Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities.

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.

Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists' preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.

The Functional Relevance of Task-State Functional Connectivity.

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the ability of the human brain to adaptively alter its functionality via rapid changes in inter-regional relationships. We used activity flow mapping-an approach for building empirically derived network models-to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.SIGNIFICANCE STATEMENT Human cognition is highly dynamic, yet the functional network organization of the human brain is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the intrinsic (resting-state) network organization of the brain strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

Combining Multiple Functional Connectivity Methods to Improve Causal Inferences.

Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet, approaches that characterize relationships among neural time series-functional connectivity (FC) methods-are dominated by methods that assess bivariate statistical associations rather than causal interactions. Such bivariate approaches result in substantial false positives because they do not account for confounders (common causes) among neural populations. A major reason for the dominance of methods such as bivariate Pearson correlation (with functional MRI) and coherence (with electrophysiological methods) may be their simplicity. Thus, we sought to identify an FC method that was both simple and improved causal inferences relative to the most popular methods. We started with partial correlation, showing with neural network simulations that this substantially improves causal inferences relative to bivariate correlation. However, the presence of colliders (common effects) in a network resulted in false positives with partial correlation, although this was not a problem for bivariate correlations. This led us to propose a new combined FC method (combinedFC) that incorporates simple bivariate and partial correlation FC measures to make more valid causal inferences than either alone. We release a toolbox for implementing this new combinedFC method to facilitate improvement of FC-based causal inferences. CombinedFC is a general method for FC and can be applied equally to resting-state and task-based paradigms.

Cardiotrophin-1 decreases liver apoptosis through calpastatin induction.

BACKGROUND: Cardiotrophin-1 (CT1) has been used to prevent cell death in different models of liver injury in rats. D-galactosamine induces cell death in culture rat and human hepatocytes. The present study evaluated the cytoprotective effects of CT1 in an experimental model of apoptosis induced by D-galactosamine in hepatocytes. METHODS: DNA fragmentation, calpain activity and Western blots of caspase-3, calpastatin and Stat3, and Akt phosphorylation were measured. Stat3 and Akt inhibitors were used to analyze the mechanisms of action of CT1. RESULTS: CT1 caused an increase in Stat3 and Akt phosphorylation and a decrease of DNA fragmentation, calpain activity, and caspase-3 induced by D-galactosamine. The reduction of calpain activity by CT1 was associated with an increase of calpastatin (its endogenous inhibitor). The effects of CT1 were also dependent on the activation of Sta3 or Akt. CONCLUSIONS: CT1 decreases cell death through a mechanism related to Stat3 and Akt phosphorylation and activation of calpastatin in D-galactosamine-treated hepatocytes.

Study of tamoxifen urinary metabolites in rat by ultra-high-performance liquid chromatography time-of-flight mass spectrometry.

Tamoxifen (TMX) is a nonsteroidal estrogen antagonist drug used for the treatment of breast cancer. It is also included in the list of banned substances of the World Anti Doping Agency (WADA) prohibited in and out of competition. In this work, the excretion of urinary metabolites of TMX after a single therapeutic dose administration in rats has been studied using ultra-high-performance liquid chromatography electrospray time-of-flight mass spectrometry (UHPLC-TOFMS). A systematic strategy based on the search of typical biotransformations that a xenobiotic can undergo in living organisms, based on their corresponding molecular formula modification and accurate mass shifts, was applied for the identification of TMX metabolites. Prior to UHPLC-TOFMS analyses, a solid-phase extraction step with polymeric cartridges was applied to urine samples. Up to 38 TMX metabolites were detected. Additional collision induced dissociation (CID) MS/MS fragmentation was performed using UHPLC-QTOFMS. Compared with recent previous studies in human urine and plasma, new metabolites have been reported for the first time in urine. Metabolites identified in rat urine include the oxygen addition, owing to different possibilities for the hydroxylation of the rings in different positions (m/z 388.2271), the incorporation of two oxygen atoms (m/z 404.2220) (including dihydroxylated derivatives or alternatives such as epoxidation plus hydroxylation or N-oxidation and hydroxylation), epoxide formation or hydroxylation and dehydrogenation [m/z 386.2114 (+O -H2 )], hydroxylation of the ring accompanied by N-desmethylation (m/z 374.2115), combined hydroxylation and methoxylation (m/z 418.2377), desaturated TMX derivate (m/z 370.2165) and its N-desmethylated derivate (m/z 356.2009), the two latter modifications not previously being reported in urine. These findings confirm the usefulness of the proposed approach based on UHPLC-TOFMS.

