An ICA with reference approach in identification of genetic variation and associated brain networks

A Glucocorticoid-Sensitive Hippocampal Gene Network Moderates the Impact of Early-Life Adversity on Mental Health Outcomes

BACKGROUND

Early stress increases the risk for psychiatric disorders. Glucocorticoids are stress mediators that regulate transcriptional activity and morphology in the hippocampus, which is implicated in the pathophysiology of multiple psychiatric conditions. We aimed to establish the relevance of hippocampal glucocorticoid-induced transcriptional activity as a mediator of the effects of early life on later psychopathology in humans.

METHODS

RNA sequencing was performed with anterior and posterior hippocampal dentate gyrus from adult female macaques (n = 12/group) that were chronically treated with betamethasone (glucocorticoid receptor agonist) or vehicle. Coexpression network analysis identified a preserved gene network in the posterior hippocampal dentate gyrus that was strongly associated with glucocorticoid exposure. The single nucleotide polymorphisms in the genes in this network were used to create an expression-based polygenic score in humans.

RESULTS

The expression-based polygenic score significantly moderated the association between early adversity and psychotic disorders in adulthood (UK Biobank, women, n = 44,519) and on child peer relations (ALSPAC [Avon Longitudinal Study of Parents and Children], girls, n = 1666 for 9-year-olds and n = 1594 for 11-year-olds), an endophenotype for later psychosis. Analyses revealed that this network was enriched for glucocorticoid-induced epigenetic remodeling in human hippocampal cells. We also found a significant association between single nucleotide polymorphisms from the expression-based polygenic score and adult brain gray matter density.

CONCLUSIONS

We provide an approach for the use of transcriptomic data from animal models together with human data to study the impact of environmental influences on mental health. The results are consistent with the hypothesis that hippocampal glucocorticoid-related transcriptional activity mediates the effects of early adversity on neural mechanisms implicated in psychiatric disorders.

Data-driven multimodal fusion: approaches and applications in psychiatric research

In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

Auditory oddball hypoactivation in schizophrenia

Individuals with schizophrenia (SZ) show aberrant activations, assessed via functional magnetic resonance imaging (fMRI), during auditory oddball tasks. However, associations with cognitive performance and genetic contributions remain unknown. This study compares individuals with SZ to healthy volunteers (HVs) using two cross-sectional data sets from multi-center brain imaging studies. It examines brain activation to auditory oddball targets, and their associations with cognitive domain performance, schizophrenia polygenic risk scores (PRS), and genetic variation (loci). Both sample 1 (137 SZ vs. 147 HV) and sample 2 (91 SZ vs. 98 HV), showed hypoactivation in SZ in the left-frontal pole, and right frontal orbital, frontal pole, paracingulate, intracalcarine, precuneus, supramarginal and hippocampal cortices, and right thalamus. In SZ, precuneus activity was positively related to cognitive performance. Schizophrenia PRS showed a negative correlation with brain activity in the right-supramarginal cortex. GWA analyses revealed significant single-nucleotide polymorphisms associated with right-supramarginal gyrus activity. RPL36 also predicted right-supramarginal gyrus activity. In addition to replicating hypoactivation for oddball targets in SZ, this study identifies novel relationships between regional activity, cognitive performance, and genetic loci that warrant replication, emphasizing the need for continued data sharing and collaborative efforts.

Cognitive Development and Brain Gray Matter Susceptibility to Prenatal Adversities: Moderation by the Prefrontal Cortex Brain-Derived Neurotrophic Factor Gene Co-expression Network

Background: Previous studies focused on the relationship between prenatal conditions and neurodevelopmental outcomes later in life, but few have explored the interplay between gene co-expression networks and prenatal adversity conditions on cognitive development trajectories and gray matter density. Methods: We analyzed the moderation effects of an expression polygenic score (ePRS) for the Brain-derived Neurotrophic Factor gene network (BDNF ePRS) on the association between prenatal adversity and child cognitive development. A score based on genes co-expressed with the prefrontal cortex (PFC) BDNF was created, using the effect size of the association between the individual single nucleotide polymorphisms (SNP) and the BDNF expression in the PFC. Cognitive development trajectories of 157 young children from the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) cohort were assessed longitudinally in 4-time points (6, 12, 18, and 36 months) using the Bayley-II mental scales. Results: Linear mixed-effects modeling indicated that BDNF ePRS moderates the effects of prenatal adversity on cognitive growth. In children with high BDNF ePRS, higher prenatal adversity was associated with slower cognitive development in comparison with those exposed to lower prenatal adversity. Parallel-Independent Component Analysis (pICA) suggested that associations of expression-based SNPs and gray matter density significantly differed between low and high prenatal adversity groups. The brain IC included areas involved in visual association processes (Brodmann area 19 and 18), reallocation of attention, and integration of information across the supramodal cortex (Brodmann area 10). Conclusion: Cognitive development trajectories and brain gray matter seem to be influenced by the interplay of prenatal environmental conditions and the expression of an important BDNF gene network that guides the growth and plasticity of neurons and synapses.

Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease

Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.

Prefrontal cortex VAMP1 gene network moderates the effect of the early environment on cognitive flexibility in children

During development, genetic and environmental factors interact to modify specific phenotypes. Both in humans and in animal models, early adversities influence cognitive flexibility, an important brain function related to behavioral adaptation to variations in the environment. Abnormalities in cognitive functions are related to changes in synaptic connectivity in the prefrontal cortex (PFC), and altered levels of synaptic proteins. We investigated if individual variations in the expression of a network of genes co-expressed with the synaptic protein VAMP1 in the prefrontal cortex moderate the effect of early environmental quality on the performance of children in cognitive flexibility tasks. Genes overexpressed in early childhood and co-expressed with the VAMP1 gene in the PFC were selected for study. SNPs from these genes (post-clumping) were compiled in an expression-based polygenic score (PFC-ePRS-VAMP1). We evaluated cognitive performance of the 4 years-old children in two cohorts using similar cognitive flexibility tasks. In the first cohort (MAVAN) we utilized two CANTAB tasks: (a) the Intra-/Extra-dimensional Set Shift (IED) task, and (b) the Spatial Working Memory (SWM) task. In the second cohort, GUSTO, we used the Dimensional Change Card Sort (DCCS) task. The results show that in 4 years-old children, the PFC-ePRS-VAMP1 network moderates responsiveness to the effects of early adversities on the performance in attentional flexibility tests. The same result was observed for a spatial working memory task. Compared to attentional flexibility, reversal learning showed opposite effects of the environment, as moderated by the ePRS. A parallel ICA analysis was performed to identify relationships between whole-brain voxel based gray matter density and SNPs that comprise the PFC-ePRS-VAMP1. The early environment predicts differences in gray matter content in regions such as prefrontal and temporal cortices, significantly associated with a genetic component related to Wnt signaling pathways. Our data suggest that a network of genes co-expressed with VAMP1 in the PFC moderates the influence of early environment on cognitive function in children.

Amygdala 5-HTT Gene Network Moderates the Effects of Postnatal Adversity on Attention Problems: Anatomo-Functional Correlation and Epigenetic Changes

Variations in serotoninergic signaling have been related to behavioral outcomes. Alterations in the genome, such as DNA methylation and histone modifications, are affected by serotonin neurotransmission. The amygdala is an important brain region involved in emotional responses and impulsivity, which receives serotoninergic input. In addition, studies suggest that the serotonin transporter gene network may interact with the environment and influence the risk for psychiatric disorders. We propose to investigate whether/how interactions between the exposure to early life adversity and serotonin transporter gene network in the amygdala associate with behavioral disorders. We constructed a co-expression-based polygenic risk score (ePRS) reflecting variations in the function of the serotonin transporter gene network in the amygdala and investigated its interaction with postnatal adversity on attention problems in two independent cohorts from Canada and Singapore. We also described how interactions between ePRS-5-HTT and postnatal adversity exposure predict brain gray matter density and variation in DNA methylation across the genome. We observed that the expression-based polygenic risk score, reflecting the function of the amygdala 5-HTT gene network, interacts with postnatal adversity, to predict attention and hyperactivity problems across both cohorts. Also, both postnatal adversity score and amygdala ePRS-5-HTT score, as well as their interaction, were observed to be associated with variation in DNA methylation across the genome. Variations in gray matter density in brain regions linked to attentional processes were also correlated to our ePRS score. These results confirm that the amygdala 5-HTT gene network is strongly associated with ADHD-related behaviors, brain cortical density, and epigenetic changes in the context of adversity in young children.

Prefrontal Cortex Dopamine Transporter Gene Network Moderates the Effect of Perinatal Hypoxic-Ischemic Conditions on Cognitive Flexibility and Brain Gray Matter Density in Children

BACKGROUND

Genetic polymorphisms of the dopamine transporter gene (DAT1) and perinatal complications associated with poor oxygenation are risk factors for attentional problems in childhood and may show interactive effects.

METHODS

We created a novel expression-based polygenic risk score (ePRS) reflecting variations in the function of the DAT1 gene network (ePRS-DAT1) in the prefrontal cortex and explored the effects of its interaction with perinatal hypoxic-ischemic-associated conditions on cognitive flexibility and brain gray matter density in healthy children from two birth cohorts-MAVAN from Canada (n = 139 boys and girls) and GUSTO from Singapore (n = 312 boys and girls).

RESULTS

A history of exposure to several perinatal hypoxic-ischemic-associated conditions was associated with impaired cognitive flexibility only in the high-ePRS group, suggesting that variation in the prefrontal cortex expression of genes involved in dopamine reuptake is associated with differences in this behavior. Interestingly, this result was observed in both ethnically distinct birth cohorts. Additionally, parallel independent component analysis (MAVAN cohort, n = 40 children) demonstrated relationships between single nucleotide polymorphism-based ePRS and gray matter density in areas involved in executive (cortical regions) and integrative (bilateral thalamus and putamen) functions, and these relationships differ in children from high and low exposure to hypoxic-ischemic-associated conditions.

CONCLUSIONS

These findings reveal that the impact of conditions associated with hypoxia-ischemia on brain development and executive functions is moderated by genotypes associated with dopamine signaling in the prefrontal cortex. We discuss the potential impact of innovative genomic and environmental measures for the identification of children at high risk for impaired executive functions.

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities. The main focus has been on extracting "networks" represented as the underlying latent sources. While the broad success in learning latent representations from multiple datasets has promoted the wide presence of BSS in modern neuroscience, it also introduced a wide variety of objective functions, underlying graphical structures, and parameter constraints for each method. Such diversity, combined with a host of datatype-specific know-how, can cause a sense of disorder and confusion, hampering a practitioner's judgment and impeding further development. We organize the diverse landscape of BSS models by exposing its key features and combining them to establish a novel unifying view of the area. In the process, we unveil important connections among models according to their properties and subspace structures. Consequently, a high-level descriptive structure is exposed, ultimately helping practitioners select the right model for their applications. Equipped with that knowledge, we review the current state of BSS applications to neuroimaging. The gained insight into model connections elicits a broader sense of generalization, highlighting several directions for model development. In light of that, we discuss emerging multi-dataset multidimensional (MDM) models and summarize their benefits for the study of the healthy brain and disease-related changes.

