About

Anders M Dale is an Emeritus Professor of Neurosciences at UCSD, with additional positions in Psychiatry, Cognitive Science, and Data Science. He completed his Ph.D. at UCSD in 1994, after earning a Master's degree from Harvard University in 1990 and a B.A. in Computer Science from the University of Texas in 1985. His research activities focus on brain imaging, genetics, and neuroinformatics, with involvement in numerous national and international consortia related to neuroimaging, neurogenetics, and psychiatric disorders. Dale has held leadership roles such as Director of the UCSD Center for Multimodal Imaging & Genetics and the Center for Translational Imaging and Personalized Medicine, as well as Vice Chair for Research in the Department of Radiology. His work has contributed to understanding the genetic architecture of brain morphology, neuroimaging biomarkers in Alzheimer's disease, and the genetic relationships among mental disorders, autoimmune diseases, and other complex conditions. He has been recognized with awards including the Young Investigator Award for Human Brain Mapping and memberships in prestigious scientific societies.

Research topics

• Medicine
• Psychology
• Neuroscience
• Computer Science
• Biology
• Genetics
• Psychiatry
• Audiology
• Radiology
• Gerontology
• Clinical psychology
• Internal medicine
• Physics
• Nuclear medicine
• Developmental psychology
• Artificial Intelligence
• Cognitive psychology
• Environmental health
• Mathematics
• Data science
• Pediatrics
• Evolutionary biology
• Pathology
• Family medicine

Selected publications

• A common <i>IFI44L</i> variant strongly predicts clinical trajectories in aging and neurodegeneration

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  Abstract Background Dysregulation of the interferon (IFN) response is emerging as a major pathobiological contributor across multiple forms of neurodegeneration, but much less is known about how genetic variation in the IFN pathway modulates the course of neurodegenerative disease. IFN signaling represents a primary cellular mechanism for the antiviral response. Furthermore, both viral infection and vaccination are increasingly recognized for their roles in modifying neurodegenerative disease risk. We hypothesized that common variation in IFN‐stimulated gene IFI44L , which is enriched in IFN‐responsive immune‐cell subsets and strongly associated with vaccine response, would be associated with differences in clinical trajectories in neurodegenerative disease and normal aging. Method We performed longitudinal analyses using linear mixed‐effects models on measures of clinical severity and cognitive impairment across four clinical diagnoses and two independent cohorts, including the University of California, San Francisco Memory and Aging Center (MAC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Clinical measures included the Clinical Dementia Rating Scale Sum of Boxes (CDR‐SB) and the Mini‐Mental State Examination. IFI44L rs273259 genotype was measured by whole‐genome sequencing (MAC) and Illumina GWAS BeadChips (ADNI). Result After controlling for baseline age, sex, education, APOE ε4 dosage, and self‐reported race, IFI44L genotype strongly associated with clinical trajectories in clinically normal (CN) individuals and those with mild cognitive impairment (MCI) in the ADNI cohort, with the rs273259 alternate (G) allele displaying a highly significant, dose‐dependent relationship with worse clinical trajectories (for CDR‐SB, in CN, p &lt; 2 x 10 ‐16 ; in MCI, p = 1.1 x 10 ‐6 ; Figure 1A‐B). In the MAC cohort, the alternate allele was also associated with worse CDR‐SB trajectories in CN ( p = 0.05), frontotemporal dementia ( p = 3.9 x 10 ‐10 ; Figure 1C) and early‐onset AD ( p = 3.3 x 10 ‐3 ; Figure 1D). Among subjects with neuropathologically defined frontotemporal lobar degeneration (FTLD) with tau or TDP‐43 proteinopathy, IFI44L genotype was associated with clinical trajectories in both subtypes ( p = 3.8 x 10 ‐5 , FTLD‐tau; p = 0.03, FTLD‐TDP). Conclusion A common IFI44L variant is significantly associated with clinical trajectories in normal aging and in multiple forms of neurodegenerative disease, confirming an important role for IFN signaling in aging and neurodegeneration (Figure 2).