Non-Gaussian methods and high-pass filters in the estimation of effective connections.

We consider several alternative ways of exploiting non-Gaussian distributional features, including some that can in principle identify direct, positive feedback relations (graphically, 2-cycles) and combinations of methods that can identify high dimensional graphs. All of the procedures are implemented in the TETRAD freeware (Ramsey et al., 2013). We show that in most cases the limited accuracy of the several non-Gaussian methods in the Smith et al. (2011) simulations can be attributed to the high-pass Butterworth filter used in that study. Without that filter, or with the filter in the widely used FSL program (Jenkinson et al., 2012), the directional accuracies of several of the non-Gaussian methods are at or near ceiling in many conditions of the Smith et al. simulation. We show that the improvement of an apparently Gaussian method (Patel et al., 2006) when filtering is removed is due to non-Gaussian features of that method introduced by the Smith et al. implementation. We also investigate some conditions in which multi-subject data help with causal structure identification using higher moments, notably with non-stationary time series or with 2-cycles. We illustrate the accuracy of the methods with more complex graphs with and without 2-cycles, and with a 500 node graph; to illustrate applicability and provide a further test we apply the methods to an empirical case for which aspects of the causal structure are known. Finally, we note a number of cautions and issues that remain to be investigated, and some outstanding problems for determining the structure of effective connections from fMRI data.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities.

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which in turn modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (N=149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

Plasmatic nitric oxide correlates with weight and red cell distribution width in exercised rats supplemented with quercetin.

Quercetin is suggested as a nitric oxide regulator which may in turn influence blood parameters and weight gain. Wistar rats were classified as: quercetin-exercise training, QT; placebo-exercise training, PT; quercetin-sedentary, QS; and placebo sedentary, PS. After 6 weeks of treatment with quercetin and/or exercise, an incremental test was run to measure oxygen consumption. QT had lower levels of NO compared with PS (p = 0.029) and QS (p = 0.002). Red cell distribution width increased in both exercised groups, especially in the QT group (p < 0.001). Pearson correlation analysis showed that nitric oxide levels were associated with weight (r = 0.675) and red distribution width (r = -0814) in the QT group. Quercetin effect on NO production seems to be more powerful when it is supplemented during exercise training. Moreover, RDW relationship with NO production need to be further investigated in regards to health.

[Myocardiopericarditis in adult still's disease]. / Miocardiopericarditis en la enfermedad de still del adulto.

Cardiac involvement in Adult-onset Still's Disease (AOSD) usually manifests as a pericardial disease, that has a benign course. The myocarditis is a rare complication with 7% of prevalence. The diagnosis of AOSD is based in the Yamaguchi or Fautrel criteria. The treatment with steroids and methotrexate is the first and second therapeutic lines, respectively, the combination is effective in 70% of cases. We report a case of AOSD with unusual presentation due to myocardial and pericardial commitment.

The hypoxic preconditioning agent deferoxamine induces poly(ADP-ribose) polymerase-1-dependent inhibition of the mitochondrial respiratory chain.

We previously reported that treatment with a single dose of deferoxamine (DFO), which acts as a hypoxic-mimetic agent, only induces reactive oxygen species (ROS) production in the presence of poly(ADP-ribose) polymerase (PARP-1). Given that mitochondria are one of the main sources of ROS, the present study was designed to assess the effect of DFO treatment on the activity of mitochondrial respiratory chain complexes, and more importantly, to determine whether this effect is modulated by PARP-1. We found that DFO treatment induced a progressive decline in complex II and IV activity, but that this activity was preserved in PARP-1 knock-out cells, demonstrating that this decrease is mediated by PARP-1. We also confirmed that complex II inhibition after DFO treatment occurs in parallel with poly-ADP ribosylation. Consequently, we recommend that PARP-1 activation be taken into account when using DFO as a hypoxia-mimetic agent, because it mediates alteration of the mitochondrial respiratory chain.

HER2 blockade in metastatic collecting duct carcinoma (CDC) of the kidney: a case report.

BACKGROUND: Collecting duct carcinoma of the kidney (CDC) is a rare cancer associated with bad prognosis and, at present, with no specific effective therapies. CASE REPORT: We report a clinical case with disseminated highgrade CDC presenting with widespread metastasis to both lungs, pelvic bones, axial skeleton, and the central nervous system (posterior fossa, both hemispheres and pituitary-hypothalamic). The primary tumor in the kidney was demonstrated (by fluorescence in situ hybridization and immunohistochemistry with Herceptest (3+ score)) to significantly overexpress HER2. Critically ill at presentation, the patient received oral capecitabine together with double HER2 blockade with both intravenous trastuzumab and oral lapatinib. His clinical response was a dramatic improvement and a progressive decline in the radiological size of all of his multiple cancer lesions. CONCLUSION: Double HER2 blockade is an effective therapy in disseminated CDC even in the presence of brain metastases.

Neuroanatomy of episodic and semantic memory in humans: a brief review of neuroimaging studies.

One of the most basic functions in every individual and species is memory. Memory is the process by which information is saved as knowledge and retained for further use as needed. Learning is a neurobiological phenomenon by which we acquire certain information from the outside world and is a precursor to memory. Memory consists of the capacity to encode, store, consolidate, and retrieve information. Recently, memory has been defined as a network of connections whose function is primarily to facilitate the long-lasting persistence of learned environmental cues. In this review, we present a brief description of the current classifications of memory networks with a focus on episodic memory and its anatomical substrate. We also present a brief review of the anatomical basis of memory systems and the most commonly used neuroimaging methods to assess memory, illustrated with magnetic resonance imaging images depicting the hippocampus, temporal lobe, and hippocampal formation, which are the main brain structures participating in memory networks.

Protocol for activity flow mapping of neurocognitive computations using the Brain Activity Flow Toolbox.

Traditional cognitive neuroscience uses task-evoked activations to map neurocognitive processes (and information) to brain regions; however, how those processes are generated is unknown. We developed activity flow mapping to identify and empirically validate network mechanisms underlying the generation of neurocognitive processes. This approach models the movement of task-evoked activity over brain connections to predict task-evoked activations. We present a protocol for using the Brain Activity Flow Toolbox (https://colelab.github.io/ActflowToolbox/) to identify network mechanisms underlying neurocognitive processes of interest. For complete details on the use and execution of this protocol, please refer to Cole et al., 2021.

Short-term effects of caffeine intake on binocular accommodative facility: a quantitative and qualitative analysis.

CLINICAL RELEVANCE: Caffeine intake has been demonstrated to influence several physiological measures, including some related to eye physiology. The ability to focus at different distances is of paramount importance in real-world situations, and thus, the possible impact of caffeine intake on accommodative facility may have important clinical implications. BACKGROUND: This placebo-controlled, double-blind, balanced crossover study aimed to assess the acute effects of caffeine ingestion on the frequency and precision of the binocular accommodative facility. METHODS: Twenty university students (21.9 ± 3.4 years) ingested a capsule of caffeine (4 mg/kg) or placebo (300 mg of corn-starch) on two different days and counterbalanced order. The binocular accommodative facility was objectively assessed, using the WAM-5500 binocular open-field autorefractometer, after 60 min of capsule ingestion (caffeine/placebo). Perceived levels of activation was also assessed in each experimental condition. RESULTS: The ingestion of a single administration of caffeine (~ 4 mg/kg) causes an increase in the number of cycles performed per minute (p = 0.023, Cohen's d = 0.55), whereas no effects were observed for the mean magnitude of accommodative change between the far and near targets (p = 0.794), and the percentage of incorrect cycles of accommodation and dis-accommodation (p = 0.271 and 0.396, respectively). Participants reported a perceived level of activation of 6.8 ± 1.5 and 7.6 ± 1.8 in the placebo and caffeine conditions, respectively (p = 0.059). CONCLUSION: Caffeine intake improves quantitative, but not qualitative, measures of accommodative facility. These results corroborate the impact of caffeine on visual function and suggest that this ergogenic effect of caffeine may be used to enhance visual performance in applied situations.

