bioRxiv Science⌕ Search

Biology subjects

Ding, M.

Publications and source records attributed to Ding, M..

At least 55 records · Page 3Linked to original sources
bioRxiv2024

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Mendoza-Halliday, Major et al.1 (referred to as "Mendoza-Halliday et al." for brevity), advocate for a local field potential (LFP)-based approach to functional identification of cortical layers during "laminar" (simultaneous recordings from all cortical layers) multielectrode recordings in nonhuman primates (NHPs). They describe a "ubiquitous spectrolaminar motif" in the primate neocortex: 1) 75-150 Hz power peaks in the supragranular layers, 2) 10-19 Hz power peaks in the infragranular layers and 3) the crossing point of their laminar power gradients identifies Layer 4 (L4). Identification of L4 is critical in general, but especially for Mendoza-Halliday et al. as the "motif" discovery is couched within a framework whose central hypothesis is that gamma activity originates in the supragranular layers and reflects feedforward activity, while alpha-beta activity originates in the infragranular layers and reflects feedback activity. In an impressive scientific effort, Mendoza-Halliday et al. analyzed laminar data from 14 cortical areas in 2 prior macaque studies and compared them to marmoset, mouse, and human data to further bolster the canonical nature of the motif. Identification of such canonical principles of brain operation is clearly a topic of broad scientific interest. Similarly, a reliable online method for L4 identification would be of broad scientific value for the rapidly increasing use of laminar recordings using numerous evolving technologies. Despite Mendoza-Halliday et al.s papers strengths, and its potential for scientific impact, a series of concerns that are fundamental to the analysis and interpretation of laminar activity profile data in general, and local field potential (LFP) signals in particular, led us to question its conclusions. Here, we address four key questions: Q1) Is the spectrolaminar motif ubiquitous, i.e. "found everywhere" or "universal"? Q2) Do features of the motif reliably identify Layer (L) 4? Q3) Are Mendoza-Halliday et al.s newly introduced methods (FLIP and vFLIP) reliable? And Q4) Are the proposed biophysical mechanisms underlying the motif well justified? We used new sets of data comprised of stimulus-evoked laminar response profiles from primary and higher-order auditory cortices (A1 and belt cortex), and primary visual cortex (V1) to test these questions. The rationale for using these areas as a test bed for new methods is that, in contrast to higher-order cortical areas, their laminar anatomy and physiology have already been extensively characterized by prior studies, and there is general agreement across laboratories on key matters like L4 identification. In short, we find that Mendoza-Halliday et al.s findings do not generalize well to these cortical areas. Specifically, regarding Q1: Though we can find a spectrolaminar gradient that is qualitatively consistent with Mendoza-Halliday et al., it is quantifiable in only 61-64% of our recordings, indicating that the motif is common but by no means universal (see "Evaluation of the FLIP method [...]" and "Evaluation of vFLIP [...]"). Regarding Q2: The motifs high/low frequency gradient cross point identified L4 in only 29-33% of our recordings. Regarding Q3: FLIP and vFLIP exhibit marked variability across studies, across brain areas, and spuriously detect cortical layer inversions (see "FLIPs fitting process [...]" and "Evaluation of vFLIP [...]"). Regarding Q4: the biophysical modeling findings cited to support Mendoza-Hallidays conclusions (see "Going forward - in silico [...]") do not reproduce the LFP data trends. While our findings are in many respects at odds with those of Mendoza-Halliday et al., the paper already has, and will continue to spark debate and further experimentation. Hopefully this countervailing presentation will lead to robust collegial efforts to define optimal strategies for applying laminar recording methods in future studies.

neuroscience↗
bioRxiv2024

Ventral tegmental area dopamine neural activity switches simultaneously with rule representations in the prefrontal cortex and hippocampus

