bioRxiv Subject Collection: Neuroscience's Journal

Hemispheric specialization of functions are tuned by conduction velocities of neuronal propagation in large-scale brain networks
We propose a tuning-by-delay hypothesis to explain the hemispheric specialization of function that has intrigued psychologists, philosophers, neurologists and neuroscientists for decades. In the auditory domain, speech and melody processing are understood to be lateralized in the left and the right hemispheric brain areas, respectively. Thus, analogous to the notion of pleiotropy where one gene can influence two or three unrelated traits, there exists a shared structural connectome encompassing both hemispheres underlying speech and melody processing, which however gets segregated along left vs right hemispheres, respectively. Here, we demonstrate how empirical observations of hemispheric specialization of speech and melody are shaped by the computational time-scales of information integration in cortical networks. First, we demonstrate that context-specific causal outflow of information emerging from primary auditory cortices (PAC) drives the hemispheric specialization of speech and melody processing when human volunteers listened to a cappella songs placed within a delayed match to sample task while electroencephalogram (EEG) were recorded. Second, together with participant specific whole-brain connectome model guided by diffusion weighted imaging, we predicted individual specific lateralization indices of inflow in cortical sources - after rigorous source time series reconstruction of EEG spectra. High levels of accuracy in the prediction of laterality indices in the extended large-scale auditory related regions can be achieved after optimizing conduction speeds of information propagation over a neuro-oscillatory network - a novel way to interpret about the neural mechanisms. We demonstrate that parametric modulation of conduction speeds that effectively controls the transmission delays - a key metric for understanding information processing and control of any biological network. Thus, the transmission delay in turn, acts as the switch and triggered by the spectro-temporal complexity of the task context to select the geometry of lateralization.

Effects of Classical Psychedelics on Implicit and Explicit Emotional Empathy and Cognitive Empathy: A Meta-analysis of MET task
This meta-analysis investigates the effect of classic psychedelic drugs on empathy and focuses on cognitive and emotional empathy measured using the Multifaceted Empathy Test (MET). Empathy entails the ability to understand and share the feelings of another and is a significant component of social interaction. Several studies have examined the effects of psychedelic drugs such as LSD, psilocybin and ayahuasca on empathy, yet their overall effect has not been studied so far. In this meta analysis, we reviewed data from studies up to November 2023 with the aim of examining the effects of various psychedelic drugs on empathic abilities broadly. Our findings suggest that classical psychedelics significantly enhance explicit and implicit emotional empathy without affecting measures of cognitive empathy. The results emphasize the need to continue testing the therapeutic potential of classic psychedelic drugs.

Differential control of intestine function by genetically defined enteric neurons
The function of the intestine is regulated by direct innervation from a combination of enteric, sensory, and autonomic neurons. A central question in neurobiology is how these distinct peripheral neuron populations collectively control intestinal function. However, disambiguating the functions of intestine-innervating neuronal populations has been a challenge. Using intersectional genetic approaches in mice, we enable precise manipulations of defined neuronal populations within the intestinal tract. We examined enteric neurons, which represent the majority of intestine-innervating neurons, by genetically isolating neuronal subclasses, identifying their morphological specializations, and defining subclass-specific influences on intestinal functions. We further found that food consumption can be modulated by select enteric neuron populations via the spinal sensory afferent pathway. Taken together, the presented molecular genetic characterization of intestine-innervating neurons establishes a foundation for detailed studies of the enteric nervous system and its interactions with the broader neural networks of the body.

Insulin evokes release of endozepines from astrocytes of the NTS to modulate glucose metabolism
The central nervous system (CNS) plays a key role in regulating metabolic functions, but conditions like obesity and diabetes can disrupt this balance. Within the CNS, the nucleus of the solitary tract (NTS) in the dorsal vagal complex (DVC) controls glucose metabolism and feeding behaviour. In rodents, the NTS senses insulin and communicates with the liver to regulate glucose production. Even short term exposure to a high fat diet (HFD) can lead to insulin resistance and impair NTS function. However, we still know little about which cells in the NTS are sensitive to insulin. Our study aimed to identify these insulin sensitive cells and understand how they affect glucose metabolism. We found that insulin receptors in astrocytes are crucial for the NTS ability to regulate glucose production in the liver. Insulin evokes the release of endozepines from astrocytes, and injecting endozepines into the NTS reduces glucose production. The effect of endozepines within the NTS is mimicked by GABAA antagonists and prevented by an agonist, suggesting that insulin prompts astrocytes to release endozepines, which then attenuate GABAA receptor activity, ultimately reducing glucose production in the liver. Our study is the first to show that insulin dependent release of endozepines from NTS astrocytes is fundamental to control blood glucose levels, providing valuable insights into the mechanisms underlying insulin function within this specific region of the CNS.

Cardiovascular responses to natural and auditory evoked slow waves predict post-sleep cardiac function
The interplay between slow-wave sleep and cardiovascular health is increasingly recognized. Our prior research showed that auditory-enhanced slow waves can boost cardiac function, yet the mechanisms behind this remain unclear. Advancing these findings, our current analysis dissected the effects of two slow wave types on cardiovascular function, using data from 18 middle-aged men across three nights. We found that the strength of heart rate and blood pressure responses concurrent with slow waves predicts cardiac function post-sleep. Notably, we identified that highly synchronized type I slow waves, as opposed to lower-amplitude type II slow waves, primarily co-occur with these cardiovascular pulsations. While auditory stimulation enhances both types of slow waves, they exhibit distinct temporal dynamics, pointing to different underlying biological mechanisms. This study crucially addresses how distinct slow wave types can affect cardiovascular function, implying that targeted slow wave stimulation could be a strategic approach to improve heart health.

Daily light-induced transcription in visual cortex neurons drives downward Firing Rate Homeostasis and stabilizes sensory processing
Balancing plasticity and stability in neural circuits is essential for an animal's ability to learn from its environment while preserving the proper processing and perception of sensory information. However, unlike the mechanisms that drive plasticity in neural circuits, the activity-induced molecular mechanisms that convey functional stability remain poorly understood. Focusing on the visual cortex of adult mice and combining transcriptomics, electrophysiology and 2-photon imaging, we find that the daily appearance of light induces in excitatory neurons a large gene program along with rapid and transient shifts in the ratio of excitation and inhibition (E/I-ratio) and ongoing neural activity. Furthermore, we find that the light-induced transcription factor NPAS4 drives these daily normalizations of E/I-ratio and neural activity rates and that it stabilizes the neurons' response properties. These findings indicate that daily sensory-induced transcription normalizes E/I-ratio and drives downward Firing Rate Homeostasis to maintain proper sensory processing and perception.

Synchrony, oscillations, and phase relationships in collective neuronal activity: a highly comparative overview of methods
Neuronal activity is organized in collective patterns that are critical for information coding, generation, and communication between brain areas. These patterns are often described in terms of synchrony, oscillations, and phase relationships. Many methods have been proposed for the quantification of these collective states of dynamic neuronal organization. However, it is difficult to determine which method is best suited for which experimental setting and research question. This choice is further complicated by the fact that most methods are sensitive to a combination of synchrony, oscillations, and other factors; in addition, some of them display systematic biases that can complicate their interpretation. To address these challenges, we adopt a highly comparative approach, whereby spike trains are represented by a diverse library of measures. This enables unsupervised or supervised classification in the space of measures, or in that of spike trains. We compile a battery of 122 measures of synchrony, oscillations, and phase relationships, complemented with 9 measures of spiking intensity and variability. We first apply them to sets of synthetic spike trains with known statistical properties, and show that all measures are confounded by extraneous factors such as firing rate or population frequency, but to different extents. Then, we analyze spike trains recorded in different species---rat, mouse, and monkey---and brain areas---primary sensory cortices and hippocampus---and show that our highly comparative approach provides a high-dimensional quantification of collective network activity that can be leveraged for both unsupervised and supervised classification of firing patterns. Overall, the highly comparative approach provides a detailed description of the empirical properties of multineuron spike train analysis methods, including practical guidelines for their use in experimental settings, and advances our understanding of neuronal coordination and coding.

Effects of cognitive load and years of experience on phase-amplitude coupling in simultaneous interpretation
Simultaneous interpretation is a highly cognitively demanding task that requires constant attention switching between languages. Interest continues to grow in the contribution of phase-amplitude coupling (PAC), which involves the cooperative interaction of multiple oscillations and working memory. In this study, we established subjective definitions for cognitive load levels based on the subjective word familiarity of simultaneous interpretation, categorizing them as low, medium, or high. We then compared the changes in the PAC patterns between experienced interpreters and beginners. Experienced interpreters exhibited an increase in PAC, including theta-gamma PAC, which is linked to working memory, as well as theta-beta PAC, alpha-beta PAC and alpha-gamma PAC, with rising cognitive load levels in simultaneous interpreting. This suggests that experienced simultaneous interpreters choose a more adaptive neural processing strategy in response to the cognitive demands of interpretation language. In contrast, beginner interpreters do not show such changes in PACs, indicating either an underdeveloped or a different neurological approach to the cognitive load levels of interpretation language. The difference in PAC responses between the two groups reflects varying cognitive and interpretive strategies in the brain, where experienced interpreters might utilize more advanced neural mechanisms to manage higher levels of difficulty in simultaneous interpretation.

Statistical signature of subtle behavioural changes inlarge-scale behavioural assays
The central nervous system can generate various behaviours, including motor responses, which we can observe through video recordings. Recent advancements in genetics, automated behavioural acquisition at scale, and machine learning enable us to link behaviours to their underlying neural mechanisms causally. Moreover, in some animals, such as the Drosophila larva, this mapping is possible at unprecedented scales of millions of animals and single neurons, allowing us to identify the neural circuits generating particular behaviours. These high-throughput screening efforts are invaluable, linking the activation or suppression of specific neurons to behavioural patterns in millions of animals. This provides a rich dataset to explore how diverse nervous system responses can be to the same stimuli. However, challenges remain in identifying subtle behaviours from these large datasets, including immediate and delayed responses to neural activation or suppression, and understanding these behaviours on a large scale. We introduce several statistically robust methods for analyzing behavioural data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioural spaces aimed at detecting subtle deviations in behaviour. 3) A generative model for larval behavioural sequences, providing a benchmark for identifying complex behavioural changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioural screen focused on responses to an air puff, analyzing data from 280,716 larvae across 568 genetic lines.

Insulin resistance compromises midbrain organoid neural activity and metabolic efficiency predisposing to Parkinsons disease pathology
Growing evidence indicates that Type 2 Diabetes (T2D) is associated with an increased risk of developing Parkinsons disease through shared disease mechanisms. Studies show that insulin resistance, which is the driving pathophysiological mechanism of T2D plays a major role in neurodegeneration by impairing neuronal functionality, metabolism, and survival. To investigate insulin resistance caused pathological changes in the human midbrain, which could predispose a healthy midbrain to PD development, we exposed iPSC-derived human midbrain organoids from healthy individuals to either high insulin concentrations, promoting insulin resistance, or to more physiological insulin concentrations restoring insulin signalling function. We combined experimental methods with metabolic modelling to identify the most insulin resistance-dependent pathogenic processes. We demonstrate that insulin resistance compromises organoid metabolic efficiency, leading to increased levels of oxidative stress. Additionally, insulin-resistant midbrain organoids showed decreased neural activity and reduced amount of dopaminergic neurons, highlighting insulin resistance as a significant target in PD prevention.

Optimized AAV capsids for diseases of the basal ganglia show robust potency and distribution in adult nonhuman primates
Huntington's disease and other disorders of the basal ganglia create challenges for biomolecule-based medicines given the poor accessibility of these deep brain structures following intracerebral or intravascular delivery. Additionally, for adeno-associated viruses (AAVs) intravascular delivery exposes peripheral tissues to the vast majority of the therapy, increasing the risk of immune responses and the quantity and associated cost of goods required for therapeutically relevant brain penetration levels. Here, we found that low dose, low volume delivery of unbiased AAV libraries into a focused brain region allowed recovery of novel capsids capable of broad access to key deep brain and cortical structures relevant for human therapies at doses orders of magnitude lower than used in current clinical trials. One such capsid, AAV-DB-3, provided transduction of up to 45% of medium spiny neurons in the adult NHP striatum, along with substantial transduction of relevant deep layer neurons in the cortex. Notably, AAV-DB-3 behaved similarly in mice as in NHPs and also potently transduced human neurons derived from induced pluripotent stem cells. Thus, AAV-DB-3 provides a unique AAV for network level brain gene therapies that translates up and down the evolutionary scale for preclinical studies and eventual clinical use.

Verbal working memory and syntactic comprehension segregate into the dorsal and ventral streams
Syntactic processing and verbal working memory are both essential components to sentence comprehension. Nonetheless, the separability of these systems in the brain remains unclear. To address this issue, we performed causal-inference analyses based on lesion and connectome network mapping using MRI and behavioral testing in 103 individuals with chronic post-stroke aphasia. We employed a rhyme judgment task with heavy working memory load without articulatory confounds, controlling for the overall ability to match auditory words to pictures and to perform a metalinguistic rhyme judgment, isolating the effect of working memory load. We assessed noncanonical sentence comprehension, isolating syntactic processing by incorporating residual rhyme judgment performance as a covariate for working memory load. Voxel-based lesion analyses and structural connectome-based lesion symptom mapping controlling for total lesion volume were performed, with permutation testing to correct for multiple comparisons (4,000 permutations). We observed that effects of working memory load localized to dorsal stream damage: posterior temporal-parietal lesions and frontal-parietal white matter disconnections. These effects were differentiated from syntactic comprehension deficits, which were primarily associated with ventral stream damage: lesions to temporal lobe and temporal-parietal white matter disconnections, particularly when incorporating the residual measure of working memory load as a covariate. Our results support the conclusion that working memory and syntactic processing are associated with distinct brain networks, largely loading onto dorsal and ventral streams, respectively.

Development of the overlapping network modules in the human brain
Developmental connectomic studies have shown that the modular organization of functional networks in the human brain undergoes substantial reorganization with age to support cognitive growth. However, these studies implicitly assume that each brain region belongs to one and only one specific network module, ignoring the potential spatial overlap between functional modules. How the overlapping functional modular architecture develops and whether this development is related to structural signatures remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6-14 years), we investigated the development of the overlapping modular architecture of functional networks, and further explored their structural associations. Specifically, an edge-centric network model was used to identify the overlapping functional modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a remarkable regional inhomogeneity in module overlap in children, with higher entropy in the ventral attention, somatomotor, and subcortical networks and lower entropy in the visual and default-mode networks. Furthermore, the overlapping modules developed in a linear, spatially dissociable manner from childhood to adolescence, with significantly reduced entropy in the prefrontal cortex and putamen and increased entropy in the parietal lobules. Personalized overlapping modular patterns capture individual brain maturity as characterized by brain age. Finally, the overlapping functional modules can be significantly predicted by integrating gray matter morphology and white matter network properties. Our findings highlight the maturation of overlapping network modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Hippocampal ripples mediate motor learning during brief rest breaks in humans
Although research on the hippocampus has largely focused on its role in active learning, critical aspects of learning and memory happen offline, during both wake and sleep. When healthy young people learn a motor sequence task, most of their performance improvement happens not while typing, but offline, during interleaved rest breaks. Although patients with dense amnesia due to hippocampal damage show a normal amount of motor sequence learning, they show a different pattern. They actually lose speed over the breaks and compensate while typing. This indicates that an intact hippocampus is necessary for offline motor learning during wake but does not specify its mechanism. Here, we studied epilepsy patients (n=20) undergoing direct intracranial EEG monitoring of the hippocampus as they learned the same motor sequence task. Like healthy young people, they showed greater speed gains across rest breaks than while typing. They also showed a higher ripple rate during the breaks that predicted offline gains in speed. This suggests that motor learning during brief rest breaks during wake is mediated by hippocampal ripples. These findings complement rodent studies showing memory replay during hippocampal ripples in the wakeful rest that follows learning. Disrupting these ripples impairs memory, consistent with a causal role. The findings also provide a mechanistic explanation of human neuroimaging reports of increased hippocampal activation and sequential motor memory replay during rest breaks that predict performance improvement. Finally, they expand our understanding of the role of hippocampal ripples beyond declarative memory to include enhancing motor procedural memory.

Viral overexpression of human alpha-synuclein in mouse substantia nigra dopamine neurons results in hyperdopaminergia but no neurodegeneration
Loss of select neuronal populations such as midbrain dopamine (DA) neurons is a pathological hallmark of Parkinson disease (PD). The small neuronal protein alpha-synuclein has been related both genetically and neuropathologically to PD, yet how it contributes to selective vulnerability remains elusive. Here, we describe the generation of a novel adeno-associated viral vector (AAV) for Cre-dependent overexpression of wild-type human alpha-synuclein. Our strategy allows us to restrict alpha-synuclein to select neuronal populations and hence investigate the cell-autonomous effects of elevated alpha-synuclein in genetically-defined cell types. Since DA neurons in the substantia nigra pars compacta (SNc) are particularly vulnerable in PD, we investigated in more detail the effects of increased alpha-synuclein in these cells. AAV-mediated overexpression of wildtype human alpha-synuclein in SNc DA neurons increased the levels of alpha-synuclein within these cells and augmented phosphorylation of alpha-synuclein at serine-129, which is considered a pathological feature of PD and other synucleinopathies. However, despite abundant alpha-synuclein overexpression and hyperphosphorylation we did not observe any DA neurodegeneration up to 90 days post virus infusion. In contrast, we noticed that overexpression of alpha-synuclein resulted in increased locomotor activity and elevated striatal DA levels suggesting that alpha-synuclein enhanced dopaminergic activity. We therefore conclude that cell-autonomous effects of elevated alpha-synuclein are not sufficient to trigger acute DA neurodegeneration.

Cortical networks responsive to phrase structure and subject island violations
In principle, functional neuroimaging provides uniquely informative data in addressing linguistic questions, because it can indicate distinct processes that are not apparent from behavioral data alone. This could involve adjudicating the source of unacceptability via the different patterns of elicited brain responses to different ungrammatical sentence types. However, it is difficult to interpret brain activations to syntactic violations. Such responses could reflect processes that have nothing intrinsically related to linguistic representations, such as domain-general executive function abilities. In order to facilitate the potential use of functional neuroimaging methods to identify the source of different syntactic violations, we conducted an fMRI experiment to identify the brain activation maps associated with two distinct syntactic violation types: phrase structure (created by inverting the order of two adjacent words within a sentence) and subject islands (created by extracting a wh-phrase out of an embedded subject). The comparison of these violations to control sentences surprisingly showed no indication of a generalized violation response, with almost completely divergent activation patterns. Phrase structure violations seemingly activated regions previously implicated in verbal working memory and structural complexity in sentence processing, whereas the subject islands appeared to activate regions previously implicated in conceptual-semantic processing, broadly defined. We review our findings in the context of previous research on syntactic and semantic violations using event-related potentials. We suggest that functional neuroimaging is a potentially fruitful technique in unpacking the distinct sets of cognitive processes elicited by theoretically-relevant syntactic violations, when interpreted with care and paired with appropriate control conditions.

