George A. Mashour

Transcranial low-intensity focused ultrasound (LIFU) offers a rare ability to noninvasively modulate human brain activity in superficial and deep neural structures. Yet the field still cannot reliably predict how a given acoustic protocol will affect a specific brain target. We synthesize 230 LIFU experiments targeting the human cerebrum from 136 articles reporting sufficient acoustic information and neuromodulatory outcomes. We compare associations between acoustic parameters and reported outcomes across cortical and subcortical targets. This reveals divergent parameter-effect landscapes across cortical and subcortical targets. Cortical outcomes follow a nonmonotonic organization across pulse repetition frequency and duty cycle, with distinct zones associated with facilitation, suppression, and null or ambiguous effects. Subcortical outcomes instead vary predominantly along duty cycle, shifting from suppression at lower values toward facilitation at higher values. Intensity and dose factors do not provide a consistent target-independent explanation for outcome polarity, and current evidence is insufficient to identify parameters promoting lasting effects. These findings challenge the assumption that acoustic protocols have generalizable effects across the brain and identify anatomical target as a central dimension that should inform LIFU protocol design. We provide SonoMap, an open, interactive web resource that supports a more systematic, evidence-based approach to human LIFU neuromodulation.

Background: Minimum alveolar concentration (MAC) is a standard dosing metric for general anesthesia. Although influences of MAC have been identified in controlled studies, the determinants of clinical delivery of MAC ratio are largely unknown. To address this knowledge gap, the authors performed this single-center retrospective study. The primary objective was to understand factors associated with MAC ratio, as indexed by the end-tidal volatile anesthetic concentration, for a given case. It was hypothesized that mean arterial pressure (MAP) was strongly associated with anesthetic delivery; a secondary objective was to perform a genome-wide association study to identify genetic variants associated with MAC ratio in clinical practice. Methods: The primary outcome was mean age-adjusted MAC ratio during the maintenance phase of anesthesia. The primary exposure variable was mean MAP during the same time window. The correlation between the outcome, exposure, and a variety of demographic, laboratory, procedural, and anesthetic covariates was assessed in adjusted multivariable linear regressions. Next, the authors performed two genome-wide association studies of clinically delivered MAC ratio. Results: Of the 30,125 cases included in the final dataset, mean ± SD age-adjusted MAC ratio was 1.066 ± 0.173, and mean MAP was 82 ± 10 mmHg. MAP was associated with MAC ratio in the overall model (β = 0.0028; 95% CI, 0.0024 to 0.0032; P < 0.0001). A variety of demographic ( e.g. , female sex: β = –0.0182; 95% CI, –0.0219 to –0.0144; P < 0.0001), laboratory, surgical, and anesthetic factors were also associated with anesthetic agent concentration. Eight variants located near six genes ( DPH6 , CPM , EEFSEC , SGSM1 , CDH9 , and DISC1 ) reached the lower suggestive threshold ( P < 1 × 10 ^-6 ) in one but not both models; none exceed the threshold for genome-wide significance ( P < 5 × 10 ^-8 ). Conclusions: The hypothesis was verified that MAP was associated with clinical delivery of MAC ratio. The authors also characterized practice patterns of inhalational anesthetic management and identified numerous covariates that need to be accounted for when modeling anesthetic requirements. Female patients received lower doses of anesthetic, despite evidence that they have a higher requirement. While genome-wide association studies failed to identify novel variants at the level of genome-wide significance ( P < 5 × 10 ^-8 ), multiple mechanistically plausible genes were suggested. Notably, the DISC1 gene has been shown to impact resting-state brain activations under general anesthesia with isoflurane and has been linked to abnormal sleep/wake patterns.

When complex systems move away from criticality—a balance between order and chaos—they are no longer optimized. Furthermore, when criticality is lost too quickly, or recovery is delayed, system damage can result. However, the mechanism for these abnormally fast or slow critical transitions remains unknown. Here, we show that the proximity of a complex network to explosive synchronization (ES), a first-order phase transition, determines the trajectories of criticality loss and recovery after perturbations. Our computational models revealed characteristic dynamics based on network proximity to ES, enabling us to infer network phase transition types from empirical data and predict criticality transition patterns. We validated our predictions using empirical data from the human brain under anesthesia and the stock market during an economic crisis, demonstrating that early and prolonged recoveries can be systematically predicted. This study has implications for designing resilient networks that withstand perturbations and recover quickly.

