ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size, and have limited clinical relevance. These concerns have prompted a paradigm shift towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Hence, we uniquely built/validated supervised neuroanatomical machine learning (ML) models for individual-level inferences, using the largest up to date neuroimaging database on youth anxiety disorders: ENIGMA Anxiety Consortium (N=3,343; Age: 10-25 years; Global Sites: 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (Panic Disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status, and symptom severity (AUC’s 0.59-0.63). Classifications were driven by neuroanatomical features (cortical thickness/surface area, subcortical volumes) in fronto-striato-limbic and temporo-parietal regions. This benchmark study provides estimates on individual-level classification performances that can be realistically achieved with ML using neuroanatomical data, within a large, heterogenous, and multi-site sample of youth with anxiety disorders

Strong Carbon Features and a Red Early Color in the Underluminous Type Ia SN 2022xkq

We present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining type Ia supernova (SN Ia) in NGC 1784 ($\mathrm{D}\approx31$ Mpc), from $<1$ to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess which is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived C I 1.0693 $\mu$m feature which persists until 5 days post-maximum. We also detect C II $\lambda$6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic dataset of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes which produce faint SNe Ia.Comment: 38 pages, 16 figures, accepted for publication in ApJ, the figure 15 input models and synthetic spectra are now available at https://zenodo.org/record/837925

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Searching for Bumps in the Cosmological Road: Do Type Ia Supernovae with Early Excesses Have Biased Hubble Residuals?

Abstract Flux excesses in the early-time light curves of Type Ia supernovae (SNe Ia) are predicted by multiple theoretical models and have been observed in a number of nearby SNe Ia over the last decade. However, the astrophysical processes that cause these excesses may affect their use as standardizable candles for cosmological parameter measurements. We perform a systematic search for early-time excesses in SNe Ia observed by the Zwicky Transient Facility (ZTF) to study whether SNe Ia with these excesses yield systematically different Hubble residuals. We analyze two compilations of SN Ia light curves from ZTF’s first year of operations: 127 high-cadence light curves from Y. Yao et al. and 305 light curves from the ZTF cosmology data release of S. Dhawan et al. We detect significant early-time excesses for 17 SNe Ia in these samples and find that the excesses have a median g − r color of 0.10 ± 0.11 mag; we do not find a clear preference for blue excesses as predicted by several models. Using the SALT3 model, we measure Hubble residuals for these two samples, finding that excess-having SNe Ia may have lower Hubble residuals (HR) after correcting for shape, color, and host-galaxy mass, at ∼2–3σ significance; our baseline result is ΔHR = −0.056 ± 0.026 mag (2.2σ). We compare the host-galaxy masses of excess-having and no-excess SNe Ia and find they are consistent, though at marginal significance excess-having SNe Ia may prefer lower-mass hosts. Additional discoveries of early excess SNe Ia will be a powerful way to understand potential biases in SN Ia cosmology and probe the physics of SN Ia progenitors.</jats:p

Extreme Nuclear Transients Resulting from the Tidal Disruption of Intermediate Mass Stars

International audienceModern transient surveys now routinely discover flares resulting from tidal disruption events (TDEs) which occur when stars, typically $\sim0.5-2$ M$_{\odot}$, are ripped apart after passing too close to a supermassive black hole. We present three examples of a new class of extreme nuclear transients (ENTs) that we interpret as the tidal disruption of intermediate mass ($\sim3-10$ M$_{\odot}$) stars. Each is coincident with their host-galaxy nucleus and exhibits a smooth ($150$ days) flare. ENTs are extremely rare ($\geq1\times10^{-3}$ Gpc$^{-1}$ yr$^{-1}$) compared to any other known class of transients. They are at least twice as energetic ($0.5-2.5\times 10^{53}$ erg) as any other known transient and these extreme energetics rule out stellar origins

Strong Carbon Features and a Red Early Color in the Underluminous Type Ia SN 2022xkq

International audienceWe present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining type Ia supernova (SN Ia) in NGC 1784 ($\mathrm{D}\approx31$ Mpc), from $<1$ to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess which is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived C I 1.0693 $\mu$m feature which persists until 5 days post-maximum. We also detect C II $\lambda$6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic dataset of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes which produce faint SNe Ia

Strong Carbon Features and a Red Early Color in the Underluminous Type Ia SN 2022xkq

International audienceWe present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining type Ia supernova (SN Ia) in NGC 1784 ($\mathrm{D}\approx31$ Mpc), from $<1$ to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess which is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived C I 1.0693 $\mu$m feature which persists until 5 days post-maximum. We also detect C II $\lambda$6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic dataset of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes which produce faint SNe Ia

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors