Presentations/Journal Clubs
Check here for various didactic and journal club recordings. Please note that some recordings as missing, as not every presenter gives consent to be recorded
Improving neuroimaging of individual differences: Bayesian individualized parcellation
Colin DeYoung and Tyler Sassenberg - November 29, 2021
The DeYoung Personality Lab studies the structure and sources of personality and risk for psychopathology. We will give a brief overview of our conceptual framework before turning to the question of how to deal with a major challenge for individual-differences research in neuroimaging: namely, the fact that the brain’s functional organization, especially in the cortex, is not fixed relative to anatomical landmarks. One common method has been ICA with dual regression, but this has serious limitations. We describe a better approach based on a Bayesian technique that adjusts the boundaries of a standard atlas to fit each subject optimally, and we illustrate it with data from our lab and HCP.
Parallel hippocampal-parietal circuits for self- and goal-oriented processing
Annie Zheng - October 18, 2021
Using individualized approaches to creating functional network partitions in the hippocampus, we found differential connectivity along the anterior-posterior axis of the hippocampus. We found the anterior hippocampus (head and body) to be preferentially connected to the default mode network as expected. The hippocampal tail, however, was strongly preferentially connected to the parietal memory network, which supports goal-oriented cognition and stimulus recognition.The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel, but distinct circuits between the hippocampus and medial parietal cortex for self vs. goal-oriented processing.
Amandine Van Rinsveld - October 4, 2021
Can we predict the basic math skills of an individual from the brain functional architecture? In this talk, you will hear about how brain wide associations (BWAS) and polyneuro risk scores (PNRS) can be used to predict math abilities as measured in behavioral tasks. We used data from the ABCD data set to study the functional networks related to math skills in a larger sample. This approach shows that networks supporting math skills are associated to focal effects rather than being distributed across the entire brain
Brain Wide Associations (BWAS) to model the link between brain features and behavior
Oscar Miranda Dominguez - September 20, 2021
In this talk, you will hear about how brain wide associations (BWAS) and polyneuro risk scores (PNRS) are used to model associations between brain features (e.g., resting-state functional connectivity, cortical thickness) and behavior using large samples (N>1000). The speaker will describe the rationale behind following this approach and, using data from the ABCD study, will show how BWAS/PNRS can be useful to disambiguate between focal or globally distributed effects.
CNNs and GANs in T1w-to-T2w Image Estimation
Anders Perrone - August 23, 2021
Deep learning based methods for image estimation offer unique solutions to many MR image processing challenges. One such challenge is the issue of missing or poor quality anatomical image modalities that are required for minimal preprocessing of functional data, particularly in neonatal cohorts. Here, we present an overview of deep learning methods to generate realistic T2w images from T1w images in multiple neonatal cohorts and demonstrate their efficacy in salvaging fcMRI processing that would otherwise have been lost due to missing or poor quality T2w images.
Resting state functional connectivity networks predict motor behavior in Parkinson’s Disease
Anjani Ragothaman - August 9, 2021
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by multiple motor symptoms following dopamine depletion in the substantia nigra. While the negative effects of this deficit in the basal ganglia and motor cortical areas is well documented, the involvement of other cortical areas in PD is not well understood. Functional MRI offers a unique opportunity to non-invasively characterize brain function. In this study, we hypothesized that the motor dysfunctions observed in PD not only depend on motor networks, but also involve connectivity in higher-level attention networks.
Personalized functional networks: sex differences and development Part One - Sheila and Part Two - Adam
Sheila Shanmugan and Adam Pines - July 12 and 26, 2021
Individualized mapping of functional networks has revealed substantial inter-individual variability in the topography of these networks. Here, we found systematic sex differences in functional topography, which are linked to regional sex-specificity in gene expression. Additionally, we used multi-scale individualized functional networks to delineate patterns of hierarchical network coupling in neurocognitive development.
Gareth Harman - June 28, 2021
Covering a recent publication in which we built models to predict suicidal ideation and suicide attempt using the ABCD sample. We will be discussing some of the challenges of this task and looking to have an open discussion at the end about some of these limitations and future directions.
The MIDB Precision Functional Atlas and a potential use case for non-invasive neuromodulation
Robert Hermosillo - June 14, 2021
A Robust and Unified Framework for Estimating Heritability using Generalized Estimating Equations
Sanoli Basu - May 17, 2021
Researchers are often interested in estimating heritability for non‐normal outcomes such as binary, counts, skewed, or heavy‐tailed continuous traits. I will discuss a robust generalized estimating equations (GEE2) framework for estimating heritability for non‐normal traits. I will show that this approach allows adjusting for covariates in a flexible way and provides a computationally fast alternative to likelihood-based techniques.
