Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.

Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks informed by prior biological knowledge, referred to as visible networks. These neural networks offer insights into the decision-making process and can unveil novel perspectives on the underlying biological mechanisms associated with traits and complex diseases. We tested the performance, interpretability and generalizability for inferring smoking status, subject age and LDL levels using genome-wide RNA expression and CpG methylation data from the blood of the BIOS consortium (four population cohorts, Ntotal = 2940). In a cohort-wise cross-validation setting, the consistency of the diagnostic performance and interpretation was assessed. Performance was consistently high for predicting smoking status with an overall mean AUC of 0.95 (95% CI: 0.90-1.00) and interpretation revealed the involvement of well-replicated genes such as AHRR, GPR15 and LRRN3. LDL-level predictions were only generalized in a single cohort with an R2 of 0.07 (95% CI: 0.05-0.08). Age was inferred with a mean error of 5.16 (95% CI: 3.97-6.35) years with the genes COL11A2, AFAP1, OTUD7A, PTPRN2, ADARB2 and CD34 consistently predictive. For both regression tasks, we found that using multi-omics networks improved performance, stability and generalizability compared to interpretable single omic networks. We believe that visible neural networks have great potential for multi-omics analysis; they combine multi-omic data elegantly, are interpretable, and generalize well to data from different cohorts.

Proteogenomics in cerebrospinal fluid and plasma reveals new biological fingerprint of cerebral small vessel disease.

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia with no specific mechanism-based treatment. We used Mendelian randomization to combine a unique cerebrospinal fluid (CSF) and plasma pQTL resource with the latest European-ancestry GWAS of MRI-markers of cSVD (white matter hyperintensities, perivascular spaces). We describe a new biological fingerprint of 49 protein-cSVD associations, predominantly in the CSF. We implemented a multipronged follow-up, across fluids, platforms, and ancestries (Europeans and East-Asian), including testing associations of direct plasma protein measurements with MRI-cSVD. We highlight 16 proteins robustly associated in both CSF and plasma, with 24/4 proteins identified in CSF/plasma only. cSVD-proteins were enriched in extracellular matrix and immune response pathways, and in genes enriched in microglia and specific microglial states (integration with single-nucleus RNA sequencing). Immune-related proteins were associated with MRI-cSVD already at age twenty. Half of cSVD-proteins were associated with stroke, dementia, or both, and seven cSVD-proteins are targets for known drugs (used for other indications in directions compatible with beneficial therapeutic effects. This first cSVD proteogenomic signature opens new avenues for biomarker and therapeutic developments.

Quantifying dysmorphologies of the neurocranium using artificial neural networks.

BACKGROUND: Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for evaluating cranial morphology to enhance patient treatment. Current subjective assessments often lead to inconsistent outcomes. This study introduces a novel, quantitative approach to classify craniosynostosis and measure its severity. METHODS: An artificial neural network was trained to classify normocephalic, trigonocephalic, and scaphocephalic head shapes based on a publicly available dataset of synthetic 3D head models. Each 3D model was converted into a low-dimensional shape representation based on the distribution of normal vectors, which served as the input for the neural network, ensuring complete patient anonymity and invariance to geometric size and orientation. Explainable AI methods were utilized to highlight significant features when making predictions. Additionally, the Feature Prominence (FP) score was introduced, a novel metric that captures the prominence of distinct shape characteristics associated with a given class. Its relationship with clinical severity scores was examined using the Spearman Rank Correlation Coefficient. RESULTS: The final model achieved excellent test accuracy in classifying the different cranial shapes from their low-dimensional representation. Attention maps indicated that the network's attention was predominantly directed toward the parietal and temporal regions, as well as toward the region signifying vertex depression in scaphocephaly. In trigonocephaly, features around the temples were most pronounced. The FP score showed a strong positive monotonic relationship with clinical severity scores in both scaphocephalic (ρ = 0.83, p < 0.001) and trigonocephalic (ρ = 0.64, p < 0.001) models. Visual assessments further confirmed that as FP values rose, phenotypic severity became increasingly evident. CONCLUSION: This study presents an innovative and accessible AI-based method for quantifying cranial shape that mitigates the need for adjustments due to age-specific size variations or differences in the spatial orientation of the 3D images, while ensuring complete patient privacy. The proposed FP score strongly correlates with clinical severity scores and has the potential to aid in clinical decision-making and facilitate multi-center collaborations. Future work will focus on validating the model with larger patient datasets and exploring the potential of the FP score for broader applications. The publicly available source code facilitates easy implementation, aiming to advance craniofacial care and research.

