Changes in Resting-State Brain Activity After Cognitive Behavioral Therapy for Chronic Pain: A Magnetoencephalography Study.

Cognitive behavioral therapy (CBT) is believed to be an effective treatment for chronic pain due to its association with cognitive and emotional factors. Nevertheless, there is a paucity of magnetoencephalography (MEG) investigations elucidating its underlying mechanisms. This study investigated the neurophysiological effects of CBT employing MEG and analytical techniques. We administered resting-state MEG scans to 30 patients with chronic pain and 31 age-matched healthy controls. Patients engaged in a 12-session group CBT program. We conducted pretreatment (T1) and post-treatment (T2) MEG and clinical assessments. MEG data were examined within predefined regions of interest, guided by the authors' and others' prior magnetic resonance imaging studies. Initially, we selected regions displaying significant changes in power spectral density and multiscale entropy between patients at T1 and healthy controls. Then, we examined the changes within these regions after conducting CBT. Furthermore, we applied support vector machine analysis to MEG data to assess the potential for classifying treatment effects. We observed normalization of power in the gamma2 band (61-90 Hz) within the right inferior frontal gyrus (IFG) and multiscale entropy within the right dorsolateral prefrontal cortex (DLPFC) of patients with chronic pain after CBT. Notably, changes in pain intensity before and after CBT positively correlated with the alterations of multiscale entropy. Importantly, responders predicted by the support vector machine classifier had significantly higher treatment improvement rates than nonresponders. These findings underscore the pivotal role of the right IFG and DLPFC in ameliorating pain intensity through CBT. Further accumulation of evidence is essential for future applications. PERSPECTIVE: We conducted MEG scans on 30 patients with chronic pain before and after a CBT program, comparing results with 31 healthy individuals. There were CBT-related changes in the right IFG and DLPFC. These results highlight the importance of specific brain regions in pain reduction through CBT.

Insula-cortico-subcortical networks predict interoceptive awareness and stress resilience.

BACKGROUND: Interoception, the neural sensing of visceral signals, and interoceptive awareness (IA), the conscious perception of interoception, are crucial for life survival functions and mental health. Resilience, the capacity to overcome adversity, has been associated with reduced interoceptive disturbances. Here, we sought evidence for our Insula Modular Active Control (IMAC) model that suggest that the insula, a brain region specialized in the processing of interoceptive information, realizes IA and contributes to resilience and mental health via cortico-subcortical connections. METHODS: 64 healthy participants (32 females; ages 18-34 years) answered questionnaires that assess IA and resilience. Mental health was evaluated with the Beck Depression Inventory II that assesses depressive mood. Participants also underwent a 15 minute resting-state functional resonance imaging session. Pearson correlations and mediation analyses were used to investigate the relationship between IA and resilience and their contributions to depressive mood. We then performed insula seed-based functional connectivity analyzes to identify insula networks involved in IA, resilience and depressive mood. RESULTS: We first demonstrated that resilience mediates the relationship between IA and depressive mood. Second, shared and distinct intra-insula, insula-cortical and insula-subcortical networks were associated with IA, resilience and also predicted the degree of experienced depressive mood. Third, while resilience was associated with stronger insula-precuneus, insula-cerebellum and insula-prefrontal networks, IA was linked with stronger intra-insula, insula-striatum and insula-motor networks. CONCLUSIONS: Our findings help understand the roles of insula-cortico-subcortical networks in IA and resilience. These results also highlight the potential use of insula networks as biomarkers for depression prediction.

Insula neuroanatomical networks predict interoceptive awareness.

Interoceptive awareness (IA), the subjective and conscious perception of visceral and physiological signals from the body, has been associated with functions of cortical and subcortical neural systems involved in emotion control, mood and anxiety disorders. We recently hypothesized that IA and its contributions to mental health are realized by a brain interoception network (BIN) linking brain regions that receive ascending interoceptive information from the brainstem, such as the amygdala, insula and anterior cingulate cortex (ACC). However, little evidence exists to support this hypothesis. In order to test this hypothesis, we used a publicly available dataset that contained both anatomical neuroimaging data and an objective measure of IA assessed with a heartbeat detection task. Whole-brain Voxel-Based Morphometry (VBM) was used to investigate the association of IA with gray matter volume (GMV) and the structural covariance network (SCN) of the amygdala, insula and ACC. The relationship between IA and mental health was investigated with questionnaires that assessed depressive symptoms and anxiety. We found a positive correlation between IA and state anxiety, but not with depressive symptoms. In the VBM analysis, only the GMV of the left anterior insula showed a positive association with IA. A similar association was observed between the parcellated GMV of the left dorsal agranular insula, located in the anterior insula, and IA. The SCN linking the right dorsal agranular insula with the left dorsal agranular insula and left hyper-granular insula were positively correlated with IA. No association between GMV or SCN and depressive symptoms or anxiety were observed. These findings revealed a previously unknown association between IA, insula volume and intra-insula SCNs. These results may support development of non-invasive neuroimaging interventions, e.g., neurofeedback, seeking to improve IA and to prevent development of mental health problems, such anxiety disorders.

The neuroanatomy of social trust predicts depression vulnerability.

Trust attitude is a social personality trait linked with the estimation of others' trustworthiness. Trusting others, however, can have substantial negative effects on mental health, such as the development of depression. Despite significant progress in understanding the neurobiology of trust, whether the neuroanatomy of trust is linked with depression vulnerability remains unknown. To investigate a link between the neuroanatomy of trust and depression vulnerability, we assessed trust and depressive symptoms and employed neuroimaging to acquire brain structure data of healthy participants. A high depressive symptom score was used as an indicator of depression vulnerability. The neuroanatomical results observed with the healthy sample were validated in a sample of clinically diagnosed depressive patients. We found significantly higher depressive symptoms among low trusters than among high trusters. Neuroanatomically, low trusters and depressive patients showed similar volume reduction in brain regions implicated in social cognition, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial PFC, posterior cingulate, precuneus, and angular gyrus. Furthermore, the reduced volume of the DLPFC and precuneus mediated the relationship between trust and depressive symptoms. These findings contribute to understanding social- and neural-markers of depression vulnerability and may inform the development of social interventions to prevent pathological depression.

Understanding the Adsorption and Desorption of Sitagliptin Phosphate Monohydrate on SS2343, Glass, and PTFE Using QCM-D and Raman Spectroscopy.

Cross-contamination during pharmaceutical drug manufacturing can result in expensive recalls. To counter that, companies spend significant time and resources to ensure equipment cleanliness, often relying on the compound solubility data in various solvents as the main indicator of cleaning success. The aim of this work is to provide an alternative way to analyze the fouling and cleaning of surfaces in pharmaceutical manufacturing processes by using the quartz crystal microbalance with dissipation (QCM-D) and Raman spectroscopy. In this study, we chose an active pharmaceutical ingredient (API), sitagliptin phosphate monohydrate (SIT), as the model drug compound and observed its adsorption and desorption on stainless steel (SS2343), borosilicate glass (glass), and polytetrafluoroethylene (PTFE) surfaces. SIT was selected as the model API since it is a product manufactured on a large scale and is part of the widely used dipeptidyl peptidase-IV inhibitor class of oral hypoglycemics used to treat type 2 diabetes mellitus, while the chosen surfaces mimic the wall materials of manufacturing equipment and components such as reactors, transfer lines, and valves. Both the QCM-D and Raman spectroscopy results show the highest physisorption on PTFE, followed by SS2343 and glass. Additionally, QCM-D revealed a harder removal of SIT from SS2343 compared to glass and PTFE. Raman analysis of the chemical interactions disclosed C-F and C═O bond interactions between SIT and the surfaces, and the lack of a peak shift suggested dipole-dipole interactions. Furthermore, contact angle measurements indicate that hydrophobic attraction contributed to SIT adhesion to the PTFE surface. Subsequently, SIT coverage upon deposition on a PTFE surface has a significantly smaller surface area than on SS2343 and glass due to surface hydrophobicity, hence resulting in a longer removal time. These results provide a practical use of QCM-D and Raman spectroscopy to enhance the understanding of fouling and improve the cleaning of complex small molecules on relevant surfaces during the pharmaceutical manufacturing process.

A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea.

Objective. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.Approach. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.Main results. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.Significance. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.

Longitudinal dispersion of microplastics in aquatic flows using fluorometric techniques.

Microplastics are an emerging environmental contaminant. Existing knowledge on the precise transport processes involved in the movement of microplastics in natural water bodies is limited. Microplastic fate-transport models rely on numerical simulations with limited empirical data to support and validate these models. We adopted fluorometric principles to track the movement of both fluorescent dye and florescent stained microplastics (polyethylene) in purpose-built laboratory flumes with standard fibre-optic fluorometers. Neutrally buoyant microplastics behaved in the same manner as a solute (Rhodamine) and more importantly displayed classical fundamental dispersion theory in uniform open channel flow. This suggests Rhodamine, a fluorescent tracer, can be released into the natural environment with the potential to mimic microplastic movement in the water column.

Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition.

The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.

Unique Fluorescent Imaging Probe for Bacterial Surface Localization and Resistant Enzyme Imaging.

Emergence of antibiotic bacterial resistance has caused serious clinical issues worldwide due to increasingly difficult treatment. Development of a specific approach for selective visualization of resistant bacteria will be highly significant for clinical investigations to promote timely diagnosis and treatment of bacterial infections. In this article, we present an effective method that not only is able to selectively recognize drug resistant AmpC ß-lactamases enzyme but, more importantly, is able to interact with bacterial cell wall components, resulting in a desired localization effect on the bacterial surface. A unique and specific enzyme-responsive cephalosporin probe (DFD-1) has been developed for the selective recognition of resistance bacteria AmpC ß-lactamase, by employing fluorescence resonance energy transfer with an "off-on" bioimaging. To achieve the desired localization, a lipid-azide conjugate (LA-12) was utilized to facilitate its penetration into the bacterial surface, followed by copper-free click chemistry. This enables the probe DFD-1 to be anchored onto the cell surface. In the presence of AmpC enzymes, the cephalosporin ß-lactam ring on DFD-1 will be hydrolyzed, leading to the quencher release, thus generating fluorescence for real-time resistant bacterial screening. More importantly, the bulky dibenzocyclooctyne group in DFD-1 allowed selective recognition toward the AmpC bacterial enzyme instead of its counterpart ( e.g., TEM-1 ß-lactamase). Both live cell imaging and cell cytometry assays showed the great selectivity of DFD-1 to drug resistant bacterial pathogens containing the AmpC enzyme with significant fluorescence enhancement (∼67-fold). This probe presented promising capability to selectively localize and screen for AmpC resistance bacteria, providing great promise for clinical microbiological applications.

Gold(i)-catalyzed 6-endo-dig azide-yne cyclization: efficient access to 2H-1,3-oxazines.

Denitrogenative 6-endo-dig azide-yne cyclization of α-propargyloxy-ß-haloalkylazides was enabled by gold catalysis, thus providing 2H-1,3-oxazines. This rare cyclization mode in gold-catalyzed reactions of azide-yne substrates was demonstrated to be facilitated and controlled by electronic and resonance effects of the alkyne substituents. Molecular transformations of the as-prepared 2H-1,3-oxazines were also investigated.

Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals.

Functional connectivity calculated using multiple channels of electromagnetic brain signals is often over- or underestimated due to volume conduction or field spread. Considering connectivity measures, coherence is suitable for the detection of rhythmic synchronization, whereas temporal correlation is appropriate for transient synchronization. This paper presents a beamformer-based imaging method, called spatiotemporal imaging of linearly-related source component (SILSC), which is capable of estimating connectivity at the cortical level by extracting the source component with the maximum temporal correlation between the activity of each targeted region and a reference signal. The spatiotemporal correlation dynamics can be obtained by applying SILSC at every brain region and with various time latencies. The results of six simulation studies demonstrated that SILSC is sensitive to detect the source activity correlated to the specified reference signal and is accurate and robust to noise in terms of source localization. In a facial expression imitation experiment, the correlation dynamics estimated by SILSC revealed the regions with mirror properties and the regions involved in motor control network when performing the imitation and execution tasks, respectively, with the left inferior frontal gyrus specified as the reference region.

Selection of independent components based on cortical mapping of electromagnetic activity.

Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones. In this work, we proposed two component selection methods, both of which first estimate the cortical distribution of the brain activity for each component, and then determine the functional components based on the parcellation of brain activity mapped onto the cortical surface. Among all independent components, the first method can identify the dominant components, which have strong activity in the selected dominant brain regions, whereas the second method can identify those inter-regional associating components, which have similar component spectra between a pair of regions. For a targeted region, its component spectrum enumerates the amplitudes of its parceled brain activity across all components. The selected functional components can be remixed to reconstruct the focused electromagnetic signals for further analysis, such as source estimation. Moreover, the inter-regional associating components can be used to estimate the functional brain network. The accuracy of the cortical activation estimation was evaluated on the data from simulation studies, whereas the usefulness and feasibility of the component selection methods were demonstrated on the magnetoencephalography data recorded from a gender discrimination study.

Maximum multiple-correlation beamformer for estimating source connectivities in electromagnetic brain activities.

Synchrony is a phenomenon of local-scale and long-range integrations within a brain circuit. Synchronous activities manifest themselves in similar temporal structures that can be statistically quantified by temporal correlation. In previous studies, synchronous activities were estimated by calculating the correlation coefficient or coherence between a single reference signal and the activity in a brain region. However, a brain circuit may involve multiple brain regions and these regions may communicate to each other through different temporal patterns. Therefore, temporal correlation to multiple reference signals is effective in quantify the source connectivities in the brain. This paper proposes a novel algorithm to calculate the maximum multiple-correlation for each brain region which has an activity estimated by a beamformer. Furthermore, this algorithm can accommodate various latencies of activities in a circuit. Experimental results demonstrate that the proposed method can accurately detect source activities correlated to the given multiple reference signals, even when unknown latencies exist between the source and references.