Publications by Year: 2026

PURPOSE: In the course of diffusion, water molecules encounter varying values for the relaxation-time properties of the underlying tissue. This factor, which has rarely been accounted for in diffusion MRI (dMRI), is modeled in this work, allowing for the estimation of the gradient of relaxation-time properties from the dMRI signal.

METHODS: With the aim of mining the dMRI data for information about spatial variations in tissue relaxation-time properties, a new mathematical relationship between the diffusion signal and the spatial gradient of the image is derived, enabling the estimation of the latter from the former. The hypothesis was validated on human brain dMRI images from three datasets: the public Human Connectome Project Young Adults database, 10 healthy volunteers and 1 ex vivo sample scanned in-house with stimulated-echo diffusion encoding and a long diffusion time of 1 s (which we have made publicly available), and three subjects from the public Multi-TE database. The effects of the confounding factor of "fiber continuity" were furthermore measured.

RESULTS: The spatial image gradient estimated from the diffusion signal was compared to the gold-standard spatial gradient approximated using the finite difference method. The former gradient was significantly related to the latter in all datasets (i.e., with a difference significantly smaller than chance), with an effect distinct from fiber continuity.

CONCLUSION: The results support the hypothesized relationship between within-voxel dMRI signal and image gradient, with an effect that was not explainable by the confounding factor of fiber continuity.

BACKGROUND: Over the past decade, emerging evidence has shed light on the role of the gut microbiota in the interface between diet and brain health. Olive oil, particularly virgin olive oil, a key component and major fat source in the Mediterranean diet, has exhibited widespread healthful benefits, including improvements in gut microbiota and cognitive health. Despite insights from preclinical studies into the relationship between virgin olive oil consumption, gut microbiota, and cognitive function, human research in this area remains limited. Therefore, our study aims to investigate the interplay between total olive oil consumption and its subtypes, gut microbiota, and changes in cognitive function in older adults who were cognitively healthy at baseline but at high risk of cognitive decline.

METHODS: In this prospective cohort study, we assessed a total of 656 participants aged 55 to 75y (mean age 65.0 ± 4.9y, 47.9% women) with overweight/obesity and metabolic syndrome who provided stool samples and completed a validated semi-quantitative food frequency questionnaire at baseline and a comprehensive battery of neuropsychological tests at baseline and at a 2-y follow-up.

RESULTS: Results from the multivariable linear regression models showed that higher consumption of virgin olive oil was associated with improved cognitive function over a 2-y follow-up, and a more diverse gut microbiota overall structure at baseline. Conversely, increased consumption of common olive oil is linked to lower alpha diversity of the microbial communities, and accelerated cognitive decline. Mediation analysis suggests that gut microbiota and particularly the Adlercreutzia, may serve as a mediator taxon in the association between virgin olive oil consumption and positive changes in general cognitive function.

CONCLUSIONS: Higher consumption of virgin olive oil was associated with cognitive preservation, possibly mediated by favorable alterations in gut microbiota composition. Our study provides novel insights into the complex interplay between different types of olive oil consumption, gut microbiota, and changes in cognitive function. These findings underscore the potential of microbiota-targeted dietary strategies to promote cognitive health in aging populations, though further high-quality and clinical cohort studies are required. Video Abstract.

The Association of Chiefs and Leaders of General Internal Medicine (ACLGIM), a group of division chiefs and other division leaders within the Society for General Internal Medicine (SGIM), convened the Hess Initiative Work Group on Learner Experience in Primary Care in 2023 to (1) identify the gaps between the current state of resident continuity clinic and current standards for a high-functioning primary care setting, (2) review the evidence to identify solutions, and (3) develop a set of evidence-based recommendations. These recommendations align with the current Accreditation Council for Graduate Medical Education (ACGME) IM milestones, with the goal to improve the experience of resident continuity clinic and meet the needs of the three key stakeholders of GME: residents, patients, and the healthcare system. We recruited experts in internal medicine ambulatory training from across the country, representing a variety of US regions, membership in professional societies and organizations related to internal medicine, community and academic settings, and residency program sizes during national and regional meetings at SGIM between 2023 and 2025. Using standard frameworks of high-functioning primary care practice, we performed an extensive review of the literature to identify discrepancies between these standards and resident continuity clinics, then, using the expertise of the group, produced a set of recommendations, which were reviewed and endorsed by the SGIM Council in September 2025.

