Chapter 26: the temporal dynamics of emotional responding: implications for well-being and health from the MIDUS

In this chapter findings are reviewed from the MIDUS Neuroscience Project that underscore the value in examining the temporal dynamics of responses to brief emotional provocation for understanding linkages among emotions and factors contributing to health and well-being across the MIDUS study. This rich dataset has allowed the exploration of associations between individual differences in the affective chronometry of negative and positive emotional responses in vulnerable vs. resilient profiles. Findings from functional magnetic resonance imaging (fMRI) as well as electromyographic recordings (EMG) of the facial muscles to objectively measure emotional responses demonstrate that the temporal dynamics of emotional responses to affective stimuli are associated with aging, personality, psychopathology, stress exposure, biomarkers, and well-being. Overall, these findings suggest that variation in health and well-being are differentially predicted by specific temporal parameters of the emotional response, such as the magnitude of the immediate response to the presence of a stimulus (i.e., reactivity), residual activity and its duration after stimulus offset (i.e., recovery), as well as the change in response – or habituation - across repeated presentations of similarly-valenced stimuli. Besides the seemingly obvious import of recovering quickly from negative or unpleasant provocations, the chronometry of positive emotional responses appears to be particularly vital for determining how emotional processes may take a physiological toll or promote resiliency in the face of stress and disease. By examining such temporal dynamics in response to affective stimuli in MIDUS, a better understanding of the brain-behavior associations underlying emotion, and how emotions “get under the skin” to impact well-being and health across the lifespan is gained

Redshift Filtering by Swift Apparent X-ray Column Density

We remark on the utility of an observational relation between the absorption column density in excess of the Galactic absorption column density, $\Delta N_{\rm H} = N_{\rm H, fit} - N_{\rm H, gal}$, and redshift, z, determined from all 55 Swift-observed long bursts with spectroscopic redshifts as of 2006 December. The absorption column densities, $N_{\rm H, fit}$, are determined from powerlaw fits to the X-ray spectra with the absorption column density left as a free parameter. We find that higher excess absorption column densities with $\Delta N_{\rm H} > 2\times 10^{21}$ cm$^{-2}$ are only present in bursts with redshifts z$<$2. Low absorption column densities with $\Delta N_{\rm H} < 1\times 10^{21}$ cm$^{-2}$ appear preferentially in high-redshift bursts. Our interpretation is that this relation between redshift and excess column density is an observational effect resulting from the shift of the source rest-frame energy range below 1 keV out of the XRT observable energy range for high redshift bursts. We found a clear anti-correlation between $\Delta N_{\rm H}$ and z that can be used to estimate the range of the maximum redshift of an afterglow. A critical application of our finding is that rapid X-ray observations can be used to optimize the instrumentation used for ground-based optical/NIR follow-up observations. Ground-based spectroscopic redshift measurements of as many bursts as possible are crucial for GRB science.Comment: revised version including updates and the referee's comments, accepted for publication in the Astronomical Journal, 12 pages, 2 figures, 2 tables - v3 contains an update on the reference lis

Swift and XMM-Newton Observations of the Extraordinary GRB 060729: An afterglow with a more than 100 days X-ray light curve

We report the results of the Swift and XMM observations of the Swift-discovered long Gamma-Ray Burst GRB 060729 ($T_{90}$=115s). The afterglow of this burst was exceptionally bright in X-rays as well as at UV/Optical wavelengths showing an unusually long slow decay phase ($\alpha$=0.14\plm0.02) suggesting a larger energy injection phase at early times than in other bursts. The X-ray light curve displays a break at about 60 ks after the burst. The X-ray decay slope after the break is $\alpha$=1.29\plm0.03. Up to 125 days after the burst we do not detect a jet break, suggesting that the jet opening angle is larger than 28 degrees. In the first 2 minutes after the burst (rest frame) the X-ray spectrum of the burst changed dramatically from a hard X-ray spectrum to a very soft one. We find that the X-ray spectra at this early phase can all be fitted by an absorbed single power law model or alternatively by a blackbody plus power law model. The power law fits show that the X-ray spectrum becomes steeper while the absorption column density decreases. In Swift's UV/Optical telescope the afterglow was clearly detected up to 9 days after the burst in all 6 filters and even longer in some of the UV filters with the latest detection in the UVW1 31 days after the burst. A break at about 50 ks is clearly detected in all 6 UVOT filters from a shallow decay slope of about 0.3 and a steeper decay slope of 1.3. In addition to the \swift observations we also present and discuss the results from a 61 ks ToO observation by XMM. (Abriviated)Comment: Accepted to be published in the Astrophysical Journal, 28 pages, 10 figure

