About

Christopher Schwarz is a Professor of Finance at the Paul Merage School of Business at the University of California Irvine. He serves as the Faculty Director for the Center for Investment and Wealth Management. His research interests include the management, disclosure, and operational risk of the investment fund industry, the impact of manager incentives and structure on investment fund performance, and the behavior of retail investors and retail market structure. His work has been published in leading academic journals such as the Journal of Finance, Journal of Financial Economics, Review of Financial Studies, and the Journal of Financial and Quantitative Analysis. Schwarz's research has been cited by prominent media outlets including the New York Times, LA Times, Wall Street Journal, Financial Times, Bloomberg, and Barron's, and has appeared on CNBC. Additionally, his research has been included in testimony before the U.S. Congress House Financial Services Committee. Prior to his tenure at UCI, he earned his Ph.D. from the University of Massachusetts Amherst, during which he was a Visiting Doctoral Fellow at Yale University's International Center of Finance. He also holds a B.S. from Babson College.

Research topics

• Medicine
• Psychology
• Internal medicine
• Pathology
• Neuroscience
• Oncology
• Biology
• Artificial Intelligence
• Business
• Computer Science
• Genetics
• Chemistry
• Financial economics
• Radiology
• Economics
• Audiology
• Monetary economics
• Finance
• Geography
• Nuclear medicine

Selected publications

• Default mode network failure across the Alzheimer’s disease spectrum

  Brain · 2026-04-21

  article

  Alzheimer's disease (AD) emerges from multi-scale interactions between molecular pathology and disruptions in large-scale brain network dynamics. Understanding how these processes co-evolve and relate to disease stages is essential for advancing complex systems models of aging and AD, and for developing system-informed interventions. However, progress has been limited by a lack of large-scale longitudinal data. To address this, we examined the longitudinal relationship between subsystems of the default mode network (DMN) (posterior DMN, ventral DMN, anterior dorsal DMN) using task-free functional MRI (fMRI) and amyloid positron emission tomography (PET) imaging in a large longitudinal cohort spanning the clinico-biological spectrum of AD (n = 1,451; 2,763 time points) using mixed-effect models. We also assessed whether patterns of DMN connectivity predicted conversion to amyloid positivity, mild cognitive impairment (MCI), and dementia using Cox proportional hazards models. Our findings reveal a dynamic interplay between amyloid accumulation and connectivity within and between DMN subsystems, with both hyper- and hypoconnectivity emerging across DMN subsystems in association with increasing amyloid burden. Importantly, survival models showed that DMN connectivity patterns predicted conversion to critical stages of the disease, including not only conversion to MCI and dementia, but also conversion to amyloid positivity in otherwise clinically unimpaired individuals who were amyloid negative at baseline. These associations were independent of age, APOE4 status, sex, education, and in-scanner motion. These results support a model in which breakdowns in tightly regulated feedback loops governing DMN physiology represent a core systems-level pathophysiology of AD. Notably, this functional dyshomeostasis precedes detectable amyloidosis on imaging. Future studies should focus on the development of robust biomarkers of brain function that can be applied at the individual level, which could in turn help support the development of therapeutic approaches targeting system-level pathophysiology.

  PublisherDOI

• Multi-Contrast MRI Inputs Enable Self-Consistent Tissue Segmentation & Robust Perivascular Space Identification

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-07

  articleOpen access

  Different MRI image contrasts are designed to highlight various tissue properties and combining them allows extension of probabilistic segmentation beyond the commonly used "gray-white-CSF" models. This work describes a fully automated method that combines T1-weighted, T2-FLAIR, and conventional T2-weighted images to provide internal consistency across prediction of tissue segmentations including segmentation of superficial and deep gray matter, white matter hyperintensities, and MR-visible perivascular spaces. Results from 773 imaging datasets from 403 participants in the Mayo Clinic Study of Aging and Mayo Clinic Alzheimer's Disease Research Center (ADRC) are presented.

  PublisherDOI

• An FDG-PET–Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders

  Neurology · 2025-06-27 · 13 citations

  articleOpen access

  BACKGROUND AND OBJECTIVES: Distinguishing neurodegenerative diseases is a challenging task requiring neurologic expertise. Clinical decision support systems (CDSSs) powered by machine learning (ML) and artificial intelligence can assist with complex diagnostic tasks by augmenting user capabilities, but workflow integration poses many challenges. We propose that a modeling framework based on fluorodeoxyglucose PET (FDG-PET) imaging can address these challenges and form the basis of an effective CDSS for neurodegenerative disease. METHODS: This retrospective study focused on FDG-PET images in a discovery cohort drawn from 3 research studies plus routine clinical patients. When selecting research study participants, the inclusion criterion was the availability of an FDG-PET image from within 2.5 years of diagnosis with 1 of 9 specific neurodegenerative syndromes or designation as unimpaired. Participants from disease groups were recruited from the clinical patient population while unimpaired participants came primarily from a population study. The discovery cohort was used to develop a clinical decision support framework we call StateViewer, which applies a neighbor matching algorithm to detect the presence of 9 different neurodegenerative phenotypes. The ML performance of this framework was evaluated in the discovery cohort by nested cross-validation and externally validated in the Alzheimer's Disease Neuroimaging Initiative. Potential for clinical integration was demonstrated in a radiologic reader study focused on differentiating posterior cortical atrophy from Lewy body dementia. RESULTS: The discovery cohort contained 3,671 individuals with a mean age of 68 years and consisted of 49% reported female. Our model framework was able to detect the presence of 9 different neurodegenerative phenotypes with a sensitivity of 0.89 ± 0.03 and an area under the receiver operating characteristic curve of 0.93 ± 0.02. In the radiologic reader study, readers using our model were found to have 3.3 ± 1.1 times greater odds of making a correct diagnosis than readers using a current standard-of-care workflow. DISCUSSION: Our proposed framework provides strong classification performance with high interpretability, and it addresses many of the challenges that face clinical integration of ML-based decision support tools. One limitation of this study is a uniform discovery cohort that is not representative of other patient populations in some regards.

  PublisherOA PDFDOI

• Cortical microstructural abnormalities in dementia with Lewy bodies and their associations with Alzheimer’s disease copathologies

  npj Parkinson s Disease · 2025-05-12 · 8 citations

  articleOpen access

  Dementia with Lewy bodies (DLB) frequently coexists with Alzheimer's disease pathology, yet the pattern of cortical microstructural injury and its relationship with amyloid, tau, and cerebrovascular pathologies remains unclear. We applied neurite orientation dispersion and density imaging (NODDI) to assess cortical microstructural integrity in 57 individuals within the DLB spectrum and 57 age- and sex-matched cognitively unimpaired controls by quantifying mean diffusivity (MD), tissue-weighted neurite density index (tNDI), orientation dispersion index (ODI), and free water fraction (FWF). Amyloid and tau levels were measured using PiB and Flortaucipir PET imaging. Compared to controls, DLB exhibited increased MD and FWF, reduced tNDI across multiple regions, and focal ODI reductions in the occipital cortex. Structural equation modeling revealed that APOE genotype influenced amyloid levels, which elevated tau, leading to microstructural injury. These findings highlight the role of AD pathology in DLB neurodegeneration, advocating for multi-target therapeutic approaches addressing both AD and DLB-specific pathologies.

