Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state‐level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS‐fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS‐fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS‐fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.

Diffusion tensor imaging (DTI) is a powerful method to visualize white matter, but its use in patients with acute stroke remains limited because of the lack of corresponding histologic information. In this study, we addressed this issue using a hypoxia-ischemia (HI)-induced thrombotic model of stroke in adult mice. At 6, 15, and 24 hours after injury, animals were divided into three groups for (1) in vivo T2-and diffusion-weighted magnetic resonance imaging, followed by histochemistry, (2) ex vivo DTI and electron microscopy, and (3) additional biochemical or immunochemical assays. The temporal changes of diffusion anisotropy and histopathology were compared in the fimbria, internal capsule, and external capsule. We found that HI caused a rapid reduction of axial and radial diffusivities in all three axonal bundles. A large decrease in fractional anisotropy, but not in axial diffusivity per se, was associated with structural breakdown of axons. Furthermore, the decrease in radial diffusivity correlated with swelling of myelin sheaths and compression of the axoplasma. The gray matter of the hippocampus also exhibited a high level of diffusion anisotropy, and its reduction signified dendritic degeneration. Taken together, these results suggest that crossevaluation of multiple DTI parameters may provide a fuller picture of axonal and dendritic injury in acute ischemic stroke.

Disruption of the integrity of the blood-brain barrier (BBB) is an important mechanism of cerebrovascular diseases, including neonatal cerebral hypoxia-ischemia (HI). Although both tissue-type plasminogen activator (tPA) and matrix metalloproteinase-9 (MMP-9) can produce BBB damage, their relationship in neonatal cerebral HI is unclear. Here we use a rodent model to test whether the plasminogen activator (PA) system is critical for MMP-9 activation and HI-induced brain injury in newborns. To test this hypothesis, we examined the therapeutic effect of intracerebroventricular injection of plasminogen activator inhibitor-1 (PAI-1) in rat pups subjected to unilateral carotid artery occlusion and systemic hypoxia. We found that the injection of PAI-1 greatly reduced the activity of both tPA and urokinase-type plasminogen activator after HI. It also blocked HI-induced MMP-9 activation and BBB permeability at 24 h of recovery. Furthermore, magnetic resonance imaging and histological analysis showed the PAI-1 treatment reduced brain edema, axonal degeneration, and cortical cell death at 24 -48 h of recovery. Finally, the PAI-1 therapy provided a dose-dependent decrease of brain tissue loss at 7 d of recovery, with the therapeutic window at 4 h after the HI insult. Together, these results suggest that the brain PA system plays a pivotal role in neonatal cerebral HI and may be a promising therapeutic target in infants suffering hypoxic-ischemic encephalopathy.

There currently remains considerable variability in stroke survivor recovery. To address this, developing individualized treatment has become an important goal in stroke treatment. As a first step, it is necessary to determine brain dynamics associated with stroke and recovery. While recent methods have made strides in this direction, we still lack physiological biomarkers. The Virtual Brain (TVB) is a novel application for modeling brain dynamics that simulates an individual’s brain activity by integrating their own neuroimaging data with local biophysical models. Here, we give a detailed description of the TVB modeling process and explore model parameters associated with stroke. In order to establish a parallel between this new type of modeling and those currently in use, in this work we establish an association between a specific TVB parameter (long-range coupling) that increases after stroke with metrics derived from graph analysis. We used TVB to simulate the individual BOLD signals for 20 patients with stroke and 10 healthy controls. We performed graph analysis on their structural connectivity matrices calculating degree centrality, betweenness centrality, and global efficiency. Linear regression analysis demonstrated that long-range coupling is negatively correlated with global efficiency (P = 0.038), but is not correlated with degree centrality or betweenness centrality. Our results suggest that the larger influence of local dynamics seen through the long-range coupling parameter is closely associated with a decreased efficiency of the system. We thus propose that the increase in the long-range parameter in TVB (indicating a bias toward local over global dynamics) is deleterious because it reduces communication as suggested by the decrease in efficiency. The new model platform TVB hence provides a novel perspective to understanding biophysical parameters responsible for global brain dynamics after stroke, allowing the design of focused therapeutic interventions.