Discerning the physical origins of cosmological Gamma-ray bursts based on multiple observational criteria: the cases of z=6.7 GRB 080913, z=8.3 GRB 090423, and some short/hard GRBs

(Abridged) The two high-redshift gamma-ray bursts, GRB 080913 at z=6.7 and GRB 090423 at z=8.3, recently detected by Swift appear as intrinsically short, hard GRBs. They could have been recognized by BATSE as short/hard GRBs should they have occurred at z <= 1. We perform a more thorough investigation on two physically distinct types (Type I/II) of cosmological GRBs and their observational characteristics. We reiterate the definitions of Type I/II GRBs and review the observational criteria and their physical motivations. Contrary to the traditional approach of assigning the physical category based on the gamma-ray properties (duration, hardness, and spectral lag), we take an alternative approach to define the Type I and Type II Gold Samples using several criteria that are more directly related to the GRB progenitors, and study the properties of the two Gold Samples and compare them with the traditional long/soft and short/hard samples. We find that the Type II Gold Sample reasonably tracks the long/soft population, although it includes several intrinsically short (shorter than 1s in the rest frame) GRBs. The Type I Gold Sample only has 5 GRBs, 4 of which are not strictly short but have extended emission. Other short/hard GRBs detected in the Swift era represent the BATSE short/hard sample well, but it is unclear whether all of them belong to Type I. We suggest that some (probably even most) high-luminosity short/hard GRBs instead belong to Type II. We suggest that GRB 080913 and GRB 090423 are more likely Type II events. We re-emphasize the importance of invoking multiple observational criteria, and cautiously propose an operational procedure to infer the physical origin of a given GRB with available multiple observational criteria, with various caveats laid out.Comment: 32 pages, ApJ, in press. The strengths and weaknesses of physical classification and its relation to phenomenological classification are fully discussed in a newly added section 3. Discussions on GRBs 090423, 090426, and 090510 are include

Very Early Optical Afterglows of Gamma-Ray Bursts: Evidence for Relative Paucity of Detection

Very early observations with the Swift satellite of gamma-ray burst (GRB) afterglows reveal that the optical component is not detected in a large number of cases. This is in contrast to the bright optical flashes previously discovered in some GRBs (e.g. GRB 990123 and GRB 021211). Comparisons of the X-ray afterglow flux to the optical afterglow flux and prompt gamma-ray fluence is used to quantify the seemingly deficient optical, and in some cases X-ray, light at these early epochs. This comparison reveals that some of these bursts appear to have higher than normal gamma-ray efficiencies. We discuss possible mechanisms and their feasibility for explaining the apparent lack of early optical emission. The mechanisms considered include: foreground extinction, circumburst absorption, Ly-alpha blanketing and absorption due to high redshift, low density environments, rapid temporal decay, and intrinsic weakness of the reverse shock. Of these, foreground extinction, circumburst absorption, and high redshift provide the best explanations for most of the non-detections in our sample. There is tentative evidence of suppression of the strong reverse shock emission. This could be because of a Poynting-flux-dominated flow or a pure non-relativistic hydrodynamical reverse shock.Comment: 22 pages, 5 figures. Accepted for publication in Ap

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Effects of long-term exposure to an electronic containment system on the behaviour and welfare of domestic cats

