bioRxiv Subject Collection: Neuroscience's Journal
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Thursday, April 25th, 2024
| Time |
Event |
| 2:18a |
Novel inhibitors of acute, axonal DLK palmitoylation are neuroprotective and avoid the deleterious side effects of cell-wide DLK inhibition
Dual leucine-zipper kinase (DLK) drives acute and chronic forms of neurodegeneration, suggesting that inhibiting DLK signaling could ameliorate diverse neuropathological conditions. However, direct inhibition of the kinase domain of DLK in human patients and conditional knockout of DLK in mice both cause unintended side effects, including elevated plasma neurofilament levels, indicative of neuronal cytoskeletal disruption. Indeed, we found that a DLK kinase domain inhibitor acutely disrupted the axonal cytoskeleton and caused vesicle aggregation in cultured dorsal root ganglion (DRG) neurons, further cautioning against this therapeutic strategy. In seeking a more precise intervention, we found that retrograde (axon-to-soma) pro-degenerative signaling requires acute, axonal palmitoylation of DLK and hypothesized that modulating this post-translational modification might be more specifically neuroprotective than cell-wide DLK inhibition. To address this possibility, we screened >28,000 compounds using a high-content imaging assay that quantitatively evaluates the palmitoylation-dependent subcellular localization of DLK. Of the 33 hits that significantly altered DLK localization in non-neuronal cells, several reduced DLK retrograde signaling and protected cultured DRG neurons from DLK-dependent neurodegeneration. Mechanistically, the two most neuroprotective compounds selectively prevent stimulus-dependent palmitoylation of axonal pools of DLK, a process crucial for the recruitment of DLK to axonal vesicles. In contrast, these compounds minimally impact DLK localization and signaling in healthy neurons and avoid the cytoskeletal disruption associated with direct DLK inhibition. Importantly, our hit compounds also reduce pro-degenerative retrograde signaling in vivo, suggesting that modulating the palmitoylation-dependent localization of DLK could be a novel neuroprotective strategy. | | 3:31a |
Deviation from typical brain activity during naturalistic stimulation predicts personality traits
The relationship between personality and brain activity has been an increasingly popular topic of neuroscientific research. However, the limitations of both personality measures and neuroimaging, as well as methodological issues, continue to pose challenges to its understanding. The naturalistic viewing condition has been shown to enhance individual differences and might, therefore, be of benefit to the endeavor. Here, we thus examine this relationship using naturalistic fMRI of 82 healthy subjects. We implemented a simple dimensionality reduction method to characterize brain activity by its 'typicality', assessed a range of personality traits using widely-used personality inventories, and tested the relationship between the two. We found that there is, indeed, a relationship between personality traits and the typicality of brain activity, most consistently manifested by lower typicality in subjects with higher Neuroticism/Harm Avoidance. Our results highlight the usefulness of naturalistic viewing data for exploring the relationship between individual differences in personality and brain activity. | | 3:31a |
A recurrent neural circuit in Drosophila deblurs visual inputs
A critical goal of vision is to detect changes in light intensity, even when these changes are blurred by the spatial resolution of the eye and the motion of the animal. Here we describe a recurrent neural circuit in Drosophila that compensates for blur and thereby selectively enhances the perceived contrast of moving edges. Using in vivo, two-photon voltage imaging, we measured the temporal response properties of L1 and L2, two cell types that receive direct synaptic input from photoreceptors. These neurons have biphasic responses to brief flashes of light, a hallmark of cells that encode changes in stimulus intensity. However, the second phase was often much larger than the first, creating an unusual temporal filter. Genetic dissection revealed that recurrent neural circuitry strongly shapes the second phase of the response, informing the structure of a dynamical model. By applying this model to moving natural images, we demonstrate that rather than veridically representing stimulus changes, this temporal processing strategy systematically enhances them, amplifying and sharpening responses. Comparing the measured responses of L2 to model predictions across both artificial and natural stimuli revealed that L2 tunes its properties as the model predicts in order to deblur images. Since this strategy is tunable to behavioral context, generalizable to any time-varying sensory input, and implementable with a common circuit motif, we propose that it could be broadly used to selectively enhance sharp and salient changes. | | 5:04a |
Non-apoptotic role of EGL-1 in exopher production and neuronal health in Caenorhabditis elegans