A perspective on muscle phenotyping in musculoskeletal research.

Musculoskeletal research should synergistically investigate bone and muscle to inform approaches for maintaining mobility and to avoid bone fractures. The relationship between sarcopenia and osteoporosis, integrated in the term 'osteosarcopenia', is underscored by the close association shown between these two conditions in many studies, whereby one entity emerges as a predictor of the other. In a recent workshop of Working Group (WG) 2 of the EU Cooperation in Science and Technology (COST) Action 'Genomics of MusculoSkeletal traits Translational Network' (GEMSTONE) consortium (CA18139), muscle characterization was highlighted as being important, but currently under-recognized in the musculoskeletal field. Here, we summarize the opinions of the Consortium and research questions around translational and clinical musculoskeletal research, discussing muscle phenotyping in human experimental research and in two animal models: zebrafish and mouse.

3D Analysis of the Cranial and Facial Shape in Craniosynostosis Patients: A Systematic Review.

With increasing interest in 3D photogrammetry, diverse methods have been developed for craniofacial shape analysis in craniosynostosis patients. This review provides an overview of these methods and offers recommendations for future studies. A systematic literature search was used to identify publications on 3D photogrammetry analyses in craniosynostosis patients until August 2023. Inclusion criteria were original research reporting on 3D photogrammetry analyses in patients with craniosynostosis and written in English. Sixty-three publications that had reproducible methods for measuring cranial, forehead, or facial shape were included in the systematic review. Cranial shape changes were commonly assessed using heat maps and curvature analyses. Publications assessing the forehead utilized volumetric measurements, angles, ratios, and mirroring techniques. Mirroring techniques were frequently used to determine facial asymmetry. Although 3D photogrammetry shows promise, methods vary widely between standardized and less conventional measurements. A standardized protocol for the selection and documentation of landmarks, planes, and measurements across the cranium, forehead, and face is essential for consistent clinical and research applications.

Predicting skin cancer risk from facial images with an explainable artificial intelligence (XAI) based approach: a proof-of-concept study.

Background: Efficient identification of individuals at high risk of skin cancer is crucial for implementing personalized screening strategies and subsequent care. While Artificial Intelligence holds promising potential for predictive analysis using image data, its application for skin cancer risk prediction utilizing facial images remains unexplored. We present a neural network-based explainable artificial intelligence (XAI) approach for skin cancer risk prediction based on 2D facial images and compare its efficacy to 18 established skin cancer risk factors using data from the Rotterdam Study. Methods: The study employed data from the Rotterdam population-based study in which both skin cancer risk factors and 2D facial images and the occurrence of skin cancer were collected from 2010 to 2018. We conducted a deep-learning survival analysis based on 2D facial images using our developed XAI approach. We subsequently compared these results with survival analysis based on skin cancer risk factors using cox proportional hazard regression. Findings: Among the 2810 participants (mean Age = 68.5 ± 9.3 years, average Follow-up = 5.0 years), 228 participants were diagnosed with skin cancer after photo acquisition. Our XAI approach achieved superior predictive accuracy based on 2D facial images (c-index = 0.72, 95% CI: 0.70-0.74), outperforming that of the known risk factors (c-index = 0.59, 95% CI 0.57-0.61). Interpretation: This proof-of-concept study underscores the high potential of harnessing facial images and a tailored XAI approach as an easily accessible alternative over known risk factors for identifying individuals at high risk of skin cancer. Funding: The Rotterdam Study is funded through unrestricted research grants from Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. G.V. Roshchupkin is supported by the ZonMw Veni grant (Veni, 549 1936320).

Automated three-dimensional analysis of facial asymmetry in patients with syndromic coronal synostosis: A retrospective study.