Repeated concussion traumatic brain injury (TBI) results in long-term brain damage and cognitive dysfunctions, leading to neurodegenerative diseases. The brain clearance system plays a crucial role in TBI recovery and neurodegenerative disease amelioration by draining waste macromolecules from the brain. Pharmacological therapeutics have failed to demonstrate benefits in human TBI. Photobiomodulation (PBM) has gained interest in neuroscience and has been shown to improve brain drainage. Here, we evaluated the efficiency of PBM in the treatment of multiple concussions in mice and the augmentation of the brain clearance system. Three consecutive closed-head concussive TBIs were induced with a 1-h interval to the left hemisphere in C57BL/6 male mice. A near-infrared irradiation (1270 nm, 10 mW/cm2) was used for PBM 4 h after the last TBI and the following 3 days twice a day. Laser speckle contrast imaging was used to assess cerebral blood flow (rCBF). In vivo 2-photon laser scanning microscopy assessed PBM effects on cerebral microcirculation, tissue oxygen supply (NADH), and meningeal lymphatics clearance. Brain compliance was evaluated by intracranial pressure waveform analysis. Neurological severity scores were obtained at 0-3 days after TBI. Two-way ANOVA for multiple comparisons was used to test intergroup differences, with the statistical significance set at p < 0.05. Multiple concussions progressively impaired rCBF, cortical microcirculation, tissue oxygen supply, and brain drainage function (p < 0.05). Compared to the sham-treated group, PBM improved rCBF, microcirculation, tissue oxygenation, and the brain drainage system (p < 0.05). Neurological function was more preserved in the PBMT group than in sham-treated mice (p < 0.05). Our study demonstrated that PBMT can be used as an adjunct therapy even in the acute period of TBI.

The planning of sample size for research studies often focuses on obtaining a significant result given a specified level of power, significance, and an anticipated effect size. This planning requires prior knowledge of the study design and a statistical analysis to calculate the proposed sample size. However, there may not be one specific testable analysis from which to derive power (Silberzahn et al., Advances in Methods and Practices in Psychological Science, 1(3), 337356, 2018) or a hypothesis to test for the project (e.g., creation of a stimuli database). Modern power and sample size planning suggestions include accuracy in parameter estimation (AIPE, Kelley, Behavior Research Methods, 39(4), 755-766, 2007; Maxell et al., Annual Review of Psychology, 59, 537-563, 2008) and simulation of proposed analyses (Chalmers & Adkins, The Quantitative Methods for Psychology, 16(4), 248-280, 2020). These toolkits offer flexibility in traditional power analyses that focus on the if-this, then-that approach. However, both AIPE and simulation require either a specific parameter (e.g., mean, effect size, etc.) or a statistical test for planning sample size. In this tutorial, we explore how AIPE and simulation approaches can be combined to accommodate studies that may not have a specific hypothesis test or wish to account for the potential of a multiverse of analyses. Specifically, we focus on studies that use multiple items and suggest that sample sizes can be planned to measure those items adequately and precisely, regardless of the statistical test. This tutorial also provides multiple code vignettes and package functionality that researchers can adapt and apply to their own measures.

