Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

Nondestructive Evaluation of Mechanical and Histological Properties of the Human Aorta With Near-Infrared Spectroscopy.

INTRODUCTION: Ascending aortic dilatation is a well-known risk factor for aortic rupture. Indications for aortic replacement in its dilatation concomitant to other open-heart surgery exist; however, cut-off values based solely on aortic diameter may fail to identify patients with weakened aortic tissue. We introduce near-infrared spectroscopy (NIRS) as a diagnostic tool to nondestructively evaluate the structural and compositional properties of the human ascending aorta during open-heart surgeries. During open-heart surgery, NIRS could provide information regarding tissue viability in situ and thus contribute to the decision of optimal surgical repair. MATERIALS AND METHODS: Samples were collected from patients with ascending aortic aneurysm (n = 23) undergoing elective aortic reconstruction surgery and from healthy subjects (n = 4). The samples were subjected to spectroscopic measurements, biomechanical testing, and histological analysis. The relationship between the near-infrared spectra and biomechanical and histological properties was investigated by adapting partial least squares regression. RESULTS: Moderate prediction performance was achieved with biomechanical properties (r = 0.681, normalized root-mean-square error of cross-validation = 17.9%) and histological properties (r = 0.602, normalized root-mean-square error of cross-validation = 22.2%). Especially the performance with parameters describing the aorta's ultimate strength, for example, failure strain (r = 0.658), and elasticity (phase difference, r = 0.875) were promising and could, therefore, provide quantitative information on the rupture sensitivity of the aorta. For the estimation of histological properties, the results with α-smooth muscle actin (r = 0.581), elastin density (r = 0.973), mucoid extracellular matrix accumulation(r = 0.708), and media thickness (r = 0.866) were promising. CONCLUSIONS: NIRS could be a potential technique for in situ evaluation of biomechanical and histological properties of human aorta and therefore useful in patient-specific treatment planning.

Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes.

In a context of growing interest in breeding more resilient animals, a noninvasive indicator of resilience would be very valuable. We hypothesized that the time-course of concentrations of several milk metabolites through a short-term underfeeding challenge could reflect the variation of resilience mechanisms to such a challenge. We submitted 138 one-year-old primiparous goats, selected for extreme functional longevity (i.e., productive longevity corrected for milk yield [60 low longevity line goats and 78 high longevity line goats]), to a 2-d underfeeding challenge during early lactation. We measured the concentration of 13 milk metabolites and the activity of 1 enzyme during prechallenge, challenge, and recovery periods. Functional principal component analysis summarized the trends of milk metabolite concentration over time efficiently without preliminary assumptions concerning the shapes of the curves. We first ran a supervised prediction of the longevity line of the goats based on the milk metabolite curves. The partial least square analysis could not predict the longevity line accurately. We thus decided to explore the large overall variability of milk metabolite curves with an unsupervised clustering. The large year × facility effect on the metabolite concentrations was precorrected for. This resulted in 3 clusters of goats defined by different metabolic responses to underfeeding. The cluster that showed higher ß-hydroxybutyrate, cholesterol, and triacylglycerols increase during the underfeeding challenge was associated with poorer survival compared with the other 2 clusters. These results suggest that multivariate analysis of noninvasive milk measures show potential for deriving new resilience phenotypes.

Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project.

This study characterized person-specific rates of change of total daily physical activity (TDPA) and identified correlates of this change. TDPA metrics were extracted from multiday wrist-sensor recordings from 1083 older adults (average age 81 years; 76% female). Thirty-two covariates were collected at baseline. A series of linear mixed-effect models were used to identify covariates independently associated with the level and annual rate of change of TDPA. Though, person-specific rates of change varied during a mean follow-up of 5 years, 1079 of 1083 showed declining TDPA. The average decline was 16%/year, with a 4% increased rate of decline for every 10 years of age older at baseline. Following variable selection using multivariate modeling with forward and then backward elimination, age, sex, education, and 3 of 27 non-demographic covariates including motor abilities, a fractal metric, and IADL disability remained significantly associated with declining TDPA accounting for 21% of its variance (9% non-demographic and 12% demographics covariates). These results show that declining TDPA occurs in many very old adults. Few covariates remained correlated with this decline and the majority of its variance remained unexplained. Further work is needed to elucidate the biology underlying TDPA and to identify other factors that account for its decline.

Multivariate semi-blind deconvolution of fMRI time series.

Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition. Most of existing approaches in the literature works on task-fMRI data and relies on the experimental paradigm as a surrogate of neural activity, hence remaining inoperative on resting-stage fMRI (rs-fMRI) data. To cope with this issue, recent works have performed either a two-step analysis to detect large neural events and then characterize the HRF shape or a joint estimation of both the neural and haemodynamic components in an univariate fashion. In this work, we express the neural activity signals as a combination of piece-wise constant temporal atoms associated with sparse spatial maps and introduce an haemodynamic parcellation of the brain featuring a temporally dilated version of a given HRF model in each parcel with unknown dilation parameters. We formulate the joint estimation of the HRF shapes and spatio-temporal neural representations as a multivariate semi-blind deconvolution problem in a paradigm-free setting and introduce constraints inspired from the dictionary learning literature to ease its identifiability. A fast alternating minimization algorithm, along with its efficient implementation, is proposed and validated on both synthetic and real rs-fMRI data at the subject level. To demonstrate its significance at the population level, we apply this new framework to the UK Biobank data set, first for the discrimination of haemodynamic territories between balanced groups (n=24 individuals in each) patients with an history of stroke and healthy controls and second, for the analysis of normal aging on the neurovascular coupling. Overall, we statistically demonstrate that a pathology like stroke or a condition like normal brain aging induce longer haemodynamic delays in certain brain areas (e.g. Willis polygon, occipital, temporal and frontal cortices) and that this haemodynamic feature may be predictive with an accuracy of 74 % of the individual's age in a supervised classification task performed on n=459 subjects.

Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches.

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.

Data-Driven Modeling of the Bicalutamide Dissolution from Powder Systems.

Low solubility of active pharmaceutical compounds (APIs) remains an important challenge in dosage form development process. In the manuscript, empirical models were developed and analyzed in order to predict dissolution of bicalutamide (BCL) from solid dispersion with various carriers. BCL was chosen as an example of a poor water-soluble API. Two separate datasets were created: one from literature data and another based on in-house experimental data. Computational experiments were conducted using artificial intelligence tools based on machine learning (AI/ML) with a plethora of techniques including artificial neural networks, decision trees, rule-based systems, and evolutionary computations. The latter resulting in classical mathematical equations provided models characterized by the lowest prediction error. In-house data turned out to be more homogeneous, as well as formulations were more extensively characterized than literature-based data. Thus, in-house data resulted in better models than literature-based data set. Among the other covariates, the best model uses for prediction of BCL dissolution profile the transmittance from IR spectrum at 1260 cm-1 wavenumber. Ab initio modeling-based in silico simulations were conducted to reveal potential BCL-excipients interaction. All crucial variables were selected automatically by AI/ML tools and resulted in reasonably simple and yet predictive models suitable for application in Quality by Design (QbD) approaches. Presented data-driven model development using AI/ML could be useful in various problems in the field of pharmaceutical technology, resulting in both predictive and investigational tools revealing new knowledge.

A MATLAB toolbox for multivariate analysis of brain networks.

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).

Ultrasound imaging of the median nerve as a prognostic factor for carpal tunnel decompression.

INTRODUCTION: The diagnostic value of ultrasound imaging in carpal tunnel syndrome is established, but reports on its prognostic value have been contradictory. METHODS: This investigation was an observational study of subjective surgical results, evaluated by symptom severity and functional status scales, and an ordinal scale for overall outcome, for 145 carpal tunnel decompressions in relation to preoperative measurement of median nerve cross-sectional area. RESULTS: The surgical success rate was 86%. In univariate analyses no significant correlation existed between outcome and preoperative cross-sectional area, nor with preoperative nerve conduction studies or patient variables, except for body mass index and gender. A multivariate model including electrophysiological, imaging, and patient variables was moderately predictive of success with an area under the receiver operating characteristic curve of 0.82. CONCLUSIONS: Cross-sectional area alone is unlikely to be a sufficiently reliable predictor of outcome for use in counseling individual patients, but imaging results may be useful in multivariate prognostic models.

Elucidation of the retention mechanism on a reverse-phase cyano column by modeling.