  PublisherOA PDFDOI

• Predictive Value of Plasma Biomarkers in Tau‐ <scp>PET</scp> Transitions

  Annals of Neurology · 2025-09-10

  articleOpen access

  OBJECTIVE: The objective of this study was to determine the predictive value of amyloid-positron emission tomography (PET) versus the plasma ratio of phosphorylated tau at threonine 217 (p-tau217) to non-phosphorylated tau217 (%p-tau217) for tau-PET transitions (T- to T+). The added value of combining plasma amyloid-β 42 and amyloid-β 40 (Aβ42/40) and %p-tau217 into an amyloid probability score (APS2) was also assessed. METHODS: Mayo Clinic Study of Aging (MCSA) participants had plasma markers measured at via mass spectrometry (MS), an amyloid-PET scan, and a tau-PET (meta-temporal region of interest [ROI]) negative scan (standardized uptake value ratio [SUVR] <1.29) at the index (baseline) date, along with one or more follow-up tau-PET scans. The BioFINDER-2 cohort was used for validation. Cox proportional hazards models adjusted for age, sex, and apolipoprotein (APOE) ε4 were used to assess predictors, with scaling to the interquartile range (IQR) for comparability of hazard ratios (HR). RESULTS: Among 255 tau-PET negative MCSA participants (median age: 71.9 years), 37 converted to tau-PET positive (median follow-up time: 3.81 years). Higher %p-tau217 (HR: 1.52 [95% CI: 1.28-1.80]), amyloid-PET centiloid (HR: 1.47 [95% CI: 1.20-1.79]), and APS2 (HR: 1.62 [95% CI: 1.22-2.16]) predicted tau-PET conversion. However, Aβ42/40 (HR: 0.94 [95% CI: 0.54-1.66]) was not associated with tau-PET conversion. In the BioFINDER-2 cohort (605 tau-negative, median age: 70.2), 33 converted to tau-positive (median follow-up time: 2 years), with higher %p-tau217 (HR: 1.80 [95% CI: 1.50-2.17]), amyloid-PET centiloid (HR: 2.29 [95% CI: 1.77-2.97]), and lower Aβ42/40 (HR: 2.38 [95% CI: 1.17-4.83]) predicting conversion. INTERPRETATION: In two cohorts, %p-tau217 was associated with tau-PET conversion, comparable to amyloid-PET. APS2 also predicted conversion in the MCSA cohort, whereas Aβ42/40 predicted conversion in the BioFINDER-2 cohort, which had more individuals with cognitive impairment. ANN NEUROL 2025;98:1249-1260.

  PublisherOA PDFDOI

• Posterior cortical atrophy: reorganization of the dorsal attention network and its implications on volume loss and clinical performance

  Journal of Neurology · 2025-10-22

  articleOpen access

  Posterior cortical atrophy (PCA) is associated with visual attention, episodic memory, and working memory deficits, in addition to the typical visual dysfunction. The dorsal attention network (DAN) plays a critical role in modulating these functions. However, little is known about the relationship of DAN with other core networks (visual and default mode networks (DMN)) and its relationships to volume loss and memory function in PCA. Fifty-seven PCA patients were compared to 60 cognitively unimpaired (CU) individuals. Within-network connectivity was calculated within the frontal eye field (FEF) and intraparietal sulcus (IPS) and the entire DAN. Between-network connectivity was calculated with default mode network (DMN), frontoparietal, and visual networks. Models were fit to compare network connectivity between both groups and assess relationships between connectivity, gray matter volumes, and clinical test scores in PCA. PCA showed reduced within-network connectivity in DAN, specifically within the IPS, compared to CU individuals. The DAN, particularly the FEF, showed an increase in between-network connectivity with the frontoparietal network but no relationship to the DMN and visual networks. Lower DAN connectivity was associated with a trend for smaller volumes in the entire network and significantly lower scores on the auditory verbal learning test-recognition percent correct and Wechsler Memory Scale III-digit span backward in PCA patients. Our results showed disruptions in DAN connectivity, particularly in the posterior regions, which could be contributing to episodic and working memory deficits in PCA. Heightened connectivity between the DAN and the frontoparietal network suggests a compensatory mechanism to preserve attention function.

  PublisherOA PDFDOI

• Revisiting Centiloids using AI

  Research Square · 2025-07-08

  preprintOpen access
  PublisherOA PDFDOI

• Lifetime and 10-year absolute risk of cognitive impairment in relation to amyloid PET severity: a retrospective, longitudinal cohort study

  The Lancet Neurology · 2025-11-13 · 7 citations

  articleOpen access
  PublisherOA PDFDOI

• DICOM datasets for reproducible neuroimaging research across manufacturers and software versions

  Scientific Data · 2025-07-09

  articleOpen access

  DICOM is an industry-standard for medical imaging data targeted at interoperability across systems. This enables transfer, storage and processing of imaging data regardless of the manufacturer. Pragmatically, manufacturers often store detailed acquisition parameters in private rather than public DICOM tags. In parallel, the DICOM standard itself has gradually evolved by introducing new public tags and properties to better capture emerging imaging technologies. Accurately extracting these details is essential for reproducible neuroimaging research. To address this need, we created a series of DICOM datasets illustrating how various manufacturers encode acquisition details that are critical for modern processing and analysis. These minimal test cases, covering CT and MR modalities, highlight manufacturer-specific conventions, including the use of public tags, private tags, and proprietary data structures. For each DICOM dataset, we provide corresponding NIfTI-formatted images with metadata JSON files following the BIDS standard, using consistent terminology to mitigate variations in how manufacturers encode acquisition details. Our repository provides validation datasets for any tool that is intended to extract acquisition details from medical imaging data.

  PublisherOA PDFDOI

• Cervical cord atrophy correlates with intracranial lesion burden in tumefactive multiple sclerosis

  Journal of Neurology · 2025-10-21

  articleOpen access

  Spinal cord atrophy associates with motor disability in multiple sclerosis (MS). The influence of intracranial lesion burden (ILB) on spinal cord atrophy requires further investigation. Tumefactive MS (TMS) offers a model for studying the contribution of ILB on spinal cord atrophy. Determine the relationship between upper cervical cord (UCC) area, ILB, and progressive TMS. Individuals with tumefactive demyelinating disease (TDD) undergoing UCC area analysis (C1-C3) were stratified into three groups based on ILB: single-lesion (SL-TDD), multiple-lesion (ML-TDD), and tumefactive MS (TMS). Descriptive characteristics and UCC area were compared across radiological and clinical phenotypes. Of 109 individuals, six (6%) were SL-TDD, 28 (26%) ML-TDD, and 75 (69%) TMS. All seven (6%) with progressive MS met TMS criteria. TMS had more spinal cord (63% vs. 26%; p = 0.003), and lateral tract lesions (54% vs. 14%; p = 0.001), and a higher final EDSS [median 2.5 (IQR 1.5,3.0) vs. 2.0 (0.0,2.0); p = 0.01] than ML-TDD. After excluding individuals with UCC lesions, there was an inverse trend between median C2 area and ILB across groups: SL-TDD, 56.3 mm2 (47.9,69.0); ML-TDD, 53.4 mm2 (37.2,63.2), and TMS, 50.8 mm2 (32.0,64.3); p = 0.08. During the course of TMS, early disability may be driven by a single tumefactive lesion while late disability is related to the accrual of intracranial lesions, spinal cord disease, and UCC atrophy. Across the TMS spectrum, there appears to be an inverse relationship between UCC area and ILB, partially independent of UCC lesions which trended towards statistical significance, warranting further investigation.

  PublisherOA PDFDOI

Recent grants

• Protecting the Confidentiality of Participants in Studies of Alzheimer's Disease and Related Dementias by Replacing Face Imagery in MRI

  NIH · $3.2M · 2021–2026

Frequent coauthors

• Clifford R. Jack

  WinnMed

  544 shared

• Matthew L. Senjem

  Mayo Clinic

  480 shared

• Val J. Lowe

  WinnMed

  417 shared

• David S. Knopman

  Mayo Clinic

  407 shared

• Ronald C. Petersen

  Mayo Clinic in Florida

  406 shared

• Kejal Kantarci

  Mayo Clinic

  329 shared

• Jonathan Graff‐Radford

  WinnMed

  310 shared

• Bradley F. Boeve

  Mayo Clinic

  273 shared

Awards & honors

• Red Rock Finance Conference, Best Paper Award (2023)
• Excellence in Teaching, Fully-Employed MBA (2012-2024)
• Utah Winter Finance Conference, Best Paper Award (2023)
• Financial Research Association (FRA), Best Paper Award (2022…
• UCI Paul Merage School of Business Faculty Service Award (20…