About

Jonathan Drake is an Assistant Professor of Neurology and a Clinician Educator at Brown University. He holds a medical degree from New York Medical College obtained in 2013, a Master of Science from the University of Massachusetts, Boston, earned in 2009, a Master of Arts from Boston University School of Medicine in 2007, and a Bachelor of Science from the University of Massachusetts, Amherst, completed in 1993. His professional role involves both clinical practice and educational responsibilities within the Department of Neurology at Brown University.

Research topics

• Medicine
• Internal medicine
• Psychology
• Oncology
• Neuroscience

Selected publications

• “If I’m not getting oxygen, neither is my baby”: A qualitative study of Australian women’s experiences of asthma management in pregnancy

  Women and Birth · 2025-04-15 · 1 citations

  articleOpen access

  BACKGROUND: Optimal management of asthma during pregnancy is an important element in improving maternal and neonatal outcomes. Asthma effects 12.7 % of pregnant women in Australia. Despite consistent management recommendations available via clinical practice guidelines for asthma in pregnancy, pregnant women with asthma are not receiving guideline recommended care. This study builds on previous research and aims to explore the asthma management experiences of pregnant women with asthma. Specifically, to gain insight into pregnant women's understanding of their asthma; previous and current exposure to asthma education; and attitudes towards their asthma management and medication use. METHODS: This qualitative descriptive study involved individual semi-structured interviews with pregnant women with asthma. Data were transcribed and analysed using content analysis. The participants were recruited from those enrolled in the Breathing For Life Trial (BLT), an RCT of inflammation-guided asthma management in pregnancy versus usual care. RESULTS: A total of 24 women were interviewed between June 2018 and May 2020. Three main themes: "Did not think asthma was an issue", "If I'm not getting oxygen in then neither is my baby" and "Beyond pregnancy care" were identified along with 9 sub-themes which showed asthma knowledge, attitude and medication adherence variation depending on experiences with asthma management. CONCLUSIONS: This study highlighted the varied experiences of this cohort of pregnant women with asthma and identified the need for ongoing consistent asthma management to improve the knowledge, attitude, and medication adherence of women with asthma before during and after pregnancy, and in turn improve maternal and neonatal outcomes.

  PublisherDOI

• The Alzheimer's Disease Continuum - A New Diagnostic Approach.

  PubMed · 2025-05-01

  review1st authorCorresponding

  The management of Alzheimer's disease (AD) is in the process of transitioning into a new era, enabled by 50 years of scientific progress elucidating biological and clinical aspects of the AD continuum. Newly FDA-approved disease modifying therapies have driven greater access to amyloid positron emission tomography imaging, and fluid biomarker technology has produced the first blood-based biomarkers for AD that are currently entering the marketplace. Community practitioners are increasingly finding themselves on the front lines of advanced AD biomarker decision-making that was in the very recent past the domain of subspecialty memory center providers. The goal of this brief review is to orient community practitioners to fundamental principles necessary for informed AD diagnostic decision-making as biomarker technologies evolve and point out some emerging diagnostic challenges that have arisen as a consequence of more readily available advanced diagnostic options.

  Publisher

• Associations between Digital Cognitive Assessment and Amyloid Burden in Cognitively Unimpaired Older Adults

