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The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

BackgroundChildhood disadvantage is a prominent risk factor for cognitive and brain aging. Childhood disadvantage is associated with poorer episodic memory in late midlife and functional and structural brain abnormalities in the default mode network (DMN). Although age-related changes in DMN are associated with episodic memory declines in older adults, it remains unclear if childhood disadvantage has an enduring impact on this later-life brain-cognition relationship earlier in the aging process. Here, within the DMN, we examined whether its cortical microstructural integrity-an early marker of structural vulnerability that increases the risk for future cognitive decline and neurodegeneration-is associated with episodic memory in adults at ages 56-66, and whether childhood disadvantage moderates this association.MethodsCortical mean diffusivity (MD) obtained from diffusion magnetic resonance imaging was used to measure microstructural integrity in 350 community-dwelling men. We examined both visual and verbal episodic memory in relation to DMN MD and divided participants into disadvantaged and nondisadvantaged groups based on parental education and occupation.ResultsHigher DMN MD was associated with poorer visual memory but not verbal memory (β = -0.11, p = .040 vs β = -0.04, p = .535). This association was moderated by childhood disadvantage and was significant only in the disadvantaged group (β = -0.26, p = .002 vs β = -0.00, p = .957).ConclusionsLower DMN cortical microstructural integrity may reflect visual memory vulnerability in cognitively normal adults earlier in the aging process. Individuals who experienced childhood disadvantage manifested greater vulnerability to cortical microstructure-related visual memory dysfunction than their nondisadvantaged counterparts who exhibited resilience in the face of low cortical microstructural integrity.

BackgroundPlasma neurofilament light chain (NfL) is a promising biomarker of neurodegeneration with potential clinical utility in monitoring the progression of neurodegenerative diseases. However, the cross-sectional associations of plasma NfL with measures of cognition and brain have been inconsistent in community-dwelling populations.MethodsWe examined these associations in a large community-dwelling sample of early old age men (N = 969, mean age = 67.57 years, range = 61-73 years), who are either cognitively unimpaired (CU) or with mild cognitive impairment (MCI). Specifically, we investigated five cognitive domains (executive function, episodic memory, verbal fluency, processing speed, visual-spatial ability), as well as neuroimaging measures of gray and white matter.ResultsAfter adjusting for age, health status, and young adult general cognitive ability, plasma NfL level was only significantly associated with processing speed and white matter hyperintensity (WMH) volume, but not with other cognitive or neuroimaging measures. The association with processing speed was driven by individuals with MCI, as it was not detected in CU individuals.ConclusionsThese results suggest that in early old age men without dementia, plasma NfL does not appear to be sensitive to cross-sectional individual differences in most domains of cognition or neuroimaging measures of gray and white matter. The revealed plasma NfL associations were limited to WMH for all participants and processing speed only within the MCI cohort. Importantly, considering cognitive status in community-based samples will better inform the interpretation of the relationships of plasma NfL with cognition and brain and may help resolve mixed findings in the literature.

BackgroundHigh b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa.MethodsOne hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b = 0, 500, 1000, and 2000 s/mm2 using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b = 2000 s/mm2 via extrapolation based on mono-exponential decay, using b = 0 and b = 500 s/mm2 (sDWI500) and b = 0, b = 500 s/mm2, and b = 1000 s/mm2 (sDWI1000). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC).ResultsWithin the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI1000 and -67 ± 24% for sDWI500. AUC for aDWI, sDWI500, sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively.ConclusionsDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.

As pediatric youth sports involvement has increased, there has been an increase in meniscus tears associated with acute pediatric knee injuries. The meniscus of pediatric patients has a more robust blood supply which may help its healing potential. The discoid meniscus is an anatomical variant that is more prone to meniscal tears in pediatric patients. Meniscectomy and saucerization are usually the treatment of choice for the complex meniscus tear and the discoid meniscus tear. Meniscus repair should be attempted when at all possible due to the good outcomes associated with meniscus repair and poor results associated with meniscectomy in pediatric patients. [ABSTRACT FROM AUTHOR]

Objectives Childhood disadvantage is associated with lower general cognitive ability (GCA) and brain structural differences in midlife and older adulthood. However, the neuroanatomical mechanisms underlying childhood disadvantage effects on later-life GCA remain poorly understood. Although total surface area (SA) has been linked to lifespan GCA differences, total SA does not capture the nonuniform nature of childhood disadvantage effects on neuroanatomy, which varies across unimodal and transmodal cortices. Here, we examined whether cortical SA profile—the extent to which the spatial patterning of SA deviates from the normative unimodal–transmodal cortical organization—is a mediator of childhood disadvantage effects on later-life GCA. Methods In 477 community-dwelling men aged 56–72 years old, childhood disadvantage index was derived from four indicators of disadvantages and GCA was assessed using a standardized test. Cortical SA was obtained from structural magnetic resonance imaging. For cortical SA profile, we calculated the spatial similarity between maps of individual cortical SA and MRI-derived principal gradient (i.e. unimodal–transmodal organization). Mediation analyses were conducted to examine the indirect effects of childhood disadvantage index through cortical SA profile on GCA. Results Around 1.31% of childhood disadvantage index effects on later-life GCA were mediated by cortical SA profile, whereas total SA did not. Higher childhood disadvantage index was associated with more deviation of the cortical SA spatial patterning from the principal gradient, which in turn related to lower later-life GCA. Discussion Childhood disadvantage may contribute to later-life GCA differences partly by influencing the spatial patterning of cortical SA in a way that deviates from the normative cortical organizational principle. [ABSTRACT FROM AUTHOR]

Aims: Anxiety disorders are prevalent and anxiety symptoms (ANX) co‐occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders. Methods: We included a genome‐wide association study of ANX (meta‐analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention‐deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders. Results: Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k–11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MD n=47, BIP n=33, SCZ n=71, ADHD n=20, and ASD n=5. Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci. Conclusions: Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross‐disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets. [ABSTRACT FROM AUTHOR]

Background: Balanced steady‐state free precession (bSSFP) imaging is commonly used in cardiac cine MRI but prone to image artifacts. Ferumoxytol‐enhanced (FE) gradient echo (GRE) has been proposed as an alternative. Utilizing the abundance of bSSFP images to develop a computationally efficient network that is applicable to FE GRE cine would benefit future network development. Purpose: To develop a variable‐splitting spatiotemporal network (VSNet) for image reconstruction, trained on bSSFP cine images and applicable to FE GRE cine images. Study Type: Retrospective and prospective. Subjects: 41 patients (26 female, 53 ± 19 y/o) for network training, 31 patients (19 female, 49 ± 17 y/o) and 5 healthy subjects (5 female, 30 ± 7 y/o) for testing. Field Strength/Sequence: 1.5T and 3T, bSSFP and GRE. Assessment: VSNet was compared to VSNet with total variation loss, compressed sensing and low rank methods for 14× accelerated data. The GRAPPA×2/×3 images served as the reference. Peak signal‐to‐noise‐ratio (PSNR), structural similarity index (SSIM), left ventricular (LV) and right ventricular (RV) end‐diastolic volume (EDV), end‐systolic volume (ESV), and ejection fraction (EF) were measured. Qualitative image ranking and scoring were independently performed by three readers. Latent scores were calculated based on scores of each method relative to the reference. Statistics: Linear mixed‐effects regression, Tukey method, Fleiss' Kappa, Bland–Altman analysis, and Bayesian categorical cumulative probit model. A P‐value <0.05 was considered statistically significant. Results: VSNet achieved significantly higher PSNR (32.7 ± 0.2), SSIM (0.880 ± 0.004), rank (2.14 ± 0.06), and latent scores (−1.72 ± 0.22) compared to other methods (rank >2.90, latent score < −2.63). Fleiss' Kappa was 0.52 for scoring and 0.61 for ranking. VSNet showed no significantly different LV and RV ESV (P = 0.938) and EF (P = 0.143) measurements, but statistically significant different (2.62 mL) EDV measurements compared to the reference. Conclusion: VSNet produced the highest image quality and the most accurate functional measurements for FE GRE cine images among the tested 14× accelerated reconstruction methods. Level of Evidence: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa). Methods: This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75 ms and once at TE = 90 ms (TEmin1, TEmin2, and TE90, respectively). A linear regression model was determined to match the C‐maps of TE90 to the reference C‐maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at the 98th percentile within each patient's prostate. We compared RSIrs from calibrated TE90 (RSIrsTE90corr) and uncorrected TE90 (RSIrsTE90) to RSIrs from reference TEmin1 (RSIrsTEmin1) and repeated TEmin2 (RSIrsTEmin2). Calibration performance was evaluated with sensitivity, specificity and area under the ROC curve (AUC). Results: Scaling factors for C1, C2, C3, and C4 were estimated as 1.68, 1.33, 1.02, and 1.13, respectively. In non‐csPCa cases, the 98th percentile of RSIrsTEmin2 and RSIrsTEmin1 differed by 0.27 ± 0.86SI (mean ± standard deviation), whereas RSIrsTE90 differed from RSIrsTEmin1 by 1.82 ± 1.20SI. After calibration, this bias was reduced to ‐0.51 ± 1.21SI, representing a 72% reduction in absolute error. For patients with csPCa, the difference was 0.54 ± 1.98SI between RSIrsTEmin2 and RSIrsTEmin1 and 2.28 ± 2.06SI between RSIrsTE90 and RSIrsTEmin1. After calibration, the mean difference decreased to ‐1.03SI, a 55% reduction in absolute error. At the Youden index for patient‐level classification of csPCa (8.94SI), RSIrsTEmin1 has a sensitivity of 66% and a specificity of 72%. Conclusions: The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE‐induced error by 72% and 55% for non‐csPCa and csPCa, respectively. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBO's gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping. Author summary: Genome-Wide Association Studies report the effect size of each genomic variant as summary statistics. Due to the correlated structure of the genomic variants, it may not be straightforward to determine the actual causal genomic variants from these summary statistics. Finemapping studies aim to identify these causal SNPs using different approaches. Here, we presented a novel finemapping method, called Finemap-MiXeR, to determine the actual causal variants using summary statistics data and weighted linkage disequilibrium matrix as input. Our method is based on Variational Bayesian inference on MiXeR model and Evidence Lower Bound of the model is determined to obtain a tractable optimization function. Afterwards, we determined the first derivatives of this Evidence Lower Bound, and finally, Adaptive Moment Estimation is applied to perform optimization. Our method has been validated on synthetic and real data, and similar or better performance than the existing finemapping tools has been observed. [ABSTRACT FROM AUTHOR]

