Biology subjects

Silva, R. F.

Publications and source records attributed to Silva, R. F..

11 recordsLinked to original sources

bioRxiv2025

Viral Landscape of Gastric Adenocarcinoma Reveals Clinically Relevant Viruses

BackgroundGastric cancer (GC) ranks among the most common and lethal cancers worldwide, with poor prognosis mainly due to late diagnosis. Accumulating evidence highlights the role of the gastric microbiome in carcinogenesis through inflammation, genomic instability, and immune modulation. Unlike the bacterial component, the gastric virome remains largely unexplored despite its potential contribution to tumor development. The present study aimed to characterize the virome present in tumor and peritumoral tissues from patients with GC. Materials and Methods105 tumor and 85 peritumoral gastric tissues were analyzed. RNA was extracted, libraries were prepared, and sequencing was performed on the Illumina NextSeq 500. Viral reads were classified with Kraken2. Taxonomic profiles, viral abundance and diversity metrics were computed in R, with group differences assessed by Wilcoxon tests and PERMANOVA (p < 0.05). ResultsIn this study, 38 viral orders and 329 viral genera were identified in gastric tumor and peritumoral tissues. Tumor tissues harbored 210 viral genera, including 109 exclusive to this microenvironment, with bacteriophages comprising the majority, alongside human-infecting and other eukaryotic viruses. Lymphocryptovirus, Cytomegalovirus, and Alphapapillomavirus were enriched in tumors. Alpha and beta diversity analyses revealed no significant differences between tumor and peritumoral tissues, indicating comparable viral richness and composition. ConclusionThese findings underscore the complexity of the gastric virome and provide a foundation for future investigations into the interactions and mechanisms through which the viral community could influence the development of gastric cancer, highlighting its potential role in gastric health and disease.

genetics↗

bioRxiv2025

aNy-way ICA and its application to estimate cortico-thalamo-cerebellar functional links in schizophrenia

Disrupted large-scale brain network interactions are a hallmark of schizophrenia, yet the circuit-level mechanisms underlying cognitive deficits remain incompletely understood. In particular, cortico-thalamo-cerebellar circuitry has been implicated in "cognitive dysmetria," but its functional organization across subdomains has not been fully characterized. Here, we apply a flexible multimodal data fusion framework (aNy-way independent component analysis) to integrate 4D domain-specific resting-state fMRI data from the cortex, thalamus, and cerebellum. Using a large discovery cohort (FBIRN, N=311) and two independent replication datasets (COBRE, N=148; MPRC, N=364), we identify a robust cortico-thalamo-cerebellar circuit involving higher-order thalamic nuclei, visual cortex, default mode network, and cerebellar posterior regions. This circuit shows altered connectivity in schizophrenia, characterized by reduced anticorrelation between thalamus and cortex, and is consistently associated with cognitive impairments, including processing speed, working memory, and reasoning deficits across cohorts. Importantly, these findings are largely replicated across independent cohorts. Together, these results provide data-driven evidence for a cortico-thalamo-cerebellar mechanism underlying cognitive dysfunction in schizophrenia, supporting the cognitive dysmetria hypothesis. This work also demonstrates the utility of aNy-way independent component analysis for identifying clinically relevant brain circuits in psychiatric disorders. HighlightsO_LIIdentified cortico-thalamo-cerebellar circuits in schizophrenia C_LIO_LIAltered thalamocortical interactions differentiated schizophrenia C_LIO_LICircuit alterations were associated with cognitive impairment C_LIO_LIFindings are replicated across three independent multisite datasets C_LIO_LIMultidomain fusion enabled subdomain-specific circuit characterization C_LI

neuroscience↗

bioRxiv2025

An information theory framework for capturing multi-connectivity via spatial network encoding reveals reduced population count (Hamming weight) localized to auditory, visual, and motor networks

