bioRxiv Subject Collection: Neuroscience's Journal
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Sunday, February 16th, 2025
| Time |
Event |
| 8:30a |
Quantitative evaluation of normal cerebrospinal fluid flow in the Sylvian aqueduct and perivascular spaces of the middle cerebral artery and circle of Willis via 2D phase-contrast MR imaging
Recently, it was proposed that CSF flow constitutes a critical part of the glymphatic system, playing a critical role in various brain abnormalities from Alzheimers disease to hydrocephalus. Thus, measurement of CSF flow has been increasingly used for diagnostic and clinical monitoring purposes. Phase-contrast MRI has been used to determine CSF flow. However, CSF flow in the periarterial spaces of the circle of Willis and the middle cerebral artery which are important conduits remain unexplored. We employed phase-contrast MRI to explore CSF flow along the perivascular spaces of the circle of Willis and the middle cerebral artery to establish baseline parameters of CSF and compare them with the Sylvian aqueduct. To analyze CSF flow in the perivascular space, we developed a new, semi-automated method for outlining the perivascular space and extracting CSF flow parameters. The twenty-four healthy participants were recruited to achieve an even distribution by age (mean: 40 +/- 11) and gender (13 males). We validated our routine for CSF flow measurements by comparing CSF flow in the Sylvian aqueduct (0.00700 mL/s) with the range of literature values, 0.0049-0.0432 mL/s. For all CSF parameters, the circle of Willis and middle cerebral artery were differed from the Sylvian aqueduct. For most CSF flow parameters, the 95% confidence intervals of the circle of Willis and middle cerebral artery overlap. The linear mixed models and general linear mixed models for flow indicate strong effects of the conduits. CSF velocity in these conduits were lower 0.159 cm/s and 0.198 cm/s respectively than in the Sylvian aqueduct. Overall, differences in CSF flow parameters between sex and age groups were negligible. In this study, we have validated our routine and established baseline values of CSF flow along the circle of Willis and the middle cerebral artery as well as highlighted the limited influence of sex and/or age. | | 12:02p |
Full inter-hemispheric integration sustained by a fraction of posterior callosal fibers
The dynamic integration of the lateralized and specialized capacities of the two cerebral hemispheres constitutes a hallmark feature of human brain function. This inter-hemispheric exchange of information is thought to critically depend upon the corpus callosum. Classical anatomical descriptions of callosal organization outline a topographic gradient from front to back, such that specific transcallosal fibers support distinct aspects of integrated brain function. Here we present a challenge to this conventional model. Using neuroimaging data obtained from a new cohort of adult callosotomy patients, we leverage modern network neuroscience techniques to show - for the first time - that full inter-hemispheric integration can be achieved via a small proportion of posterior callosal fibers. Partial callosotomy patients with spared callosal fibers retained widespread patterns of inter-hemispheric functional connectivity and showed no signs of behavioral disconnection syndromes, even with only 1 cm of the splenium intact. Conversely, only complete corpus callosotomy patients demonstrated sweeping disruptions of inter-hemispheric network architectures, aligning with disconnection syndromes long-thought to reflect diminished information propagation and communication across the brain. These findings motivate a novel mechanistic understanding of synchronized inter-hemispheric neural activity for large-scale human brain function and behavior. | | 12:02p |
The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension
Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brain's linguistic capabilities. ECoG offers a high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas. Here, we share a dataset of nine ECoG participants with 1,330 electrodes listening to a 30-minute audio podcast. The richness of this naturalistic stimulus can be used for various research endeavors, from auditory perception to semantic integration. In addition to the neural data, we extract linguistic features of the stimulus ranging from phonetic information to large language model word embeddings. We use these linguistic features in encoding models that relate stimulus properties to neural activity. Finally, we provide detailed tutorials for preprocessing raw data, extracting stimulus features, and running encoding analyses that can serve as a pedagogical resource or a springboard for new research. | | 12:02p |
Neuropeptide CRH prevents premature differentiation of OPCs following CNS injury and in early postnatal development
The role of neuropeptides and their receptors in oligodendrocyte progenitor cells (OPCs) has largely been overlooked so far. Here, we describe a new subpopulation of corticotropin-releasing hormone (CRH)-expressing OPCs that aggregate around acute brain injuries and exhibit an elevated capacity to differentiate into myelinating oligodendrocytes (OLs). We found that CRH expression in OPCs is rapidly induced de novo as a transient response within the first 72 hours after injury. As target cells, we identified CRH receptor type 1 (CRHR1)-expressing OPCs which show a decreased differentiation velocity. We demonstrate that CRH/CRHR1 system inactivation increases the speed of OL generation compromising the long-term survival of OLs after acute injury. Furthermore, we prove that a CRH/CRHR1 system deficiency under non-injury conditions leads to increased early postnatal oligodendrogenesis and alterations in adult myelination. Altogether, we show that OPC-derived CRH not only actively influences the injury environment through the interaction with CRHR1-expressing OPCs, but also identify the G-protein coupled receptor CRHR1 as a critical modulator of oligodendrogenesis at early postnatal stages with lasting effects on adult myelination. | | 12:02p |
A neuro-immune axis of transcriptomic dysregulation within the subgenual anterior cingulate cortex in schizophrenia
Many genes are linked to psychiatric disorders, but genome-wide association studies (GWAS) and differential gene expression (DGE) analyses in post-mortem brain tissue often implicate distinct gene sets. This disconnect impedes therapeutic development, which relies on integrating genetic and genomic insights. We address this issue using a novel multivariate technique that reduces DGE bias by leveraging gene co-expression networks and controlling for confounds such as drug exposure. Deep RNA sequencing was performed in bulk post-mortem sgACC from individuals with bipolar disorder (BD; N=35), major depression (MDD; N=51), schizophrenia (SCZ; N=44), and controls (N=55). Toxicology data dimensionality was reduced using multiple correspondence analysis; case-control gene expression was then analyzed using 1) traditional DGE and 2) group regularized canonical correlation analysis (GRCCA) - a multivariate regression method that accounts for feature interdependence. Gene set enrichment analyses compared results with established neuropsychiatric risk genes, gene ontology pathways, and cell type enrichments. GRCCA revealed a significant association with SCZ (Pperm=0.001; no significant BD or MDD association), and the resulting gene weight vector correlated with DGE SCZ-control t-statistics (R=0.53; P<0.05). Both methods indicated down-regulation of immune and microglial genes and upregulation of ion transport and excitatory neuron genes. However, GRCCA - at both the gene and transcript level - showed stronger enrichments (FDR<0.05). Notably, GRCCA results were enriched for SCZ GWAS-implicated genes (FDR<0.05), while DGE results were not. These findings identify a SCZ-specific sgACC gene expression pattern that highlights SCZ risk genes and implicates neuro-immune pathways, thus demonstrating the utility of multivariate approaches to integrate genetic and genomic signals. | | 12:02p |
Voltage-gated ion channel diversity underlies neuronal excitability and nervous system evolution
All nervous systems function via electrical excitability mediated by ion channels. However, channels and excitability are both ancient, preneuronal features that many cells use to rapidly adjust behavior. We systematically studied the evolutionary paths to neuronal excitability by characterizing the voltage-gated ion channel complements (VGL-chanomes) of 623 organisms, dissecting their expression patterns in 11 whole-body cell atlases and 3 entire nervous systems, and recording electrical properties of primitive neurons in the sea anemone. We find a disconnect between ion channel availability and organismal or nervous system complexity and find instead an association with lifestyle and behavior. Cell type restricted chanome expression predated the emergence of nervous systems in multicellular organisms. Multiple gene-family expansions and contractions independently specialized or diversified VGL-chanomes leading to a surprising convergent pattern: not the number of channels but their diversity and restrictive recruitment is a hallmark of neuronal complexity. These findings suggest that the evolution of highly complex nervous systems was not a stepwise progression of expanding complexity. | | 12:02p |
Capicua refines mossy fiber-CA3 axon targeting in the late postnatal hippocampus
Proper brain wiring relies on the precise distribution of axonal projections to specific subcellular domains of their target neurons. These spatially confined connections establish the anatomical foundation for neural circuit assembly. The mossy fiber (MF)-CA3 pathway in the hippocampus is an excellent system to study the mechanisms underlying lamina-specific connectivity. In rodents, MF projections develop postnatally and reach their mature configuration by the end of the second postnatal week. MF axons synapse on the proximal segments of the dendrites but avoid the somas of CA3 pyramidal neurons. As dentate gyrus granule neurons are continuously generated and integrated into the existing hippocampal circuit throughout the postnatal period and adulthood, the mechanisms that guide MF axons to achieve lamina-specific targeting of these later-born granule neurons remain unclear. Here, we show that deletion of the neurodevelopmental disorder-associated protein capicua (CIC) results in abnormal MF targeting in the mouse hippocampus. Notably, this defect emerges after the second postnatal week and persists into adulthood, distinguishing it from classical MF guidance defects, which typically manifest during the first postnatal week. We also demonstrate that this miswiring is due to CIC loss in dentate gyrus granule neurons rather than CA3 pyramidal neurons. Single-nucleus transcriptomics and trajectory analysis reveal a loss of a mature granule neuron subtype and dysregulation of axon guidance genes that are normally downregulated as granule neurons mature. Our findings uncover a previously unrecognized role for CIC in hippocampus development and offer insights into the regulation of lamina-specific MF connectivity in the postnatal brain. | | 12:02p |
Divergent modulation of dopaminergic neurons by hypocretin/orexin receptors-1 and -2 shapes dopaminergic cell activity and socio-emotional behavior
Many neuropsychiatric disorders involve dysregulation of the dopaminergic (DA) input to the forebrain. Of particular relevance are DA projections originating from the midbrain ventral tegmental area (VTA). A key neuromodulatory influence onto DAVTA neurons arises from the dense axonal projections emanating from lateral hypothalamic area hypocretin/orexin (OX) neurons. Despite being a major input, the differential action of orexin peptides A and B (OXA and OXB) on orexin receptors 1 and 2 (OX1R and OX2R) in DA cells is poorly characterized. We thus genetically engineered mice whose DA neurons are selectively unresponsive to OX input via OX1R (DAOx1R-KO mice) or OX2R (DAOx2R-KO mice) and compared their behavior and DA cell electrophysiology to genetic controls. We previously showed a profound functional divergence between OX1R- and OX2R-mediated modulation of DA neurons in controlling vigilance states, brain oscillations and cognitive behavior. Inactivation of OX2R, but not OX1R, in DA neurons dramatically increased time spent in EEG theta-rich wakefulness, improved reward-driven learning and attentional skills, while it impaired inhibitory control. Here, we interrogate DAOx1R-KO and DAOx2R-KO mice in further behavioral domains. We show that mice with DA-specific OX1R loss exhibit hyperactivity, or anxiety-like responding, in context-dependent manners. OX2R loss in contrast decreases sociability and aversion-driven learning. We next investigate the underlying electrophysiological substrates and uncover previously unrecognized effects of OX peptides on DAVTA cell responses. In WT and control mice, we show that while OXA enhances, OXB diminishes DAVTA neuronal excitability. OX1R-deficient DA cells lose OXA responding and OX2R-deficient DA cells lose OXB responding. We altogether evidence strikingly distinct functions of OX1 vs OX2R signaling in modulating the intrinsic excitability of DAVTA neurons and influencing DA-related behaviors. Our data position OX[->]DA neurotransmission via OX1 or OX2R as relevant to endophenotypes observed in the context of disorders such as obsessive-compulsive, attention-deficit/hyperactivity, and autism spectrum disorders. | | 12:02p |
Hierarchical encoding of natural sounds mixtures in ferret auditory cortex
Extracting relevant auditory signals from complex natural scenes is a fundamental challenge for the auditory system. Sounds from multiple sources overlap in time and frequency. In particular, dynamic "foreground" sounds are often masked by more stationary "background" sounds. Human auditory cortex exhibits a hierarchical organization where background-invariant representations are progressively enhanced along the processing stream, from primary to non-primary regions. However, we do not know whether this organizational principle is conserved across species and which neural mechanisms drive this invariance. To address these questions, we investigated background invariance in ferret auditory cortex using functional ultrasound imaging (fUSI), which enables large-scale, high-resolution recordings of hemodynamic responses. We measured responses across primary, secondary, and tertiary auditory cortical regions as ferrets passively listened to mixtures of natural sounds and their components in isolation. We found a hierarchical gradient of background invariance, mirroring findings in humans: responses in primary auditory cortex reflected contributions from both foreground and background sounds, while background invariance increased in higher-order auditory regions. Using a spectrotemporal filter-bank model, we found that in ferrets, this hierarchical structure could be largely explained by tuning to low-order acoustic features. However, this model failed to fully account for background invariance in human non-primary auditory cortex, suggesting that additional, higher-order mechanisms are crucial for background segregation in humans. | | 12:02p |
Linguistic coupling between neural systems for speech production and comprehension during real-time dyadic conversations
The core use of human language is communicating complex ideas from one mind to another in everyday conversations. In conversations, comprehension and production processes are intertwined, as speakers soon become listeners, and listeners become speakers. Nonetheless, the neural systems underlying these faculties are typically studied in isolation using paradigms that cannot fully engage our capacity for interactive communication. Here, we used an fMRI hyperscanning paradigm to measure neural activity simultaneously in pairs of subjects engaged in real-time, interactive conversations. We used contextual word embeddings from a large language model to quantify the linguistic coupling between production and comprehension systems within and across individual brains. We found a highly overlapping network of regions involved in both production and comprehension spanning much of the cortical language network. Our findings reveal that shared representations for both processes extend beyond the language network into areas associated with social cognition. Together, these results suggest that the specialized neural systems for speech perception and production align on a common set of linguistic features encoded in a broad cortical network for language and communication. | | 1:16p |
Partial Input Loss Differentially Modifies Neural Pathways
Following input loss from degeneration, injury, and/or aging, downstream circuits undergo modifications that can impact fundamental sensory computations. Using the retina to leverage known cell types, well-defined circuitry, and molecular tools, we show how multiple pathways adjust their functional properties differently to common input loss and further locate these changes within each pathway. Specifically, we asked if two OFF ganglion cell types, alpha OFF-sustained (AOFF-S) and OFF-transient (AOFF-T) cells, and their respective dominant presynaptic partners, type 2 and type 3a cone bipolar cells, respond differentially to partial cone loss. We find that AOFF-T ganglion cells exhibit more circuit changes than AOFF-S ganglion cells, resulting in altered spatiotemporal tuning following partial cone loss. We show that the underlying mechanisms include changes in glutamatergic, GABAergic, and glycinergic circuits in the pathway of AOFF-T ganglion cells. In response to common input loss, our study finds different locations of circuit modifications across OFF pathways. These distinct functional changes in two pathways contribute to maintaining perceptually relevant information, preserving key visual features despite input loss. These findings provide insight into how sensory systems can compensate to ultimately serve vision. | | 4:49p |
Degeneracy explains diversity in interneuronal regulation of pattern separation in heterogeneous dentate gyrus networks
Background and motivations: The Marr-Albus theory postulates that pattern separation is realized by divergent feedforward excitatory connectivity. Yet, there are several lines of evidence for strong but differential regulation of pattern separation by local circuit connectivity, even when feedforward connectivity is divergent. What are the relative contributions of divergent feedforward connectivity and local circuit interactions to pattern separation? How do we reconcile the contrasting lines of evidence on local circuit regulation of pattern separation in circuits endowed with divergent feedforward connectivity? In this study, we quantitatively address these questions in a population of heterogeneous dentate gyrus (DG) networks, where we enforced feedforward connectivity to be identically divergent. Methodology: We generated tens of thousands of random spiking neuronal models to arrive at thousands of non-repeating heterogeneous single-neuron models of four different DG neuronal subtypes, each satisfying their respective functional characteristics. We connected these heterogeneous populations of neurons with subtype proportions and local connectivity that reflected the DG microcircuit. In a second level of unbiased search, we generated 20,000 identical networks that differed from each other only in their synaptic weight values. Thus, within the Marr-Albus framework, these networks that were identical in terms of neuronal composition and divergent feedforward connectivity should all be capable of performing effective pattern separation. To test this, we fed these networks with morphed sets of input patterns, recorded granule cell outputs, and computed similarity metrics based on correlation measures across input or output patterns. We developed novel quantitative metrics for pattern separation from plots of output similarity vs. input similarity and validated each network with bounds on these metrics. Results: Despite being identical in terms of divergent feedforward connectivity, we found only a very small proportion (47 of 20,000 or 0.23%) of the randomly generated networks to manifest effective pattern separation. We tested the specific contributions of interneurons by assessing pattern separation in all pattern-separating networks after individually deleting each of the three interneuron subtypes. Strikingly, we found pronounced network-to-network variability in how each interneuron subtype contributed to granule cell sparsity and pattern separation. We traced this variability to differences in local synaptic connectivity, which also resulted in network-to-network variability in firing rates and sparsity of different interneurons. Finally, we found heterogeneous DG networks to be more resilient to synaptic jitter compared to their homogeneous counterparts, with specific reference to pattern separation computed through average firing rate correlations. Implications: Our population-of-networks approach clearly shows that divergent connectivity of afferent inputs does not guarantee pattern separation in DG networks. Instead, we demonstrate strong yet variable roles for interneurons and local connectivity in implementing pattern separation. Importantly, our analyses unveil degeneracy in DG circuits, whereby similar pattern separation efficacy was achieved through disparate local-circuit interactions. These observations, alongside network-to-network variability in dependencies on different interneurons, strongly advocate the complex adaptive systems approach as a unifying framework to study DG pattern separation. | | 5:18p |
Depolarizing afterpotentials in rodent mEC layer II neurons vary gradually across all principal cell types
Principal cells in Layer II (LII) of the rodent medial entorhinal cortex (mEC) often discharge in short burst episodes, whose biophysical mechanism and biological function are not fully understood. As shown by in vitro recordings in rats and in vivo recordings in mice, action potentials of stellate cells (SCs) and pyramidal cells (PCs) in mEC LII can trigger depolarizing afterpotentials (DAPs) that facilitate spike doublets and burst firing. In vitro recordings in mice confirm these findings for SCs and two additional cell types intermediate between SCs and PCs but suggest that mouse PCs generate minute DAPs only. To better understand the diversity and physiology of the intermediate LII mEC cell types, we performed slice experiments in Long Evans rats of both sexes and analyzed the data using clustering techniques. The analysis suggests four distinct cell clusters, reminiscent of previous observations in mice. On purpose, DAP properties were not used for the cluster analysis so that they could be studied post-hoc in an unbiased manner. Our results show that characteristic features of DAPs and other physiological markers are broadly distributed within each cell class and vary gradually from cluster to cluster, forming a continuum of biophysical properties. | | 9:30p |
3D markerless tracking of speech movements with submillimeter accuracy
Speech movements are highly complex and require precise tuning of both spatial and timing of oral articulators to support intelligible communication. These properties also make measurement of speech movements challenging, often requiring extensive physical sensors placed around the mouth and face that are not easily tolerated by certain populations such as young children. Recent progress in machine learning-based markerless facial landmark tracking technology demonstrated its potential to provide lip tracking without the need for physical sensors, but whether such technology can provide submillimeter precision and accuracy in 3D remains unknown. Moreover, it is also unclear whether such technology can be applied to track speech movements in young children. Here, we developed a novel approach that integrates Shape Preserving Facial Landmarks with Graph Attention Networks (SPIGA), a facial landmark detector, and CoTracker, a transformer-based neural network model that jointly tracks dense points across a video sequence. We further examined and validated this novel approach by assessing its tracking precision and accuracy. The findings revealed that our approach that integrates SPIGA and CoTracker was more precise ({approx} 0.15 mm in standard deviation) than SPIGA alone ({approx} 0.35 mm). In addition, its 3D tracking performance was comparable to electromagnetic articulography ({approx} 0.29 mm RMSE against simultaneously recorded articulograph data). Importantly, the approach performed similarly well across adults and young children (i.e., 3- and 4-year-olds). Because our framework is built upon open-source pretrained models that are fully trained, it promotes accessibility and open science while saving computing resources. Furthermore, given that this framework combines a landmark detection model (SPIGA) with a tracker model (CoTracker) to improve precision/accuracy, our novel approach serves as a proof-of-concept for enhancing the performance of a wide variety of commonly used markerless tracking applications in biology and neuroscience. |
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