Amygdala Perfusion Is Predicted by Its Functional Connectivity with the Ventromedial Prefrontal Cortex and Negative Affect

Background: Previous studies have shown that the activity of the amygdala is elevated in people experiencing clinical and subclinical levels of anxiety and depression (negative affect). It has been proposed that a reduction in inhibitory input to the amygdala from the prefrontal cortex and resultant over-activity of the amygdala underlies this association. Prior studies have found relationships between negative affect and 1) amygdala over-activity and 2) reduced amygdala-prefrontal connectivity. However, it is not known whether elevated amygdala activity is associated with decreased amygdala-prefrontal connectivity during negative affect states. Methods: Here we used resting-state arterial spin labeling (ASL) and blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) in combination to test this model, measuring the activity (regional cerebral blood flow, rCBF) and functional connectivity (correlated fluctuations in the BOLD signal) of one subregion of the amygdala with strong connections with the prefrontal cortex, the basolateral nucleus (BLA), and subsyndromal anxiety levels in 38 healthy subjects. Results: BLA rCBF was strongly correlated with anxiety levels. Moreover, both BLA rCBF and anxiety were inversely correlated with the strength of the functional coupling of the BLA with the caudal ventromedial prefrontal cortex. Lastly, BLA perfusion was found to be a mediator of the relationship between BLA-prefrontal connectivity and anxiety. Conclusions: These results show that both perfusion of the BLA and a measure of its functional coupling with the prefrontal cortex directly index anxiety levels in healthy subjects, and that low BLA-prefrontal connectivity may lead to increased BLA activity and resulting anxiety. Thus, these data provide key evidence for an often-cited circuitry model of negative affect, using a novel, multi-modal imaging approach

Impairment in acquisition of conditioned fear in schizophrenia

Individuals with schizophrenia show impairments in associative learning. One well-studied, quantifiable form of associative learning is Pavlovian fear conditioning. However, to date, studies of fear conditioning in schizophrenia have been inconclusive, possibly because they lacked sufficient power. To address this issue, we pooled data from four independent fear conditioning studies that included a total of 77 individuals with schizophrenia and 74 control subjects. Skin conductance responses (SCRs) to stimuli that were paired (the CS + ) or not paired (CS−) with an aversive, unconditioned stimulus were measured, and the success of acquisition of differential conditioning (the magnitude of CS + vs. CS− SCRs) and responses to CS + and CS− separately were assessed. We found that acquisition of differential conditioned fear responses was significantly lower in individuals with schizophrenia than in healthy controls (Cohen’s d = 0.53). This effect was primarily related to a significantly higher response to the CS− stimulus in the schizophrenia compared to the control group. Moreover, the magnitude of this response to the CS− in the schizophrenia group was correlated with the severity of delusional ideation (p = 0.006). Other symptoms or antipsychotic dose were not associated with fear conditioning measures. In conclusion, individuals with schizophrenia who endorse delusional beliefs may be over-responsive to neutral stimuli during fear conditioning. This finding is consistent with prior models of abnormal associative learning in psychosis

A parametric study of fear generalization to faces and non-face objects: relationship to discrimination thresholds

Fear generalization is the production of fear responses to a stimulus that is similar—but not identical—to a threatening stimulus. Although prior studies have found that fear generalization magnitudes are qualitatively related to the degree of perceptual similarity to the threatening stimulus, the precise relationship between these two functions has not been measured systematically. Also, it remains unknown whether fear generalization mechanisms differ for social and non-social information. To examine these questions, we measured perceptual discrimination and fear generalization in the same subjects, using images of human faces and non-face control stimuli (“blobs”) that were perceptually matched to the faces. First, each subject’s ability to discriminate between pairs of faces or blobs was measured. Each subject then underwent a Pavlovian fear conditioning procedure, in which each of the paired conditioned stimuli (CS) were either followed (CS+) or not followed (CS−) by a shock. Skin conductance responses (SCRs) were also measured. Subjects were then presented with the CS+, CS− and five levels of a CS+-to-CS− morph continuum between the paired stimuli, which were identified based on individual discrimination thresholds. Finally, subjects rated the likelihood that each stimulus had been followed by a shock. Subjects showed both autonomic (SCR-based) and conscious (ratings-based) fear responses to morphs that they could not discriminate from the CS+ (generalization). For both faces and non-face objects, fear generalization was not found above discrimination thresholds. However, subjects exhibited greater fear generalization in the shock likelihood ratings compared to the SCRs, particularly for faces. These findings reveal that autonomic threat detection mechanisms in humans are highly sensitive to small perceptual differences between stimuli. Also, the conscious evaluation of threat shows broader generalization than autonomic responses, biased towards labeling a stimulus as threatening

A Data-Driven Investigation of Gray Matter–Function Correlations in Schizophrenia during a Working Memory Task

