Rare Genetic Variants in LDLR, APOB, and PCSK9 are Associated with Aortic Stenosis.

Background: Despite a proposed causal role for low-density lipoprotein cholesterol (LDL-C) in aortic stenosis (AS), randomized controlled trials of lipid-lowering therapy failed to prevent severe AS. We aimed to assess the impact on AS and peak velocity across the aortic valve conferred by lifelong alterations in LDL-C levels mediated by protein-disrupting variants in three clinically significant genes for LDL metabolism (LDLR, APOB, PCSK9). Methods: We utilized sequencing data and electronic health records from UK Biobank (UKB) and All of Us and magnetic resonance imaging data from UKB. We identified predicted protein-disrupting variants with the LOFTEE and AlphaMissense algorithms and evaluated their associations with LDL-C and peak velocity across the aortic valve (UK Biobank), as well as diagnosed AS and aortic valve replacement (UK Biobank + All of Us). Results: We included 421,049 unrelated participants (5,621 with AS) in UKB and 195,519 unrelated participants (1,087 with AS) in All of Us. Carriers of protein-disrupting variants in LDLR had higher mean LDL-C (UKB: +42.6 mg/dl, P=4.4e-237) and greater risk of AS (meta-analysis: odds ratio [OR] =3.52 [95% CI 2.39-5.20], P=2.3e-10) and aortic valve replacement (meta-analysis: OR=3.78 [95% CI 2.26-6.32], P=4.0e-7). Carriers of protein-disrupting variants in APOB or PCSK9 had lower mean LDL-C (UKB: -32.3 mg/dl, P<5e-324) and lower risk of AS (meta-analysis: OR=0.49 [0.31-0.75], P=0.001) and aortic valve replacement (meta-analysis: OR=0.54 [0.30-0.97], P=0.04). Among 57,371 UKB imaging substudy participants, peak velocities across the aortic valve were greater in carriers of protein-disrupting variants in LDLR (+12.2cm/s, P=1.6e-5) and lower in carriers of protein-disrupting variants in PCSK9 (-6.9cm/s, P=0.022). Conclusions: Rare genetic variants that confer lifelong higher or lower LDL-C levels are associated with substantially increased and decreased risk of AS, respectively. Early and sustained lipid-lowering therapy may slow or prevent AS development.

Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation.

Background: AF risk estimation is feasible using clinical factors, inherited predisposition, and artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis. Objective: To test whether integrating these distinct risk signals improves AF risk estimation. Methods: In the UK Biobank prospective cohort study, we estimated AF risk using three models derived from external populations: the well-validated Cohorts for Aging in Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) clinical score, a 1,113,667-variant AF polygenic risk score (PRS), and a published AI-enabled ECG-based AF risk model (ECG-AI). We estimated discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic (AUROC) and average precision (AP). Results: Among 49,293 individuals (mean age 65±8 years, 52% women), 825 (2.4%) developed AF within 5 years. Using single models, discrimination of 5-year incident AF was higher using ECG-AI (AUROC 0.705 [95%CI 0.686-0.724]; AP 0.085 [0.071-0.11]) and CHARGE-AF (AUROC 0.785 [0.769-0.801]; AP 0.053 [0.048-0.061]) versus the PRS (AUROC 0.618, [0.598-0.639]; AP 0.038 [0.028-0.045]). The inclusion of all components ("Predict-AF3") was the best performing model (AUROC 0.817 [0.802-0.832]; AP 0.11 [0.091-0.15], p<0.01 vs CHARGE-AF+ECG-AI), followed by the two component model of CHARGE-AF+ECG-AI (AUROC 0.802 [0.786-0.818]; AP 0.098 [0.081-0.13]). Using Predict-AF3, individuals at high AF risk (i.e., 5-year predicted AF risk >2.5%) had a 5-year cumulative incidence of AF of 5.83% (5.33-6.32). At the same threshold, the 5-year cumulative incidence of AF was progressively higher according to the number of models predicting high risk (zero: 0.67% [0.51-0.84], one: 1.48% [1.28-1.69], two: 4.48% [3.99-4.98]; three: 11.06% [9.48-12.61]), and Predict-AF3 achieved favorable net reclassification improvement compared to both CHARGE-AF+ECG-AI (0.039 [0.015-0.066]) and CHARGE-AF+PRS (0.033 [0.0082-0.059]). Conclusions: Integration of clinical, genetic, and AI-derived risk signals improves discrimination of 5-year AF risk over individual components. Models such as Predict-AF3 have substantial potential to improve prioritization of individuals for AF screening and preventive interventions.

