Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease.

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment.

BACKGROUND: In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. RESULTS: DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. CONCLUSION: Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.

Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification.

MOTIVATION: Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype-phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype-phenotype associations. RESULTS: In this article, we propose a new joint multitask learning method, named MT-SCCALR, which absorbs the merits of both SCCA and logistic regression. MT-SCCALR learns genotype-phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype-phenotype pattern. Meanwhile, MT-SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT-SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype-phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders. AVAILABILITY AND IMPLEMENTATION: The software is publicly available at https://github.com/dulei323/MTSCCALR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort.

MOTIVATION: Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. RESULTS: We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer's Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. AVAILABILITY AND IMPLEMENTATION: The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Joint between-sample normalization and differential expression detection through ℓ0-regularized regression.

BACKGROUND: A fundamental problem in RNA-seq data analysis is to identify genes or exons that are differentially expressed with varying experimental conditions based on the read counts. The relativeness of RNA-seq measurements makes the between-sample normalization of read counts an essential step in differential expression (DE) analysis. In most existing methods, the normalization step is performed prior to the DE analysis. Recently, Jiang and Zhan proposed a statistical method which introduces sample-specific normalization parameters into a joint model, which allows for simultaneous normalization and differential expression analysis from log-transformed RNA-seq data. Furthermore, an ℓ0 penalty is used to yield a sparse solution which selects a subset of DE genes. The experimental conditions are restricted to be categorical in their work. RESULTS: In this paper, we generalize Jiang and Zhan's method to handle experimental conditions that are measured in continuous variables. As a result, genes with expression levels associated with a single or multiple covariates can be detected. As the problem being high-dimensional, non-differentiable and non-convex, we develop an efficient algorithm for model fitting. CONCLUSIONS: Experiments on synthetic data demonstrate that the proposed method outperforms existing methods in terms of detection accuracy when a large fraction of genes are differentially expressed in an asymmetric manner, and the performance gain becomes more substantial for larger sample sizes. We also apply our method to a real prostate cancer RNA-seq dataset to identify genes associated with pre-operative prostate-specific antigen (PSA) levels in patients.

Preconfigured patterns are the primary driver of offline multi-neuronal sequence replay.

Spontaneous neuronal ensemble activity in the hippocampus is believed to result from a combination of preconfigured internally generated dynamics and the unique patterns of activity driven by recent experience. Previous research has established that preconfigured sequential neuronal patterns (i.e., preplay) contribute to the expression of future place cell sequences, which in turn contribute to the sequential neuronal patterns expressed post-experience (i.e., replay). The relative contribution of preconfigured and of experience-related factors to replay and to overall sequential activity during post-run sleep is believed to be highly biased toward the recent run experience, despite never being tested directly. Here, we use multi-neuronal sequence analysis unbiased by firing rate to compute and directly compare the contributions of internally generated and of recent experience-driven factors to the sequential neuronal activity in post-run sleep in naïve adult rats. We find that multi-neuronal sequences during post-run sleep are dominantly contributed by the pre-run preconfigured patterns and to a much smaller extent by the place cell sequences and associated awake rest multi-neuronal sequences experienced during de novo run session, which are weakly and similarly correlated with pre- and post-run sleep multi-neuronal sequences. These findings indicate a robust default internal organization of the hippocampal network into sequential neuronal ensembles that withstands a de novo spatial experience and suggest that integration of novel information during de novo experience leading to lasting changes in sequential network patterns is much more subtle than previously assumed.

A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics.

MOTIVATION: Brain imaging genetics, which studies the linkage between genetic variations and structural or functional measures of the human brain, has become increasingly important in recent years. Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ1-norm or its variants to induce sparsity. The ℓ0-norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem. RESULTS: In this paper, we propose the truncated ℓ1-norm penalized SCCA to improve the performance and effectiveness of the ℓ1-norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning τ. It can avoid the time intensive parameter tuning if given a reasonable small τ. Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations. AVAILABILITY AND IMPLEMENTATION: The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/tlpscca/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Widely tunable eye-safe optical parametric oscillator with noncollinear phase-matching in a ring cavity.

An effective method for wavelength tuning in an optical parametric oscillator (OPO) was proposed using noncollinear phase-matching (PM) in a ring cavity. This method was particularly useful for noncritically phase-matched (NCPM) KTP/KTA OPOs where changing the crystal orientation or working temperature is ineffective. A wide tuning range in the eye-safe band from 1572.9 nm to 1684.2 nm was realized pumped by an Nd:YAG laser at 1.06 µm in an NCPM KTP OPO while the internal noncollinear angle was tuned from 0 to 3.1° or the external angle from 0° to 5.8°, with slight variation of the deflection angle of one cavity mirror. The good beam quality and high spectrum intensity of the narrow-linewidth Nd:YAG laser resulted in 33.3% conversion efficiency for the collinear case and above 11% throughout the tuning range. Such OPOs have many potential applications where tunable eye-safe lasers are required, and the proposed wavelength tuning method can be extended to all kinds of OPOs.

Dual-wavelength intracavity Raman laser driven by a coaxially pumped dual-crystal fundamental laser.

An actively Q-switched dual-wavelength intracavity Raman laser based on a coaxially pumped dual-crystal (Nd:YAG and b-cut Nd:YAP) fundamental configuration was theoretically and experimentally investigated. Stable dual-wavelength Stokes output at 1176 nm and 1195 nm was subsequently obtained by the Raman conversion from an a-cut YVO4 crystal. A total average power of 1.8 W was produced at 10 kHz pulse repetition frequency under an incident diode pump power of 15.8 W, in which the power levels at the two Stokes wavelengths were nearly equivalent. The power proportion and the interval between the dual-wavelength Stokes pulses could be manipulated actively by changing the pump focal position or pump wavelength.

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules.

MOTIVATION: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. RESULTS: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype. AVAILABILITY AND IMPLEMENTATION: The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/. CONTACT: shenli@iu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Contrast-dependent OFF-dominance in cat primary visual cortex facilitates discrimination of stimuli with natural contrast statistics.

Both theoretical and experimental studies suggest that response properties in the visual system are shaped by signals in the natural environment. Recent studies showed that, in the primary visual cortex (V1), neurons preferring light decrements (OFF stimuli) outnumber those preferring light increments (ON stimuli). However, it is not clear whether the OFF-dominance in V1 neurons is related to the contrast statistics in natural images. By analysing the distribution of negative and positive contrasts in natural images at several spatial scales, we showed that optimal coding of the natural contrast signals would lead to a contrast-dependent OFF-dominant response, with a stronger degree of OFF-dominance at a higher contrast. Using bright and dark stimuli at various contrast levels to measure the receptive fields of neurons in cat V1, we found an increasing degree of OFF-dominance of the neuronal population as the contrast was increased. By modeling receptive fields exhibiting OFF- and ON-dominance, we found that contrast-dependent OFF-dominance facilitated the discrimination of stimuli with natural contrast distribution. Thus, by matching contrast-dependent OFF-dominance to the statistics of contrast distribution in natural images, V1 neurons may better discriminate contrast information in natural scenes.

Deciphering Microbiome, Transcriptome, and Metabolic Interactions in the Presence of Probiotic Lactobacillus acidophilus against Salmonella Typhimurium in a Murine Model.

Salmonella enterica serovar Typhimurium (S. Typhimurium), a foodborne pathogen that poses significant public health risks to humans and animals, presents a formidable challenge due to its antibiotic resistance. This study explores the potential of Lactobacillus acidophilus (L. acidophilus 1.3251) probiotics as an alternative strategy to combat antibiotic resistance associated with S. Typhimurium infection. In this investigation, twenty-four BALB/c mice were assigned to four groups: a non-infected, non-treated group (CNG); an infected, non-treated group (CPG); a group fed with L. acidophilus but not infected (LAG); and a group fed with L. acidophilus and challenged with Salmonella (LAST). The results revealed a reduction in Salmonella levels in the feces of mice, along with restored weight and improved overall health in the LAST compared to the CPG. The feeding of L. acidophilus was found to downregulate pro-inflammatory cytokine mRNA induced by Salmonella while upregulating anti-inflammatory cytokines. Additionally, it influenced the expression of mRNA transcript, encoding tight junction protein, oxidative stress-induced enzymes, and apoptosis-related mRNA expression. Furthermore, the LEfSe analysis demonstrated a significant shift in the abundance of critical commensal genera in the LAST, essential for maintaining gut homeostasis, metabolic reactions, anti-inflammatory responses, and butyrate production. Transcriptomic analysis revealed 2173 upregulated and 506 downregulated differentially expressed genes (DEGs) in the LAST vs. the CPG. Functional analysis of these DEGs highlighted their involvement in immunity, metabolism, and cellular development. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis indicated their role in tumor necrosis factor (TNF), mitogen-activated protein kinase (MAPK), chemokine, Forkhead box O (FOXO), and transforming growth factor (TGF-ß) signaling pathway. Moreover, the fecal metabolomic analysis identified 929 differential metabolites, with enrichment observed in valine, leucine, isoleucine, taurine, glycine, and other metabolites. These findings suggest that supplementation with L. acidophilus promotes the growth of beneficial commensal genera while mitigating Salmonella-induced intestinal disruption by modulating immunity, gut homeostasis, gut barrier integrity, and metabolism.

Interfacial engineering of POM-stabilized Ni quantum dots on porous titanium mesh for high-rate and stable alkaline hydrogen production.

The development of low-cost, high-efficiency, and stable electrocatalysts for the alkaline hydrogen evolution reaction (HER) is a key challenge because the alkaline HER kinetics is slowed by an additional water dissociation step. Herein, we report an interfacial engineering strategy for polyoxometalate (POM)-stabilized nickel (Ni) quantum dots decorated on the surface of porous titanium mesh (POMs-Ni@PTM) for high-rate and stable alkaline hydrogen production. Benefiting from the strong interfacial interactions among POMs, Ni atoms, and PTM substrates, as well as unique POM-Ni quantum dot structures, the optimized POMs-Ni@PTM electrocatalyst exhibits a remarkable alkaline HER performance with an overpotential (η10) of 30.1 mV to reach a current density of 10 mA cm-2, which is much better than those of bare Ni decorated porous titanium mesh (Ni@PTM) (η10 = 171.1 mV) and POM decorated porous titanium mesh (POMs@PTM) electrocatalysts (η10 = 493.6 mV), comparable to that of the commercial 20 wt% platinum/carbon (20% Pt/C) electrocatalyst (η10 = 20 mV). Moreover, the optimized POMs-Ni@PTM electrocatalyst demonstrates excellent stability under continuous alkaline water-splitting at a current density of ∼100 mA cm-2 for 100 h, demonstrating great potential for its practical application.

A State-of-the-Art Review of Organic Polymer Modifiers for Slope Eco-Engineering.

In slope ecological restoration projects, reinforcing soil and promoting vegetation growth are essential measures. Guest soil spraying technology can be used to backfill modified soil and vegetation seeds onto the slope surface, resulting in successful ecological restoration. The use of organic polymer modifiers to reinforce soil has several benefits, such as high strength, effective results, and low pollution levels. Organic polymer soil modifiers can be divided into two categories: synthetic polymer modifiers and biopolymer modifiers. This paper provides a thorough review of the properties and interaction mechanisms of two types of polymer modifiers in soil consolidation. The properties of organic polymer modifiers make them applicable in soil and vegetation engineering on slopes. These modifiers can enhance soil mechanics, infiltration, and erosion resistance and promote vegetation growth. Therefore, the suitability of organic polymer modifiers for soil and vegetation engineering on slopes is demonstrated by their properties and potential for improvement in key areas. Furthermore, challenges and future prospects for slope protection technology using organic polymer modifiers are suggested.

Measuring the suicidal mind: The 'open source' Suicidality Scale, for adolescents and adults.

Clinicians are expected to provide accurate and useful mental health assessments, sometimes in emergency settings. The most urgent challenge may be in calculating suicide risk. Unfortunately, existing instruments often fail to meet requirements. To address this situation, we used a sustainable scale development approach to create a publicly available Suicidality Scale (SS). Following a critical review of current measures, community input, and panel discussions, an international item pool survey included 5,115 English-speaking participants aged 13-82 years. Revisions were tested with two follow-up cross-sectional surveys (Ns = 814 and 626). Pool items and SS versions were critically examined through item response theory, hierarchical cluster, factor and bifactor analyses, resulting in a unidimensional eight-item scale. Psychometric properties were high (loadings > .77; discrimination > 2.2; test-retest r = .87; internal consistency, ω = .96). Invariance checks were satisfied for age, gender, ethnicity, rural/urban residence, first language, self-reported psychiatric diagnosis and suicide attempt history. The SS showed stronger psychometric properties, and significant differences in bivariate associations with depressive symptoms, compared with included suicide measures. The 'open source' Suicidality Scale represents a significant step forward in accurate assessment for people aged 13+, and diverse populations. This study provides an example of sustainable scale development utilizing community input, emphasis on strong psychometric evidence from diverse samples, and a free-to-use license allowing instrument revisions. These methods can be used to develop a wide variety of psychosocial instruments that can benefit clinicians, researchers, and the public.

Laboratory Investigation of the Low-Temperature Crack Resistance of Wood Tar-Based Rejuvenated Asphalt Mixture Based on the Semi-Circular Bend and Trabecular Bending Test.

Low-temperature crack resistance is the core issue affecting the promotion of rejuvenated asphalt, but most current studies do not consider the creep relaxation characteristics of rejuvenated asphalt mixture at low temperatures, which is inconsistent with the actual situation. To explore the low-temperature crack resistance of a wood tar-based rejuvenated asphalt mixture, we observed the low-temperature crack resistance of styrene butadiene styrene (SBS) modified asphalt, wood tar-based rejuvenated asphalt, and RA-102 rejuvenated asphalt and their mixtures using laboratory tests. Our results showed that the low temperature crack resistance of the wood tar-based rejuvenated asphalt mixture was better than that of the RA-102 rejuvenated asphalt mixture, but slightly worse than that of the original SBS asphalt mixture. After the synergistic action of wood tar and biomass fiber, wood tar can be fully mixed into the new asphalt, effectively alleviating the bonding failure between asphalt and aggregate and improving the stiffness of the mixture, so that the toughness and crack resistance of rejuvenated asphalt mixture at low temperatures are evidently improved. Wood tar-based rejuvenated asphalt mixture has a good creep deformation ability at low temperatures. The established creep damage model can better describe the flexural creep performance of rejuvenated asphalt mixtures at low temperatures, and can be used to infer the deformation characteristics at other low temperatures.

Study on the Durability of Acid Rain Erosion-Resistant Asphalt Mixtures.

Acid erosion can accelerate the process of early damage of asphalt pavement and decrease the durability of asphalt pavement. However, there are limited research results for asphalt mixtures that can resist acid rain erosion. To systematically evaluate the impact and action law of acid rain erosion on the durability of asphalt mixtures, three gradation schemes were used: periodic dry-wet cycle immersion test, contact angle test and road performance test. The acid rain erosion resistance of epoxy asphalt mixture, SBS-modified asphalt mixture and 70# matrix asphalt mixture were tested from three aspects of anti-aging performance, freeze-thaw cycle performance and fatigue performance. The results show that the erosion of acid rain can significantly decrease the adhesion between asphalt and aggregate, and affects the road performance of the asphalt mixture. Acid rain erosion can significantly decrease the mechanical properties, adhesion and durability of asphalt mixtures. Epoxy asphalt has better physical properties, adhesion and acid rain erosion resistance than 70# matrix asphalt and SBS-modified asphalt. Epoxy asphalt has excellent adhesion due to its polar group, high cohesion and thermosetting resin with low shrinkage, which can effectively resist moisture erosion, spalling and temperature stress cracking, thereby effectively resisting the erosion of acid rain. Epoxy asphalt mixture has the strongest acid rain erosion resistance, which can be further enhanced when used together with waste rubber powder and modified bamboo fiber. On the whole, asphalt mixture with high-density structure and thicker asphalt film can effectively resist acid rain erosion. The durability of asphalt concrete (AC)-type gradation mixture and stone mastic asphalt (SMA)-type gradation mixture are equivalent, and both are superior to open-graded friction courses (OGFC)-type gradation mixture. The gradation of asphalt mixtures and the type of asphalt binder have great influence on their acid rain erosion resistance and durability. In order to realize the directional control of the acid rain erosion resistance and durability of different asphalt mixtures, a multi-parameter comprehensive assessment indicator system between the type and property of asphalt, the gradation of asphalt mixture, and the acid rain resistance and durability of the mixture need to be established in the future.

Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics.

The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulation data, our proposed model has achieved the best performance in terms of canonical correlation coefficients, estimation accuracy, and feature selection accuracy. On the real imaging genetic data, our proposed model has revealed promising features of single-nucleotide polymorphisms and brain regions related to sleep. The identified features can be used to improve clinical score prediction using promising imaging genetic biomarkers. An interesting future direction is to apply our model to additional neurological or psychiatric cohorts such as patients with Alzheimer's or Parkinson's disease to demonstrate the generalizability of our method.

Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer's Disease.

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

Orientation selectivity enhances context generalization and generative predictive coding in the hippocampus.

We learn and remember multiple new experiences throughout the day. The neural principles enabling continuous rapid learning and formation of distinct representations of numerous sequential experiences without major interference are not understood. To understand this process, here we interrogated ensembles of hippocampal place cells as rats explored 15 novel linear environments interleaved with sleep sessions over continuous 16 h periods. Remarkably, we found that a population of place cells were selective to environment orientation and topology. This orientation selectivity property biased the network-level discrimination and re/mapping between multiple environments. Novel environmental representations emerged rapidly as more generic, but repeated experience within the environments subsequently enhanced their discriminability. Generalization of prior experience with different environments consequently improved network predictability of future novel environmental representations via strengthened generative predictive codes. These coding schemes reveal a high-capacity, high-efficiency neuronal framework for rapid representation of numerous sequential experiences with optimal discrimination-generalization balance and reduced interference.