Hope Center member publications

List of publications for the week of October 7, 2021

Diagnostic criteria for blepharospasm: A multicenter international study” (2021) Parkinsonism and Related Disorders

Diagnostic criteria for blepharospasm: A multicenter international study
(2021) Parkinsonism and Related Disorders, 91, pp. 109-114. 

Defazio, G.a , Jinnah, H.A.b , Berardelli, A.c , Perlmutter, J.S.d , Berkmen, G.K.b , Berman, B.D.e , Jankovic, J.f , Bäumer, T.g , Comella, C.h , Cotton, A.C.b , Ercoli, T.a , Ferrazzano, G.c , Fox, S.i , Kim, H.-J.j , Moukheiber, E.S.k , Richardson, S.P.l , Weissbach, A.g m , Wrigth, L.J.d , Hallett, M.n

a Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
b Department of Neurology and Human Genetics, Emory University, Atlanta, GA, United States
c Sapienza University of Rome, Rome, and IRCSS NEUROMEDPozzilli (Is), Italy
d Washington University in St. Louis, St LouisMO, United States
e Virginia Commonwealth University, Richmond, VA, United States
f Parkinson’s Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, United States
g Institute of Systems Motor Science, University of Luebeck, Luebeck, Germany
h Rush University Medical Center, New PhiladelphiaOH, United States
i Toronto Western Hospital, University of Toronto, Canada
j Department of Neurology and Movement Disorder Centre, Seoul National University Hospital, Seoul, South Korea
k Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
l Department of Neurology, University of New Mexico, Albuquerque, NM, United States
m Institute of Neurogenetics, University of Luebeck, Luebeck, Germany
n Human Motor Control Section, NINDS, NIH, BethesdaMD, United States

Background: There are no widely accepted criteria to aid the physician in diagnosing BSP. Objective: To validate recently proposed diagnostic criteria for blepharospasm in a larger and geographically diverse population and to develop a screening system for blepharospasm. Methods: Video-recordings from 211 blepharospasm patients and 166 healthy/disease controls were examined by 8 raters. Agreement for presence of orbicularis oculi spasms, sensory trick, and increased blinking was measured by k statistics. Inability to voluntarily suppress the spasms was asked by the examiner but not captured in the video. Patients/controls were also requested to fill a self-administered questionnaire addressing relevant blepharospasm clinical aspects. The diagnosis at each site was the gold standard for sensitivity/specificity. Results: All the study items yielded satisfactory inter/intra-observer agreement. Combination of items rather than each item alone reached satisfactory sensitivity/specificity. The combined algorithm started with recognition of spasms followed by sensory trick. In the absence of a sensory trick, including “increased blinking” or “inability to voluntarily suppress the spasms” or both items yielded 88–92% sensitivity and 79–83% specificity. No single question of the questionnaire yielded high sensitivity/specificity. Serial application of the questionnaire to our blepharospasm and control subjects and subsequent clinical examination of subjects screening positive by the validated diagnostic algorithms yielded 78–81% sensitivity and 83–91% specificity. Conclusion: These results support the use of proposed diagnostic criteria in multi-ethnic, multi-center cohorts. We also propose a case-finding procedure to screen blepharospasm in a given population with less effort than would be required by examination of all subjects. © 2021

Author Keywords
Blepharospasm;  Diagnosis;  Dystonia

Funding details
National Institutes of HealthNIH
U.S. Department of DefenseDODW81XWH-19-CTRR-CTA
National Institute of Neurological Disorders and StrokeNINDSTR 001456, U54 NS065701, U54 NS116025
National Center for Research ResourcesNCRR
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Parkinson’s Disease FoundationPDF
Dystonia Medical Research FoundationDMRF
CHDI FoundationCHDI
National Center for Advancing Translational SciencesNCATSU54 TR001456, UL1TR001449, UNM CTSC KL21TR001448-01
Teva Pharmaceutical Industries
International Parkinson and Movement Disorder SocietyMDS
Benign Essential Blepharospasm Research FoundationBEBRF
Parkinson’s FoundationPF
National Spasmodic Dysphonia AssociationNSDA
Dystonia Coalition
Government of South Australia
Rare Diseases Clinical Research NetworkRDCRN
Deutsche ForschungsgemeinschaftDFGP20 GM109899, WE 5919/2-1
Else Kröner-Fresenius-StiftungEKFS2018_A55
Institute for Information and Communications Technology PromotionIITP
Merz Pharmaceuticals

Document Type: Article
Publication Stage: Final
Source: Scopus

Identifying individuals with Alzheimer’s disease-like brains based on structural imaging in the Human Connectome Project Aging cohort” (2021) Human Brain Mapping

Identifying individuals with Alzheimer’s disease-like brains based on structural imaging in the Human Connectome Project Aging cohort
(2021) Human Brain Mapping, . 

Li, B.a b c , Jang, I.b , Riphagen, J.b d , Almaktoum, R.b , Yochim, K.M.b , Ances, B.M.e , Bookheimer, S.Y.f , Salat, D.H.b c g , For the Alzheimer’s Disease Neuroimaging Initiativeh

a Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
b MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
c Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
d Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, Netherlands
e Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
f Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, United States
g Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, United States

Given the difficulty in factoring out typical age effects from subtle Alzheimer’s disease (AD) effects on brain structure, identification of very early, as well as younger preclinical “at-risk” individuals has unique challenges. We examined whether age-correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross-sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP-A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer’s Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP-A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD-like from the HCP-A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross-validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD-specific biomarkers and worse cognition. In an independent HCP-A cohort, 8.8% were identified as AD-like, and they trended toward worse cognition. An “AD risk” score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder. © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Author Keywords
aging;  Alzheimer’s disease;  classifier;  machine learning

Funding details
National Institutes of HealthNIHU01 AG024904
U.S. Department of DefenseDODW81XWH‐12‐2‐0012
National Institute on AgingNIA
National Institute of Biomedical Imaging and BioengineeringNIBIB
NIH Blueprint for Neuroscience ResearchU01AG052564‐S1
Alzheimer’s AssociationAA
Alzheimer’s Drug Discovery FoundationADDF
Eli Lilly and Company
Janssen Research and DevelopmentJRD
GE Healthcare
Alzheimer’s Disease Neuroimaging InitiativeADNI
University of WashingtonUW
McDonnell Center for Systems Neuroscience
Northern California Institute for Research and EducationNCIRE
Johnson and Johnson Pharmaceutical Research and DevelopmentJ&JPRD
Canadian Institutes of Health ResearchIRSC
Fujirebio Europe
Shanghai Rising-Star Program21QA1405800

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