Functional connectivity of the language area in migraine: a preliminary classification model.

BACKGROUND: Migraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and treatment. METHODS: Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI(rfMRI), and diffusion weighted scans were obtained from 31 patients with migraine, and 17 controls. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into diagnostic groups based on functional connectivity (FC) and derive networks and parcels contributing to the model. PageRank centrality analysis was also performed on the structural connectome to identify changes in hubness. RESULTS: Our model attained an area under the receiver operating characteristic curve (AUC-ROC) of 0.68, which rose to 0.86 following hyperparameter tuning. FC of the language network was most predictive of the model's classification, though patients with migraine also demonstrated differences in the accessory language, visual and medial temporal regions. Several analogous regions in the right hemisphere demonstrated changes in PageRank centrality, suggesting possible compensation. CONCLUSIONS: Although our small sample size demands caution, our preliminary findings demonstrate the utility of our method in providing a network-based perspective to diagnosis and treatment of migraine.

Connectomics underlying motor functional outcomes in the acute period following stroke.

Objective: Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify markers of functional outcomes. Methods: Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke. Sixteen models were constructed to predict performance across six tests of motor impairment, spasticity, and activities of daily living, using either whole-brain structural or functional connectivity. Feature importance analysis was also performed to identify brain regions and networks associated with performance in each test. Results: The area under the receiver operating characteristic curve ranged from 0.650 to 0.868. Models utilizing functional connectivity tended to have better performance than those utilizing structural connectivity. The Dorsal and Ventral Attention Networks were among the top three features in several structural and functional models, while the Language and Accessory Language Networks were most commonly implicated in structural models. Conclusions: Our study highlights the potential of machine learning methods combined with connectivity analysis in predicting outcomes in neurorehabilitation and disentangling the neural correlates of functional impairments, though further longitudinal studies are necessary.

Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia.

Objective: Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities. Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups. Results: Subjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls. Conclusion: Our methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia.

Connectomic disturbances underlying insomnia disorder and predictors of treatment response.

Objective: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy. Materials and methods: 51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up. Results: Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change. Conclusion: Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.

Converging 2D Nanomaterials and 3D Bioprinting Technology: State-of-the-Art, Challenges, and Potential Outlook in Biomedical Applications.

The development of next-generation of bioinks aims to fabricate anatomical size 3D scaffold with high printability and biocompatibility. Along with the progress in 3D bioprinting, 2D nanomaterials (2D NMs) prove to be emerging frontiers in the development of advanced materials owing to their extraordinary properties. Harnessing the properties of 2D NMs in 3D bioprinting technologies can revolutionize the development of bioinks by endowing new functionalities to the current bioinks. First the main contributions of 2D NMS in 3D bioprinting technologies are categorized here into six main classes: 1) reinforcement effect, 2) delivery of bioactive molecules, 3) improved electrical conductivity, 4) enhanced tissue formation, 5) photothermal effect, 6) and stronger antibacterial properties. Next, the recent advances in the use of each certain 2D NMs (1) graphene, 2) nanosilicate, 3) black phosphorus, 4) MXene, 5) transition metal dichalcogenides, 6) hexagonal boron nitride, and 7) metal-organic frameworks) in 3D bioprinting technology are critically summarized and evaluated thoroughly. Third, the role of physicochemical properties of 2D NMSs on their cytotoxicity is uncovered, with several representative examples of each studied 2D NMs. Finally, current challenges, opportunities, and outlook for the development of nanocomposite bioinks are discussed thoroughly.

Biodegradable and biocompatible graphene-based scaffolds for functional neural tissue engineering: A strategy approach using dental pulp stem cells and biomaterials.

Neural tissue engineering aims to restore the function of nervous system tissues using biocompatible cell-seeded scaffolds. Graphene-based scaffolds combined with stem cells deserve special attention to enhance tissue regeneration in a controlled manner. However, it is believed that minor changes in scaffold biomaterial composition, internal porous structure, and physicochemical properties can impact cellular growth and adhesion. The current work aims to investigate in vitro biological effects of three-dimensional (3D) graphene oxide (GO)/sodium alginate (GOSA) and reduced GOSA (RGOSA) scaffolds on dental pulp stem cells (DPSCs) in terms of cell viability and cytotoxicity. Herein, the effects of the 3D scaffolds, coating conditions, and serum supplementation on DPSCs functions are explored extensively. Biodegradation analysis revealed that the addition of GO enhanced the degradation rate of composite scaffolds. Compared to the 2D surface, the cell viability of 3D scaffolds was higher (p < 0.0001), highlighting the optimal initial cell adhesion to the scaffold surface and cell migration through pores. Moreover, the cytotoxicity study indicated that the incorporation of graphene supported higher DPSCs viability. It is also shown that when the mean pore size of the scaffold increases, DPSCs activity decreases. In terms of coating conditions, poly- l-lysine was the most robust coating reagent that improved cell-scaffold adherence and DPSCs metabolism activity. The cytotoxicity of GO-based scaffolds showed that DPSCs can be seeded in serum-free media without cytotoxic effects. This is critical for human translation as cellular transplants are typically serum-free. These findings suggest that proposed 3D GO-based scaffolds have favorable effects on the biological responses of DPSCs.

Advancing fabrication and properties of three-dimensional graphene-alginate scaffolds for application in neural tissue engineering.

Neural tissue engineering provides enormous potential for restoring and improving the function of diseased/damaged tissues and promising opportunities in regenerative medicine, stem cell technology, and drug discovery. The conventional 2D cell cultures have many limitations to provide informative and realistic neural interactions and network formation. Hence, there is a need to develop three-dimensional (3D) bioscaffolds to facilitate culturing cells with matched microenvironment for cell growth and interconnected pores for penetration and migration of cells. Herein, we report the synthesis and characterization of 3D composite bioscaffolds based on graphene-biopolymer with porous structure and improved performance for tissue engineering. A simple, eco-friendly synthetic method is introduced and optimized for synthesis of this hybrid fibrous scaffold by combining Graphene Oxide (GO) and Sodium Alginate (Na-ALG) which are specifically selected to match the mechanical strength of the central nervous system (CNS) tissue and provide porous structure for connective tissue engineering. Properties of the developed scaffold in terms of the structure, porosity, thermal stability, mechanical properties, and electrical conductivity are presented. These properties were optimised through key synthesis conditions including GO concentrations, reduction process and crosslinking time. In contrast to other studies, the presented structure maintains its stability in aqueous media and uses a bio-friendly reducing agent which enable the structure to enhance neuron cell interactions and act as nerve conduits for neurological diseases.

Carbon-Based Nanobiohybrid Thin Film for Amperometric Glucose Sensing.

This pioneering study involved the fabrication of a new class of nanohybrid-based electrochemical glucose biosensor. First, three-dimensional (3D) graphene was fabricated as a platform of multiwalled carbon nanotube (MWCNT). Then, it was used to immobilize glucose oxidase (GOD) on nanohybrid thin film via the entrapment technique. The modified glucose biosensor indicated excellent biocatalytic activity toward the glucose measurment with a sensitivity of up to 49.58 µA mM-1 cm-2 and a wide linear dynamic range up to 16 mM. The fabricated biosensor shows an excellent stability of 87.8%, with its current diminishing after 3 months. This facile and simple electrochemical method for glucose monitoring using a modified glassy carbon electrode (GCE) by 3DG-MWCNT-GOD could open new avenues in producing of a inexpensive and sensitive glucose nanobiosensors.

The influence of topography on tissue engineering perspective.

The actual in vivo tissue scaffold offers a three-dimensional (3D) structural support along with a nano-textured surfaces consist of a fibrous network in order to deliver cell adhesion and signaling. A scaffold is required, until the tissue is entirely regenerated or restored, to act as a temporary ingrowth template for cell proliferation and extracellular matrix (ECM) deposition. This review depicts some of the most significant three dimensional structure materials used as scaffolds in various tissue engineering application fields currently being employed to mimic in vivo features. Accordingly, some of the researchers' attempts have envisioned utilizing graphene for the fabrication of porous and flexible 3D scaffolds. The main focus of this paper is to evaluate the topographical and topological optimization of scaffolds for tissue engineering applications in order to improve scaffolds' mechanical performances.