Some mental health problems such as depression and anxiety are more common in females, while others such as autism and attention deficit/hyperactivity (AD/H) are more common in males. However, the neurobiological origins of these sex differences are poorly understood. Animal studies have shown substantial sex differences in neuronal and glial cell structure, while human brain imaging studies have shown only small differences, which largely reflect overall body and brain size. Advanced diffusion MRI techniques can be used to examine intracellular, extracellular, and free water signal contributions and provide unique insights into microscopic cellular structure. However, the extent to which sex differences exist in these metrics of subcortical gray matter structures implicated in psychiatric disorders is not known. Here, we show large sex-related differences in microstructure in subcortical regions, including the hippocampus, thalamus, and nucleus accumbens in a large sample of young adults. Unlike conventional T1-weighted structural imaging, large sex differences remained after adjustment for age and brain volume. Further, diffusion metrics in the thalamus and amygdala were associated with depression, anxiety, AD/H, and antisocial personality problems. Diffusion MRI may provide mechanistic insights into the origin of sex differences in behavior and mental health over the life course and help to bridge the gap between findings from experimental, epidemiological, and clinical mental health research. [ABSTRACT FROM AUTHOR]

Background: Guidelines advocate for minimization of physical restraint (PR) use in intensive care units (ICU). Interprofessional team perspectives on PR practices can inform the design and implementation of successful PR minimization interventions. Aim: To identify ICU staff perspectives of contextual influences on PR practices and minimization strategies. Study Design: A qualitative descriptive study in a single ICU in Toronto, Canada. One‐on‐one semi‐structured interviews were conducted with 14 ICU staff. A deductive content analysis of interviews was undertaken using the integrated–Promoting Action on Research Implementation in Health Services (i‐PARIHS) framework. Results: Five themes were developed: risk‐averse culture, leadership, practice monitoring and feedback processes, environmental factors, and facilitation. Participants described a risk‐averse culture where prophylactic application of PR for intubated patients was used to prevent unplanned extubation thereby avoiding blame from colleagues. Perceived absence of leadership and interprofessional team involvement situated nurses as the primary decision‐maker for restraint application and removal. Insufficient monitoring of restraint practices, lack of access to restraint alternatives, and inability to control environmental contributors to delirium and agitation further increased PR use. Recommendations as to how to minimize restraint use included a nurse facilitator to advance leadership‐team collaboration, availability of restraints alternatives, and guidance on situations for applying and removing restraints. Conclusions: This analysis of contextual influences on PR practices and minimization using the i‐PARIHS framework revealed potentially modifiable barriers to successful PR minimization, including a lack of leadership involvement, gaps in practice monitoring, and collaborative decision‐making processes. A team approach to changing behaviour and culture should be considered for successful implementation and sustainability of PR minimization. Relevance to Practice: The establishment of an interprofessional facilitation team that addresses risk‐averse culture and promotes collaboration among ICU stakeholders will be crucial to the success of any approach to restraint minimization. [ABSTRACT FROM AUTHOR]

