Dementia-associated atrophy predicted by brain networks

By Samantha Black, PhD, ScienceBoard editor in chief

October 14, 2019 -- A new study uses brain maps to predict how brain atrophy spreads in patients with frontotemporal dementia (FTD). The experiment, conducted by UC San Francisco scientists, adds to a growing body of data suggesting that brain cell loss associated with dementia spreads via synaptic connections between established brain networks.

Results from the study were published in Neuron on October 14, advance knowledge on how neurodegeneration spreads and could lead to new clinical tools to evaluate treatments and predict disease trajectory.

FTD is the most common form of dementia in people under the age of 60, and is a clinical syndrome associated with shrinking of the frontal and temporal anterior lobes of the brain. In this condition, patients experience neurodegeneration with diverse linguistic and behavioral symptoms. As with other neurodegenerative diseases, symptoms and severity varies greatly, making it difficult for scientists to determine the exact cause of FTD. The rate of disease spread through the brain can also span from as little as 2 years up to 10 years.

"Knowing how dementia spreads opens a window onto the biological mechanisms of the disease -- what parts of our cells or neural circuits are most vulnerable," said study lead author Jesse Brown, PhD, an assistant professor of neurology at the UCSF Memory and Aging Center and UCSF Weill Institute for Neurosciences. "You can't really design a treatment until you know what you're treating."

The UCSF researchers set out to examine if neural network maps based on brain scans can predict brain atrophy in FTD patients over the course of a year. The idea was generated by senor author, William Seeley's team in previous research proposes that there are patterns of brain atrophy that are similar to brain networks. Rather, that groups of functionally related brain regions work cooperatively via synaptic connections, sometimes of long distances. This could suggest that atrophy does not spread evenly in all directions but can jump from one part of the brain to another.

The researchers used scans from healthy individuals to develop baseline maps as comparison tools. These brain maps allowed researchers to determine patient-specific "epicenters" in the healthy functional connectome. Using 175 different brain regions of 75 healthy adults, the team identified which of the networks best matched the pattern of brain atrophy seen in FTD patients and marked it as a likely epicenter of the patient's degeneration.

42 behavioral variant FTD patients and 30 semantic variant primary progressive aphasia (a type of FTD) were recruited from the UCSF Memory and Aging Center. The patients has baseline MRI scans to assess the extent of existing brain degeneration and a year later had follow-up scans to measure how their disease progressed.

The researchers used the healthy brain maps to predict where the FTD patient's brain atrophy would most likely to spread in the follow-up scans one year later. They compared the accuracy of the predictions to others that didn't take the healthy brain maps into account. The found that two region-wise graph theoretical metrics to predict future atrophy:

  • shortest path length to the epicenter, number of synaptic "steps" that region was from the estimated disease epicenter
  • nodal hazard, the cumulative atrophy of a region's first-degree neighbors

The researchers showed that these two measures more accurately predicted spread of disease than straight-line distance from existing atrophy. In many cases the disease spread bypassed adjacent areas, jumping to other functionally-related sections.

"It's like with an infectious disease, where your chances of becoming infected can be predicted by how many degrees of separation you have from 'Patient Zero' but also by how many people in your immediate social network are already sick," Brown said.

Although this method shows great promise, the researchers emphasize that it is not yet ready for clinical use. They hope to improve the accuracy of their predictions by -- among other approaches -- using individualized network maps for each patient rather than using average connectivity maps, and by developing more specialized prediction models for particular subtypes of FTD.

"We are excited about this result because it represents an important first step toward a more precision medicine type of approach to predicting progression and measuring treatment effects in neurodegenerative disease," Seeley said.

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