While traditionally studied for their pro-apoptotic functions, recent research suggests BH3-only proteins also have non-apoptotic roles. Here, we find that EGL-1, the BH3-only protein in Caenorhabditis elegans, promotes the cell-autonomous production of exophers in adult neurons. Exophers are large, micron-scale vesicles that are ejected from the cell and contain cellular components such as mitochondria. EGL-1 facilitates exopher production potentially through regulation of mitochondrial dynamics. Moreover, an endogenous, low level of EGL-1 expression appears to benefit dendritic health. Our findings provide insights into the mechanistic role of BH3-only protein in mitochondrial dynamics, downstream exopher production, and ultimately neuronal health. | | 7:50a |
Non-resolving neuroinflammation regulates axon regeneration in chronic spinal cord injury
Chronic spinal cord injury (SCI) lesions retain increased densities of microglia and macrophages. In acute SCI, macrophages induce growth cone collapse, facilitate axon retraction away from lesion boundaries, as well as play a key role in orchestrating the growth-inhibitory glial scar. Little is known about the role of sustained inflammation in chronic SCI, or whether chronic inflammation affects repair and regeneration. We performed transcriptional analysis using the Nanostring Neuropathology panel to characterize the resolution of inflammation into chronic SCI, to characterize the chronic SCI microenvironment, as well as to identify spinal cord responses to macrophage depletion and repopulation using the CSF1R inhibitor, PLX-5622. We determined the ability for macrophage depletion and repopulation to augment axon growth into chronic lesions both with and without regenerative stimulation using neuronal-specific PTEN knockout (PTEN-KO). PTEN-KO was delivered with spinal injections of retrogradely transported adeno associated viruses (AAVrgs). Both transcriptional analyses and immunohistochemistry revealed the ability for PLX-5622 to significantly deplete inflammation around and within chronic SCI lesions, with a return to pre-depleted inflammatory densities after treatment removal. Neuronal-specific transcripts were significantly elevated in mice after inflammatory repopulation, but no significant effects were observed with macrophage depletion alone. Axon densities significantly increased within the lesion after PLX-5622 treatment with a more consistent effect observed in mice with inflammatory repopulation. PTEN-KO did not further increase axon densities within the lesion beyond effects induced by PLX-5622. We identified that PLX-5622 increased axon densities within the lesion that are histologically identified as 5-HT+ and CGRP+, both of which are not robustly transduced by AAVrgs. Our work identified that increased macrophage/microglia densities in the chronic SCI environment may be actively retained by homeostatic mechanisms likely affiliated with a sustained elevated expression of CSF1 and other chemokines. Finally, we identify a novel role of sustained inflammation as a prospective barrier to axon regeneration in chronic SCI. | | 10:31a |
A theory of temporal self-supervised learning in neocortical layers
The neocortex constructs an internal representation of the world, but the underlying circuitry and computational principles remain unclear. Inspired by self-supervised learning algorithms, we introduce a computational model wherein layer 2/3 (L2/3) learns to predict incoming sensory stimuli by comparing previous sensory inputs, relayed via layer 4, with current thalamic inputs arriving at layer 5 (L5). We demonstrate that our model accurately predicts sensory information in a contextual temporal task, and that its predictions are robust to noisy or partial sensory input. Additionally, our model generates layer-specific sparsity and latent representations, consistent with experimental observations. Next, using a sensorimotor task, we show that the model's L2/3 and L5 prediction errors mirror mismatch responses observed in awake, behaving mice. Finally, through manipulations, we offer testable predictions to unveil the computational roles of various cortical features. In summary, our findings suggest that the multi-layered neocortex empowers the brain with self-supervised learning capabilities. | | 10:31a |
Hierarchical cortical entrainment orchestrates the multisensory processing of biological motion
When observing others' behaviors, we continuously integrate their movements with the corresponding sounds to achieve efficient perception and develop adaptive responses. However, how human brains integrate these complex audiovisual cues based on their natural temporal correspondence remains unknown. Using electroencephalogram, we demonstrated that cortical oscillations entrained to hierarchical rhythmic structures in audiovisually congruent human walking movements and footstep sounds. Remarkably, the entrainment effects at different time scales exhibit distinct modes of multisensory integration, i.e., an additive integration effect at a basic-level integration window (step-cycle) and a super-additive multisensory enhancement at a higher-order temporal integration window (gait-cycle). Moreover, only the cortical tracking of higher-order rhythmic structures is specialized for the multisensory integration of human motion signals and correlates with individuals' autistic traits, suggesting its functional relevance to biological motion perception and social cognition. These findings unveil the multifaceted roles of entrained cortical activity in the multisensory perception of human motion, shedding light on how hierarchical cortical entrainment orchestrates the processing of complex, rhythmic stimuli in natural contexts. | | 10:31a |
The hippocampus pre-orders movements for skilled action sequences
Plasticity in the subcortical motor basal ganglia-thalamo-cerebellar network plays a key role in the acquisition and control of long-term memory for new procedural skills, from the formation of population trajectories controlling trained motor skills in the striatum to the adaptation of sensorimotor maps in the cerebellum. However, recent findings demonstrate the involvement of a wider cortical and subcortical brain network in the consolidation and control of well-trained actions, including an area traditionally associated with declarative memory - the hippocampus. Here, we probe which role these subcortical areas play in skilled motor sequence control, from sequence feature selection during planning to their integration during sequence execution. An fMRI dataset collected after participants learnt to produce four finger sequences entirely from memory with high accuracy over several days was examined for both changes in BOLD activity and their informational content in subcortical regions of interest. Although there was a widespread activity increase in effector-related striatal, thalamic and cerebellar regions, the associated activity did not contain information on the motor sequence identity. In contrast, hippocampal activity increased during planning and predicted the order of the upcoming sequence of movements. Our findings show that the hippocampus pre-orders movements for skilled action sequences, thus contributing to the higher-order control of skilled movements. These findings challenge the traditional taxonomy of episodic and procedural memory and carries implications for the rehabilitation of individuals with neurodegenerative disorders. | | 10:31a |
Models optimized for real-world tasks reveal the necessity of precise temporal coding in hearing
Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action potentials phase lock to sound with sub- millisecond precision, but its behavioral relevance is uncertain. To investigate the role of this temporal coding, we optimized machine learning models to perform real-world hearing tasks with simulated cochlear input. We asked how precise auditory nerve spike timing needed to be to reproduce human behavior. Models with high-fidelity phase locking exhibited more human-like sound localization and speech perception than models without, consistent with an essential role in human hearing. Degrading phase locking produced task-dependent effects, revealing how the use of fine-grained temporal information reflects both ecological task demands and neural implementation constraints. The results link neural coding to perception and clarify conditions in which prostheses that fail to restore high-fidelity temporal coding could in principle restore near-normal hearing. | | 12:34p |
Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features
A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics. However, those methods can be dominated by a select few features and obscure key dynamics related to less dominant features. This study presents an iterative feature learning approach to identify a maximally significant and minimally complex subset of dFNC features within the default mode network (DMN) in schizophrenia (SZ). Utilizing dFNC data from individuals with SZ and healthy controls (HC), our approach uncovers a subset of features that has a greater number of dFNC states with disorder-related dynamics than is found when all features are present in the clustering. We find that anterior cingulate cortex/posterior cingulate cortex (ACC/PCC) interactions are consistently related to SZ across the most significant iterations of the feature learning analysis and that individuals with SZ tend to spend more time in states with greater intra-ACC anticorrelation and almost no time in a state of high intra-ACC correlation that HCs periodically enter. Our findings highlight the need for nuanced analyses to reveal disorder-related dynamics and advance our understanding of neuropsychiatric disorders. | | 4:45p |
A morphing map model for place field organization in large environments
Navigation and spatial memory, essential for animal survival, are founded on the hippocampal formation's ability to host spatial neurons marking an animal's position. Within controlled settings, the place cells of the hippocampus are activated in specific regions, the 'place fields'. In this paper, we introduce a "morphing map algorithm" based on bat data from a 200-meter tunnel, highlighting a multifield, multiscale spatial representation of place fields. This algorithm posits that in vast environments, bats opt to select landmarks or 'anchors' for spatial representation. The method introduces a hyperbolic geometry in spatial representation, aligning with bat data from vast areas. We apply Procrustes distance to evaluate similarities between neural maps of bats and model-generated maps, revealing shared structure across bat maps, which might be dictated by common anchor points. Further explorations into anchor point ablation provide insights into their crucial role in map stability. The results present an evolved neural navigation strategy in expansive habitats and guide future spatial representation research across species. | | 6:02p |
