Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity.

Background: The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates. Methods: Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed. Results: Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM. Discussion: Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics.

Slow oscillation-spindle cross-frequency coupling predicts overnight declarative memory consolidation in older adults.

Cross-frequency coupling (CFC) between brain oscillations during non-rapid-eye-movement (NREM) sleep (e.g. slow oscillations [SO] and spindles) may be a neural mechanism of overnight memory consolidation. Declines in CFC across the lifespan might accompany coinciding memory problems with ageing. However, there are few reports of CFC changes during sleep after learning in older adults, controlling for baseline effects. Our objective was to examine NREM CFC in healthy older adults, with an emphasis on spindle activity and SOs from frontal electroencephalogram (EEG), during a learning night after a declarative learning task, as compared to a baseline night without learning. Twenty-five older adults (M [SD] age = 69.12 [5.53] years; 64% female) completed a two-night study, with a pre- and post-sleep word-pair associates task completed on the second night. SO-spindle coupling strength and a measure of coupling phase distance from the SO up-state were both examined for between-night differences and associations with memory consolidation. Coupling strength and phase distance from the up-state peak were both stable between nights. Change in coupling strength between nights was not associated with memory consolidation, but a shift in coupling phase towards (vs. away from) the up-state peak after learning predicted better memory consolidation. Also, an exploratory interaction model suggested that associations between coupling phase closer to the up-state peak and memory consolidation may be moderated by higher (vs. lower) coupling strength. This study supports a role for NREM CFC in sleep-related memory consolidation in older adults.

Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia.

Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigated dynamical properties of the resting-state electroencephalogram of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. We then studied the relation of these dynamic properties with the perturbational complexity index (PCI), which has shown remarkably high sensitivity in detecting consciousness independent of behavior. All participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams)., enabling an experimental dissociation between unresponsiveness and unconsciousness. We estimated (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related measures, and found that states of unconsciousness were characterized by a distancing from both the edge of activity propagation and the edge of chaos. We were then able to predict individual subjects' PCI (i.e., PCImax) with a mean absolute error below 7%. Our results establish a firm link between the PCI and criticality and provide further evidence for the role of criticality in the emergence of consciousness.

Dissecting the phase separation and oligomerization activities of the carboxysome positioning protein McdB.

Across bacteria, protein-based organelles called bacterial microcompartments (BMCs) encapsulate key enzymes to regulate their activities. The model BMC is the carboxysome that encapsulates enzymes for CO2 fixation to increase efficiency and is found in many autotrophic bacteria, such as cyanobacteria. Despite their importance in the global carbon cycle, little is known about how carboxysomes are spatially regulated. We recently identified the two-factor system required for the maintenance of carboxysome distribution (McdAB). McdA drives the equal spacing of carboxysomes via interactions with McdB, which associates with carboxysomes. McdA is a ParA/MinD ATPase, a protein family well studied in positioning diverse cellular structures in bacteria. However, the adaptor proteins like McdB that connect these ATPases to their cargos are extremely diverse. In fact, McdB represents a completely unstudied class of proteins. Despite the diversity, many adaptor proteins undergo phase separation, but functional roles remain unclear. Here, we define the domain architecture of McdB from the model cyanobacterium Synechococcus elongatus PCC 7942, and dissect its mode of biomolecular condensate formation. We identify an N-terminal intrinsically disordered region (IDR) that modulates condensate solubility, a central coiled-coil dimerizing domain that drives condensate formation, and a C-terminal domain that trimerizes McdB dimers and provides increased valency for condensate formation. We then identify critical basic residues in the IDR, which we mutate to glutamines to solubilize condensates. Finally, we find that a condensate-defective mutant of McdB has altered association with carboxysomes and influences carboxysome enzyme content. The results have broad implications for understanding spatial organization of BMCs and the molecular grammar of protein condensates.

Cells contain many millions of protein molecules that must be in the right place at the right time to carry out their roles. A process called phase separation, in which a well-mixed solution separates into two phases ­ one concentrated and one dilute ­ is thought to help organize the contents of various cell types. The single-celled bacteria Synechococcus elongatus converts carbon dioxide from the air into sugars using internal reaction centers. This process depends on a protein called McdB which is crucial for spatially organizing these centers. McdB readily phase separates on its own in a test tube, raising the possibility that this phenomenon could be involved in the carbon dioxide-capturing process. To investigate, Basalla et al. identified the parts of McdB responsible for phase separation and modified them to make a version that was less able to separate. When viewed under the microscope, Synechococcus elongatus cells containing the altered McdB showed changes in the organization and structure of the reaction centers. This suggests that phase separation by McdB is required for optimal carbon capture by this bacterium. In the future, manipulation of McdB phase separation could be used to improve carbon capture technologies or enhance crop growth. Phase separation is also known to influence complex disease. Therefore, further understanding of the process could be important for developing new disease treatments.

In Salmonella enterica, the pathogenicity island 2 (SPI-2) regulator PagR regulates its own expression and the expression of a five-gene operon that encodes transketolase C.

The enteropathogen Salmonella enterica subsp. enterica sv. Typhimurium str. LT2 (hereafter S. Typhimurium) utilizes a cluster of genes encoded within the pathogenicity island 2 (SPI-2) of its genome to proliferate inside macrophages. The expression of SPI-2 is controlled by a complex network of transcriptional regulators and environmental cues, which now include a recently characterized DNA-binding protein named PagR. Growth of S. Typhimurium in low-phosphate, low-magnesium medium mimics conditions inside macrophages. Under such conditions, PagR ensures SPI-2 induction by upregulating the transcription of slyA, which encodes a known activator of SPI-2. Here, we report that PagR represses the expression of a divergently transcribed polycistronic operon that encodes the two subunits of transketolase TktC (i.e., tktD, tktE) of this bacterium. Transketolases contribute to the nonredox rearrangements of phosphorylated sugars of the pentose phosphate pathway, which provide building blocks for amino acids, nucleotides, cofactors, etc. We also demonstrate that PagR represses the expression of its own gene and define two PagR-binding sites between stm2344 and pagR.

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data.

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.

Myocardial Characterization with Extracellular Volume Mapping with a First-Generation Photon-counting Detector CT with MRI Reference.

Background Photon-counting detector (PCD) CT provides comprehensive spectral data with every acquisition, but studies evaluating myocardial extracellular volume (ECV) quantification with use of PCD CT compared with an MRI reference remain lacking. Purpose To compare ECV quantification for myocardial tissue characterization between a first-generation PCD CT system and cardiac MRI. Materials and Methods In this single-center prospective study, adults without contraindication to iodine-based contrast media underwent same-day cardiac PCD CT and MRI with native and postcontrast T1 mapping and late gadolinium enhancement for various clinical indications for cardiac MRI (the reference standard) between July 2021 and January 2022. Global and midventricular ECV were assessed with use of three methods: single-energy PCD CT, dual-energy PCD CT, and MRI T1 mapping. Quantitative comparisons among all techniques were performed. Correlation and reliability between different methods of ECV quantification were assessed with use of the Pearson correlation coefficient (r) and the intraclass correlation coefficient. Results The final sample included 29 study participants (mean age ± SD, 54 years ± 17; 15 men). There was a strong correlation of ECV between dual- and single-energy PCD CT (r = 0.91, P < .001). Radiation dose was 40% lower with dual-energy versus single-energy PCD CT (volume CT dose index, 10.1 mGy vs 16.8 mGy, respectively; P < .001). In comparison with MRI, dual-energy PCD CT showed strong correlation (r = 0.82 and 0.91, both P < .001) and good to excellent reliability (intraclass correlation coefficients, 0.81 and 0.90) for midventricular and global ECV quantification, but it overestimated ECV by approximately 2%. Single-energy PCD CT showed similar relationship with MRI but underestimated ECV by 3%. Conclusion Myocardial tissue characterization with photon-counting detector CT-based quantitative extracellular volume analysis showed a strong correlation to MRI. © RSNA, 2023 Supplemental material is available for this article.

Interventions to improve the implementation of evidence-based healthcare in prisons: a scoping review.

BACKGROUND: There are challenges to delivering high quality primary care within prison settings and well-recognised gaps between evidence and practice. There is a growing body of literature evaluating interventions to implement evidence-based practice in the general population, yet the extent and rigour of such evaluations in incarcerated populations are unknown. We therefore conducted a scoping literature review to identify and describe evaluations of implementation interventions in the prison setting. METHODS: We searched EMBASE, MEDLINE, CINAHL Plus, Scopus, and grey literature up to August 2021, supplemented by hand searching. Search terms included prisons, evidence-based practice, and implementation science with relevant synonyms. Two reviewers independently selected studies for inclusion. Data extraction included study populations, study design, outcomes, and author conclusions. We took a narrative approach to data synthesis. We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance for scoping reviews. RESULTS: Fifteen studies reported in 17 papers comprised one randomised controlled trial, one controlled interrupted time series analysis and 13 uncontrolled before and after studies. Eight studies took place in the US and four in the UK. Ten studies evaluated combined (multifaceted) interventions, typically including education for staff or patients. Interventions most commonly targeted communicable diseases, mental health and screening uptake. Thirteen studies reported adherence to processes of care, mainly testing, prescribing and referrals. Fourteen studies concluded that interventions had positive impacts. CONCLUSIONS: There is a paucity of high-quality evidence to inform strategies to implement evidence-based health care in prisons, and an over-reliance on weak evaluation designs which may over-estimate effectiveness. Whilst most evaluations have focused on recognised priorities for the incarcerated population, relatively little attention has been paid to long-term conditions core to primary care delivery. Initiatives to close the gaps between evidence and practice in prison primary care need a stronger evidence base.

How critical is brain criticality?

Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks.

Processing visual ambiguity in fractal patterns: Pareidolia as a sign of creativity.

