bioRxiv Subject Collection: Neuroscience's Journal

Molecular Insights into Neuronal Dysfunction in GM2 Gangliosidoses
Glycosphingolipids (GSL) are important bioactive components of cellular membranes. Complex GSLs, containing one or more sialic acid residues are known as gangliosides and are highly abundant in the brain. Diseases of ganglioside metabolism often result in severe, early-onset neurodegeneration. The ganglioside GM2 is the substrate of the hydrolytic lysosomal {beta}-hexosaminidase A (HexA) enzyme and when subunits of this enzyme are non-functional, GM2 lipid accumulates in cells leading to the GM2 gangliosidoses, Tay-Sachs and Sandhoff diseases. We have developed i3Neuron-based models of Tay-Sachs and Sandhoff diseases, which recapitulate cellular features of these diseases including endolysosomal storage of GM2 and formation of membrane whorls. Using proteomic approaches, we identify the molecular changes occurring within these neurons including accumulation of endolysosomal proteins consistent with disease phenotypes. Importantly, we demonstrate that in addition to lysosomal dysfunction, these diseases also result in significant changes in proteins associated with trafficking, as well as changes in the lipid and protein composition of the plasma membrane (PM). One of the mechanisms driving these changes is the exocytosis of lysosomal material resulting in the aberrant accumulation of lysosomal proteins and lipids on the cell surface. The PM analysis also revealed changes in the abundance of several key synaptic proteins and measurements of neuronal electrical activity reveals a reduced threshold for depolarisation, consistent with neuronal hyperactivity. This work provides deep molecular insights into the mechanisms driving neuronal dysfunction in the GM2 gangliosidoses with potential relevance more broadly to other lysosomal storage diseases and for late-onset diseases with sphingolipid dysregulation.

A neural basis for mutant ATAXIN-1 induced respiratory dysfunction in mouse models of Spinocerebellar ataxia type 1
Spinocerebellar ataxia type 1 (SCA1), a dominantly inherited neurodegenerative disorder caused by an expanded trinucleotide repeat in the ATAXIN-1 (ATXN1) gene, is characterized by motor dysfunction, cognitive impairment, and death from compromised swallowing and respiration. To delineate specific cell types that contribute to respiratory dysfunction, we utilized the floxed conditional knock-in f-ATXN1[146Q/2Q] mouse. Whole body plethysmography during spontaneous respiration and respiratory challenge showed that f-ATXN1[146Q/2Q] mice exhibit a spontaneous respiratory phenotype characterized by elevated respiratory frequency, volumes, and respiratory output. Consequently, the ability of f-ATXN1[146Q/2Q] mice to increase ventilation during the challenge is impaired. To investigate the role of mutant ATXN1 expression in neural and skeletal muscle lineages, f-ATXN1[146Q/2Q] mice were bred to Nestin-Cre and Acta1-Cre mice respectively. These analyses revealed that the abnormal spontaneous respiration in f-ATXN1[146Q/2Q] mice involved two aspects: a behavioral phenotype in which SCA1 mice exhibit increased motor activity during respiratory testing and functional dysregulation of central respiratory control centers. Both aspects of spontaneous respiration were partially ameliorated by removing mutant ATXN1 from neural, but not skeletal muscle, cell lineages.

Nicotine engages a VTA-NAc feedback loop to inhibit amygdala-projecting dopamine neurons and induce anxiety.
Nicotine activates ventral tegmental area (VTA) dopaminergic (DA) neurons projecting to the nucleus accumbens (NAc) to drive its reinforcing effects. Simultaneously, it inhibits those projecting to the amygdala (Amg) to mediate anxiety through a process that remains unknown. Here we show that NAc- and Amg-projecting DA neurons respond with similar polarities to ethanol and nicotine, suggesting a shared network-based mechanism underlying the inhibitory effect of these otherwise pharmacologically-distinct drugs. Selective activation of NAc-projecting DA neurons, using genetic or optogenetic strategies, produced inhibition of Amg-projecting DA neurons, through a GABAergic feedback loop. Furthermore, optogenetically silencing this feedback loop prevented nicotine from inducing both inhibition of DA neurons and anxiety-like behavior. Therefore, nicotine-induced inhibition of the VTA-Amg DA pathway results from a VTA-NAc inhibitory feedback loop, mediating anxiety.

Dev-Atlas: A reference atlas of functional brain networks for typically developing adolescents
Adolescence is a critical period for neural changes, including maturation of the brain's cognitive networks, but also a period of increased vulnerability to psychopathology. It is well accepted that the brain is functionally organized into multiple interacting networks and extensive literature has demonstrated that the spatial and functional organization of these networks shows major age-related changes across the lifespan, but particularly during adolescence. Yet, there is limited option for a reference functional brain atlas derived from typically developing adolescents, which is especially problematic as the reliable and reproducible identification of functional brain networks crucially depends on the use of such reference functional atlases. In this context, we utilized resting-state functional MRI data from a total of 1,391 typically developing youth between the ages of 8 and 17 years to create a new adolescent-specific reference atlas of functional brain networks. We further investigated the impact of age and sex on these networks. Using a multiscale individual component clustering algorithm (MICCA), we identified 24 reliable functional brain networks, classified within six domains: Default-Mode (5 networks), Control (4 networks), Salience (3 networks), Attention (4 networks), Somatomotor (5 networks), and Visual (3 networks). We identified reliable and large effects of age on the spatial topography of these majority of networks, as well as on the functional network connectivity (FNC) between networks. The DMN showed reduced FNC with the other networks with older age. Sex effects were not as widespread. No significant sex-by-age interactions were detected. Overall, we created a novel brain atlas, named Dev-Atlas, focused on a typically developing sample, with the hope that this atlas can be used in future independent developmental network neuroscience studies. Dev-Atlas is freely available to the research community.

Distinct roles of prefrontal cortex neurons in set shifting
Cognitive flexibility, the ability to adjust behavioral strategies in response to changing environmental contingencies, requires adaptive processing of internal states and contextual cues to guide goal-oriented behavior, and is dependent on prefrontal cortex (PFC) functions. However, the neurophysiological underpinning of how the PFC supports cognitive flexibility is not well understood and has been under active investigation. We recorded spiking activity from single PFC neurons in mice performing the attentional set-shifting task, where mice learned to associate different contextually relevant sensory stimuli to reward. We identified subgroups of PFC neurons encoding task context, choice and trial outcome. Putative fast-spiking neurons were more involved in representing outcome and choice than putative regular-spiking neurons. Regression model further revealed that task context and trial outcome modulated the activity of choice-encoding neurons in rule-dependent and cell type-dependent manners. Together, our data provide new evidence to elucidate PFC's role in cognitive flexibility, suggesting differential cell type-specific engagement during set shifting, and that both contextual rule representation and trial outcome monitoring underlie PFC's unique capacity to support flexible behavioral switching.

Beta Waves in Action: Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction
The role of beta band activity in cortico-basal ganglia interactions during motor control has been studied extensively in resting-state and for simple movements, such as button pressing. However, little is known about how beta oscillations change and interact in more complex situations involving rapid changes of movement in various contexts. To close this knowledge gap, we combined magnetoencephalography (MEG) and local field potential recordings from the subthalamic nucleus (STN) in Parkinsons disease patients to study beta dynamics during initiation, stopping, and rapid reversal of rotational movements. The action prompts were manipulated to be predictable vs. unpredictable. We observed movement-related beta suppression at motor sequence start, and a beta rebound after motor sequence stop in STN power, motor cortical power, and STN-cortex coherence. Despite involving a brief stop of movement, no clear rebound was observed during reversals of turning direction. On the cortical level, beta power decreased bilaterally following reversals, but more so in the hemisphere ipsilateral to movement, due to a floor effect on the contralateral side. In the STN, power modulations varied across patients, with patients revealing brief increases or decreases of high-beta power. Importantly, cue predictability affected these modulations. Event-related changes of STN-cortex beta coherence were generally stronger in the unpredictable than in the predictable condition. In summary, this study reveals the influence of movement context on beta oscillations in basal ganglia-cortex loops when humans change ongoing movements according to external cues. We find that movement scenarios requiring higher levels of caution involve enhanced modulations of subthalamo-cortical beta synchronization. Further, our results confirm that beta oscillations reflect the start and end of motor sequences better than movement changes within a sequence.

Boosting Proteasome Activity: A Novel Mechanism of NMDAR Blockers Against Neurodegeneration
NMDAR antagonists, such as memantine and ketamine, have shown efficacy in treating neurodegenerative diseases and major depression. The mechanism by which these drugs correct the aforementioned diseases is still unknown. Our study reveals that these antagonists significantly enhance 20S proteasome activity, crucial for degrading intrinsically disordered, oxidatively damaged, or misfolded proteins, factors pivotal in neurodegenerative diseases like Alzheimer's and Parkinson's. In a mouse model, ketamine administration notably altered brain synaptic protein profiles within two hours, downregulating proteins linked to neurodegenerative conditions. Furthermore, the altered proteins exhibited enrichment in terms related to plasticity and potentiation, including retrograde endocannabinoid signaling-a pivotal pathway in both short- and long-term plasticity that may elucidate the long-lasting effects of ketamine in major depression. Via the ubiquitin-independent 20S proteasome pathway (UIPS), these drugs maintain cellular protein homeostasis, crucial as proteasome activity declines with age leading to protein aggregation and disease symptoms. The elucidation of the mechanistic pathways underlying the therapeutic effects of NMDAR antagonists holds promise for developing new treatment strategies for brain diseases, including schizophrenia, Alzheimer's, and Parkinson's.

GPT-4V shows human-like social perceptual capabilities at phenomenological and neural levels
Humans navigate the social world by rapidly perceiving social features from other people and their interaction. Recently, large-language models (LLMs) have achieved high-level visual capabilities for detailed object and scene content recognition and description. This raises the question whether LLMs can perceive nuanced and tacit social information form images and videos, and whether the high-dimensional perceptual structure aligns with that of humans. We collected social perceptual evaluations for 138 social features from GPT-4V for images (N=468) and videos (N=234) that are derived from social movie scenes. These evaluations were compared with human evaluations (N=2254). The comparisons established that GPT-4V can achieve human-like social perceptual capabilities at the level of individual features as well as at the level of high-dimensional perceptual representations. We also modelled hemodynamic responses (N=97) to viewing socioemotional movie clips with feature annotations by human observers and GPT-4V. These results demonstrated that GPT-4V can also reproduce the social perceptual space at the neural level highly similar to reference human evaluations. These human-like social perceptual capabilities of LLMs could have wide range of real-life applications ranging from health care to business and would open exciting new avenues for behavioural and psychological research.

Neither fifty percent slow-wave sleep suppression nor fifty percent rapid eye movement sleep suppression does impair memory consolidation
Establishing well-defined relationships between sleep features and memory consolidation is essential in comprehending the pathophysiology of cognitive decline commonly seen in patients with insomnia, depression, and other sleep-disrupting conditions. Twenty-eight volunteers participated in two experimental sessions: a session with selective SWS suppression during one night and a session with undisturbed night sleep (as a control condition). Fifteen of them also participated in a third session with REM suppression. Suppression was achieved by presenting an acoustic tone. In the evening and the morning, the participants completed procedural and declarative memory tasks and the Psychomotor vigilance task (PVT). Heart rate variability analysis and salivary cortisol were used to control possible stress reactions to sleep interference. SWS and REM suppression led to more than a 50 percent reduction in the amount of these stages. Neither vigilance nor memory consolidation was impaired after SWS or REM suppression. Unexpectedly, a beneficial effect of selective SWS suppression on PVT performance was found. Similarly, after a night with SWS suppression, the overnight improvement in procedural skills was higher than after a night with REM suppression and after a night with undisturbed sleep. Our data brings into question the extent to which SWS and REM are truly necessary for effective memory consolidation to proceed. Moreover, SWS suppression may even improve the performance of some tasks, possibly by reducing sleep inertia associated with undisturbed sleep.

Optimization of TMS target engagement: novel evidence based on combined TMS-EEG and dMRI tractography of brain circuitry
Neuromodulation is based on the principle that brain stimulation produces plastic changes in cerebral circuitry. Given the intersubject structural and functional variability, neuromodulation has a personalized effect in the brain. Moreover, because of cerebral dominance and interhemispheric functional and structural differences in the same individual, the characterization of specific brain circuitries involved is currently not feasible. This notion is extremely important for neuromodulation treatments applied in neuropsychiatry. Specifically, the efficacy of the neuromodulation treatments is critically dependent on the anatomical precision of the brain target and the circuitry which has been affected. However, a complete understanding of how the brain behaves under stimulation needs a combined characterization of its neurophysiological response. This can be achieved by TMS-EEG guided by current multimodal neuroimaging techniques in real time. Herein, we present novel data based on dMRI tractography-guided TMS-EEG on one healthy young adult volunteer.

Learning and embodied decisions in active inference
Biological organisms constantly face the necessity to act timely in dynamic environments and balance choice accuracy against the risk of missing valid opportunities. As formalized by embodied decision models, this might require brain architectures wherein decision-making and motor control interact reciprocally, in stark contrast to traditional models that view them as serial processes. Previous studies have assessed that embodied decision dynamics emerge naturally under active inference - a computational paradigm that considers action and perception as subject to the same imperative of free energy minimization. In particular, agents can infer their targets by using their own movements (and not only external sensations) as evidence, i.e., via self-evidencing. Such models have shown that under appropriate conditions, action-generated feedback can stabilize and improve decision processes. However, how adaptation of internal models to environmental contingencies influences embodied decisions is yet to be addressed. To shed light on this challenge, in this study we systematically investigate the learning dynamics of an embodied model of decision-making during a two-alternative forced choice task, using a hybrid (discrete and continuous) active inference framework. Our results show that active inference agents can adapt to embodied contexts by learning various statistical regularities of the task - namely, prior preferences for the correct target, cue validity, and response strategies that prioritize faster or slower (but more accurate) decisions. Crucially, these results illustrate the efficacy of learning discrete preferences and strategies using sensorimotor feedback from continuous dynamics.

EEG Biomarkers of Age-Related Memory Change
The current study investigates whether electroencephalographic (EEG) activity reflects age-related memory changes during encoding. We recorded scalp EEG in 151 young adults (aged 18-30) and 37 older adults (aged 60-85) as they memorized lists of words. Subjects studied the word lists either under full attention or while performing a secondary task that required them to make semantic judgments about each word. Although the secondary task reduced recall among all subjects, differences in recall performance between the age groups were smaller when participants performed a secondary task at encoding. Older adults also exhibited distinct neural subsequent memory effects, characterized by less negativity in the alpha frequencies compared to young adults. Multivariate classifiers trained on neural features successfully predicted subsequent memory at the trial level in both young and older adults, and captured the differential effects of task demands on memory performance between young and older adults. The findings indicate that neural biomarkers of successful memory vary with both cognitive aging and task demands.

Investigating the role of axonal localization of sex peptide receptor in post-mating responses of female Drosophila melanogaster
This study elucidates the molecular mechanisms underlying the axonal localization of the sex peptide receptor, a pivotal G-protein coupled receptor in the Drosophila melanogaster post-mating response cascade. Utilizing transgenic expression, neuronal labeling, and bioinformatics analyses, we demonstrate that the N-terminal domain of SPR is indispensable for its axonal targeting in both Drosophila larval ventral nerve cord neurons and mouse hippocampal neurons. Deletion of the N-terminal domain abolished axonal localization, highlighting its critical role in this process. Intriguingly, the C-terminal domain of SPR appears to play a subordinate role in axonal targeting. Bioinformatical analysis revealed a striking homology between the N-terminus of SPR and the Broad-complex, Tramtrack, and Bric-a-brac/poxvirus and zinc finger family of proteins. The BTB domain, a conserved protein-protein interaction domain within this family, is implicated in diverse cellular processes and axonal targeting. Further investigation into the role of the BTB domain-like region in SPR could provide valuable insights into the molecular underpinnings of axonal targeting and post-mating responses in Drosophila. This research contributes to our understanding of the intricate mechanisms governing GPCR localization and function in the context of reproductive biology and neuronal signaling.

Neural dynamics of visual streams interactions during memory-guided actions investigated by intracranial EEG
The dorsal and ventral visual streams play distinct roles in visual processing for action: the dorsal stream is assumed to support real-time actions, while the ventral stream facilitates memory-guided actions. As the recent evidence suggests a more integrated function of these streams, we investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG. We tracked neural activity in the inferior parietal lobule in the dorsal stream, and ventral temporal cortex in the ventral stream as well as hippocampus during a delayed action task. We found increased alpha power in both streams during the delay, indicating their role in maintaining visual information. We also observed an increase in theta band synchronization between the inferior parietal lobule and ventral temporal cortex, and between the inferior parietal lobule and hippocampus during the delay. Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams, supporting an integrated processing model.

Evidence for belief updating in decision-variable space: past decisions with finer granularity attract future ones more strongly
Essential to adaptive intelligence is the ability to create mental spaces where knowledge from past experiences cumulates and integrates with newly acquired information. When engaged in decision- making tasks, humans are known to create such a space and therein form decision variables, which integrate task-essential information from multiple sources in a generalizable form. Much effort has focused on the cognitive and neural processes involved in forming decision variables. However, there is limited understanding of how decision variables, once formed, are utilized to adapt to the environment. Considering the abstract and generalizable nature of decision variables, we reason that decision-makers would benefit from shaping and updating probabilistic knowledge-known as belief-within the decision-variable space. As one such belief updating, we hypothesize that an act of decision commitment restricts the current belief about the decision variable to a range of states corresponding to that decision. This implies that past decisions not only attract future ones but also exert a greater pull when those decisions are made with finer granularity-dubbed the granularity effect. Here, we present the findings of seven psychophysical experiments that consistently confirm these implications while ruling out the stimulus and action space as potential loci of the granularity effect. Further, as a principled and unified account of the granularity effect and other history effects found in various perceptual tasks, we offer a Bayesian model where beliefs are updated separately in the stimulus and decision-variable spaces. Our work demonstrates how humans leverage the abstract and generalizable nature of the decision-variable space to effectively adapt to their surroundings, expanding the gamut of human intellect.

Data acquisition strategies to reduce cardiac-induced noise in brain maps of R2* and magnetic susceptibility
Purpose: Cardiac pulsation increases the noise level in brain MR images. Maps of the transverse relaxation rate R2* and magnetic susceptibility (QSM) are particularly affected by cardiac-induced noise as they are computed from gradient-echo data acquired at multiple echo times. Here, we introduce two data acquisition strategies to mitigate the impact of cardiac-induced noise in brain maps of R2* and QSM. Methods: The proposed strategies are based on the higher level of cardiac-induced noise near the k-space centre. Using a pseudo-spiral sampling trajectory, the first strategy allows for the acquisition of a specific number of averages at each k-space location, set from the local level of cardiac-induced noise. The second strategy synchronizes the acquisition with the cardiac cycle in real time. We compared in 10 healthy volunteers, the variability of data acquired across 4 repetitions and in the same session, using both strategies and with a standard linear trajectory. Results: Compared to linear sampling, the pseudo-spiral trajectory reduced the variability of R2* and QSM maps across repetitions by 26/28/22% and 19/18/16% in the brainstem/cerebellum/whole brain, for a 14% increase in scan time. The pseudo-spiral sampling also reduced the level of aliasing artifacts from pulsating blood vessels. The cardiac-triggered trajectory did not reduce the variability of R2* or QSM maps. Conclusion: Pseudo-spiral k-space trajectories can be designed to mitigate cardiac-induced noise in brain maps of the MRI parameters R2* and QSM. Synchronization of the acquisition with the cardiac cycle in real time did not lead to any reduction in cardiac-induced noise.

Immediate TMS-EEG responses reveal motor cortex excitability
Background: Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is widely used to probe cortical excitability at the network level, but technical challenges have prevented its application to investigate local excitability of the stimulated area. A recent study revealed immediate TMS-evoked potentials (i-TEPs) after primary motor cortex (M1) stimulation, suggesting that it may represent a local response. Here, we aimed at testing if this activity is physiological in nature and what it represents. Methods: We analyzed a TMS-EEG dataset from 28 healthy participants recorded at 9.6 kHz including two M1 stimulation conditions with opposite biphasic current directions. We localized the brain sources of i-TEPs, calculated the immediate TMS-related power (i-TRP) to distinguish between two oscillatory components that may contribute to i-TEPs, and investigated the relationship between i-TRP and motor-evoked potentials (MEPs). In an additional recording, we stimulated a control site evoking a muscular response to understand the contribution of the TMS-related muscle artifact. Results: Results confirmed i-TEPs with similar characteristics as previously described. The i-TRP revealed strong activity in two ranges 600-800 Hz and 100-200 Hz; The former was positively associated with MEPs amplitude for both current direction conditions. Moreover, i-TEPs were localized in the precentral gyrus of the stimulated hemisphere and the muscular response generated by the control stimulation site differed from i-TEPs and i-TRP. Discussion: These findings provide first evidence on the physiological nature of i-TEPs and i-TRP following M1 stimulation and that i-TRP represents a direct measure of excitability of the stimulated cortex.

Transcriptional Dynamics of Sleep Deprivation and Subsequent Recovery Sleep in the Male Mouse Cortex
Sleep is an essential, tightly regulated biological function. Sleep is also a homeostatic process, with the need to sleep increasing as a function of being awake. Acute sleep deprivation (SD) increases sleep need, and subsequent recovery sleep (RS) discharges it. SD is known to alter brain gene expression in rodents, but it remains unclear which changes are linked to sleep homeostasis, SD-related impairments, or non-sleep-specific effects. To investigate this question, we analyzed RNA-seq data from adult wild-type male mice subjected to 3 and 5-6 hours of SD and 2 and 6 hours of RS after SD. We hypothesized molecular changes associated with sleep homeostasis mirror sleep pressure dynamics as defined by brain electrical activity, peaking at 5-6 hours of SD, and are no longer differentially expressed after 2 hours of RS. We report 5-6 hours of SD produces the largest effect on gene expression, affecting approximately half of the cortical transcriptome, with most differentially expressed genes (DEGs) downregulated. The majority of DEGs normalize after 2 hours of RS and are involved in redox metabolism, chromatin regulation, and DNA damage/repair. Additionally, RS affects gene expression related to mitochondrial metabolism and Wnt-signaling, potentially contributing to its restorative effects. DEGs associated with cholesterol metabolism and stress response do not normalize within 6 hours and may be non-sleep-specific. Finally, DEGs involved in insulin signaling, MAPK signaling, and RNA-binding may mediate the impairing effects of SD. Overall, our results offer insight into the molecular mechanisms underlying sleep homeostasis and the broader effects of SD.

Anti-NMDAR encephalitis alters intrinsic spatiotemporal coding by enhancing neuronal coupling and clustering
Autoimmune anti-NMDA-receptor encephalitis is characterized by severe neuropsychiatric symptoms including memory dysfunction and seizures. However, it remains enigmatic what functional changes at the multi-neuronal level mediate network dysfunction. We used two-photon in vivo recording in a passive-transfer mouse model with patient's monoclonal anti-GluN1-autoantibodies during slow-wave sleep-like conditions, a critical phase for memory processing. We find enhanced functional coupling and clustering between hippocampal CA1 pyramidal neurons (PNs), which intrinsically expose the network to hypersynchrony. These connectivity changes are associated with a selective preservation of strong excitatory synapses despite overall reduced excitation, thus enhancing hub-like properties of functionally connected PNs. Furthermore, we find abnormal PN firing characteristics, decreased transmission failure, and increased similarity of spontaneous spatiotemporal activity patterns, all affecting CA1 intrinsic neuronal coding. Collectively, the functional rewiring of hippocampal networks and altered intrinsic information processing provide new mechanistic insights into the NMDAR-hypofunction consequences and pathomechanisms of anti-NMDAR encephalitis symptomatology.

Revisiting equivalent optical properties for cerebrospinal fluid to improve diffusion-based modeling accuracy in the brain
Significance: The diffusion approximation (DA) is used in functional near-infrared spectroscopy (fNIRS) studies despite its known limitations due to the presence of cerebrospinal fluid (CSF). Nearly all of these studies rely on a set of empirical CSF optical properties, recommended by a previous simulation study, that were not selected for the purpose of minimizing DA modeling errors. Aim: We aim to directly quantify the accuracy of DA solutions in brain models by comparing those with the gold-standard solutions produced by the mesh-based Monte Carlo (MMC), based on which we derive updated recommendations. Approach: For both a 5-layer head and Colin27 atlas models, we obtain DA solutions by independently sweeping the CSF absorption (a) and reduced scattering ('s) coefficients. Using an MMC solution with literature CSF optical properties as reference, we compute the errors for surface fluence, total brain sensitivity and brain energy-deposition, and identify the optimized settings where the such error is minimized. Results: Our results suggest that previously recommended CSF properties can cause significant errors (8.7% to 52%) in multiple tested metrics. By simultaneously sweeping a and 's, we can identify infinite numbers of solutions that can exactly match DA with MMC solutions for any single tested metric. Furthermore, it is also possible to simultaneously minimize multiple metrics at multiple source/detector separations, leading to our new recommendation of setting 's = 0.15 mm-1 while maintaining physiological a for CSF in DA simulations. Conclusion: Our new recommendation of CSF equivalent optical properties can greatly reduce the model mismatches between DA and MMC solutions at multiple metrics without sacrificing computational speed. We also show that it is possible to eliminate such a mismatch for a single or a pair of metrics of interest.

Behavioral and neural correlates of diverse conditioned fear responses in male and female rats
Pavlovian fear conditioning is a widely used tool that models associative learning in rodents. For decades the field has used predominantly male rodents and focused on a sole conditioned fear response: freezing. However, recent work from our lab and others has identified darting as a female-biased conditioned response, characterized by an escape-like movement across a fear conditioning chamber. It is also accompanied by a behavioral phenotype: Darters reliably show decreased freezing compared to Non-darters and males and reach higher velocities in response to the foot shock (shock response). However, the relationship between shock response and conditioned darting is not known. This study investigated if this link is due to differences in general processing of aversive stimuli between Darters, Non-darters and males. Across a variety of modalities, including corticosterone measures, the acoustic startle test, and sensitivity to thermal pain, Darters were found not to be more reactive or sensitive to aversive stimuli, and, in some cases, they appear less reactive to Non-darters and males. Analyses of cFos activity in regions involved in pain and fear processing following fear conditioning identified discrete patterns of expression among Darters, Non-darters, and males exposed to low and high intensity foot shocks. The results from these studies further our understanding of the differences between Darters, Non-darters and males and highlight the importance of studying individual differences in fear conditioning as indicators of fear state.

mCLAS adaptively rescues disease-specific sleep and wake phenotypes in neurodegeneration
Sleep alterations are hallmarks of prodromal Alzheimer's (AD) and Parkinson's disease (PD), with fundamental neuropathological processes of both diseases showing susceptibility of change upon deep sleep modulation. However, promising pharmacological deep sleep enhancement results are hindered by specificity and scalability issues, thus advocating for noninvasive slow-wave activity (SWA) boosting methods to investigate the links between deep sleep and neurodegeneration. Accordingly, we have recently introduced mouse closed-loop auditory stimulation (mCLAS), which is able to successfully boost SWA during deep sleep in neurodegeneration models. Here, we aim at further exploring mCLAS' acute effect onto disease-specific sleep and wake alterations in AD (Tg2576) and PD (M83) mice. We found that mCLAS adaptively rescues pathological sleep and wake traits depending on the disease-specific impairments observed at baseline in each model. Notably, in AD mice mCLAS significantly increases NREM long/short bout ratio, decreases vigilance state distances by decreasing transition velocities and increases the percentage of cumulative time spent in NREM sleep in the last three hours of the dark period. Contrastingly, in PD mice mCLAS significantly decreases NREM sleep consolidation, by potentiating faster and more frequent transitions between vigilance states, decreases average EMG muscle tone during REM sleep and increases alpha power in WAKE and NREM sleep. Overall, our results indicate that mCLAS selectively prompts an acute alleviation of neurodegeneration-associated sleep and wake phenotypes, by either potentiating sleep consolidation and vigilance state stability in AD or by rescuing bradysomnia and decreasing cortical hyperexcitability in PD. Further experiments assessing the electrophysiological, neuropathological and behavioural long-term effects of mCLAS in neurodegeneration may majorly impact the clinical establishment of sleep-based therapies.

Flexible modeling of large-scale neural network stimulation: electrical and optical extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX)
Computational models that predict effects of neural stimulation can be used as a preliminary tool to inform in-vivo research, reducing the costs, time, and ethical considerations involved. However, current models do not support the diverse neural stimulation techniques used in-vivo, including the expanding selection of electrodes, stimulation modalities, and stimulation paradigms. To develop a more comprehensive software, we created several extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX), the MATLAB-based neural stimulation tool from Newcastle University. VERTEX simulates input currents in a large population of multi-compartment neurons within a small cortical slice to model electric field stimulation, while recording local field potentials (LFPs) and spiking activity. Our extensions to its existing electric field stimulation framework include multiple pairs of parametrically defined electrodes and biphasic, bipolar stimulation delivered at programmable delays. To support the growing use of optogenetic approaches for targeted neural stimulation, we introduced a feature that models optogenetic stimulation through an additional VERTEX input function that converts irradiance to currents at optogenetically responsive neurons. Finally, we added extensions to allow complex stimulation protocols including paired-pulse, spatiotemporal patterned, and closed-loop stimulation. We demonstrated our novel features using VERTEX's built-in functionalities, illustrating how these extensions can be used to efficiently and systematically test diverse, targeted, and individualized stimulation patterns.

Spatial and morphological organization of mitochondria across a connectome
Neuronal function depends critically on the cell biological organization of mitochondria, which regulate calcium signals and produce energy, among other roles. However, little is known about how mitochondria are organized within the circuits of neurons that make up each brain. To uncover the systematic rules that govern mitochondria shape and position in a connectome, we analyzed the morphological and spatial organization of more than 100,000 mitochondria in over 1,000 visual projection neurons in the Drosophila connectome. We found that mitochondrial shape and size differ systematically between cell types, and are distinct enough between cell types to serve as an identifying fingerprint. Moreover, we derived three quantitative rules that describe how mitochondria are positioned within neurons relative to synapses and other subcellular features: (1) they are positioned with a precision of 2-3 microns; (2) their relative preference for pre- and postsynaptic sites and other subcellular features differs between axons and dendrites; (3) their positions were specialized to different cell types. These organizing rules correlated with functional and anatomical properties of the cells, including visual responses and input connectivity. We also find that, in the fly's olfactory associative learning circuits, mitochondria are enriched at presynapses to particular postsynaptic cells by accumulating in functional sub-compartments of axons. Overall, our findings reveal a robust set of organizing principles for mitochondria within and between cells, uncovering cell biology that maps onto the organization of the connectome and adding new dimensions for understanding circuit function in the connectome.

Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics
Biophysical neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the question of how to identify the parameters of detailed biophysical models such that they match physiological measurements at scale or such that they perform computational tasks. Here, we describe a framework for simulation of detailed biophysical models in neuroscience---Jaxley---which addresses this challenge. By making use of automatic differentiation and GPU acceleration, Jaxley opens up the possibility to efficiently optimize large-scale biophysical models with gradient descent. We show that Jaxley can learn parameters of biophysical neuron models with several hundreds of parameters to match voltage or two photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. We then demonstrate that Jaxley makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a feedforward network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. Our analyses show that Jaxley dramatically improves the ability to build large-scale data- or task-constrained biophysical models, creating unprecedented opportunities for investigating the mechanisms underlying neural computations across multiple scales.