bioRxiv Subject Collection: Neuroscience's Journal

A mutation in the transmembrane domain of Adenylate cyclase 3 impairs enzymatic function to cause sex-specific depression- and anxiety-like behaviors and food seeking in a rat model
We have previously demonstrated that a transmembrane domain mutation in Adenylate cyclase 3 (Adcy3) causes increased adiposity and negative emotion-like behaviors in a rat model. We set out to replicate and expand upon our previous study by conducting comprehensive behavioral testing, and we also investigated the molecular changes that result from this mutation. Rats with a mutation in the second transmembrane helix of ADCY3 (Adcy3mut/mut) and wild-type rats were fed a high-fat diet for 12 weeks. We measured body weight, body composition, and depression-like and anxiety-like behaviors using the following tests: sucrose splash test, sucrose preference test, forced swim test, open field test, elevated plus maze, successive alleys test, and novelty-suppressed feeding. We also measured serum leptin levels, hypothalamic cyclic AMP (cAMP) production, and membrane fraction ADCY3 content. Adcy3mut/mut male and female rats had increased adiposity. Adcy3mut/mut males showed increased despair- and anxiety-like behaviors, food seeking, and higher leptin levels relative to wild-type males. Adcy3mut/mut females showed only mildly increased anxiety-like behaviors relative to wild-type females. Adcy3mut/mut rats of both sexes had decreased cAMP production in the hypothalamus, with no changes in ADCY3 content in the membrane fraction. We conclude that the transmembrane domain of ADCY3 plays a critical role regulating adiposity and behavior, as well as cAMP production. There were key differences between males and females for the observed phenotypes. This study supports the idea that Adcy3 contributes to emotion-like behaviors and potentially mental health disorders, and that the transmembrane domain of ADCY3 is important for protein function.

Bidirectional Modulation of Somatostatin-expressing Interneurons in the Basolateral Amygdala Reduces Neuropathic Pain Perception in Mice
Neuropathic pain is characterized by mechanical allodynia and thermal (heat and cold) hypersensitivity, yet the underlying neural mechanisms remain poorly understood. This study examines the role of inhibitory interneurons in the basolateral amygdala (BLA) in modulating pain perception following nerve injury. Chemogenetic excitation of parvalbumin-positive (PV+) interneurons significantly alleviated mechanical allodynia but had minimal effects on thermal hypersensitivity. However, inhibition of PV+ interneurons did not produce significant changes in pain sensitivity, suggesting that reductions in perisomatic inhibition do not contribute to chronic pain states. In contrast, bidirectional modulation of somatostatin-positive (SST+) interneurons influenced pain perception in a modality-specific manner. Both excitation and inhibition of SST+ interneurons alleviated mechanical allodynia, indicating a potential compensatory role in nociceptive processing. Additionally, SST+ neuron excitation reduced cold hypersensitivity without affecting heat hypersensitivity, whereas inhibition improved heat hypersensitivity but not cold responses. These findings suggest that, in addition to PV+ neurons, SST+ interneurons in the BLA play a complex role in modulating neuropathic pain following nerve injury and may serve as a potential target for future neuromodulation interventions in chronic pain management.

Common and distinct neural mechanisms of aversive and appetitive pain-related learning
Appetitive and aversive conditioning are both fundamental to adaptive behaviour, yet there remains limited understanding of how they differ on the behavioural and neural level. We investigated the two processes during acquisition and extinction using functional magnetic resonance imaging and behavioural measures. In a within-subject differential conditioning paradigm (preregistration DRKS00027448), aversive learning was induced by pairing visual cues with a temperature increase (pain rise), while appetitive learning involved a temperature decrease (pain reduction). Valence and contingency ratings confirmed successful learning for both types of learning, though only the appetitive condition showed a return to baseline ratings during extinction, suggesting incomplete extinction in the aversive condition. On the neural level, both engaged the visual cortex during acquisition (with increased functional connectivity with the right frontal operculum) and the ventromedial prefrontal cortex (vmPFC) during extinction. However, aversive learning showed a stronger activation increase in the mediodorsal thalamus with heightened connectivity with the locus coeruleus during acquisition, as well as sustained parahippocampal activity during extinction. Moreover, incomplete extinction in the aversive condition (as indicated by contingency ratings) was associated with sustained activity in the visual cortex during pain anticipation. These results suggest that while appetitive and aversive learning share activation in regions involved in sensory processing (occipital lobe) and learning (vmPFC), aversive learning uniquely engages areas promoting rapid acquisition (mediodorsal thalamus and locus coeruleus) and cautious unlearning, in line with the notion of a "better-safe-than-sorry" strategy.

Non-linear and time-domain sleep qEEG features predict CSF protein damage markers in early Alzheimers disease
IntroductionIn preclinical Alzheimers disease (AD), oxidative stress induces non-enzymatic protein damage--detected as cerebrospinal fluid (CSF) biomarkers--and disrupts sleep-related networks, altering sleep electroencephalographic patterns. Due to the invasiveness of CSF sampling, quantitative electroencephalography (qEEG) is proposed as a non-invasive alternative for predicting oxidatively modified protein levels via Machine Learning (ML).

MethodsForty-two mild-to-moderate AD patients underwent polysomnography (PSG). qEEG features were extracted. CSF protein oxidation markers levels --glutamic semialdehyde, aminoadipic semialdehyde, N{varepsilon}-carboxyethyl-lysine, N{varepsilon}-carboxymethyl-lysine, and N{varepsilon}-malondialdehyde-lysine --were assessed by gas chromatography/mass spectrometry, and ML models trained to predict CSF biomarker levels.

ResultsqEEG features from slow-wave sleep (SWS) and rapid eye movement (REM) sleep, particularly over frontal and central regions, yielded R2 > 0.9 and RMSE < 0.1 for biomarker prediction.

ConclusionqEEG is a non-invasive, scalable tool for detecting AD-related oxidative stress, with potential implications for early diagnosis and risk stratification.

APOE genotype-dependent differences in human astrocytic energy metabolism
The main genetic risk factor for Alzheimers disease (AD) is the presence of the apolipoprotein E4 (APOE4) allele. While APOE4 increases the risk of developing AD, the APOE2 allele is protective and APOE3 is risk-neutral. In the brain, APOE is primarily expressed by astrocytes and plays a key role in various processes including cholesterol and lipid transport, neuronal growth, synaptic plasticity, immune response and energy metabolism. Disruptions in brain energy metabolism are considered a major contributor to AD pathophysiology, raising a key question about how different APOE isoforms affect the energy metabolism of human astrocytes. In this study, we generated astrocytes (iAstrocytes) from APOE-isogenic human induced pluripotent stem cells (iPSCs), expressing either APOE2, APOE3, APOE4 or carrying an APOE knockout (APOE-KO), and investigated APOE genotype-dependent changes in energy metabolism. ATP Seahorse assay revealed a reduced mitochondrial and glycolytic ATP production in APOE4 iAstrocytes. In contrast, proteomic GO enrichment analysis and mitochondrial stress tests indicated increased mitochondrial respiration and activity in APOE4 iAstrocytes, accompanied with elevated proton leak, while mitochondrial fusion and fission protein levels remain unchanged. Glycolysis stress tests also demonstrated enhanced glycolysis and glycolytic capacity in APOE4 iAstrocytes while genetically encoded nanosensor-based FLIM analysis revealed that APOE does not affect lactate dynamics. Mass spectrometry-based metabolomic analysis identified various energy and glucose metabolism-related pathways that were differentially regulated in APOE4 compared to the other genotypes, including mitochondrial electron transport chain and glycolysis. In general, APOE2 and APOE-KO iAstrocytes showed a very similar phenotype in all functional assays and differences between APOE2/APOE-KO and APOE4 were stronger than between APOE3 and APOE4. Our study provides evidence for APOE genotype-dependent effects on astrocyte energy metabolism and highlights alterations in the bioenergetic processes of the brain as important pathomechanisms in AD.

Mapping the coupling between tract reachability and cortical geometry of the human brain
The study of cortical geometry and connectivity is prevalent in research on the human brain. However, these two aspects of brain structure are usually examined separately, leaving the essential connections between the brain's folding patterns and white matter connectivity unexplored. In this study, we aimed to elucidate fundamental links between cortical geometry and white matter tract connectivity. We developed the concept of tract-geometry coupling (TGC) by optimizing the alignment between tract connectivity to the cortex and multiscale cortical geometry. Specifically, spectral analyses of the cortical surface yielded a set of geometrical eigenmodes, which were then used to explain the locations on the cortical surface reached by specific white matter tracts, referred to as tract reachability. In two independent datasets, we confirmed that tract reachability was well characterized by cortical geometry. We further observed that TGC had high test-retest ability and was specific to each individual. Interestingly, low-frequency TGC was found to be heritable and more informative than the high-frequency components in behavior prediction. Finally, we found that TGC could reproduce task-evoked cortical activation patterns. Collectively, our study provides a new approach to mapping coupling between cortical geometry and connectivity, highlighting how these two aspects jointly shape the connected brain.

The extended language network: Language selective brain areas whose contributions to language remain to be discovered
Although language neuroscience has largely focused on core left frontal and temporal brain areas and their right-hemisphere homotopes, numerous other areas - cortical, subcortical, and cerebellar - have been implicated in linguistic processing. However, these areas' contributions to language remain unclear given that the evidence for their recruitment comes from diverse paradigms, many of which conflate language processing with perceptual, motor, or task-related cognitive processes. Using fMRI data from 772 participants performing an extensively-validated language localizer paradigm that isolates language processing from other processes, we a) delineate a comprehensive set of areas that respond reliably to language across written and auditory modalities, and b) evaluate these areas' selectivity for language relative to a demanding non-linguistic task. In line with prior claims, many areas outside the core fronto-temporal network respond during language processing, and most of them show selectivity for language relative to general task demands. These language-selective areas of the extended language network include areas around the temporal poles, in the medial frontal cortex, in the hippocampus, and in the cerebellum, among others. Although distributed across many parts of the brain, the extended language-selective network still only comprises ~1.2% of the brain's volume and is about the size of a strawberry, challenging the view that language processing is broadly distributed across the cortical surface. These newly identified language-selective areas can now be systematically characterized to decipher their contributions to language processing, including testing whether these contributions differ from those of the core language areas.

Neural Tracking of the Maternal Voice in the Infant Brain
Infants preferentially process familiar social signals, but the neural mechanisms underlying continuous processing of maternal speech remain unclear. Using EEG-based neural encoding models based on temporal response functions, we investigated how 7-month-old infants track maternal vs. unfamiliar speech and whether this affects simultaneous face processing. Infants showed stronger neural tracking of their mother's voice, independent of acoustic properties, suggesting an early neural signature of voice familiarity. Face tracking responses differed depending on the voice infants heard. When listening to a stranger's voice, face-tracking accuracy at central electrodes increased with occipital face tracking, suggesting heightened attentional engagement. However, we found no evidence for differential processing of happy vs. fearful faces, contrasting previous findings on early emotion discrimination. Our results reveal interactive effects of voice familiarity on multimodal processing in infancy: while maternal speech enhances neural tracking, it may also alter how other social cues, such as faces, are processed. The findings suggest that early auditory experiences shape how infants allocate cognitive resources to social stimuli, emphasizing the need to consider cross-modal influences in early development.

Aging reduces excitatory bandwidth, alters spectral tuning curve diversity, and reduces sideband inhibition in L2/3 of primary auditory cortex
Presbycusis, or age-related hearing loss, is caused by changes in both the peripheral and the central auditory system. Many of the peripheral structures that degrade with age have been identified and characterized, but there is still a dearth of information pertaining to what changes occur in the aging central auditory pathway that are independent of peripheral degradation. The primary auditory cortex (A1) of aging mice shows reduced suppressive responses and reduced diversity of temporal responses suggesting alteration of inhibitory processing. To gain a better understanding of how tuning features of the auditory cortex change with age, we performed in vivo 2-photon Ca2+ imaging on L2/3 of the auditory cortex of both adult (n=14, 11-24 weeks old) and aging (n=12, 12-17 months old) mice that retain peripheral hearing in old age. To reveal inhibitory inputs to L2/3 neurons we characterized spectral receptive fields with pure tones and two tone complexes. We find that in contrast to adult mice, L2/3 excitatory neurons from aging mice showed fewer distinct categories of spectral receptive fields, though in a subset of FRA types, we found increased diversity. We also noted a decrease in excitatory bandwidth with age among broadly tuned neurons, but that sideband inhibition became weaker across all FRA types due to a reduced amplitude in inhibitory responses. These results suggest that aging causes changes in circuit organization leading to more homogenous spectrotemporal receptive fields and that the lack of response diversity contributes to a decreased encoding capacity observed in aging A1.

Sensory responsivity and its connection to sympathetic activation and deactivation: implications for stress and learning
This study introduces sensory responsivity (SR), which describes individual differences in sensory stimuli response strength and is hypothesised to affect stress responses in students and, in turn, their learning. To investigate this, a scale to assess SR was developed and linked to physiological responses. To this end, electrodermal activity (EDA) and sensory gating data was collected from a laboratory study with 100 students (12-21 y) and EDA data was collected in a classroom study with 35 students (17-18 y). In the lab study, sympathetic activation was generally lower for high SR groups, whilst in the classroom study, sympathetic activation was higher for high SR groups in line with differential susceptibility theory. The high SR groups demonstrated overall lower composite EDA values, which negatively correlate with learning, indicating a potential learning benefit. Thus, sensory responsivity moderates sympathetic activation based on the environmental sensory-intensiveness, which may impact learning and stress-related outcomes.

Cortical motor activity modulates respiration and reduces apnoea in neonates
Respiration is governed by a widespread network of cortical and subcortical structures. This complex communication between the brain and lungs is altered in pathological conditions. Apnoea - the cessation of respiration - is a common condition in infants, particularly those born prematurely. Apnoea in infants is believed to relate to immaturity of brainstem respiratory centres; involvement of the cortex in respiration in infants has yet to be explored. We investigated if cortical control of respiration occurs in newborn humans and whether it relates to apnoea. Using simultaneous electroencephalography (EEG) and impedance pneumography, we show that cortico-respiratory coupling is present in premature and term newborns (104 recordings from 68 infants; 34.5 {+/-} 2.6 weeks post-menstrual age), identifying an interplay between breathing phase and EEG amplitude. We further shed light on the biological meaning by revealing that the strongest coupling occurs during inspiration and that cortical activity precedes respiration, with coupling strongest over frontocentral regions. These findings support the notion that the cortico-respiratory coupling observed here primarily constitutes communication between cortical motor areas and lung effectors. Moreover, we show that cortico-respiratory coupling is negatively correlated with the rate of apnoea, revealing novel mechanistic insight into this common and potentially life-threatening neonatal pathology.

Excitatory synaptic transmission is differentially modulated by opioid receptors along the claustro-cingulate pathway
The anterior cingulate cortex (ACC) plays a pivotal role in processing pain and emotion, communicating with both cortical and subcortical regions involved in these functions. The claustrum (CLA), a subcortical region with extensive connectivity to the ACC also plays a critical role in pain perception and consciousness. Both ACC and CLA express Kappa (KOR), Mu (MOR), and Delta (DOR) opioid receptors, yet whether and how opioid receptors modulate this circuit is poorly understood. This study investigates the effects of opioid receptor activation on glutamatergic signaling in CLA-ACC circuitry using spatial transcriptomics, slice electrophysiology, optogenetics, and pharmacological approaches in mice. Our results demonstrated that excitatory inputs generated by the CLA onto layer 5 pyramidal cells (L5 PYR) in the ACC are reduced by KOR, MOR, and DOR agonists. However, only KOR agonists reduce monosynaptic transmission from the CLA onto L5 ACC PYR cells, highlighting the unique role of KOR in modulating the CLA-ACC pathway. MOR agonists had a heterogeneous effect on optically-evoked excitatory postsynaptic currents (oEPSCs), significantly reducing longer-latency excitatory responses while only modestly inhibiting the short latency excitatory postsynaptic currents. DOR agonists only reduce slower, longer-latency recurrent excitatory responses. These findings provide new insights into how opioid receptors regulate the claustro-cingulate circuit and demonstrate the distinct, receptor-specific modulation of synaptic transmission within this network.

GPR34 regulates microglia state and loss-of-function rescues TREM2 metabolic dysfunction
Microglia are implicated in modifying neurodegenerative disease risk in the central nervous system (CNS). GPR34 is a microglia-enriched G-protein coupled receptor that detects cytotoxic lipids upregulated in Alzheimers Disease (AD). Since dysregulated lipid metabolism occurs in disease, we hypothesized GPR34 could act with other lipid sensors, such as TREM2, to regulate microglial function. Here, we report that GPR34 knockout (KO) rescues dysregulated cholesterol metabolism in TREM2 KO iPSC-derived microglia (iMG) and alone promotes fatty acid catabolism without the proton leak observed in TREM2 KO. Loss of GPR34 downregulated ERK signaling, while its agonism promoted interaction with and activation of ERK. In healthy and amyloid mouse models, Gpr34 KO accelerated the conversion of homeostatic microglia to disease-associated microglia (DAM) states. Additionally, in Gpr34 KO amyloid mouse brain, the frequency of large plaques was increased compared to WT, indicating that Gpr34 KO microglia may promote amyloid aggregation. Overall, our data suggest GPR34 as a therapeutic target for modulating microglial function to slow AD progression.

Neural entrainment to pitch changes of auditory targets in noise
Neural entrainment to certain acoustic features can predict speech-in-noise perception, but these features are difficult to separate. We measured neural responses to both natural speech-in-noise and stimuli (auditory figure-ground) that simulate speech-in-noise without any acoustic or linguistic confound such as stress contour and semantics. The figure-ground stimulus is formed by multiple temporally coherent pure-tone components embedded in a random tone cloud. Previous work has shown that discrimination of dynamic figure-ground based on the fundamental frequency (F0) of natural speech predicts speech-in-noise recognition independent of hearing and age. In this study, we compared the brain substrate for the figure-ground analysis based on the F0 contour and a statistically similar 1/f contour with speech-in-noise. We used the temporal response function to predict the electroencephalography responses to the frequency trajectories of the auditory targets. We demonstrate that the brain significantly tracked the pitch changes in both AFG conditions (F0 and 1/F tracking) and a sentence-in-noise condition (F0 tracking) at similar latencies, but at similar magnitudes only when tracking the F0 contour. The pitch-tracking accuracy was consistently high across the delta and theta bands for the AFG condition but not for speech. Sensor-space analysis revealed that speech-in-noise performance correlated with the positive peak amplitude of the F0 figure-ground at 100 ms. Source-space analysis revealed bilateral temporal lobe and hippocampal generators, and strong tracking in the superior parietal lobe for auditory figures and natural speech. In conclusion, our findings demonstrate that the human brain reliably tracks the F0 trajectory of both speech and a non-linguistic figure in noise, with speech tracking showing reduced accuracy in the theta band compared to figure-ground tracking. Despite the difference in prediction accuracy, we reveal striking similarities in neural entrainment patterns and source locations between the two paradigms. These results suggest that neural entrainment engages high-level cortical mechanisms independent of linguistic content. Furthermore, we show that TRF peak amplitude serves as a potential biomarker for speech-in-noise ability, highlighting possible clinical applications.

White Matter Hyperintensities Precede other Biomarkers in GRN Frontotemporal Dementia
INTRODUCTION: Increased white matter hyperintensities (WMHs) have been reported in genetic frontotemporal dementia (FTD) in small studies, but the sequence of WMH abnormalities relative to other biomarkers is unclear. METHODS: Using a large dataset (n=763 GENFI2 participants), we measured WMHs and examined them across genetic FTD variants and stages. Cortical and subcortical volumes were parcellated, and serum neurofilament light chain (NfL) levels were measured. Biomarker progression was assessed with discriminative event-based and regression modeling. RESULTS: Symptomatic GRN carriers showed elevated WMHs, primarily in the frontal lobe, while no significant increase was observed in C9orf72 or MAPT carriers. WMH abnormalities preceded NfL elevation, ventricular enlargement, and cortical atrophy. Longitudinally, baseline WMHs predicted subcortical changes, while subcortical volumes did not predict WMH changes, suggesting WMHs may precede neurodegeneration. DISCUSSION: WMHs are elevated in a subset of GRN-related FTD. When present, they appear early and should be considered in disease progression models.

Millisecond-Scale White Matter Dynamics Underlying Visuomotor Integration
In the conventional neuropsychological model, nonverbal visuospatial processing is predominantly handled by the right hemisphere, whereas verbal processing occurs in the left, with right-hand responses governed by the left motor cortex. Using intracranial EEG and MRI tractography, we investigated the timing and white matter networks involved in processing nonverbal visuospatial stimuli, forming response decisions, and generating motor outputs. Within 200 ms of stimulus onset, we observed widespread increases in functional connectivity and bidirectional neural flows from visual to association cortices, predominantly in the right hemisphere. Engagement of the right anterior middle frontal gyrus improved response accuracy; however, the accompanying enhancement in intra-hemispheric connectivity delayed response times. In the final 100 ms before right-hand response, functional connectivity and bidirectional communication via the corpus callosum between the right and left motor cortices became prominent. These findings provide millisecond-level support for the established model of hemispheric specialization, while highlighting a trade-off between accuracy and speed governed by the right dorsolateral prefrontal network. They also underscore the critical timing of callosal transmission of response decisions formed in right-hemispheric networks to the left-hemispheric motor system.

The Organization of Serotonergic Fibers in the Pacific Angelshark Brain: Neuroanatomical and Supercomputing Analyses
Serotonergic axons (fibers) are a universal feature of all vertebrate brains. They form meshworks, typically quantified with regional density measurements, and appear to support neuroplasticity. The self-organization of this system remains poorly understood, partly because of the strong stochasticity of individual fiber trajectories. In an extension to our previous analyses of the mouse brain, serotonergic fibers were investigated in the brain of the Pacific angelshark (Squatina californica), a representative of a unique (ray-like) lineage of the squalomorph sharks. First, the fundamental cytoarchitecture of the angelshark brain was examined, including the expression of ionized calcium binding adaptor molecule 1 (Iba1, AIF-1) and the mesencephalic trigeminal nucleus. Second, serotonergic fibers were visualized with immunohistochemistry, which showed that fibers in the forebrain have the tendency to move toward the dorsal pallium and also accumulate at higher densities at pial borders. Third, a population of serotonergic fibers was modeled inside a digital model of the angelshark brain by using a supercomputing simulation. The simulated fibers were defined as sample paths of fractional Brownian motion (FBM), a continuous-time stochastic process. The results reproduced key features of serotonergic fiber densities in the telencephalon, a brain division with a considerable physical uniformity and no major "obstacles" (dense axon tracts). The study provides further evidence that serotonergic fibers can be successfully modeled as paths of a rigorously-defined stochastic process, and that a rich repertoire of self-organizing behaviors can be produced by axons that are inherently stochastic but also respond to external forces.

Heterogeneity of human insular cortex: Five principles of functional organization across multiple cognitive domains
The insular cortex serves as a critical hub for human cognition, but how its anatomically distinct subregions coordinate diverse cognitive, emotional, and social functions remains unclear. Using the Human Connectome Project's multi task fMRI dataset (N=524), we investigated how insular subregions dynamically engage during seven different cognitive tasks spanning executive function, social cognition, emotion, language, and motor control. Our findings reveal five key principles of human insular organization. First, insular subregions maintain distinct functional signatures that enable reliable differentiation based on activation and connectivity patterns across cognitive domains. Second, these subregions dynamically reconfigure their network interactions in response to specific task demands while preserving their core functional architecture. Third, clear functional specialization exists along the insula's dorsal ventral axis: the dorsal anterior insula selectively responds to cognitive control demands through interactions with frontoparietal networks, while the ventral anterior insula preferentially processes emotional and social information via connections with limbic and default mode networks. Fourth, we observed counterintuitive connectivity patterns during demanding cognitive tasks, with the dorsal anterior insula decreasing connectivity to frontoparietal networks while increasing connectivity to default mode networks, suggesting a complex information routing mechanism rather than simple coactivation of task relevant networks. Fifth, while a basic tripartite model captures core functional distinctions, finer grained parcellations revealed additional cognitive domain specific advantages that are obscured by simpler parcellation approaches. Our results illuminate how the insula's organization supports its diverse functional roles through selective engagement of distinct neural networks, providing a new framework for understanding both normal cognitive function and clinical disorders involving insular dysfunction.

Accelerated learning of a noninvasive human brain-computer interface via manifold geometry
Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. We hypothesized that human BCI learning could be accelerated by leveraging the naturally occurring geometric structure of brain activity, or its intrinsic manifold, extracted using a data-diffusion process. We trained participants on a noninvasive BCI that allowed them to gain real-time control of an avatar in a virtual reality game by modulating functional magnetic resonance imaging (fMRI) activity in brain regions that support spatial navigation. We then perturbed the mapping between fMRI activity patterns and the movement of the avatar to test our manifold hypothesis. When the new mapping respected the intrinsic manifold, participants succeeded in regaining control of the BCI by aligning their brain activity within the manifold. When the new mapping did not respect the intrinsic manifold, participants could not learn to control the avatar again. These findings show that the manifold geometry of brain activity constrains human learning of a complex cognitive task in higher-order brain regions. Manifold geometry may be a critical ingredient for unlocking the potential of future human neurotechnologies.

Accumbal Dopamine and Acetylcholine Dynamics during Psychostimulant Sensitization
Behavioral sensitization to repeated psychostimulant exposure is believed to contribute to the development of addiction. Nucleus accumbens (NAcc) dopamine (DA) is known to be a key substrate in sensitization, though recent work suggests that striatal acetylcholine (ACh) may also play a critical role. However, underlying ACh changes and their relationship to DA signaling have not been characterized. Here, we used dual-color fiber photometry to simultaneously measure DA and ACh in the NAcc shell of mice across repeated injections of cocaine or amphetamine. Repeated exposure progressively elevated locomotor activity and increased slow extracellular DA while attenuating transient DA release. Psychostimulants reduced phasic ACh transient amplitude and frequency, an effect that sensitized with repeated injections. However, the temporal coupling of DA and ACh remained unchanged. To determine whether D2 receptors (D2Rs) on cholinergic interneurons (CINs) drive this effect, we generated CIN-selective D2R knockout (KO) mice. Surprisingly, KOs continued to show an acute decrease in ACh and intact DA-ACh correlations after psychostimulant administration. However, they failed to exhibit sensitization of either DA or ACh in response to repeated psychostimulant administration. Despite this lack of sensitization in underlying neuromodulator signaling, the KO mice nevertheless exhibited behavioral sensitization, though at a slower rate than wild-type. These findings suggest that neural sensitization to psychostimulants is dependent on D2R expressed on CINs, but that behavioral sensitization is not dependent on sensitization of these underlying signals.

Does the Brain's E:I Balance Really Shape Long-Range Temporal Correlations? Lessons Learned from 3T MRI
A 3T multimodal MRI study of healthy adults (n=19; 10 female; 21.3 - 53.4 years) was performed to investigate the relationship between fMRI long-range temporal correlations and excitatory/inhibitory balance. The study objective was to determine if the Hurst exponent (H) - an estimate of the self-correlation and signal complexity - of the blood-oxygen-level-dependent signal is correlated with the excitatory-inhibitory (E:I) ratio. E:I has been proposed to serve as a control parameter for brain criticality - the theory that the brain operates near a critical point between order and disorder, optimizing information processing and adaptability - which H is believed to be a measure of. Thus, understanding if H and E:I are correlated would clarify this relationship. Moreover, findings in this domain have implications for neurological and neuropsychiatric conditions with disrupted E:I balance, such as autism spectrum disorder, schizophrenia, and Alzheimer's disease. From a practical perspective, H is easier to accurately measure than E:I ratio at 3T MRI. If H can serve as a proxy for E:I, it may serve as a more practical clinical biomarker for this imbalance and for neuroscience research in general. The study collected functional MRI and magnetic resonance spectroscopy data during rest and movie-watching. H was found to increase with movie-watching compared to rest, while E:I (glutamate/GABA) did not change between conditions. H and E:I were not correlated during either movie-watching or rest. This study represents the first attempt to investigate this connection in vivo in humans. We conclude that, at 3T and with our particular methodologies, no association was found. We end with lessons learned and suggestions for future research.

Adolescents with major depression featured by sensory-association subtyping show divergent information dynamics and streams
Adolescent major depressive disorder (MDD) exhibits complex and heterogeneous alterations of brain functional organization. To understand the neurobiological basis of adolescent MDD, we adopted resting-state functional MRI data and used various matrix decomposition approaches to obtain the organization gradients, temporal dynamics, and information streams. With clustering sensory-association gradient features in our exploratory sample (NMDD = 250 and NControls = 203), we identified two MDD subtypes. Subtype 1 was characterized by sensory contraction and subtype 2 was associated with association expansion. In addition, two subtypes showed divergent bottom-up and top-down information flows in sensory and association areas using temporal dynamics analysis. These subtypes exhibit distinct age-related changes and reorganization trajectories along sensory-association and auditory-visual axes, highlighting that cortical information flow patterns systematically vary and relate differently to sensory integration, cognitive complexity, and aging. These network distinctions are linked to clinical severity and molecular mechanisms. Subtype 1 is predominantly associated with early neurodevelopmental abnormalities and emotional regulation deficits, while Subtype 2 is more related to synaptic dysfunction and reduced neuronal excitability. These results could be largely replicated in another independent sample (NMDD = 73 and NControls = 28). We therefore construct a sensory-association dual functional framework to characterize MDD heterogeneity in adolescent MDD. Itl integrates cortical hierarchy, developmental trajectories, and genetic influences, offering novel insights into MDD pathophysiology and providing a theoretical foundation for precision psychiatry, facilitating personalized diagnosis and intervention strategies.

Neural correlates of perceptual decision making in primary somatosensory cortex
The brain is thought to produce decisions by gradual accumulation of sensory evidence1 through a hierarchically organized feedforward cascade of neuronal activities that transforms early stimulus representations in the primary somatosensory cortex (S1) [2] to a perceptual decision processed in pre-motor areas [3-6]. Recently, this prevailing view has been challenged by observation of choice-correlated neural activity as early in the hierarchy as S1 [7-13]. Here, to reconcile these seemingly controversial observations, we employ ethological whisker-guided navigation of mice in a tactile virtual reality paradigm [14-16] combined with dense electrophysiological recordings in whisker-related wS1. Leaving only a pair of C2 whiskers for mice to navigate with, we effectively designed an information bottleneck for sensory input to decision making16. We show that neural activity in principal whisker wS1 barrel recorded during untrained and unrewarded two alternative forced choice (2AFC) decision-making consists of fast (50ms) and slow (200-300ms) stages that are mostly orthogonal to each other and directly precede the decision execution. The fast component represents detection of a deviant signal in sensory perception, that triggers dramatic collapse of the high-dimensional spiking activity to just a single latent variable followed by a slower and almost synchronous ramping up across the whole cortical column. We show that this variable is consistent with models of gradual accumulation of noisy sensory evidence to a decision bound [1,5,17,18]. These observations indicate that S1 may directly participate in a categorical coding of all-or-none decision variable via cortico-cortical feedback loops through which sensory information reverberates [17,18] to be transformed into perception and action.

Glucose levels are associated with mood, but the association is mediated by ratings of metabolic state
Hunger is commonly linked to negative mood and mood shifts are thought to arise from sensing the body's internal state. However, it is unclear whether circulating glucose levels affect mood subconsciously or via subjectively sensed metabolic states. Here, we continuously monitored the interstitial glucose levels of 90 healthy adults for four weeks while they completed ecological momentary assessments (EMA; M=47 runs per participant) to rate their mood and metabolic states. As expected, hungry participants reported lower mood, and metabolic state ratings were associated with glucose levels. Although glucose levels were associated with mood, the metabolic state ratings fully mediated this association. Individual differences reflecting metabolic health (i.e., BMI and insulin resistance) did not affect the interaction between glucose and metabolic state ratings on mood. Notably, individuals with higher interoceptive accuracy had fewer fluctuations in mood ratings. We conclude that hunger-related mood shifts depend on conscious sensing of the body's internal state instead of acting subconsciously. Our study highlights the relevance of considering the self-report of bodily signals in understanding mood shifts, offering new fundamental insights into mood regulation mechanisms.

brain network dynamics during multi-task demands predict children math achievement
Dynamic reconfiguration of neural network and flexible information integration across multiple tasks has been considered critical to characterize individual difference in complex cognition and general intelligence. A promising and underexplored question is how these neurocognitive processes related to children's academic achievements, a hallmark of high-order cognitive abilities that integrate attention, memory and problem solving. By using of a multitasking paradigm which bridging outside-in and inside-out approaches, we investigated the dynamic neural mechanisms underlying two core domains of academic performance: math and reading. We first apply partial least squares regression (PLSR) to examine static neural patterns and find that the first latent component-reflecting a generalized brain functional system-predicts math achievement but not reading. The multiple-demand system and the somato-cognitive action network (SCAN) are consistently engaged across diverse task demands. Furthermore, we use a Hidden Markov Model (HMM) to examine dynamic features of brain activity and identify distinct integrated and segregated brain states. Notably, the segregated state-characterized by heightened cortical network segregation-is associated with better math performance. Information-theoretic analyses further reveal that greater complexity in the temporal sequence of the segregated brain functional networks, along with stronger cerebrocerebellar functional coupling, correlates with higher math achievement. By means of multitasks design, these findings suggest that flexible engagement of specialized brain network and automatic information processing is crucial for math learning in children.

Sleep rescues age-associated loss of glial engulfment
Neuronal injury due to trauma or neurodegeneration is a common feature of aging. The clearance of damaged neurons by glia is thought to be critical for maintenance of proper brain function. Sleep loss has been shown to inhibit the motility and function of glia that clear damaged axons while enhancement of sleep promotes clearance of damaged axons. Despite the potential role of glia in maintenance of brain function and protection against neurodegenerative disease, surprisingly little is known about how sleep loss impacts glial function in aged animals. Axotomy of the Drosophila antennae triggers Wallerian degeneration, where specialized olfactory ensheathing glia engulf damaged neurites. This glial response provides a robust model system to investigate the molecular basis for glial engulfment and neuron-glia communication. Glial engulfment is impaired in aged and sleep-deprived animals, raising the possibility that age-related sleep loss underlies deficits in glial function. To define the relationship between sleep- and age-dependent reductions in glial function, we restored sleep to aged animals and examined the effects on glial clearance of damaged axons. Both pharmacological and genetic induction of sleep restores clearance of damaged neurons in aged flies. Further analysis revealed that sleep restored post-injury induction of the engulfment protein Draper to aged flies, fortifying the notion that loss of sleep contributes to reduced glial-mediated debris clearance in aged animals. To identify age-related changes in the transcriptional response to neuronal injury, we used single-nucleus RNA-seq of the central brains from axotomized young and old flies. We identified broad transcriptional changes within the ensheathing glia of young flies, and the loss of transcriptional induction of autophagy-associated genes. We also identify age-dependent loss of transcriptional induction of 18 transcripts encoding for small and large ribosomal protein subunits following injury in old flies, suggesting dysregulation of ribosomal biogenesis contributes to loss of glial function. Together, these findings demonstrate a functional link between sleep loss, aging and Wallerian degeneration.

Linking Aβ and tau in the amyloid cascade through the intersection of their proteostasis networks
Amyloid plaques and neurofibrillary tangles are molecular hallmarks of Alzheimer's disease. According to the amyloid cascade hypothesis, aberrant A{beta} and tau behaviours contribute synergistically to accelerate the Alzheimer's pathology. However, the complex molecular mechanisms linking A{beta} and tau dysregulation remain to be fully characterised. To address this problem, we investigated the connection between A{beta} and tau through the protein homeostasis (proteostasis) network. We asked whether A{beta} proteostasis is linked to tau proteostasis. To answer to this question, we first mapped the proteostasis networks of A{beta} and tau, and then studied the interplay of these two networks, identifying the molecular chaperone HSP90 as a central hub. To test this hub role of HSP90, we observed in a cell model that HSP90 and its co-chaperone SUGT1 mediate tau phosphorylation via GSK-3{beta} in an A{beta}42-dependent manner. Furthermore, we also observed that in turn HSP90 and SUGT1 increase the intracellular concentration of A{beta}42. These results suggest that the HSP90/SUGT1 system may act as a hub in the amyloid cascade by lying at the intersection of the A{beta} and tau proteostasis networks.

Modification and validation of a GAD-GFP mouse line without accelerated aging-related hearing loss.
GABAergic neurons in the inferior colliculus (IC) play a crucial role in auditory processing by extracting specific features of sounds (Ono et al., 2005). The Gad67-GFP mouse model developed by Tamamaki et al. in 2003 on a Swiss background facilitates studying these neurons by using a green fluorescent protein that is expressed endogenously via the GAD67 promoter. Unfortunately, this mouse suffers from accelerated aging-related hearing loss, limiting its utility in studying the auditory system. Here, we report the results of an 8-generation backcross of this line onto CBA/CaJ mice, which produces mice with stable low-threshold hearing while retaining GFP expression in GAD+ neurons. Additionally, this study investigates mechanisms that underlie hearing loss in the Gad67-GFP mouse model by focusing specifically on cochlear hair cells (HCs) and ribbon synapses, which may contribute to both model-specific hearing loss and clinical disorders like presbycusis. Findings revealed the newly generated F1 mouse model that resulted from the Gad67-GFP x CBA/CaJ backcross maintained better hearing thresholds when compared to ABR data for Gad67 and Swiss mice and very closely resembled those of the CBA/CaJ mice, mirroring progression of presbycusis in humans. Additionally, all morphological changes observed in cochlear structure correlated to ABR thresholds. F1 mice continued maintained expression of the GAD67 promoter in the IC via immunostaining.

Neuroimaging Biomarkers of Neuroprotection: Impact of Voluntary versus Enforced Exercise in Alzheimers Disease Models
Exercise is a promising strategy for preventing or delaying Alzheimers disease (AD), yet its mechanisms remain unclear. We investigated how exercise influences brain structure, function, and behavior in a familial AD model. Mice underwent voluntary, voluntary plus enforced exercise, or remained sedentary. Neuroimaging included in vivo manganese-enhanced MRI (MEMRI). perfusion, and ex vivo diffusion MRI to assess morphometry, activity, cerebral blood flow (CBF), microstructural integrity and connectivity. Both exercise regimens induced structural and functional brain adaptations while reducing anhedonia. Voluntary exercise increased cortical and limbic volumes, particularly in the hippocampus, cingulate, and entorhinal cortex, supporting cognitive and emotional regulation. Adding enforced exercise influenced subcortical and sensory regions, including visual, motor and associative areas, supporting sensory-motor integration. MEMRI revealed increased activity in sensorimotor, limbic, and associative cortices, with voluntary exercise enhancing limbic and associative regions, and enforced exercise strengthening sensorimotor and subcortical circuits. White matter integrity improved in memory-associate pathways such as the corpus callosum, cingulum, and hippocampal commissure. Synaptic remodeling was observed in the cingulate cortex, anterior thalamic nuclei, and amygdala. Voluntary exercise enhanced CBF in the motor cortex and hippocampus, while enforced exercise limited these increases. Connectivity analyses revealed exercise-responsive networks spanning the cingulate cortex, entorhinal cortex, anterior thalamic nuclei, and basolateral amygdala, and associated tracts. Graph analyses linked running distance with increased thalamic, brainstem, and cerebellar connectivity, associating exercise intensity with plasticity. These findings highlight the ability of chronic exercise to modulate neuroimaging biomarkers through distinct but complementary pathways, reinforcing its potential as a neuroprotective intervention for AD.

Data-driven classification of tissue water populations by massively multidimensional diffusion-relaxation correlation MRI
Massively multidimensional diffusion-relaxation correlation MRI provides detailed information on tissue microstructure by analyzing water populations at a sub-voxel level. This method correlates frequency-dependent tensor-valued diffusion MRI with longitudinal and transverse relaxation rates, generating nonparametric D({omega})-R1-R2 distributions. Traditionally, D({omega})-R1-R2 distributions are separated using manual binning of the diffusivity and anisotropy space to differentiate white matter (WM), gray matter (GM), and free water (FW) in brain tissue. However, while effective, this approach oversimplifies complex tissue fractions and does not fully utilize all available diffusion-relaxation parameters. In this study, we implemented an unsupervised clustering approach to automatically classify WM, GM, and FW and explore additional water populations using all components in the D({omega})-R1-R2 distributions on ex vivo and in vivo rat brain, and in vivo human brain. Results showed that a basic separation of WM, GM, and FW is possible using unsupervised clustering even under different multidimensional diffusion-relaxation protocols of rat brain and human brain. Additionally, when there is high frequency-dependent diffusion range, it is possible to obtain a cluster characterized by restriction localized in specific high cell density regions such as the dentate gyrus and cerebellum of rat brain. These findings were compared with rat histological sections of myelin and Nissl stainings. We demonstrated that unsupervised clustering of diffusion-relaxation MRI data can reveal tissue complexity beyond traditional WM, GM, and FW segmentation in rat and human brain without parameter assumptions. The unsupervised cluster approach could be used in other body parts (e.g., prostate and breast cancer) without requiring pre-defined bin limits. Furthermore, the characterization of the clusters by diffusivities, anisotropy, and relaxation rates can provide a better understanding of the subtle changes in different cellular fractions in tissue-specific pathologies.

Optimizing Artificial Neural Network Models to Predict Brain-Age from Functional MRI
Large MRI datasets combined with deep learning methods have realized a new state of the art for brain-age prediction. Age prediction may serve as a valuable biomarker for brain health and disease given that over-estimated age based on MRI (usually as predicted by a machine learning model; sometimes called a "brain-age gap") has been associated with neurological and psychiatric disorders. However, most of these results have been achieved via the use of high-resolution structural (T1w) MRI scans. Brain-age prediction via deep learning over large volumes of functional MRI (fMRI) data is less well studied, but could help form a bridge between neural health biomarkers observed in MRI and more portable platforms like functional near infrared spectroscopy (fNIRS), which measure a hemodynamic signal similar to fMRI. In this work, we studied how to optimize deep learning model architectures and training pipelines to predict brain-age from resting state fMRI connectivity data. A wide set of pre-processing and model hyperparameters was explored that included varying the number of nodes and the composition of the input functional connectivity matrices, the size, depth and objective functions of the neural network models, and a time series sub-sampling method as a data augmentation strategy. Model performance was evaluated on both an internal validation set of held-out participants (from the multi-study corpus compiled for training), as well as numerous external corpora not seen during training, which comprised healthy controls and clinical participants. Neural network models with a variety of hyperparameter configurations supported accurate brain-age prediction using fMRI and many models generalized effectively to predict the age of healthy individuals among data sets not seen during training (< 8 years mean absolute error on the external validation dataset). However, we report mixed results regarding a brain-age gap for held out clinical populations using these methods, with a gap observed only among neurodegenerative disorders (here, Alzheimer's disease), and not among psychiatric disorders or patients with traumatic brain injury. This work constitutes a valuable step towards scalable, portable brain-age prediction but highlights a number of areas where additional work and improvements are needed.

A formal relation between two disparate mathematical algorithms is ascertained from biological circuit analyses
We simulate and formally analyze the emergent operations from the specific anatomical layout and physiological activation patterns of a particular local excitatory-inhibitory circuit architecture that occurs throughout superficial layers of cortex. The circuit carries out two effective procedures on its inputs, depending on the strength of its local feedback inhibitory cells. Both procedures can be formally characterized in terms of well-studied statistical operations: clustering, and component analyses, under high-feedback-inhibition and low-feedback-inhibition conditions, respectively. The detailed nature of these clustering and component procedures is studied in the context of extensive related literatures in statistics, machine learning, and computational neuroscience. The two operations (clustering and component analysis) have not previously been shown to contain deep connections, let alone to each be derivable from a single overarching algorithmic precursor. The identification of this deep formal mathematical connection, which arose from analysis of a detailed biological circuit, represents a rare instance of novel mathematical relations arising from biological analyses.

Sex, not estrous cycle stage, drives differences in the microglial transcriptome in the 5xFAD mouse model of amyloidosis
Alzheimer's disease (AD) presents with a sex bias where women are at higher risk and exhibit worse cognitive decline and brain atrophy compared to men. Microglia play a significant role in the pathogenesis and progression of AD and have been shown to be sexually differentiated in health and disease. Whether microglia contribute to the sex differences in AD remains to be elucidated. Herein, we characterize the sex differences in amyloid-beta (A{beta}) plaque pathology and microglia-plaque interaction using the 5xFAD mouse model of amyloidosis and further elucidate the microglial transcriptomic changes that occur in males and females. In females, we concentrate on two hormonally distinct stages of the rodent estrous cycle: proestrus and diestrus. Our results indicate that A{beta} plaque morphology is sexually distinct with females having greater plaque volume and lower plaque sphericity compared to males. Microglia also interact with plaques in a sexually distinct manner with females phagocytosing A{beta} to a greater extent compared to males. Furthermore, we found that female microglia are not overtly different at the proestrus or diestrus stages. However, we found stark sex differences between female and male microglia transcriptomes in the 5xFAD brains, where female 5xFAD microglia were enriched in genes involved in glycolytic metabolism, antigen presentation, disease-associated microglia and microglia neurodegenerative phenotype (DAM/MGnD), and interferon signaling compared to male 5xFAD microglia.

Temporal adaptation aids object recognition in deep convolutional neural networks in suboptimal viewing scenario's
The primate visual system excels in recognizing objects under challenging viewing scenario's. A neural mechanism that is thought to play a key role in this ability is rapid temporal adaptation, or the adjustment of neurons' activity based on recent history. To understand how temporal adaptation may support object recognition, previous work has incorporated a variety of temporal feedback mechanisms in deep convolutional neural networks (DCNN) and explored how these mechanisms affect object recognition performance. While multiple adaptation mechanisms have been shown to impact model behavior, it remains unclear how the origin (intrinsic or recurrent) and the way the temporal feedback is integrated (additive or multiplicative) affects object recognition. Here, we compare the impact of four different temporal adaptation mechanisms on object recognition using three different task designs, including object recognition under either noise or occlusion, and in the context of novelty detection. Our results show that the effectiveness of temporal adaptation mechanisms for robust object recognition depends on the task and dataset. For objects embedded in noise, intrinsic adaptation excels with simple, high-contrast inputs, while recurrent mechanisms perform better with complex, low-contrast inputs, highlighting their focus on different visual features. Under dynamic occlusion, recurrent adaptation mechanisms exhibit a more progressive increase in performance over time, suggesting they better maintain object coherence when parts are obscured. For novelty detection, recurrent mechanisms show higher performance compared to intrinsic adaptation mechanisms, suggesting that recurrence aids in detecting global changes caused by the presentation of new objects. All together, these findings suggest that robust object recognition likely requires multiple temporal adaptation strategies in parallel to handle the diverse challenges of naturalistic visual settings.

Characterization Eclosion Hormone Receptor function reveals differential hormonal control of ecdysis during Drosophila development.
Neuromodulators and peptide hormones play important roles in regulating animal behavior. A well-studied example is ecdysis, which is used by insects to shed their exoskeleton at the end of each molt. Ecdysis is initiated by Ecdysis Triggering Hormone (ETH) and Eclosion Hormone (EH), which interact via positive feedback to coordinate the sequence of behavioral and physiological changes that cause exoskeleton shedding. Whereas the cell types targeted by ETH are well characterized, those targeted by EH have remained largely unknown due to limited characterization of the EH receptor (EHR). A gene encoding an EHR has been described in the oriental fruit fly, B. dorsalis, and in the desert locust, Schistocerca gregaria. However, little is known in these species about its expression pattern and its precise role at ecdysis, and no other insect EHRs are known. Here we analyze CG10738, the Drosophila ortholog of the B. dorsalis gene encoding EHR, and show that expressing it in cells confers sensitivity to EH. In addition, mutations of CG10738 specifically disrupt ecdysis, phenocopying the knockout of the EH gene. Together, these results indicate that CG10738 encodes the Drosophila EHR. As in B. dorsalis, EHR is expressed in the ETH-producing Inka cells; in addition, it is expressed in many known targets of ETH, including the neurons responsible for the secretion of other ecdysis-related peptides, such as CCAP and EH itself. Our results from targeted knockdown and rescue experiments reveal that EHR is required for ecdysis in diverse cell types and that the role of EHR in different targets differs with developmental stage. Our findings indicate extensive convergence of EH and ETH signaling and provide an exemplar of the complex mechanisms by which hormones control animal behavior.

A neurocomputational basis of face recognition changes in ASD: E/I balance, internal noise, and weak neural representations
Individuals with Autism Spectrum Disorder (ASD) are known for their socio-communicative challenges, including face recognition. Despite mounting evidence in behavioral studies, the neurocomputational basis of these challenges remains unclear. Meanwhile, neurobiological theories propose that ASD may arise from an imbalance of excitatory and inhibitory signals (E/I imbalance) or excessive internal noise (IN). However, studies with humans can hardly provide causal evidence. Therefore, this study employed Conventional Neural Network (CNN) models to simulate face recognition in typical populations and ASD based on the claims of I/E imbalance and IN theories. By varying the positive slope in the ReLU activation function (simulating E/I imbalance) and random noises added to the weights (simulating internal noise), we showed that CNN models with non-optimal ReLU slope or noised weights led to poorer performance in face recognition and atypical neural representations of faces. Overall, simulations based on the E/I imbalance theory seem to encompass a broader range of behavioral profiles in ASD. Our approach to using CNN models to test neurobiological theories is highly theory-driven, and our results provided causal evidence to how neurobiological factors could influence face recognition in ASD. This framework could be easily adapted to test in other neurobiological disorders, providing a plausible bridge between neurobiological theories and behavioral and neuroimaging research on humans.

Single-Nucleus Transcriptomics Identifies Neuroblast Migration Programs Sensitive to Reelin and Cannabis in the Adolescent Nucleus Accumbens
The interplay between cannabis exposure during adolescence and genetic predisposition has been linked to increased vulnerability to psychiatric disorders. To investigate the molecular underpinnings of this interaction, we performed single-nucleus RNA sequencing of the nucleus accumbens (NAc) in a mouse model of Reln haploinsufficiency, a genetic risk factor for psychiatric disorders, following adolescent exposure to tetrahydrocannabinol (THC), the primary psychoactive component of cannabis. We identified a gene co-expression network influenced by both Reln genotype and THC, enriched in genes associated with human psychiatric disorders and predominantly expressed in a GABAergic neuroblast subpopulation. Using immunostaining, we showed that neuroblasts actively migrated in the adolescent NAc, but declined with age. Cell-to-cell communication analysis further revealed that these neuroblasts receive migratory cues from cholecystokinin interneurons, which co-express Reln and high levels of cannabinoid receptors. These findings provide mechanistic insights into how adolescent THC exposure and genetic risk factors may impair GABAergic circuit maturation.

Non-canonical role for ATPase HSC70 in driving Clathrin remodeling during compensatory endocytosis in synapse
Neurons use Clathrin-mediated endocytosis to retrieve synaptic vesicle (SV) proteins during compensatory endocytosis after presynaptic SV fusion. We have shown SV cargo to be re-sorted and pre-assembled outside the active zone into Clathrin-coated structures (CCS) of variable size and curvature, constituting a readily retrievable pool. During compensatory endocytosis CCS of the readily retrievable pool must be remodeled swiftly into spherical vesicles within 10 seconds at physiological temperature. How this is achieved remains elusive. Here we performed live-cell imaging on intact as well as scanning electron microscopy on unroofed hippocampal Xenapses, TIRFM-amenable presynaptic boutons formed en-face on microstructured and functionalized coverslips. While CCS can slowly remodel into spherical pits in unroofed Xenapses within tens of minutes, this process is highly accelerated in intact Xenapses, as evidenced by the rapid (< 10 seconds) exchange of EGFP-labelled Clathrin and AP2 adaptor after photo-bleaching. This fast remodeling of CCS was observed in the absence of stimulation and cannot be explained by constitutive endocytosis. Hence, this process must be driven by cytosolic factors which are lost during unroofing. Using membrane-permeant interfering peptides we identify Hsc70, the ATPase which along with Auxilin drives uncoating of endocytosed Clathrin-coated vesicles, to have an additional role in driving curvature of invaginating CCS.

Behavioral Efficacy of AAV FOXG1 Gene Replacement Therapy in a Mouse Model of FOXG1 Syndrome
FOXG1 syndrome is a severe neurodevelopmental disorder characterized by microcephaly, profound intellectual disability with communication deficits including lack of speech, impaired social interaction, increased anxiety, hyperkinetic/dyskinetic movements, seizures and abnormal sleep patterns. Mutations in a single allele of the FOXG1 gene cause disease, likely due to loss-of-function. However, current therapies do not target this root cause of FOXG1 syndrome and have little to modest therapeutic benefit on only a small subset of symptoms. To date, the therapeutic potential of restoring FOXG1 levels in the brain with adeno-associated virus (AAV) FOXG1 gene replacement therapy has only been reported in a Foxg1fl/+;NexCre mouse model that lacks one Foxg1 allele but does not express mutant FOXG1, and with only neuroanatomical endpoints evaluated. Here, in a FOXG1 mouse model that contains a highly prevalent, patient-specific Q84P mutation, we describe the beneficial effects of AAV human FOXG1 gene replacement therapy administered by intracerebroventricular (ICV) injection at postnatal day 6 (P6) on several behavioral deficits that are relevant to key features of human FOXG1 syndrome. Our studies demonstrate that AAV FOXG1 gene replacement therapy is a promising approach for the treatment of a subset of functional deficits in human FOXG1 syndrome.

Listening to Your Own Brain Waves Sound Enhances Your Sleep Quality and Quantity
This pilot study examined the effects of relaxing personalized sound sequences (PSS), derived from individual slow-wave brain activity on sleep in adults with subjective insomnia complaints. Thirteen participants underwent one-night polysomnography to record delta wave activity (0.5-4 Hz), which was then transformed into individualized sound sequences. A randomized, single-blind, crossover protocol was then conducted at home, including two conditions of 3 to 5 consecutive nights: listening to the PSS and a non-personalized placebo sound sequence (PLA) for 30 minutes at bedtime. Objective sleep was assessed using a dry-electroencephalographic (EEG) headband and subjective sleep with a digital sleep diary. Compared to PLA, the PSS condition significantly increased total sleep time (delta = +18.9 min, p = 0.05) and REM sleep proportion (delta = +2.3%, p < 0.05), reduced REM latency (delta = -16.6 min, p < 0.05) and improved overall sleep quality score (delta = +1.4 A.U., p < 0.05). Participants with the shorter sleep duration (< 390 min, n = 5) and longer sleep onset latencies (> 20 min, n = 4) in PLA condition experienced greater improvements with PSS. These preliminary results suggest that listening to one's own slow brain waves converted into sound may improve both sleep quality and quantity in individuals with moderate insomnia, with potentially enhanced benefits for those with more severe sleep difficulties.

Neural signatures of flexible temporal orienting under spatial and motor uncertainty
The goal-dependent use of temporal expectations enhances visual performance, even without concurrent spatial or motor predictions, yet the underlying neural mechanisms remain unclear. To identify the stages of stimulus processing influenced by temporal orienting of attention, we recorded EEG while participants performed a visual identification task manipulating relevance and temporal predictability of targets. Colored targets appeared in one of two simultaneous visual streams. One color appeared unpredictably while two others appeared predictably early or late. Targets appeared in either stream and required a localization response using the corresponding hand. On each trial, one target was cued as task-relevant. Behaviorally, participants used temporal expectations flexibly to optimize processing of goal-relevant targets. The target-defining cue elicited a contingent negative variation (CNV), modulated by the temporal predictability of anticipated early vs. late targets. Target-related event related potentials (ERP) showed strong modulation by target relevance at multiple stages. Modulations impacted target-selection (N2pc), motor preparation (LRP), and late decision-related factors (P300). Interestingly, only the P300 was additionally modulated by the temporal predictability of targets. The findings reveal how temporal attention can impact multiple stages of stimulus processing through the relevance and temporal predictability of targets even without spatial or motor certainty.

Active and predictive adjustment of pupil size for accommodating visual regularity
The visual system efficiently encodes information by summarizing visual regularities that could be quantified by summary statistics. While summary statistics are thought to be processed in several cortical and subcortical areas, we investigated whether the earliest stage of visual processing, the pupil, is involved in this process. In three experiments, participants either performed ensemble orientation estimation with or passively viewed bar arrays with different bar orientation distributions while their pupil size was recorded. We found that pupil size increased when the orientation distribution became more dispersed and was closely linked to participants' estimation performance. Furthermore, pupillary responses occurred automatically during passive viewing and could even predict ensemble orientation estimation performance. Moreover, when anticipating a dispersed distribution, the pupil dilated in advance. Our findings reveal a new cognitive role of the pupil - it actively and predictively adjusts its size to facilitate the extraction of visual regularity.

Development of Replay-DMN Coordination Predicts Entorhinal Grid Codes
Replay has been implicated in organising experiences into cognitive maps offline, yet how this process evolves through development remains unclear. We studied 106 participants (ages 8-25) who learned a hidden two-dimensional (2D) structure and then underwent magnetoencephalography (MEG) during a map-based inference task and subsequent rest, allowing us to detect spontaneous replay. Younger participants relied more on replay alone to represent the 2D associations, whereas older participants showed increasingly precise alignment between replay and the default mode network (DMN), particularly when replay events were timed to the DMN theta (2-6 Hz) trough. This alignment further predicted grid-cell-like codes in the entorhinal cortex, previously identified in the same cohort using fMRI. Resting-state fMRI indicated that DMN connectivity also strengthened with age, which explained reduced reliance on replay and faster inference across development. These findings illuminate a developmental progression where replay shifts from an isolated hippocampal process to a coordinated hippocampal-DMN mechanism. This shift may underpin maturing grid-like schema representations, offering insight into how children gradually build internal knowledge structures for flexible inference.

Glycolysis-enhancing alpha 1-adrenergic antagonists are neuroprotective in Alzheimers disease
Terazosin (TZ) is an alpha 1-adrenergic receptor antagonist that enhances glycolysis by activating the enzyme phosphoglycerate kinase 1 (PGK1). Epidemiological data suggest that TZ may be neuroprotective in Parkinsons disease and in dementia with Lewy bodies and that glycolysis-enhancing drugs might be protective in other neurodegenerative diseases involving protein aggregation, such as Alzheimers disease (AD). We investigated TZ in AD and report four main results. First, we found that TZ increased ATP levels in a Saccharomyces cerevisiae mutant with impaired energy homeostasis and reduced the aggregation of the AD-associated protein, amyloid beta (A-beta) 42. Second, in an AD transgenic mouse model (5xFAD) we found that TZ attenuated amyloid pathology in the hippocampus and rescued cognitive impairments in spatial memory and interval timing behavioral assays. Third, using the Alzheimers Disease Neuroimaging Initiative(ADNI) database, we found that AD patients newly started on TZ or related glycolysis-enhancing drugs had a slower progression of both cognitive dysfunction and neuroimaging biomarkers, such as 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), a measure of brain metabolism. Finally, in a large human administrative dataset, we found that patients taking TZ or related glycolysis-enhancing drugs had a lower hazard of being diagnosed with AD compared to those taking tamsulosin or 5-alpha reductase inhibitors. These data further implicate metabolism in neurodegenerative diseases and suggest that glycolysis-enhancing drugs may be neuroprotective in AD.

Impact of Cesarean Delivery on Reward Behavior and Neurodevelopment in Adult Prairie Vole Offspring
Accumulating clinical evidence has shown that birth by Cesarean section (CS) is associated with a higher incidence of disorders involving the dysregulation of dopamine (DA), such as attention deficit-hyperactivity disorder, autism spectrum disorder, and obesity, compared to vaginal delivery (VD). The mesolimbic (ML) system encompasses DAergic neurons that modulate reward processes underlying learning, motivation, and food intake. Previous research has shown that there are lower levels of DA in the prefrontal cortex and higher in the nucleus accumbens (NAc) of CS offspring. Alterations in the ML-DA system as a consequence of birth via CS may impact behavioral response to rewarding stimuli, such as food. Thus, we aimed to ascertain the behavioral and neurodevelopmental outcomes relevant to food reward in CS prairie vole offspring. This study utilized conditioned place preference (CPP) testing to assess learning using context-dependent conditioning, operant conditioning to assess acquisition of a conditioned response and motivation to receive a reinforcer, and immunohistochemistry (IHC) to stain for tyrosine hydroxylase (TH) in the NAc. Behavioral results showed no difference in preference for the conditioned chamber during CPP testing between CS offspring and their VD counterparts. CS prairie vole offspring had a lower average break point during progressive-ratio testing compared to VD offspring, but no difference in response during fixed-ratio 1 or 3 testing. IHC results showed CS offspring had lower levels of TH-immunoreactivity in the NAc core and shell. These findings further support that delivery by CS has long-term neurodevelopmental effects, specifically in the brain's reward system, and that CS offspring have decreased motivation toward food reward independent of deficits in learning.

Human-like monocular depth biases in deep neural networks
Human depth perception from 2D images is systematically distorted, yet the nature of these distortions is not fully understood. To gain insights into this fundamental problem, we compare human depth judgments with those of deep neural networks (DNNs), which have shown remarkable abilities in monocular depth estimation. Using a novel human-annotated dataset of natural indoor scenes and a systematic analysis of absolute depth judgments, we investigate error patterns in both humans and DNNs. Employing exponential-affine fitting, we decompose depth estimation errors into depth compression, per-image affine transformations (including scaling, shearing, and translation), and residual errors. Our analysis reveals that human depth judgments exhibit systematic and consistent biases, including depth compression, a vertical bias (perceiving objects in the lower visual field as closer), and consistent per-image affine distortions across participants. Intriguingly, we find that DNNs with higher accuracy partially recapitulate these human biases, demonstrating greater similarity in affine parameters and residual error patterns. This suggests that these seemingly suboptimal human biases may reflect efficient, ecologically adapted strategies for depth inference from inherently ambiguous monocular images. However, while DNNs capture metric-level residual error patterns similar to humans, they fail to reproduce human-level accuracy in ordinal depth perception within the affine-invariant space. These findings underscore the importance of evaluating error patterns beyond raw accuracy, providing new insights into how humans and computational models resolve depth ambiguity. Our dataset and methodology provide a framework for evaluating the alignment between computational models and human perceptual biases, thereby advancing our understanding of visual space representation and guiding the development of models that more faithfully capture human depth perception.

iNeurons are sweet, maybe too sweet? Exploring the impact of media composition on PINK1-dependent mitophagy
Parkinson's disease associated proteins PINK1 and Parkin collaboratively regulate stress-induced mitophagy. While in vitro human neuronal cultures are valuable for studying the roles of PINK1 and Parkin in a disease-relevant context, the impact of culture conditions on these processes remains largely underexplored. Here, it is shown that human induced neurons (iNeurons) cultured in N2B27 and BrainPhys medium exhibit distinct PINK1-Parkin dependent mitophagy phenotypes. Specifically, BrainPhys-cultured iNeurons show greater resistance to PINK1-dependent mitophagy initiation, linked to a reduction in glucose availability and reduced PINK1 protein availabilities, leading to decreases in stress-induced and basal mitophagy fluxes. These findings highlight the critical impact of culture conditions on mitophagy dynamics and emphasise the need to account for media-specific differences when using in vitro models to investigate mitophagy mechanisms in human neurons.

Localization of AP2α2, TRPV1 and PIEZO2 to the Large Dense Core Vesicles of Human Dorsal Root Ganglion Neurons
Dorsal Root Ganglia (DRG) consist of both peptidergic and non-peptidergic nociceptive neurons. CGRP, an inflammatory neuropeptide, is a classical marker of peptidergic nociceptors and CGRP is stored within the large dense core vesicles (LDCVs) of these neurons. In addition to storing large peptide neurotransmitters, LDCVs might also serve to transport key membrane proteins to the peripheral terminals. This immunohistochemical study investigated the localization of different membrane proteins to the LDCVs of human DRG neurons. Previously validated antibodies against the endocytotic subunit AP22, the heat-activated channel TRPV1 and the mechanosensitive channel PIEZO2 were used in conjunction with an antibody against CGRP on sections of intact human DRG isolated from de-identified human subjects. Immunohistochemical studies were also performed on human synovial tissue to examine peripheral terminals. High magnification confocal microscopy was used to determine the co-localization signal of these membrane proteins with CGRP. We observed a strong co-localization of AP22 with the CGRP containing LDCVs signifying its role in membrane recycling. Moreover, we also observed a strong colocalization of TRPV1 and PIEZO2 with CGRP suggesting that LDCV release controls the trafficking of these channels to the membrane. It is likely that during injury, bulk exocytosis of CGRP will concomitantly increase the surface expression of TRPV1 and PIEZO2 channels enhancing the responsiveness of these neurons to painful stimuli. This model suggests that neurons that co-localize TRPV1 and PIEZO2 to CGRP containing LDCVs are likely silent nociceptors.

Multi-modal Monte Carlo MRI simulator of tissue microstructure
Monte Carlo simulation of MRI signals is a powerful tool for modelling and quantifying tissue microstructure. While these methods have been used in MRI for decades, they typically focus on one aspect of microstructure and one type of MRI contrast at a time. In this paper, we present a new Monte Carlo MRI (MCMR) simulator for investigating the impact of tissue microstructure on arbitrary MRI sequences. A key feature of the proposed simulator is that substrates can incorporate multiple microstructural features (diffusion, T1/T2 relaxation, membrane permeability, local off-resonance fields, surface relaxation and magnetisation transfer) simultaneously. This provides a single forward model from tissue microstructure to MRI signals that captures a broad range of contrasts typically considered to be of different MRI "modalities". We validate the results of the simulator by reproducing previous findings in well-established sequences, namely diffusion-weighted MRI, magnetisation transfer imaging, and gradient and spin echo sequences. The simulator is fully open source and packaged along with detailed documentation and tutorials.

Too little and too much: balanced hippocampal, but not medial prefrontal, neural activity is required for intact novel object recognition in rats
Impaired GABAergic inhibition, so-called neural disinhibition, in the prefrontal cortex and hippocampus has been linked to cognitive deficits. The novel object recognition (NOR) task has been used widely to study cognitive deficits in rodents. However, the contribution of prefrontal cortex and hippocampal GABAergic inhibition to NOR task performance has not been established. Here, we investigated NOR task performance in male Lister Hooded rats following regional neural disinhibition or functional inhibition, using intra-cerebral microinfusion of the GABA-A receptor antagonist picrotoxin or agonist muscimol, respectively. Our infusion targets were the medial prefrontal cortex (mPFC), dorsal hippocampus and ventral hippocampus. Using a within-subjects design, we compared NOR task performance (1-min retention delay) following bilateral regional saline, picrotoxin or muscimol infusions made before the acquisition phase. In mPFC, neither functional inhibition nor neural disinhibition affected object recognition memory. However, in both dorsal and ventral hippocampus, neural disinhibition impaired NOR relative to saline control, mainly by reducing novel object exploration time. In addition, functional inhibition of dorsal hippocampus impaired NOR, whereas ventral hippocampal functional inhibition tended to reduce novel object exploration at the highest dose used (alongside substantial non-specific behavioural effects). Overall, our data suggest that hippocampal, but not prefrontal, GABAergic inhibition contributes to NOR at a 1-min retention delay. Moreover, such NOR performance likely requires balanced neural activity in the dorsal hippocampus, with both too little and too much dorsal hippocampal activity impairing NOR memory. Our findings support that the NOR task can be used to investigate hippocampal GABAergic dysfunction in rodent models.

Performance anxiety is associated with biases in learning from reward and punishment in skilled individuals
Many individuals experience performance anxiety (PA) in high-stakes situations, from public speaking to the performing arts. While debilitating PA is associated with physiological, cognitive, and affective alterations, its underlying mechanisms remain unclear. Using behavioural analysis, computational modelling, and electroencephalography, we investigated whether PA predisposes individuals to learn faster from punishment than reward, particularly under high task uncertainty. Across three experiments with 95 skilled pianists, participants learned hidden melody dynamics through reinforcement with graded reward or punishment feedback. Bayesian hierarchical modelling revealed that performers with greater PA levels learn faster from punishment in low-uncertainty environments but increasingly rely on reward as uncertainty escalates. These biases were mediated by reinforcement-driven modulation of motor variability, increasing following poor outcomes, and shifts in frontal theta (4-7 Hz) activity encoding feedback changes and signalling upcoming motor adjustments. The findings reveal that PA alters the weighting of reward and punishment signals based on task uncertainty.

Regional susceptibility of PV interneurons in an hAPP-KI mouse model of Alzheimer's disease pathology
Early-stage Alzheimer's pathology correlates with disrupted neuronal excitability, which can drive network and cognitive dysfunction even prior to neurodegeneration. However, the emergence and extent of these changes may vary by brain region and cell types situated in those regions. Here we aimed to investigate the effects of AD pathology on different neuron subtypes in both the entorhinal cortex, a region with enhanced pathology in early AD, and the primary visual cortex, a relatively unaffected region in early-stage AD. We designed and employed a semi-automated patch clamp electrophysiology apparatus to record from fast-spiking parvalbumin interneurons and excitatory neurons in these regions, recording from over 150 cells in young adult APP-KI mice. In entorhinal cortex, amyloid overproduction resulted in PV interneuron hypoexcitability, whereas excitatory neurons were concurrently hyperexcitable. Conversely, neurons of either subclass were largely unaffected in the visual cortex. Together, these findings suggest that fast-spiking parvalbumin interneurons in the entorhinal cortex, but not in the visual cortex, play an integral role in AD progression.

Pumping Up your Predictive Power for Cognitive State Detection with the Proper GAINS
Detecting cognitive states and impairments through EEG signals is crucial for applications in aviation and medicine and has broad applications in the field of human-machine interaction. However, existing methods often fail to capture the fine-grained neural dynamics of critical brain processes due to limited temporal resolution and inadequate signal decomposition techniques. To address this, we introduce the Spectral Intensity Stability (SIS) algorithm, a novel technique that analyzes the stability and competition of dominant brain frequency oscillations across granular timescales (~4 ms). Unlike traditional spectral methods, SIS captures rapid neural transitions and hierarchical frequency dynamics, enabling more accurate characterization of task-specific cognitive processes. Our study focuses on EEG data from pilots performing multitasking simulations under hypoxic and non-hypoxic conditions, a high-stakes scenario where cognitive performance is crucial. We divided this multitasking scenario into specific cognitive states, such as task precursor, interruption, execution, and recovery. Our algorithm SIS achieved a 29.8% improvement in cognitive state classification compared to conventional methods, demonstrating superior accuracy in distinguishing both task states and hypoxic impairments. This work is novel because it bridges gaps left by traditional methods by revealing the role of hierarchical spectral dynamics in maintaining cognitive performance. Through the Granular Analysis Informing Neural Stability (GAINS) framework, we reveal how neuronal groups self-organize across fine-grained time scales, providing new understanding of task-switching, neural communication, and criticality. The findings highlight the potential for developing real-time cognitive monitoring systems to enhance safety and performance in environments where cognitive impairments can have serious consequences. Future research should extend these insights by incorporating transient behaviors and spatial dynamics to achieve a more comprehensive framework for characterizing cognitive states.

Neuronal modulation of the superior colliculus associated with visual spatial attention represents perceptual sensitivity, independent of perceptual decision and motor biases
Neurons in the superior colliculus (SC), like those in the cerebral cortex, are strongly modulated in response to shifts in attention, but make a contribution that is distinct from attention-related modulations in visual cortex. It has been a point of contention whether attention-related enhancement of neuronal activity in SC is associated with selective increase in behavioral sensitivity (d') in neurons' response fields or with animals' decision bias, which is closely linked with motor planning. By independently controlling monkeys' perceptual decision and motor criterion, we show that SC activity is strongly correlated with perceptual sensitivity at the neuron's response field. Responses of the same SC neurons were unchanged in the face of correspondingly large changes in the perceptual decision criterion. Furthermore, the SC activity did not convey information about perceptual detection on individual trials. These results suggest that the SC contributes to the component of attentional states related to heightened perceptual sensitivity.

Anisotropic light propagation in human brain white matter
Significance: Accurate modeling of light diffusion in the human brain is crucial for applications in optogenetics and spectroscopy diagnostic techniques. White matter tissue is composed of myelinated axon bundles, suggesting the occurrence of enhanced light diffusion along their local orientation direction, which however has never been characterized experimentally. Existing diffuse optics models assume isotropic properties, limiting their accuracy. Aim: We aim to characterize the anisotropic scattering properties of human white matter tissue by directly measuring its tensor scattering components along different directions, and to correlate them with the local axon fiber orientation. Approach: Using a time- and space-resolved setup, we image the transverse propagation of diffusely reflected light across two perpendicular directions in a ex vivo human brain sample. Local fiber orientation is independently determined using light sheet fluorescence microscopy (LSFM). Results: The directional dependence of light propagation in organized myelinated axon bundles is characterized via Monte Carlo (MC) simulations accounting for a tensor scattering coefficient, revealing a lower scattering rate parallel to the fiber orientation. The effects of white matter anisotropy are further assessed by simulating a typical time-domain near-infrared spectroscopy measurement in a four-layer human head model. Conclusions: This study provides a first characterization of the anisotropic scattering properties in ex vivo human white matter, highlighting its direct correlation with axon fiber orientation, and opening to the realization of quantitatively accurate anisotropy-aware human head 3D meshes for diffuse optics applications.

Tonotopic Specialization of MYO7A Isoforms in Auditory Hair Cells
Mutations in Myo7a cause Usher syndrome type 1B and non-syndromic deafness, but the precise function of MYO7A in sensory hair cells remains unclear. We identify and characterize a novel isoform, MYO7A-N, expressed in auditory hair cells alongside the canonical MYO7A-C. Isoform-specific knock-in mice reveal that inner hair cells primarily express MYO7A-C, while outer hair cells express both isoforms in opposing tonotopic gradients. Both localize to the upper tip-link insertion site, consistent with a role in the tip link for mechanotransduction. Loss of MYO7A-N leads to outer hair cell degeneration and progressive hearing loss. Cryo-EM structures reveal isoform-specific differences at actomyosin interfaces, correlating with distinct ATPase activities. These findings reveal an unexpected layer of molecular diversity within the mechanotransduction machinery. We propose that MYO7A isoform specialization enables fine-tuning of tip-link tension, thus hearing sensitivity, and contributes to the frequency-resolving power of the cochlea.

Efficient Gaussian Process-based Motor Hotspot Hunting with Concurrent Optimization of TMS Coil Location and Orientation
BACKGROUND. Transcranial magnetic stimulation (TMS) is a widely used non-invasive brain stimulation technique in neuroscience research and clinical applications. TMS-based motor hotspot hunting, the process of identifying the optimal scalp location to elicit robust and reliable motor responses, is critical to ensure reproducibility and efficiency, as well as to determine safe and precise stimulation intensities in both healthy participants and patients. Typically, this process targets motor responses in contralateral short hand muscles. However, hotspot hunting remains challenging due to the vast parameter space and time constraints. OBJECTIVE. To address this, we present an approach that concurrently optimizes both spatial and angular TMS parameters for hotspot hunting using Gaussian processes and Bayesian optimization. METHODS. We systematically evaluated five state-of-the-art acquisition functions on electromyographic TMS data from eight healthy individuals enhanced by simulated data from generative models. RESULTS. Our results consistently demonstrate that optimizing spatial and angular TMS parameters simultaneously enhances the efficacy and spatial precision of hotspot hunting. Furthermore, we provide mechanistic insights into the acquisition function behavior and the impact of coil rotation constraints, revealing critical limitations in current hotspot-hunting strategies. Specifically, we show that arbitrary constraints on coil rotation angle are suboptimal, as they reduce flexibility and fail to account for individual variability. We further demonstrate that acquisition functions differ in sampling strategies and performance. Functions overly emphasizing exploitation tend to converge prematurely to local optima, whereas those balancing exploration and exploitation-particularly Thompson sampling-achieve superior performance. CONCLUSION. These findings highlight the importance of acquisition function selection and the necessity of removing restrictive coil rotation constraints for effective hotspot hunting. Our work advances TMS-based hotspot identification, potentially reducing participant burden and improving safety in both research and clinical applications beyond the motor cortex.

Neural signatures of motor memories emerge in neural network models
Animals can learn and seamlessly perform a great number of behaviors. However, it is unclear how neural activity can accommodate new behaviors without interfering with those an animal has already acquired. Recent studies in monkeys performing motor and brain-computer interface (BCI) learning tasks have identified neural signatures - so-called "memory traces" and "uniform shifts" - that appear in the neural activity of a familiar task after learning a new task. Here we asked when these signatures arise and how they are related to continual learning. By modeling a BCI learning paradigm, we show that both signatures emerge naturally as a consequence of learning, without requiring a specific mechanism. In general, memory traces and uniform shifts reflected savings by capturing how information from different tasks coexisted in the same neural activity patterns. Yet, although the properties of these two different signatures were both indicative of savings, they were uncorrelated with each other. When we added contextual inputs that separated the activity for the different tasks, these signatures decreased even when savings were maintained, demonstrating the challenges of defining a clear relationship between neural activity changes and continual learning.