Sleep and Activity Patterns in Autism Spectrum Disorder
Background: Autism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impaired social interactions, repetitive behaviors, and a wide range of comorbidities. Between 44-83% of autistic individuals report sleep disturbances, which may share an underlying neurodevelopmental basis with ASD. Methods: We recruited 382 ASD individuals and 223 of their family members to obtain quantitative ASD-related traits and wearable device-based accelerometer data spanning three consecutive weeks. An unbiased approach identifying traits associated with ASD was achieved by applying the elastic net machine learning algorithm with five-fold cross-validation on 6,878 days of data. The relationship between sleep and physical activity traits was examined through linear mixed-effects regressions using each night of data. Results: This analysis yielded 59 out of 242 actimetry measures associated with ASD status in the training set, which were first validated in a test set (AUC: 0.777). For several of these traits (e.g. total light physical activity), the day-to-day variability, in addition to the mean value, was associated with ASD. Individuals with ASD were found to have a stronger correlation between physical activity and sleep, where less physical activity decreased their sleep more significantly than that of their non-ASD relatives. Conclusions: The average duration of sleep/physical activity and the variation in the average duration of sleep/physicial activity strongly predict ASD status. Physical activity measures were correlated with sleep quality, traits, and regularity, with ASD individuals having stronger correlations. Interventional studies are warranted to investigate whether improvements in both sleep and increased physical activity may improve the core symptoms of ASD.

Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum
Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here we investigate this suggestion by harnessing a biologically-constrained spiking model of the cortico-basal ganglia-thalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect pathway spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits.

Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone, the brain region generating seizures, for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non-epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data-driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO-networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy.

Individual variability in the relationship between physiological and resting-state fMRI metrics
Cerebrovascular Reactivity (CVR), the brain's vascular response to a vasodilatory stimulus, can be measured using fMRI during breathing challenges that modulate arterial CO2 levels. CVR is an important indicator of cerebrovascular health, although its estimation can be challenging due to the extra experimental setup and/or the subject compliance required. To overcome these limitations, summary metrics based on resting state fluctuations (RSF), such as the (fractional) amplitude of low frequency fluctuations (f/ALFF) and the resting state fluctuation amplitude (RSFA), have been proposed as alternative estimates of CVR, as they are frequently associated with vascular and physiological factors. Previous studies have reported a significant relationship between CVR estimates obtained by means of respiratory paradigms and RSF metrics. However, the total sample sizes, considering both subjects and sessions, are typically small, and not all studies agree on the degree of the relationship. Furthermore, to our knowledge, these studies have only reported cross-sectional analyses, whereas intra-subject longitudinal relationships between CVR estimates and RSF metrics are unknown. Leveraging a unique dense sampling dataset in which resting state and breath-hold multi-echo fMRI were collected in 7 subjects with 10 sessions each, we provide evidence of high individual variability in the inter-session (i.e. intra-subject) relationship between RSF metrics and CVR. These results indicate that RSF metrics might not be a suitable proxy of CVR in clinical settings or for BOLD signal calibration as they may not properly account for intra-individual physiological variations in BOLD fMRI data.

Alcohol-induced sleep dysregulation in Drosophila is dependent on the neuropeptide PDF
Alcohol exposure is known to trigger homeostatic adaptations in the brain that lead to the development of tolerance and dependence. These adaptations are also believed to be the root of a series of disturbances in sleep patterns that often manifest during the development of alcoholism and can have significant clinical and economic consequences. Unfortunately, the neuronal and genetic pathways that control the effects of alcohol on sleep are currently unknown, thus limiting our efforts to find effective treatment. In this study, we conduct a mechanistic exploration of the relationships between alcohol and sleep alterations using a Drosophila model system. We show that the genetic manipulation of the ventral lateral neurons (LNv) -a set of neurons known to control sleep in Drosophila- disrupts alcohol sensitivity and tolerance. Moreover, we show that alcohol exposure induces a series of alterations in sleep patterns that last for several days. Our results demonstrate that a single alcohol exposure promotes daytime sleep, alters the structure of sleep during the night, and reduces morning anticipatory behavior. In addition, we show that some of these alterations partially depend on the activity of the neuropeptide PDF, a key element in regulating sleep architecture. We propose that alcohol-induced sleep disruption stems from alterations in the activity of the PDF-releasing LNv neurons and that these alterations are similar to those that produce alcohol tolerance.

Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals
Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful patterns embedded within complex and rich data sources. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a range of biosensors (calcium, norepinephrine, ATP, acetylcholine, dopamine), cell types (astrocytes, oligodendrocytes, microglia, neurons), organs (brains and spinal cords), animal models (zebrafish and mouse), and imaging modalities (confocal, two-photon, light sheet). As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Cortical circuit principles predict patterns of trauma induced tauopathy in humans
Connections in the cortex of diverse mammalian species are predicted reliably by the Structural Model for direction of pathways and signal processing (reviewed in 1,2). The model is rooted in the universal principle of cortical systematic variation in laminar structure and has been supported widely for connection patterns in animals but has not yet been tested for humans. Here, in postmortem brains of individuals neuropathologically diagnosed with chronic traumatic encephalopathy (CTE) we studied whether the hyperphosphorylated tau (p-tau) pathology parallels connection sequence in time by circuit mechanisms. CTE is a progressive p-tau pathology that begins focally in perivascular sites in sulcal depths of the neocortex (stages I-II) and later involves the medial temporal lobe (MTL) in stages III-IV. We provide novel quantitative evidence that the p-tau pathology in MTL A28 and nearby sites in CTE stage III closely follows the graded laminar patterns seen in homologous cortico-cortical connections in non-human primates. The Structural Model successfully predicted the laminar distribution of the p-tau neurofibrillary tangles and neurites and their density, based on the relative laminar (dis)similarity between the cortical origin (seed) and each connection site. The findings were validated for generalizability by a computational progression model. By contrast, the early focal perivascular pathology in the sulcal depths followed local columnar connectivity rules. These findings support the general applicability of a theoretical model to unravel the direction and progression of p-tau pathology in human neurodegeneration via a cortico-cortical mechanism. Cortical pathways converging on medial MTL help explain the progressive spread of p-tau pathology from focal cortical sites in early CTE to widespread lateral MTL areas and beyond in later disease stages.

Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking
Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed stacking that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Study. For predictability, stacked models led to out-of-sample r~.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.

Protection of savings by reducing the salience of opposing errors
When humans encounter the same disturbance twice, they adapt to it faster during the second exposure. To examine how subconscious learning systems contribute to this savings process, previous studies have suppressed explicit awareness of the perturbation by gradually increasing its magnitude during initial learning. This has produced mixed effects, with some studies demonstrating faster relearning, and others observing no acceleration during relearning. Here we examined whether these differences might be due to the nature of a de-adaptation period that separates two learning periods. To test this idea, we manipulated the magnitude of washout errors by de-adapting participants abruptly, gradually, or by removing feedback entirely. Empirical analyses indicated that the different classes of washout errors had a profound effect on savings: large washout errors nullified the ability to save, whereas small errors or the absence of error protected savings. Model-based analyses suggested that changes in learning rates were mediated by an increase in sensitivity to error that could be reversed by experience with oppositely-oriented washout errors. This suggests that the experience of error produces both a facilitation of learning for similar errors and a reduction in learning for dissimilar errors. The latter can abolish the expression of savings following gradual adaptation.

Perceptual consequences of retinal stabilization with a high-frequency LCD display
Several recent studies have shown decreased sensitivity when stimuli are immobilized on the retina, a procedure known as retinal stabilization. Because of the technical challenges inherent in this procedure, studies have either used fast-phosphor CRT displays or directly updated the stimulus on the retina by means of adaptive optics scanning laser ophthalmoscopes. Both display systems provide brief pulses to the retina, raising the question of whether this flicker contributed to perceptual effects. Here we report the results of retinal stabilization experiments conducted with LCD monitors at high refresh rates. Results replicate previous findings of high-frequency impairments under retinal stabilization. These data provide further support for a functional role of small eye movements during visual fixation.

A Deep Learning-Based Segmentation of Cells and Analysis (DL-SCAN)
With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to study cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises, if fluorophores employed exhibit low brightness and low Signal-to-Noise ratio (SNR). As a consequence, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we have developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using a Deep Learning algorithm called Stardist. We demonstrate its ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na+ and Ca2+ imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types.

DeepLeMiN: Deep-learning-empowered Physics-aware Lensless Miniscope
Mask-based lensless fluorescence microscopy is a compact, portable imaging technique promising for biomedical research. It forms images through a thin optical mask near the camera without bulky optics, enabling snapshot three-dimensional imaging and a scalable field of view (FOV) without increasing device thickness. Lensless microscopy relies on computational algorithms to solve the inverse problem of object reconstruction. However, there has been a lack of efficient reconstruction algorithms for large-scale data. Furthermore, the entire FOV is typically reconstructed as a whole, which demands substantial computational resources and limits the scalability of the FOV. Here, we developed DeepLeMiN, a lensless microscope with a custom designed optical mask and a multi-stage physics-informed deep learning model. This not only enables the reconstruction of localized FOVs, but also significantly reduces the computational resource demands and facilitates real-time reconstruction. Our deep learning algorithm can reconstruct object volumes over 4x6x0.6 mm3, achieving lateral and axial resolution of ~10 m and ~50 m respectively. We demonstrated significant improvement in both reconstruction quality and speed compared to traditional methods, across various fluorescent samples with dense structures. Notably, we achieved high-quality reconstruction of 3D motion of hydra and the neuronal activity with cellular resolution in awake mouse cortex. DeepLeMiN holds great promise for scalable, large FOV, real-time, 3D imaging applications with compact device footprint.

Novel clock neuron subtypes regulate temporal aspects of sleep
Circadian neurons within animal brains orchestrate myriad physiological processes and behaviors, But the contribution of these neurons to the regulation of sleep is not well understood. To address this deficiency, we leveraged single cell RNA sequencing to generate a new and now comprehensive census of transcriptomic cell types of Drosophila clock neurons. We focused principally on the enigmatic DN3s, which constitute about half of the 75 pairs of clock neurons in the fly brain and were previously almost completely uncharacterized. These DN3s are organized into 12 clusters with unusual gene expression features compared to the more well-studied clock neurons. We further show that different DN3 subtypes with distinct projection patterns promote sleep at specific times of the day through a common G protein coupled receptor, TrissinR. Our findings indicate an intricate regulation of sleep behavior by clock neurons and highlight their remarkable diversity in gene expression, projection patterns and functional properties.

Daily glucocorticoids promote glioblastoma growth and circadian synchrony to the host
Glioblastoma (GBM) is the most common primary brain tumor in adults with a poor prognosis despite aggressive therapy. A recent, retrospective clinical study found that administering Temozolomide in the morning increased patient overall survival by 6 months compared to evening. Here, we tested the hypothesis that daily host signaling regulates tumor growth and synchronizes circadian rhythms in GBM. We found daily Dexamethasone promoted or suppressed GBM growth depending on time of day of administration and on the clock gene, Bmal1. Blocking circadian signals, like VIP or glucocorticoids, dramatically slowed GBM growth and disease progression. Finally, mouse and human GBM models have intrinsic circadian rhythms in clock gene expression in vitro and in vivo that entrain to the host through glucocorticoid signaling, regardless of tumor type or host immune status. We conclude that GBM entrains to the circadian circuit of the brain, which modulates its growth through clock-controlled cues, like glucocorticoids.

RealtimeDecoder: A fast software module for online clusterless decoding
Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing neural activity. That in turn allows experimenters to test hypotheses about the role of that content in driving downstream activity patterns and behaviors. While multiple real-time systems have been developed, they are typically implemented in C++ and are locked to a specific data acquisition system, making them difficult to adapt to new experiments.Here we present a Python software system that implements online clusterless decoding using state space models in a manner independent of data acquisition systems. The parallelized system processes neural data with temporal resolution of 6 ms and median computational latency <50 ms for medium- to large-scale (32+ tetrodes) rodent hippocampus recordings without the need for spike sorting. It also executes auxiliary functions such as detecting sharp wave ripples from local field potential (LFP) data. Performance is similar to state-of-the-art solutions which use compiled programming languages. We demonstrate this system use in a rat behavior experiment in which the decoder allowed closed loop neurofeedback based on decoded hippocampal spatial representations . This system provides a powerful and easy-to-modify tool for real-time feedback experiments.