bioRxiv Subject Collection: Neuroscience's Journal

Left-right-alternating theta sweeps in the entorhinal-hippocampal spatial map
Place cells in the hippocampus and grid cells in the entorhinal cortex are elements of a neural map of self- position. To benefit navigation, this representation must be dynamically related to surrounding locations. A candidate mechanism for linking places along an animal's path has been described in place cells, where the sequence of spikes within each cycle of the hippocampal theta oscillation encodes a trajectory from the animal's current location towards upcoming locations. In mazes that bifurcate, such trajectories alternately traverse the two upcoming arms as the animal approaches the choice point, raising the possibility that the trajectories express available forward paths encoded on previous trials. However, to bridge the animal's path with the wider environment, beyond places previously or subsequently visited, an experience-independent spatial sampling mechanism might be required. Here we show in freely moving rats, that within individual theta cycles, ensembles of grid cells and place cells encode a position signal that sweeps linearly outwards from the animal's location into the ambient environment, with sweep direction alternating stereotypically between left and right across successive theta cycles. These sweeps were accompanied by, and aligned with, a similarly alternating directional signal in a discrete population of parasubiculum cells with putative connections to grid cells via conjunctive gridxdirection cells. Sweeps extended into never-visited locations that were inaccessible to the animal and persisted during REM sleep. Sweep directions could be explained by an algorithm that maximizes cumulative coverage of surrounding space. The sustained and unconditional expression of theta-patterned left-right-alternating sweeps in the entorhinal-hippocampal positioning system provides an efficient 'look-around' mechanism for sampling locations beyond the travelled path.

Differential modulation of sensory response dynamics by cilia structure and intraflagellar transport within and across chemosensory neurons
Sensory neurons contain morphologically diverse primary cilia that are built by intraflagellar transport (IFT) and house sensory signaling molecules. Since both ciliary structural and signaling proteins are trafficked via IFT, it has been challenging to decouple the contributions of IFT and cilia structure to neuronal responses. By acutely inhibiting IFT without altering cilia structure and vice versa, here we describe the differential roles of ciliary trafficking and sensory ending morphology in shaping chemosensory responses in C. elegans. We show that a minimum cilium length but not continuous IFT is necessary for a subset of responses in the ASH nociceptive neurons. In contrast, neither cilia nor continuous IFT are necessary for odorant responses in the AWA olfactory neurons. Instead, continuous IFT differentially modulates response dynamics in AWA. Upon acute inhibition of IFT, cilia-destined odorant receptors are shunted to ectopic branches emanating from the cilia base. Spatial segregation of receptors in these branches from a cilia-restricted regulatory kinase results in odorant desensitization defects, highlighting the importance of precise organization of signaling molecules at sensory endings in regulating response dynamics. We also find that adaptation of AWA responses upon repeated exposure to an odorant is mediated by IFT-driven removal of its cognate receptor, whereas adaptation to a second odorant is regulated via IFT-independent mechanisms. Our results reveal unexpected complexity in the contribution of IFT and cilia organization to the regulation of responses even within a single chemosensory neuron type, and establish a critical role for these processes in the precise modulation of olfactory behaviors.

Impaired sleep-dependent memory consolidation predicted by reduced sleep spindles in Rolandic epilepsy
Background and Objectives: Sleep spindles are prominent thalamocortical brain oscillations during sleep that have been mechanistically linked to sleep-dependent memory consolidation in animal models and healthy controls. Sleep spindles are decreased in Rolandic epilepsy and related sleep-activated epileptic encephalopathies. We investigate the relationship between sleep spindle deficits and deficient sleep dependent memory consolidation in children with Rolandic epilepsy. Methods: In this prospective case-control study, children were trained and tested on a validated probe of memory consolidation, the motor sequence task (MST). Sleep spindles were measured from high-density EEG during a 90-minute nap opportunity between MST training and testing using a validated automated detector. Results: Twenty-three children with Rolandic epilepsy (14 with resolved disease), and 19 age- and sex-matched controls were enrolled. Children with active Rolandic epilepsy had decreased memory consolidation compared to control children (p=0.001, mean percentage reduction: 25.7%, 95% CI [10.3, 41.2]%) and compared to children with resolved Rolandic epilepsy (p=0.007, mean percentage reduction: 21.9%, 95% CI [6.2, 37.6]%). Children with active Rolandic epilepsy had decreased sleep spindle rates in the centrotemporal region compared to controls (p=0.008, mean decrease 2.5 spindles/min, 95% CI [0.7, 4.4] spindles/min). Spindle rate positively predicted sleep-dependent memory consolidation (p=0.004, mean MST improvement of 3.9%, 95% CI [1.3, 6.4]%, for each unit increase in spindles per minute). Discussion: Children with Rolandic epilepsy have a sleep spindle deficit during the active period of disease which predicts deficits in sleep dependent memory consolidation. This finding provides a mechanism and noninvasive biomarker to aid diagnosis and therapeutic discovery for cognitive dysfunction in Rolandic epilepsy and related sleep activated epilepsy syndromes.

Variation in high-amplitude events across the human lifespan
Edge time series decompose functional connections into their fine-scale, framewise contributions. Previous studies have demonstrated that global high-amplitude "events" in edge time series can be clustered into distinct patterns. To date, however, it is unknown whether events and their patterns change or persist throughout the human lifespan. Here, we directly address this question by clustering event frames using the Nathan Kline Institute-Rockland sample that includes subjects with ages spanning the human lifespan. We find evidence of two main clusters that appear across subjects and age groups. We also find that these patterns of clusters systematically change in magnitude and frequency with age. Our results also demonstrate that such event clusters have distinct, heterogeneous relationships with structural connectivity-derived communication measures, which change with age. Finally, event clusters were found to outperform non-events in predicting phenotypes regarding human intelligence and achievement. Collectively, our findings fill several gaps in current knowledge about co-fluctuation patterns in edge time series and human aging, setting the stage for future investigation into the causal origins of changes in functional connectivity throughout the human lifespan.

Functional benefits of continuous vs. categorical listening strategies on the neural encoding and perception of noise-degraded speech
Acoustic information in speech changes continuously, yet listeners form discrete perceptual categories to ease the demands of perception. Being a more continuous/gradient as opposed to a discrete/categorical listener may be further advantageous for understanding speech in noise by increasing perceptual flexibility and resolving ambiguity. The degree to which a listener's responses to a continuum of speech sounds are categorical versus continuous can be quantified using visual analog scaling (VAS) during speech labeling tasks. Here, we recorded event-related brain potentials (ERPs) to vowels along an acoustic-phonetic continuum (/u/ to /a/) while listeners categorized phonemes in both clean and noise conditions. Behavior was assessed using standard two alternative forced choice (2AFC) and VAS paradigms to evaluate categorization under task structures that promote discrete (2AFC) vs. continuous (VAS) hearing, respectively. Behaviorally, identification curves were steeper under 2AFC vs. VAS categorization but were relatively immune to noise, suggesting robust access to abstract, phonetic categories even under signal degradation. Behavioral slopes were positively correlated with listeners' QuickSIN scores, suggesting a behavioral advantage for speech in noise comprehension conferred by gradient listening strategy. At the neural level, electrode level data revealed P2 peak amplitudes of the ERPs were modulated by task and noise; responses were larger under VAS vs. 2AFC categorization and showed larger noise-related delay in latency in the VAS vs. 2AFC condition. More gradient responders also had smaller shifts in ERP latency with noise, suggesting their neural encoding of speech was more resilient to noise degradation. Interestingly, source-resolved ERPs showed that more gradient listening was also correlated with stronger neural responses in left superior temporal gyrus. Our results demonstrate that listening strategy (i.e., being a discrete vs. continuous listener) modulates the categorical organization of speech and behavioral success, with continuous/gradient listening being more advantageous to speech in noise perception.

Preclinical evaluation of the novel CHDI-650 PET ligand for non-invasive quantification of mutant huntingtin aggregates in Huntington's disease
Purpose: Positron emission tomography (PET) imaging of mutant huntingtin (mHTT) aggregates is a potential tool to monitor disease progression as well as the efficacy of candidate therapeutic interventions for Huntington's disease (HD). To date, the focus has been mainly on the investigation of 11C radioligands; however, favorable 18F radiotracers will facilitate future clinical translation. This work aimed at characterizing the novel [18F]CHDI-650 PET radiotracer using a combination of in vivo and in vitro approaches in a mouse model of HD. Methods: After characterizing [18F]CHDI-650 using in vitro autoradiography, we assessed in vivo plasma and brain radiotracer stability as well as kinetics through dynamic PET imaging in the heterozygous (HET) zQ175DN mouse model of HD and wild-type (WT) littermates at 9 months of age. Additionally, we performed a head-to-head comparison study at 3 months with the previously published [11C]CHDI-180R radioligand. Results: Plasma and brain radiometabolite profiles indicated a suitable metabolic profile for in vivo imaging of [18F]CHDI-650. Both in vitro autoradiography and in vivo [18F]CHDI-650 PET imaging at 9 months of age demonstrated a significant genotype effect (p<0.0001) despite the poor test-retest reliability. [18F]CHDI-650 PET imaging at 3 months of age displayed higher differentiation between genotypes when compared to [11C]CHDI-180R. Conclusion: Overall, [18F]CHDI-650 allows for discrimination between HET and WT zQ175DN mice at 9 and 3 months of age. [18F]CHDI-650 represents the first suitable 18F radioligand to image mHTT aggregates in mice and its clinical evaluation is underway.

DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging
Significance: Voltage imaging is a powerful tool for studying the dynamics of neuronal activities in the brain. However, voltage imaging data are fundamentally corrupted by severe Poisson noise in the low-photon regime, which hinders the accurate extraction of neuronal activities. Self-supervised deep learning denoising methods have shown great potential in addressing the challenges in low-photon voltage imaging without the need for ground truth, but usually suffer from the tradeoff between spatial and temporal performance. Aim: We present DeepVID v2, a novel self-supervised denoising framework with decoupled spatial and temporal enhancement capability to significantly augment low-photon voltage imaging. Approach: DeepVID v2 is built on our original DeepVID framework, which performs frame-based denoising by utilizing a sequence of frames around the central frame targeted for denoising to leverage temporal information and ensure consistency. The network further integrates multiple blind pixels in the central frame to enrich the learning of local spatial information. Additionally, DeepVID v2 introduces a new edge extraction branch to capture fine structural details in order to learn high spatial resolution information. Results: We demonstrate that DeepVID v2 is able to overcome the tradeoff between spatial and temporal performance, and achieve superior denoising capability in resolving both high-resolution spatial structures and rapid temporal neuronal activities. We further show that DeepVID v2 is able to generalize to different imaging conditions, including time-series measurements with various signal-to-noise ratios (SNRs) and in extreme low-photon conditions. Conclusions: Our results underscore DeepVID v2 as a promising tool for enhancing voltage imaging. This framework has the potential to generalize to other low-photon imaging modalities and greatly facilitate the study of neuronal activities in the brain.

Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity
Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of 5-GABAA receptors (5-PAM) offers a promising effective treatment. However, testing the effect of 5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and 5-PAM effects, to simulate EEG of virtual subjects across depression severity and 5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for 5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of 5-PAM treatment to clinical use.

Balancing postural control and motor inhibition during gait initiation
This study explores the link between cancelling a prepared gait initiation in response to sudden environmental changes and maintaining body stability. To address this, we developed a gait initiation version of the Stop Signal Task (SST), a widely used tool for evaluating motor inhibition during different forms of action control. To understand the relevance of postural control during gait initiation and suppression, we evaluated the underlying changes in anticipatory postural adjustments (APA). We report that specific trial-level variables, like the time to initiate or cancel stepping, interacted with biomechanical factors such as the center of mass displacement relative to the base of support, impacting performance. We identified a critical biomechanical threshold beyond which halting the movement became improbable. These findings underscore the tight connection between limb action control and overall body equilibrium, providing a framework within an established motor control paradigm. By incorporating biomechanical elements into this model, we demonstrate its efficacy for simulating complex real-life scenarios. This approach defines essential variables for studying neural correlations between action and postural control, guiding the development of tools for injury prevention and rehabilitation devices for individuals with movement and posture impairments, including those suffering of neurodegenerative disorders.

The cost of behavioral flexibility: reversal learning driven by a spiking neural network
To survive in a changing world, animals often need to suppress an obsolete behavior and acquire a new one. This process is known as reversal learning (RL). The neural mechanisms underlying RL in spatial navigation have received limited attention and it remains unclear what neural mechanisms maintain behavioral flexibility. We extended an existing closed-loop simulator of spatial navigation and learning, based on spiking neural networks (Ghazinouri et al. 2023). The activity of place cells and boundary cells were fed as inputs to action selection neurons, which drove the movement of the agent. When the agent reached the goal, behavior was reinforced with spike-timing-dependent plasticity (STDP) coupled with an eligibility trace which marks synaptic connections for future reward-based updates. The modeled RL task had an ABA design, where the goal was switched between two locations A and B every 10 trials. Agents using symmetric STDP excel initially on finding target A, but fail to find target B after the goal switch, persevering on target A. Using asymmetric STDP, using many small place fields, and injecting short noise pulses to action selection neurons were effective in driving spatial exploration in the absence of rewards, which ultimately led to finding target B. However, this flexibility came at the price of slower learning and lower performance. Our work shows three examples of neural mechanisms that achieve flexibility at the behavioral level, each with different characteristic costs.

Intermittent theta burst stimulation (iTBS)-induced changes of resting-state brain entropy (BEN)
Intermittent theta burst stimulation (iTBS) is a novel protocol of repetitive transcranial magnetic stimulation (rTMS). While iTBS has shown better therapeutic effects for depression than conventional high-frequency rTMS (HF-rTMS), its underlying neuronal mechanism remains elusive. Brain entropy (BEN), a measure of irregularity of brain activity, has recently emerged as a novel marker of regional brain activity. Our previous studies have shown the sensitivity of BEN to depression and HF-rTMS, suggesting BEN as a sensitive tool for understanding the brain mechanism of iTBS. To assess this possibility, we calculated BEN using resting state fMRI data provided by an open dataset in OpenNeuro. Sixteen healthy participants underwent 600 pulses of iTBS applied over the left dorsolateral prefrontal cortex (L-DLPFC) at two intensities (90% and 120% of individual resting motor threshold (rMT)) on separate days. We assessed the pre-post stimulation BEN difference and its associations with neurotransmitter receptor and transporter binding maps. Our results showed that subthreshold iTBS (90% rMT) decreased striatal BEN, while suprathreshold iTBS (120% rMT) increased striatal BEN. We also found significant differences in the spatial correlation between BEN changes induced by different stimulation intensities and various neurotransmitters. These results suggest that differences in BEN caused by iTBS stimulation intensity may be related to the release of other neurotransmitters. The study underscores the significance of iTBS stimulation intensity and provides a basis for future clinical investigations to identify stimulation intensities with good therapeutic benefits.

The number of effectors limits the relative sleep gain: Insights from seven finger tapping experiments
The role of sleep in motor memory consolidation is still a matter of ongoing debate. A classic task to investigate mechanisms of motor memory consolidation is the finger tapping task, which reliably shows small effects in performance enhancement after sleep but not after a corresponding wake interval. However, variants of the task with a varying number of effectors (e.g., one hand) failed to demonstrate this effect on motor memory consolidation. Thus, in a series of seven experiments we investigate five variants of the classic finger tapping task in which the number of effectors (1 or 2 hands combined with 1, 2 or 4 fingers) used to perform the task are systematically varied. For the groups, where sleep immediately followed learning, a beneficial effect of sleep in comparison with a corresponding wake interval was found, except for the task variant where the finger tapping task was performed with 1 hand and 1 finger. However, no clear-cut pattern could be identified for the numbers of effectors used to perform the task and the magnitude of offline motor memory consolidation. Furthermore, for groups with an intervening wake interval between learning and sleep no differences between the post-sleep and post-wake gain were observed.

A neuroimaging dataset during sequential color qualia similarity judgments with and without reports
Recent neuroscientific research has advanced our understanding of consciousness, yet the connection between specific qualitative aspects of consciousness, known as "qualia," and particular brain regions or networks remains elusive. Traditional methods that rely on verbal descriptions from participants pose challenges in neuroimaging studies. To address this, our group has introduced a novel "qualia structure" paradigm that leverages exhaustive, structural, and relational comparisons among qualia instead of verbal reports. In this study, we present the first fMRI dataset that captures relational similarity judgments among two out of nine color qualia per trial from 35 participants. This dataset also includes a "no-report" condition in half of the trials to assess the impact of overt reporting. Additionally, each participant's color discriminability was evaluated with a hue test conducted outside the scanner. Our data offer valuable insights into the brain functions associated with color qualia and contribute to a deeper understanding of the neural foundations of consciousness.

ninjaCap: A fully customizable and 3D printable headgear for fNIRS and EEG brain imaging
Significance: Accurate sensor placement is vital for non-invasive brain imaging, particularly for functional near infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT), which lack standardized layouts like EEG. Custom, manually prepared probe layouts on textile caps are often imprecise and labor-intensive. Aim: We introduce a method for creating personalized, 3D-printed headgear, enabling accurate translation of 3D brain coordinates to 2D printable panels for custom fNIRS and EEG sensor layouts, reducing costs and manual labor. Approach: Our approach uses atlas-based or subject-specific head models and a spring-relaxation algorithm for flattening 3D coordinates onto 2D panels, using 10-5 EEG coordinates for reference. This process ensures geometrical fidelity, crucial for accurate probe placement. Probe geometries and holder types are customizable and printed directly on the cap, making the approach agnostic to instrument manufacturers and probe types. Results: Our ninjaCap method offers 2.2 {+/-} 1.5 mm probe placement accuracy. Over the last five years, we have developed and validated this approach with over 50 cap models and 500 participants. A cloud-based ninjaCap generation pipeline along with detailed instructions is now available at openfnirs.org. Conclusions: The ninjaCap marks a significant advancement in creating individualized neuroimaging caps, reducing costs and labor while improving probe placement accuracy, thereby reducing variability in research.

Alternative splicing across the C. elegans nervous system
Alternative splicing is a key mechanism that shapes neuronal transcriptomes, helping to define neuronal identity and modulate function. Here, we present an atlas of alternative splicing across the nervous system of Caenorhabditis elegans. Our analysis identifies novel alternative splicing in key neuronal genes such as unc-40/DCC and sax-3/ROBO. Globally, we delineate patterns of differential alternative splicing in almost 2,000 genes, and estimate that a quarter of neuronal genes undergo differential splicing. We introduce a web interface for examination of splicing patterns across neuron types. We explore the relationship between neuron type and splicing patterns, and between splicing patterns and differential gene expression. We identify RNA features that correlate with differential alternative splicing, and describe the enrichment of microexons. Finally, we compute a splicing regulatory network that can be used to generate hypotheses on the regulation and targets of alternative splicing in neurons.

Feature selective adaptation of numerosity perception
Perceptual adaptation has been used to infer the existence of numerosity detectors, which allow humans to quickly estimate the number of objects in a scene. While adaptation was originally thought to affect numerosity perception regardless of the low-level features of the items, a recent study demonstrated that adaptation is more pronounced when the adapting and adapted (test) stimuli share the same color, compared to when they were colored differently. In this study we explored whether such adaptation reduction depends on a novelty effect induced by changes in stimulus features or whether this effect is observed only when implying an identity change of the stimuli. To this aim, we performed six experiments in which numerosity adaptation was investigated in conditions in which adapting and adapted stimuli were either matched or differed for several low-level (color, luminance, shape, and motion) or high-level (letters' identity, face emotions) features. Numerosity adaptation was consistently observed across all conditions, but it was reduced when adaptor and test differed in color, luminance and shape. However, when stimuli differed in their motion profile, a very salient perceptual change that does not imply a change in items' identity, adaptation selectivity vanished. Moreover, adaptation selectivity was not observed when items' identity was changed by spatial rotations of the same stimulus (letters) or when stimuli were matched for the global configuration (face outline) but differed for the arrangement of local features (mouth, nose, eyes). Interestingly, image dissimilarity between test and adaptor, as quantified by Gabor filters simulating a simplified model of the primary visual cortex, nicely predicted the strength of numerosity adaptation across all conditions. Overall, changes in stimulus identity defined by low-level features, rather than novelty in general, determined the strength of the adaptation effects, provided that the changes were readily noticeable. Our findings suggest that numerosity mechanisms may be able to operate on segregated and categorized visual items in addition to the total quantity of the set, with part of the aftereffects induced by numerosity adaptation occurring after feature-binding.

