Projects

Wheelock Neuroimaging Lab

Network Level Analysis Software

Determining the mechanisms by which the human brain generates cognition, perception and emotion hinges upon quantifying the relationships between coordinated brain activity and behavior. Contemporary connectome research views the brain as a large-scale, complex network composed of nonadjacent, yet connected, brain regions. These connectomes are frequently composed of tens of thousands of connections between brain regions, which poses a challenge for identifying biologically meaningful and reproducible associations with behavioral or clinical outcomes.

Our lab develops software implementing statistical approaches for connectome-wide association studies. We adopt methods from the genome-wide association literature, frequently referred to as ‘pathway analysis,’ ‘enrichment analysis’ or ‘over-representation analysis.’ We are combining these statistical approaches under one umbrella ‘Network-Level Analysis’ toolbox for use by the neuroscience community. This toolbox includes a graphical user interface as well as command line code and offers options for the analysis of between groups differences, individual variability, nested site/scanner/family effects, and can be applied across the lifespan.

Wheelock et al., (2020) Cerebral Cortex
Functional Connectivity Network Disruption Underlies Domain-Specific Impairments in Attention for Children Born Very Preterm | Cerebral Cortex | Oxford Academic (oup.com)

Li et al., (2024) Network Neuroscience
Network-level enrichment provides a framework for biological interpretation of machine learning results | Network Neuroscience | MIT Press

K99/R00 EB029343

For more information regarding using this software toolbox, contact Magaly Perales.

*Reproduced from Wheelock et al., (2022) Cerebral Cortex*

Organization of the Developing Connectome

Brain connectivity changes over the course of development to support the acquisition of new skills. Early in development, this includes brain systems supporting primary auditory, visual and motor functions, and later in development, this includes higher-order brain systems supporting complex functions such as language, attention and emotion regulation. Accurately modeling brain development provides valuable insights into healthy and aberrant behaviors. These insights can lead to improvements in early diagnostic and detection tools, a deeper understanding of neuroplasticity and specialization within the brain, and a clearer comprehension of how early experiences impact brain connectivity.

Our lab focuses on understanding the developmental trajectories of brain functional connectivity during prenatal, early postnatal and childhood stages. Our work emphasizes the optimization of neuroimaging methods to better understand early brain development. To improve the reliability, interpretability and comparability of neuroimaging studies in the developing brain, our lab has evaluated the application of adult functional area parcels and network models in infant FC data in addition to developing infant-specific area parcels and network models.

Wheelock et al., (2019) DCN
Sex differences in functional connectivity during fetal brain development – ScienceDirect

Tu et al., (2024) bioRxiv, In revision at DCN
A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development | bioRxiv

Tu et al., (2024) bioRxiv, Under Review at Cerebral Cortex
Early Life Neuroimaging: The Generalizability of Cortical Area Parcellations Across Development | bioRxiv

R01 HD115540

Connectome Degeneration in Alzheimer Disease

In Alzheimer’s disease (AD), a cascade of events — including the accumulation of amyloid-beta (Ab) plaques, neurofibrillary tau tangles, cortical thinning, hypometabolism and disrupted brain connectivity — leads to severe cognitive deficits. Blood-based assays detecting neurofilament light chain (NfL), a structural protein indicating axonal damage, have emerged as potential biomarkers for neurodegeneration and disease progression in AD. Additionally, hub regions that are crucial for efficient brain communication have shown promise as potential neuroimaging markers due to their vulnerability to pathology and degeneration in AD.

Despite strong evidence supporting these AD markers, our lab focuses on assessing their connectome-wide associations. We have published work detailing the use of resting-state fMRI to evaluate how increased NfL levels and activity-dependent hub degeneration disrupt functional connectivity. Our findings enhance the understanding of brain network organization in AD and contribute to early diagnostic tools in developing precise therapeutic interventions.

Wheelock et al., (2023) Brain
Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer’s disease | Brain | Oxford Academic

Tu (2024) Network Neuroscience
Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease | Network Neuroscience | MIT Press

ADRC Developmental Project 5.3

B. Cell Death & Neurofilament Release — *Reproduced from Wheelock et al., (2023) Brain*

EEG-fMRI

Determining accurate models of the developing brain’s functional architecture during the first two years of life is necessary to make predictions about developmental and clinical outcomes. Crucially, developmental differences in sleep cycles between infants and toddlers and individual variability in time spent in each sleep stage pose a challenge for both understanding early brain development as well as mental health outcome prediction reproducibility and accuracy in extant pediatric, sleeping state fMRI studies. Disentangling the relative contributions of sleep stage and age in early brain network development will be transformative, providing a state-based understanding of early developmental brain networks and a framework for accurate developmental outcome predictions.

Our primary aim is to optimize EEG, MRI and concurrent EEG-fMRI acquisition and analysis pipelines in a sample of infants, toddlers and adults. Afterwards, our lab will focus on integrating this technology to understand the interaction between sleep-stages and brain development on functional connectivity estimates.

R01 HD115540

For more information regarding using this software toolbox, contact Magaly Perales.