Lysosome-acidifying nanoparticles rescue A30P α-synuclein induced neuronal death in cellular and Drosophila models of Parkinson's disease
Parkinson's disease (PD) is an age-related neurodegenerative disease characterized by histopathological hallmarks of Lewy bodies formed by accumulation of -synuclein (Syn) and progressive loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain, with clinical symptoms of motor deficits. Toxic protein accumulation of Syn in PD is associated with autolysosomal acidification dysfunction that contributes to defective autophagy-lysosomal degradation system. While lysosome-acidifying nanoparticles have been applied as therapeutics to ameliorate dopaminergic neurodegeneration in neurotoxin mediated or Syn aggregates induced mouse model of sporadic PD, lysosome-targeted approach has not yet been applied in synucleinopathy models of familial PD. Here, we report the first application of the new poly(ethylene tetrafluorosuccinate-co-succinate) (PEFSU)-based acidic nanoparticles (AcNPs) in A30P Syn overexpressing SH-SY5Y cells and Drosophila models of PD. In the cellular model, we showed that AcNPs restore lysosomal acidification, promote autophagic clearance of Syn, improve mitochondrial turnover and function, and rescue A30P Syn induced death in SH-SY5Y cells. In the Drosophila model, we demonstrated that AcNPs enhance clearance of Syn and rescue dopaminergic neuronal loss in fly brains and improve their locomotor activity. Our results highlight AcNPs as a new class of lysosome-acidifying therapeutic for treatment of PD and other proteinopathies in general. | | 6:30p |
Modeling conditional distributions of neural and behavioral data with masked variational autoencoders
Extracting the relationship between high-dimensional recordings of neural activity and complex behavior is a ubiquitous problem in systems neuroscience. Toward this goal, encoding and decoding models attempt to infer the conditional distribution of neural activity given behavior and vice versa, while dimensionality reduction techniques aim to extract interpretable low-dimensional representations. Variational autoencoders (VAEs) are flexible deep-learning models commonly used to infer low-dimensional embeddings of neural or behavioral data. However, it is challenging for VAEs to accurately model arbitrary conditional distributions, such as those encountered in neural encoding and decoding, and even more so simultaneously. Here, we present a VAE-based approach for accurately calculating such conditional distributions. We validate our approach on a task with known ground truth and demonstrate the applicability to high-dimensional behavioral time series by retrieving the conditional distributions over masked body parts of walking flies. Finally, we probabilistically decode motor trajectories from neural population activity in a monkey reach task and query the same VAE for the encoding distribution of neural activity given behavior. Our approach provides a unifying perspective on joint dimensionality reduction and learning conditional distributions of neural and behavioral data, which will allow for scaling common analyses in neuroscience to today's high-dimensional multi-modal datasets. | | 7:45p |
Behavioral and cortical arousal from sleep, muscimol-induced coma, and anesthesia by direct optogenetic stimulation of cortical neurons
The cerebral cortex is widely considered part of the neural substrate of consciousness. However, while several studies have demonstrated that stimulation of subcortical nuclei can produce EEG activation and restore consciousness, so far no direct causal evidence has been available for the cortex itself. Here we tested in mice whether optogenetic activation of cortical neurons in posterior parietal cortex (PtA) or medial prefrontal cortex (mPFC) is sufficient for arousal from three behavioral states characterized by progressively deeper unresponsiveness: sleep, a coma-like state induced by muscimol injection in the midbrain, and deep sevoflurane-dexmedetomidine anesthesia. We find that cortical stimulation always awakens the mice from both NREM sleep and REM sleep, with PtA requiring weaker/shorter light pulses than mPFC. Moreover, in most cases light pulses produce both cortical activation (decrease in low frequencies) and behavioral arousal (recovery of the righting reflex) from brainstem coma, as well as cortical activation from anesthesia. These findings provide evidence that direct activation of cortical neurons is sufficient for behavioral and/or cortical arousal from sleep, brainstem coma, and anesthesia. | | 8:16p |
A Comparison of Techniques to Determine Active Motor Threshold for Quadriceps Transcranial Magnetic Stimulation Research
The determination of active motor threshold (AMT) is a critical step in transcranial magnetic stimulation (TMS) research protocols involving voluntary muscle contractions. As AMT is frequently determined using an absolute electromyographic (EMG) threshold (e.g., 200 microvolts peak-to-peak amplitude), wide variation in EMG recordings across participants has given reason to consider a relative threshold (e.g., = 2x background EMG) for AMT determination. However, these approaches have never been systemically compared. PURPOSE: We sought to compare the AMT values derived from absolute and relative criteria commonly used to determine AMT in the quadriceps muscles, and assess the test-retest reliability of each approach (absolute = 200 microvolts vs. relative = 2x background EMG). METHODS: Eighteen young adults (9 males and 9 females; mean +/- SD age = 23 +/- 2 years) visited the research laboratory on two occasions. All testing was conducted on the dominant limb. During each laboratory visit, maximal voluntary isometric contraction (MVIC) quadriceps torque was measured, with all subsequent TMS procedures conducted as participants maintained 10% of MVIC torque. AMT values were derived from each criterion using motor evoked potentials recorded from the vastus lateralis (VL) and defined as the lowest stimulator output (SO%) needed to meet the specified criteria within at least 5/10 pulses. The order of criteria (i.e., absolute vs. relative) was randomized during the first laboratory visit, and counterbalanced during the second visit. A paired samples t-test, 95% confidence intervals and the effect size were used to compare mean differences in AMT values obtained from each criterion during the second laboratory visit. Paired samples t-tests, intraclass correlation coefficients (ICC2,1), standard errors of measurement (SEMs), and the minimal difference (MD) scores were calculated to assess test-retest reliability of each AMT criterion. RESULTS: Differences between the AMT criteria were small and not statistically significant (absolute criterion mean = 48.9%, relative criterion mean = 47.4%; p = .309, Cohens d = 0.247). The absolute criterion demonstrated moderate to excellent reliability (ICC2,1= .866 [0.648 , 0.950], SEM = 7.9%, MD = 10.4%), but higher AMTs were observed in the second visit compared to the first (p = 0.043). The relative criteria demonstrated good-to-excellent test-retest reliability (ICC2,1= .894 [0.746 , 0.959], SEM = 6.9%, MD = 8.9%) and AMTs were not different between visits (p = 0.420). CONCLUSION: Quantifying AMT with an absolute voltage threshold of 200 microvolts peak-to-peak amplitude and a relative voltage threshold 2x background EMG resulted in similar values within a single testing session. However, the relative voltage criterion demonstrated superior test-rest reliability. TMS researchers aiming to track cortical or corticospinal characteristics across visits should consider implementing relative criterion approaches during their AMT determination protocol. | | 8:16p |
Mapping polarization-sensitive optical coherence tomography and ultra-high-field diffusion MRI in the macaque brain
This paper provides comparisons between microstructure and two-dimensional fiber orientations measured optically using polarization-sensitive optical coherence tomography (PS-OCT) and those estimated from ultra-high-field diffusion MRI (dMRI) at 10.5T in the macaque brain. The PS-OCT imaging is done at an in-plane resolution of ~10 microns in and around the thalamus. Whole brain dMRI is acquired at an isotropic resolution of 0.75 mm. We provide comparisons between cross-polarization and optical orientation from PS-OCT with the fractional anisotropy and two-dimensional orientations extracted from dMRI using a diffusion tensor model. The orientations from PS-OCT are also extracted computationally using a structure tensor. Additionally, we demonstrate the utility of mesoscale, PS-OCT imaging in improving the MRI resolution by learning the mapping between these contrasts using a super-resolution Generative Adversarial Network. | | 8:16p |
300 Hz transcutaneous auricular vagus nerve stimulation (taVNS) impacts pupil size nonlinearly as a function of intensity
Transcutaneous auricular vagus nerve stimulation (taVNS) is a neuromodulatory technique that may have numerous potential health and human performance benefits. However, optimal stimulation parameters for maximizing taVNS efficacy are unknown. Progress is impeded by disagreement on the identification of a biomarker that reliably indexes activation of neuromodulatory systems targeted by taVNS, including the locus coeruleus-norepinephrine (LC-NE) system. Pupil size varies with LC-NE activity and is one potential taVNS biomarker that has shown inconsistent sensitivity to taVNS in prior studies. The present study examined the relationship between pupil size and taVNS using stimulation parameters that have shown promising behavioral effects in prior studies but have received comparatively little attention. Participants received 30-second trains of 50 s taVNS pulses delivered below perceptual threshold at 300 Hz to the left external acoustic meatus (EAM) while pupil size was recorded during a pupillary light reflex task. Analysis of pupil size using generalized additive mixed modelling (GAMM) revealed a nonlinear relationship between taVNS intensity and pupil diameter. Active taVNS increased pupil size during stimulation for participants who received taVNS between 2 and approximately 4.8 mA, but not for participants who received higher intensity taVNS (up to 8.1 mA). In addition, taVNS effects persisted in subsequent blocks, mitigating decreases in pupil size over the course of the task. These findings suggest 300 Hz taVNS activates the LC-NE system when applied to the EAM, but its effects may be counteracted at higher intensities. |
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