  PublisherOA PDFDOI

• A genome-wide analysis of the shared genetic risk architecture of complex neurological and psychiatric disorders

  Nature Neuroscience · 2025-11-11 · 13 citations

  article
  PublisherDOI

• Distinct metabolic signatures of Alzheimer’s and Parkinson’s disease revealed through genetic overlap with metabolic markers

  medRxiv · 2025-07-31 · 4 citations

  preprintOpen access

  ABSTRACT Metabolic dysfunction is increasingly implicated in neurodegenerative diseases, yet the genetic architecture linking metabolic markers with Alzheimer’s disease (AD) and Parkinson’s disease (PD) remains unclear. We systematically analysed phenotypic and genetic relationships between 249 circulating metabolites with AD and PD, comparing patterns to body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD) and stroke. Using linkage disequilibrium score regression and bivariate Gaussian mixture modeling, we identified distinct genetic overlap. AD correlated positively with cardiometabolic traits (BMI, r s =0.11; T2D, r s =0.23; CAD, r s =0.22; stroke, r s =0.18), whereas PD showed opposing patterns (AD–PD r s =−0.36). Mendelian randomization identified bi-directional causal effects of lipid measures on AD and divergent effects of glutamine on AD and PD. Conjunctional FDR analyses mapped 1,377 shared genes, implicating lipid metabolism in AD and synaptic processes in PD. These findings disentangle disease-specific pathways and inform therapeutic strategies targeting metabolic health.

  PublisherOA PDFDOI

• Age associations with cortical and subcortical brain structure in adolescents age 9-17

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-19

  preprintOpen accessSenior author

  Introduction: Adolescence is a pivotal period in brain structural development and maturation. However, investigation of cortical and subcortical brain changes during this time have been limited by small sample size and have generally examined the brain at the level of predetermined regions of interest. The recently developed Fast Efficient Mixed-Effects Algorithm (FEMA) allows for increased computational speed using mixed-effects models applied at the voxel or vertex level, as well as across multiple regions of interest. Methods: Study (22,651 observations from 10,521 unique subjects aged 9.00-17.77) to study the age-related trajectories of tabulated cortical and subcortical volumes, vertexwise cortical thickness and surface area, and voxelwise volume assessed using the Jacobian. Models are reported separately in males and females. Results: Global volume variables, including total subcortical gray matter volume, peaked near 13 years in females and 15 years in males. Vertexwise cortical surface area followed an inverted U-shaped curve, whereas vertexwise cortical thickness followed a monotonic decrease during the age range studied. Voxelwise imaging analysis revealed regional differences in age trajectories at the subregional level. Discussion: The results of this work replicate and extend prior findings related to adolescent brain development, and illustrate distinct spatiotemporal patterns of structural changes in subcortical regions. The updated FEMA framework is publicly available for use in similar large datasets.

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• W14. IMPLICATING NEURAL CELL TYPES FOR PSYCHIATRIC DISORDERS AND NEUROLOGICAL DISEASES WITH NOVEL MIXER METHODS

  European Neuropsychopharmacology · 2025-10-01

  articleOpen access

  Psychiatric disorders and neurological diseases are complex brain-related traits with shared and distinctive neurobiology. Previous genetic studies implicate various neuronal cell types for psychiatric disorders while brain-resident immune cells are implicated for several neurological diseases. Studies typically investigate each disorder separately. However, much can be gained from the cross-trait modelling of genetic signal. Therefore, we have expanded the novel gene set analysis MiXeR (GSA-MiXeR) tool to model bivariate partitioned-heritability. We apply bivariate GSA-MiXeR to pairs of psychiatric disorders and neurological diseases to identify shared and distinct genetic signal for various neural cell-types. We use genome-wide association study summary statistics for three psychiatric disorders (bipolar disorder (BD), depression, and schizophrenia (SCZ)) and three neurological diseases (Alzheimer’s disease (AD), multiple sclerosis, and Parkinson’s disease) as well as largescale single-cell RNA sequencing data from fetal and/or adult brain donors. For each single-cell dataset, we estimate an expression proportion for each gene to rank their cell-type specificity. The top 10% of genes for each cell type is used to define gene sets for all analyses. In a sample of 267 BD and 270 SCZ cases with 745 controls, we apply the recently developed MiXeR-Pred tool to compare performance of a cell-type polygenic risk score (PRS) derived from a single trait (BD or SCZ) to a PRS derived from a pair of traits (i.e., BD-with-SCZ or SCZ-with-BD). To develop and validate the bivariate GSA-MiXeR tool, we run extensive simulations. A main feature of the tool is the ability to model trait-specific and shared genetic signal which contribute to a cross-trait Akaike Information Criterion (AIC) value. This value can be used for selection of relevant gene sets for one or both modelled traits, similar to an omnibus test. Current simulations reveal that gene sets with shared genetic signal between traits have a higher cross-trait AIC than gene sets that are specific to a single trait. Consistent with previous reports, using bivariate GSA-MiXeR we find neurological diseases exhibit heritability fold-enrichment for brain-resident immune cells. Comparatively, enrichment in psychiatric disorders for the same immune cells was lower. Psychiatric disorders exhibited high enrichment for subclasses of inhibitory neurons. In general, the shared cross-trait signal across cell types was larger amongst psychiatric disorders than neurological disorders. For pairs of traits with shared genetic signal for a cell-type, PRS analyses reveal a subtle increase in performance using a bivariate cell-type PRS compared to trait-specific. For example, there was shared genetic signal for BD and SCZ for neuronal genes. The MiXeR-Pred neuronal-PRS for BD sourcing SCZ genetic signal had a larger R-squared than the neuronal-PRS derived from BD alone. Comparatively, astrocyte genes did not have shared genetic signal for BD and SCZ and the astrocyte-PRS for BD sourcing SCZ did not outperform the astrocyte-PRS for BD alone. Altogether, we illustrate neural cell associations with psychiatric disorders and neurological diseases using the novel bivariate GSA-MiXeR. We observe shared genetic signal across traits and validate these findings using MiXeR-Pred PRS performance. These tools can be further utilized to probe and understand the underlying biology of differing traits.