Craniosynostosis, characterized by premature fusion of one or more cranial sutures, results in a distorted skull shape. Only three studies have assessed facial asymmetry manually in unicoronal synostosis patients. It is therefore important to understand how uni- and bicoronal synostosis affect facial asymmetry with a minimum risk of human bias. An automated algorithm was developed to quantify facial asymmetry from three-dimensional images, generating a mean facial asymmetry (MFA) value in millimeters to reflect the degree of asymmetry. The framework was applied to analyze postoperative 3D images of syndromic patients (N = 35) diagnosed with Muenke syndrome, Saethre-Chotzen syndrome, and TCF12-related craniosynostosis with respect to MFA values from a healthy control group (N = 89). Patients demonstrated substantially higher MFA values than controls: Muenke syndrome (unicoronal 1.74 ± 0.40 mm, bicoronal 0.77 ± 0.21 mm), Saethre-Chotzen syndrome (unicoronal 1.15 ± 0.20 mm, bicoronal 0.69 ± 0.16 mm), and TCF12-related craniosynostosis (unicoronal 1.40 ± 0.51 mm, bicoronal 0.66 ± 0.05 mm), compared with controls (0.49 ± 0.12 mm). Longitudinal analysis identified an increasing MFA trend in unicoronal synostosis patients. Our study revealed higher MFA in syndromic patients with uni- and bicoronal synostosis compared with controls, with the most pronounced MFA in Muenke syndrome patients with unilateral synostosis. Bicoronal synostosis patients demonstrated higher facial asymmetry than expected given the condition's symmetrical presentation.

Predicting amyloid-beta pathology in the general population.

INTRODUCTION: Reliable models to predict amyloid beta (Aß) positivity in the general aging population are lacking but could become cost-efficient tools to identify individuals at risk of developing Alzheimer's disease. METHODS: We developed Aß prediction models in the clinical Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n = 4,119) including a broad range of easily ascertainable predictors (demographics, cognition and daily functioning, health and lifestyle factors). Importantly, we determined the generalizability of our models in the population-based Rotterdam Study (n = 500). RESULTS: The best performing model in the A4 Study (area under the curve [AUC] = 0.73 [0.69-0.76]), including age, apolipoprotein E (APOE) ε4 genotype, family history of dementia, and subjective and objective measures of cognition, walking duration and sleep behavior, was validated in the independent Rotterdam Study with higher accuracy (AUC = 0.85 [0.81-0.89]). Yet, the improvement relative to a model including only age and APOE ε4 was marginal. DISCUSSION: Aß prediction models including inexpensive and non-invasive measures were successfully applied to a general population-derived sample more representative of typical older non-demented adults.

Reliability and Agreement of Automated Head Measurements From 3-Dimensional Photogrammetry in Young Children.

This study aimed to assess the reliability and agreement of automated head measurements using 3-dimensional (3D) photogrammetry in young children. Specifically, the study evaluated the agreement between manual and automated occipitofrontal circumference (OFC) measurements (n = 264) obtained from 3D images of 188 patients diagnosed with sagittal synostosis using a novel automated method proposed in this study. In addition, the study aimed to determine the interrater and intrarater reliability of the automatically extracted OFC, cephalic index, and volume. The results of the study showed that the automated OFC measurements had an excellent agreement with manual measurements, with a very strong regression score ( R2 = 0.969) and a small mean difference of -0.1 cm (-0.2%). The limits of agreement ranged from -0.93 to 0.74 cm, falling within the reported limits of agreement for manual OFC measurements. High interrater and intrarater reliability of OFC, cephalic index, and volume measurements were also demonstrated. The proposed method for automated OFC measurements was found to be a reliable alternative to manual measurements, which may be particularly beneficial in young children who undergo 3D imaging in craniofacial centers as part of their treatment protocol and in research settings that require a reproducible and transparent pipeline for anthropometric measurements. The method has been incorporated into CraniumPy, an open-source tool for 3D image visualization, registration, and optimization, which is publicly available on GitHub ( https://github.com/T-AbdelAlim/CraniumPy ).

Oral and craniofacial research in the Generation R study: an executive summary.

OBJECTIVES: Oral conditions are of high prevalence and chronic character within the general population. Identifying the risk factors and determinants of oral disease is important, not only to reduce the burden of oral diseases, but also to improve (equal access to) oral health care systems, and to develop effective oral health promotion programs. Longitudinal population-based (birth-)cohort studies are very suitable to study risk factors on common oral diseases and have the potential to emphasize the importance of a healthy start for oral health. In this paper, we provide an overview of the comprehensive oral and craniofacial dataset that has been collected in the Generation R study: a population-based prospective birth cohort in the Netherlands that was designed to identify causes of health from fetal life until adulthood. METHODS: Within the multidisciplinary context of the Generation R study, oral and craniofacial data has been collected from the age of 3 years onwards, and continued at the age of six, nine, and thirteen. Data collection is continuing in 17-year-old participants. RESEARCH OUTCOMES: In total, the cohort population comprised 9749 children at birth, and 7405 eligible participants at the age of seventeen. Based on questionnaires, the dataset contains information on oral hygiene, dental visits, oral habits, oral health-related quality of life, orthodontic treatment, and obstructive sleep apnea. Based on direct measurements, the dataset contains information on dental caries, developmental defects of enamel, objective orthodontic treatment need, dental development, craniofacial characteristics, mandibular cortical thickness, and 3D facial measurements. CONCLUSIONS: Several research lines have been set up using the oral and craniofacial data linked with the extensive data collection that exists within the Generation R study. CLINICAL RELEVANCE: Being embedded in a multidisciplinary and longitudinal birth cohort study allows researchers to study several determinants of oral and craniofacial health, and to provide answers and insight into unknown etiologies and oral health problems in the general population.

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease.

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.

Sagittal Craniosynostosis: Comparing Surgical Techniques Using 3D Photogrammetry.

BACKGROUND: The aim of this study was to compare three surgical interventions for correction of sagittal synostosis-frontobiparietal remodeling (FBR), extended strip craniotomy (ESC), and spring-assisted correction (SAC)-based on three-dimensional (3D) photogrammetry and operation characteristics. METHODS: Patients who were born between 1991 and 2019 and diagnosed with nonsyndromic sagittal synostosis who underwent FBR, ESC, or SAC and had at least one postoperative 3D photogrammetry image taken during one of six follow-up appointments until age 6 were considered for this study. Operative characteristics, postoperative complications, reinterventions, and presence of intracranial hypertension were collected. To assess cranial growth, orthogonal cranial slices and 3D photocephalometric measurements were extracted automatically and evaluated from 3D photogrammetry images. RESULTS: A total of 322 postoperative 3D images from 218 patients were included. After correcting for age and sex, no significant differences were observed in 3D photocephalometric measurements. Mean cranial shapes suggested that postoperative growth and shape gradually normalized with higher occipitofrontal head circumference and intracranial volume values compared with normal values, regardless of type of surgery. Flattening of the vertex seems to persist after surgical correction. The authors' cranial 3D mesh processing tool has been made publicly available as a part of this study. CONCLUSIONS: The findings suggest that until age 6, there are no significant differences among the FBR, ESC, and SAC in their ability to correct sagittal synostosis with regard to 3D photocephalometric measurements. Therefore, efforts should be made to ensure early diagnosis so that minimally invasive surgery is a viable treatment option. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.

Subcortical brain structures and the risk of dementia in the Rotterdam Study.

INTRODUCTION: Volumetric and morphological changes in subcortical brain structures are present in persons with dementia, but it is unknown if these changes occur prior to diagnosis. METHODS: Between 2005 and 2016, 5522 Rotterdam Study participants (mean age: 64.4) underwent cerebral magnetic resonance imaging (MRI) and were followed for development of dementia until 2018. Volume and shape measures were obtained for seven subcortical structures. RESULTS: During 12 years of follow-up, 272 dementia cases occurred. Mean volumes of thalamus (hazard ratio [HR] per standard deviation [SD] decrease 1.94, 95% confidence interval [CI]: 1.55-2.43), amygdala (HR 1.66, 95% CI: 1.44-1.92), and hippocampus (HR 1.64, 95% CI: 1.43-1.88) were strongly associated with dementia risk. Associations for accumbens, pallidum, and caudate volumes were less pronounced. Shape analyses identified regional surface changes in the amygdala, limbic thalamus, and caudate. DISCUSSION: Structure of the amygdala, thalamus, hippocampus, and caudate is associated with risk of dementia in a large population-based cohort of older adults.

Genome-wide profiling of circulatory microRNAs associated with cognition and dementia.

