The blood-brain barrier (BBB), comprised of brain endothelial cells with tight junctions (TJ) between them, regulates the extravasation of molecules and cells into and out of the central nervous system (CNS). Overcoming the difficulty of delivering therapeutic agents to specific regions of the brain presents a major challenge to treatment of a broad range of brain disorders. Current strategies for BBB opening are invasive, not specific, and lack precise control over the site and timing of BBB opening, which may limit their clinical translation. In the present report, we describe a novel approach based on a combination of stem cell delivery, heat-inducible gene expression and mild heating with high-intensity focused ultrasound (HIFU) under MRI guidance to remotely permeabilize BBB. The permeabilization of the BBB will be controlled with, and limited to where selected pro-inflammatory factors will be secreted secondary to HIFU activation, which is in the vicinity of the engineered stem cells and consequently both the primary and secondary disease foci. This therapeutic platform thus represents a non-invasive way for BBB opening with unprecedented spatiotemporal precision, and if properly and specifically modified, can be clinically translated to facilitate delivery of different diagnostic and therapeutic agents which can have great impact in treatment of various disease processes in the central nervous system.

MRI connectomics methods treat the brain as a network and provide new information about its organization, efficiency, and mechanisms of disruption. The most commonly used method of defining network nodes is to register the brain to a standardized anatomical atlas based on the Brodmann areas. This approach is limited by inter-subject variability and can be especially problematic in the context of brain maturation or neuroplasticity (cerebral reorganization after brain damage). In this study, we combined different image processing and network theory methods and created a novel approach that enables atlas-free construction and connection-wise comparison of diffusion MRI-based brain networks. We illustrated the proposed approach in three age groups: neonates, 6-month-old infants, and adults. First, we explored a data-driven method of determining the optimal number of equal-area nodes based on the assumption that all cortical areas of the brain are connected and, thus, no part of the brain is structurally isolated. Second, to enable a connection-wise comparison, alignment to a "reference brain" was performed in the network domain within each group using a matrix alignment algorithm with simulated annealing. The correlation coefficients after pair-wise network alignment ranged from 0.6102 to 0.6673. To test the method's reproducibility, one subject from the 6-month-old group and one from the adult group were scanned twice, resulting in correlation coefficients of 0.7443 and 0.7037, respectively. While being less than 1 due to parcellation and noise, statistically, these values were significantly higher than inter-subject values. Rotation of the parcellation largely explained the variability. Through the abstraction from anatomy, the developed framework allows for a fully network-driven analysis of structural MRI connectomes and can be applied to subjects at any stage of development and with substantial differences in cortical anatomy.

Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment.

High Intensity Focused Ultrasound (HIFU) is an emerging noninvasive, nonionizing physical energy based modality to ablate solid tumors with high power, or increase local permeability in tissues/tumors in pulsed mode with relatively low power. Compared with traditional ablative HIFU, nondestructive pulsed HIFU (pHIFU) is present in the majority of novel applications recently developed for enhancing the delivery of drugs and genes. Previous studies have demonstrated the capability of pHIFU to change tissue local permeability for enhanced drug delivery in both mouse tumors and mouse muscle. Further study based on bulk tissues in large animals and clinical HIFU system revealed correlation between therapeutic effect and thermal parameters, which was absent in the previous mouse studies. In this study, we further investigated the relation between the therapeutic effect of pHIFU and thermal parameters in bulky normal muscle tissues based on a rabbit model and a preclinical HIFU system. Correlation between therapeutic effect and thermal parameters was confirmed in our study on the same bulk tissues although different HIFU systems were used. Following the study in bulky normal muscle tissues, we further created bulky tumor model with VX2 tumors implanted on both hind limbs of rabbits and investigated the feasibility to enhance tumor permeability in bulky VX2 tumors in a rabbit model using pHIFU technique. A radiolabeled peptidomimetic integrin antagonist, ¹¹¹In-DOTA-IA, was used following pHIFU treatment in our study to target VX2 tumor and serve as the radiotracer for follow-up single-photon emission computed tomography (SPECT) scanning. The results have shown significantly elevated uptake of ¹¹¹In-DOTA-IA in the area of VX2 tumors pretreated by pHIFU compared with the control VX2 tumors not being pretreated by pHIFU, and statistical analysis revealed averaged 34.5% enhancement 24h after systematic delivery of ¹¹¹In-DOTA-IA in VX2 tumors pretreated by pHIFU compared with the control VX2 tumors.

Cognitive training interventions are a promising approach to mitigate cognitive deficits common in aging and, ultimately, to improve functioning in older adults. Baseline neural factors, such as properties of brain networks, may predict training outcomes and can be used to improve the effectiveness of interventions. Here, we investigated the relationship between baseline brain network modularity, a measure of the segregation of brain sub-networks, and training-related gains in cognition in older adults. We found that older adults with more segregated brain sub-networks (i.e., more modular networks) at baseline exhibited greater training improvements in the ability to synthesize complex information. Further, the relationship between modularity and training-related gains was more pronounced in sub-networks mediating "associative" functions compared with those involved in sensory-motor processing. These results suggest that assessments of brain networks can be used as a biomarker to guide the implementation of cognitive interventions and improve outcomes across individuals. More broadly, these findings also suggest that properties of brain networks may capture individual differences in learning and neuroplasticity. Trail Registration: ClinicalTrials.gov, NCT#00977418.

Cooperativeness is an essential behavioral trait evolved to facilitate group living. Social and cognitive mechanisms involved in cooperation (e.g., motivation, reward encoding, action evaluation, and executive functions) are sub-served by the striatal-projected circuits, whose physical existence has been confirmed by animal studies, human postmortem studies, and in vivo human brain studies. The current study investigated the associations between Cooperativeness and fiber connectivities from the striatum to nine subcortical and cortical regions, including the amygdala, hippocampus, medial orbitofrontal cortex, lateral orbitofrontal cortex, ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, posterior cingulate cortex/retrosplenial cortex, dorsal cingulate cortex, and rostral cingulate cortex. Results showed that Cooperativeness was negatively correlated with fiber connectivity for the cognitive control system (from the dorsal caudate to the rostral cingulate cortex and ventrolateral prefrontal cortex), but not with fiber connectivity for the social cognitive system (e.g., connectivity with the medial prefrontal cortex and amygdala). These results partially supported Declerck et al.'s (2013) cognitive neural model of the role of cognitive control and social cognition in cooperation.

Efforts to emulate the formidable information processing capabilities of the brain through neuromorphic engineering have been bolstered by recent progress in the fabrication of nonlinear, nanoscale circuit elements that exhibit synapse-like operational characteristics. However, conventional fabrication techniques are unable to efficiently generate structures with the highly complex interconnectivity found in biological neuronal networks. Here we demonstrate the physical realization of a self-assembled neuromorphic device which implements basic concepts of systems neuroscience through a hardware-based platform comprised of over a billion interconnected atomic-switch inorganic synapses embedded in a complex network of silver nanowires. Observations of network activation and passive harmonic generation demonstrate a collective response to input stimulus in agreement with recent theoretical predictions. Further, emergent behaviors unique to the complex network of atomic switches and akin to brain function are observed, namely spatially distributed memory, recurrent dynamics and the activation of feedforward subnetworks. These devices display the functional characteristics required for implementing unconventional, biologically and neurally inspired computational methodologies in a synthetic experimental system.