Publications

Hope Center Member Publications

Scopus list of publications for October 29, 2023

The herbicide glyphosate inhibits hippocampal long-term potentiation and learning through activation of pro-inflammatory signaling” (2023) Scientific Reports

The herbicide glyphosate inhibits hippocampal long-term potentiation and learning through activation of pro-inflammatory signaling
(2023) Scientific Reports, 13 (1), art. no. 18005, . 

Izumi, Y.a b , O’Dell, K.A.a b , Zorumski, C.F.a b

a Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
b The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Glyphosate, a herbicide marketed as Roundup, is widely used but there are concerns this exposure could impair cognitive function. In the CA1 region of rat hippocampal slices, we investigated whether glyphosate alters synaptic transmission and long-term potentiation (LTP), a cellular model of learning and memory. Our hypothesis is that glyphosate alters neuronal function and impairs LTP induction via activation of pro-inflammatory processes. Roundup depressed excitatory synaptic potentials(EPSPs) in a dose-dependent manner with complete suppression at 2000 mg/L. At concentrations ≤ 20 mg/L Roundup did not affect basal transmission, but 4 mg/L Roundup administered for 30 min inhibited LTP induction. Acute administration of 10–100 μM glyphosate also inhibited LTP induction. Minocycline, an inhibitor of microglial activation, and TAK-242, an inhibitor of toll-like receptor 4 (TLR4), both overcame the inhibitory effects of 100 µM glyphosate. Similarly, lipopolysaccharide from Rhodobacter sphaeroides (LPS-RS), a different TLR4 antagonist, overcame the inhibitory effects. In addition, ISRIB (integrated stress response inhibitor) and quercetin, an inhibitor of endoplasmic reticulum stress, overcame the inhibitory effects. We also observed that in vivo glyphosate injection (16.9 mg/kg i.p.) impaired one-trial inhibitory avoidance learning. This learning deficit was overcome by TAK-242. These observations indicate that glyphosate can impair cognitive function through pro-inflammatory signaling in microglia. © 2023, Springer Nature Limited.

Funding details
National Institute of Mental HealthNIMHMH122379
Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine in St. Louis

Document Type: Article
Publication Stage: Final
Source: Scopus

Innovative Technologies in CNS Trials: Promises and Pitfalls for Recruitment, Retention, and Representativeness” (2023) Innovations in Clinical Neuroscience

Innovative Technologies in CNS Trials: Promises and Pitfalls for Recruitment, Retention, and Representativeness
(2023) Innovations in Clinical Neuroscience, 20 (7-9), pp. 40-46. 

Lutz, J.a b c , Pratap, A.d e f g , Lenze, E.J.h , Bestha, D.i , Lipschitz, J.M.j k , Karantzoulis, S.l , Vaidyanathan, U.e m , Robin, J.n , Horan, W.o p q , Brannan, S.p , Mittoux, A.r , Davis, M.C.s , Lakhan, S.E.a t , Keefe, R.u

a Click Therapeutics, Inc, New York, NY, United States
b Biogen Digital Health, Cambridge, MA, United States
c Boston University School of Medicine, Boston, MA, United States
d Center for Addiction & Mental Health, Toronto, Canada
e Boehringer Ingelheim, Ridgefield, CT, United States
f King’s College London, London, United Kingdom
g Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
h Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
i Atrium Health, Charlotte, NC, United States
j Brigham and Women’s Hospital, Boston, MA, United States
k Harvard Medical School, Boston, MA, United States
l IQVIA, New York, NY, United States
m Sublimus, Ridgefield, CT, United States
n Winterlight Labs, Inc, Toronto, Canada
o WCG VeraSci, Durham, NC, United States
p Karuna Therapeutics, Boston, MA, United States
q University of California, Los Angeles, CA, United States
r Lundbeck, Paris, France
s Usona Institute, Madison, WI, United States
t School of Neuroscience, Virginia Tech, Blacksburg, VA, United States
u Department of Psychiatry, Duke University Medical Center, Durham, NC, United States

Abstract
Objective: Recruitment of a sufficiently large and representative patient sample and its retention during central nervous system (CNS) trials presents major challenges for study sponsors. Technological advances are reshaping clinical trial operations to meet these challenges, and the COVID-19 pandemic further accelerated this development. Method of Research: The International Society for CNS Clinical Trials and Methodology (ISCTM; www.isctm.org) Innovative Technologies for CNS Trials Working Group surveyed the state of technological innovations for improved recruitment and retention and assessed their promises and pitfalls. Results: Online advertisement and electronic patient registries can enhance recruitment, but challenges with sample representativeness, conversion rates from eligible prescreening to enrolled patients, data privacy and security, and patient identification remain hurdles for optimal use of these technologies. Electronic medical records (EMR) mining with artificial intelligence (AI)/machine learning (ML) methods is promising but awaits translation into trials. During the study treatment phase, technological innovations increasingly support participant retention, including adherence with the investigational treatment. Digital tools for adherence and retention support take many forms, including patient-centric communication channels between researchers and participants, real-time study reminders, and digital behavioral interventions to increase study compliance. However, such tools add technical complexities to trials, and their impact on the generalizability of results are largely unknown. Conclusion: Overall, the group found a scarcity of systematic data directly assessing the impact of technological innovations on study recruitment and retention in CNS trials, even for strategies with already high adoption, such as online recruitment. Given the added complexity and costs associated with most technological innovations, such data is needed to fully harness technologies for CNS trials and drive further adoption. © 2023, Matrix Medical Communications. All rights reserved.

