Discussion
The goal of this analysis was to determine whether an atrophy-based risk score could predict the onset of dementia in asymptomatic or questionably symptomatic individuals who are at high risk due to autosomal dominant mutations. Our results provide preliminary indication that quantification of each person’s unique pattern of atrophy can separate those with dementia from those with no symptoms with 93 percent accuracy. Furthermore, in an independent subset of the data composed of longitudinal observations, we were able to show that this atrophy-based risk score statistically significantly increased the risk for progression to dementia from the asymptomatic (CDR~=~0) or questionably symptomatic state (CDR~=~0.5). These results suggest that quantification of individualized atrophy patterns is a promising technique that may be useful for developing new treatments for neurodegenerative disease and for guiding the use of these treatments once they are approved.
This approach for creation of atrophy maps addresses formidable challenges in developing treatments for neurodegenerative disease. Because the age when symptoms develop varies widely across individuals with FTLD, drug trials seeking to delay the onset of symptoms must identify biological markers which reliably indicate that symptoms will develop within a short time, allowing enrichment of the trial cohort with these participants. The current approach can potentially identify brain changes that herald the onset of symptoms regardless of where they occur in the brain. Similarly, each person’s w-map can potentially be used to define a region of interest for tracking the effect of a drug in slowing atrophy. Once treatments are approved, this type of risk score can be used to avoid potential adverse effects of approved drugs if treatment is delayed until the time when symptoms are more likely to develop. Furthermore, these considerations are not limited to FTLD. For instance, a substantial minority of patients with Alzheimer’s disease (AD) present with symptoms of visuospatial, frontal, or language symptoms~\cite{Ossenkoppele2015}, and measurements targeted at the hippocampal or entorhinal cortex regions typically affected in AD may miss early brain changes indicative of oncoming symptoms. This idea could be examined in amyloid positive individuals at high risk of developing AD due to genetic risk.
While this method appears promising, future refinements can be envisioned that will likely improve its utility. Technical factors will need to be addressed, such as the impact of specific scanner and field strength on the estimate. Although this was a multisite study, many of the subjects were studied at UCSF, and all subjects were scanned at 3T. Replication of these findings in an independent dataset will be important. In addition, the regression model was created using patients who had already developed dementia an average of X years before the onset of the study. Creation of a model based on patients who were observed to convert to dementia within the last year would likely improve its sensitivity. Such patients will likely be available through projects such as ARTFL and LEFFTDS in the future {REF}. Furthermore, this model was created using all three types of FTLD mutations as one group. With larger numbers of patients, mutation-specific models can be created. This would likely improve sensitivity by having the model be more representative of the types of changes linked to dementia in each mutation. For instance, the weighting of each region in our model depicted in Fig.~3 shows that right sided regions were heavily weighted, and only a limited number of left hemisphere regions were useful in prediction of dementia. In a group with only GRN carriers, there would be a higher likelihood of asymmetric involvement and left sided involvement, so that the left hemisphere might carry more weight for prediction. In addition, it is likely that additional variables indicative of inflammation or neuronal injury, or other types of imaging or clinical data, when added to this score, could significantly improve prediction in survival analyses. Despite these limitations, these results are an important step towards predicting disease onset in neurodegenerative conditions.