INTRODUCTION: MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression. Their role in the pathophysiology of dementia and potential as biomarkers remains undetermined. METHODS: We conducted a single- (one-by-one) and multi-marker (joint) analysis to identify well-expressed circulating miRNAs in plasma (total = 591) associated with general cognition and incident dementia, for 1615 participants of the population-based Rotterdam Study. RESULTS: During single-marker analysis, 47 miRNAs were nominally (P ≤ .05) associated with cognition and 18 miRNAs were nominally associated with incident dementia, after adjustment for potential confounders. Three miRNAs were common between cognition and dementia (miR-4539, miR-372-3p, and miR-566), with multi-marker analysis revealing another common miRNA (miR-7106-5p). In silico analysis of these four common miRNAs led to several putative target genes expressed in the brain, highlighting the mitogen-activated protein kinase signaling pathway. DISCUSSION: We provide population-based evidence on the relationship between circulatory miRNAs with cognition and dementia, including four common miRNAs that may elucidate downstream mechanisms. HIGHLIGHTS: MicroRNAs (miRNAs) are involved in the (dys)function of the central nervous system. Four circulating miRNAs in plasma are associated with cognition and incident dementia. Several predicted target genes of these four miRNAs are expressed in the brain. These four miRNAs may be linked to pathways underlying dementia. Although miRNAs are promising biomarkers, experimental validation remains essential.

3D surface imaging technology for objective automated assessment of facial interventions: A systematic review.

BACKGROUND: The incidence of facial skin cancer increases worldwide, resulting in more surgical resections and reconstructions. Reconstructive surgery aims to approach a normal facial anatomy to optimize the quality of life. Objective automated assessment of the esthetic outcome of facial reconstructions could provide feedback for the improvement of surgical techniques and preoperative patient expectation management. OBJECTIVE: This systematic literature review aimed to assess whether modern technologies can create automated objective measurements of surgical and non-surgical facial interventions outcomes using 3D surface imaging technology. METHODS: A systematic literature search was conducted in Embase, Medline (Ovid), Web of Science, and Cochrane on May 19, 2021. All English literature was collected on surgical and non-surgical invasive facial interventions in which 3D surface imaging technology was used for objective automated assessment of outcomes. RESULTS: Fourteen articles were included in the systematic review. 3D surface imaging technology and automated assessment techniques were found for skin malignancy, cleft lip repair, rhinoplasty, orthognathic surgery, and injectables. Ten 3D surface imaging technology hardware systems and 12 software systems were described. Four studies compared 3D surface imaging techniques to conventional methods. Ten studies used 3D surface imaging techniques for the evaluation of the surgical outcome, without comparison to 2D photography, validated scores, or a panel. Two studies validated the hardware system. CONCLUSION: This systematic literature review shows that 3D surface imaging technology has the potential for automated objective assessment of facial intervention outcomes. Future studies are necessary to study and validate these tools for standard clinical use in patients with facial interventions.

Stroke genetics informs drug discovery and risk prediction across ancestries.

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.

Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found well-replicated genes such as HERC2 and OCA2 for hair and eye color, and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.

Tinnitus and Its Central Correlates: A Neuroimaging Study in a Large Aging Population.

OBJECTIVES: To elucidate the association between tinnitus and brain tissue volumes and white matter microstructural integrity. DESIGN: Two thousand six hundred sixteen participants (mean age, 65.7 years [SD: 7.5 years]; 53.9% female) of the population-based Rotterdam Study underwent tinnitus assessment (2011 to 2014) and magnetic resonance imaging of the brain (2011 to 2014). Associations between tinnitus (present versus absent) and total, gray, and white matter volume and global white matter microstructure were assessed using multivariable linear regression models adjusting for demographic factors, cardiovascular risk factors, depressive symptoms, Mini-Mental State Examination score, and hearing loss. Finally, potential regional gray matter density and white matter microstructural volume differences were assessed on a voxel-based level again using multivariable linear regression. RESULTS: Participants with tinnitus (21.8%) had significantly larger brain tissue volumes (difference in SD, 0.09; 95% confidence interval, 0.06 to 0.13), driven by larger white matter volumes (difference, 0.12; 95% confidence interval, 0.04 to 0.21) independent of hearing loss. There was no association between tinnitus and gray matter volumes nor with global white matter microstructure. On a lobar level, tinnitus was associated with larger white matter volumes in each lobe, not with gray matter volume. Voxel-based results did not show regional specificity. CONCLUSIONS: We found that tinnitus in older adults was associated with larger brain tissue volumes, driven by larger white matter volumes, independent of age, and hearing loss. Based on these results, it may be hypothesized that tinnitus potentially has a neurodevelopmental origin in earlier life independent of aging processes.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.