Department of Biostatistics
Neurostatistics Working Group

2014 - 2015

Coordinators: Dr. Rebecca Betensky, and Adam Sullivan

Schedule: Wednesdays, 12:30-1:30 p.m.
HSPH2, Room 426 (unless otherwise notified)

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Seminar Description
This working group provides a forum for presentation and discussion of completed, ongoing, or planned statistical analyses of neurological data. Such data include, for example, in vivo human brain images (anatomic, functional and spectroscopic magnetic resonance imaging), gene expression studies of human and non-human animal brain tissue (brightfield and immunofluorescence microscopy, DNA microarrays, laser micro-dissection), in vivo micro-dialysis, clinical trials data for a variety of neurologic diseases, and genetic data from family studies. Non-statistical presentations of neurological, psychiatric and technological background material will also be included. Through this seminar, statisticians will gain exposure to the statistical issues that arise in the broad field of neurology and brain imaging psychiatry and to the diverse ongoing research in this area throughout Harvard and the world. A main goal of the seminar is to stimulate statistical interest in neuroscience and neurology and to develop strategies for collaboration within these fields.


October 8

Sharon Xiangwen Xie, Ph.D.*
Associate Professor of Biostatistics at the Hospital of the University of Pennsylvania (HUP), University of Pennsylvania Perelman School of Medicine

Survival Analysis with Uncertain Endpoints Using an Internal Validation Subsample"
ABSTRACT: When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when obtaining the true endpoint is too invasive or costly. We develop nonparametric and semiparametric estimated likelihood functions that incorporate both uncertain endpoints available for all participants and true endpoints available for only a subset of participants. We propose maximum estimated likelihood estimators of the discrete survival function of time to the true endpoint and of a hazard ratio representing the effect of a binary or continuous covariate assuming a proportional hazards model. We show that the proposed estimators are consistent and asymptotically normal and develop the analytical forms of the variance estimators. Through extensive simulations, we also show that the proposed estimators have little bias compared to the nave estimator, which uses only uncertain endpoints, and are more efficient with moderate missingness compared to the complete-case estimator, which uses only available true endpoints. We illustrate the proposed method by estimating the risk of developing Alzheimer's disease using data from the Alzheimer's Disease Neuroimaging Initiative.

*Joint work with Jarcy Zee.
October 29

Yael Reijmer, Ph.D.
Postdoctoral Researcher, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital

"Brain Network Disruption and Cognitive Impairment in Small Vessel Disease"
ABSTRACT: Small vessel disease (SVD) is an important risk factor for cognitive impairment and dementia. The mechanisms linking SVD to cognitive impairment are not well understood. We hypothesized that multiple small, spatially distributed vascular lesions affect cognition through disruption of brain connectivity. We therefore examined local and global network alterations in patients with SVD and examined the relationship between network efficiency, markers of SVD burden on MRI and PET, and potential clinical consequences.

November 5

Carlos R. Ponce, M.D., Ph.D.
Research Fellow in Neurobiology, Harvard Medical School

"How Do Inferotemporal Cortex Cells Use Different Cortical Inputs for Image Categorization?"
ABSTRACT: Our ability to recognize visual objects, such as faces, is realized by neurons in the inferotemporal cortex (IT). These cells show preferences for individual images and image categories (and are thus selective), and are able to maintain these preferences even if one introduces irrelevant contextual changes (they are tolerant to changes in retinal size, position or viewpoint). To perform these computations, posterior IT neurons (pIT) require feedforward anatomical projections from over a dozen cortical regions, predominantly from area V4 and anterior IT, but also from areas V3 and V2. We do not know why multiple projections to pIT are required. In this study, we are defining the contributions of areas V2, V3 and V4 towards selectivity and tolerance in pIT neurons. By reversibly inactivating these visual regions, we can observe selective changes in response selectivity of IT neurons. We can interpret these changes using multivariate statistical techniques, such as multidimensional scaling, affinity propagation and linear classifiers. Our preliminary findings suggest that these input clusters to IT are concerned with different but overlapping computations.

