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

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

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

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

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

Regions showing a correlation between perfusion and anxiety levels.

<p>Results of a whole-brain voxel-wise regression analysis of the regional cerebral blood flow (rCBF) data, using anxiety levels as a regressor, are listed. Sites which showed a significant positive correlation between rCBF and anxiety levels are listed below. Also, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097466#pone-0097466-g002" target="_blank">Figure 2</a>. BA = Brodmann Area; Hemi = hemisphere; Tal = Talaraich coordinates.</p

Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation

BackgroundWearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. ObjectiveThis study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest. MethodsThe pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward–sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each. ResultsActigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. ConclusionsWe discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments