The effect of intellectual ability on functional activation in a neurodevelopmental disorder: preliminary evidence from multiple fMRI studies in Williams syndrome

BACKGROUND: Williams syndrome (WS) is a rare genetic disorder caused by the deletion of approximately 25 genes at 7q11.23 that involves mild to moderate intellectual disability (ID). When using functional magnetic resonance imaging (fMRI) to compare individuals with ID to typically developing individuals, there is a possibility that differences in IQ contribute to between-group differences in BOLD signal. If IQ is correlated with BOLD signal, then group-level analyses should adjust for IQ, or else IQ should be matched between groups. If, however, IQ is not correlated with BOLD signal, no such adjustment or criteria for matching (and exclusion) based on IQ is necessary. METHODS: In this study, we aimed to test this hypothesis systematically using four extant fMRI datasets in WS. Participants included 29 adult subjects with WS (17 men) demonstrating a wide range of standardized IQ scores (composite IQ mean = 67, SD = 17.2). We extracted average BOLD activation for both cognitive and task-specific anatomically defined regions of interest (ROIs) in each individual and correlated BOLD with composite IQ scores, verbal IQ scores and non-verbal IQ scores in Spearman rank correlation tests. RESULTS: Of the 312 correlations performed, only six correlations (2%) in four ROIs reached statistical significance at a P value < 0.01, but none survived correction for multiple testing. All six correlations were positive. Therefore, none supports the hypothesis that IQ is negatively correlated with BOLD response. CONCLUSIONS: These data suggest that the inclusion of subjects with below normal IQ does not introduce a confounding factor, at least for some types of fMRI studies with low cognitive load. By including subjects who are representative of IQ range for the targeted disorder, findings are more likely to generalize to that population

Rethinking Peer Review: Detecting and Addressing Medical Malpractice Claims Risk

A medical center department chair has just been notified that a physician in his department, Dr. G, is being sued for the fifth time in seven years. The CEO of co-defendant hospital wants the chair to solve Dr. G\u27s claims problems. At the chair\u27s request, the hospital peer review committee evaluates Dr. G\u27s malpractice cases. While committee members note some minor concerns in the cases, they conclude that in each circumstance he has met the standard of care. They cannot identify any specific technical or educational need, nor can they supply justification for a disciplinary action. The chair is in a vexing situation. Is Dr. G. the victim of bad luck, or is something more systematic at work? Is there some failure or deficiency other than technical incompetence which is making this physician vulnerable to malpractice suits? If so, is it remediable? In this Article, we analyze the ability of peer review to recognize and reduce physicians\u27 risk of medical malpractice claims. Critics argue that peer review neither consistently identifies substandard physicians, nor ensures their removal, while it unfairly targets colleagues for reasons such as economic competition. They suggest that the solution may be to modify statutes governing privilege and immunity, or to increase penalties for healthcare institutions that violate reporting statutes. Critics\u27 concerns may be misplaced. We will argue that peer review is not deficient in its basic conception, but rather aspects of its design and implementation which often do not directly link it to an institution\u27s risk management activities. We assert that peer review can effectively identify a physician\u27s risk of generating a disproportionate share of medical malpractice claims ex ante, and present a sample methodology which allows peer review to more effectively help physicians address that risk. Part I of this Article discusses the background and authority for peer review. Part II outlines common criticisms of peer review and discusses shortcomings in these analyses. Part III describes background medical malpractice research and introduces the Patient Advocacy Reporting System ( PARSSM ) program for peer review. In Part IV we conclude with a discussion of programmatic elements which, if incorporated into the legal framework for peer review, may allow peer review committees to systematically evaluate, monitor, and, potentially reduce physicians\u27 medical malpractice claims risk

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

ENIGMA-anxiety working group : Rationale for and organization of large-scale neuroimaging studies of anxiety disorders

Altres ajuts: Anxiety Disorders Research Network European College of Neuropsychopharmacology; Claude Leon Postdoctoral Fellowship; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, 44541416-TRR58); EU7th Frame Work Marie Curie Actions International Staff Exchange Scheme grant 'European and South African Research Network in Anxiety Disorders' (EUSARNAD); Geestkracht programme of the Netherlands Organization for Health Research and Development (ZonMw, 10-000-1002); Intramural Research Training Award (IRTA) program within the National Institute of Mental Health under the Intramural Research Program (NIMH-IRP, MH002781); National Institute of Mental Health under the Intramural Research Program (NIMH-IRP, ZIA-MH-002782); SA Medical Research Council; U.S. National Institutes of Health grants (P01 AG026572, P01 AG055367, P41 EB015922, R01 AG060610, R56 AG058854, RF1 AG051710, U54 EB020403).Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders

Amygdala temporal dynamics: temperamental differences in the timing of amygdala response to familiar and novel faces

Abstract Background Inhibited temperament - the predisposition to respond to new people, places or things with wariness or avoidance behaviors - is associated with increased risk for social anxiety disorder and major depression. Although the magnitude of the amygdala's response to novelty has been identified as a neural substrate of inhibited temperament, there may also be differences in temporal dynamics (latency, duration, and peak). We hypothesized that persons with inhibited temperament would have faster responses to novel relative to familiar neutral faces compared to persons with uninhibited temperament. We used event-related functional magnetic resonance imaging to measure the temporal dynamics of the blood oxygen level dependent (BOLD) response to both novel and familiar neutral faces in participants with inhibited or uninhibited temperament. Results Inhibited participants had faster amygdala responses to novel compared with familiar faces, and both longer and greater amygdala response to all faces. There were no differences in peak response. Conclusion Faster amygdala response to novelty may reflect a computational bias that leads to greater neophobic responses and represents a mechanism for the development of social anxiety.</p

The effect of intellectual ability on functional activation in a neurodevelopmental disorder: preliminary evidence from multiple fMRI studies in Williams syndrome

Abstract Background Williams syndrome (WS) is a rare genetic disorder caused by the deletion of approximately 25 genes at 7q11.23 that involves mild to moderate intellectual disability (ID). When using functional magnetic resonance imaging (fMRI) to compare individuals with ID to typically developing individuals, there is a possibility that differences in IQ contribute to between-group differences in BOLD signal. If IQ is correlated with BOLD signal, then group-level analyses should adjust for IQ, or else IQ should be matched between groups. If, however, IQ is not correlated with BOLD signal, no such adjustment or criteria for matching (and exclusion) based on IQ is necessary. Methods In this study, we aimed to test this hypothesis systematically using four extant fMRI datasets in WS. Participants included 29 adult subjects with WS (17 men) demonstrating a wide range of standardized IQ scores (composite IQ mean = 67, SD = 17.2). We extracted average BOLD activation for both cognitive and task-specific anatomically defined regions of interest (ROIs) in each individual and correlated BOLD with composite IQ scores, verbal IQ scores and non-verbal IQ scores in Spearman rank correlation tests. Results Of the 312 correlations performed, only six correlations (2%) in four ROIs reached statistical significance at a P value Conclusions These data suggest that the inclusion of subjects with below normal IQ does not introduce a confounding factor, at least for some types of fMRI studies with low cognitive load. By including subjects who are representative of IQ range for the targeted disorder, findings are more likely to generalize to that population.</p

Neurobiological candidate endophenotypes of social anxiety disorder

Social anxiety disorder (SAD) is a disabling psychiatric disorder with a complex pathogenesis. Studies indicate a genetic component in the development of SAD, but the search for genetic mechanisms underlying this vulnerability is complicated. A focus on endophenotypes instead of the disorder itself may provide a fruitful path forward. Endophenotypes are measurable characteristics related to complex psychiatric disorders and reflective of genetically-based disease mechanisms, and could shed light on the ways by which genes contribute to the development of SAD. We review evidence for candidate MRI endophenotypes of SAD and discuss the extent to which they meet the criteria for an endophenotype, focussing on the amygdala, the medial prefrontal cortex, whole-brain functional connectivity and structural-anatomical changes. Strongest evidence is present for the primary endophenotype criterion of association between the candidate endophenotypes and SAD, while the other criteria, involving trait-stability, heritability and co-segregation of the endophenotype with the disorder within families, warrant further investigation. We highlight the potential of neuroimaging endophenotypes and stress the need for family studies into SAD endophenotypes