Multiple brain regions need to coordinate activity to support cognitive flexibility and behavioral adaptation. Neural activity in both the hippocampus (HPC) and prefrontal cortex (PFC) is known to represent spatial context and is sensitive to reward and rule alterations. Midbrain dopamine (DA) activity is key in reward seeking behavior and learning. There is abundant evidence that midbrain DA modulates HPC and PFC activity. However, it remains underexplored how these networks engage dynamically and coordinate temporally when animals must adjust their behavior according to changing reward contingencies. In particular, is there any relationship between DA reward prediction change during rule switching, and rule representation changes in PFC and CA1? We addressed these questions using simultaneous recording of neuronal population activity from the hippocampal area CA1, PFC and ventral tegmental area (VTA) in male TH-Cre rats performing two spatial working memory tasks with frequent rule switches in blocks of trials. CA1 and PFC ensembles showed rule-specific activity both during maze running and at reward locations, with PFC rule coding more consistent across animals compared to CA1. Optogenetically tagged VTA DA neuron firing activity responded to and predicted reward outcome. We found that the correct prediction in DA emerged gradually over trials after rule-switching in coordination with transitions in PFC and CA1 ensemble representations of the current rule after a rule switch, followed by behavioral adaptation to the correct rule sequence. Therefore, our study demonstrates a crucial temporal coordination between the rule representation in PFC/CA1, the dopamine reward signal and behavioral strategy. Significance StatementThis study examines neural activity in mammalian brain networks that support the ability to respond flexibly to changing contexts. We use a rule-switching spatial task to examine whether the key reward-responsive and predictive dopamine (DA) activity changes in coordination with changes in rule representations in key cognitive regions, the prefrontal cortex (PFC) and hippocampus. We first established distinct rule representations in PFC and hippocampus, and predictive coding of reward outcomes by DA neuronal activity. We show that the rule-specific DA reward prediction after a rule switch develops in temporal coordination with changes in rule representations in PFC, eventually leading to behavioral changes. These results thus provide an integrated understanding of reward prediction, cognitive representations of rules and behavioral adaptation.

neuroscience↗
bioRxiv2024

Stimulus repetition induces a two-stage learning process in primary visual cortex

Repeated stimulus exposure alters the brains response to the stimulus. We investigated the underlying neural mechanisms by recording functional MRI data from human observers passively viewing 120 presentations of two Gabor patches (each Gabor repeating 60 times). We evaluated support for two prominent models of stimulus repetition, the fatigue model and the sharpening model. Our results uncovered a two-stage learning process in the primary visual cortex. In Stage 1, univariate BOLD activation in V1 decreased over the first twelve repetitions of the stimuli, replicating the well-known effect of repetition suppression. Applying MVPA decoding along with a moving window approach, we found that (1) the decoding accuracy between the two Gabors decreased from above-chance level ([~]60% to [~]70%) at the beginning of the stage to chance level at the end of the stage ([~]50%). This result, together with the accompanying weight map analysis, suggested that the learning dynamics in Stage 1 were consistent with the predictions of the fatigue model. In Stage 2, univariate BOLD activation for the remaining 48 repetitions of the two stimuli exhibited significant fluctuations but no systematic trend. The MVPA decoding accuracy between the two Gabor patches was at chance level initially and became progressively higher as stimulus repetition continued, rising above and staying above chance level starting at the [~]35th repetition. Thus, results from the second stage supported the notion that sustained and prolonged stimulus repetition prompts sharpened representations. Additional analyses addressed (1) whether the neural patterns within each learning stage remained stable and (2) whether new neural patterns were evoked in Stage 2 relative to Stage 1.

neuroscience↗
bioRxiv2024

Decoding in the fourth dimension: Classification of temporal patterns and their generalization across locations

Neuroscience research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the features used for decoding are spatial in nature, including voxel patterns and electrode locations. However, the strength of many neurophysiological recording techniques such as electroencephalography or magnetoencephalography is in their rich temporal, rather than spatial, content. The present report proposes a new decoding method that relies on the time information contained in neural time series. This information is then used in a subsequent step, generalization across location (GAL), which characterizes the relationship between sensor locations based on their ability to cross-decode. Two datasets are used to demonstrate usage of this method, referred to as time-GAL, involving (1) event-related potentials in response to affective pictures and (2) steady-state visual evoked potentials in response to aversively conditioned grating stimuli. In both cases, experimental conditions were successfully decoded based on the temporal features contained in the neural time series. Cross-decoding occurred in regions known to be involved in visual and affective processing. We conclude that the time-GAL approach holds promise for analyzing neural time series from a wide range of paradigms and measurement domains providing an assumption-free method to quantifying differences in temporal patterns of neural information processing and whether these patterns are shared across sensor locations. Author summaryDecoding and classification approaches are widely used in computational biology. In the field of neuroscience, pattern classification approaches typically use spatial information. In many instances, neural time series however are best defined by the their temporal, rather than spatial, features. Here, we propose a novel decoding approach taking advantage of the temporal information. Specifically, we utilize the waveform of neural time series as features for decoding experimental conditions and quantify each decoders generalization across locations (GAL) in multi-channel recordings. We illustrate the usage of our open source toolbox using two datasets, showing the sensitivity of the method to systematic condition differences in temporal dynamics along with its ability to capture and quantify spatial dependencies between recording locations.

neuroscience↗
bioRxiv2024

Quantifying Population-level Neural Tuning Functions Using Ricker Wavelets and the Bayesian Bootstrap

Experience changes the tuning of sensory neurons, including neurons in retinotopic visual cortex, as evident from work in humans and non-human animals. In human observers, visuo-cortical re-tuning has been studied during aversive generalization learning paradigms, in which the similarity of generalization stimuli (GSs) with a conditioned threat cue (CS+) is used to quantify tuning functions. This work utilized pre-defined tuning shapes reflecting prototypical generalization (Gaussian) and sharpening (Difference-of-Gaussians) patterns. This approach may constrain the ways in which re-tuning can be characterized, for example if tuning patterns do not match the prototypical functions or represent a mixture of functions. The present study proposes a flexible and data-driven method for precisely quantifying changes in neural tuning based on the Ricker wavelet function and the Bayesian bootstrap. The method is illustrated using data from a study in which university students (n = 31) performed an aversive generalization learning task. Oriented gray-scale gratings served as CS+ and GSs and a white noise served as the unconditioned stimulus (US). Acquisition and extinction of the aversive contingencies were examined, while steady-state visual event potentials (ssVEP) and alpha-band (8-13 Hz) power were measured from scalp EEG. Results showed that the Ricker wavelet model fitted the ssVEP and alpha-band data well. The pattern of re-tuning in ssVEP amplitude across the stimulus gradient resembled a generalization (Gaussian) shape in acquisition and a sharpening (Difference-of-Gaussian) shape in an extinction phase. As expected, the pattern of re-tuning in alpha-power took the form of a generalization shape in both phases. The Ricker-based approach led to greater Bayes factors and more interpretable results compared to prototypical tuning models. The results highlight the promise of the current method for capturing the precise nature of visuo-cortical tuning functions, unconstrained by the exact implementation of prototypical a-priori models. HighlightsO_LITuning functions are a common way for describing sensory responses, primarily in the visual cortex. C_LIO_LIThe quantification and interpretation of tuning functions has faced computational and conceptual problems. C_LIO_LIWe demonstrated how the Ricker function can be used as a simple and interpretable way for measuring tuning functions. C_LIO_LIWe applied a Ricker function together with a Bayesian Bootstrap approach across a gradient of stimulus features in a generalization conditioning task to characterize visual tuning in the human EEG data. C_LI

neuroscience↗
bioRxiv2024

CFI-1/ARID3 functions unilaterally to restrict gap junction formation in C. elegans

Electrical coupling is vital to neural communication, facilitating synchronized activity among neurons. Despite its significance, the precise mechanisms governing the establishment of gap junction connections between specific neurons remain elusive. Here, we identified that the PVC interneuron in Caenorhabditis elegans forms gap junction connections with the PVR interneuron. The transcriptional regulator CFI-1/ARID3 is specifically expressed in the PVC but not PVR interneuron. Reducing cfi-1 expression in the PVC interneuron leads to enhanced gap junction formation in the PVR neuron, while ectopic expression of cfi-1 in the PVR neuron restores the proper level of gap junction connections in the PVC neuron, along with the normal touch response. These findings unveil the pivotal role of CFI-1/ARID3 in bidirectionally regulating the formation of gap junctions within a specific neuronal pair, shedding light on the intricate molecular mechanisms governing neuronal connectivity in vivo. SUMMARY STATEMENTProper electrical coupling could be achieved by either of the partner cells; however distinctive influence on neuron circuits maybe achieved.

developmental biology↗
bioRxiv2024

Myocardin-related transcription factor drives epithelial fibrogenesis in polycystic kidney disease

Polycystic kidney disease (PKD) is characterized by extensive cyst formation and progressive fibrosis. However, the molecular mechanisms whereby the loss/loss-of-function of Polycystin 1 or 2 (PC1/2) provokes fibrosis are largely unknown. The small GTPase RhoA has been recently implicated in cystogenesis, and we have shown that the RhoA/cytoskeleton/myocardin-related transcription factor (MRTF) pathway is a key mediator of epithelium-induced fibrogenesis. Therefore, we hypothesized that MRTF is activated by PC1/2 loss and plays a critical role in fibrogenic reprogramming of the epithelium. Loss of PC1 or PC2 induced by siRNA in vitro activated RhoA, caused cytoskeletal remodeling and robust nuclear MRTF translocation and overexpression. These phenomena were also manifest in PKD1 (RC/RC) and PKD2 (WS25/-) mice, with MRTF translocation and overexpression occurring predominantly in dilated tubules and in cyst-lining epithelium, respectively. In epithelial cells, a large cohort of PC1/PC2 downregulation-induced genes was MRTF-dependent, including cytoskeletal, integrin-related, and matricellular/fibrogenic proteins. Epithelial MRTF was necessary for paracrine priming of fibroblast-myofibroblast transition. Thus, MRTF is a critical novel mediator of the PC1/2 loss-induced profibrotic epithelial phenotype, and consequently PKD-related fibrosis.

cell biology↗
bioRxiv2024

Spatiotemporal recruitment of the ubiquitin-specific protease USP8 directs endosome maturation

The spatiotemporal transition of small GTPase Rab5 to Rab7 is crucial for early-to-late endosome maturation, yet the precise mechanism governing Rab5-to-Rab7 switching remains elusive. USP8, a ubiquitin-specific protease, plays a prominent role in the endosomal sorting of a wide range of transmembrane receptors and is a promising target in cancer therapy. Here, we identified that USP8 is recruited to Rab5-positive carriers by Rabex5, a guanine nucleotide exchange factor (GEF) for Rab5. The recruitment of USP8 dissociates Rabex5 from early endosomes (EEs) and meanwhile promotes the recruitment of the Rab7 GEF SAND-1/Mon1. In USP8-deficient cells, the level of active Rab5 is increased, while the Rab7 signal is decreased. As a result, enlarged EEs with abundant intraluminal vesicles accumulate and digestive lysosomes are rudimentary. Together, our results reveal an important and unexpected role of a deubiquitinating enzyme in endosome maturation.

cell biology↗
bioRxiv2023

An ancient transcription activator is required for the multiciliogenesis program

Multiciliogenesis is an evolutionarily conserved process required a unique transcriptional program. The mechanisms governing multiciliogenesis are incompletely understood. Here we show that an ancient transcription activator, Edf1, is essential for the multiciliogenesis program. Mice lacking Edf1 exhibit postnatal hydrocephalus and delayed multiciliated cells (MCCs) differentiation. Functional studies reveal that MCCs in Edf1-/- mice have defects in tissue-level polarity and reduced motility. These defects result in abnormal cerebrospinal fluid (CSF) dynamics in vivo, potentially contributing to the development of hydrocephalus. Intriguingly, we found that the expression of pivotal ciliary transcription factors was decreased in Edf1-/- mice. Collectively, our data suggest that Edf1 modulates the multiciliogenesis by regulating the transcription of important ciliary transcription factors required for ciliary gene expression.

cell biology↗
bioRxiv2023

Complex relationship among vessel diameter, shear stress and blood pressure controlling vessel pruning during angiogenesis

Blood vessel pruning during angiogenesis is the optimization process of the branching pattern to improve the transport properties of a vascular network. Recent studies show that part of endothelial cells (ECs) subjected to lower shear stress migrate toward vessels with higher shear stress in opposition to the blood flow for vessel regression. While dynamic changes of blood flow and local mechano-stress could coordinately modulate EC migration for vessel regression within the closed circulatory system, the effect of complexity of haemodynamic forces and vessel properties on vessel pruning remains elusive. Here, we reconstructed a 3-dimentsional (3D) vessel structure from 2D confocal images of the growing vessels in the mouse retina, and numerically obtained the local information of blood flow, shear stress and blood pressure in the vasculature. Moreover, we developed a predictive model for vessel pruning based on machine learning. We found that the combination of shear stress and blood pressure with vessel radius was tightly corelated to vessel pruning sites. Our results highlighted that orchestrated contribution of local haemodynamic parameters was important for the vessel pruning. Authors SummaryBlood vessel networks formed by angiogenic vessel growth subsequently undergo extensive vascular remodeling process by regression of selected vascular branches. Optimization of the branching pattern in the vasculature is critical to ensure sufficient blood supply throughout the entire tissue. Recent studies have highlighted a strong relationship between the vessel remodeling and the shear stress acting on the vessel wall. However, its detailed mechanisms remain elusive due to the difficulties of estimating local haemodynamic parameters and relating them to vessel remodeling. Here, we have numerically simulated local haemodynamic parameters within the vascular network of the postnatal day 6 (P6) mouse retinal vasculature. Then, the relationship among the local shear stress, blood pressure, and vessel radius with the vessel pruning was examined. Moreover, we developed a predictive model for the vessel pruning based on the local haemodynamic parameters by a machine learning technique. Importantly, our results indicate that the combination of shear stress and blood pressure with vessel radius is tightly correlated to vessel pruning sites. Given the ongoing clinical approach to suppress tumor growth via blood vessel normalization, our results provide important knowledge for developing future medicine such as nanomedicine based on drug delivery systems.

developmental biology↗
bioRxiv2023

Top-Down Biasing of Visual Cortical Activity Encodes Attended Information and Facilitates Behavioral Performance in Visual Spatial Attention

In models of visual spatial attention control, it is commonly held that top-down control signals originate in the dorsal attention network, propagating to the visual cortex to modulate baseline neural activity and bias sensory processing. However, the precise distribution of these top-down influences across different levels of the visual hierarchy is debated. In addition, it is unclear whether these changes in baseline neural activity directly translate into improved performance. We analyzed attention-related baseline activity during the anticipatory period of a trial-by-trial voluntary spatial attention task, using two independent fMRI datasets, and two different analytic approaches. First, as in prior studies, univariate analysis showed that covert attention significantly enhanced baseline neural activity in higher-order visual areas contralateral to the attended visual hemifield, while effects in lower-order visual areas (e.g., V1) were weaker and more variable. Second, in contrast, multivariate pattern analysis (MVPA) revealed significant decoding of attention conditions across all visual cortical areas, with lower-order visual areas exhibiting higher decoding accuracies than higher-order areas. Third, decoding accuracy, rather than the magnitude of univariate activation, was a better predictor of a subjects stimulus discrimination performance. Finally, the MVPA results were replicated across two experimental conditions, where the direction of spatial attention was either externally instructed by a cue or based on the participants free choice decision about where to attend. Together, these findings offer new insights into the extent of attentional biases in the visual hierarchy under top-down control, and how these biases influence both sensory processing and behavioral performance. HighlightsO_LIMultivariate pattern analysis revealed the presence of top-down attentional biasing signals in all areas of the visual hierarchy whereas univariate analysis was not able to reveal the full extent of attentional biasing in the visual cortex. C_LIO_LIThe decoding accuracy derived from the MVPA analysis but not the magnitude difference derived from the univariate analysis predicted the subjects behavioral performance in stimulus discrimination. C_LIO_LIThe MVPA results were consistent across two experimental conditions where the direction of spatial attention was driven either by external instructions or from purely internal decisions. C_LI

neuroscience↗
bioRxiv2023

Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks

Multivoxel pattern analysis (MVPA) examines the differences in fMRI activation patterns associated with different cognitive conditions and provides information not possible with the conventional univariate analysis. Support vector machines (SVMs) are the predominant machine learning method in MVPA. SVMs are intuitive and easy to apply. The limitation is that it is a linear method and mainly suitable for analyzing data that are linearly separable. Convolutional neural networks (CNNs), a class of AI models originally developed for object recognition, are known to have the ability to approximate nonlinear relationships. CNNs are rapidly becoming an alternative to SVMs. The purpose of this study is to compare the two methods when they are applied to the same datasets. Two datasets were considered: (1) fMRI data collected from participants during a cued visual spatial attention task (the attention dataset) and (2) fMRI data collected from participants viewing natural images containing varying degrees of affective content (the emotion dataset). We found that (1) both SVM and CNN are able to achieve above chance level decoding accuracies for attention control and emotion processing in both the primary visual cortex and the whole brain with, (2) the CNN decoding accuracies are consistently higher than that of the SVM, (3) the SVM and CNN decoding accuracies are generally not correlated with each other, and (4) the heatmaps derived from SVM and CNN are not significantly overlapping. These results suggest that (1) there are both linearly separable features and nonlinearly separable features in fMRI data that distinguish cognitive conditions and (2) applying both SVM and CNN to the same data may yield a more comprehensive understanding of neuroimaging data. Key pointsO_LIWe compared the performance and characteristics of SVM and CNN, two major methods in MVPA analysis of neuroimaging data, by applying them to the same two fMRI datasets. C_LIO_LIBoth SVM and CNN achieved decoding accuracies above chance level for both datasets in the chosen ROIs and the CNN decoding accuracies were consistently higher than those of SVM. C_LIO_LIThe heatmaps derived from SVM and CNN, which assess the contribution of voxels or brain regions to MVPA decoding performance, showed no significant overlap, providing evidence that the two methods depend on distinct brain activity patterns for decoding cognitive conditions. C_LI

bioinformatics↗
bioRxiv2023

Transcranial stimulation of alpha oscillations modulates brain state dynamics in sustained attention

The brain operates an advanced complex system to support mental activities. Cognition is thought to emerge from dynamic states of the complex brain system, which are organized spatially through large- scale neural networks and temporally via neural synchrony. However, specific mechanisms underlying these processes remain obscure. Applying high-definition alpha-frequency transcranial alternating-current stimulation (HD -tACS) in a continuous performance task (CPT) during functional resonance imaging (fMRI), we causally elucidate these major organizational architectures in a key cognitive operation-- sustained attention. We demonstrated that -tACS enhanced both electroencephalogram (EEG) alpha power and sustained attention, in a correlated fashion. Akin to temporal fluctuations inherent in sustained attention, our hidden Markov modeling (HMM) of fMRI timeseries uncovered several recurrent, dynamic brain states, which were organized through a few major neural networks and regulated by the alpha oscillation. Specifically, during sustain attention, -tACS regulated the temporal dynamics of the brain states by suppressing a Task-Negative state (characterized by activation of the default mode network/DMN) and Distraction state (with activation of the ventral attention and visual networks). These findings thus linked dynamic states of major neural networks and alpha oscillations, providing important insights into systems-level mechanisms of attention. They also highlight the efficacy of non-invasive oscillatory neuromodulation in probing the functioning of the complex brain system and encourage future clinical applications to improve neural systems health and cognitive performance.

neuroscience↗
bioRxiv2023

Interleukin-16/STAT6 recruits CBP/p300 to upregulate TIMP-3 and promote atherosclerotic plaque stability

BackgroundAtherosclerotic plaque rupture increases the risk of ischemic heart disease and stroke and commonly causes sudden death. High levels of circulating or intraplaque interleukin 16 (IL-16) are clinically associated with a reduced incidence of cardiovascular events. Here, we investigated the effects of IL-16 on plaque phenotypic modification and identified the molecules involved in smooth muscle cells (SMCs). MethodsWe deleted IL-16 in ApoE-/- mice to generate IL16-/-ApoE-/- mice, and the double mutant was used for plaque phenotype analysis after a 24-week high-fat diet. RNA sequencing was performed to identify the changes in cellular processes and molecule expression in response to IL-16 defects. Affinity purification-mass spectrometry was used to identify the STAT6 binding protein. Bone marrow transplantation was used to investigate the effects of hematopoietic IL-16 deficiency or reconstitution on plaque stability. ResultsIL-16 deficiency reduced collagen deposition and increased the necrotic core area in the plaques of the brachiocephalic artery and aortic root lesions. Intraplaque TIMP-3 levels were found to be decreased in association with an increase in the proteolytic activity of MMPsinIL16-/-ApoE-/- mice. Next, we demonstrated that IL-16 activates the CD4/JAK2/STAT6 pathway and that STAT6 directly binds the TIMP-3 promoter in SMCs. Furthermore, IL-16 treatment increased the interaction of cAMP-response element binding protein (CBP)/p300 with STAT6, which promoted STAT6 acetylation and increased histone H3 acetylation in the TIMP-3 promoter. Inhibition of CBP/p300 resulted in decreased acetylation of STAT6 and TIMP-3 promoter histone H3 and TIMP-3 expression, suggesting a requirement for CBP/p300 as a coactivator. Finally, we found that hematopoietic-derived IL-16 from ApoE-/- mice or overexpression of TIMP-3 successfully attenuated plaque instability in IL16-/-ApoE-/- mice. ConclusionsIL-6 upregulates TIMP-3 expression and remodels the intraplaque extracellular matrix toward a stable phenotype, suggesting IL-16 as a potential target for intervening in atherosclerosis at later stages.

pathology↗
bioRxiv2023

Emergence of Emotion Selectivity in A Deep Neural Network Trained to Recognize Visual Objects

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories. Author SummaryThe present study shows that emotion selectivity can emerge in deep neural networks trained to recognize visual objects and the existence of the emotion-selective neurons underlies the ability of the network to recognize the emotional qualities in visual images. Obtained using two affective datasets (IAPS and NAPS) and replicated on two CNNs (VGG-16 and AlexNet), these results support the idea that the visual system may have an intrinsic ability to represent the motivational significance of sensory input and CNNs are a valuable platform for testing neuroscience ideas in a way that is not practical in empirical studies.

neuroscience↗
bioRxiv2023

Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction

RNA splicing is an important post-transcriptional process of gene expression in eukaryotic cells. Predicting RNA splicing from primary sequences can facilitate the interpretation of genomic variants. In this study, we developed a novel self-supervised pre-trained language model, SpliceBERT, to improve sequence-based RNA splicing prediction. Pre-training on pre-mRNA sequences from vertebrates enables SpliceBERT to capture evolutionary conservation information and characterize the unique property of splice sites. SpliceBERT also improves zero-shot prediction of variant effects on splicing by considering sequence context information, and achieves superior performance for predicting branchpoint in the human genome and splice sites across species. Our study highlighted the importance of pre-training genomic language models on a diverse range of species and suggested that pre-trained language models were promising for deciphering the sequence logic of RNA splicing.

bioinformatics↗
bioRxiv2022

Imaging the Neural Substrate of Trigeminal Neuralgia Pain Using Deep Learning

Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were six regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex(dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions represent signature centers of TN pain and provide target areas for future studies relating to central mechanisms of TN.

neuroscience↗