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ObjectiveThe effects of temporal lobe epilepsy (TLE) on subcortical arousal structures remain incompletely understood. Here, we evaluate thalamic arousal network functional connectivity in TLE and examine changes after epilepsy surgery.MethodsWe examined 26 adult patients with TLE and 26 matched control participants and used resting-state functional MRI (fMRI) to measure functional connectivity between the thalamus (entire thalamus and 19 bilateral thalamic nuclei) and both neocortex and brainstem ascending reticular activating system (ARAS) nuclei. Postoperative imaging was completed for 19 patients >1 year after surgery and compared with preoperative baseline.ResultsBefore surgery, patients with TLE demonstrated abnormal thalamo-occipital functional connectivity, losing the normal negative fMRI correlation between the intralaminar central lateral (CL) nucleus and medial occipital lobe seen in controls (p < 0.001, paired t-test). Patients also had abnormal connectivity between ARAS and CL, lower ipsilateral intrathalamic connectivity, and smaller ipsilateral thalamic volume compared with controls (p < 0.05 for each, paired t-tests). Abnormal brainstem–thalamic connectivity was associated with impaired visuospatial attention (ρ = −0.50, p = 0.02, Spearman’s rho) while lower intrathalamic connectivity and volume were related to higher frequency of consciousness-sparing seizures (p < 0.02, Spearman’s rho). After epilepsy surgery, patients with improved seizures showed partial recovery of thalamo-occipital and brainstem–thalamic connectivity, with values more closely resembling controls (p < 0.01 for each, analysis of variance).ConclusionsOverall, patients with TLE demonstrate impaired connectivity in thalamic arousal networks that may be involved in visuospatial attention, but these disturbances may partially recover after successful epilepsy surgery. Thalamic arousal network dysfunction may contribute to morbidity in TLE.

•Low-dimensional representations of fMRI data may contain vigilance information.•These representations can be clustered into two or more putative vigilance states.•Low-dimensional fMRI exhibited alignment with behavioral and EEG vigilance.•More frequent anti-correlated cortical activity occurred during higher vigilance. The human brain exhibits rich dynamics that reflect ongoing functional states. Patterns in fMRI data, detected in a data-driven manner, have uncovered recurring configurations that relate to individual and group differences in behavioral, cognitive, and clinical traits. However, resolving the neural and physiological processes that underlie such measurements is challenging, particularly without external measurements of brain state. A growing body of work points to underlying changes in vigilance as one driver of time-windowed fMRI connectivity states, calculated on the order of tens of seconds. Here we examine the degree to which the low-dimensional spatial structure of instantaneous fMRI activity is associated with vigilance levels, by testing whether vigilance-state detection can be carried out in an unsupervised manner based on individual BOLD time frames. To investigate this question, we first reduce the spatial dimensionality of fMRI data, and apply Gaussian Mixture Modeling to cluster the resulting low-dimensional data without any a priori vigilance information. Our analysis includes long-duration task and resting-state scans that are conducive to shifts in vigilance. We observe a close alignment between low-dimensional fMRI states (data-driven clusters) and measurements of vigilance derived from concurrent electroencephalography (EEG) and behavior. Whole-brain coactivation analysis revealed cortical anti-correlation patterns that resided primarily during higher behavioral- and EEG-defined levels of vigilance, while cortical activity was more often spatially uniform in states corresponding to lower vigilance. Overall, these findings indicate that vigilance states may be detected in the low-dimensional structure of fMRI data, even within individual time frames.

Aposematic organisms defend themselves through various means to increase their unprofitability to predators which they advertise with conspicuous warning signals. Predators learn to avoid aposematic prey through associative learning that leads to lower predation. However, when these visual signals become unreliable (e.g., through automimicry or Batesian mimicry), predators may switch from using visual signals to taste sampling prey to choose among them. In this experiment, we tested this possibility in a field experiment where we released a total of 4800 mealworm prey in two clusters consisting of either: (i) undefended prey (injected with water) and (ii) model-mimics (injected with either quinine sulphate [models] or water [mimics]). Prey were deployed at 12 sites, with the mimic frequency of the model-mimics ranging between 0 and 1 (at 0.2 intervals). We found that taste rejection peaked at moderate mimic frequencies (0.4 and 0.6), supporting the idea that taste sampling and rejection of prey is related to signal reliability and predator uncertainty. This is the first time that taste-rejection has been shown to be related to the reliability of prey signals in a mimetic prey system.

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

While traditionally regarded as “noise”, blood-oxygenation-level-dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) fluctuations coupled to systemic physiology—such as heart rate and respiratory changes—also hold valuable information about brain vascular properties and autonomic function. In this study, we leverage these physiological signals to characterize age-related changes in brain physiology, drawing on a large dataset from the Lifespan Human Connectome Project Aging study. Our findings reveal that aging is associated with globally slower respiratory fMRI responses, alongside faster cardiac fMRI responses and enhanced brain-cardiac signal coupling. Moreover, we show that the impact of age on physiological fMRI signals exhibits a notable turning point after age 60, suggesting a critical role of declining vascular health and autonomic function in aging. The potential impact of age-related changes in brain structure, tissue perfusion, and in-scan arousal states on the identified physiological fMRI patterns is also tested and discussed. Altogether, our results underscore significant age effects in the fMRI signatures of systemic physiology, emphasizing the pivotal role of altered vascular properties and autonomic function in aging. Methodologically, this study also demonstrates the utility of resting-state fMRI for extracting multi-parametric information about brain physiology, offering new biomarker opportunities that complement established functional connectivity metrics. Functional magnetic resonance imaging signals linked to heart rate and respiration reveal age-related alterations in vascular physiology and autonomic function.