  PublisherDOI

• Genomic relationship between polycystic ovary syndrome and bipolar disorder

  Research Square · 2025-09-23

  preprintOpen access
  PublisherOA PDFDOI

• Acute and transient change in brain water content induced by a single electroconvulsive therapy session

  Brain stimulation · 2025-10-28 · 1 citations

  articleOpen access

  BACKGROUND: While electroconvulsive therapy (ECT) is the most effective treatment for severe depression, there is a concern regarding its potential adverse effects on the brain. It is unclear whether a single ECT session (i.e., an electrically induced seizure) leads to physiological microstructural changes or causes lasting adverse biological effects. METHODS: This study examined longitudinal changes in multishell diffusion MRI-derived metrics in 25 individuals with depression, with scans acquired 2 h before and after their first ECT session. Follow-up scans were collected within 14 days and 6 months after the last ECT session. To control for potential confounding effects of anesthesia and repeated measurements, two additional groups were included: 16 individuals undergoing short-acting anesthesia and 16 healthy controls without interventions. A multicompartment model was applied to explore extracellular free water and intracellular/extracellular compartments. RESULTS: Whole-brain voxel-wise analyses identified increased extracellular free water in bilateral periventricular and subcortical regions surrounding the hippocampus, with minimal involvement of cortical regions, following a single ECT session. These changes were not observed in either control group, and were not associated with post-ictal reorientation time (r = 0.11, p = 0.92). Follow-up assessments confirmed that the alterations in tissue free water resolved within 14 days. CONCLUSIONS: A single ECT-induced seizure induces a transient increase in extracellular water content without evidence of cytotoxic edema indicative of cellular injury. Our findings suggest that ECT-related brain water shifts are reversible and unlikely to reflect permanent damage to brain tissue.

  PublisherDOI

• Genetic Architecture of Placental Efficiency for Term Infants: Evidence from Monoaminergic Pathways and Placental Tissue Expression in the Norwegian Mother, Father and Child Cohort Study (MoBa)

  medRxiv · 2025-10-09

  preprintOpen access

  Abstract The placenta plays a central role in supporting fetal growth. Placental efficiency (PlE) defined as the birthweight-to-placental weight ratio proves to be a key measure of its capacity to adapt to the fetal developmental demands. Although the genetic architecture of birthweight (BW) and placental weight (PW) have been explored, the biology underlying PlE remains largely unknown. Here, we report the first genome-wide association study (GWAS) of PlE in 63,894 at term singleton births from the Norwegian Mother, Father and Child cohort (MoBa), complemented by maternal (N = 60,472) and paternal (N = 40,116) analyses. Across offspring and maternal genomes, we identified multiple genome-wide significant loci, with TSNAX-DISC1 consistently implicated across analyses. Comparative genetic analyses revealed strong overlap between PlE and PW, but minimal overlap with BW, suggesting that PlE captures distinct aspects of placental adaptation beyond overall growth. Gene-set enrichment highlighted significant involvement of monoaminergic pathways, particularly norepinephrine uptake and transport, while tissue-specific analyses demonstrated strong enrichment in placental tissue. Notably, mapped genes including SLC6A2, SLC22A2, and SLC22A3 link PlE to regulation of monoamine signaling, aligning with the placenta’s potential role in neurodevelopmental vulnerability. Together, these findings establish PlE as a genetically distinct phenotype, provide insight into the biology of placental adaptation, and suggest shared genetic pathways connecting placental function and offspring neurodevelopment.

  PublisherOA PDFDOI

• INSIGHTS INTO THE METABOLIC ORIGINS OF ANOREXIA NERVOSA THROUGH GENOMICS AND NEUROIMAGING

  European Neuropsychopharmacology · 2025-10-01

  article
  PublisherDOI

• Urethra contours on MRI: Multidisciplinary consensus educational atlas and reference standard for artificial intelligence benchmarking

  Radiotherapy and Oncology · 2025-10-25 · 2 citations

  articleOpen access

  INTRODUCTION: The urethra is a recommended avoidance structure for prostate cancer treatment. However, even subspecialist physicians often struggle to accurately identify it on available imaging. Automated segmentation tools show promise, but a lack of reliable ground truth or appropriate evaluation standards has hindered validation and clinical adoption. This study aims to establish a reference-standard dataset with expert consensus contours, define clinically meaningful evaluation metrics, and assess the performance and generalizability of a deep-learning-based segmentation model. MATERIALS AND METHODS: A multidisciplinary panel of four experienced subspecialists in prostate MRI generated consensus urethra contours on MRI data for 71 patients from 6 centers, establishing a reference standard. Four of these patients were previously used in an international study (PURE-MRI) where 62 physicians contoured the prostate and urethra. Using an independent training dataset (n = 151 patients, 1 center), we developed a deep-learning AI model for urethra segmentation. We evaluated the AI tool in the consensus reference dataset and compared it to human performance using Dice, percent urethra coverage, and maximum 2D (axial, in-plane) Hausdorff Distance (HD) from the reference standard. RESULTS: The AI model outperformed most physicians, achieving median Dice of 0.41 (vs. 0.33 for physicians), Coverage of 81 % (vs. 36 %), and Max 2D HD of 1.8 mm (vs. 1.6 mm) in the four PURE-MRI cases. In the full reference dataset, AI performance remained consistent, with Dice of 0.40, Coverage of 89 %, and Max 2D HD of 2.0 mm, indicating strong generalizability across a broader patient population and more varied imaging conditions. CONCLUSION: We established a multidisciplinary consensus benchmark for segmentation of the urethra. The deep-learning model performs comparably to specialist physicians and demonstrates consistent results across multiple institutions. It shows promise as a clinical decision-support tool for accurate and reliable urethra segmentation in prostate cancer radiotherapy planning and studies of dose-toxicity associations.

  PublisherDOI

Recent grants

• NIH Grant R01EB000307

  NIH · $453k · 2003

• Healthy Brain and Child Development National Consortium Data Coordinating Center

  NIH · $30.3M · 2021–2026

• NIH Grant S10RR029050

  NIH · $870k · 2011

• The VETSA Longitudinal MRI Twin Study of Aging (VETSA MRI 4)

  NIH · $8.7M · 2022–2027

• NIH Grant R01NS039581

  NIH · $933k · 2003

Frequent coauthors

• Ole A. Andreassen

  Oslo University Hospital

  818 shared

• Srdjan Djurovic

  University of Oslo

  438 shared

• Oleksandr Frei

  324 shared

• Olav B. Smeland

  Oslo University Hospital

  285 shared

• Wesley K. Thompson

  University of Tulsa

  280 shared

• Alexey Shadrin

  Oslo University Hospital

  267 shared

• Bruce Fischl

  Harvard University

  258 shared

• Terry L. Jernigan

  University of California, San Diego

  252 shared

Labs

• UCSD Neurosciences, Psychiatry, and Cognitive Science and Data SciencePI

Education

• Ph.D., Neuroscience

  University of California, San Diego

  1991

• B.S., Psychology

  University of California, San Diego

  1984

Awards & honors

• Young Investigator Award for Human Brain Mapping (1998)
• Career Development Award from Norwegian Research Council (19…
• Fulbright Fellowship from Harvard University (1988)
• Member of Norwegian Academy of Science and Letters (2016)
• Distinguished Scientist Appointment at Halicioglu Data Scien…