Academic Minute
5:00 am
Fri April 4, 2014

Dr. Nathan Spreng, Cornell University - The Aging Brain Network

Breakthroughs in how we understand the human brain's structure and internal communication networks are helping scientists track neurological changes over time.

Dr. Nathan Spreng, assistant professor at Cornell University's Department of Human Development, is using advancement in neuroimaging to better understand how the brain functions and changes as we age.

At Cornell University, Dr. Nathan Spreng is assistant professor and the director of the Laboratory of Brain and Cognition in the Department of Human Development. He is also a Rebecca Q. and James C. Morgan Sesquicentennial Faculty Fellow. His research currently focuses on large scale brain dynamics and their function in cognition. He earned his PhD from the University of Toronto.

About Dr. Spreng

The College of Human Ecology

Dr. Nathan Spreng - The Aging Brain Network

Over the last several decades there have been incredible advances in our ability to study the structure and function of the brain in living humans. Advanced neuroimaging techniques such as Magnetic Resonance Imaging, or MRI, are rapidly opening up new frontiers for studying the brain as it ages and undergoes pathological changes in neurodegenerative disorders such as Alzheimer’s Disease. 

One of the most exciting frontiers in this regard is the reconceptualization of the brain as a complex system of many large and constantly interacting networks of brain regions. Seen through the lens of a ‘networked brain’, cognition is considered to be an emergent property of synchronized neural activity within and between these networks. As a direct corollary of this model, cognitive decline in aging and brain disease is increasingly ascribed to reductions in structure and/or function within these large-scale brain networks.

Our research investigated whether brain changes associated with the transition from healthy aging to Alzheimer’s Disease might follow a coherent trajectory of decline within large-scale networks. Our data suggest that this is indeed the case. We’ve now demonstrated that reductions in brain density in the default network, a collection of brain regions implicated in aspects of internal thought such as memory, are greater in older adults with mild cognitive impairment who go onto to develop Alzheimer’s Disease than in those who do not. Thus structural changes in the default network may be an important early marker of disease onset. We have also shown that reductions in brain density within the default network are robustly associated with reduced cognitive status as well as increased genetic risk for developing the disease.

Rapid and dramatic advances in neuroimaging are providing the tools to study the network architecture of the human brain in health and disease. Our work continues to leverage these advances to investigate aging and neurodegeneration through the lens of the networked brain – and ultimately to derive clinically useful diagnostic and prognostic neural markers of the transition from healthy aging to Alzheimer’s Disease.

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