bioRxiv Subject Collection: Neuroscience's Journal
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Monday, February 10th, 2025
| Time |
Event |
| 4:42a |
A Semi-Automated MEA Spike sorting (SAMS) method for high throughput assessment of cultured neurons
Neurons derived from human pluripotent stem cells (hPSCs) are valuable models for studying brain development and developing therapies for brain disorders. Evaluating human-derived neurons requires assessing their electrical activity, which can be achieved using multi-electrode arrays (MEAs) for extracellular recordings. Because each electrode channel generally detects activity from multiple neurons, resolving the activity of single neurons requires a process called spike sorting. However, currently available spike sorting methods are not optimized for the analysis of hPSC-derived neurons, and require complex workflows and time-consuming manual intervention. Here, we introduce a Semi-Automated MEA Spike sorting software (SAMS) designed specifically for low-density MEA recordings of cultured neurons. SAMS outperforms commercially available automated spike sorting algorithms in terms of accuracy and greatly reduces computational and human processing time. By providing an accessible, efficient, and integrated platform for spike sorting, SAMS enhances the resolution and utility of MEA in disease modeling and drug development using human-derived neurons. | | 9:21a |
Spaces and sequences in the hippocampus: a homological perspective
Topological techniques have become a popular tool for studying information flows in neural networks. In particular, simplicial homology theory is used to analyze how cognitive representations of space emerge from large conglomerates of independent neuronal contributions. Meanwhile, a growing number of studies suggest that many cognitive functions are sustained by serial patterns of activity. Here, we investigate stashes of such patterns using path homology theory--an impartial, universal approach that does not require a priori assumptions about the sequences' nature, functionality, underlying mechanisms, or other contexts. We focus on the hippocampus---a key enabler of learning and memory in mammalian brains---and quantify the ordinal arrangement of its activity similarly to how its topology has previously been studied in terms of simplicial homologies. The results reveal that the vast majority of sequences produced during spatial navigation are structurally equivalent to one another. Only a few classes of distinct sequences form an ordinal schema of serial activity that remains stable as the pool of sequences consolidates. Importantly, the structure of both maps is upheld by combinations of short sequences, suggesting that brief activity motifs dominate physiological computations. This ordinal organization emerges and stabilizes on timescales characteristic of spatial learning, displaying similar dynamics. Yet, the ordinal maps generally do not reflect topological affinities---spatial and sequential analyses address qualitatively different aspects of spike flows, representing two complementary formats of information processing. | | 9:21a |
Cholesterol-mediated Lysosomal Dysfunction in APOE4 Astrocytes Promotes α-Synuclein Pathology in Human Brain Tissue
The pathological hallmark of neurodegenerative disease is the aberrant post-translational modification and aggregation of proteins leading to the formation of insoluble protein inclusions. Genetic factors like APOE4 are known to increase the prevalence and severity of tau, amyloid, and -Synuclein inclusions. However, the human brain is largely inaccessible during this process, limiting our mechanistic understanding. Here, we developed an iPSC-based 3D model that integrates neurons, glia, myelin, and cerebrovascular cells into a functional human brain tissue (miBrain). Like the human brain, we found pathogenic phosphorylation and aggregation of -Synuclein is increased in the APOE4 miBrain. Combinatorial experiments revealed that lipid-droplet formation in APOE4 astrocytes impairs the degradation of -synuclein and leads to a pathogenic transformation that seeds neuronal inclusions of -Synuclein. Collectively, this study establishes a robust model for investigating protein inclusions in human brain tissue and highlights the role of astrocytes and cholesterol in APOE4-mediated pathologies, opening therapeutic opportunities. | | 9:21a |
Functional Specialization for Language Processing in Inferior Frontal Regions During Early Childhood: Evidence from functional near-infrared spectroscopy individual functional channels of interest approach
Early language acquisition represents a fundamental achievement in cognitive development, yet the neural mechanisms underlying this process remain debated. Using functional near-infrared spectroscopy (fNIRS) with an innovative functional Channel of Interest (fCOI) approach, we investigated the functional specialization for language processing in bilateral inferior frontal regions during early childhood. In two experiments involving adults (N=20) and toddlers (N=22, ages 2-4 years), participants completed language processing and cognitive control tasks. Results demonstrated early functional specialization in the language-selective region of left inferior frontal gyrus, which showed selective responses to linguistic content while remaining insensitive to cognitive demand manipulations in both age groups. However, language selectivity in the homologous right hemisphere region was present only in adults, suggesting continued development of language organization beyond early childhood. The MD regions showed complementary patterns, with right-hemispheric selectivity for cognitive control emerging early. These findings provide evidence for early neural specialization of language processing in the left hemisphere, while revealing ongoing development in right hemispheric organization. Our results support models of early language-specific neural circuits rather than gradual differentiation from domain-general mechanisms, while highlighting the protracted development of language organization. | | 5:20p |
Dynamic fMRI networks of emotion
The experience of emotions is that of dynamic, time changing processes. Yet, many fMRI studies of emotion average across time to focus on maps of static activations, overlooking the temporal dimension of emotional responses. In this study, we used time-resolved fMRI, group spatial independent component analysis (ICA), dual regression, and Gaussian curve fitting to examine both the spatial and temporal properties of whole-brain networks during a behavioral task. This task included trials that spanned over 25 seconds of watching short, emotionally evocative movie clips, making emotion-related decisions, and an intertrial rest period. We identified four whole-brain networks with unique spatial and temporal features that mapped onto different stages of the task. A network activated early in the course of the task included perceptual and affective evaluation regions, while two later networks supported semantic interpretation and decision-making, and a final network aligned with default mode activity. Both spatial and temporal properties of all four networks were modulated by the emotional content of the movie clips. Our findings extend current models of emotion by integrating temporal dynamics with large-scale network activity, offering a richer framework for understanding how emotions unfold across distributed circuits. Such temporal-spatial markers of emotional processing may prove valuable for identifying and tracking alterations in clinical populations. | | 5:20p |
Activity-Dependent Changes in Ion Channel Voltage-Dependence Influence the Activity Patterns Targeted by Neurons
Neurons can maintain stable activity profiles over their lifetimes despite ion channel turnover over minutes to days. Neurons also exhibit flexibility in electrical activity by regulating the voltage-dependence of ion channels. This flexibility, combined with channel turnover can, in principle, work together to maintain a stable activity profile. We augment a classical model of activity-dependent ion channel density regulation with a mechanism that adjusts channel voltage-dependence based on activity. Our findings reveal that the timescale of these adjustments shapes the range of electrical patterns that meet an activity target. This work highlights a potentially distinct role for activity-dependent regulation of channel voltage-dependence in maintaining stable neuronal activity profiles. |
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