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  Abstract Background Digital cognitive screening is poised to improve early detection of Alzheimer's disease and related dementias (ADRD), but existing tools are relatively insensitive to subtle cognitive changes in the preclinical stage of disease. Our digital assessment, the Rhode Island Mobile Cognitive Assessment Tool (RIMCAT), distinguishes cognitively impaired (MCI/early dementia) and unimpaired older adults with high sensitivity and specificity, but it has not yet been examined in the context of AD biomarker status. The goal of this study was to 1) enhance RIMCAT's sensitivity to early/preclinical AD risk by examining four cognitive paradigms sensitive to brain areas affected earliest in the course of disease; and 2) to examine associations between digital assessments and plasma amyloid Ab42/40 levels in cognitively unimpaired older adults. Method Potential participants were cognitively unimpaired older adults currently enrolled in an ongoing longitudinal study of cognitive aging. We invited a random sample ( N = 41) to participate, and 24 individuals ( M age = 73 years, M education = 16 years, 54% male) were enrolled. Plasma samples were analyzed on the PrecivityAD platform to yield an amyloid probability score (APS; based on APOE status and Ab 42‐40 ). Eleven (18% e4 carrier) had an APS < 35, suggesting low amyloid burden, and 13 (92% e4 carrier) had an APS >58, suggesting elevated amyloid burden. Participants completed RIMCAT and four new computerized subtests assessing perceptual discrimination, familiarity memory, feature binding, and response inhibition. Result Participants in the elevated amyloid group performed worse on the RIMCAT and perceptual discrimination, familiarity memory, and feature binding tasks (non‐parametric Mann‐Whitney U tests p 's < .05; medium to large effect sizes, r = .28‐.51) compared to the non‐elevated amyloid group. When examined as a continuous variable across all participants, higher Ab42/40 ratio (less amyloid pathology) was associated with better performance on RIMCAT ( r = .27) and all four new subtests ( r = .23‐.40). Conclusion We demonstrated the preliminary sensitivity of novel digital cognitive screening measures to amyloid burden in cognitively unimpaired older adults. Future research will validate these findings in a larger sample and examine associations with tau and other AD biomarkers.

  PublisherOA PDFDOI

• Longitudinal cognitive outcomes are strongly correlated with the Alzheimer’s Disease microbiome

  Alzheimer s & Dementia · 2024-12-01

  articleOpen access

  BACKGROUND: It has been shown that dysbiosis, or dysfunction of the gastrointestinal (gut) microbiome is associated with Alzheimer's disease (AD). Here, we aimed to expand on beyond our previously reported findings of the gut microbiome associating with AD and explore if the gut microbiome is predictive of cognitive performance in individuals with AD. We sought to identity what cognitive domains are associated with the microbiome in our cohort of AD patients and healthy controls without dementia. METHOD: Older individuals residing in the general community of central Massachusetts were enrolled in our study. At each visit, fecal samples and clinical variables were collected in addition to cognitive testing using the ADAS-Cog-13 tool, such as delayed memory, word recall, recognition etc. Metagenomic profiling was performed on longitudinal fecal samples. Z-scores for different cognitive domains, including memory, executive function and language were generated for the study population. Mixed-effect random forest regression (MERFR) models were created to identify metagenomic features informative of cognitive performance across these different cognitive tests and domains. RESULT: Replicating our previous work, among AD diagnosed individuals, MERFR models predicted performance on ADAS-Cog 13 from microbial abundance and pathways with a strong accuracy. The ADAS-Cog 13 was not well predicted by the microbiome in the healthy controls. Additionally, in our new analysis across different cognitive domains, Z-Scores were well predicted by MERFR models using microbial abundance and encoded pathways. CONCLUSION: Not only is the gut microbiome composition highly predictive of AD diagnosis, but there is also a strong correlation of the gut microbiome and cognitive functioning. This is true across the multiple domains of cognition including memory, executive function and language, however different bacterial species were significant in associating with each domain. This work highlights the complexity of the microbiome-gut-brain axis and how the microbiome community makeup might play a role in cognitive decline.

  PublisherOA PDFDOI

• Prognostic value of plasma biomarkers in a clinical trial of mild‐to‐moderate Alzheimer’s Disease

  Alzheimer s & Dementia · 2023-12-01

  articleOpen access

  Abstract Background Informative, readily accessible plasma biomarkers of pathology, neuroinflammation, and neurodegeneration in Alzheimer’s disease (AD) could enhance targeted approaches to AD trial design and treatment. In post‐hoc analyses, we examined whether plasma biomarkers (Aβ1‐40, Aβ1‐42, total tau, p‐tau‐181, NfL, and GFAP) predicted 48‐week change in cognitive (ADAS‐Cog11), global function (CDR‐SB), and volumetric MRI (6 cortical regions and 2 whole brain measures) outcomes in T2 Protect AD, a phase‐2 placebo‐controlled RCT of troriluzole in mild‐to‐moderate AD (NCT03605667). Method At least one baseline biomarker measurement was available for 319 of 350 trial participants, forming the analytic sample. No significant treatment effects were found in T2 Protect AD (Feldman 2021, JPAD 8(4):s46); thus, treatment arms were pooled for this analysis. Baseline levels of, and 48‐week changes in, plasma biomarkers were assessed for association with 48‐week change in outcomes using linear regression adjusted for relevant covariates. Combinations of baseline plasma biomarkers that best predicted 48‐week decline on the ADAS‐Cog11 and CDR‐SB were identified using LASSO regression. Statistical significance level was 5%; p‐values were Bonferroni corrected for multiple comparisons. Result Participant characteristics are presented in Table 1. Higher baseline plasma NfL predicted greater 48‐week decline on ADAS‐Cog11 (p = 0.026) and CDR‐SB (p = 0.048; Fig. 1A). LASSO revealed that the combination of baseline plasma NfL, total tau, and Aβ42/40 ratio best predicted 48‐week decline on ADAS‐Cog11, whereas baseline NfL alone best predicted 48‐week decline on CDR‐SB. Regarding MRI outcomes, baseline NfL predicted increase in ventricular volume (p = 0.018; Fig 1A). Baseline NfL, GFAP, and p‐tau‐181 each predicted 48‐week decline in mid‐temporal cortical volume (all p<0.02; Fig.1B); LASSO results were similar. The only significant association between 48‐week biomarker change and clinical outcomes was between increased plasma NfL and worsening CDR‐SB. Conclusion Elevated baseline plasma NfL predicted greater 48‐week decline on cognitive, global function, and MRI measures in a clinical trial of mild‐to‐moderate AD. Furthermore, greater increase in plasma NfL over time was associated with greater clinical decline. Plasma NfL is an easily accessible biomarker that may enhance AD clinical trial design and treatment strategies. Acknowledgement: Trial funding by Biohaven Pharmaceuticals; data and trial coordination by the Alzheimer’s Disease Cooperative Study (ADCS).

  PublisherOA PDFDOI

• The effects of oral contraceptives on resting autonomic function and the autonomic response to physiological stressors: a systematic review

  Clinical Autonomic Research · 2023-11-16 · 3 citations

  review
  PublisherDOI

• Rest-activity patterns associated with delirium in patients with intracerebral hemorrhage

  Journal of the Neurological Sciences · 2023-10-04 · 1 citations

  article
  PublisherDOI

• Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage

  Frontiers in Neurology · 2023-06-09 · 11 citations

  articleOpen access

  Objective Delirium is associated with worse outcomes in patients with stroke and neurocritical illness, but delirium detection in these patients can be challenging with existing screening tools. To address this gap, we aimed to develop and evaluate machine learning models that detect episodes of post-stroke delirium based on data from wearable activity monitors in conjunction with stroke-related clinical features. Design Prospective observational cohort study. Setting Neurocritical Care and Stroke Units at an academic medical center. Patients We recruited 39 patients with moderate-to-severe acute intracerebral hemorrhage (ICH) and hemiparesis over a 1-year period [mean (SD) age 71.3 (12.20), 54% male, median (IQR) initial NIH Stroke Scale 14.5 (6), median (IQR) ICH score 2 (1)]. Measurements and main results Each patient received daily assessments for delirium by an attending neurologist, while activity data were recorded throughout each patient's hospitalization using wrist-worn actigraph devices (on both paretic and non-paretic arms). We compared the predictive accuracy of Random Forest, SVM and XGBoost machine learning methods in classifying daily delirium status using clinical information alone and combined with actigraph data. Among our study cohort, 85% of patients ( n = 33) had at least one delirium episode, while 71% of monitoring days ( n = 209) were rated as days with delirium. Clinical information alone had a low accuracy in detecting delirium on a day-to-day basis [accuracy mean (SD) 62% (18%), F1 score mean (SD) 50% (17%)]. Prediction performance improved significantly ( p < 0.001) with the addition of actigraph data [accuracy mean (SD) 74% (10%), F1 score 65% (10%)]. Among actigraphy features, night-time actigraph data were especially relevant for classification accuracy. Conclusions We found that actigraphy in conjunction with machine learning models improves clinical detection of delirium in patients with stroke, thus paving the way to make actigraph-assisted predictions clinically actionable.

  PublisherOA PDFDOI

• PATIENT AGE AND SEX DO NOT APPEAR TO INFLUENCE DECISION MAKING AROUND BIOMARKER TESTING FOR COGNITIVE IMPAIRMENT

  Innovation in Aging · 2023-12-01

  articleOpen access

  Abstract The QUIP I Study (ClinicalTrials.gov Identifier: NCT05477056) was a prospective, single-arm study which included 347 older persons (average age 74, 56% women) presenting with signs and symptoms of cognitive impairment. In a subgroup analysis, we measured the effect of a person’s age and sex on clinical decision making around the PrecivityAD® blood biomarker (BBM) test result. The test result was reported as the Amyloid Probability Score (APS), which measures the likelihood of a positive result on an amyloid PET scan. Clinical decision making was recorded by clinician survey pre- and post-BBM testing. Clinician-reported probability of Alzheimer’s disease (“AD”) changed pre-test to post-test from 58% to 23% (Low APS group) and from 71% to 89% (High APS group) (p < 0.0001 for all APS groups). The relationships between APS and change in diagnostic certainty were not significantly different as analyzed by age (p=0.344 for Low APS, p=0.292 for High APS) or sex (p=0.167 for Low APS, p=0.213 for High APS). Overall use of AD drug therapy decreased from 48% to 26% (Low APS group) and increased from 56% to 88% (High APS group) (p < 0.0001 for all APS groups). The relationships between APS and change in medication prescribing were not significantly different as analyzed by age (p=0.4534 for Low APS, p=0.9939 for High APS) or sex (p=1 for Low APS, p=0.931 for High APS). We believe that the current study results help to underscore the usefulness and generalizability of this BBM among older adults in clinical care pathways.

  PublisherOA PDFDOI

• A blood biomarker test for brain amyloid impacts the clinical evaluation of cognitive impairment

  Annals of Clinical and Translational Neurology · 2023-08-07 · 33 citations

  articleOpen access

  OBJECTIVE: The objective of this study was to examine clinicians' patient selection and result interpretation of a clinically validated mass spectrometry test measuring amyloid beta and ApoE blood biomarkers combined with patient age (PrecivityAD® blood test) in symptomatic patients evaluated for Alzheimer's disease (AD) or other causes of cognitive decline. METHODS: The Quality Improvement and Clinical Utility PrecivityAD Clinician Survey (QUIP I, ClinicalTrials.gov Identifier: NCT05477056) was a prospective, single-arm cohort study among 366 patients evaluated by neurologists and other cognitive specialists. Participants underwent blood biomarker testing and received an amyloid probability score (APS), indicating the likelihood of a positive result on an amyloid positron emission tomography (PET) scan. The primary study outcomes were appropriateness of patient selection as well as result interpretation associated with PrecivityAD blood testing. RESULTS: A 95% (347/366) concordance rate was noted between clinicians' patient selection and the test's intended use criteria. In the final analysis including these 347 patients (median age 75 years, 56% women), prespecified test result categories incorporated 133 (38%) low APS, 162 (47%) high APS, and 52 (15%) intermediate APS patients. Clinicians' pretest and posttest AD diagnosis probability changed from 58% to 23% in low APS patients and 71% to 89% in high APS patients (p < 0.0001). Anti-AD drug therapy decreased by 46% in low APS patients (p < 0.0001) and increased by 57% in high APS patients (p < 0.0001). INTERPRETATION: These findings demonstrate the clinical utility of the PrecivityAD blood test in clinical care and may have added relevance as new AD therapies are introduced.

  PublisherOA PDFDOI

Frequent coauthors

• Brian R. Ott

  Brown University

  123 shared

• Lori A. Daiello

  Brown University

  121 shared

• Akiva Mintz

  109 shared

• Bryan M. Spann

  San Francisco VA Medical Center

  105 shared

• Marwan N. Sabbagh

  98 shared

• Andrew J. Saykin

  Indiana University

  95 shared

• Kwangsik Nho

  Indiana University School of Medicine

  83 shared

• John C. Morris

  Washington University in St. Louis

  78 shared

Education

• M.D.

  New York Medical College

  2013

• M.S.

  University of Massachusetts, Boston

  2009

• M.A.

  Boston University School of Medicine

  2007

• B.S.

  University of Massachusetts, Amherst

  1993