Neuroimaging in aphasia treatment research: Consensus and practical guidelines for data analysis

Abstract

Functional magnetic resonance imaging is the most widely used imaging technique to study treatment-induced recovery in post-stroke aphasia. The longitudinal design of such studies adds to the challenges researchers face when studying patient populations with brain damage in cross-sectional settings. The present review focuses on issues specifically relevant to neuroimaging data analysis in aphasia treatment research identified in discussions among international researchers at the Neuroimaging in Aphasia Treatment Research Workshop held at Northwestern University (Evanston, Illinois, USA). In particular, we aim to provide the reader with a critical review of unique problems related to the pre-processing, statistical modeling and interpretation of such data sets. Despite the fact that data analysis procedures critically depend on specific design features of a given study, we aim to discuss and communicate a basic set of practical guidelines that should be applicable to a wide range of studies and useful as a reference for researchers pursuing this line of research.

The canonical semantic network supports residual language function in chronic post‐stroke aphasia

“…In order to account for variability in the time-to-peak of the HRF, time and dispersion derivatives were included as basis functions [Meinzer et al, 2013]. Note that since data were collected using a blocked design, separate modeling of correct vs. incorrect trials was not possible.…”

“…Our choice to use the voxel-level threshold of 0.01 was primarily motivated by its prevalence in the neuroimaging literature of post-stroke aphasia, and because detection power is reduced for group analyses of highly heterogeneous groups such as stroke patients [e.g. Meinzer et al, 2013]. In addition, we note that for the whole-brain regression analyses, we employed a more stringent whole-brain cluster-correction threshold of 0.01 (as opposed to the conventional 0.05 threshold), even when our analyses were restricted to only the right hemisphere.…”

The relationships between the amount of spared tissue, percent signal change, and accuracy in semantic processing in aphasia

“…Motion parameters were included in the model as regressors. The data were not smoothed in order to avoid interpolation from nearby regions or CSF which might extend into the boundaries of specific ROIs selected in this study (Meinzer et al, 2013). …”

Can neuroimaging help aphasia researchers? Addressing generalizability, variability, and interpretability

“…However, as fMRI gained popularity among aphasia researchers, they have gradually recognized that adapting neuroimaging methodologies – originally developed for the study of neurologically intact populations – had to confront the same challenges faced by behavioral methods: establishing generalizability, taking into account inter-individual variability, and maintaining interpretability. These issues have prompted collaborative efforts to develop methodological guidelines for the use of fMRI in aphasia research (Crosson et al, 2007; Kiran et al, 2013; Meinzer et al, 2013; Price, Crinion, & Friston, 2006; Rapp, Caplan, Edwards, Visch-Brink, & Thompson, 2013; Veldsman, Cumming, & Brodtmann, 2015; Wilson, Bautista, Yen, Lauderdale, & Eriksson, 2016). Naturally, in striving to meet the three desiderata, a critical focus of these efforts has been to negotiate the relative benefits (and respective costs) of group and single case studies.…”