Author Keywords
Clinical trials;  CNS;  e-consent;  recruitment;  technology;  virtual trials

Funding details
Patient-Centered Outcomes Research InstitutePCORI
Janssen Pharmaceuticals

Document Type: Article
Publication Stage: Final
Source: Scopus

T1 and FLAIR signal intensities are related to tau pathology in dominantly inherited Alzheimer disease” (2023) Human Brain Mapping

T1 and FLAIR signal intensities are related to tau pathology in dominantly inherited Alzheimer disease
(2023) Human Brain Mapping, .

Rahmani, F.a , Brier, M.R.a , Gordon, B.A.a , McKay, N.a , Flores, S.a , Keefe, S.a , Hornbeck, R.a , Ances, B.a , Joseph-Mathurin, N.a , Xiong, C.a , Wang, G.a , Raji, C.A.a , Libre-Guerra, J.J.a , Perrin, R.J.a , McDade, E.a , Daniels, A.a , Karch, C.a , Day, G.S.b , Brickman, A.M.c , Fulham, M.d , Jack, C.R., Jr.e , la La Fougère, C.f g h , Reischl, G.f g h , Schofield, P.R.i j , Oh, H.k , Levin, J.l m n , Vöglein, J.l m n , Cash, D.M.o p , Yakushev, I.l m n , Ikeuchi, T.q , Klunk, W.E.r , Morris, J.C.a , Bateman, R.J.a , Benzinger, T.L.S.a

a Washington University School of Medicine, St. Louis, MO, United States
b Mayo Clinic, Department of Neurology, Jacksonville, FL, United States
c Taub Institute for Research on Alzheimer’s Disease & the Aging Brain, and Department of Neurology College of Physicians and Surgeons, Columbia University, New York, NY, United States
d Royal Prince Alfred Hospital (RPA), Sydney, Australia
e Mayo Clinic, Rochester, MN, United States
f Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tübingen, Germany
g German Center for Neurodegenerative Diseases (DZNE) Tuebingen, Tübingen, Germany
h Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tübingen, Tübingen, Germany
i Neuroscience Research Australia, Sydney, NSW, Australia
j School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
k Brown University, Providence, RI, United States
l Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
m German Center for Neurodegenerative Diseases (DZNE), site Munich, Munich, Germany
n Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
o UK Dementia Research Institute at University College London, London, United Kingdom
p Dementia Research Centre, UCL Queen Square Institute of Neurology, London, United Kingdom
q Niigata University, Brain Research Institute, Niigata, Japan
r University of Pittsburgh, Pittsburgh, PA, United States

Abstract
Carriers of mutations responsible for dominantly inherited Alzheimer disease provide a unique opportunity to study potential imaging biomarkers. Biomarkers based on routinely acquired clinical MR images, could supplement the extant invasive or logistically challenging) biomarker studies. We used 1104 longitudinal MR, 324 amyloid beta, and 87 tau positron emission tomography imaging sessions from 525 participants enrolled in the Dominantly Inherited Alzheimer Network Observational Study to extract novel imaging metrics representing the mean (μ) and standard deviation (σ) of standardized image intensities of T1-weighted and Fluid attenuated inversion recovery (FLAIR) MR scans. There was an exponential decrease in FLAIR-μ in mutation carriers and an increase in FLAIR and T1 signal heterogeneity (T1-σ and FLAIR-σ) as participants approached the symptom onset in both supramarginal, the right postcentral and right superior temporal gyri as well as both caudate nuclei, putamina, thalami, and amygdalae. After controlling for the effect of regional atrophy, FLAIR-μ decreased and T1-σ and FLAIR-σ increased with increasing amyloid beta and tau deposition in numerous cortical regions. In symptomatic mutation carriers and independent of the effect of regional atrophy, tau pathology demonstrated a stronger relationship with image intensity metrics, compared with amyloid pathology. We propose novel MR imaging intensity-based metrics using standard clinical T1 and FLAIR images which strongly associates with the progression of pathology in dominantly inherited Alzheimer disease. We suggest that tau pathology may be a key driver of the observed changes in this cohort of patients. © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Author Keywords
amyloid PET;  dominantly inherited Alzheimer disease;  quantitative MR imaging;  T1 and FLAIR signal intensity;  tau PET