Determination of permeability data and 3-D modelling of the host rock and sinters from a geothermal field: Los Geysers, northern Trans-Mexican Volcanic Field.

This data article describes the connected pore cluster data from segmented nano-images of rocks related to a geothermal system. The collected samples include two (2) vesicle-amygdaloidal basalt (host rock) and four (4) horizons collected from a siliceous sinter mound (rock precipitated from hot waters). All the samples have undergone computed tomography scanning using a SkyScan 2211 multiscale X-ray nano-CT system (Bruker®), and the slices were analyzed using the Digital Rock Physics (DRP) approach. Pore volume and fluid permeability in the three directions were calculated with scripts of Python (v.3.9) and the visualizations of the 3D models were run with Paraview (v.5.10) software. The petrophysical properties, diagrams, and figures were produced by stacking the 2D projections (8-bit grayscale *.png images format) from the scanning. Raw data (images) were deposited in a repository, which has granted a persistent identifier (Mendeley Data: https://data.mendeley.com/datasets/srpxhpd37p/2). This article provides a study case to handle the data that test the interconnectivity and ability to transport fluids and/or exogenous matter carried during high-flow events in rocks outcropping at the surface level of a geothermal system.

Acute Effects of Caffeine on Dynamic Accommodative Response and Pupil Size: A Placebo-controlled, Double-blind, Balanced Crossover Study.

OBJECTIVES: To evaluate the acute effect of caffeine consumption on the accuracy and variability of accommodation, as well as its impact on pupil size and perceived levels of activation. METHODS: 22 university students (21.68 ± 3.67 years old) ingested a capsule of caffeine (4 mg/kg) or placebo (300 mg of corn-starch) in two different days and counterbalanced order. After 30 min of capsule ingestion, we objectively measured the accuracy and variability of accommodation, and pupil size using the WAM-5500 binocular open-field autorefractometer for 2 min at each of the six viewing distances (5 m, 50 cm, 40 cm, 33 cm, 25 cm, and 20 cm). Subjective levels of activation to check the effectiveness of caffeine/placebo manipulation were also reported. RESULTS: We found that after 30 min of caffeine/placebo ingestion, participant perceived higher levels of activation in the caffeine condition (p = .047, Cohen´s d = 0.48). Caffeine consumption induced a statistically significant dilator effect on pupil size (p = .011, η2 = 0.271), and reduced variability of accommodative response (p = .027, η2 = 0.211). However, no differences were obtained for the accuracy of accommodation (p = .321). CONCLUSIONS: Our data suggest that caffeine consumption reduced the variability of accommodative response and induced pupil dilation. Nevertheless, the accuracy of accommodation was insensitive to caffeine intake. These findings may be explained by the bidirectional relationship between ocular functioning and the nervous system´s state of activation.

Poly(ADP-ribose) polymerase-1 modulation of in vivo response of brain hypoxia-inducible factor-1 to hypoxia/reoxygenation is mediated by nitric oxide and factor inhibiting HIF.

Poly(ADP-ribose) polymerase-1 (PARP-1) is a nuclear protein that once activated by genotoxic agents, modulates its own activity and that of several other nuclear proteins. The absence or pharmacological inhibition of this protein has been proven to be beneficial in the treatment of different diseases involving a hypoxic situation. We previously reported that PARP-1 modulates the hypoxia-inducible factor-1 (HIF-1) response in vitro, but this effect has not yet been demonstrated in vivo. The brain is especially susceptible to hypoxic injury, and the present study demonstrates that PARP-1 plays a major role in the post-hypoxic response of HIF-1alpha in the cerebral cortex. Immediate post-hypoxic HIF-1alpha accumulation was higher in the presence of PARP-1, and this differential response was mediated by nitric oxide and to a lesser extent, reactive oxygen species. PARP-1 was also found to induce a more rapid but less sustained HIF-1 transcriptional activity by up-regulating the factor inhibiting HIF. The implication of PARP-1 in these results was further demonstrated by pharmacologically inhibiting PARP in wild-type mice. In conclusion, our data suggest that PARP-1 has an important regulatory role in the in vivo response of brain HIF-1 to hypoxia/reoxygenation.

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods.

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

Advancing functional connectivity research from association to causation.

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.