Precision Brain Morphometry Using Cluster Scanning

Abstract

Measurement error limits the statistical power to detect group differences and longitudinal change in structural MRI morphometric measures (e.g., hippocampal volume, prefrontal thickness). Recent advances in scan acceleration enable extremely fast T1-weighted scans (∼1 minute) to achieve morphometric errors that are close to the errors in longer traditional scans. As acceleration allows multiple scans to be acquired in rapid succession, it becomes possible to pool estimates to increase measurement precision, a strategy known as “cluster scanning.” Here we explored brain morphometry using cluster scanning in a test-retest study of 40 individuals (12 younger adults, 18 cognitively unimpaired older adults, and 10 adults diagnosed with mild cognitive impairment or Alzheimer’s Dementia). Morphometric errors from a single compressed sensing (CS) 1.0mm scan with 6x acceleration (CSx6) were, on average, 12% larger than a traditional scan using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) protocol. Pooled estimates from four clustered CSx6 acquisitions led to errors that were 34% smaller than ADNI despite having a shorter total acquisition time. Given a fixed amount of time, a gain in measurement precision can thus be achieved by acquiring multiple rapid scans instead of a single traditional scan. Errors were further reduced when estimates were pooled from eight CSx6 scans (51% smaller than ADNI). Neither pooling across a break nor pooling across multiple scan resolutions boosted this benefit. We discuss the potential of cluster scanning to improve morphometric precision, boost statistical power, and produce more sensitive disease progression biomarkers.

Reliability of structural brain change in cognitively healthy adult samples

“…Takao's reliability of cortical thinning is consistent with our estimations at 2 years of follow-up, but not those of volumetric data. This approach, however, assumes that the measurements acquired within the same session have independent measurement errors, an assumption that is likely greatly violated when employing signal intensity as a core measure (Elliott et al, 2023b).…”

“…Spacing between observations (i.e., the temporal division) is a third factor – together with study duration and number of observations - that influences longitudinal reliability, though it has not formally been studied here because most datasets available have roughly equispaced observations. Assuming measurement error is independent between close measurements (see Elliott et al, 2023b; Maclaren et al, 2014), the closer measurements are taken towards the beginning and the end of the study period, the better in terms of longitudinal reliability (Rast and Hofer, 2014; Willett, 1989). Future datasets can leverage cluster-like acquisitions of rapid MRI scans to boost reliability and power to detect differences (Elliott et al, 2023a).…”