We study brain aging—and the critical windows for change.

The brain changes across the lifespan, but not all changes are equal. While some brain changes support cognitive vitality, others signal vulnerability to decline and disease.

We are a cognitive neuroscience laboratory focused on understanding why some individuals remain cognitively resilient while others develop Alzheimer's disease, with particular emphasis on midlife as a pivotal period shaping risk and resilience in aging.

Our lab develops quantitative imaging-based biomarkers of brain network organization to identify early predictors of cognitive decline and Alzheimer's disease. We integrate cross-species models to isolate mechanisms driving these changes.

Our research uses non-invasive neuroimaging to study brain structure and function in healthy and clinical populations across the lifespan.

Scroll down to learn about our key research areas

Large-scale Brain Networks

The brain functions as a network and our research is focused on defining and understanding the large-scale organization of brain networks using neuroimaging. This work is done primarily in human participants, but we have also recently expanded our research to include rodents and non-human primates. We use network analysis methods to explore how brain areas (large groups of neurons that are functionally related) interact and assemble into more complex patterns of organization. Our goal is to uncover how brain network organization gives rise to cognition (e.g., long-term memory, attention, language), how it changes over years and decades, and how it is disrupted by diseases.

For representative overviews see:

Wig et al. Annals of the New York Academy of Sciences (2011)
Wig et al. Neuroimage (2014)
Wig. Trends in Cognitive Sciences (2017)
Petersen et al. Neuron (2024)

*Organization of functional brain networks measured using resting-state fMRI.*

Brain Network Changes Across the Adult Lifespan

We study how the organization and function of brain networks change across the adult lifespan. This research involves examining individuals from different age segments of adulthood (e.g., comparing 20- to 50- to 90-year-old individuals), and also repeatedly studying the same individual longitudinally (i.e., over multiple years or decades). We also study individuals across different degrees of brain health (e.g., varying levels of dementia severity). This multi-prong approach allows us to gain a deeper understanding of trajectories of healthy vs unhealthy aging and enables identification of imaging-based biomarkers of impending cognitive problems prior to the onset of disease.

For representative publications see:

Chan et al. Proceedings of the National Academy of Sciences USA (2014)
Chan et al. Journal of Neuroscience (2017)
Han et al., Cerebral Cortex (2018)
Chan et al. Nature Aging (2021)

*Large-scale brain network decline in aging mice and humans*

Cross-species Models of Brain Network Aging

We are developing cross-species models of large-scale brain network organization that serve as translational bridges for understanding human brain aging. These approaches allow us to isolate the mechanisms driving changes in brain network organization and their impact on cognition and behavior, and to identify conserved principles of brain organization that shape risk and resilience across the lifespan.

For representative publications see:

Winter-Nelson et al. Proceedings of the National Academy of Sciences USA (2026)

*Impacts of increasing AD dementia severity on brain network interactions*

Alzheimer’s Disease

While Alzheimer's Disease is pathologically defined by amyloid plaques and tau tangles, cognitive impairment results from disruptions to brain function across distributed brain networks. Our research quantifies how these network alterations emerge and progress in individuals who vary in disease severity, as well as in cognitively healthy individuals at elevated risk for Alzheimer's disease. Our goals are to develop imaging-based biomarkers of brain network organization that serve as prognostic markers of future cognitive decline, and diagnostic measures of disease severity. In doing so, we aim to establish critical windows for intervention and enable earlier, more targeted treatment approaches.

For representative publications see:

Wig. Issues in Science and Technology (2019)
Chan et al. Nature Aging (2021)
Zhang et al. The Journal of Neuroscience (2023)

*US County-level maps (2016) of Median Household Income and Education level*

Lifecourse exposures and Brain Health

Our environment and lifecourse exposures define our interactions with the social and material world. We want to understand how these interactions shape and constrain our brains as we grow older. Do differences in access to resources, stimulation and environmental stressors contribute to differences in aging brain health? If so when does this start, and can we identify the most vulnerable individuals? Accomplishing this work necessitates studying groups of individuals who vary in their social, economic, and health-related exposures.

CURRENT RESEARCH PROJECT:

The Midlife Brain and Environment Study (MBES), is a longitudinal study focusing on identifying the health and lifestyle factors that contribute to brain and cognitive aging during middle-age adulthood (supported by a research project grant from the NIH-NIA). The project’s second wave of testing was recently completed.

For representative publications see:

Chan et al. Proceedings of the National Academy of Sciences USA (2018)
Chan et al. Nature Aging (2021)
Wig et al. Trends in Cognitive Sciences (2024)
Wig et al. Encyclopedia of the Human Brain, 2nd Edition (2025)

Network-targeted Interventions

Gaining a deeper understanding of individual differences in brain networks and cognitive ability has led us to formulate novel hypotheses about brain network function. To test these hypotheses, we are conducting experiments to test whether targeted interventions can modify the organization and function of an individual’s brain network and cognitive abilities. This research is being done using interventions that incorporate behavioral training and non-invasive brain stimulation.

For representative publications see:

Wig et al. Nature Neuroscience (2005)