This article demonstrates an inverse emulsion scheme to polymerize a variety of ionizable and non-ionizable monomers to form stimuli-responsive nanoparticles with potential applications in biomaterials science.

Functional connectivity (FC) between brain regions tracks symptom severity in many neuropsychiatric disorders. Transcranial magnetic stimulation (TMS) directly alters regional activity and indirectly alters FC. Predicting how FC will change following TMS is difficult, but would allow novel therapies that target FC to improve symptoms. We address this challenge by proposing a predictive model that explains how TMS-induced activation can change the strength of FC. Here, we focus on the FC of the frontoparietal (FPS) and default mode (DMS) systems given the importance of their FC in executive function and affective disorders. We fit this model to neuroimaging data in 29 individuals who received TMS to the frontal cortex and evaluated the FC between the FPS and DMS. For each individual, we measured the TMS-induced change in FC between the FPS and DMS (the FC network), and the structural coupling between the stimulated area and the FPS and DMS (the structural context network (SCN)). We find that TMS-induced FC changes are best predicted when the model accounts for white matter fibers from the stimulated area to the two systems. We find that the correlation between these two networks (structure-function coupling) - and therefore the predictability of the TMS-induced modulation - was highest when the SCN contained a dense core of intraconnected regions, indicating that the stimulated area had ample access to an anatomical module. Further, we found that when the core of the SCN overlapped with the FPS and DMS, we observed the greatest change in the strength of their FC. Broadly, our findings explain how the structural connectivity of a stimulated region modulates TMS-induced changes in the brain's functional network. Efforts to account for such structural connections could improve predictions of TMS response, further informing the development of TMS protocols for clinical translation.

Recent advancements in molecular recognition have provided additional diagnostic and treatment approaches for multiple diseases, including autoimmune disorders and cancers. Research investigating how the composition of biological fluids is altered during disease progression, including differences in the expression of the small molecules, proteins, RNAs, and other components present in patient tears, saliva, blood, urine, or other fluids, has provided a wealth of potential candidates for early disease screening; however, adoption of biomarker screening into clinical settings has been challenged by the need for more robust, low-cost, and high-throughput assays. This review examines current approaches in molecular recognition and biosensing for the quantification of biomarkers for disease screening and diagnostic outcomes.

Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.

Autoimmune diseases are a group of debilitating illnesses that are often idiopathic in nature. The steady rise in the prevalence of these conditions warrants new approaches for diagnosis and treatment. Stimuli-responsive biomaterials also known as "smart," "intelligent," or "recognitive" biomaterials are widely studied for their applications in drug delivery, biosensing, and tissue engineering due to their ability to produce thermal, optical, chemical, or structural changes upon interacting with the biological environment. Studies within the last decade that harness the recognitive capabilities of these biomaterials toward the development of novel detection and treatment options for autoimmune diseases are critically analyzed.

Engineered microscale hydrogels have emerged as promising therapeutic approaches for the treatment of various diseases. These microgels find wide application in the biomedical field because of the ease of injectability, controlled release of therapeutics, flexible means of synthesis, associated tunability, and can be engineered as stimuli-responsive. While bulk hydrogels of several length-scale dimensions have been used for over two decades in drug delivery applications, their use as microscale carriers of drug and cell-based therapies is relatively new. Herein, we critically summarize the fundamentals of hydrogels based on their equilibrium and dynamics of their molecular structure, as well as solute diffusion as it relates to drug delivery. In addition, examples of common microgel synthesis techniques are provided. The ability to tune microscale hydrogels to obtain controlled release of therapeutics is discussed, along with microgel considerations for cell encapsulation as it relates to the development of cell-based therapies. We conclude with an outlook on the use of microgels for cell sequencing, and the convergence of the use of microscale hydrogels for drug delivery, cell therapy, and cell sequencing based systems.

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.

Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging -- and to assess their dynamics during learning -- remain limited. Here, we describe an approach based on a novel application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning.

In the emerging field of network neuroscience, the brain is represented as a network of discrete yet functionally and structurally interconnected areas. Mathematical and computational tools to characterize the organization of this network can provide insights into the principles guiding brain structure and function, and can pinpoint differences between healthy individuals and individuals suffering from psychiatric disease or neurological disorders. The field is now faced with the question of how to devise clinical interventions that target these network alterations. Potential solutions to this question include the combination of emerging theories of network control with cutting-edge interventions such as neurofeedback. Each of these techniques may now be mature enough to combine to obtain a theoretically-motivated framework informing viable neuropsychiatric therapies.

A major challenge in neuroimaging is understanding the mapping of neurophysiological dynamics onto cognitive functions. Traditionally, these maps have been constructed by examining changes in the activity magnitude of regions related to task performance. Recently, network neuroscience has produced methods to map connectivity patterns among many regions to certain cognitive functions by drawing on tools from network science and graph theory. However, these two different views are rarely addressed simultaneously, largely because few tools exist that account for patterns between nodes while simultaneously considering activation of nodes. We address this gap by solving the problem of community detection on weighted networks with continuous (non-integer) annotations by deriving a generative probabilistic model. This model generates communities whose members connect densely to nodes within their own community, and whose members share similar annotation values. We demonstrate the utility of the model in the context of neuroimaging data gathered during a motor learning paradigm, where edges are task-based functional connectivity and annotations to each node are beta weights from a general linear model that encoded a linear decrease in blood-oxygen-level-dependent signal with practice. Interestingly, we observe that individuals who learn at a faster rate exhibit the greatest dissimilarity between functional connectivity and activation magnitudes, suggesting that activation and functional connectivity are distinct dimensions of neurophysiology that track behavioral change. More generally, the tool that we develop offers an explicit, mathematically principled link between functional activation and functional connectivity, and can readily be applied to a other similar problems in which one set of imaging data offers network data, and a second offers a regional attribute.

BACKGROUND:Neonatal seizures can result in chronic epilepsy and long-term behavioral and cognitive deficits. Levetiracetam (LEV), an antiepileptic drug that binds to the synaptic vesicle protein 2A (SV2A), has been increasingly used off-label for the therapy of neonatal seizures. Preclinical data regarding the acute or long-term efficacy of LEV are lacking.METHODS:We tested the anticonvulsant efficacy of LEV in a rat model of hypoxia-induced neonatal seizures. In addition, we evaluated the protective effects of postnatal day (P)10 LEV treatment on later-life kainic acid (KA)-induced seizure susceptibility and seizure-induced neuronal injury. Western blot and immunohistochemistry were used to assess the developmental regulation of SV2A in the rat and human brain.RESULTS:LEV pretreatment at P10 significantly decreased the cumulative duration of behavioral and electrographic seizures at both 25 and 50 mg/kg. At P40, KA-induced seizures and neuronal loss were significantly diminished in rats previously treated with LEV. LEV target SV2A is present in both neonatal rat and human brain and increases steadily to adulthood.CONCLUSION:LEV suppressed acute seizures induced by perinatal hypoxia and diminished later-life seizure susceptibility and seizure-induced neuronal injury, providing evidence for disease modification. These results support consideration of a clinical trial of LEV in neonatal seizures.

D-2 and H-2 Stark recurrence spectra have been measured at scaled energies of epsilon = -2.3 and epsilon = -3.2. An isotope shift in the scaled-action locations of recurrence peaks is found and explained by the increased density of states associated with perturbing rotational series. This reinforces the insight gained in previous work on the correspondence between the structures of a quantum spectrum and its classical periodic orbits.

D${}_{2}$ and H${}_{2}$ Stark recurrence spectra have been measured at scaled energies of $\ensuremath{\epsilon}=\ensuremath{-}2.3$ and $\ensuremath{\epsilon}=\ensuremath{-}3.2$. An isotope shift in the scaled-action locations of recurrence peaks is found and explained by the increased density of states associated with perturbing rotational series. This reinforces the insight gained in previous work on the correspondence between the structures of a quantum spectrum and its classical periodic orbits.

Models of premature brain injury have largely focused on the white matter injury thought to underlie periventricular leukomalacia (PVL). However, with increased survival of very low birth weight infants, injury patterns involving grey matter are now recognized. We aimed to determine how grey matter lesions relate to hypoxic-ischemic- (HI) mediated white matter injury by modifying our rat model of PVL. Following HI, microglial infiltration, astrocytosis, and neuronal and axonal degeneration increased in a region-specific manner dependent on the severity of myelin loss in pericallosal white matter. The spectrum of injury ranged from mild, where diffuse white matter abnormalities were dominant and were associated with mild axonal injury and local microglial activation, to severe HI injury characterized by focal MBP loss, widespread neuronal degeneration, axonal damage, and gliosis throughout the neocortex, caudate putamen, and thalamus. In sum, selective regional white matter loss occurs in the preterm rat concomitantly with a clinically relevant spectrum of grey matter injury. These data demonstrate an interspecies similarity of brain injury patterns and further substantiates the reliable use of this model for the study of preterm brain injury.

With improvements in surgical techniques, implant design, and patient caremaps, surgeons have sought to accelerate early rehabilitation after total hip arthroplasty. Many authors have reported results of fundamentally similar protocols to achieve this end. These protocols focus on multi-modal pain management, early therapy, tissue-preserving surgical technique, and careful blood management. We present the implementation and results of such a protocol involving a different surgical approach, and highlight the published literature on this topic.