Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research

Adenylylation of Gyrase and Topo IV by FicT Toxins Disrupts Bacterial DNA Topology

Toxin-antitoxin (TA) modules are ubiquitous molecular switches controlling bacterial growth via the release of toxins that inhibit cell proliferation. Most of these toxins interfere with protein translation, but a growing variety of other mechanisms hints at a diversity that is not yet fully appreciated. Here, we characterize a group of FIC domain proteins as toxins of the conserved and abundant FicTA family of TA modules, and we reveal that they act by suspending control of cellular DNA topology. We show that FicTs are enzymes that adenylylate DNA gyrase and topoisomerase IV, the essential bacterial type IIA topoisomerases, at their ATP-binding site. This modification inactivates both targets by blocking their ATPase activity, and, consequently, causes reversible growth arrest due to the knotting, catenation, and relaxation of cellular DNA. Our results give insight into the regulation of DNA topology and highlight the remarkable plasticity of FIC domain proteins

Proton MR spectroscopy of the brain at 3 T: An update

Proton magnetic resonance spectroscopy (1H-MRS) provides specific metabolic information not otherwise observable by any other imaging method. 1H-MRS of the brain at 3 T is a new tool in the modern neuroradiological armamentarium whose main advantages, with respect to the well-established and technologically advanced 1.5-T 1H-MRS, include a higher signal-to-noise ratio, with a consequent increase in spatial and temporal resolutions, and better spectral resolution. These advantages allow the acquisition of higher quality and more easily quantifiable spectra in smaller voxels and/or in shorter times, and increase the sensitivity in metabolite detection. However, these advantages may be hampered by intrinsic field-dependent technical issues, such as decreased T2 signal, chemical shift dispersion errors, J-modulation anomalies, increased magnetic susceptibility, eddy current artifacts, challenges in designing and obtaining appropriate radiofrequency coils, magnetic field instability and safety hazards. All these limitations have been tackled by manufacturers and researchers and have received one or more solutions. Furthermore, advanced 1H-MRS techniques, such as specific spectral editing, fast 1H-MRS imaging and diffusion tensor 1H-MRS imaging, have been successfully implemented at 3 T. However, easier and more robust implementations of these techniques are still needed before they can become more widely used and undertake most of the clinical and research 1H-MRS applications. © Springer-Verlag 2007

Quantification of muscle fat in patients with low back pain: Comparison of multi-echo MR imaging with single-voxel MR spectroscopy

Purpose:To compare lumbar muscle fat-signal fractions derived from three-dimensional dual gradient-echo magnetic resonance (MR) imaging and multiple gradient-echo MR imaging with fractions from single-voxel MR spectroscopy in patients with low back pain.Materials and Methods:This prospective study had institutional review board approval, and written informed consent was obtained from all study participants. Fifty-six patients (32 women; mean age, 52 years ± 15 [standard deviation]; age range, 20-79 years) with low back pain underwent standard 1.5-T MR imaging, which was supplemented by dual-echo MR imaging, multi-echo MR imaging, and MR spectroscopy to quantify fatty degeneration of bilateral lumbar multifidus muscles in a region of interest at the intervertebral level of L4 through L5. Fat-signal fractions were determined from signal intensities on fat- and water-only images from both imaging data sets (dual-echo and multi-echo fat-signal fractions without T2* correction) or directly obtained, with additional T2* correction, from multi-echo MR imaging. The results were compared with MR spectroscopic fractions. The Student t test and Bland-Altman plots were used to quantify agreement between fat-signal fractions derived from imaging and from spectroscopy.Results:In total, 102 spectroscopic measurements were obtained bilaterally (46 of 56) or unilaterally (10 of 56). Mean spectroscopic fat-signal fraction was 19.6 ± 11.4 (range, 5.4-63.5). Correlation between spectroscopic and all imaging-based fat-signal fractions was statistically significant (R(2) = 0.87-0.92; all P < .001). Mean dual-echo fat-signal fractions not corrected for T2* and multi-echo fat-signal fractions corrected for T2* significantly differed from spectroscopic fractions (both P < .01), but mean multi-echo fractions not corrected for T2* did not (P = .11). There was a small measurement bias of 0.5% (95% limits of agreement: -6.0%, 7.2%) compared with spectroscopic fractions.Conclusion:Large-volume image-based (dual-echo and multi-echo MR imaging) and spectroscopic fat-signal fractions agree well, thus allowing fast and accurate quantification of muscle fat content in patients with low back pain.© RSNA, 2012

Effects of inner volume field-of-view reduction on myocardial T2 mapping

da inserir