A Position Paper published in The Lancet Psychiatry in 2020 suggested an agenda for research about the effects of the COVID-19 pandemic on mental health, following which an interdisciplinary Lancet Psychiatry standing commission was established in 2022 to examine the emerging evidence and refine recommendations for more research. In this first Series paper from the standing commission, we focus on changes in the delivery of clinical mental health care during the COVID-19 pandemic. The second paper in the Series focuses on public mental health and policy perspectives, and the third will address neuropsychiatric consequences of infection by SARS-CoV-2. Evidence from high-quality longitudinal studies with pre-pandemic baseline data, controlled intervention trials, or systematic reviews took time to accrue. During the early months of the COVID-19 pandemic, symptoms of anxiety and depression became more prevalent, and many mental health services were compromised by pandemic-related factors; however, whether the COVID-19 pandemic accelerated pre-existing long-term trends of increasing incidence of mental health disorders, especially in children and adolescents, is unclear. Little research has been done in low-income and middle-income countries, or regarding post-COVID-19 condition (also known as long COVID), which emerged as a multisystem condition with mental health implications. Vulnerable populations, including socioeconomically disadvantaged and minoritised groups, faced disproportionate mental health impacts and limited access to care during the COVID-19 pandemic, reflecting systemic, pre-pandemic inequalities. Bold implementation of existing evidence-based mental health support for vulnerable communities, ambitious trials of novel interventions, and systematic pooling of rapidly accumulating evidence about best healh care should be priorities in future pandemics.

Real-world evidence (RWE) is increasingly used to complement findings from randomized controlled trials (RCTs), contextualizing the effectiveness and safety of medical interventions as delivered in routine clinical practice. Advances in the curation and accessibility of electronic health record (EHR) data present the opportunity to utilize real-world data (RWD) to investigate therapeutic areas including oncology, where administrative healthcare claims databases alone are often not fit-for-purpose. The RCT DUPLICATE initiative has previously evaluated when RWE can most appropriately draw causal conclusions by emulating trials for nononcology indications. Here, we present the design and trial selection for the emulation of comparative oncology trials with real-world evidence (ENCORE) project, which extends this work to oncology. ENCORE is designed to emulate 12 RCTs in four oncology-specialized EHR databases across four different cancer indications, specifically non-small-cell lung cancer, breast cancer, colorectal cancer, and multiple myeloma. It will place special emphasis on systematic evaluation of fitness of data in relation to the study design and statistical analysis for a particular research question and preregistration of study protocols prior to initiation and analysis. Prespecified criteria will assess agreement of treatment effect estimates between RCTs and their respective emulations. Through extensive sensitivity analyses benchmarked against RCT results, the ENCORE project aims to inform understanding of how measurement, design, and analytic decisions influence the interpretation of results from emulated oncology trials using RWD.

Microbiological tests for tuberculosis (TB) disease in children have suboptimal accuracy and respiratory samples are often challenging to obtain. Using liquid chromatography/mass spectrometry, we performed plasma high-resolution metabolomics (HRM) to identify blood-based biomarkers associated with TB disease in children. We analyzed plasma samples from 438 children 0-14 years being evaluated for TB disease in India, Peru, Uganda, The Gambia, and South Africa. All children underwent a standard clinical evaluation and were followed up after 3 months. Children were classified as Confirmed (n = 104), Unconfirmed (n = 108), or Unlikely TB (n = 226) as per NIH consensus definitions. Controlling for age and study site, we found creatine, alanine, retinol, citrulline, fumarate, and tryptophan to be significantly decreased in children with Confirmed TB disease versus those with Unlikely TB, while cortisol, nicotinamide, and butyrylcarnitine were increased (FDR-corrected p-value < 0.2). Using logistic regression, we found this nine-metabolite signature had an area under the receiver operator characteristic curve (AUC) of 0.72 (95% CI (0.67 - 0.82) in the test set of participants with Confirmed and Unlikely TB and an AUC of 0.49 (95% CI 0.42 - 0.55) in the Unconfirmed TB group. These results show a nine-metabolite plasma signature has moderate accuracy for identification of Confirmed TB disease in children.