Differences in functional connectivity distribution after transcranial direct-current stimulation: A connectivity density point of view

Bohao Tang, Yi Zhao, Archana Venkataraman, Kyrana Tsapkini, Martin A. Lindquist, James Pekar, Brian Caffo

Research output: Contribution to journalArticlepeer-review

Abstract

In this manuscript, we consider the problem of relating functional connectivity measurements viewed as statistical distributions to outcomes. We demonstrate the utility of using the distribution of connectivity on a study of resting-state functional magnetic resonance imaging association with an intervention. The method uses the estimated density of connectivity between nodes of interest as a functional covariate. Moreover, we demonstrate the utility of the procedure in an instance where connectivity is naturally considered an outcome by reversing the predictor/response relationship using case/control methodology. The method utilizes the density quantile, the density evaluated at empirical quantiles, instead of the empirical density directly. This improved the performance of the method by highlighting tail behavior, though we emphasize that by being flexible and non-parametric, the technique can detect effects related to the central portion of the density. To demonstrate the method in an application, we consider 47 primary progressive aphasia patients with various levels of language abilities. These patients were randomly assigned to two treatment arms, transcranial direct-current stimulation and language therapy versus sham (language therapy only), in a clinical trial. We use the method to analyze the effect of direct stimulation on functional connectivity. As such, we estimate the density of correlations among the regions of interest and study the difference in the density post-intervention between treatment arms. We discover that it is the tail of the density, rather than the mean or lower order moments of the distribution, that demonstrates a significant impact in the classification. The new approach has several benefits. Among them, it drastically reduces the number of multiple comparisons compared with edge-wise analysis. In addition, it allows for the investigation of the impact of functional connectivity on the outcomes where the connectivity is not geometrically localized.

Original languageEnglish (US)
Pages (from-to)170-185
Number of pages16
JournalHuman Brain Mapping
Volume44
Issue number1
DOIs
StatePublished - Jan 2023

Keywords

  • density regression
  • functional connectivity
  • random graph

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Anatomy

Fingerprint

Dive into the research topics of 'Differences in functional connectivity distribution after transcranial direct-current stimulation: A connectivity density point of view'. Together they form a unique fingerprint.

Cite this