Partial least squares and quantitative structure-retention relationship models have been used mainly to explain and then to predict the retention mechanism on a cyanopropyl high-performance liquid chromatography column. Developing and applying the models involves studying the chromatographic behavior of 100 probes. Characterization of the probes took place under optimized isocratic conditions at variable proportions of two mobile phase mixtures. Retention time was correlated with numerous physicochemical properties and structural features of the probes. The goodness-of-fit for both models was estimated by the coefficient of multiple determinations, while the prediction of a test set was achieved by the root mean square error of prediction. The contribution of the descriptors in partial least squares is confirmed by the information derived from the variable importance in the projection and loadings plots, while a quantitative structure-retention relationship reflects the behavior model. In both cases, the descriptors determining the retention mechanism are lipophilicity, solubility in water, molecular volume and the presence of -COOH and/or condensed rings. Such techniques are proven useful tools for visualizing, exploring, and modeling the complex interactions between solutes and the mobile and stationary phase while at the same time this information can be quantified.

Multivariate investigation of aging in mouse models expressing the Alzheimer's protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics.

APOE allelic variation is critical in brain aging and Alzheimer's disease (AD). The APOE2 allele associated with cognitive resilience and neuroprotection against AD remains understudied. We employed a multipronged approach to characterize the transition from middle to old age in mice with APOE2 allele, using behavioral assessments, image-derived morphometry and diffusion metrics, structural connectomics, and blood transcriptomics. We used sparse multiple canonical correlation analyses (SMCCA) for integrative modeling, and graph neural network predictions. Our results revealed brain sub-networks associated with biological traits, cognitive markers, and gene expression. The cingulate cortex emerged as a critical region, demonstrating age-associated atrophy and diffusion changes, with higher fractional anisotropy in males and middle-aged subjects. Somatosensory and olfactory regions were consistently highlighted, indicating age-related atrophy and sex differences. The hippocampus exhibited significant volumetric changes with age, with differences between males and females in CA3 and CA1 regions. SMCCA underscored changes in the cingulate cortex, somatosensory cortex, olfactory regions, and hippocampus in relation to cognition and blood-based gene expression. Our integrative modeling in aging APOE2 carriers revealed a central role for changes in gene pathways involved in localization and the negative regulation of cellular processes. Our results support an important role of the immune system and response to stress. This integrative approach offers novel insights into the complex interplay among brain connectivity, aging, and sex. Our study provides a foundation for understanding the impact of APOE2 allele on brain aging, the potential for detecting associated changes in blood markers, and revealing novel therapeutic intervention targets.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample.

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

Clinical neuroscience research is going through a translational crisis largely due to the challenges of producing meaningful and generalizable results. Two critical limitations within clinical neuroscience research are the use of univariate statistics and between-study methodological variation. Univariate statistics may not be sensitive enough to detect complex relationships between several variables, and methodological variation poses challenges to the generalizability of the results. We compared two widely used multivariate statistical approaches, canonical correlations analysis (CCA) and partial least squares correlation (PLS), to determine the generalizability and stability of their solutions. We show that the properties of the measures inputted into the analysis likely play a more substantial role in the generalizability and stability of results compared to the specific approach applied (i.e., CCA or PLS).

Current evidence supporting associations of DNA methylation measurements with survivorship burdens in cancer survivors: A scoping review.

INTRODUCTION: Identifying reliable biomarkers that reflect cancer survivorship symptoms remains a challenge for researchers. DNA methylation (DNAm) measurements reflecting epigenetic changes caused by anti-cancer therapy may provide needed insights. Given lack of consensus describing utilization of DNAm data to predict survivorship issues, a review evaluating the current landscape is warranted. OBJECTIVE: Provide an overview of current studies examining associations of DNAm with survivorship burdens in cancer survivors. METHODS: A literature review was conducted including studies if they focused on cohorts of cancer survivors, utilized peripheral blood cell DNAm data, and evaluated the associations of DNAm and survivorship issues. RESULTS: A total of 22 studies were identified, with majority focused on breast (n = 7) or childhood cancer (n = 9) survivors, and half studies included less than 100 patients (n = 11). Survivorship issues evaluated included those related to neurocognition (n = 5), psychiatric health (n = 3), general wellness (n = 9), chronic conditions (n = 5), and treatment specific toxicities (n = 4). Studies evaluated epigenetic age metrics (n = 10) and DNAm levels at individual CpG sites or regions (n = 12) for their associations with survivorship issues in cancer survivors along with relevant confounding factors. Significant associations of measured DNAm in the peripheral blood samples of cancer survivors and survivorship issues were identified. DISCUSSION/CONCLUSION: Studies utilizing epigenetic age metrics and differential methylation analysis demonstrated significant associations of DNAm measurements with survivorship burdens. Associations were observed encompassing diverse survivorship outcomes and timeframes relative to anti-cancer therapy initiation. These findings underscore the potential of these measurements as useful biomarkers in survivorship care and research.

