Brain classification reveals the right cerebellum as the best biomarker of dyslexia

Background Developmental dyslexia is a specific cognitive disorder in reading acquisition that has genetic and neurological origins. Despite histological evidence for brain differences in dyslexia, we recently demonstrated that in large cohort of subjects, no differences between control and dyslexic readers can be found at the macroscopic level (MRI voxel), because of large variances in brain local volumes. In the present study, we aimed at finding brain areas that most discriminate dyslexic from control normal readers despite the large variance across subjects. After segmenting brain grey matter, normalizing brain size and shape and modulating the voxels' content, normal readers' brains were used to build a 'typical' brain via bootstrapped confidence intervals. Each dyslexic reader's brain was then classified independently at each voxel as being within or outside the normal range. We used this simple strategy to build a brain map showing regional percentages of differences between groups. The significance of this map was then assessed using a randomization technique. Results The right cerebellar declive and the right lentiform nucleus were the two areas that significantly differed the most between groups with 100% of the dyslexic subjects (N = 38) falling outside of the control group (N = 39) 95% confidence interval boundaries. The clinical relevance of this result was assessed by inquiring cognitive brain-based differences among dyslexic brain subgroups in comparison to normal readers' performances. The strongest difference between dyslexic subgroups was observed between subjects with lower cerebellar declive (LCD) grey matter volumes than controls and subjects with higher cerebellar declive (HCD) grey matter volumes than controls. Dyslexic subjects with LCD volumes performed worse than subjects with HCD volumes in phonologically and lexicon related tasks. Furthermore, cerebellar and lentiform grey matter volumes interacted in dyslexic subjects, so that lower and higher lentiform grey matter volumes compared to controls differently modulated the phonological and lexical performances. Best performances (observed in controls) corresponded to an optimal value of grey matter and they dropped for higher or lower volumes. Conclusion These results provide evidence for the existence of various subtypes of dyslexia characterized by different brain phenotypes. In addition, behavioural analyses suggest that these brain phenotypes relate to different deficits of automatization of language-based processes such as grapheme/phoneme correspondence and/or rapid access to lexicon entries. article available here: http://www.biomedcentral.com/1471-2202/10/6

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

Persistent Genital Herpes Simplex Virus-2 Shedding Years Following the First Clinical Episode

Background. Patients with newly acquired genital herpes simplex virus 2 (HSV-2) infection have virus frequently detected at the genital mucosa. Rates of genital shedding initially decrease over time after infection, but data on long-term viral shedding are lacking

Comprehensive Myocardial Assessment by Computed Tomography: Impact on Short-Term Outcomes After Transcatheter Aortic Valve Replacement

BACKGROUND: Quantification of myocardial changes in severe aortic stenosis (AS) is prognostically important. The potential for comprehensive myocardial assessment pre–transcatheter aortic valve replacement (TAVR) by computed tomography angiography (CTA) is unknown. OBJECTIVES: The study sought to evaluate whether quantification of left ventricular (LV) extracellular volume—a marker of myocardial fibrosis—and global longitudinal strain—a marker of myocardial deformation—at baseline CTA associate with post-TAVR outcomes. METHODS: Consecutive patients with symptomatic severe AS between January 2021 and June 2022 who underwent pre-TAVR CTA were included. Computed tomography extracellular volume (CT-ECV) was derived from septum tracing after generating the 3-dimensional CT-ECV map. Computed tomography global longitudinal strain (CT-GLS) used semi-automated feature tracking analysis. The clinical endpoint was the composite outcome of all-cause mortality and heart failure hospitalization. RESULTS: Among the 300 patients (80.0 ± 9.4 years of age, 45% female, median Society of Thoracic Surgeons Predicted Risk of Mortality score 2.80%), the left ventricular ejection fraction (LVEF) was 58 ± 12%, the median CT-ECV was 28.5% (IQR: 26.2% to 32.1%), and the median CT-GLS was −20.1% (IQR: −23.8% to −16.3%). Over a median follow-up of 16 months (IQR: 12 to 22 months), 38 deaths and 70 composite outcomes occurred. Multivariable Cox proportional hazards model, accounting for clinical and echocardiographic variables, demonstrated that CT-ECV (HR: 1.09 [95% CI: 1.02-1.16]; P = 0.008) and CT-GLS (HR: 1.07 [95% CI: 1.01-1.13]; P = 0.017) associated with the composite outcome. In combination, elevated CT-ECV and CT-GLS (above median for each) showed a stronger association with the outcome (HR: 7.14 [95% CI: 2.63-19.36]; P < 0.001). CONCLUSIONS: Comprehensive myocardial quantification of CT-ECV and CT-GLS associated with post-TAVR outcomes in a contemporary low-risk cohort with mostly preserved LVEF. Whether these imaging biomarkers can be potentially used for the decision making including timing of AS intervention and post-TAVR follow-up will require integration into future clinical trials