Visual network modularity and communication alterations in ADHD subtypes: evidence from source localized EEG and graph theoretical analysis
The neurobiological basis of ADHD and its subtypes remains unclear, with inconsistent findings from studies using electrophysiology and neuroimaging. Some studies suggest ADHD-I is a distinct disorder, but there is also evidence of similar neural basis in ADHD-I and ADHD-C subtypes. This study investigates the neural basis of ADHD and its subtypes using a subnetwork modularity approach based on graph theoretical analysis of EEG data from 35 children aged 7-11. EEG was recorded in the eyes open condition and preprocessed. After preprocessing, data was analyzed using LORETA algorithm to estimate current densities in 84 regions of interest (ROIs) in the cortex and calculate functional connectivity between these ROIs in different EEG frequency bands. Then, we evaluated modularity of five functional brain networks (default mode, central control, salience, visual, and sensorimotor) using Newman modularity algorithm. Further, we evaluated edge betweenness centrality to assess communications between these functional brain networks. The study found that different brain networks have modularity in certain frequency bands, and ADHD groups showed reduced modularity of the visual network compared to normal groups in the alpha1 band (8-10 Hz). The communication between the visual network and other brain networks, except the salience network, was also reduced in ADHD groups (in the alpha1 band). However, there were no significant differences in the modularity of brain networks and communication among them between two ADHD subtypes. The results suggest a novel mechanism for ADHD involving lower intrinsic modularity in the visual network, disturbed communication between the visual network and other networks, and potential impact on the function of control and sensorimotor networks. Further, our results suggest that there may be a common neural basis for both subtypes, involving a shared disturbance in the modularity and connectivity of the ventral network. This supports the idea that ADHD-I and ADHD-C are subtypes within the same category and contradicts previous studies that suggest they are separate disorders.

FlyBox: A Flexible Open-Source Behavior Monitoring System
Over the past two decades, the vast majority of circadian behavior in Drosophila has been recorded in Drosophila Activity Monitor (DAM) boards. Though simple and robust, locomotor behavior recording via DAM boards can be prohibitively expensive, especially when taking incubator costs into consideration. Furthermore, their simplicity limits their experimental options and resolution. Here, we present the FlyBox: a simple, open-source benchtop locomotor activity recording system. FlyBox was designed to monitor activity in animals loaded into a standard laboratory multi-well plate. It features light-tight construction and multiple programmable LEDs for simulating day/night cycles and optogenetic manipulation. In total, a single FlyBox costs approximately $750 to build and around two days of labor. In addition, we also present the FlyBoxScanner software to simplify activity monitoring while maintaining compatibility with DAM analysis software. FlyBox is an attractive and affordable package for behavior monitoring that also offers considerable room for customization. Materials and instructions for the FlyBox are available at https://github.com/Rosbash-Lab-FlyBox/FlyBox, and FlyBoxScanner is available at https://github.com/jose-elias-alvarez/flybox-scanner.