Metabolomics and Lipidomics Analyses Aid Model Classification of Type 2 Diabetes in Non-Human Primates.

Type 2 diabetes (T2D) is a global public health issue characterized by excess weight, abdominal obesity, dyslipidemia, hyperglycemia, and a progressive increase in insulin resistance. Human population studies of T2D development and its effects on systemic metabolism are confounded by many factors that cannot be controlled, complicating the interpretation of results and the identification of early biomarkers. Aged, sedentary, and overweight/obese non-human primates (NHPs) are one of the best animal models to mimic spontaneous T2D development in humans. We sought to identify and distinguish a set of plasma and/or fecal metabolite biomarkers, that have earlier disease onset predictability, and that could be evaluated for their predictability in subsequent T2D studies in human cohorts. In this study, a single plasma and fecal sample was collected from each animal in a colony of 57 healthy and dysmetabolic NHPs and analyzed for metabolomics and lipidomics. The samples were comprehensively analyzed using untargeted and targeted LC/MS/MS. The changes in each animal's disease phenotype were monitored using IVGTT, HbA1c, and other clinical metrics, and correlated with their metabolic profile. The plasma and fecal lipids, as well as bile acid profiles, from Healthy, Dysmetabolic (Dys), and Diabetic (Dia) animals were compared. Following univariate and multivariate analyses, including adjustments for weight, age, and sex, several plasma lipid species were identified to be significantly different between these animal groups. Medium and long-chain plasma phosphatidylcholines (PCs) ranked highest at distinguishing Healthy from Dys animals, whereas plasma triglycerides (TG) primarily distinguished Dia from Dys animals. Random Forest (RF) analysis of fecal bile acids showed a reduction in the secondary bile acid glycoconjugate, GCDCA, in diseased animals (AUC 0.76[0.64, 0.89]). Moreover, metagenomics results revealed several bacterial species, belonging to the genera Roseburia, Ruminococcus, Clostridium, and Streptococcus, to be both significantly enriched in non-healthy animals and associated with secondary bile acid levels. In summary, our results highlight the detection of several elevated circulating plasma PCs and microbial species associated with fecal secondary bile acids in NHP dysmetabolic states. The lipids and metabolites we have identified may help researchers to differentiate individual NHPs more precisely between dysmetabolic and overtly diabetic states. This could help assign animals to study groups that are more likely to respond to potential therapies where a difference in efficacy might be anticipated between early vs. advanced disease.

Modeling considerations for using expression data from multiple species.

Although genome-wide expression data sets from multiple species are now more commonly generated, there have been few studies on how to best integrate this type of correlated data into models. Starting with a single-species, linear regression model that predicts transcription factor binding sites as a case study, we investigated how best to take into account the correlated expression data when extending this model to multiple species. Using a multivariate regression model, we accounted for the phylogenetic relationships among the species in two ways: (i) a repeated-measures model, where the error term is constrained; and (ii) a Bayesian hierarchical model, where the prior distributions of the regression coefficients are constrained. We show that both multiple-species models improve predictive performance over the single-species model. When compared with each other, the repeated-measures model outperformed the Bayesian model. We suggest a possible explanation for the better performance of the model with the constrained error term.

Alzheimer's disease-specific cytokine secretion suppresses neuronal mitochondrial metabolism.

Introduction: Neuroinflammation and metabolic dysfunction are early alterations in Alzheimer's disease brain that are thought to contribute to disease onset and progression. Glial activation due to protein deposition results in cytokine secretion and shifts in brain metabolism, which have been observed in Alzheimer's disease patients. However, the mechanism by which this immunometabolic feedback loop can injure neurons and cause neurodegeneration remains unclear. Methods: We used Luminex XMAP technology to quantify hippocampal cytokine concentrations in the 5xFAD mouse model of Alzheimer's disease at milestone timepoints in disease development. We used partial least squares regression to build cytokine signatures predictive of disease progression, as compared to healthy aging in wild-type littermates. We applied the disease-defining cytokine signature to wild-type primary neuron cultures and measured downstream changes in gene expression using the NanoString nCounter system and mitochondrial function using the Seahorse Extracellular Flux live-cell analyzer. Results: We identified a pattern of up-regulated IFNγ, IP-10, and IL-9 as predictive of advanced disease. When healthy neurons were exposed to these cytokines in proportions found in diseased brain, gene expression of mitochondrial electron transport chain complexes, including ATP synthase, was suppressed. In live cells, basal and maximal mitochondrial respiration were impaired following cytokine stimulation. Conclusions: An Alzheimer's disease-specific pattern of cytokine secretion reduces expression of mitochondrial electron transport complexes and impairs mitochondrial respiration in healthy neurons. We establish a mechanistic link between disease-specific immune cues and impaired neuronal metabolism, potentially causing neuronal vulnerability and susceptibility to degeneration in Alzheimer's disease.

Amyloid-ß Pathology-Specific Cytokine Secretion Suppresses Neuronal Mitochondrial Metabolism.

Introduction: Neuroinflammation and metabolic dysfunction are early alterations in Alzheimer's disease (AD) brain that are thought to contribute to disease onset and progression. Glial activation due to protein deposition results in cytokine secretion and shifts in brain metabolism, which have been observed in AD patients. However, the mechanism by which this immunometabolic feedback loop can injure neurons and cause neurodegeneration remains unclear. Methods: We used Luminex XMAP technology to quantify hippocampal cytokine concentrations in the 5xFAD mouse model of AD at milestone timepoints in disease development. We used partial least squares regression to build cytokine signatures predictive of disease progression, as compared to healthy aging in wild-type littermates. We applied the disease-defining cytokine signature to wild-type primary neuron cultures and measured downstream changes in gene expression using the NanoString nCounter system and mitochondrial function using the Seahorse Extracellular Flux live-cell analyzer. Results: We identified a pattern of up-regulated IFNγ, IP-10/CXCL10, and IL-9 as predictive of advanced disease. When healthy neurons were exposed to these cytokines in proportions found in diseased brain, gene expression of mitochondrial electron transport chain complexes, including ATP synthase, was suppressed. In live cells, basal and maximal mitochondrial respiration were impaired following cytokine stimulation. Conclusions: We identify a pattern of cytokine secretion predictive of progressing amyloid-ß pathology in the 5xFAD mouse model of AD that reduces expression of mitochondrial electron transport complexes and impairs mitochondrial respiration in healthy neurons. We establish a mechanistic link between disease-specific immune cues and impaired neuronal metabolism, potentially causing neuronal vulnerability and susceptibility to degeneration in AD. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-023-00782-y.

Forecasting patient-specific dosimetric benefit from daily online adaptive radiotherapy for cervical cancer.

Objective. Adaptive Radiotherapy (ART) is an emerging technique for treating cancer patients which facilitates higher delivery accuracy and has the potential to reduce toxicity. However, ART is also resource-intensive, Requiring extra human and machine time compared to standard treatment methods. In this analysis, we sought to predict the subset of node-negative cervical cancer patients with the greatest benefit from ART, so resources might be properly allocated to the highest-yield patients.Approach. CT images, initial plan data, and on-treatment Cone-Beam CT (CBCT) images for 20 retrospective cervical cancer patients were used to simulate doses from daily non-adaptive and adaptive techniques. We evaluated the coefficient of determination (R2) between dose and volume metrics from initial treatment plans and the dosimetric benefits to theBowelV40Gy,BowelV45Gy,BladderDmean,andRectumDmeanfrom adaptive radiotherapy using reduced 3 mm or 5 mm CTV-to-PTV margins. The LASSO technique was used to identify the most predictive metrics forBowelV40Gy.The three highest performing metrics were used to build multivariate models with leave-one-out validation forBowelV40Gy.Main results. Patients with higher initial bowel doses were correlated with the largest decreases in BowelV40Gyfrom daily adaptation (linear best fit R2= 0.77 for a 3 mm PTV margin and R2= 0.8 for a 5 mm PTV margin). Other metrics had intermediate or no correlation. Selected covariates for the multivariate model were differences in the initialBowelV40GyandBladderDmeanusing standard versus reduced margins and the initial bladder volume. Leave-one-out validation had an R2of 0.66 between predicted and true adaptiveBowelV40Gybenefits for both margins.Significance. The resulting models could be used to prospectively triage cervical cancer patients on or off daily adaptation to optimally manage clinical resources. Additionally, this work presents a critical foundation for predicting benefits from daily adaptation that can be extended to other patient cohorts.

Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach.

Recent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks.