Harvard Catalyst Profiles

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Daniel M. Goldenholz, M.D.,Ph.D.

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Biography
education and training
University of Wisconsin-Madison, Madison, WIB.S.05/2000Electrical and computer engineering
Boston University, Boston, MAMD, PhD05/2008Medicine (MD) and Biomedical Engineering (PhD)
Mass General Hospital, Charleston, MAPostdoc fellowship06/2009Radiology
Alameda County Medical Center, Oakland, CAInternship06/2010Internal medicine
University of California-Davis, Sacramento, CAResidency06/2013Neurology
National Institutes of Health, Bethesda, MDClinical fellowship06/2014Epilepsy
National Institutes of Health, Bethesda, MDClinical fellowship06/2015Clinical Neurophysiology
National Institutes of Health, Bethesda, MDClinical fellowship05/2017Advanced Epilepsy
Duke University, Durham, NCMHsc05/2017Health Science

Overview
overview
Key research interests in data science applied to epilepsy:
1. Natural seizure patterns - this includes understanding the natural fluctuations of seizure counts, seizure clustering, and seizure forecasting.
2. Biosensor dynamics - here we are using biosensors such as ECG and pulse oximetry to understand hidden markers of risk for sudden cardiac death and/or sudden unexpected death in epilepsy (SUDEP).
3. Clinical trial improvements - developing techniques for more rapid, more efficient yet less expensive clinical trials in epilepsy that will accelerate bringing novel treatments to this disease.
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Research
research activities and funding
The research activities and funding listed below are automatically derived from NIH ExPORTER and other sources, which might result in incorrect or missing items. Faculty can login to make corrections and additions.
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  1. K23NS124656 (GOLDENHOLZ, DANIEL M) Sep 1, 2022 - May 31, 2027
    NIH
    Non-Invasive Seizure Forecasting System Using E-Diaries, Internal and External Factors
    Role: Principal Investigator
  2. K23NS124656 (Goldenholz) Jun 1, 2022 - May 31, 2027
    NIH/NINDS
    Non-invasive seizure forecasting system using e-diaries, internal and external factors
    Role Description: This study will evaluate the ability for non-invasive intrinsic factors to forecast seizure risk in patients with drug-resistant temporal lobe epilepsy, using electronic-diaries from three months prior and deep learning to identify patterns in the diaries.
    Role: PI
  3. Schilder Family Fund (Daniel Goldenholz) Jul 1, 2018 - Jul 1, 2019
    BIDMC Department of Neurology
    Forecasting Real-Time Seizure Risk using Deep Learning on Seizure Database
    Role: PI
  4. KL2TR002542 (BREDELLA, MIRIAM ANTOINETTE ;RUTKOVE, SEWARD B.) May 1, 2018 - Apr 30, 2023
    NIH
    Institutional Career Development Core
    Role: Co-Investigator

Bibliographic
selected publications
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Goldenholz's Networks
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© 2024 President and Fellows of Harvard College
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.