I am generally interested in using image analysis techniques to improve detection, diagnosis and treatment of diseases. My research interests particularly lie in the areas of non-linear statistics and machine learning applied to translational neuroscience.
Importance: Maturation of white matter fiber systems subserves cognitive, behavioral, emotional, and motor development during adolescence. Hazardous drinking during this active neurodevelopmental period may alter the trajectory of white matter microstructural development, potentially increasing risk for developing alcohol-related dysfunction and alcohol use disorder in adulthood.Objective: To identify disrupted adolescent microstructural brain development linked to drinking onset and to assess whether the disruption is more pronounced in younger rather than older adolescents.Design, Setting, and Participants: This case-control study, conducted from January 13, 2013, to January 15, 2019, consisted of an analysis of 451 participants from the National Consortium on Alcohol and Neurodevelopment in Adolescence cohort. Participants were aged 12 to 21 years at baseline and had at least 2 usable magnetic resonance diffusion tensor imaging (DTI) scans and up to 5 examination visits spanning 4 years. Participants with a youth-adjusted Cahalan score of 0 were labeled as no-to-low drinkers; those with a score of greater than 1 for at least 2 consecutive visits were labeled as heavy drinkers. Exploratory analysis was conducted between no-to-low and heavy drinkers. A between-group analysis was conducted between age- and sex-matched youths, and a within-participant analysis was performed before and after drinking.Exposures: Self-reported alcohol consumption in the past year summarized by categorical drinking levels.Main Outcomes and Measures: Diffusion tensor imaging measurement of fractional anisotropy (FA) in the whole brain and fiber systems quantifying the developmental change of each participant as a slope.Results: Analysis of whole-brain FA of 451 adolescents included 291 (64.5%) no-to-low drinkers and 160 (35.5%) heavy drinkers who indicated the potential for a deleterious association of alcohol with microstructural development. Among the no-to-low drinkers, 142 (48.4%) were boys with mean (SD) age of 16.5 (2.2) years and 149 (51.2%) were girls with mean (SD) age of 16.5 (2.1) years and 192 (66.0%) were White participants. Among the heavy drinkers, 86 (53.8%) were boys with mean (SD) age of 20.1 (1.5) years and 74 (46.3%) were girls with mean (SD) age of 20.5 (2.0) years and 142 (88.8%) were White participants. A group analysis revealed FA reduction in heavy-drinking youth compared with age- and sex-matched controls (t154=-2.7, P=.008). The slope of this reduction correlated with log of days of drinking since the baseline visit (r156=-0.21, 2-tailed P=.008). A within-participant analysis contrasting developmental trajectories of youths before and after they initiated heavy drinking supported the prediction that drinking onset was associated with and potentially preceded disrupted white matter integrity. Age-alcohol interactions (t152=3.0, P=.004) observed for the FA slopes indicated that the alcohol-associated disruption was greater in younger than older adolescents and was most pronounced in the genu and body of the corpus callosum, regions known to continue developing throughout adolescence.Conclusions and Relevance: This case-control study of adolescents found a deleterious association of alcohol use with white matter microstructural integrity. These findings support the concept of heightened vulnerability to environmental agents, including alcohol, associated with attenuated development of major white matter tracts in early adolescence.
View details for DOI 10.1001/jamapsychiatry.2020.4064
View details for PubMedID 33377940
The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs-fMRI either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. Simultaneously, the model learns the importance of graph edges within ST-GCN to gain insight into the functional connectivities contributing to the prediction. In analyzing the rs-fMRI of the Human Connectome Project (HCP, N = 1,091) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N = 773), ST-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals. Furthermore, the brain regions and functional connections significantly contributing to the predictions of our model are important markers according to the neuroscience literature.
View details for DOI 10.1007/978-3-030-59728-3_52
View details for PubMedID 33257918
The application of data-driven deep learning to identify sex differences in developing brain structures of pre-adolescents has heretofore not been accomplished. Here, the approach identifies sex differences by analyzing the minimally processed MRIs of the first 8,144 participants (age 9 and 10 years) recruited by the Adolescent Brain Cognitive Development (ABCD) study. The identified pattern accounted for confounding factors (i.e., head size, age, puberty development, socioeconomic status) and comprised cerebellar (corpus medullare, lobules III, IV/V, and VI) and subcortical (pallidum, amygdala, hippocampus, parahippocampus, insula, putamen) structures. While these have been individually linked to expressing sex differences, a novel discovery was that their grouping accurately predicted the sex in individual pre-adolescents. Another novelty was relating differences specific to the cerebellum to pubertal development. Finally, we found that reducing the pattern to a single score not only accurately predicted sex but also correlated with cognitive behavior linked to working memory. The predictive power of this score and the constellation of identified brain structures provide evidence for sex differences in pre-adolescent neurodevelopment and may augment understanding of sex-specific vulnerability or resilience to psychiatric disorders and presage sex-linked learning disabilities.
View details for DOI 10.1016/j.neuroimage.2020.117293
View details for PubMedID 32841716
View details for DOI 10.1111/adb.12746
View details for Web of Science ID 000528674100024
Exogenous causes, such as alcohol use, and endogenous factors, such as temperament and sex, can modulate developmental trajectories of adolescent neurofunctional maturation. We examined how these factors affect sexual dimorphism in brain functional networks in youth drinking below diagnostic threshold for alcohol use disorder (AUD). Based on the 3-year, annually acquired, longitudinal resting-state functional magnetic resonance imaging (MRI) data of 526 adolescents (12-21 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) cohort, developmental trajectories of 23 intrinsic functional networks (IFNs) were analyzed for (1) sexual dimorphism in 259 participants who were no-to-low drinkers throughout this period; (2) sex-alcohol interactions in two age- and sex-matched NCANDA subgroups (N = 76 each), half no-to-low, and half moderate-to-heavy drinkers; and (3) moderating effects of gender-specific alcohol dose effects and a multifactorial impulsivity measure on IFN connectivity in all NCANDA participants. Results showed that sex differences in no-to-low drinkers diminished with age in the inferior-occipital network, yet girls had weaker within-network connectivity than boys in six other networks. Effects of adolescent alcohol use were more pronounced in girls than boys in three IFNs. In particular, girls showed greater within-network connectivity in two motor networks with more alcohol consumption, and these effects were mediated by sensation-seeking only in girls. Our results implied that drinking might attenuate the naturally diminishing sexual differences by disrupting the maturation of network efficiency more severely in girls. The sex-alcohol-dose effect might explain why women are at higher risk of alcohol-related health and psychosocial consequences than men.
View details for DOI 10.1111/adb.12914
View details for PubMedID 32428984
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of those relationships often results in spurious and biased associations. Traditional machine learning and statistical models minimize the impact of confounders by, for example, matching data sets, stratifying data, or residualizing imaging measurements. Alternative strategies are needed for state-of-the-art deep learning models that use end-to-end training to automatically extract informative features from large set of images. In this article, we introduce an end-to-end approach for deriving features invariant to confounding factors while accounting for intrinsic correlations between the confounder(s) and prediction outcome. The method does so by exploiting concepts from traditional statistical methods and recent fair machine learning schemes. We evaluate the method on predicting the diagnosis of HIV solely from Magnetic Resonance Images (MRIs), identifying morphological sex differences in adolescence from those of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), and determining the bone age from X-ray images of children. The results show that our method can accurately predict while reducing biases associated with confounders. The code is available at https://github.com/qingyuzhao/br-net .
View details for DOI 10.1038/s41467-020-19784-9
View details for PubMedID 33243992
In a previous study using Jacobian mapping to evaluate the morphological effects on the brain of binge (4-day) intragastric ethanol (EtOH) on wild-type Wistar rats, we reported reversible thalamic shrinkage and lateral ventricular enlargement, but persistent superior and inferior colliculi shrinkage in response to binge EtOH treatment.Herein, we used similar voxel-based comparisons of Magnetic Resonance Images collected in EtOH-exposed relative to control animals to test the hypothesis that regardless of the intoxication protocol or the rat strain, the hippocampi, thalami, and colliculi would be affected.Two experiments [binge (4-day) intragastric EtOH in Fisher 344 rats and chronic (1-month) vaporized EtOH in Wistar rats] showed similarly affected brain regions including retrosplenial and cingulate cortices, dorsal hippocampi, central and ventroposterior thalami, superior and inferior colliculi, periaqueductal gray, and corpus callosum. While most of these regions showed significant recovery, volumes of the colliculi and periaqueductal gray continued to show response to each proximal alcohol exposure but at diminished levels with repeated exposures.Given the high metabolic rate of these enduringly affected regions, the current findings suggest that EtOH per se may affect cellular respiration leading to brain volume deficits. Further, responsivity greatly diminished likely reflecting neuroadaptation to repeated alcohol exposure. In summary, this unbiased, in vivo based approach demonstrating convergent brain systems responsive to two EtOH exposure protocols in two rat strains highlights regions that warrant further investigation in both animal models of alcoholism and in humans with Alcohol Use Disorder.
View details for DOI 10.1111/acer.14496
View details for PubMedID 33119896
Despite mounting evidence for the role of inflammation in Major Depressive Disorder (MDD), in vivo preclinical investigations of inflammation-induced negative affect using whole brain imaging modalities are scarce, precluding a valid model within which to evaluate pharmacological interventions. Here we used an E. coli lipopolysaccharide (LPS)-based model of inflammation-induced depressive signs in rats to explore brain changes using multimodal neuroimaging methods. During the acute phase of the LPS response (2 h post injection), prior to the emergence of a task-quantifiable depressive phenotype, striatal glutamine levels and splenial, retrosplenial, and peri-callosal hippocampal cortex volumes were greater than at baseline. LPS-induced depressive behaviors observed at 24 h, however, occurred concurrently with lower than control levels of striatal glutamine and a reversibility of volume expansion (i.e., shrinkage of splenial, retrosplenial, and peri-callosal hippocampal cortex to baseline volumes). In both striatum and hippocampus at 24 h, mRNA expression in LPS relative to control animals demonstrated alterations in enzymes and transporters regulating glutamine homeostasis. Collectively, the observed behavioral, in vivo structural and metabolic, and mRNA expression alterations suggest a critical role for astrocytic regulation of inflammation-induced depressive behaviors.
View details for DOI 10.1016/j.jneuroim.2020.577367
View details for PubMedID 32866714
With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better understanding of how a disease impacts the brain, the studies visualize the salience maps of the ConvNet highlighting voxels within the brain majorly contributing to the prediction. However, these salience maps are generally confounded, i.e., some salient regions are more predictive of confounding variables (such as age) than the diagnosis. To avoid such misinterpretation, we propose in this paper an approach that aims to visualize confounder-free saliency maps that only highlight voxels predictive of the diagnosis. The approach incorporates univariate statistical tests to identify confounding effects within the intermediate features learned by ConvNet. The influence from the subset of confounded features is then removed by a novel partial back-propagation procedure. We use this two-step approach to visualize confounder-free saliency maps extracted from synthetic and two real datasets. These experiments reveal the potential of our visualization in producing unbiased model-interpretation.
View details for DOI 10.1007/978-3-030-32692-0_38
View details for PubMedID 32549051
Due to difficulties in collecting sufficient training data, recent advances in neural-network-based methods have not been fully explored in the analysis of brain Magnetic Resonance Imaging (MRI). A possible solution to the limited-data issue is to augment the training set with synthetically generated data. In this paper, we propose a data augmentation strategy based on regional feature substitution. We demonstrate the advantages of this strategy with respect to training a simple neural-network-based classifier in predicting when individual youth transition from no-to-low to medium-to-heavy alcohol drinkers solely based on their volumetric MRI measurements. Based on 20-fold cross-validation, we generate more than one million synthetic samples from less than 500 subjects for each training run. The classifier achieves an accuracy of 74.1% in correctly distinguishing non-drinkers from drinkers at baseline and a 43.2% weighted accuracy in predicting the transition over a three year period (5-group classification task). Both accuracy scores are significantly better than training the classifier on the original dataset.
View details for DOI 10.1007/978-3-030-33642-4_4
View details for PubMedID 32924031
The tracking of dynamic functional connectivity (dFC) states in resting-state fMRI scans aims to reveal how the brain sequentially processes stimuli and thoughts. Despite the recent advances in statistical methods, estimating the high dimensional dFC states from a small number of available time points remains a challenge. This paper shows that the challenge is reduced by linear covariance shrinkage, a statistical method used for the estimation of large covariance matrices from small number of samples. We present a computationally efficient formulation of our approach that scales dFC analysis up to full resolution resting-state fMRI scans. Experiments on synthetic data demonstrate that our approach produces dFC estimates that are closer to the ground-truth than state-of-the-art estimation approaches. When comparing methods on the rs-fMRI scans of 162 subjects, we found that our approach is better at extracting functional networks and capturing differences in rs-fMRI acquisition and diagnosis.
View details for DOI 10.1007/978-3-030-32391-2_4
View details for PubMedID 32924030
View details for DOI 10.1016/j.laa.2019.04.009
View details for Web of Science ID 000468261100007
The World Health Organization estimates a 12-month prevalence rate of 8+% for an alcohol use disorder (AUD) diagnosis in people age 15years and older in the United States and Europe, presenting significant health risks that have the potential of accelerating age-related functional decline. According to neuropathological studies, white matter systems of the cerebellum are vulnerable to chronic alcohol dependence. To pursue the effect of AUD on white matter structure and functions in vivo, this study used T1-weighted, magnetic resonance imaging (MRI) to quantify the total corpus medullare of the cerebellum and a finely grained analysis of its surface in 135 men and women with AUD (mean duration of abstinence, 248d) and 128 age- and sex-matched control participants; subsets of these participants completed motor testing. We identified an AUD-related volume deficit and accelerated aging in the total corpus medullare. Novel deformation-based surface morphometry revealed regional shrinkage of surfaces adjacent to lobules I-V, lobule IX, and vermian lobule X. In addition, accelerated aging was detected in the regional surface areas adjacent to lobules I-V, lobule VI, lobule VIIB, and lobules VIII, IX, and X. Sex differences were not identified for any measure. For both volume-based and surface-based analyses, poorer performance in gait and balance, manual dexterity, and grip strength were linked to greater regional white matter structural deficits. Our results suggest that local deformation of the corpus medullare has the potential of identifying structurally and functionally segregated networks affected in AUD.
View details for PubMedID 30932270
View details for DOI 10.1002/hbm.24541
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised regression. Based on recent advances in learning disentangled representations, the novel generative process explicitly models the conditional distribution of latent representations with respect to the regression target variable. Performing a variational inference procedure on this model leads to joint regularization between the VAE and a neural-network regressor. In predicting the age of 245 subjects from their structural Magnetic Resonance (MR) images, our model is more accurate than state-of-the-art methods when applied to either region-of-interest (ROI) measurements or raw 3D volume images. More importantly, unlike simple feed-forward neural-networks, disentanglement of age in latent representations allows for intuitive interpretation of the structural developmental patterns of the human brain.
View details for DOI 10.1007/978-3-030-32245-8_91
View details for Web of Science ID 000548438900091
View details for PubMedID 32705091
View details for PubMedCentralID PMC7377006
Resting-state functional connectivity states are often identified as clusters of dynamic connectivity patterns. However, existing clustering approaches do not distinguish major states from rarely occurring minor states and hence are sensitive to noise. To address this issue, we propose to model major states using a non-linear generative process guided by a Gaussian-mixture distribution in a low-dimensional latent space, while separately modeling the connectivity patterns of minor states by a non-informative uniform distribution. We embed this truncated Gaussian-Mixture model in a Variational Autoencoder framework to obtain a general joint clustering and outlier detection approach, tGM-VAE. When applied to synthetic data with known ground-truth, tGM-VAE is more accurate in clustering connectivity patterns than existing approaches. On the rs-fMRI of 593 healthy adolescents, tGM-VAE identifies meaningful major connectivity states. The dwell time of these states significantly correlates with age.
View details for DOI 10.1007/978-3-030-20351-1_68
View details for PubMedID 32699491
View details for PubMedCentralID PMC7375028
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and behavioral treatment interventions have shown promise for young children with ASD. However, there is limited progress in understanding the effect of each type of treatment. In this project, we aim to detect structural changes in the brain after treatment and select structural features associated with treatment outcomes. The difficulty in building large databases of patients who have received specific treatments and the high dimensionality of medical image analysis problems are the challenges in this work. To select predictive features and build accurate models, we use the sure independence screening (SIS) method. SIS is a theoretically and empirically validated method for ultra-high dimensional general linear models, and it achieves both predictive accuracy and correct feature selection by iterative feature selection. Compared with step-wise feature selection methods, SIS removes multiple features in each iteration and is computationally efficient. Compared with other linear models such as elastic-net regression, support vector regression (SVR) and partial least squares regression (PSLR), SIS achieves higher accuracy. We validated the superior performance of SIS in various experiments: First, we extract brain structural features from FreeSurfer, including cortical thickness, surface area, mean curvature and cortical volume. Next, we predict different measures of treatment outcomes based on structural features. We show that SIS achieves the highest correlation between prediction and measurements in all tasks. Furthermore, we report regions selected by SIS as biomarkers for ASD.
View details for DOI 10.1109/ISBI.2019.8759156
View details for PubMedID 32256966
View details for PubMedCentralID PMC7119202
View details for DOI 10.1007/978-3-030-20351-1_68
View details for Web of Science ID 000493380900068
View details for Web of Science ID 000485040000088
Increasing numbers of neuroimaging studies are acquiring data to examine changes in brain architecture by investigating intrinsic functional networks (IFN) from longitudinal resting-state functional MRI (rs-fMRI). At the subject level, these IFNs are determined by cross-sectional procedures, which neglect intra-subject dependencies and result in suboptimal estimates of the networks. Here, a novel longitudinal approach simultaneously extracts subject-specific IFNs across multiple visits by explicitly modeling functional brain development as an essential context for seeking change. On data generated by an innovative simulation based on real rs-fMRI, the method was more accurate in estimating subject-specific IFNs than cross-sectional approaches. Furthermore, only group-analysis based on longitudinally consistent estimates identified significant developmental effects within IFNs of 246 adolescents from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The findings were confirmed by the cross-sectional estimates when the corresponding group analysis was confined to the developmental effects. Those effects also converged with current concepts of neurodevelopment.
View details for PubMedID 30806009
View details for DOI 10.3389/fnana.2018.00108
View details for Web of Science ID 000452849300001
View details for DOI 10.1016/j.neuroimage.2018.08.022
View details for Web of Science ID 000447750200038
Human Immunodeficiency Virus (HIV) infection continues to have major adverse public health and clinical consequences despite the effectiveness of combination Antiretroviral Therapy (cART) in reducing HIV viral load and improving immune function. As successfully treated individuals with HIV infection age, their cognition declines faster than reported for normal aging. This phenomenon underlines the importance of improving long-term care, which requires better understanding of the impact of HIV on the brain. In this paper, automated identification of patients and brain regions affected by HIV infection are modeled as a classification problem, whose solution is determined in two steps within our proposed Chained-Regularization framework. The first step focuses on selecting the HIV pattern (i.e., the most informative constellation of brain region measurements for distinguishing HIV infected subjects from healthy controls) by constraining the search for the optimal parameter setting of the classifier via group sparsity (?2,1-norm). The second step improves classification accuracy by constraining the parameterization with respect to the selected measurements and the Euclidean regularization (?2-norm). When applied to the cortical and subcortical structural Magnetic Resonance Images (MRI) measurements of 65 controls and 65 HIV infected individuals, this approach is more accurate in distinguishing the two cohorts than more common models. Finally, the brain regions of the identified HIV pattern concur with the HIV literature that uses traditional group analysis models.
View details for PubMedID 30138676
Group analysis of brain magnetic resonance imaging (MRI) metrics frequently employs generalized additive models (GAM) to remove contributions of confounding factors before identifying cohort specific characteristics. For example, the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) used such an approach to identify effects of alcohol misuse on the developing brain. Here, we hypothesized that considering confounding factors before group analysis removes information relevant for distinguishing adolescents with drinking history from those without. To test this hypothesis, we introduce a machine-learning model that identifies cohort-specific, neuromorphometric patterns by simultaneously training a GAM and generic classifier on macrostructural MRI and microstructural diffusion tensor imaging (DTI) metrics and compare it to more traditional group analysis and machine-learning approaches. Using a baseline NCANDA MR dataset (N?=?705), the proposed machine learning approach identified a pattern of eight brain regions unique to adolescents who misuse alcohol. Classifying high-drinking adolescents was more accurate with that pattern than using regions identified with alternative approaches. The findings of the joint model approach thus were (1) impartial to confounding factors; (2) relevant to drinking behaviors; and (3) in concurrence with the alcohol literature.
View details for PubMedID 29844507
Individuals aged 12-20 years drink 11% of all alcohol consumed in the United States with more than 90% consumed in the form of binge drinking. Early onset alcohol use is a strong predictor of future alcohol dependence. The study of the effects of excessive alcohol use on the human brain is hampered by limited information regarding the quantity and frequency of exposure to alcohol. Animal models can control for age at alcohol exposure onset and enable isolation of neural substrates of exposure to different patterns and quantities of ethanol (EtOH). As with humans, a frequently used binge exposure model is thought to produce dependence and affect predominantly corticolimbic brain regions. in vivo neuroimaging enables animals models to be examined longitudinally, allowing for each animal to serve as its own control. Accordingly, we conducted 3 magnetic resonance imaging (MRI) sessions (baseline, binge, recovery) to track structure throughout the brains of wild type Wistar rats to test the hypothesis that binge EtOH exposure affects specific brain regions in addition to corticolimbic circuitry. Voxel-based comparisons of 13 EtOH- vs. 12 water- exposed animals identified significant thalamic shrinkage and lateral ventricular enlargement as occurring with EtOH exposure, but recovering with a week of abstinence. By contrast, pretectal nuclei and superior and inferior colliculi shrank in response to binge EtOH treatment but did not recover with abstinence. These results identify brainstem structures that have been relatively underreported but are relevant for localizing neurocircuitry relevant to the dynamic course of alcoholism.
View details for PubMedID 30618652
View details for DOI 10.1007/978-3-030-00931-1_17
View details for Web of Science ID 000477769700017
Even though the number of longitudinal resting-state-fMRI studies is increasing, accurately characterizing the changes in functional connectivity across visits is a largely unexplored topic. To improve characterization, we design a Riemannian framework that represents the functional connectivity pattern of a subject at a visit as a point on a Riemannian manifold. Geodesic regression across the 'sample' points of a subject on that manifold then defines the longitudinal trajectory of their connectivity pattern. To identify group differences specific to regions of interest (ROI), we map the resulting trajectories of all subjects to a common tangent space via the Lie group action. We account for the uncertainty in choosing the common tangent space by proposing a test procedure based on the theory of latent p-values. Unlike existing methods, our proposed approach identifies sex differences across 246 subjects, each of them being characterized by three rs-fMRI scans.
View details for DOI 10.1007/978-3-030-00931-1_17
View details for PubMedID 33005907
View details for PubMedCentralID PMC7526985
Latest information on COVID-19
Stanford Medicine is closely monitoring the COVID-19 pandemic. Get the latest news on COVID-19 testing, treatment, tracking data, and medical research.
Racism and discrimination are direct affronts to Stanford Medicine?s values. Read our leaders? pledge on racial equity.
A leader in the biomedical revolution, Stanford Medicine has a long tradition of leadership in pioneering research, creative teaching protocols and effective clinical therapies.
Stanford Medicine now provides a service that harnesses the power of genome sequencing to identify the source of diseases and help target treatments.
Our scientists have launched dozens of research projects as part of the global response to COVID-19. Some aim to prevent, diagnose and treat the disease; others aim to understand how it spreads and how people?s immune systems respond to it.
Thanks to deep brain stimulation, an artist with a common but lesser known neurological disorder can paint again with a steady hand.
Physician assistant Thanh Khong manages testing and vaccination operations for Stanford Health Care.
The Pfizer and Moderna COVID-19 vaccines are the first to use the RNA coding molecule to prompt our bodies to fight the virus.
Latest information on COVID-19
Stanford Medicine is closely monitoring the COVID-19 pandemic. Get the latest news on COVID-19 testing, treatment, tracking data, and medical research.
Racism and discrimination are direct affronts to Stanford Medicine?s values. Read our leaders? pledge on racial equity.
A leader in the biomedical revolution, Stanford Medicine has a long tradition of leadership in pioneering research, creative teaching protocols and effective clinical therapies.
Stanford Medicine now provides a service that harnesses the power of genome sequencing to identify the source of diseases and help target treatments.
Our scientists have launched dozens of research projects as part of the global response to COVID-19. Some aim to prevent, diagnose and treat the disease; others aim to understand how it spreads and how people?s immune systems respond to it.
Thanks to deep brain stimulation, an artist with a common but lesser known neurological disorder can paint again with a steady hand.
Physician assistant Thanh Khong manages testing and vaccination operations for Stanford Health Care.
The Pfizer and Moderna COVID-19 vaccines are the first to use the RNA coding molecule to prompt our bodies to fight the virus.
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