1.The human brain operates as a complex system where functional networks evolve and interact across spatially distributed regions. In traditional neuroimaging analyses, functional connectivity (FC), based on pairwise correlations or statistical dependencies of temporal fluctuations in the BOLD signal, has been a primary method for exploring interactions between brain regions and decoding brain function. However, traditional FC methods often overlook the intricate, multi-way interplay among brain elements that emerge from the brains densely interconnected nature. To overcome these limitations, we introduce a novel voxel-centric framework that captures the multi-way interactions between voxels and networks identified via high-model order independent component analysis. This framework posits that individual voxels serve as critical mediators of multi-network communication, reflecting the brains complex functional architecture. By encoding voxel contributions from brain networks into binary representations and quantifying the population count at each voxel via Hamming weights, the proposed method prioritizes high-contribution voxels that facilitate inter-network interactions. This approach provides new insights into the brains functional organization, revealing previously unrecognized patterns of voxel-to-network entanglement. Specifically, in the context of schizophrenia, our method enables the identification of spatial patterns that may underpin the cognitive and perceptual disturbances characteristic of the disorder. This enhanced understanding could improve diagnostic precision and help tailor interventions that target specific dysfunctional networks, offering a pathway to more effective treatments and better patient outcomes in schizophrenia.

neuroscience↗

bioRxiv2024

Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion

Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.

neuroscience↗

bioRxiv2023

Multimodal subspace independent vector analysis captures latent subspace structures in large multimodal neuroimaging studies

A key challenge in neuroscience is to infer the relationships between brain structure and function from high-dimensional, multimodal neuroimaging data. While conventional multivariate approaches often simplify statistical assumptions and estimate one-dimensional independent sources shared across modalities, the true relationships between latent sources are likely more complex--statistical dependence may exist both within and between modalities, and possibly span more than one dimension per modality. Here we present Multimodal Subspace Independent Vector Analysis (MSIVA), a methodology to capture both joint and unique vector sources from multiple data modalities by defining both cross-modal and unimodal subspaces with variable dimensions. In particular, MSIVA enables flexible estimation of varying-size independent subspaces within modalities and their one-to-one linkage to corresponding subspaces across modalities. A key advantage is its ability to capture subjectlevel variability at the voxel level within independent subspaces, in contrast to traditional methods that share the same independent components across subjects. We evaluated three initialization workflows with five candidate subspace structures in multiple synthetic datasets and two large multimodal neuroimaging datasets, both including structural MRI (sMRI) and functional MRI (fMRI). After confirming that MSIVA successfully recovered the ground-truth subspace structures in the synthetic data, we then applied MSIVA to identify the latent subspace structure in the neuroimaging data. Subsequent subspace-specific canonical correlation analysis, brain-phenotype prediction, and voxelwise brain-age delta analysis suggest that the estimated sources from MSIVA with the optimal subspace structure are strongly associated with multiple phenotype variables, including age, sex, schizophrenia, lifestyle factors, and cognitive functions. Further, we identified modality- and group-specific brain regions related to multiple phenotype measures such as age (for example, cerebellum, precentral gyrus, and cingulate gyrus in sMRI; occipital lobe and superior frontal gyrus in fMRI), sex (for example, cerebellum in sMRI, frontal lobe in fMRI, and precuneus in both sMRI and fMRI), schizophrenia (for example, cerebellar, frontal, and insular cortices in sMRI; occipital pole, lingual gyrus, and precuneus in fMRI), shedding light on phenotypic and neuropsychiatric biomarkers of linked brain structure and function.

neuroscience↗

bioRxiv2022

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among many existing methods and tools for analysis of brain anatomy based on structural magnetic resonance imaging (sMRI) data, data-driven source based morphometry (SBM) focuses on the exploratory detection of such patterns. Constrained source-based morphometry (constrained SBM) is a widely used semi-blind extension of SBM that enables extracting maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. In order to operate, constrained SBM needs the data to be locally accessible. However, there exist many reasons (e.g., the concerns of revealing identifiable rare disease information, or violating strict IRB policies) that may preclude access to data from different sites. In this scenario, constrained SBM fails to leverage the benefits of decentralized data. To mitigate this problem, we present a novel approach: decentralized constrained source-based morphometry (dcSBM). In dcSBM, the original data never leaves the local site. Each site operates constrained ICA on their private local data while using a common distributed computation platform. Then, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to find out the significant sources. In our approach, we first use UK Biobank sMRI data to investigate the reliability of our dcSBM algorithm. Finally, we utilize two additional multi-site patient datasets to validate our model by comparing the resulting group difference estimates from both centralized and decentralized constrained SBM.

