Frontiers in Neuroscience

We completed a video-based, four-night, in-home, level 3 sleep apnea study of healthy, low-risk pregnant participants and their bed partners in order to characterize sleep physiology in the third trimester of pregnancy. Demographic, anthropometric, and baseline sleep health characteristics were recorded, and the NightOwl home sleep apnea test device was used to measure sleep breathing, posture, and architecture parameters. Symptoms of restless legs syndrome were elicited in the exit interview. Forty-one pregnant participants and 36 bed partners completed the study. Bed partners had a significantly higher prevalence of sleep apnea than their pregnant co-sleepers (31% vs. 5.9%). Bed partners also had more severe sleep apnea than their pregnant co-sleepers, and this persisted on an adjusted analysis for baseline differences in factors known to increase risk of sleep apnea. In pregnant participants, increasing gestational age was found to be protective against mild respiratory events but not more severe events. While the correlation between STOP-Bang score and measures of sleep apnea severity was weak, an affirmative response to the witnessed apneas item on the STOP-Bang questionnaire was a strong predictor of more severe sleep apnea for all participants. Smoking history also increased sleep apnea risk. Pregnant participants had lower sleep efficiency and longer self-reported sleep onset latency. Restless legs syndrome was experienced by 39.5% of the pregnant participants but no bed partners. From a sleep breathing perspective, people with healthy, low-risk pregnancies have better sleep than their bed partners despite lower sleep efficiency and higher rates of restless legs syndrome.

The Drosophila adult central brain contains 240 circadian neurons, of which there are more than 25 different neuron subtypes based on connectomic data. Recent single cell RNA-seq (scRNAseq) characterization of these neurons "around the clock" also indicates a similar number of molecular subtypes of circadian neurons, but other conclusions from these transcriptomic studies warranted verifying and extending with other approaches. To this end: 1) We used a genetic multiplexing strategy to profile the transcriptomes of circadian neurons from multiple time points in a single experiment, reducing confounding technical variation between timepoints; 2) Large numbers of single nuclei were sequenced (snRNA-seq), which was enabled because the new method EL-INTACT purifies nuclei from frozen heads; 3) We assayed 12 time points under both light-dark (LD) and constant darkness (DD) conditions. These approaches showed dramatic transcriptional differences between time points in many circadian neuron types and enhanced time-of-day gene expression analysis. The data indicate that most of this regulation is transcriptional and circadian. There were however a small number of light-dependent transcripts, including a few that correspond to mammalian immediate-early genes. They probably play a role in the light-regulation of gene expression and behavior in specific neurons, perhaps circadian entrainment or phase-shifting. The results taken together provide a more comprehensive picture of gene expression heterogeneity within adult Drosophila circadian neurons including how intrinsic clock mechanisms and light cues are integrated across circadian neuron subtypes.

Research in cognitive neuroscience has relied on simple, highly controlled stimuli due to the difficulty in developing standardized, ecologically valid stimulus sets. However, there is a consensus that using ecologically valid stimuli is imperative to generalize results beyond controlled laboratory settings. The current study introduces a naturalistic audio stimulus database, consisting of short, recognizable, and emotionally rated stimuli. To create such a database, the current study collected 291 audio files from a wide range of sources. 361 participants rated the audio clips on emotionality, arousal, and recognizability, and subsequently freely described the audios by typing what they believed the sound to be. The text responses of the participants were embedded and clustered using an unsupervised machine-learning algorithm to derive a participant-grounded organization of auditory object categories. The results indicate audio clips were easily recognizable, while emotionality and arousal ratings showed broad variability, making the database suitable for diverse experimental needs. Furthermore, the final database comprises 10 distinct semantic categories, providing a diverse set of auditory stimuli.

Currently, implantation of electroencephalogram (EEG) electrodes in laboratory animals is time-consuming and requires specialized equipment. We present a novel method for EEG recordings in mice that utilizes thin needle electrodes. These electrodes are inserted into the skull at predetermined locations by gently pressing them against the bone surface. To ensure stable fixation of the implant, hook-shaped needles are positioned along the lateral aspects of the skull. The electrodes are connected to a multipin connector and secured to the skull using dental composite, after which the animal is allowed to recover from anesthesia. Importantly, procedures such as skull drilling and screw placement are not required, allowing the entire surgery to be completed in less than 15 minutes. Consequently, this EEG implantation approach is rapid and minimally invasive. Results of our studies indicate that EEG recordings obtained with needle electrodes are not inferior to those obtained with screw electrodes. Overall, the method is designed to enhance the accuracy and efficiency of EEG recording studies while improving animal welfare. O_LISimplifies the placement of EEG electrodes. C_LIO_LIReduces the time required for electrode implantation. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/715731v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@e5608org.highwire.dtl.DTLVardef@1325ea4org.highwire.dtl.DTLVardef@1e37202org.highwire.dtl.DTLVardef@1521bb8_HPS_FORMAT_FIGEXP M_FIG C_FIG

The human auditory system decomposes complex sounds into distinct components via a collection of processing steps. Knowing whether Spiral Ganglion Cells (SGCs) play an active role in the decoding of complex sounds can facilitate the development of Cochlear Implant (CI) coding strategies and clinical assessment tools. Early animal studies reported SGCs being similar across different characteristic frequencies (CFs). In this study, human electrically evoked compound action potentials (eCAPs) were analysed to probe the relationship between the reciprocal of CF and the duration of the eCAP. A significant relationship could indicate that SGCs may not simply be passive cables. eCAP datasets from 6 published studies (175 CI users, 1243 recordings) were analysed and their peaks were automatically labelled. The n1p2 latency was derived for each recording as a proxy of the action potential duration. The CF of each recording was estimated by mapping the average insertion angle of the electrode to the human SGC map. A weak but statistically significant relationship was observed between the n1p2 latency and the reciprocal of CF (random-effects model with random intercepts for subject, r = 0.09, p = 0.024, n= 450) supporting the hypothesis that lower CF is associated with slower repolarisation (longer n1p2 latency) in human spiral ganglion cells.

ObjectiveNicotinic acetylcholinergic receptors (nAChRs), comprising of and {beta} subunits are present in the brain and whole body. The less abundant 2-subunit is a fast-acting receptor subtype and plays an important role in cognition and learning. To understand cellular functional consequences, this study evaluated glucose metabolism using [18F]FDG PET/CT in 2 knockout (2KO) and 2 hypersensitive (2HS) mice. MethodsControl (CN; 4M, 4F), 2 knockout (2KO; 4M, 4F) and 2 hypersensitive (2HS; 4M,4F), 12-16 month old mice were used. Mice were fasted and injected with [18F]FDG (3-5 MBq) while awake. After 40 minutes they underwent whole body PET/CT. On a separate day, nicotine challenge [18F]FDG studies were done. Reconstructed images were analyzed to obtain standard uptake values (SUV) of [18F]FDG in brain and interscapular brown adipose tissue (IBAT). Statistical analysis was performed. ResultsThe 2HS male mice exhibited the largest brain increase in [18F]FDG compared to 2KO male mice. The rank order of brain [18F]FDG uptake in the 3 groups: 2HS[male]> CN[male]> 2KO[male]> CN[female]= 2KO[female][≥] 2HS[female]. Nicotine treatment reduced brain [18F]FDG uptake in all mice. Females had lower [18F]FDG uptake compared to males and were less sensitive to 2 nAChR. In the case of IBAT, 2KO mice had significantly higher baseline [18F]FDG uptake compared to the other two groups: 2KO[male]> 2KO[female]> 2HS[female]> 2HS[male]> CN[female]> CN[male]. Nicotine decreased IBAT in 2KO mice rather than increase as observed in CN and 2HS mice. Conclusions2 nAChRs plays a significant role in brain activation as exhibited by the increase in [18F]FDG in 2HS mice. In the absence of regulatory control by the 2 nAChR, the 2KO mice IBAT exhibited higher [18F]FDG IBAT compared to controls and 2HS mice. Female mice were less affected by nicotine compared to the male mice. Overall, 2 nAChRs played a significant role in glucose metabolism in the brain and IBAT.

Conductive hearing loss (CHL) with a normal otoscopic exam can be difficult to diagnose because routine clinical measures such as audiometric air-bone gaps (ABGs) can identify a conductive component but often cannot distinguish among specific underlying mechanical pathologies (e.g., stapes fixation versus superior canal dehiscence, which may produce similar audiograms). Wideband tympanometry (WBT) is a fast, noninvasive test that can provide additional mechanical information across a broad range of frequencies (200 Hz to 8 kHz). However, WBT metrics are influenced by variations in ear canal geometry and probe placement and can be challenging to interpret clinically. In this study, we extend prior WBT absorbance-based classification work by estimating the middle ear input impedance at the tympanic membrane (ZME), a WBT-derived metric intended to reduce ear canal effects. To estimate ZME, we fit an analog circuit model of the ear canal, middle ear, and inner ear to raw WBT data collected at tympanometric peak pressure (TPP). Data from 27 normal ears, 32 ears with superior canal dehiscence, and 38 ears with stapes fixation were analyzed. A multinomial logistic regression classifier was trained using principal component analysis (retaining 90% variance) and stratified 5-fold cross-validation with regularization. We compared feature sets based on ABGs alone, ABGs combined with absorbance, and ABGs combined with the magnitude of ZME. The combination of ABGs and the magnitude of ZME produced the best performance, achieving an overall accuracy of 85.6% compared to 80.4% for ABGs alone and 78.4% for ABGs combined with absorbance. These results suggest that incorporating model-derived middle ear impedance features with standard audiometric measures (ABGs) can improve automated pathology classification for stapes fixation and superior canal dehiscence.

The SAFE MRI ECMO (NCT05469139) study established the safety of ultra-low-field 64mT MRI in patients receiving extracorporeal membrane oxygenation (ECMO) in the setting of intensive care and demonstrated that these images were highly sensitive in detecting acquired brain injuries. This retrospective analysis of prospectively collected observational data sought to expand on these findings in light of the crucial need for neurological monitoring while patients receive ECMO by evaluating the feasibility of volumetric analyses derived from ultra-low-field MR images. T2-weighted scans from thirty patients who received ultra-low-field MRI while undergoing ECMO at Johns Hopkins Hospital were analyzed using a volumetric pipeline to determine whole brain volume and volumes of total grey matter, total white matter, subcortical grey matter, ventricles, left hemisphere, right hemisphere, telencephalon, left and right lateral ventricles, the total intracranial volume, and the cerebellum. Segmented brain volumes in patients undergoing ECMO were comparable to measurements obtained using conventional field and ultra-low-field MRI in the absence of ECMO instrumentation. The subgroup analysis demonstrated subtle volumetric differences between patients supported with venoarterial ECMO and those receiving venovenous ECMO. These data provide the first evidence that ultra-low-field MRI provides volumetric measurements comparable to conventional field-strength MRI, even in the presence of ECMO circuitry, supporting its feasibility for neuroimaging in critically ill patients.

The early development of the prefrontal cortex is crucial for higher cognitive functions. However, current research presents inconsistent findings regarding whether intra-prefrontal connectivity increases or decreases in infants younger than six months. Do dynamic changes in connection strength across different states over time carry information about prefrontal maturation? This study used functional near-infrared spectroscopy (fNIRS) to record prefrontal brain activity in 48 healthy infants aged 1-8 months during natural sleep and auditory stimulation. By analyzing the fluctuations in frequency-domain characteristics of functional connectivity (FC) and various brain network properties, we found that: under auditory stimulation, the intensity of FC fluctuations in the ultra-low frequency range was positively correlated with age; while in the resting state, the fluctuation intensity of network properties in relatively higher frequency bands decreased with age. Furthermore, auditory stimulation reconfigured the energy distribution of network fluctuations, shifting it towards higher frequency bands. These results suggest that the early development of the infant prefrontal internal network is characterized by state-dependent optimization of its dynamic fluctuation properties, shedding light on the developmental tuning of functional network dynamics in infancy.

Understanding the mechanical properties of brain tissue may provide crucial insights into brain development, injury, disease and surgical planning. Conventionally, these properties are measured ex vivo or in vivo during surgical procedures, while non-invasive in vivo alternatives are sparse. This study investigates whether fractional anisotropy (FA) derived from diffusion-weighted magnetic resonance imaging can serve as a surrogate marker for brain tissue stiffness in healthy human brains. MRI data were collected from three body donor brains, 28 healthy adults, and a publicly available independent dataset of 26 adults. FA values were compared with mechanical properties from ex vivo mechanical testing of brain tissue. Statistical analysis revealed a strong negative correlation between FA and the mechanical response for small strains expressed as shear modulus of a one-term hyperelastic Ogden model, indicating that higher FA values are associated with lower tissue stiffness. The nonlinearity parameter alpha exhibited a qualitatively similar, but considerably weaker correlation with FA. These findings were consistent across datasets. The findings suggest that FA can be a robust, non-invasive marker for estimating mechanical properties of brain tissue, with potential applications in clinical diagnosis and computational modeling of brain mechanics and the study of brain development. Further research is needed to clarify the relationship in lesional tissues and to optimize clinical utility.

Functional magnetic resonance imaging (fMRI) has been widely utilized to explore the neural mechanisms underlying speech processing. However, the intertwining of perception and production that exists in real-world scenarios remains underexplored due to challenges such as gradient noise and head motion artifacts from speaking. Previous research has often employed sparse-sampling designs, pausing image acquisition intermittently to present auditory stimuli or record overt speech. While this approach mitigates some challenges, it cannot capture continuous brain activity during speech processing and does not separate the mixed hemodynamic responses to external and self-generated speech occurring in succession. We overcame these limitations and continuously scanned thirty-one participants as they listened to and recited English sentences. Through independent component analysis (ICA), we decomposed each functional scan into spatially independent components (ICs), identifying task-related ICs in the superior temporal cortex, inferior frontal gyrus, and orofacial sensorimotor cortex. These ICs demonstrated time-resolved hemodynamic responses corresponding to distinct stages of speech perception, planning, and production. A linear subtraction between the IC time courses from the listening-reciting (perception-to-production) and listening (perception-only) tasks further revealed a secondary hemodynamic response to self-generated speech in the superior temporal cortex. Furthermore, we established precise temporal relationships between overt speech output and the peak, rise, and fall of hemodynamic responses for each independent component. Together, we present a methodological framework that can inform future fMRI studies on naturalistic tasks involving the perception of external auditory stimuli and monitoring of self-generated sounds.

Scientists have for decades attempted to automate the manual sleep staging problem not only for human polysomnography data but also for rodent data. No model has, however, succeeded in fully replacing the manual procedure across clinics and laboratories. We hypothesize that this is due to the models limited ability to generalize to data from unseen laboratories. Our findings show that despite the high performance of four state-of-the-art models reported in initial publications, the published models struggle to generalize to other laboratories. We further show a significant improvement in model performance across labs by re-training them on a diverse dataset from five different sites. To assess the contribution of variability in manual scoring, ten experts from five laboratories all labelled the same nine mouse sleep recordings. The result revealed substantial scoring variability, particularly for rapid eye movement (REM) sleep, both within and between labs. In conclusion our study demonstrates that key challenges in the generalizability of state-of-the-art sleep scoring models are signal variability and label noise. Our study highlights the need for a standardized set of mouse sleep scoring guidelines to enable consistency and collaboration across the field. Until such a consensus is reached, we present four sufficiently robust models trained on diverse datasets that can serve as standardized tools across labs.

Hypoxic-ischemic (HI) brain injury in neonates is one of the leading causes of lifelong neurological disability. Behavioral tests in preclinical rodent models are widely used to assess motor and cognitive outcomes after HI injury; however, these assays usually depend on subjective and labor-intensive manual scoring. Recent advances in markerless pose estimation offer new opportunities for automated and reproducible behavioral quantification in animal and infant recordings, but their use in neonatal HI preclinical studies remains limited. Wistar rat pups underwent HI injury using the Rice-Vannucci model at postnatal day 7 (P7). Three developmental behavioral tests included righting reflex (P8), negative geotaxis (P14), and wire hang (P16), were recorded and analyzed by both a human rater and an automated pipeline using DeepLabCut (DLC), an open source markerless pose estimation framework. Automated measurements were compared with manual scores using Intraclass Correlation Coefficients (ICC), Bland-Altman analysis, and Pearson correlation. DLC-derived measurements demonstrated strong agreement with manual scoring across all assays. ICC values were 0.929 (95% CI 0.648-0.971) for righting reflex, 0.965 (0.888-0.989) for negative geotaxis, and 0.958 (0.876-0.985) for wire hang. An automated behavioral analysis framework integrating DLC-based pose estimation with rule based quantification and supervised machine learning offers a reliable and objective alternative to manual scoring in neonatal HI models, enabling more efficient and reproducible behavioral assessment.

Compared with model-based and phenomenological descriptions of the spatiotemporal dynamics of resting-brain activity, statistical characterizations of resting-state fMRI (rs-fMRI) data remain relatively underexplored. Some sophisticated analysis techniques, such as Mapper-based topological data analysis (TDA) and innovation-driven coactivation pattern analysis (iCAP), can distinguish real data from phase-randomized (PR) surrogates, suggesting that rs-fMRI data are not as simple as stationary Gaussian processes. However, the exact statistical properties that distinguish real rs-fMRI data from PR surrogates have not yet been determined. In this study, we conducted system identification analysis and surrogate data analysis to specify key statistical properties that allow TDA and iCAP to discriminate real rs-fMRI data from PR surrogates. We first analyzed rs-fMRI data concatenated across scans using autoregressive (AR) modeling and found that the scan-concatenated rs-fMRI data were weakly non-Gaussian. However, non-Gaussianity alone was insufficient to reproduce realistic TDA and iCAP results because of non-stationarity across scans. AR modeling of single-scan data revealed that rs-fMRI data were statistically indistinguishable from a Gaussian distribution within a single scan, although TDA and iCAP results still differed between the real data and PR surrogates. A new surrogate dataset designed to preserve non-stationarity successfully reproduced realistic TDA and iCAP results, suggesting that TDA and iCAP likely capture the non-stationarity of rs-fMRI data to distinguish it from PR surrogates. Together, these results indicate approximate Gaussianity and non-stationarity in rs-fMRI data, providing a data-driven and statistical characterization of resting-state brain activity that can serve as a quantitative reference for whole brain simulations and generative models.

Ischemic stroke triggers a cascade of molecular and cellular processes leading to fibrotic scar formation, entailing activation of brain platelet-derived growth factor receptor (PDGFR){beta}+ cells. Kruppel-like factor (KLF)4 plays an important role in regulating the activation of peripheral PDGFR{beta}+ perivascular cells in response to hypoxia/ischemia. Herein, we aimed to characterize the spatiotemporal responses of brain PDGFR{beta}+ cells while assessing the contribution of KLF4. This was achieved using transgenic mice that enable tracking or conditionally depleting KLF4 in PDGFR{beta}+ cells, which were subjected to experimental ischemic stroke. Next, we employed point pattern analysis (PPA) and topological data analysis (TDA) to quantitatively characterize cell phenotypic changes and spatial distribution over injury progression after ischemic stroke. We show that brain PDGFR{beta}+ cells rapidly become reactive and early localize to regions prone to irreversible damage. We report the emergence of parenchymal PDGFR{beta}+ cells, which cannot be causally linked to proliferation or vascular detachment. Moreover, our analysis reveals that KLF4 is barely expressed in brain PDGFR{beta}+ cells under normal conditions, and that its expression is slightly induced in reactive cells in the injured brain. Notably, specific attenuation of KLF4 induced expression in PDGFR{beta}+ cells does not affect cell reactivity and spatiotemporal distribution, nor scar formation and injury severity. These observations suggest that in contrast with the periphery, KLF4 is not implicated in regulating the responses of brain PDGFR{beta}+ cells. Our results indicate that the reactivity of brain PDGFR{beta}+ cells after stroke is spatiotemporally diverse, evolve over injury progression, and is distinct from peripheral perivascular cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/712632v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1149c62org.highwire.dtl.DTLVardef@26edaaorg.highwire.dtl.DTLVardef@1bd3d35org.highwire.dtl.DTLVardef@fd8030_HPS_FORMAT_FIGEXP M_FIG C_FIG

Introduction: Auditory brainstem response (ABR) is a standard objective method for estimating hearing threshold, especially in patients who cannot reliably participate in behavioral audiometry. However, ABR interpretation is usually performed by an expert. This study evaluated whether two general-purpose artificial intelligence (AI) multimodal large language model (LLM) chatbots, ChatGPT and Qwen, can accurately estimate ABR hearing thresholds from ABR waveform images. The accuracy was measured by comparisons with the judgements of 3 expert audiologists. Methods: A total of 500 images each containing several ABR waveforms recorded at different stimulus intensities were analyzed. Three expert audiologists established the reference auditory thresholds based on visual identification of wave V at the lowest stimulus intensity, with the most frequent judgment among the three used as the reference. Each waveform image was independently submitted to ChatGPT (version 5.1) and Qwen (version 3Max) using the same standardized prompt and without additional clinical context. Agreement with the expert thresholds was assessed as mean errors and correlations. Sensitivity and specificity for detecting hearing loss (>20 dB nHL) were also calculated. In cases where the AI and expert thresholds nominally matched, corresponding latency measures were also compared. Results: Auditory thresholds derived from both LLMs correlated strongly with expert opinion, with Pearson r = 0.954 for ChatGPT and r = 0.958 for Qwen. ChatGPT showed a mean error of +5.5 dB and Qwen showed a mean error of -2.7 dB. Exact nominal agreement with expert values was achieved in 34.6% of ChatGPT estimates and 35.6% of Qwen estimates; agreement within +/-10 dB was observed in 75.6% and 80.0% of cases, respectively. For hearing-loss classification, ChatGPT achieved 100% sensitivity but low specificity (20.4%), whereas Qwen showed a more balanced profile with 91.6% sensitivity and 67.5% specificity. Curiously, estimates of wave V latency were markedly poor for both LLMs, with systematic underestimation and weak correlations with the expert judgements. Conclusion: ChatGPT and Qwen demonstrated a moderate ability to estimate ABR thresholds from waveform images, although their performance was not good enough for independent clinical use. Both models captured general patterns of hearing loss severity, but there was systematic bias, limited specificity and sensitivity balance, and poor latency estimation. General-purpose multimodal LLMs may have potential as assistive or preliminary tools, but clinically reliable ABR interpretation will likely require specialized, domain-trained AI systems with expert oversight.

The hippocampal CA1 subregion supports learning, memory formation, and spatial navigation. Although its three-layered architecture has been described in ex-vivo investigations, the in-vivo microstructural profile of CA1 and its relation to individual variations in memory performance remain poorly characterized. In this study, we used ultra-high field structural MRI at 7 Tesla to investigate the depth-dependent myelination patterns (measured by quantitative T1) of CA1 in younger adults, their relation to the local arterial architecture, and their association with individual differences in cognitive functions, specifically memory performance. Results show that left and right CA1 present depth-dependent patterns of myelination, with the outer and inner compartments showing higher myelination than the middle compartment. No significant relationship between layer-specific myelination of CA1 and distance to the nearest artery was observed. Right CA1 was found to be more myelinated than left CA1. Pairwise correlations and regression models showed that higher left CA1 myelination is linked to higher accuracy in object localization. Together, our data demonstrates the feasibility of describing the three layered myelin architecture of CA1 in vivo, and provides information on how alterations in the architecture of CA1 may relate to alterations in cognitive performance in younger adults.

The visual receptive field (RF) characterizes the spatiotemporal properties of the visual pathway and serves as a fundamental unit for information encoding. While RFs have been extensively studied across various neural modalities, such as functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), and Magnetoencephalography (MEG), their investigation via Electroencephalography (EEG) remains limited. In this study, we introduce a stimulation paradigm that combines white noise image sequences with a letter detection task to elicit central visual field EEG responses. Using the aligned/shuffled reverse correlation, we estimate RFs across different resolutions and demonstrate that the resulting RFs exhibit rich spatiotemporal characteristics. To validate the reliability of the estimated RFs, we constructed a visual EEG reconstruction model, which achieved good performance in a classification task. The same RF estimation method was subsequently applied to high-density EEG recordings to investigate the information gain afforded by high-density configurations in visual space. This work fills a gap in the study of visual RFs regarding the EEG modality and may inform the paradigm design of visual brain-computer interfaces.

Information flow between visual areas is central to perception, but difficult to measure in vivo. To characterize causal interactions across human visual cortex, we stimulated electrodes during intracranial EEG recordings in 17 patients undergoing evaluation for drug resistant epilepsy. This allowed us to construct a map of causal interactions between human visual cortical areas. Stimulation of early visual areas elicited robust feedforward influences on dorsal, lateral, and ventral visual streams, whereas feedback influences were weaker and more spatially selective. Cross-stream interactions showed a bias toward stronger temporal to parietal influence compared to parietal to temporal influence. These findings suggest that early visual areas and the ventral stream act as primary sources of influence, whereas dorsal and lateral streams act as integrators. HighlightsO_LIThis study shares an initial matrix of causal interactions between human visual areas C_LIO_LIFeedforward influences dominate over feedback influences C_LIO_LICross-stream communication is spatially selective and asymmetric C_LIO_LIVisual areas play distinct source and integrative roles in the visual cortical system C_LI

Background: Peri-lead edema (PLE) occurs in up to 15% of Deep Brain Stimulation (DBS) cases, can cause morbidity, and its etiology remains unknown. We hypothesized that PLE represents a secondary brain injury modulated by hypoxemia, and that patients with obstructive sleep apnea (OSA) are at elevated risk. Methods: We conducted a retrospective case-control study of 121 Parkinson's disease (PD) patients undergoing DBS at a single center (2019-2024). PLE severity was quantified by CT volumetric segmentation and Hounsfield unit (HU) measures. Perioperative SpO2 and PaO2 were recorded. Polysomnography (PSG) was available in 26 patients; and the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) was administered retrospectively. Results: Symptomatic PLE occurred in 12 patients (9.9%), with onset at 3.5 (2-9) days postoperatively. PLE patients had higher body mass index (p = 0.022) and higher OSA prevalence (75% vs. 30%; p = 0.002). Perioperative SpO2 was lower in the PLE group in both the operating room and post-anesthesia care unit (PACU; p < 0.05); PaO2 was lower in the PACU (p = 0.037). In the PSG subgroup, REM Sleep Behavior Disorder (RBD) incidence was lower in PLE patients (20% vs. 60%; unadjusted p = 0.048), and PLE severity correlated significantly with sleep-related hypoxemia and respiratory indices. RBDSQ scores were positively associated with edema density (normalized HU: rho = 0.86, p = 0.024). Conclusions: OSA and perioperative hypoxemia are associated with symptomatic PLE following DBS, while RBD appears protective. Preoperative sleep evaluation and optimized perioperative airway management warrant prospective investigation as PLE prevention strategies.