Frontiers in Neuroscience

BackgroundFrom the radiographic perspective, the septum pellucidum (SP) and septum verum (SV) Complex (SPVC) has been tacitly understood. Microdissection and diffusion tensor imaging (DTI) have now well established that they are not mere membranes but contain septal nuclei and nerve fibers; the Superior (SF) and Inferior fascicles (IF) forming the SP, and precommissural fibers of the fornix (PrCFx) in SV. ObjectiveWe aimed to delineate the topography of normal and abnormal SPVC using ultrasound (US), T2-weighted magnetic resonance imaging (MRI), and DTI in fetuses and provide an algorithm for prenatal diagnosis and evaluation of septopreoptic holoprosencephaly (SPrH). MethodsTwenty-nine fetuses included in the study were divided based on US into Group 1 (five of 29): normal Cavum Septum Pellucidum (CSP) on axial transthalamic (aTTP) and transventricular (aTVP) planes; Group 2 (eleven of 29): non-visualization of the SP in aTVP, coronal transcaudate plane (cTCP) and beyond; Group 3 (three of 29): single septum in aTVP; Group 4 (ten of 29): small /echogenic CSP in aTTP and aTVP. ResultsAll three fascicles forming the SPVC were demonstrated in all cases prenatally and/or postnatally on US, MRI and DTI. All fetuses in Groups 2 to 4 showed an abnormal hypointense band bridging the region of septal and/or preoptic nuclei on T2-weighted fetal and postnatal MR, suggestive of SPrH. ConclusionThis study contributes to understanding the topography of normal and abnormal SPVC by prenatal US, MRI, and DTI. Based on this understanding, we outline an algorithm for prenatal diagnosis and evaluation of SPrH. HighlightsO_LIFetal Septum pellucidum/verum complex (SPVC) contain septal nuclei and 3 nerve fiber groups: Superior fascicle, Inferior fascicle and Precommissural fornix C_LIO_LIThese are seen on ultrasound, T2-weighted MRI and Diffusion tensor imaging in cases with both normal and abnormal cavum septum pellucidum (CSP). C_LIO_LIHypointense band in the septopreoptic region on T2-weighted MRI in fetuses with abnormal CSP are potential markers of septopreoptic holoprosencephaly C_LIO_LIRecognition of this entity may help in prenatal counselling and prognosis C_LI

We investigated the anatomical localization of the frontal eye field (FEF) and its relationship to the eyelid motor area (EMA) and precentral motor cortex. We performed functional mapping using electrical cortical stimulation (ECS) and correlated electrode position by non-linear co-registration techniques using postoperative MRI. We studied 22 patients who underwent chronic implantation of subdural electrodes for epilepsy surgery. Eye movements were elicited at 52 electrodes overall. The majority of the movements were conjugated, saccadic eye deviation contralateral to the side of ECS. Head turning and non-saccadic eye deviation more frequently occurred in the vicinity of the precentral sulcus. Anatomically, FEF was located at Brodmanns area 6 in the most-caudal region of the middle frontal gyrus and in the adjacent part of the superior frontal sulcus and precentral sulcus. Functionally, FEF was situated at the level of the hand motor area, more dorsal than was described in Penfields motor homunculus. The FEF is situated anteriorly from the precentral motor cortex. The EMA was situated within the precentral motor cortex, partially overlapping with but distinctly ventral and caudal to FEF, and dorsal to the lower face motor area. A standardized map of the FEF and precentral motor homunculus is provided as a reference for human system neuroscience research.

Musical rhythm is often experienced with a periodic beat, serving as a temporal reference for coordination with the rhythm. Thus far, models of beat processing have mainly relied on representing sensory inputs as patterns of onset timing, with limited consideration of other sensory features. Here, we challenge this view by showing that the internal representation of beat is affected by other temporal features of the stimulus beyond onset timing alone. We recorded electroencephalography (EEG) while participants listened to rhythmic sequences designed to elicit a beat. Across conditions, we manipulated the duration of the tones conveying the rhythms, while keeping all other parameters identical, including overall intensity, speed, and rhythmic pattern structure. Crucially, the beat periodicity was enhanced in neural activity with increased sound duration, even though the beat periodicity was not prominent in the acoustic features, thus ruling out basic sensory confounds. These results demonstrate the preferential role of longer sound durations in fostering temporal scaffolding processes that integrate fast rhythmic inputs into behavior-relevant internal structures such as the beat. More generally, our findings are compatible with a holistic processing account whereby a range of features beyond onset timing may be integrated into a neural representation of rhythm. Graphical Abstract: Fig. 2EEG was recorded while listeners heard rhythmic sequences eliciting a beat. Sound duration (sonic duty cycle) was varied across four conditions while speed, pattern, and intensity stayed constant. Beat-related EEG responses increased with longer sounds, and were enhanced in all conditions compared to auditory nerve model envelopes, which did not show prominent energy at the beat periodicity, ruling out sensory confounds. Results support holistic rhythm processing beyond onset timing alone. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/721298v1_fig2.gif" ALT="Figure 2"> View larger version (27K): org.highwire.dtl.DTLVardef@10a0599org.highwire.dtl.DTLVardef@f5a95forg.highwire.dtl.DTLVardef@42d1ceorg.highwire.dtl.DTLVardef@dc58a7_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO EEG and auditory nerve model output analysis based on magnitude spectrum and autocorrelation. Each row represents a duty cycle condition. The two columns on the left represent the magnitude spectrum-based analysis. The first column represents the group-level averaged magnitude spectra at a pool of fronto-central electrodes, across conditions. Beat-related frequencies are shown in red, and beat-unrelated frequencies are shown in blue. Scalp topographies of the neural activity measured at the average magnitudes of beat-related (in red circle) and unrelated (in blue circle) frequencies are represented as insets. The second column represents the normalized magnitude spectra obtained from the auditory nerve model output for each duty cycle sequence. The two columns on the right represent the autocorrelation-based analysis (for visualization purposes, only a subset of lags from 0 to 2.4 s corresponding to the pattern duration is shown). The first column represents the group-level averaged autocorrelation function measured from the same pool of fronto-central electrodes, across conditions. Beat-related lags are shown in red, and beat-unrelated lags are shown in blue. The second column represents the autocorrelation function of the auditory nerve model output for each duty cycle sequence. C_FIG

Ferroptosis is a form of non-apoptotic cell death in which iron catalyzes the formation of reactive oxygen species, leading to lipid peroxidation. Experimentally, this process has recently been associated with seizures based on the increased levels of specific markers (4-hydroxynonenal and malondialdehyde) in the brain and plasma. Clinically, iron deposits have been identified in resected tissue from patients with refractory temporal lobe epilepsy. Quantitative susceptibility mapping (QSM) offers an opportunity to detect these accumulations in vivo. In this study, we investigated how pilocarpine-induced status epilepticus contributes to the generation of iron deposits in diverse cerebral regions and whether QSM can detect these deposits longitudinally. We scanned 14 animals (n=10 experimental; n=4 control) at five different time points (pre-status epilepticus induction and 1, 7, 14, 21 days post-induction) using QSM. We identified iron deposits in the caudate putamen, hippocampus, thalamus, and primary somatosensory cortex of experimental animals, which is consistent with histological findings. The initial size of the hippocampal iron deposits significantly increased over the following weeks. None of these effects was observed in the control animals. The presence of cerebral iron depositions in an animal model of pilocarpine-induced status epilepticus suggests that ferroptosis may be involved in the onset, development, and progression of spontaneous recurrent seizures. Furthermore, non-invasive, longitudinal in vivo mapping of brain iron deposits could be a potential imaging marker in neurological disorders such as epilepsy. Future experiments will be required to determine the origin of the iron and avoid its progressive accumulation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/712677v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@14abf67org.highwire.dtl.DTLVardef@5c08fborg.highwire.dtl.DTLVardef@51c40forg.highwire.dtl.DTLVardef@1eb5f9_HPS_FORMAT_FIGEXP M_FIG C_FIG

For a vision system, estimating the speed and direction of movement at the retinal input stage is an essential function for survival in many organisms. Retinal ganglion cells specific to this movement function were identified using multi-electrode array recordings in neonatal chick retina. Motion-evoked "visual streaks" and direction selective responses were observed in chick ganglion cells upon sequential activation as a response to moving bar stimuli. These characteristics were preserved in the sub-retinal prosthetic consisting of a semiconductor polymer film coupled to the blind chick retina which generated spatiotemporal activity patterns resembling those in natural vision. The motion parameters of direction and speed inferred from these recordings demonstrate that polymer-based prostheses can evoke physiologically relevant activity patterns, suggesting their potential to restore motion perception in degenerative retinae.

Statistical parametric mapping (SPM) software was implemented in the early 1990s so that neuroscientists could test spatially extended hypotheses using functional imaging data, usually in 3D space and allowing for a mass univariate approach to hypothesis testing that is agnostic to where any significant effects may lie. Here, we apply the same approach to gaze duration data, i.e. visual fixations, collected using a virtual reality headset, which extends across a large 2D area of visual space, measuring 32{degrees} either side of central fixation and 24{degrees} above and below this point. In order to evaluate this novel method, we measured the locus of average gaze in a group of 17 patients with hemispatial inattention to the left, a neurological condition caused by damage to the right parieto-frontal brain networks, that induces a systematic bias in lateralised visual attention. This causes people to experience difficulty in paying attention to one side of space, both in their extrapersonal world and relative to their own bodies. We used a free visual exploration paradigm (viewing multiple naturalistic scenes for 7 seconds), which is sensitive to spatial biases encountered in this condition. 23 age-matched and neurologically healthy controls also took part. The visual stimuli were original and mirror flipped versions (Left to Right ie L-R) to correct for any lateralised informational biases inherent in the images. When compared with age-matched controls, the patients exhibited an average spatial shift of attention of 18{degrees} to the right of the midline. We demonstrated this approach using patients with hemispatial inattention, but it can be applied to any fixation-based or dwell time data. This is an advance on current methods that generated visual heatmaps or attentional maps, as our technique allows formal testing of spatially extended hypotheses on gaze duration data using a standard, frequentist statistical approach.

This study assessed neural responses to continuous speech to classify listening state, cognitive load, and selective auditory attention in complex acoustic environments. EEG was recorded while participants listened to concurrent male and female talkers under two conditions: active listening, where attention was directed to one of two competing speakers (target vs. masker), or passive listening, where attention was diverted to a visual task. Cognitive load was varied by manipulating target-to-masker (TMR) ratio (TMR: +7 dB, -7 dB), with lower TMR representing more demanding listening conditions. Spectral EEG features across frequency bands were ranked with univariate statistics and used to classify listening state (active vs passive) and cognitive load (low vs. high TMR). Auditory attention decoding (AAD) was performed using linear stimulus reconstruction to identify the target talker during active listening. Classification of listening state achieved 90.3% accuracy, and AAD reached 84.4% accuracy, demonstrating robust tracking of attentional engagement. In contrast, classification of cognitive load was near chance, suggesting that more extreme acoustic manipulations may be required to elicit distinct neural signatures. Comparable performance using a reduced set of electrodes near the ear indicates the potential for integration with wearable hearing devices. Overall, these results demonstrate that EEG can distinguish attentional states and selectively track target speech in realistic auditory scenarios. The findings provide a foundation for future applications in monitoring listening behavior, supporting auditory processing, and improving brain-controlled hearing aids in complex acoustic environments. HighlightsO_LIListening state (active vs. passive) can be classified from EEG spectral features. C_LIO_LIAttended speech can be decoded by reconstructing speech envelopes from EEG. C_LIO_LIComparable accuracy is achieved using only electrodes placed around the ears. C_LIO_LIEEG can monitor listening state and track auditory attention in two-speaker settings. C_LI Graphical AbstractEEG signals were recorded while participants listened to two concurrent speech streams, either by actively attending to one speaker or by focusing on an unrelated visual task. Spectral features of the EEG were used to classify listening state (active vs. passive) and cognitive load (low vs. high TMR). Auditory attention decoding (AAD) was performed by reconstructing the speech envelope from the EEG time signal. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/711289v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1079628org.highwire.dtl.DTLVardef@1135404org.highwire.dtl.DTLVardef@1f0d950org.highwire.dtl.DTLVardef@14b4c9a_HPS_FORMAT_FIGEXP M_FIG C_FIG Classification of listening state (active vs. passive): 90.3% accuracy. EEG difference between active and passive listening. Left, power spectrum, right, topographic map (alpha band 8-12 Hz). Classification of cognitive load (low vs high TMR): near chance level. EEG difference between low and high TMR. Left, power spectrum, right, topographic map (alpha band 8-12 Hz). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/711289v1_ufig2.gif" ALT="Figure 2"> View larger version (34K): org.highwire.dtl.DTLVardef@9229b1org.highwire.dtl.DTLVardef@1ef394corg.highwire.dtl.DTLVardef@9adecforg.highwire.dtl.DTLVardef@199f8c2_HPS_FORMAT_FIGEXP M_FIG C_FIG AAD achieved 84.4% accuracy, indicating robust decoding of the attended speaker during active listening, while performance dropped to near chance during passive listening.

Our ability to recognize sound sources in the world is critical to daily life, but is not well documented or understood in computational terms. We developed a large-scale behavioral benchmark of human environmental sound recognition, built stimulus-computable models of sound recognition, and used the benchmark to compare models to humans. The behavioral benchmark measured how sound recognition varied across source categories, audio distortions, and concurrent sound sources, all of which influenced recognition performance in humans. Artificial neural network models trained to recognize sounds in multi-source scenes reached near-human accuracy and qualitatively matched human patterns of performance in many conditions. By contrast, traditional models of the cochlea and auditory cortex that were trained to recognize sounds produced worse matches to human performance. Models trained on larger datasets exhibited stronger alignment with both human behavior and brain responses. The results suggest that many aspects of human sound recognition emerge in systems optimized for the problem of real-world recognition. The benchmark results set the stage for future explorations of auditory scene perception involving salience and attention.

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.

Understanding speech in noisy situations is challenging and often fails due to attentional rather than sensory deficits. We report here that neurofeedback can enhance a neural signature of selective attention to speech in a cocktail-party setting. Neural responses to speech were quantified in participants electroencephalogram (EEG) while they attended to one of two audiobooks, presented simultaneously. After each 22s-segment of speech, the participants N1 component was extracted from the temporal response function (TRF). The N1 component is known to be sensitive to attention. During a [~]49-min training session, N1 amplitude values were displayed visually to participants so that they could learn to strengthen their neural responses to target speech and minimise their responses to distracting speech. During neurofeedback training, we found an enhanced N1 component in the response to target audiobooks that was specific to EEG channels used to provide feedback, and not present in a control group that received sham feedback. At right-lateralised fronto-central channels, enhanced N1 components correlated with improvements in a measure of speech comprehension (multiple-choice content questions). These results indicate that neural responses to speech can be regulated through neurofeedback and open up new possibilities to train attentional listening in populations struggling to understand speech in noise.

Chronic tinnitus is a common condition with few effective treatments and no cure. Though inconsistent results across MRI studies of tinnitus have slowed mechanistic insight, converging evidence across animal and human studies clearly implicate auditory-system dysfunction. This paper presents a systematic, retrospective assessment of auditory-network function in chronic tinnitus across multiple fMRI datasets. Auditory network nodes were newly defined in this effort, including novel nodes in cerebellum previously linked with somatotopic representations of articulators (lobules VI, VIIIa). Auditory-network connectivity in cerebellum and superior olivary complex was reduced in chronic tinnitus, perhaps explaining the recent success of trigeminal stimulation in improving tinnitus. Auditory-network strength was also reduced, corroborating some recent studies and perhaps reflecting increased spontaneous neuronal activity reported in animal models. Together, these results suggest auditory-network dysconnectivity as a tinnitus biomarker, and that efferent cochlear pathways related to head-centric interoception may play a mechanistic role.

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 motor cortex is involved not only in movement execution but also in motor imagery, a process leveraged by decoding algorithms for brain-computer interface (BCI) applications in individuals with severe motor impairments. Previous work has shown that population activity during execution and imagery occupies partially overlapping regions of neural state space while also engaging distinct subspaces unique to each motor state, suggesting that decoders trained in one condition may not generalize to the other. Moreover, movement execution likely includes neural representations of both motor output and proprioceptive feedback, which themselves may occupy distinct or overlapping regions of neural state space. To explore these distinctions, we studied two individuals with incomplete spinal-cord injuries and partial residual proximal arm function performing a center-out reaching task in three conditions: motor imagery, active execution, and passive movement. We found that decoders trained on neural activity from motor imagery failed to generalize to either active or passive movements. In contrast, decoders trained on active or passive movement activity generalized reciprocally. Population analysis revealed distinct dynamics depending on limb state and proprioceptive feedback, which could explain this lack of generalization. These results suggest that motor imagery engages motor cortical representations distinct from those recruited during actual movements, either actively or passively generated, with important implications for the design of BCI decoders.

IntroductionAuditory 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. MethodsA 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. ResultsAuditory 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. ConclusionChatGPT 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 goal of the resting-state functional connectivity studies is to determine the inherent dynamics of the brain networks while the body is at rest. These networks get differentially activated when the brain is involved in various tasks such as processing of sensory inputs, initiating motor activities, or various cognitive tasks. Resting state functional connectivity networks are commonly revealed by determining Pearson Correlation Coefficients of the Blood Oxygenation Level Dependent (BOLD) signals collected from different brain regions using functional Magnetic Resonance Imaging (fMRI) while the subject is not actively performing any task. However, the functional connectivity thus determined does not correlate well with the known structural connectivity between different brain regions. Here, we used Empirical Mode decomposition (EMD), followed by Hilbert Transformation (HT), to determine the resting state functional connectivity of the somatomotor network in the human brains. We show that the time series data decomposed by this method improves correlation of the derived functional connectivity with the known structural connectivity (especially for low -TR fMRI data) as compared to the methods commonly used.

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.

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. Clinical Study RegistrationSleep in Late Pregnancy - Artificial Intelligence Development for the Detection of Disturbances and Disorders (SLeeP AID4), https://clinicaltrials.gov/study/NCT05376475, registration ID NCT05376475. Statement of SignificancePregnancy negatively impacts sleep, and poor sleep in pregnancy negatively impacts maternal and fetal health. Pregnancy represents a unique challenge to sleep breathing physiology and, thus, an opportunity to test for sleep apnea. Sleep apnea however, while increased in pregnancy, is more common in males. This novel study tested healthy people with low-risk pregnancies and their bed partners for sleep apnea in the comfort of their home over four nights in late pregnancy. Sleep apnea was more common and worse in the bed partners. Advancing gestational age was protective against mild but not severe sleep apnea, and a critical remaining knowledge gap is this interplay in high-risk pregnancies. Future sleep in pregnancy research should make efforts to include high-risk pregnancies.

Organotypic slice cultures (OSCs) are widely used to study cellular properties in a functional and developmental tissue context. With the recent advent of transgenic mouse lines and viral tools we postulated that OSCs may enable the study of multicellular glial and neuroglial interactions in development, as well homeostatic and pathological conditions. Here, we made mouse cortical OSCs and used markers for oligodendroglial, microglial states and neuronal types between 1 to 28 days in vitro (DIV). The OSC was characterized by in-vivo like cortical layering, including layer 5 pyramidal neurons and produced highly robust synchronized period bursts resembling Up- and Down states. Glial cells showed a strong cortical layer- and time-dependent development pattern: in the first week (DIV 1-7), slicing-related debris clearance and developmentally restricted sparse oligodendroglial myelination created an environment with highly phagocytic, non-homeostatic microglia (assessed with CD68 and purinergic receptor P2Y12, respectively). Between DIV 14 and 21, however, slices showed stereotypical cortical myelin patterns and the emergence of a homeostatic microglia phenotype while exhibiting continued phagocytosis. Furthermore, live two-photon imaging and morphometric analyses revealed highly ramified microglia and myelinated axons with compact myelination, exceeding lamellae count compared to age-matched in vivo axons. Lastly, from DIV 28 and onwards, myelin integrity became impaired and associated with phagocytic microglia. Together, the results indicate that between DIV14 and 21 cortical OSCs are well suited for live imaging of homeostatic and activity-dependent neuron-glia interactions, bridging the gap between in vivo investigations and primary cell cultures.

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.