Interrogating Multivariate Patterns of Functional Connectivity Related to Age in the Newborn Brain
Ashley Nielsen - May 3, 2021
Multivariate patterns of functional connectivity across the brain in children and adults carry information about maturation and/or experience that have been used to accurately predict an individual’s age. In this project, we test if functional connectivity carries such information in the newborn brain during the first month of life (38-46 weeks postmenstrual age) and interrogate what networks are important for age prediction. We also investigate the contribution of other potential sources of differences in functional connectivity related to age such as gestational age at birth, head motion, and brain size.
An Introduction to Time-to-Event Analysis, with Neuroimaging Applications
Mark Fiecas - April 19, 2021
Longitudinal designs are becoming more common, and they make it possible to ask research questions related to the occurrence and timing of events. Time-to-event analyses, however, are not common in the neuroimaging literature. In this presentation, I will give an overview on the basic concepts of time-to-event analyses, and I will give an example using the cortical thickness data from the Alzheimer’s Disease Neuroimaging Initiative to model its association with time-to-conversion from mild cognitive impairment.
Isa Stallworthy - March 22, 2021
The development of selective visual attention is critical for effectively engaging with an ever-changing world. Its optimal deployment depends upon interactions between neural, motor, and sensory systems across multiple timescales and neurocognitive loci. Previous work illustrates the spatio-temporal dynamics of these processes in adults, but less is known about this emergent phenomenon early in life. Using data (n = 190; 421 visits) collected between 3 and 35 months of age, we examined the spatio-temporal complexity of young children’s gaze patterns as they viewed stimuli varying in semantic salience. This presentation will share results from this paper (recently published in Scientific Reports) and provide an overview of a complexity science approach to studying human systems that we believe has the potential for broader application throughout the developmental and cognitive sciences.
Inherited Genetic Liability for Autism and Infant Brain Development
Jessica Girault - March 9, 2021
Prospective studies of infant siblings of children with autism spectrum disorder (ASD) have demonstrated that atypical brain development precedes the defining behavioral symptoms of the disorder. The infant sibling study design provides a powerful framework to determine what is “familial” in autism by placing the infant sibling in the context of the family and family genetics. This presentation will share findings demonstrating that the phenotypic severity of older siblings with autism can inform neurodevelopmental trajectories in their younger siblings.
Brain Volumes and Balance and Gait
Anjani Ragothaman - February 8, 2021
Presenting the relationship between ventricular, subcortical and cortical volumes to objective measures of balance and gait.
Automated Analysis of Infant Brain Imaging Data
Lilla Zollei - December 14, 2020
The development of automated tools for brain morphometric analysis in infants is a complex and challenging task that has lagged significantly behind analogous tools for adults. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements have been associated with neuropsychiatric, neurologic, and developmental disorders in children. This presentation will introduce an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. Additionally, recent algorithmic advancements using deep learning solutions will be discussed along with future application directions related to infant brain development.
Deconstructing Early Autistic Traits: Implications for Developmental Pathways to ASD
Natasha Marrus - November 30, 2020
A Whole-Brain Connectivity Measure Associated with ADHD Symptoms
Michael Mooney - November 16, 2020
Discussion of work-in-progress on the development of a whole-brain connectivity (polyconn) score derived from brain-wide association results and its relationship to ADHD symptoms.
Gender, Racial, and Ethnic Imbalance in Neuroscience Reference Lists
Max Bertolero and Jordan Dworkin - November 2, 2020
Discrimination against racial and ethnic minority groups exists in the academy, and the associated biases impact hiring and promotion, publication rates, grant funding, and awards. Precisely how racial and ethnic bias impacts the manner in which the scientific community engages with the ideas of academics in minority groups has yet to be fully elucidated. Citations are a marker of such community engagement, as well as a currency used to attain career milestones. We assessed the extent and drivers of racial and ethnic imbalance in the reference lists of papers published in five top neuroscience journals over the last 25 years.
Towards Reproducible Brain-Wide Association Studies
Scott Marek - October 5, 2020
Leveraging the Adolescent Brain Cognitive Development (ABCD) Study (N=11,878), we estimated the effect sizes and reproducibility of these brain-wide associations studies (BWAS) as a function of sample size. The very largest, replicable brain-wide associations for univariate and multivariate methods were r=0.14 and r=0.34, respectively. In smaller subsamples, typical for BWAS, effect sizes were ubiquitously inflated due to sampling variability, causing widespread replication failure. Sample sizes of N⪆2,000 are required to provide stable and reproducible brain-behavioral phenotype associations. While investigator-initiated brain-behavior research continues to generate hypotheses and propel innovation, large consortia are needed to usher in a new era of reproducible BWAS.