Enabling planetary science across light-years. Ariel Definition Study Report

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses.Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods.Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models.Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data

Effects of Mindfulness Training on Emotion Regulation in Patients With Depression: Reduced Dorsolateral Prefrontal Cortex Activation Indexes Early Beneficial Changes

OBJECTIVE Mindfulness-based interventions (MBIs) have been found to be a promising approach for the treatment of recurrent courses of depression. However, little is known about their neural mechanisms. This functional magnetic resonance imaging study set out to investigate activation changes in corticolimbic regions during implicit emotion regulation. METHODS Depressed patients with a recurrent lifetime history were randomized to receive a 2-week MBI (n = 16 completers) or psychoeducation and resting (PER; n = 22 completers). Before and after, patients underwent functional magnetic resonance imaging while labeling the affect of angry, happy, and neutral facial expressions and completed questionnaires assessing ruminative brooding, the ability to decenter from such thinking, and depressive symptoms. RESULTS Activation decreased in the right dorsolateral prefrontal cortex (dlPFC) in response to angry faces after MBI (p 3.282; 56 mm3; Montreal Neurological Institute peak coordinate: 32, 24, 40), but not after PER. This change was highly correlated with increased decentring (r = -0.52, p = .033), decreased brooding (r = 0.60, p = .010), and decreased symptoms (r = 0.82, p = .005). Amygdala activation in response to happy faces decreased after PER (p < .01, family-wise error rate corrected; 392 mm3; Montreal Neurological Institute peak coordinate: 28, -4, -16), whereas the MBI group showed no significant change. CONCLUSIONS The dlPFC is involved in emotion regulation, namely, reappraisal or suppression of negative emotions. Decreased right dlPFC activation might indicate that, after the MBI, patients abstained from engaging in elaboration or suppression of negative affective stimuli; a putatively important mechanism for preventing the escalation of negative mood.Trial Registration: The study is registered at ClinicalTrials.gov (NCT02801513; 16/06/2016)

Effects of Mindfulness Training on Emotion Regulation in Patients with Depression

OBJECTIVE Mindfulness-based interventions (MBIs) have been found to be a promising approach for the treatment of recurrent courses of depression. However, little is known about their neural mechanisms. This functional magnetic resonance imaging study set out to investigate activation changes in corticolimbic regions during implicit emotion regulation. METHODS Depressed patients with a recurrent lifetime history were randomized to receive a 2-week MBI (n = 16 completers) or psychoeducation and resting (PER; n = 22 completers). Before and after, patients underwent functional magnetic resonance imaging while labeling the affect of angry, happy, and neutral facial expressions and completed questionnaires assessing ruminative brooding, the ability to decenter from such thinking, and depressive symptoms. RESULTS Activation decreased in the right dorsolateral prefrontal cortex (dlPFC) in response to angry faces after MBI (p 3.282; 56 mm3; Montreal Neurological Institute peak coordinate: 32, 24, 40), but not after PER. This change was highly correlated with increased decentring (r = -0.52, p = .033), decreased brooding (r = 0.60, p = .010), and decreased symptoms (r = 0.82, p = .005). Amygdala activation in response to happy faces decreased after PER (p < .01, family-wise error rate corrected; 392 mm3; Montreal Neurological Institute peak coordinate: 28, -4, -16), whereas the MBI group showed no significant change. CONCLUSIONS The dlPFC is involved in emotion regulation, namely, reappraisal or suppression of negative emotions. Decreased right dlPFC activation might indicate that, after the MBI, patients abstained from engaging in elaboration or suppression of negative affective stimuli; a putatively important mechanism for preventing the escalation of negative mood.Trial Registration: The study is registered at ClinicalTrials.gov (NCT02801513; 16/06/2016)

VIRTIS: Visible Infrared Thermal Imaging Spectrometer for the Rosetta mission

The visible infrared thermal imaging spectrometer (VIRTIS) is one of the principal payloads to be launched in 2003 on ESA's Rosetta spacecraft. Its primary scientific objective s are to map the surface of the comet Wirtanen, monitor its temperature, and identify the solids and gaseous species on the nucleus and in the coma. VIRTIS will also collect data on two asteroids, one of which has been identified as Mimistrobell. The data is collected remotely using a mapping spectrometer co-boresighted with a high spectral resolution spectrometer. The mapper consists of a Shafer telescope matched to an Offner grating spectrometer capable of gathering high spatial, medium spectral resolution image cubes in the 0.25 to 5 micrometers waveband. The high spectral resolution spectrometer uses an echelle grating and a cross dispersing prism to achieve resolving powers of 1200 to 300 in the 1.9 to 5 micrometers band. Both sub-systems are passively cooled to 130 K and use two Sterling cycle coolers to enable two HgCdTe detector arrays to operate at 70 K. The mapper also uses a silicon back-side illuminated detector array to cover the ultra-violet to near-infrared optical band

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.Peer Reviewe

Cortical Abnormalities Associated With Pediatric and Adult Obsessive-Compulsive Disorder: Findings From the ENIGMA Obsessive-Compulsive Disorder Working Group

ObjectiveBrain imaging studies of structural abnormalities in OCD have yielded inconsistent results, partly because of limited statistical power, clinical heterogeneity, and methodological differences. The authors conducted meta- and mega-analyses comprising the largest study of cortical morphometry in OCD ever undertaken.MethodT1-weighted MRI scans of 1,905 OCD patients and 1,760 healthy controls from 27 sites worldwide were processed locally using FreeSurfer to assess cortical thickness and surface area. Effect sizes for differences between patients and controls, and associations with clinical characteristics, were calculated using linear regression models controlling for age, sex, site, and intracranial volume.ResultsIn adult OCD patients versus controls, we found a significantly lower surface area for the transverse temporal cortex and a thinner inferior parietal cortex. Medicated adult OCD patients also showed thinner cortices throughout the brain. In pediatric OCD patients compared with controls, we found significantly thinner inferior and superior parietal cortices, but none of the regions analyzed showed significant differences in surface area. However, medicated pediatric OCD patients had lower surface area in frontal regions. Cohen's d effect sizes varied from -0.10 to -0.33.ConclusionsThe parietal cortex was consistently implicated in both adults and children with OCD. More widespread cortical thickness abnormalities were found in medicated adult OCD patients, and more pronounced surface area deficits (mainly in frontal regions) were found in medicated pediatric OCD patients. These cortical measures represent distinct morphological features and may be differentially affected during different stages of development and illness, and possibly moderated by disease profile and medication

An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging dat

Ariel: Enabling planetary science across light-years

Ariel Definition Study ReportAriel Definition Study Report, 147 pages. Reviewed by ESA Science Advisory Structure in November 2020. Original document available at: https://www.cosmos.esa.int/documents/1783156/3267291/Ariel_RedBook_Nov2020.pdf/Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution

Ariel: Enabling planetary science across light-years

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution