bioRxiv Subject Collection: Neuroscience's Journal
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Friday, June 20th, 2025
| Time |
Event |
| 3:47a |
Enhancing Functional Connectivity Analysis in Task-Based fMRI Using the BOLD-Filter Method: Greater Network and Activation Voxel Sensitivities
Task-based functional MRI (tb-fMRI) has gained prominence for investigating brain connectivity by engaging specific functional networks during cognitive or behavioral tasks. Compared to resting-state fMRI (rs-fMRI), tb-fMRI provides greater specificity and interpretability, making it a valuable tool for examining task-relevant networks and individual differences in brain function. In this study, we evaluated the utility of the BOLD-filter--a method originally developed to extract reliable BOLD (blood oxygenation level-dependent) components from rs-fMRI--by applying it to tb-fMRI data as a preprocessing step for functional connectivity (FC) analysis. The goal was to enhance the sensitivity and specificity of detecting task-induced functional activity. Compared to the conventional preprocessing method, the BOLD-filter substantially improved the isolation of task-evoked BOLD signals. It identified over eleven times more activation voxels at a high statistical threshold and more than twice as many at a lower threshold. Moreover, FC networks derived from BOLD-filtered signals revealed clearer task-related patterns, including gender-specific differences in brain regions linked to everyday behaviors. These patterns were not detectable using standard preprocessing approaches. Our findings demonstrate that the BOLD-filter enhances the robustness and interpretability of FC analysis in tb-fMRI. By effectively isolating meaningful functional networks, this approach offers significant advantages over conventional preprocessing methods. The BOLD-filter holds promise for advancing both basic neuroscience research and clinical applications by enabling more precise characterization of task-induced brain activity. | | 8:30a |
Classifying Resting State Connectivity: Lag-One Autocorrelation and Pattern Differentiability
Resting state functional connectivity (rsFC) and resting state effective connectivity (rsEC) are two of the most common measures that can be extracted from resting state functional magnetic resonance imaging (rs-fMRI) data. RSFC is often used to indicate the statistical dependencies among different brain regions of interest, whereas rsEC describes the causal influences among them. Many studies have explored utilities of rsFC and rsEC measures for classifying psychiatric conditions. Several studies showed that rsEC were better than rsFC features for classifying major depression (Frassle et al., 2020; Geng et al., 2018) and schizophrenia ((Brodersen et al., 2014)). However, no study to-date has investigated whether rsEC is inherently better than rsFC for classifying psychiatric conditions or the impact of autocorrelation on classifying rsFC, even though autocorrelation is known to be present in rs-fMRI data. To fill these gaps, we performed a series of computational experiments, by varying the size of the network and the number of participants, to gain some insight into these two aspects of supervised classification with resting state connectivity. Contrary to what has been reported in the literature, the results from our study suggest that rsEC cannot be, in principle, better than rsFC features for classification. In fact, rsEC measures led to systematically worse classification results, compared to rsFC features. In terms of the impact of autocorrelation, we found that lag-one autocorrelation could lead to both false negative and false positive classification results for studies with a small sample size. | | 4:32p |
Comparative Transcriptomics Reveals Inflammatory and Epigenetic Programs that Actively Orchestrate Pineal Brain Sand Calcification
Background: The pineal gland secretes melatonin but paradoxically calcifies more than any other intracranial structure, forming hydroxyapatite "brain-sand" (corpora arenacea) that correlates with reduced melatonin output, sleep disruption and heightened neuro-degenerative risk. Whether this mineralization is a passive dystrophic event or an active, bone-like process remains unclear. Methods: Analyzed RNA-seq datasets from pineal glands of six vertebrate species-calcifiers Homo sapiens, Rattus norvegicus and Capra hircus, versus non-calcifiers Mus musculus, Gallus gallus and Danio rerio. Species-specific transcripts were mapped to human orthologues, merged, and filtered. Phylogenetically informed differential expression testing used Brownian-motion and Pagel's lamda phylogenetic generalized least-squares models, calibrated on a TimeTree divergence phylogeny. Genes significant in both models (|logtwoFC| > 1; FDR < 0.05; lamda < 0.7) were assigned to functional pathways and visualized by PCA, heat-mapping and volcano plots. Results: Calcifying species segregated cleanly from non-calcifiers on the first two principal components, reflecting a shared 103-gene "calcifier module". Top up-regulated transcripts included developmental morphogens (GLI4, IQCE, NOTCH4), epigenetic regulators (SETD1A, ZNF274, ATF7IP), inflammatory mediators (CSF2RB), and quality-control factors (GABARAPL2, RHOT2). Every leading candidate exhibited minimal phylogenetic signal (lamda to 0), indicating that differential expression tracks the calcified phenotype rather than shared ancestry. Conversely, only three genes (RMI2, RASL11B, GPR18) formed a non-calcifier module, suggesting potential protective roles that are down-regulated during mineralization. Conclusions: Pineal calcification is not a passive by-product of aging but a regulated, lineage- restricted program that redeploys Hedgehog, Notch and chromatin-remodeling pathways classically required for skeletal ossification. The ten-gene core signature identified offers a molecular foothold for mechanistic dissection and therapeutic targeting aimed at preserving pineal function and circadian health. Significance This is the first phylogenetically controlled transcriptomic survey to link pineal "brain-sand" formation to specific developmental and inflammatory gene networks, revealing convergent evolution of calcification programs across divergent mammalian lineages. | | 5:45p |
Tau-seed interactome analysis reveals distinct functional signatures in Alzheimer's disease across model systems.
Tau aggregates propagate through the brain in a prion-like manner in Alzheimer's disease (AD) and other tauopathies, but the molecular identity and functional partners of the seeding-competent Tau species remain poorly defined. Here, we present an unbiased proteomic profiling of a high-molecular-weight (HMW) Tau-seed isolated from AD patient brains. We contrast this interactome with that of a biochemically similar, seeding-incompetent HMW-Tau species from age-matched healthy controls. Despite comprising less than 5% of total Tau in the brain, Tau-seed associates with a distinct set of proteins enriched in synaptic, mitochondrial, and vesicle-trafficking functions. Cross-species functional screening in Drosophila and mouse models identifies interactors that modulate Tau toxicity and seeding. Spatially resolved analysis of postmortem AD brains reveals heterogenous co-deposition of these proteins with Tau aggregates, suggesting functionally distinct Tau-seed complexes. Together, this dataset provides a framework for understanding selective Tau-seed toxicity and identifies candidate regulators of Tau propagation with therapeutic potential. | | 5:45p |
Potential beneficial effects of PD-1/PD-L1 blockade in Alzheimer's disease: A systematic review and meta-analysis of preclinical and clinical studies
Programmed cell death-1 (PD-1) and its ligand (PD-L1) play key roles in cancer immune evasion and in modulating neuroinflammation. This systematic review and meta-analysis investigated the effect of PD-1/PD-L1 blockade on pathology and cognitive function in Alzheimer's disease (AD) in preclinical and clinical studies. Relevant studies were systematically identified using the MEDLINE, Embase, CENTRAL, and Web of Science databases from their inception until April 10, 2024. In total, 33 studies were included in this meta-analysis, conducted using R software. Preclinical studies revealed that blockade of PD-1 signaling reduces amyloid-beta plaque burden, tau phosphorylation, and astrocyte reactivity in AD mouse models, accompanied by improvements in cognitive function in behavioral tests. Furthermore, clinical studies demonstrated the beneficial effect of PD-1 signaling inhibitors on cognitive function in cancer patients. This study highlights the necessity for additional research to clarify the exact mechanisms by which PD-1/PD-L1 inhibition impacts AD pathology and cognitive function, opening avenues for potential therapeutic strategies targeting this pathway in AD. | | 9:15p |
Muscle control of an extra robotic digit
Controlling an extra robotic finger requires the brain to adapt existing motor signals. While most current strategies exploit physical movement, there is growing interest in harnessing muscle activity directly via surface electromyography (EMG) as a more seamless interface. We systematically compared muscle- (EMG) and movement-based (force sensor) control of a Third Thumb. Using identical instructions and a counterbalanced within-participants design, we assessed initial skill, learning, and cognitive load across a variety of tasks, enabling a blinded comparison across control modalities. Both control modalities afforded successful Third Thumb control and learning, although force control consistently delivered better performance. Despite execution differences, learning rates and cognitive loads were comparable, with a similar evoked sense of agency. Signal analyses showed performance was predicted by real-time force sensor parameters but not by EMG, reflecting distinct control dynamics. Nonetheless, EMG training led to greater skill transfer to force control, suggesting it may better support generalisable learning. These findings challenge the assumption that proximity to neural signals ensures better control. Although EMG underperformed in execution, it showed unique advantages, including enhanced generalisation and access to richer signals, highlighting the need for improved real-time decoding to fully exploit its potential. |
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