The brain is a vastly interconnected organ and methods are needed to investigate its long range structure(S)–function(F) associations to better understand disorders such as schizophrenia that are hypothesized to be due to distributed disconnected brain regions. In previous work we introduced a methodology to reduce the whole brain S–F correlations to a histogram and here we reduce the correlations to brain clusters. The application of our approach to sMRI [gray matter (GM) concentration maps] and functional magnetic resonance imaging data (general linear model activation maps during Encode and Probe epochs of a working memory task) from patients with schizophrenia (SZ, n = 100) and healthy controls (HC, n = 100) presented the following results. In HC the whole brain correlation histograms for GM–Encode and GM–Probe overlap for Low and Medium loads and at High the histograms separate, but in SZ the histograms do not overlap for any of the load levels and Medium load shows the maximum difference. We computed GM–F differential correlation clusters using activation for Probe Medium, and they included regions in the left and right superior temporal gyri, anterior cingulate, cuneus, middle temporal gyrus, and the cerebellum. Inter-cluster GM–Probe correlations for Medium load were positive in HC but negative in SZ. Within group inter-cluster GM–Encode and GM–Probe correlation comparisons show no differences in HC but in SZ differences are evident in the same clusters where HC vs. SZ differences occurred for Probe Medium, indicating that the S–F integrity during Probe is aberrant in SZ. Through a data-driven whole brain analysis approach we find novel brain clusters and show how the S–F differential correlation changes during Probe and Encode at three memory load levels. Structural and functional anomalies have been extensively reported in schizophrenia and here we provide evidences to suggest that evaluating S–F associations can provide important additional information

The genetics of endophenotypes of neurofunction to understand schizophrenia (GENUS) consortium: a collaborative cognitive and neuroimaging genetics project

Background Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection. Methods We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site. Results Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p 10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia

Perfusion of the basolateral nucleus of the amygdala (BLA) is correlated with anxiety levels.

<p>Significant correlations were found between anxiety levels and perfusion of the left (A) and right (B) basolateral amygdala (BLA), as defined using anatomical regions-of-interest. These findings were then confirmed in a voxel-wise, whole brain regression analysis (C). In C, the BLA regions-of-interest are outlined in blue; the voxel-level display threshold is p<.005 (showing only clusters surviving whole-brain correction, see Methods). Clusters that showed cluster-wise significance (p<.05, whole brain corrected) are reported in the text and in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone-0097466-t002" target="_blank">Table 2</a>. R, right.</p

Perfusion of a distributed network of regions outside of the amygdala is also correlated with anxiety levels.

<p>A voxel-wise whole brain regression analysis revealed that, in addition to the basolateral amygdala (BLA), perfusion of the superior frontal gyri and posterior cingulate cortex (A), and anterior putamen (B), among other regions (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone-0097466-t002" target="_blank">Table 2</a>), were significantly correlated with anxiety levels. Whole-brain corrected results (see Methods) are displayed here using a voxel-level threshold of p<.005. Clusters that showed cluster-wise significance (p<.05, whole brain corrected) are reported in the text and in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone-0097466-t002" target="_blank">Table 2</a>. R, right; PCC, posterior cingulate cortex; SFG, superior frontal gyri.</p

Functional connectivity between the BLA and mPFC is inversely correlated with BLA perfusion and anxiety levels.

<p>An average map of basolateral amygdala (BLA) functional connectivity is shown in A. A whole-brain voxel-wise regression revealed that the strength of connectivity between the BLA and mPFC was negatively correlated with both: anxiety levels (B, C) and BLA perfusion (D, E). In A, B, and D, voxels with positive connectivity with the BLA (A) or showing positive correlations between their connectivity with the BLA and anxiety levels (B) or BLA perfusion (D) are shown in warm colors; voxels with negative correlations are shown in cool colors. The scatter plots in C and E are derived from the accompanying voxel-wise regression maps shown in B and D and are presented for the purpose of illustrating the range of values only. Data are displayed at a threshold of p<.05. The clusters indicated with arrows in B and D met a cluster-wise correction (FWE, p<.05) within the ventral mPFC. The peaks of the clusters in B (4, 2, −7) and D (2, 4, −4) were localized to the posterior-most portion of the SGC (with both clusters extending into the hypothalamus) using two independent atlases (the Talairach and Tournoux Stereotaxic Atlas <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-Tailarach1" target="_blank">[46]</a> and the Wake Forrest University (WFU) PickAtlas <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-Maldjian1" target="_blank">[47]</a>; see Methods). Prior work further supports this localization; previously reported sites that have been localized to the SGC (BA25), as well as an architectonic mapping of BA25 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-ngr1" target="_blank">[68]</a>, overlap with the two clusters reported here, with nearby peaks: 4, 2, −4 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-Mayberg3" target="_blank">[69]</a>; −2, 6, −6 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-Mayberg2" target="_blank">[8]</a>; −2, 8, −10 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-Mayberg4" target="_blank">[70]</a>; −3, 9, −6 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-Pizzagalli1" target="_blank">[71]</a>; −4, 9, −12 & 2, 11, −7 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-Kumano1" target="_blank">[72]</a>; 0, 8, −16 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone.0097466-Nahas1" target="_blank">[73]</a>. BLA, basolateral amygdala; FC, functional connectivity; Hy, hypothalamus; SGC, subgenual cingulate gyrus; mPFC, medial prefrontal cortex.</p

Basolateral amygdala functional connectivity.

<p>Areas of the brain showing significant functional connectivity with the basolateral amygdala (BLA) are listed. Clusters that are unshaded are those with positive functional coupling with the BLA, whereas clusters that are shaded grey are those showing negative functional coupling (inverse or anti-correlations) with the BLA (following global mean regression). Sites of connectivity within or abutting the BLA are not listed because of the difficulty of interpreting these findings. Also see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone-0097466-g003" target="_blank">Figure 3</a>. BA = Brodmann Area; Hemi = hemisphere; Tal = Talaraich coordinates.</p