Creativity is a highly valued and beneficial skill that empirical research typically probes using "divergent thinking" (DT) tasks such as problem solving and novel idea generation. Here, in contrast, we examine the perceptual aspect of creativity by asking whether creative individuals are more likely to perceive recognizable forms in ambiguous stimuli -a phenomenon known as pareidolia. To this end, we designed a visual task in which participants were asked to identify as many recognizable forms as possible in cloud-like fractal images. We found that pareidolic perceptions arise more often and more rapidly in highly creative individuals. Furthermore, high-creatives report pareidolia across a broader range of image contrasts and fractal dimensions than do low creatives. These results extend the established body of work on DT by introducing divergent perception as a complementary manifestation of the creative mind, thus clarifying the perception-creation link while opening new paths for studying creative behavior in humans.

Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.

BACKGROUND: While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia. METHODS: Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months' follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy. RESULTS: After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time. CONCLUSION: These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to cognitive-behavioral therapy.

High-frequency heart rate variability during worry predicts stress-related increases in sleep disturbances.

OBJECTIVE: The aim of this study was to evaluate whether high-frequency heart rate variability (HF-HRV) during waking restfulness and during worry predicts increases in sleep disturbances in response to a stressful life event. METHODS: A longitudinal study following up 22 individuals from well-defined periods of lower and higher stress was conducted. HF-HRV during waking restfulness and in response to a worry induction was measured during a low-stress period. Sleep disturbances were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Insomnia Severity Index (ISI) during low-stress and high-stress periods. RESULTS: During both the low- and high-stress periods, lower HF-HRV during worry was associated with greater PSQI scores. Importantly, lower HF-HRV during the worry induction prospectively predicted greater increases in the PSQI score from the low-stress to the high-stress periods. CONCLUSION: HF-HRV during worry might represent an index of vulnerability to stress-induced sleep disturbances.

Sleep spindles predict stress-related increases in sleep disturbances.

BACKGROUND AND AIM: Predisposing factors place certain individuals at higher risk for insomnia, especially in the presence of precipitating conditions such as stressful life events. Sleep spindles have been shown to play an important role in the preservation of sleep continuity. Lower spindle density might thus constitute an objective predisposing factor for sleep reactivity to stress. The aim of this study was therefore to evaluate the relationship between baseline sleep spindle density and the prospective change in insomnia symptoms in response to a standardized academic stressor. METHODS: Twelve healthy students had a polysomnography recording during a period of lower stress at the beginning of the academic semester, along with an assessment of insomnia complaints using the insomnia severity index (ISI). They completed a second ISI assessment at the end of the semester, a period coinciding with the week prior to final examinations and thus higher stress. Spindle density, amplitude, duration, and frequency, as well as sigma power were computed from C4-O2 electroencephalography derivation during stages N2-N3 of non-rapid-eye-movement (NREM) sleep, across the whole night and for each NREM sleep period. To test for the relationship between spindle density and changes in insomnia symptoms in response to academic stress, spindle measurements at baseline were correlated with changes in ISI across the academic semester. RESULTS: Spindle density (as well as spindle amplitude and sigma power), particularly during the first NREM sleep period, negatively correlated with changes in ISI (p < 0.05). CONCLUSION: Lower spindle activity, especially at the beginning of the night, prospectively predicted larger increases in insomnia symptoms in response to stress. This result indicates that individual differences in sleep spindle activity contribute to the differential vulnerability to sleep disturbances in the face of precipitating factors.

Inhibition of stress-induced hepatic tryptophan 2,3-dioxygenase exhibits antidepressant activity in an animal model of depressive behaviour.

The role of hepatic tryptophan 2,3 dioxygenase (TDO) was assessed in the provocation of stress-induced depression-related behaviour in the rat. TDO drives tryptophan metabolism via the kynurenine pathway (KP) and leads to the production of neuroactive metabolites including kynurenine. A single 2 h period of restraint stress in adult male Sprague-Dawley rats provoked an increase in circulating concentrations of the glucocorticoid corticosterone and induction of hepatic TDO expression and activity. Repeated exposure to stress (10 d of 2 h restraint each day) provoked an increase in immobility in the forced swimming test (FST) indicative of depression-related behaviour. Immobility was accompanied by an increase in the circulating corticosterone concentrations, expression and activity of hepatic TDO and increase in the expression of TDO in the cerebral cortex. Increased TDO activity was associated with raised circulating kynurenine concentrations and a reduction in circulating tryptophan concentrations indicative of KP activation. Co-treatment with the TDO inhibitor allopurinol (20 mg/kg, i.p.), attenuated the chronic stress-related increase in immobility in the FST and the accompanying increase in circulating kynurenine concentrations. These findings indicate that stress-induced corticosterone and consequent activation of hepatic TDO, tryptophan metabolism and production of kynurenine provoke a depression-related behavioural phenotype. Inhibition of stress-related hepatic TDO activity promotes antidepressant activity. TDO may therefore represent a promising target for the treatment of depression associated with stress-related disorders in which there is evidence for KP activation.