Deep learning-derived splenic radiomics, genomics, and coronary artery disease.

Background: Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD, but the role of a key regulating organ, spleen, is unknown. The understudied spleen is a 3-dimensional structure of the hematopoietic system optimally suited for unbiased radiologic investigations toward novel mechanistic insights. Methods: Deep learning-based image segmentation and radiomics techniques were utilized to extract splenic radiomic features from abdominal MRIs of 42,059 UK Biobank participants. Regression analysis was used to identify splenic radiomics features associated with CAD. Genome-wide association analyses were applied to identify loci associated with these radiomics features. Overlap between loci associated with CAD and the splenic radiomics features was explored to understand the underlying genetic mechanisms of the role of the spleen in CAD. Results: We extracted 107 splenic radiomics features from abdominal MRIs, and of these, 10 features were associated with CAD. Genome-wide association analysis of CAD-associated features identified 219 loci, including 35 previously reported CAD loci, 7 of which were not associated with conventional CAD risk factors. Notably, variants at 9p21 were associated with splenic features such as run length non-uniformity. Conclusions: Our study, combining deep learning with genomics, presents a new framework to uncover the splenic axis of CAD. Notably, our study provides evidence for the underlying genetic connection between the spleen as a candidate causal tissue-type and CAD with insight into the mechanisms of 9p21, whose mechanism is still elusive despite its initial discovery in 2007. More broadly, our study provides a unique application of deep learning radiomics to non-invasively find associations between imaging, genetics, and clinical outcomes.

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity.

Importance: Epicardial and pericardial adipose tissue (EPAT) has been associated with cardiovascular diseases such as atrial fibrillation or flutter (AF) and coronary artery disease (CAD), but studies have been limited in sample size or drawn from selected populations. It has been suggested that the association between EPAT and cardiovascular disease could be mediated by local or paracrine effects. Objective: To evaluate the association of EPAT with prevalent and incident cardiovascular disease and to elucidate the genetic basis of EPAT in a large population cohort. Design, Setting, and Participants: A deep learning model was trained to quantify EPAT area from 4-chamber magnetic resonance images using semantic segmentation. Cross-sectional and prospective cardiovascular disease associations were evaluated, controlling for sex and age. Prospective associations were additionally controlled for abdominal visceral adipose tissue (VAT) volumes. A genome-wide association study was performed, and a polygenic score (PGS) for EPAT was examined in independent FinnGen cohort study participants. Data analyses were conducted from March 2022 to December 2023. Exposures: The primary exposures were magnetic resonance imaging-derived continuous measurements of epicardial and pericardial adipose tissue area and visceral adipose tissue volume. Main Outcomes and Measures: Prevalent and incident CAD, AF, heart failure (HF), stroke, and type 2 diabetes (T2D). Results: After exclusions, this study included 44 475 participants (mean [SD] age, 64.1 [7.7] years; 22 972 female [51.7%]) from the UK Biobank. Cross-sectional and prospective cardiovascular disease associations were evaluated for a mean (SD) of 3.2 (1.5) years of follow-up. Prospective associations were additionally controlled for abdominal VAT volumes for 38 527 participants. A PGS for EPAT was examined in 453 733 independent FinnGen cohort study participants. EPAT was positively associated with male sex (ß = +0.78 SD in EPAT; P < 3 × 10-324), age (Pearson r = 0.15; P = 9.3 × 10-229), body mass index (Pearson r = 0.47; P < 3 × 10-324), and VAT (Pearson r = 0.72; P < 3 × 10-324). EPAT was more elevated in prevalent HF (ß = +0.46 SD units) and T2D (ß = +0.56) than in CAD (ß = +0.23) or AF (ß = +0.18). EPAT was associated with incident HF (hazard ratio [HR], 1.29 per +1 SD in EPAT; 95% CI, 1.17-1.43), T2D (HR, 1.63; 95% CI, 1.51-1.76), and CAD (HR, 1.19; 95% CI, 1.11-1.28). However, the associations were no longer significant when controlling for VAT. Seven genetic loci were identified for EPAT, implicating transcriptional regulators of adipocyte morphology and brown adipogenesis (EBF1, EBF2, and CEBPA) and regulators of visceral adiposity (WARS2 and TRIB2). The EPAT PGS was associated with T2D (odds ratio [OR], 1.06; 95% CI, 1.05-1.07; P =3.6 × 10-44), HF (OR, 1.05; 95% CI, 1.04-1.06; P =4.8 × 10-15), CAD (OR, 1.04; 95% CI, 1.03-1.05; P =1.4 × 10-17), AF (OR, 1.04; 95% CI, 1.03-1.06; P =7.6 × 10-12), and stroke in FinnGen (OR, 1.02; 95% CI, 1.01-1.03; P =3.5 × 10-3) per 1 SD in PGS. Conclusions and Relevance: Results of this cohort study suggest that epicardial and pericardial adiposity was associated with incident cardiovascular diseases, but this may largely reflect a metabolically unhealthy adiposity phenotype similar to abdominal visceral adiposity.

Natural Language Processing for Adjudication of Heart Failure in a Multicenter Clinical Trial: A Secondary Analysis of a Randomized Clinical Trial.

Importance: The gold standard for outcome adjudication in clinical trials is medical record review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication of medical records by natural language processing (NLP) may offer a more resource-efficient alternative but this approach has not been validated in a multicenter setting. Objective: To externally validate the Community Care Cohort Project (C3PO) NLP model for heart failure (HF) hospitalization adjudication, which was previously developed and tested within one health care system, compared to gold-standard CEC adjudication in a multicenter clinical trial. Design, Setting, and Participants: This was a retrospective analysis of the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial, which compared 2 influenza vaccines in 5260 participants with cardiovascular disease at 157 sites in the US and Canada between September 2016 and January 2019. Analysis was performed from November 2022 to October 2023. Exposures: Individual sites submitted medical records for each hospitalization. The central INVESTED CEC and the C3PO NLP model independently adjudicated whether the cause of hospitalization was HF using the prepared hospitalization dossier. The C3PO NLP model was fine-tuned (C3PO + INVESTED) and a de novo NLP model was trained using half the INVESTED hospitalizations. Main Outcomes and Measures: Concordance between the C3PO NLP model HF adjudication and the gold-standard INVESTED CEC adjudication was measured by raw agreement, κ, sensitivity, and specificity. The fine-tuned and de novo INVESTED NLP models were evaluated in an internal validation cohort not used for training. Results: Among 4060 hospitalizations in 1973 patients (mean [SD] age, 66.4 [13.2] years; 514 [27.4%] female and 1432 [72.6%] male]), 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was good agreement between the C3PO NLP and CEC HF adjudications (raw agreement, 87% [95% CI, 86-88]; κ, 0.69 [95% CI, 0.66-0.72]). C3PO NLP model sensitivity was 94% (95% CI, 92-95) and specificity was 84% (95% CI, 83-85). The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% (95% CI, 92-94) and κ of 0.82 (95% CI, 0.77-0.86) and 0.83 (95% CI, 0.79-0.87), respectively, vs the CEC. CEC reviewer interrater reproducibility was 94% (95% CI, 93-95; κ, 0.85 [95% CI, 0.80-0.89]). Conclusions and Relevance: The C3PO NLP model developed within 1 health care system identified HF events with good agreement relative to the gold-standard CEC in an external multicenter clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. Further study is needed to determine whether NLP will improve the efficiency of future multicenter clinical trials by identifying clinical events at scale.

Natural Language Processing for Adjudication of Heart Failure Hospitalizations in a Multi-Center Clinical Trial.

Background: The gold standard for outcome adjudication in clinical trials is chart review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication by natural language processing (NLP) may offer a more resource-efficient alternative. We previously showed that the Community Care Cohort Project (C3PO) NLP model adjudicates heart failure (HF) hospitalizations accurately within one healthcare system. Methods: This study externally validated the C3PO NLP model against CEC adjudication in the INVESTED trial. INVESTED compared influenza vaccination formulations in 5260 patients with cardiovascular disease at 157 North American sites. A central CEC adjudicated the cause of hospitalizations from medical records. We applied the C3PO NLP model to medical records from 4060 INVESTED hospitalizations and evaluated agreement between the NLP and final consensus CEC HF adjudications. We then fine-tuned the C3PO NLP model (C3PO+INVESTED) and trained a de novo model using half the INVESTED hospitalizations, and evaluated these models in the other half. NLP performance was benchmarked to CEC reviewer inter-rater reproducibility. Results: 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was high agreement between the C3PO NLP and CEC HF adjudications (agreement 87%, kappa statistic 0.69). C3PO NLP model sensitivity was 94% and specificity was 84%. The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% and kappa of 0.82 and 0.83, respectively. CEC reviewer inter-rater reproducibility was 94% (kappa 0.85). Conclusion: Our NLP model developed within a single healthcare system accurately identified HF events relative to the gold-standard CEC in an external multi-center clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. NLP may improve the efficiency of future multi-center clinical trials by accurately identifying clinical events at scale.

The Cardiovascular Impact and Genetics of Pericardial Adiposity.

Background: While previous studies have reported associations of pericardial adipose tissue (PAT) with cardiovascular diseases such as atrial fibrillation and coronary artery disease, they have been limited in sample size or drawn from selected populations. Additionally, the genetic determinants of PAT remain largely unknown. We aimed to evaluate the association of PAT with prevalent and incident cardiovascular disease and to elucidate the genetic basis of PAT in a large population cohort. Methods: A deep learning model was trained to quantify PAT area from four-chamber magnetic resonance images in the UK Biobank using semantic segmentation. Cross-sectional and prospective cardiovascular disease associations were evaluated, controlling for sex and age. A genome-wide association study was performed, and a polygenic score (PGS) for PAT was examined in 453,733 independent FinnGen study participants. Results: A total of 44,725 UK Biobank participants (51.7% female, mean [SD] age 64.1 [7.7] years) were included. PAT was positively associated with male sex (ß = +0.76 SD in PAT), age (r = 0.15), body mass index (BMI; r = 0.47) and waist-to-hip ratio (r = 0.55) (P < 1×10-230). PAT was more elevated in prevalent heart failure (ß = +0.46 SD units) and type 2 diabetes (ß = +0.56) than in coronary artery disease (ß = +0.22) or AF (ß = +0.18). PAT was associated with incident heart failure (HR = 1.29 per +1 SD in PAT [95% CI 1.17-1.43]) and type 2 diabetes (HR = 1.63 [1.51-1.76]) during a mean 3.2 (±1.5) years of follow-up; the associations remained significant when controlling for BMI. We identified 5 novel genetic loci for PAT and implicated transcriptional regulators of adipocyte morphology and brown adipogenesis (EBF1, EBF2 and CEBPA) and regulators of visceral adiposity (WARS2 and TRIB2). The PAT PGS was associated with T2D, heart failure, coronary artery disease and atrial fibrillation in FinnGen (ORs 1.03-1.06 per +1 SD in PGS, P < 2×10-10). Conclusions: PAT shares genetic determinants with abdominal adiposity and is an independent predictor of incident type 2 diabetes and heart failure.

Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy.

Dilated cardiomyopathy (DCM) is characterized by reduced cardiac output, as well as thinning and enlargement of left ventricular chambers. These characteristics eventually lead to heart failure. Current standards of care do not target the underlying molecular mechanisms associated with genetic forms of heart failure, driving a need to develop novel therapeutics for DCM. To identify candidate therapeutics, we developed an in vitro DCM model using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) deficient in B-cell lymphoma 2 (BCL2)-associated athanogene 3 (BAG3). With these BAG3-deficient iPSC-CMs, we identified cardioprotective drugs using a phenotypic screen and deep learning. From a library of 5500 bioactive compounds and siRNA validation, we found that inhibiting histone deacetylase 6 (HDAC6) was cardioprotective at the sarcomere level. We translated this finding to a BAG3 cardiomyocyte-knockout (BAG3cKO) mouse model of DCM, showing that inhibiting HDAC6 with two isoform-selective inhibitors (tubastatin A and a novel inhibitor TYA-018) protected heart function. In BAG3cKO and BAG3E455K mice, HDAC6 inhibitors improved left ventricular ejection fraction and reduced left ventricular diameter at diastole and systole. In BAG3cKO mice, TYA-018 protected against sarcomere damage and reduced Nppb expression. Based on integrated transcriptomics and proteomics and mitochondrial function analysis, TYA-018 also enhanced energetics in these mice by increasing expression of targets associated with fatty acid metabolism, protein metabolism, and oxidative phosphorylation. Our results demonstrate the power of combining iPSC-CMs with phenotypic screening and deep learning to accelerate drug discovery, and they support developing novel therapies that address underlying mechanisms associated with heart disease.

Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes.

Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to detect drug-induced toxicity in vitro. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks that show cardiotoxic signal in iPSC-CMs. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that may protect against diseased phenotypes and deleterious mutations.

Assessment of embryo implantation potential with a cloud-based automatic software.

RESEARCH QUESTION: Is embryo selection by Dana (automatic software for embryo evaluation) associated with a higher implantation rate in IVF treatments? DESIGN: A three-phase study for Dana system's validation: creation of a data-cloud of known implantation data (KID) embryos from 1676 transferred embryos; embryo evaluation by Dana considering manual annotations and embryo development videos (389 transferred embryos); and validation of Dana automatic selection, without embryologist's intervention (147 transferred embryos); RESULTS: The implantation rate of the 1021 KID embryos from phase 1 served to set four grades of embryos referring to implantation rate: A = 34%, B = 25%, C = 24%, and D = 19%. Phase 2: a classification ranking according to the unit average distance (UAD) and implantation potential was established: top (UAD ≤0.50), high (UAD = 0.51-0.66), medium (UAD = 0.67-1.03) and low (UAD >1.03). Pregnancy rates were 59%, 46%, 36% and 28%, respectively (P < 0.001). Phase 3: embryos were automatically categorized according to Dana's classification ranking. Most implanted embryos were found in groups top, high and medium (UAD ≤1.03), whereas the implantation rate in group low (UAD >1.03) was significantly lower: 46% versus 25%, respectively (P = 0.037). The twin gestation rate was higher when number of top embryos (UAD ≤0.5) transferred were two (52%) versus one (25%) (P < 0.001). CONCLUSIONS: Embryo selection based on Dana ranking increases the success of IVF treatments at least in oocyte donation programmes. The multicentre nature of the study supports its applicability at different clinics, standardizing the embryo development's interpretation. Dana's innovation is that the system increases its accuracy as the database grows.

Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method.

Cardiac and hepatic toxicity result from induced disruption of the functioning of cardiomyocytes and hepatocytes, respectively, which is tightly related to the organization of their subcellular structures. Cellular structure can be analyzed from microscopy imaging data. However, subtle or complex structural changes that are not easily perceived may be missed by conventional image-analysis techniques. Here we report the evaluation of PhenoTox, an image-based deep-learning method of quantifying drug-induced structural changes using human hepatocytes and cardiomyocytes derived from human induced pluripotent stem cells. We assessed the ability of the deep learning method to detect variations in the organization of cellular structures from images of fixed or live cells. We also evaluated the power and sensitivity of the method for detecting toxic effects of drugs by conducting a set of experiments using known toxicants and other methods of screening for cytotoxic effects. Moreover, we used PhenoTox to characterize the effects of tamoxifen and doxorubicin-which cause liver toxicity-on hepatocytes. PhenoTox revealed differences related to loss of cytochrome P450 3A4 activity, for which it showed greater sensitivity than a caspase 3/7 assay. Finally, PhenoTox detected structural toxicity in cardiomyocytes, which was correlated with contractility defects induced by doxorubicin, erlotinib, and sorafenib. Taken together, the results demonstrated that PhenoTox can capture the subtle morphological changes that are early signs of toxicity in both hepatocytes and cardiomyocytes.

A non-invasive platform for functional characterization of stem-cell-derived cardiomyocytes with applications in cardiotoxicity testing.

We present a non-invasive method to characterize the function of pluripotent stem-cell-derived cardiomyocytes based on video microscopy and image analysis. The platform, called Pulse, generates automated measurements of beating frequency, beat duration, amplitude, and beat-to-beat variation based on motion analysis of phase-contrast images captured at a fast frame rate. Using Pulse, we demonstrate recapitulation of drug effects in stem-cell-derived cardiomyocytes without the use of exogenous labels and show that our platform can be used for high-throughput cardiotoxicity drug screening and studying physiologically relevant phenotypes.

Automated, non-invasive characterization of stem cell-derived cardiomyocytes from phase-contrast microscopy.

Stem cell-derived cardiomyocytes hold tremendous potential for drug development and safety testing related to cardiovascular health. The characterization of cardiomyocytes is most commonly performed using electrophysiological systems, which are expensive, laborious to use, and may induce undesirable cellular response. Here, we present a new method for non-invasive characterization of cardiomyocytes using video microscopy and image analysis. We describe an automated pipeline that consists of segmentation of beating regions, robust beating signal calculation, signal quantification and modeling, and hierarchical clustering. Unlike previous imaging-based methods, our approach enables clinical applications by capturing beating patterns and arrhythmias across healthy and diseased cells with varied densities. We demonstrate the strengths of our algorithm by characterizing the effects of two commercial drugs known to modulate beating frequency and irregularity. Our results provide, to our knowledge, the first clinically-relevant demonstration of a fully-automated and non-invasive imaging-based beating assay for characterization of stem cell-derived cardiomyocytes.

