Diffusion MRI‐based tractography is the most commonly‐used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory—a powerful mathematical approach for modeling complex network systems—for analyzing tractography‐based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1

Positive behavioral responses to attractive faces have led neuroscientists to investigate underlying neural mechanisms in a “reward circuit” that includes brain regions innervated by dopamine pathways. Using male faces ranging from attractive to extremely unattractive, disfigured ones, this study is the first to demonstrate heightened responses to both rewarding and aversive faces in numerous areas of this putative reward circuit. Parametric analyses employing orthogonal linear and nonlinear regressors revealed positive nonlinear effects in anterior cingulate cortex, lateral orbital frontal cortex (LOFC), striatum (nucleus accumbens, caudate, putamen), and ventral tegmental area, in addition to replicating previously documented linear effects in medial orbital frontal cortex (MOFC) and LOFC and nonlinear effects in amygdala and MOFC. The widespread nonlinear responses are consistent with single cell recordings in animals showing responses to both rewarding and aversive stimuli, and with some human fMRI investigations of non-face stimuli. They indicate that the reward circuit does not process face valence with any simple dissociation of function across structures. Perceiver gender modulated some responses to our male faces: Women showed stronger linear effects, and men showed stronger nonlinear effects, which may have functional implications. Our discovery of nonlinear responses to attractiveness throughout the reward circuit echoes the history of amygdala research: Early work indicated a linear response to threatening stimuli, including faces; later work also revealed a nonlinear response with heightened activation to affectively salient stimuli regardless of valence. The challenge remains to determine how such dual coding influences feelings, such as pleasure and pain, and guides goal-related behavioral responses, such as approach and avoidance.

The objective of this study was to investigate voxel-wise functional connectomics using arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI). Since ASL signal has an intrinsically low signal-to-noise ratio (SNR), the role of denoising is evaluated; in particular, a novel denoising method, dual-tree complex wavelet transform (DT-CWT) combined with the nonlocal means (NLM) algorithm is implemented and evaluated. Simulations were conducted to evaluate the performance of the proposed method in denoising images and in detecting functional networks from noisy data (including the accuracy and sensitivity of detection). In addition, denoising was applied to in vivo ASL datasets, followed by network analysis using graph theoretical approaches. Efficiencies cost was used to evaluate the performance of denoising in detecting functional networks from in vivo ASL fMRI data. Simulations showed that denoising is effective in detecting voxel-wise functional networks from low SNR data and/or from data with small total number of time points. The capability of denoised voxel-wise functional connectivity analysis was also demonstrated with in vivo data. We concluded that denoising is important for voxel-wise functional connectivity using ASL fMRI and that the proposed DT-CWT-NLM method should be a useful ASL preprocessing step.