Clinical applications of the functional connectome

Highlights

• Resting-state fMRI methods can lead to biomarker identification for brain disorders.

• Prospective biomarkers must be assessed with criteria for clinical tests.

• Predictive modeling approaches are providing proof-of-concept of diagnostic utility.

• The convergence of dimensional approaches, data sharing & Big Data is propitious.

Abstract

Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the functional connectome that can be attributed to clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. Here, we assess evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). We focus on current R-fMRI-based prediction efforts, and survey R-fMRI used for neurosurgical planning. We identify gaps and needs for R-fMRI-based biomarker identification, highlighting the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them. Additionally, we note the need to expand future efforts beyond identification of biomarkers for disease status alone to include clinical variables related to risk, expected treatment response and prognosis.

Introduction

As well documented in this issue, mapping the functional connectome is now in the foreground of neuroscience research, with a frequently enunciated goal of attaining clinical utility. Indeed, the rate of growth for studies incorporating resting state fMRI (R-fMRI) approaches has overtaken that of traditional task-based fMRI (Snyder and Raichle, 2012), with an increasing focus on clinical questions (Kelly et al., 2012). Despite the multiple advantages that attach to R-fMRI approaches vis-à-vis clinical samples (Fox and Greicius, 2010), progress towards advancing the clinical enterprise has been disappointingly slow. This situation was recently analyzed in the wider context of clinical neuroscience (Kapur et al., 2012) and the lessons drawn are particularly germane to R-fMRI and efforts to map the functional connectome.

In this selective overview, we focus on R-fMRI because its relatively widespread availability and amenability to large-scale aggregation across imaging centers and populations (Milham, 2012) make possible attaining data sets on scales comparable to genetic investigations (e.g., Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). We examine common elements that need to be considered to make the efforts of mapping the functional connectome relevant to clinicians. These include validity, reliability, sensitivity, specificity, positive and negative predictive values of potential biomarkers. Beyond these, our rudimentary knowledge of brain disorders also requires that we adopt intermediate strategies, as recommended by Kapur et al. (2012).

We will assess the evidence and gaps in relation to validity, reliability, sensitivity and specificity of efforts to map the functional connectome using R-fMRI, primarily in the context of diagnostic prediction studies. We also examine the nascent literature applying R-fMRI methods for neurosurgical planning, as this best exemplifies person-centered clinical applications.