Understanding neural mechanisms of consciousness remains a challenging question in neuroscience. A central debate in the field concerns whether consciousness arises from global interactions that involve multiple brain regions or focal neural activity, such as in sensory cortex. Additionally, global theories diverge between the Global Neuronal Workspace (GNW) hypothesis, which emphasizes frontal and parietal areas, and the Integrated Information Theory (IIT), which focuses on information integration within posterior cortical regions. To disentangle the global vs. local and frontoparietal vs. posterior dilemmas, we measured global functional connectivity and local neural synchrony with functional magnetic resonance imaging (fMRI) data across a spectrum of conscious states in humans induced by psychedelics, sleep, and deep sedation. We found that psychedelic states are associated with increased global functional connectivity and decreased local neural synchrony. In contrast, non-REM sleep and deep sedation displayed the opposite pattern, suggesting that consciousness arises from global brain network interactions rather than localized activity. This mirror-image pattern between enhanced and diminished states was observed in both anterior-posterior (A-P) and posterior-posterior (P-P) brain regions but not within the anterior part of the brain alone. Moreover, anterior transmodal regions played a key role in A-P connectivity, while both posterior transmodal and posterior unimodal regions were critical for P-P connectivity. Overall, these findings provide empirical evidence supporting global theories of consciousness in relation to varying states of consciousness. They also bridge the gap between two prominent theories, GNW and IIT, by demonstrating how different theories can converge on shared neuronal mechanisms.

The preoptic area of the hypothalamus is key for the control of sleep onset and sleep homeostasis. Although traditionally considered exclusively somnogenic, recent studies identified a group of preoptic glutamatergic neurons that promote wakefulness. Specifically, our previous investigations demonstrated that chemogenetic stimulation of glutamatergic neurons within the medial-lateral preoptic area (MLPO_VGLUT2) promotes wakefulness, fragments non-rapid eye movement sleep (NREMs), and suppresses REM sleep (REMs). This evidence is further supported by recent work showing that preoptic glutamatergic neurons are activated during microarousals that fragment sleep in response to stress, and optogenetic stimulation of these neurons promotes microarousals and wakefulness. Thus, while the wake-promoting function of MLPO_VGLUT2 is clear, their role in sleep homeostasis has not been assessed. We tested the hypothesis that MLPO_VGLUT2 are wake-active, and their activation will increase wakefulness and disrupt sleep homeostasis via projections to arousal-promoting systems. Using fiber photometry, we found that MLPO_VGLUT2 were highly active during REMs, wakefulness and brief arousals, and remained minimally active during NREMs. Chemogenetic stimulation of MLPO_VGLUT2 inhibited REMs onset and suppressed the REMs homeostatic response after total sleep deprivation. Chemogenetic inhibition of MLPO_VGLUT2 increased REMs time (during the light phase only) but did not influence REMs and NREMs homeostasis. Anterograde projection mapping revealed that MLPO_VGLUT2 innervate central regions that promote wakefulness and inhibit REMs. We conclude that MLPO_VGLUT2 powerfully suppress REMs and that exogenous —and possibly pathologic— activation of these neurons disrupts REMs recovery, presumably by directly or indirectly activating REMs-inhibitory mechanisms. SIGNIFICANCE STATEMENT: The preoptic area of the hypothalamus has been extensively studied and its role in sleep regulation is well-established. Importantly, recent work identified a group of preoptic glutamatergic neurons (MLPO_VGLUT2) that are wake-active and promote wakefulness. However, whether these neurons influence sleep homeostasis remains unknown. We demonstrate that MLPO_VGLUT2 are maximally active during REM sleep (REMs), wakefulness and brief arousals from sleep, and innervate wake-promoting and REMs-inhibitory regions. MLPO_VGLUT2 stimulation inhibits REMs and REMs rebound after sleep deprivation, whereas their inactivation increases REMs but does not alter REMs homeostatic response. We thus identified a preoptic mechanism that powerfully suppresses REMs, which we propose may engage during normal sleep-to-wake transitions to block REMs intrusions into subsequent wakefulness.