Monthly-mean precipitation, mean (T AVG ), maximum (T MAX ) and minimum (T MIN ) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for T AVG and T MAX than for T MIN , and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.

Brain structure and function alterations in aging are associated with cognitive decline and Alzheimer's disease and related dementias (ADRD). Characterizing the spatial distribution through which total brain volume and functional connectivity interact across the cognitive spectrum may provide additional insights into ADRD progression. A subset of Vanderbilt Memory and Aging Project participants (n = 113, 75.7±6.2years, 34% female, n = 87 cognitively unimpaired (CU), n = 17 mild cognitively impaired (MCI), and n = 9 dementia) underwent functional magnetic resonance imaging (MRI) and T1-weighted MRI at 3T. Morphometric volumes across the grey and white matter were calculated using Multi-Atlas (MA) segmentation, and functional connectivity was calculated using Pearson correlation and a fisher-Z transform in the same MA regions. Volumetric and functional data underwent permutation testing (n = 10000) via Spearman correlation to create a morphometric structure-function coupling metric covarying for age, sex, ethnicity/race, education, and intracranial volume. Region-based multiple comparisons were corrected for using false discovery rate (FDR). Structure-function coupling showed significant differences across the brain in dementia compared to both CU and MCI as well as between MCI and CU participants. The spatial distribution across all groups and a list of significant regions after FDR adjusted p-values<0.05 are illustrated in Figure 1 and summarized in Table 1 respectively. Focusing on adjusted p-values<0.01, MCI and CU participants showed differences in the left anterior cingulate, left inferior occipital gyri, and the right entorhinal cortex. Dementia and CU participants revealed differences in the left superior frontal and lateral orbital gyri and right precentral gyrus. Finally, MCI and dementia participants had differences between the right pallidum, medial frontal cortex, and precentral gyrus, as well as the left anterior cingulate gyrus. Morphometric structure-function coupling has widespread increases with worse cognitive status as seen by our significant results across CU, MCI, and dementia participants. These stronger, correlative couplings in MCI and dementia could be due to regional atrophy and disrupted functional connectivity, which causes the relationship between the two variables to be more pronounced as compensatory capacity within the brain is diminished. Future work will incorporate an increased sample size for sufficient power and will evaluate potential cardiovascular influences on the structure-function coupling.

Abstract BACKGROUND Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

Abstract BACKGROUND Focal seizures in temporal lobe epilepsy (TLE) are associated with widespread brain network perturbations and neurocognitive problems. OBJECTIVE To determine whether brainstem connectivity disturbances improve with successful epilepsy surgery, as recent work has demonstrated decreased brainstem connectivity in TLE that is related to disease severity and neurocognitive profile. METHODS We evaluated 15 adult TLE patients before and after (>1 yr; mean, 3.4 yr) surgery, and 15 matched control subjects using magnetic resonance imaging to measure functional and structural connectivity of ascending reticular activating system (ARAS) structures, including cuneiform/subcuneiform nuclei (CSC), pedunculopontine nucleus (PPN), and ventral tegmental area (VTA). RESULTS TLE patients who achieved long-term postoperative seizure freedom (10 of 15) demonstrated increases in functional connectivity between ARAS structures and fronto-parietal-insular neocortex compared to preoperative baseline (P = .01, Kruskal–Wallis), with postoperative connectivity patterns resembling controls’ connectivity. No functional connectivity changes were detected in 5 patients with persistent seizures after surgery (P = .9, Kruskal–Wallis). Among seizure-free postoperative patients, larger increases in CSC, PPN, and VTA functional connectivity were observed in individuals with more frequent seizures before surgery (P < .05 for each, Spearman's rho). Larger postoperative increases in PPN functional connectivity were seen in patients with lower baseline verbal IQ (P = .03, Spearman's rho) or verbal memory (P = .04, Mann–Whitney U). No changes in ARAS structural connectivity were detected after successful surgery. CONCLUSION ARAS functional connectivity disturbances are present in TLE but may recover after successful epilepsy surgery. Larger increases in postoperative connectivity may be seen in individuals with more severe disease at baseline.

Surface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the eastern United States. Negative correlation between the biases in insolation and precipitation suggest that these two fields are related, likely via clouds. Systematic variations in biases for regions, seasons, variables, and metrics suggest that the bias correction in applying climate model data to assess the climate impact on various sectors must be performed accordingly. Precipitation evaluation with multiple observations reveals that observational data can be an important source of uncertainties in model evaluation; thus, cross examination of observational data is important for model evaluation.