A system for automated, noninvasive, morphology-based evaluation of induced pluripotent stem cell cultures.

Due to the rapid adoption and use of human induced pluripotent stem cells (iPSCs) in recent years, there is a need for new technologies that standardize the evaluation of iPSCs to allow the objective comparison of results across different experiments and groups. In this article, we present a noninvasive, fully automated, and analytical system for morphology-based evaluation of iPSC cultures that consists of time-lapse microscopy and novel image analysis software. The presented system acquires low-light phase-contrast images of iPSC growth collected during a period of several days in culture, measures geometrical- and texture-based features of iPSC colonies throughout time, and derives a set of six biologically relevant features to automatically rank the quality of the cell culture. In a study of 94 iPSC cultures, we demonstrated the accuracy of the system by comparing the automated ranking with an independent expert evaluation based on visual review of the time-lapse movies. To our knowledge, this is the first demonstration of a fully automated and objective assessment of iPSC culture quality using noninvasive methods.

Fiber tract-driven topographical mapping (FTTM) reveals microstructural relevance for interhemispheric visuomotor function in the aging brain.

We present a novel approach - DTI-based fiber tract-driven topographical mapping (FTTM) - to map and measure the influence of age on the integrity of interhemispheric fibers and challenge their selective functions with measures of interhemispheric integration of lateralized information. This approach enabled identification of spatially specific topographical maps of scalar diffusion measures and their relation to measures of visuomotor performance. Relative to younger adults, older adults showed lower fiber integrity indices in anterior than posterior callosal fibers. FTTM analysis identified a dissociation in the microstructural-function associates between age groups: in younger adults, genu fiber integrity correlated with interhemispheric transfer time, whereas in older adults, body fiber integrity was correlated with interhemispheric transfer time with topographical specificity along left-lateralized callosal fiber trajectories. Neural co-activation from redundant targets was evidenced by fMRI-derived bilateral extrastriate cortex activation in both groups, and a group difference emerged for a pontine activation cluster that was differently modulated by response hand in older than younger adults. Bilateral processing advantages in older but not younger adults further correlated with fiber integrity in transverse pontine fibers that branch into the right cerebellar cortex, thereby supporting a role for the pons in interhemispheric facilitation. In conclusion, in the face of compromised anterior callosal fibers, older adults appear to use alternative pathways to accomplish visuomotor interhemispheric information transfer and integration for lateralized processing. This shift from youthful associations may indicate recruitment of compensatory mechanisms involving medial corpus callosum fibers and subcortical pathways.

Sheet-like white matter fiber tracts: representation, clustering, and quantitative analysis.

We introduce an automated and probabilistic method for subject-specific segmentation of sheet-like fiber tracts. In addition to clustering of trajectories into anatomically meaningful bundles, the method provides statistics of diffusion measures by establishing point correspondences on the estimated medial representation of each bundle. We also introduce a new approach for medial surface generation of sheet-like fiber bundles in order too initialize the proposed clustering algorithm. Applying the new method to a population study of brain aging on 24 subjects demonstrates the capabilities and strengths of the algorithm in identifying and visualizing spatial patterns of group differences.

Findings in schizophrenia by tract-oriented DT-MRI analysis.

This paper presents a tract-oriented analysis of diffusion tensor (DT) images of the human brain. We demonstrate that unlike the commonly used ROI-based methods for population studies, our technique is sensitive to the local variation of diffusivity parameters along the fiber tracts. We show the strength of the proposed approach in identifying the differences in schizophrenic data compared to controls. Statistically significant drops in fractional anisotropy are observed along the genu and bilaterally in the splenium, as well as an increase in principal eigenvalue in uncinate fasciculus. This is the first tract-oriented clinical study in which an anatomical atlas is used to guide the algorithm.

A unified framework for clustering and quantitative analysis of white matter fiber tracts.

We present a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.

A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis.

We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectation-maximization (EM) algorithm is used to cluster the trajectories, in which an atlas serves as the prior on the labels. The atlas guides the clustering algorithm and makes the resulting bundles anatomically meaningful. In addition, it provides the seed points for the tractography and initial settings of the EM algorithm. The proposed approach provides a robust and automated tool for tract-oriented analysis both in a single subject and over a population.