November 12

Jing Qian, Ph.D.
Assistant Professor, Department of Biostatistics, University of Massachusetts - Amherst

"Thresholding Regression with Covariate Subject to Random Censoring"
ABSTRACT: Censored covariates arise frequently in biomarker assessement in epidemiological studies and in family history studies of disease. While there is a large literature on regression models when the outcome variable is subject to censoring, there is a more limited literature on the treatment of censored covariates, especially for type II censoring. We develop threshold regression approaches for linear regression models with covariate subject to random censoring. Compared with existing methods, the proposed methods are simple but effective as they avoid complicated modeling in dealing with censored covariate values. We study the asymptotic properties of the resultant estimators. In addition to estimating the regression coefficient of the censored covariate, the threshold regression methods can also be used to test whether the effect of the censored covariate is significant. We discuss the choice of optimal threshold which yields the most powerful test. The finite sample performance of the proposed methods are assessed through simulation studies. We also apply the method to a motivation example.

November 19 (joint with Harvard Catalyst | The Harvard Clinical & Translational Science Center Biostatistics Program)

Statistical Issues in the Analysis of Neurological Studies Symposium (RSVP to mplante@hsph.harvard.edu)
Speakers will include James Berry, MD, Rebecca Betensky, PhD, Deborah Blacker, MD, ScD, Tanuja Chitnis, MD, Brian Healy, PhD, Eric Macklin, PhD, Jing Qian, PhD, Ritesh Ramchandani, David Schoenfeld, PhD, Michael Schwarzschild, MD, PhD.

Exploration of Statistical Issues that Arise in the Study of Neurologic Diseases
ABSTRACT: This Harvard Catalyst Biostatistics symposium will explore statistical issues that arise in the study of neurologic diseases. The symposium will begin with motivating clinical background and identification of pressing analytical needs in amyotrophic lateral sclerosis, Alzheimer's disease, multiple sclerosis, and Parkinson's disease. The statistical talks will focus on methods for incorporating and handling causal inference, multiple endpoints, high dimensional biomarker selection, censored covariates, and measurement issues in short-term clinical trials. The symposium is intended for statisticians and neurological disease researchers who have analytical interests.

November 25

Ani Eloyan, Ph.D.
Assistant Professor, Department of Biostatistics, Johns Hopkins University

"Matrix Decomposition Methods for Functional MRI Data"
ABSTRACT: The field of functional neuroimaging is growing very rapidly resulting in a vast amount of data for analysis. Recently, several collections of resting state functional magnetic resonance images from different laboratories have been combined in freely available datasets for analysis including the 1000 Functional Connectomes Project Dataset, ADHD 200 among others. Statistical dimension reduction techniques such as singular value decomposition (SVD), independent component analysis (ICA), etc. are routinely used by practitioners in the field of neuroimaging to analyze complex fMRI data. In this talk, the main dimension reduction approaches for fMRI data are discussed stressing the major issues in the applications and the advantages of the methods depending on the biological question at hand. Extensions of the methods to high dimensional data are presented.

December 10

Rebecca E. Amariglio, Ph.D.
Associate Psychologist, Brigham and Women's Hospital
Instructor in Neurology, Harvard Medical School

"Subject Cognitive Concerns in Preclinical Alzheimer's Disease"
ABSTRACT: Although self-reported cognitive concerns (SCC) have previously been dismissed as a sign of the "worried well", there is emerging evidence to suggest that SCC may herald initial cognitive decrements at the stage of preclinical Alzheimer's disease (AD). Recent work from our own group and others suggests that specific SCC may in fact indicate early awareness prior to objective impairment on standardized tests and may be associated with evidence of early pathology on AD biomarkers and longitudinal decline.

January 6

John Ioannidis, DS.c., M.D.
C. F. Rehnborg Professor in Disease Prevention in the School of Medicine and Professor of Health Research and Policy (Epidemiology) and, by courtesy, of Statistics

"Research Practices and Reproducible Research"
ABSTRACT: The way research is selected for funding, designed, conducted, analyzed, and published can have a substantial impact on the reproducibility of scientific results. Empirical evidence suggests that the efficiency of many currently applied research practices is suboptimal, and there is wide variability across different scientific fields in this regard. This leads to a high prevalence of biased results. Dr. Ioannidis will peruse the current landscape and discuss different possibilities that have been proposed on how to improve the adoption and implementation of research practices that could lead to more reliable, accurate, and translatable results in a reproducible manner.