neuroscience↗

bioRxiv2021

Multimodal IVA fusion for detection of linked neuroimaging biomarkers

With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal sources in multiple datasets. In this work we utilized the multimodal independent vector analysis model in MISA to directly identify meaningful linked features across three neuroimaging modalities -- structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI -- in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (the sources/components) that capture age-associated decline, schizophrenia-related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality-specific brain-age deltas were evaluated for association with non-imaging variables. In addition, each set of linked sources reveals a corresponding set of multi-tissue spatial patterns that can be studied jointly.

neuroscience↗

bioRxiv2021

Individualized Spatial Network Predictions Using Siamese Convolutional Neural Networks: A Resting-State fMRI Study of over 11,000 Unaffected Individuals

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our networks learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

neuroscience↗

bioRxiv2020

Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis

Resting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008[~]0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies. Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions. While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.

neuroscience↗

bioRxiv2020

aNy-way Independent Component Analysis

Multimodal data fusion is a topic of great interest. Several fusion methods have been proposed to investigate coherent patterns and corresponding linkages across modalities, such as joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA+jICA, disjoint subspace using ICA (DS-ICA) and parallel ICA. JICA exploits source independence but assumes shared loading parameters. MCCA maximizes correlation linkage across modalities directly but is limited to orthogonal features. While there is no theoretical limit to the number of modalities analyzed together by jICA, mCCA, or the two-step approach mCCA+jICA, these approaches can only extract common features and require the same number of sources/components for all modalities. On the other hand, DS-ICA and parallel ICA can identify both common and distinct features but are limited to two modalities. DS-ICA assumes shared loading parameters among common features, which works well when links are strong. Parallel ICA simultaneously maximizes correlation between modalities and independence of sources, while allowing different number of sources for each modality. However, only a very limited number of modalities and linkage pairs can be optimized. To overcome these limitations, we propose aNy-way ICA, a new model to simultaneously maximize the independence of sources and correlations across modalities. aNy-way ICA combines infomax ICA and Gaussian independent vector analysis (IVA-G) via a shared weight matrix model without orthogonality constraints. Simulation results demonstrate that aNy-way ICA not only accurately recovers sources and loadings, but also the true covariance/linkage patterns, whether different modalities have the same or different number of sources. Moreover, aNy-way ICA outperforms mCCA and mCCA+jICA in terms of source and loading recovery accuracy, especially under noisy conditions. Clinical RelevanceThis establishes a model for N-way data fusion of any number of modalities and linkage pairs, allowing different number of non-orthogonal sources for different modalities.

bioengineering↗

bioRxiv2019

SPARSE INFOMAX BASED ON HOYER PROJECTION AND ITS APPLICATION TO SIMULATED STRUCTURAL MRI AND SNP DATA

Independent component analysis has been widely applied to brain imaging and genetic data analyses for its ability to identify interpretable latent sources. Nevertheless, leveraging source sparsity in a more granular way may further improve its ability to optimize the solution for certain data types. For this purpose, we propose a sparse infomax algorithm based on nonlinear Hoyer projection, leveraging both sparsity and statistical independence of latent sources. The proposed algorithm iteratively updates the unmixing matrix by infomax (for independence) and the sources by Hoyer projection (for sparsity), feeding the sparse sources back as input data for the next iteration. Consequently, sparseness propagates effectively through infomax iterations, producing sources with more desirable properties. Simulation results on both brain imaging and genetic data demonstrate that the proposed algorithm yields improved pattern recovery, particularly under low signal-to-noise ratio conditions, as well as improved sparseness compared to traditional infomax.

bioengineering↗