Free-roaming cats are exposed to a variety of risks, including involvement in road traffic accidents. One way of mitigating these risks is to contain cats, for example using an electronic boundary fence system that delivers an electric ‘correction’ via a collar if a cat ignores a warning cue and attempts to cross the boundary. However, concerns have been expressed over the welfare impact of such systems. Our aim was to determine if long-term exposure to an electronic containment system was associated with reduced cat welfare. We compared 46 owned domestic cats: 23 cats that had been contained by an electronic containment system for more than 12 months (AF group); and 23 cats with no containment system that were able to roam more widely (C group). We assessed the cats’ behavioural responses and welfare via four behavioural tests (unfamiliar person test; novel object test; sudden noise test; cognitive bias test) and an owner questionnaire. In the unfamiliar person test, C group lip-licked more than the AF group, whilst the AF group looked at, explored and interacted more with the unfamiliar person than C group. In the novel object test, the AF group looked at and explored the object more than C group. No significant differences were found between AF and C groups for the sudden noise or cognitive bias tests. Regarding the questionnaire, C group owners thought their cats showed more irritable behaviour and AF owners thought that their cats toileted inappropriately more often than C owners. Overall, AF cats were less neophobic than C cats and there was no evidence of significant differences between the populations in general affective state. These findings indicate that an electronic boundary fence with clear pre-warning cues does not impair the long term quality of life of cat

A Large-Scale Rheumatoid Arthritis Genetic Study Identifies Association at Chromosome 9q33.2

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease affecting both joints and extra-articular tissues. Although some genetic risk factors for RA are well-established, most notably HLA-DRB1 and PTPN22, these markers do not fully account for the observed heritability. To identify additional susceptibility loci, we carried out a multi-tiered, case-control association study, genotyping 25,966 putative functional SNPs in 475 white North American RA patients and 475 matched controls. Significant markers were genotyped in two additional, independent, white case-control sample sets (661 cases/1322 controls from North America and 596 cases/705 controls from The Netherlands) identifying a SNP, rs1953126, on chromosome 9q33.2 that was significantly associated with RA (ORcommon = 1.28, trend Pcomb = 1.45E-06). Through a comprehensive fine-scale-mapping SNP-selection procedure, 137 additional SNPs in a 668 kb region from MEGF9 to STOM on 9q33.2 were chosen for follow-up genotyping in a staged-approach. Significant single marker results (Pcomb<0.01) spanned a large 525 kb region from FBXW2 to GSN. However, a variety of analyses identified SNPs in a 70 kb region extending from the third intron of PHF19 across TRAF1 into the TRAF1-C5 intergenic region, but excluding the C5 coding region, as the most interesting (trend Pcomb: 1.45E-06 → 5.41E-09). The observed association patterns for these SNPs had heightened statistical significance and a higher degree of consistency across sample sets. In addition, the allele frequencies for these SNPs displayed reduced variability between control groups when compared to other SNPs. Lastly, in combination with the other two known genetic risk factors, HLA-DRB1 and PTPN22, the variants reported here generate more than a 45-fold RA-risk differential

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects

Continuum Reverberation Mapping of the Accretion Disks in Two Seyfert 1 Galaxies

K.H.acknowledges support from STFC grant ST/M001296/1.We present optical continuum lags for two Seyfert 1 galaxies, MCG+08-11-011 and NGC 2617, using monitoring data from a reverberation mapping campaign carried out in 2014. Our light curves span the ugriz filters over four months, with median cadences of 1.0 and 0.6 days for MCG+08-11-011 and NGC 2617, respectively, combined with roughly daily X-ray and near-UV data from Swift for NGC 2617. We find lags consistent with geometrically thin accretion-disk models that predict a lag-wavelength relation of τ ∝ λ 4/3. However, the observed lags are larger than predictions based on standard thin-disk theory by factors of 3.3 for MCG+08-11-011 and 2.3 for NGC 2617. These differences can be explained if the mass accretion rates are larger than inferred from the optical luminosity by a factor of 4.3 in MCG+08-11-011 and a factor of 1.3 in NGC 2617, although uncertainty in the SMBH masses determines the significance of this result. While the X-ray variability in NGC 2617 precedes the UV/optical variability, the long (2.6 day) lag is problematic for coronal reprocessing models.Publisher PDFPeer reviewe