Funding details
National Institutes of HealthNIH
National Institute on AgingNIAU19AG03243808
Alzheimer’s AssociationAA
Foundation for Barnes-Jewish HospitalFBJH
Japan Agency for Medical Research and DevelopmentAMED
UK Research and InnovationUKRIMR/V03863X/1
Avid RadiopharmaceuticalsK23AG064029, U01AG057195, U01NS120901, U19AG032438
Korea Dementia Research CenterKDRC
Medical Research CouncilMRC
Alzheimer’s Society
University College LondonUCL
National Health and Medical Research CouncilNHMRC
Alzheimer’s Research UKARUKARUK‐PG2017‐1946
Korea Health Industry Development InstituteKHIDI
Ministry of Science ICT and Future PlanningMSIP
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNER01AG052550‐01A1
UCLH Biomedical Research CentreNIHR BRC
Fleni
UK Dementia Research InstituteUK DRI

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Alzheimer’s polygenic risk scores are associated with cognitive phenotypes in Down syndrome” (2023) Alzheimer’s and Dementia

Alzheimer’s polygenic risk scores are associated with cognitive phenotypes in Down syndrome
(2023) Alzheimer’s and Dementia, . 

Gorijala, P.a b , Aslam, M.M.c , Dang, L.-H.T.d e , Xicota, L.e , Fernandez, M.V.a b , Sung, Y.J.a b f , Fan, K.-H.c , Feingold, E.c , Surace, E.I.g , Chhatwal, J.P.h , Hom, C.L.i , Hartley, S.L.j , Hassenstab, J.k , Perrin, R.J.l m n , Mapstone, M.o , Zaman, S.H.p q , Ances, B.M.n , Kamboh, M.I.c , Lee, J.H.d e , Cruchaga, C.a b l

a Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
b Neurogenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, United States
c Department of Human Genetics, University of Pittsburgh, School of Public Health, Pittsburgh, PA, United States
d Department of Epidemiology, Columbia University Irving Medical Center, New York, NY, United States
e Sergievsky Center, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, and Department of Neurology, Columbia University Irving Medical Center, New York, NY, United States
f Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
g Laboratory of Neurodegenerative Diseases – Institute of Neurosciences (INEU-Fleni- CONICET), Buenos Aires, Argentina
h Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
i Dept. of Psychiatry and Human Behavior, University of California, Irvine School of MedicineCA, United States
j Waisman Center and School of Human Ecology, University of Wisconsin- Madison, Madison, WI, United States
k Department of Neurology and Psychological & Brain Sciences, Washington University, St. Louis, MO, United States
l Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, United States
m Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
n Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
o Department of Neurology, University of California-Irvine, Irvine, CA, United States
p Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, Douglas House, United Kingdom
q Cambridgeshire and Peterborough NHS Foundation Trust, Elizabeth House, Fulbourn Hospital, Cambridge, Fulbourn, United Kingdom

Abstract
INTRODUCTION: This study aimed to investigate the influence of the overall Alzheimer’s disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) biomarkers. METHODS: AD polygenic risk scores (PRS) were tested for association with DS-related traits. RESULTS: The AD risk PRS was associated with disease status in several cohorts of sporadic late- and early-onset and familial late-onset AD, but not in familial early-onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRSAPOE, p = 2.84 × 10−4; PRS excluding APOE, PRSnonAPOE, p = 1.60 × 10−2). PRSAPOE exhibited significant associations with Aβ42, tTau, pTau, and Aβ42/40 ratio in DS. DISCUSSION: These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits. Highlights: Examination of the polygenic risk of AD in DS presented here is the first of its kind. AD PRS influences memory aspects in DS individuals, independently of APOE genotype. These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population. APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults. © 2023 The Authors. Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.

Author Keywords
amyloid precursor protein;  apoliprotein E APOE;  area under the curve;  cerebrospinal fluid biomarkers;  cognitive batteries;  Dominantly Inherited Alzheimer Network;  Down syndrome;  early-onset Alzheimer’s disease;  early-onset autosomal dominant;  genetic architecture;  genetic risk factor;  late-onset Alzheimer’s disease;  polygenic risk score;  PSEN1;  PSEN2;  sporadic late-onset Alzheimer’s disease