January 21

Sedeshna Das, Ph.D.
Instructor in Neurology, Harvard Medical School
Assistant in Neuroscience, Massachusetts General Hospital
Affiliate Faculty, Harvard Stem Cell Institute
Associate Director, Massachusetts General Hospital Biomedical Informatics Core

"Linear Models to Predict ΔMMSE"
ABSTRACT: We have developed a statistical linear model to predict change in subject scores on the Mini-Mental Status Exam (MMSE) over time. Our model includes the clinical diagnosis, APOE4 alleles, an interaction between the two, the baseline MMSE score and a few SNPs chosen from literature. This project was done as part of the Alzheimer's Disease Big Data DREAM Challenge 1 whose goal was to predict the change in MMSE at the 24 month follow-up visit given clinical covariates and genotypes from a Genome Wide Association Study (GWAS). The training set consisted of 750 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, and the test set was from the The Religious Orders Study and Memory and Aging Project at Rush University. Univariate analyses was used to select clinical covariates and SNPs with a significant odds-ratio were chosen. The model with clinical covariates and APOE genotype performed reasonably well whereas SNA data was not informative.

February 4, 10:30-11:30 am Countway Library, Minot Room (Special Event)


Harvard T.H. Chan School of Public Health Office of Human Research Administration - Quality Improvement Program
Guest Speakers: Kristen Bolt, Research Data and Conflict of Interest Officer, Harvard University
Andrew Ross, Information Security Manager, Harvard Chan School
Miguel A. Sanchez, Information Security Specialist, Harvard University
Kimberly Serpico, IRB Review Specialist, Harvard Longwood Medical Area Schools

"Ensuring Data Confidentiality: IRB Considerations and IT Data Security Measures"
ABSTRACT: How are the Harvard Research Data Security Levels determined for protocols? How do you make sure that you are complying with Harvard's policies on data protection? Can you put your sensitive data on a flash drive or transmit it electronically? How can IT assist you with ensuring High Risk Confidential Information is maintained and shared securely? Come get the answers to these questions and many more straight from IT data security officers and IRB administrators. Bring your questions on your specific protocols discuss with IT and IRB staff. Click here to register.

February 4


Tim Clark, Ph.D.
Director of Informatics, MassGeneral Institute for Neurodegenerative Disease
Assistant Professor of Neurology, Harvard Medical School
Director, Massachusetts General Hospital Biomedical Informatics Core
Co-Director, Data and Statistics Core, Massachusetts Alzheimer Disease Research Center

"Reproducibility or robustness? (or something else?)"
ABSTRACT: Significant concern has recently been expressed in the scientific literature about reproducibility of research, reusability of results, and false positives being reported as fact. These concerns are underlined by periodic scandals involving outright fraud, such as the recent scandal of so-called "stimulus-transitioned" stem cells. What is reproducibility and is it a standard to which scientists should aspire? Is there a difference between reproducibility and "robustness"? This talk will probe some of the recent discussion in the literature and reaction to it in the scholarly communications community.

Some Reading material:

Begley, C.G. and Ellis, L.M. (2012) Drug development: Raise standards for preclinical cancer research, Nature, 483, 531-533. http://www.nature.com/nature/journal/v483/n7391/full/483531a.html

Colquhoun, D. (2014) An investigation of the false discovery rate and the misinterpretation of p-values, Royal Society Open Science, 1. http://rsos.royalsocietypublishing.org/content/1/3/140216

Ioannidis, J.P.A. (2005) Why Most Published Research Findings Are False, PLoS Med, 2, e124.: http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.0020124

Rekdal, O.B. (2014) Academic urban legends, Social Studies of Science, 44, 638-654. http://sss.sagepub.com/content/44/4/638.full

February 11

Shelley Hurwitz, Ph.D.
Director of Biostatistics, Center for Clinical Investigation, Brigham and Women's Hospital, Harvard Medical School

"Biostatistics and Ethics"
ABSTRACT: In my talk on Biostatistics and Ethics, I will discuss the reputation of statistics, the response by the statistical community, some associations' guidelines for the ethical practice of statistics, and the movement toward reproducibility. Biostatisticians routinely work closely with physicians and scientists and have unique insight into data, often being privy to confidential data. We work in increasingly multidisciplinary teams with potentially divergent ethics codes and sensibilities. In the last decade we've seen a rapid increase in the ability to collect massive amounts of data, with complex structure and often a sensitive nature. These unparalleled advances and opportunities present new ethical concerns for statisticians.

February 25

Eyal Y. Kimchi, M.D., Ph.D.
Clinical Fellow in Neurology, Department of Neurology, Massachusetts General Hospital

"Talk Title TBD"
ABSTRACT: None Given

April 8

Jessica Gronsbell
Doctoral Student, Department of Biostatistics, Harvard University

"Talk Title TBD"
ABSTRACT: None Given

April 29

Journal Club

Moderated by Eyal Y. Kimchi, M.D., Ph.D.; Clinical Fellow in Neurology, Department of Neurology, Massachusetts General Hospital




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