AUTHOR=Conejo Bayón Francisco , Maese Jesús , Fernandez Oliveira Aníbal , Mesas Tamara , Herrera de la Llave Estibaliz , Álvarez Avellón Tania , Menéndez-González Manuel 

TITLE=Feasibility of the Medial Temporal lobe Atrophy index (MTAi) and derived methods for measuring atrophy of the medial temporal lobe

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=6

YEAR=2014

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2014.00305

DOI=10.3389/fnagi.2014.00305

ISSN=1663-4365

ABSTRACT=<p><bold>Introduction</bold>: The Medial Temporal-lobe Atrophy index (MTAi), 2D-Medial Temporal Atrophy (2D-MTA), yearly rate of MTA (yrRMTA) and yearly rate of relative MTA (yrRMTA) are simple protocols for measuring the relative extent of atrophy in the medial temporal lobe (MTL) in relation to the global brain atrophy. Albeit preliminary studies showed interest of these methods in the diagnosis of Alzheimer’s disease (AD), frontotemporal lobe degeneration (FTLD) and correlation with cognitive impairment in Parkinson’s disease (PD), formal feasibility and validity studies remained pending. As a first step, we aimed to assess the feasibility. Mainly, we aimed to assess the reproducibility of measuring the areas needed to compute these indices. We also aimed to assess the efforts needed to start using these methods correctly.</p><p><bold>Methods</bold>: A series of 290 1.5T-MRI studies from 230 subjects ranging 65–85 years old who had been studied for cognitive impairment were used in this study. Six inexperienced tracers (IT) plus one experienced tracer (ET) traced the three areas needed to compute the indices. Finally, tracers underwent a short survey on their experience learning to compute the MTAi and experience of usage, including items relative to training time needed to understand and apply the MTAi, time to perform a study after training and overall satisfaction.</p><p><bold>Results</bold>: Learning to trace the areas needed to compute the MTAi and derived methods is quick and easy. Results indicate very good intrarater Intraclass Correlation Coefficient (ICC) for the MTAi, good intrarater ICC for the 2D-MTA, yrMTA and yrRMTA and also good interrater ICC for the MTAi, 2D-MTA, yrMTA and yrRMTA.</p><p><bold>Conclusion</bold>: Our data support that MTAi and derived methods (2D-MTA, yrMTA and yrRTMA) have good to very good intrarater and interrater reproducibility and may be easily implemented in clinical practice even if new users have no experience tracing the area of regions of interest.</p>