Funding details
W81XWH‐12‐2‐0012
P50 HD105353, U54 HD087011, U54 HD090256
U24 AG21886
National Institutes of HealthNIHP01AG003991, P01AG026276, P30AG066444, R01AG044546, R01AG064877, RF1AG053303, RF1AG058501, RF1AG074007, U01AG058922, U19AG032438
U.S. Department of DefenseDODLI‐W81XWH2010849
National Institute on AgingNIA
National Institute of Biomedical Imaging and BioengineeringNIBIB
National Institute of Child Health and Human DevelopmentNICHDP30 AG062421, P30 AG062715, P30 AG066519, P50 AG005133, P50 AG005681, P50 AG008702, P50 AG16537, U01 AG051406, U01 AG051412, U19 AG068054
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Alzheimer’s AssociationAAZEN‐22‐848604
Alzheimer’s Drug Discovery FoundationADDF
Biogen
National Center for Advancing Translational SciencesNCATSUL1 TR001414, UL1 TR001857, UL1 TR001873, UL1 TR002345, UL1 TR002373
AbbVie
Alzheimer’s Disease Neuroimaging InitiativeADNIU01 AG024904
BioClinica
Fondation Brain Canada
Japan Agency for Medical Research and DevelopmentAMED
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHD
Hope Center for Neurological Disorders
Chan Zuckerberg InitiativeCZI
Canadian Institutes of Health ResearchIRSC
Fonds de Recherche du Québec – SantéFRQS
Korea Health Industry Development InstituteKHIDI
Instituto de Salud Carlos IIIISCIII
Deutsches Zentrum für Neurodegenerative ErkrankungenDZNE
Fleni

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Improving Early Recognition of Treatment-Responsive Causes of Rapidly Progressive Dementia: The STAM3P Score” (2023) Annals of Neurology

Improving Early Recognition of Treatment-Responsive Causes of Rapidly Progressive Dementia: The STAM3P Score
(2023) Annals of Neurology, . 

Satyadev, N.a b , Tipton, P.W.a , Martens, Y.c , Dunham, S.R.d , Geschwind, M.D.e , Morris, J.C.d , Brier, M.R.d , Graff-Radford, N.R.a , Day, G.S.a

a Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, United States
b Georgia Institute of Technology, Atlanta, GA, United States
c Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, United States
d Department of Neurology, Washington University School of Medicine, Saint Louis, MO, United States
e University of California San Francisco, Department of Neurology, Memory and Aging Center, San Francisco, CA, United States

Abstract
Objective: To improve the timely recognition of patients with treatment-responsive causes of rapidly progressive dementia (RPD). Methods: A total of 226 adult patients with suspected RPD were enrolled in a prospective observational study and followed for up to 2 years. Diseases associated with RPD were characterized as potentially treatment-responsive or non-responsive, referencing clinical literature. Disease progression was measured using Clinical Dementia Rating® Sum-of-Box scores. Clinical and paraclinical features associated with treatment responsiveness were assessed using multivariable logistic regression. Findings informed the development of a clinical criterion optimized to recognize patients with potentially treatment-responsive causes of RPD early in the diagnostic evaluation. Results: A total of 155 patients met defined RPD criteria, of whom 86 patients (55.5%) had potentially treatment-responsive causes. The median (range) age-at-symptom onset in patients with RPD was 68.9 years (range 22.0–90.7 years), with a similar number of men and women. Seizures, tumor (disease-associated), magnetic resonance imaging suggestive of autoimmune encephalitis, mania, movement abnormalities, and pleocytosis (≥10 cells/mm3) in cerebrospinal fluid at presentation were independently associated with treatment-responsive causes of RPD after controlling for age and sex. Those features at presentation, as well as age-at-symptom onset <50 years (ie, STAM3P), captured 82 of 86 (95.3%) cases of treatment-responsive RPD. The presence of ≥3 STAM3P features had a positive predictive value of 100%. Interpretation: Selected features at presentation reliably identified patients with potentially treatment-responsive causes of RPD. Adaptation of the STAM3P screening score in clinical practice may minimize diagnostic delays and missed opportunities for treatment in patients with suspected RPD. ANN NEUROL 2023. © 2023 American Neurological Association.

Funding details
National Institutes of HealthNIHK23AG064029
National Institute on AgingNIA

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients” (2023) Frontiers in Oncology

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
(2023) Frontiers in Oncology, 13, art. no. 1185738, .

Bond, K.M.a b , Curtin, L.a , Ranjbar, S.a , Afshari, A.E.a , Hu, L.S.a c , Rubin, J.B.d , Swanson, K.R.a

a Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
b Hospital of University of Pennsylvania, Department of Neurosurgery, Philadelphia, PA, United States
c Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
d Departments of Neuroscience and Pediatrics, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor’s underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions. Copyright © 2023 Bond, Curtin, Ranjbar, Afshari, Hu, Rubin and Swanson.

Author Keywords
CNS tumor;  glioblastoma;  glioma;  imaging;  machine learning;  MRI;  personalized medicine;  radiomics

Funding details
National Institutes of HealthNIHU01CA22037

Document Type: Article
Publication Stage: Final
Source: Scopus