines and white matter pathology in schizophrenia. Results: Fifteen studies totaling 792 subjects (350 with schizophrenia, 346 controls, 49 with bipolar disorder, 37 with major depressive disorder and 10 with Alzheimer's disease) met all eligibility criteria. Five neuropathological and two neuroimaging studies collectively yielded consistent evidence of an association between schizophrenia and microglial activation, particularly in white rather than gray matter regions. Ultrastructural analysis revealed activated microglia near dystrophic and apoptotic oligodendroglia, demyelinating and dysmyelinating axons and swollen and vacuolated astroglia in subjects with schizophrenia but not controls. Two neuroimaging studies found an association between carrier status for a functional single nucleotide polymorphism in the interleukin-1β gene and abnormal white as well as gray matter volumes in schizophrenia but not controls. A neuropathological study found that orbitofrontal white matter neuronal density was increased in schizophrenia cases exhibiting high transcription levels of pro-inflammatory cytokines relative to those exhibiting low transcription levels and to controls. Schizophrenia was associated with decreased astroglial density specifically in subgenual cingulate white matter and anterior corpus callosum, but not other gray or white matter areas. Astrogliosis was consistently absent. Data on astroglial gene expression, mRNA expression and protein concentration were inconsistent. Conclusion: Neuroinflammation is associated with white matter pathology in people with schizophrenia, and may contribute to structural and functional disconnectivity, even at the first episode of psychosis.
    Schizophrenia Research 06/2014; 161(1). DOI:10.1016/j.schres.2014.04.041 · 3.92 Impact Factor
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    • "As well as reflecting underlying anatomical connectivity (Park and Friston, 2013), RSFC has also been shown to correspond to the brain's functional architecture in response to external stimuli (Raichle and Mintun, 2006; Smith et al., 2009), as evidenced by findings that intrinsic resting-state brain activity can predict task-evoked brain activation during different cognitive tasks (Fox et al., 2006, 2007; Mennes et al., 2010; Liu et al., 2011; Mennes et al., 2011; Zou et al., 2013). RSFC has also been used to characterize functional brain networks correlated with individual differences in behavioral traits, such as personality, autistic trait and aggression (Di Martino et al., 2009; Hoptman et al., 2010; Adelstein et al., 2011). To our knowledge, only two studies have explored the neural correlates of risk propensity using RSFC (Cox et al., 2010; Han et al., 2012). "
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    ABSTRACT: Men are more risk prone than women, but the underlying basis remains unclear. To investigate this question, we developed a trait-like measure of risk propensity which we correlated with resting-state functional connectivity to identify sex differences. Specifically, we used short- and long-range functional connectivity densities to identify associated brain regions and examined their functional connectivities in resting-state functional magnetic resonance imaging (fMRI) data collected from a large sample of healthy young volunteers. We found that men had a higher level of general risk propensity (GRP) than women. At the neural level, although they shared a common neural correlate of GRP in a network centered at the right inferior frontal gyrus, men and women differed in a network centered at the right secondary somatosensory cortex, which included the bilateral dorsal anterior/middle insular cortices and the dorsal anterior cingulate cortex. In addition, men and women differed in a local network centered at the left inferior orbitofrontal cortex. Most of the regions identified by this resting-state fMRI study have been previously implicated in risk processing when people make risky decisions. This study provides a new perspective on the brain-behavioral relationships in risky decision making and contributes to our understanding of sex differences in risk propensity.
    Frontiers in Behavioral Neuroscience 01/2014; 8:2. DOI:10.3389/fnbeh.2014.00002 · 3.27 Impact Factor
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