The focal radiation therapy (RT) boost technique was shown in a phase III randomized controlled trial (RCT) to improve prostate cancer outcomes without increasing toxicity. This technique relies on the accurate delineation of prostate tumors on MRI. A recent prospective study evaluated radiation oncologists' accuracy when asked to delineate prostate tumors on MRI and demonstrated high variability in tumor contours. We sought to evaluate the impact of contour variability and inaccuracy on predicted clinical outcomes. We hypothesized that radiation oncologists' contour inaccuracies would yield meaningfully worse clinical outcomes. Forty-five radiation oncologists and 2 expert radiologists contoured prostate tumors on 30 patient cases. Of these cases, those with CT simulation or diagnostic CT available were selected for analysis. A knowledge-based planning model was developed to generate focal RT boost plans for each contour per the RCT protocol. The probability of biochemical failure (BF) was determined using a model from the RCT. The primary metric evaluated was delta BF (DBF = Participant BF – Expert BF). An absolute increase in BF ≥5% was considered clinically meaningful. Eight patient cases and 394 target volumes for focal RT boost planning were included in this analysis. In general, participant plans were associated with worse predicted clinical outcomes compared to the expert plan, with an average absolute increase in BF of 4.3%. Of participant plans, 37% were noted to have an absolute increase in BF of 5% or more. Radiation oncologists' attempts to contour tumor targets for focal RT boost are frequently inaccurate enough to yield meaningfully inferior clinical outcomes for patients. [ABSTRACT FROM AUTHOR]

MRI is commonly used to aid breast cancer diagnosis and treatment evaluation. For patients with breast cancer, neoadjuvant chemotherapy aims to reduce the tumor size and extent of surgery necessary. The current clinical standard to measure breast tumor response on MRI uses the longest tumor diameter. Radiologists also account for other tissue properties including tumor contrast or pharmacokinetics in their assessment. Accurate longitudinal image registration of breast tissue is critical to properly compare response to treatment at different timepoints. In this study, a deformable Fast Longitudinal Image Registration (FLIRE) algorithm was optimized for breast tissue. FLIRE was then compared to the publicly available software packages with high accuracy (DRAMMS) and fast runtime (Elastix). Patients included in the study received longitudinal T 1 - weighted MRI without fat saturation at two to six timepoints as part of asymptomatic screening (n = 27) or throughout neoadjuvant chemotherapy treatment (n = 32). T 1 - weighted images were registered to the first timepoint with each algorithm. Alignment and runtime performance were compared using two-way repeated measure ANOVAs (P < 0.05). Across all patients, Pearson's correlation coefficient across the entire image volume was slightly higher with statistical significance and had less variance for FLIRE (0.98 ± 0.01 stdev) compared to DRAMMS (0.97 ± 0.03 stdev) and Elastix (0.95 ± 0.03 stdev). Additionally, FLIRE runtime (10.0 mins) was 9.0 times faster than DRAMMS (89.6 mins) and 1.5 times faster than Elastix (14.5 mins) on a Linux workstation. FLIRE demonstrates promise for time-sensitive clinical applications due to its accuracy, robustness across patients and timepoints, and speed. [ABSTRACT FROM AUTHOR]

Opioid use disorder (OUD), a serious health burden worldwide, is associated with lower cognitive function. Recent studies have demonstrated a negative genetic correlation between OUD and general cognitive ability (COG), indicating a shared genetic basis. However, the specific genetic variants involved, and the underlying molecular mechanisms remain poorly understood. Here, we aimed to quantify and identify the genetic basis underlying OUD and COG. We quantified the extent of genetic overlap between OUD and COG using a bivariate causal mixture model (MiXeR) and identified specific genetic loci applying conditional/conjunctional FDR. Finally, we investigated biological function and expression of implicated genes using available resources. We estimated that ~94% of OUD variants (4.8k out of 5.1k variants) also influence COG. We identified three novel OUD risk loci and one locus shared between OUD and COG. Loci identified implicated biological substrates in the basal ganglia. We provide new insights into the complex genetic risk architecture of OUD and its genetic relationship with COG. • Ninety four percent of opioid use disorder (OUD) genetics is captured by genetics of general cognitive abilityability (COG). • The shared portion of genetics between OUD and COG exhibits highly negative genetic correlation. • COG is twice as polygenic as OUD. • OUD is similar in polygenicity as ADHD. • Novel genetic loci identified for OUD and COG implicates genes expressed in reward circuitry. [ABSTRACT FROM AUTHOR]