Functional brain networks predicting sustained attention are not specific to perceptual modality
Sustained attention is essential for daily life and can be directed to information from different perceptual modalities including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults' visual sustained attention performance generalizes to predict auditory sustained attention performance in three independent datasets (N1=29, N2=62, N3=17; both sexes). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality-general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds
Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Effects of Concussion on the Relative Contributions of Sensorimotor Memories during Adaptation to Unpredictable Spring-Like Loads
We examined the extent to which concussion impacts how implicit sensorimotor memories are used to compensate for changes in hand-held loads during goal-directed reaching. Recently concussed individuals performed computerized cognition tests and a robotic test of sensorimotor adaptation on three occasions: as soon as possible after injury; after clearance to return to activity; three months after injury. Non-concussed individuals (controls) were tested at inter-session intervals mimicking concussed group intervals. During robotic testing, subjects grasped the handle of a horizontal planar robot while reaching repeatedly to a target. The robot exerted spring-like forces that changed unpredictably between trials; this allowed us to estimate contributions of implicit sensorimotor memories to trial-by-trial performance by fitting a computational model to the time series of reach errors and robot forces. Symptom severity varied considerably within the concussed group at the first session. Computerized cognition tests revealed longer reaction times in the concussed group relative to control group in Session 1 only. Concussed subjects likewise had slower reaction time in the reaching task during the first but not later sessions. Computational modeling found abnormally high values of effective limb compliance in concussed individuals relative to the control group in the first session only, but did not find group differences in how sensorimotor memories contribute to reach adaptation in any session. Analysis of control group models identified a practice effect affecting the memory coefficients that may have masked initial effects of concussion on how implicit memories contribute to sensorimotor adaptation. Although a practice effect and a heterogeneous concussed cohort preclude strong conclusions, our findings suggest procedural improvements that may decrease the robotic test's sensitivity to practice effects and increase its sensitivity to concussion-related changes in how implicit memories contribute to sensorimotor adaptation to unpredictable hand-held loads during reaching.

Methodology for isolation of serum, cerebrospinal fluid, and hippocampal neuron proteins from rat and their analysis using mass spectrometry-based shotgun proteomics
Emerging interests in the field of research related to diseases such as Alzheimers disease, Parkinsons disease, cognition, and other mental health-related disorders have prompted a need for a common method for the isolation of serum, CSF, and hippocampus. The hippocampus is responsible for learning and memory. It can be affected by various neurological and psychiatric disorders. However, the process of collecting samples such as CSF and hippocampal neurons is challenging, especially for small animals like rats. We have presented here a method for the isolation of serum, CSF, and hippocampal neurons that can be used for its downstream applications such as proteomics. We have used high-speed centrifugation instruments and density gradient centrifugation methods, which are easy to follow. Additionally, we have tested the proteins identified through mass spectrometry. Our method enables the study of proteins in serum, CSF, and neural cells for researching protein cross-talks and neurological disorder mechanisms.

A linear sensorimotor transformation accounts for response range-dependent biases in human heading estimation
Accurate estimation of heading direction from optic flow is a crucial aspect of human spatial perception. Previous studies have shown that humans are typically biased in their estimates of heading directions, but the reported results are inconsistent. While some studies found that humans generally underestimate heading direction (central bias), others find the opposite, an overestimation of heading direction (peripheral bias). We conducted three psychophysical experiments showing that these conflicting findings do not reflect inherent differences in heading perception. Rather we found that they are caused by the different sizes of the response range that participants were allowed to utilize when reporting their estimates. Notably, we show that participants' heading estimates monotonically scale with the size of the response range, leading to underestimation for small and overestimation for large response ranges. Additionally, neither the speed profile of the optic flow pattern nor the response method (mouse vs. keyboard) significantly affected participants' estimates. Furthermore, we derived a Bayesian observer model to quantitatively account for participants' estimation behavior. The model assumes an efficient sensory encoding of heading direction according to the natural prior of freely behaving humans. In addition, the model incorporates a motor stage that linearly maps the percept to the reported estimate with a scaling factor that depends on the size of the response range. This perception-action model accurately predicts participants' estimates both in terms of mean and variance. Our findings underscore that human heading perception follows efficient Bayesian inference; differences in participants' reported estimates can be solely attributed to differences in linear mapping from percept to probe response.

Impact of microchannel width on axons for brain-on-chip applications
Technologies for axon guidance for in vitro disease models and bottom up investigations are increasingly being used in neuroscience research. One of the most prevalent patterning methods is using polydimethylsiloxane (PDMS) microstructures due to compatibility with microscopy and electrophysiology which enables systematic tracking of axon development with precision and efficiency. Previous investigations of these guidance platforms have noted axons tend to follow edges and avoid sharp turns; however, the specific impact of spatial constraints remains only partially explored. We investigated the influence of microchannel width beyond a constriction point, as well as the number of available microchannels, on axon growth dynamics. Further, by manipulating the size of micron/submicron-sized PDMS tunnels we investigated the space restriction that prevents growth cone penetration showing that restrictions smaller than 350nm were sufficient to exclude axons. This research offers insights into the interplay of spatial constraints, axon development, and neural behavior. The findings are important for designing in vitro platforms and in vivo neural interfaces for both fundamental neuroscience and translational applications in rapidly evolving neural implant technologies.

Low Frequency Tibial Neuromodulation Increases Voiding Activity - A Computational Model
Despite its clinical adoption, there remains a lack of understanding about how tibial nerve stimulation (TNS) improves lower urinary tract symptoms. Current evidence indicates that TNS ameliorates overactive bladder by the inhibition of micturition related activity in the brainstem and spinal cord. We present a detailed computational model and demonstrate that TNS can also induce excitatory effects via brainstem specific activity, proposing non-invasive neuromodulation as a treatment for urinary retention.

Impact of deprivation and preferential usage on functional connectivity between early visual cortex and category-selective visual regions
Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how the removal of input changes brain function. However, an important question has yet to be answered: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. However, when central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with certain portions receiving preferential usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development.

Glial cell activation precedes neurodegeneration in the cerebellar cortex of the YG8-800 murine model of Friedreich's ataxia
Friedreich's ataxia is a hereditary neurodegenerative disorder resulting from reduced levels of the protein frataxin due to an expanded GAA repeat in the FXN gene. This deficiency causes progressive degeneration of specific neuronal populations in the cerebellum and the consequent loss of movement coordination and equilibrium, some of the main symptoms observed in affected individuals. Similar to other neurodegenerative diseases, previous studies suggest that glial cells could be involved in the neurodegenerative process and disease progression in Friedreich's ataxia. In this work, we have followed and characterized the progression of changes in the cerebellar cortex of the latest Friedreich's ataxia humanized mouse model, the YG8-800 (Fxnnull:YG8s(GAA)>800), which carries a human FXN transgene containing more than 800 GAA repeats. Comparative analyses of behavioral, histopathological, and biochemical parameters were conducted between Y47R control and YG8-800 mice at different time points. Our findings revealed that the YG8-800 mice display an ataxic phenotype, characterized by poor motor coordination, lower body weight, cerebellar atrophy, neuronal loss, and changes in synaptic proteins. Additionally, early activation of glial cells, predominantly astrocytes and microglia, was observed preceding neuronal degeneration along with an increased expression of key pro-inflammatory cytokines and downregulation of neurotrophic factors. Together, our results show how the YG8-800 mouse model exhibits a stronger phenotype than previous experimental murine models, reliably recapitulating some of the features observed in the human condition. Accordingly, this humanized model could represent a valuable tool to study Friedreich's ataxia molecular disease mechanisms and for preclinical evaluation of possible therapies.

Saccades Influence Functional Modularity in the Human Cortical Vision Network
Visual cortex is thought to show both dorsoventral and hemispheric modularity, but it is not known if the same functional modules emerge spontaneously from an unsupervised network analysis, or how they interact when saccades necessitate increased sharing of spatial information. Here, we address these issues by applying graph theory analysis to fMRI data obtained while human participants decided whether an object's shape or orientation changed, with or without an intervening saccade across the object. BOLD activation from 50 vision-related cortical nodes was used to identify local and global network properties. Modularity analysis revealed three sub-networks during fixation: a bilateral parietofrontal network linking areas implicated in visuospatial processing and two lateralized occipitotemporal networks linking areas implicated in object feature processing. When horizontal saccades required visual comparisons between visual hemifields, functional interconnectivity and information transfer increased, and the two lateralized ventral modules became functionally integrated into a single bilateral sub-network. This network included between-module connectivity hubs in lateral intraparietal cortex and dorsomedial occipital areas previously implicated in transsaccadic integration. These results provide support for functional modularity in the visual system and show that the hemispheric sub-networks are modified and functionally integrated during saccades.