Translational MRI Volumetry with NeuroQuant: Effects of Version and Normative Data on Relationships with Memory Performance in Healthy Older Adults and Patients with Mild Cognitive Impairment

Abstract

NeuroQuant (NQ) is a fully-automated program that overcomes several existing limitations in the clinical translation of MRI-derived volumetry. The current study characterized differences between the original (NQ1) and an updated NQ version (NQ2) by (i) replicating previously identified relationships between neuropsychological test performance and medial temporal lobe volumes, (ii) evaluating the level of agreement between NQ versions, and (iii) determining if the addition of NQ2 age-/sex-based z-scores hold greater clinical utility for prediction of memory impairment than standard percent of intracranial volume (%ICV) values. Sixty-seven healthy older adults and 65 MCI patients underwent structural MRI and completed cognitive testing, including the Immediate and Delayed Memory indices from the RBANS. Results generally replicated previous relationships between key medial temporal lobe regions and memory test performance, though comparison of NQ regions revealed statistically different values that were biased toward one version or the other depending on the region. NQ2 hippocampal z-scores explained additional variance in memory performance relative to %ICV values. Findings indicate that NQ1/2 medial temporal lobe volumes, especially age- and sex-based z-scores, hold clinical value, though caution is warranted when directly comparing volumes across NQ versions.

Comparison of Vendor-Provided Volumetry Software and NeuroQuant Using 3D T1-Weighted Images in Subjects with Cognitive Impairment: How Large is the Inter-Method Discrepancy?

“…This systematic error is probably because of the different atlas and segmentation models of the two software and can be corrected by changing the MR parameters including spatial resolution, contrast, and filtering (30) and by using the reference values. Currently, the two software appear to apply different sets of normative database (28,31). In future studies, a common normative database should be established.…”

Medial Temporal Lobe Atrophy is Related to Learning Strategy Changes in Amnestic Mild Cognitive Impairment

“…Memory impairment in individuals with aMCI is thought to be the result of early neuropathological changes in related brain structures, such as medial temporal lobe atrophy (MTA) and white matter hyperintensities (WMH), which have been observed on magnetic resonance imaging (MRI) (Fox et al, 1996). MTA, especially in the hippocampus, is considered a diagnostic biomarker of AD and is thought to be related to episodic memory impairment in patients with AD or MCI (Das, Mancuso, Olson, Arnold, & Wolk, 2016;England, Gillis, & Hampstead, 2014;La et al, 2007;Pantel, Schönknecht, Essig, & Schröder, 2004;Sanchez-Benavides et al, 2010;Shim et al, 2011;Stelmokas et al, 2017). Moreover, a brain network that includes the medial temporal lobe and contributes to the development of learning strategies has been observed in neuroimaging studies.…”

Structural neuroimaging in mild traumatic brain injury: A chronic effects of neurotrauma consortium study

“…Based on such a diverse dataset, if a cut‐point were defined to meet or exceed a ~2.0 standard deviation ( SD ) difference from the comparison mean would account for the variability and likewise use a rigorous cut‐point. This approach is exemplified by the currently available NeuroQuant® method; this clinical application of volumetric image analysis for an individual subject does not correct for type of scanner (Farid et al, ; Stelmokas et al, ; Wang et al, ). Although adjusting and accounting for site differences would reduce between‐site variance, this approach would not be feasible for broader use—not all sites would necessarily have sufficient samples to generate their own normative data.…”

“…On the other hand, expanding the clinical application of MRI-based volumetrics may need to simply embrace the noise from different scanners and platforms and not attempt to correct for site differences. (Farid et al, 2012;Stelmokas et al, 2017;Wang et al, 2016). Although adjusting and accounting for site differences would reduce betweensite variance, this approach would not be feasible for broader use-not all sites would necessarily have sufficient samples to generate their own normative data.…”