Genetic imaging consortium for addiction medicine: From neuroimaging to genes

Since the sample size of a typical neuroimaging study lacks sufficient statistical power to explore unknown genomic associations with brain phenotypes, several international genetic imaging consortia have been organized in recent years to pool data across sites. The challenges and achievements of these consortia are considered here with the goal of leveraging these resources to study addiction. The authors of this review have joined together to form an Addiction working group within the framework of the ENIGMA project, a meta-analytic approach to multisite genetic imaging data. Collectively, the Addiction working group possesses neuroimaging and genomic data obtained from over 10,000 subjects. The deadline for contributing data to the first round of analyses occurred at the beginning of May 2015. The studies performed on this data should significantly impact our understanding of the genetic and neurobiological basis of addiction.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Anatomical, functional and molecular biomarker applications of magnetic resonance neuroimaging

ABSTRACT 

MRI and magnetic resonance spectroscopy (MRS) along with computed tomography and PET are the most common imaging modalities used in the clinics to detect structural abnormalities and pathological conditions in the brain. MRI generates superb image resolution/contrast without radiation exposure that is associated with computed tomography and PET; MRS and spectroscopic imaging technologies allow us to measure changes in brain biochemistry. Increasingly, neurobiologists and MRI scientists are collaborating to solve neuroscience problems across sub-cellular through anatomical levels. To achieve successful cross-disciplinary collaborations, neurobiologists must have sufficient knowledge of magnetic resonance principles and applications in order to effectively communicate with their MRI colleagues. This review provides an overview of magnetic resonance techniques and how they can be used to gain insight into the active brain at the anatomical, functional and molecular levels with the goal of encouraging neurobiologists to include MRI/MRS as a research tool in their endeavors.

Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation

The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the “small N” problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries—the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.

A review of multivariate analyses in imaging genetics

Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference

One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p=1.64×10(-6)). The sMRI component showed a significant group difference in loading parameters between patients and controls (p=1.33×10(-15)), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p=0.04) and was predominantly contributed to by 1030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size.

Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method

Background

In recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). In addition, a few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis.

Methods

In this study, a novel sparse representation based variable selection (SRVS) method has been proposed and tested on a simulation data set to demonstrate its multi-resolution properties. Then the SRVS method was applied to an integrative analysis of two different SCZ data sets, a Single-nucleotide polymorphism (SNP) data set and a functional resonance imaging (fMRI) data set, including 92 cases and 116 controls. Biomarkers for the disease were identified and validated with a multivariate classification approach followed by a leave one out (LOO) cross-validation. Then we compared the results with that of a previously reported sparse representation based feature selection method.

Results

Results showed that biomarkers from our proposed SRVS method gave significantly higher classification accuracy in discriminating SCZ patients from healthy controls than that of the previous reported sparse representation method. Furthermore, using biomarkers from both data sets led to better classification accuracy than using single type of biomarkers, which suggests the advantage of integrative analysis of different types of data.

Conclusions

The proposed SRVS algorithm is effective in identifying significant biomarkers for complicated disease as SCZ. Integrating different types of data (e.g. SNP and fMRI data) may identify complementary biomarkers benefitting the diagnosis accuracy of the disease.

Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies

Despite significant advances in multimodal imaging techniques and analysis approaches, unimodal studies are still the predominant way to investigate brain changes or group differences, including structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and electroencephalography (EEG). Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance of multiple imaging measures. To examine the function-structure associations of the brain in a more comprehensive and integrated manner, we reviewed a number of multimodal studies that combined two or more functional (fMRI and/or EEG) and structural (sMRI and/or DTI) modalities. In this review paper, we specifically focused on multimodal neuroimaging studies on cognition, aging, disease and behavior. We also compared multiple analysis approaches, including univariate and multivariate methods. The possible strengths and limitations of each method are highlighted, which can guide readers when selecting a method based on a given research question. In particular, we believe that multimodal fusion approaches will shed further light on the neuronal mechanisms underlying the major structural and functional pathophysiological features of both the healthy brain (e.g. development) or the diseased brain (e.g. mental illness) and, in the latter case, may provide a more sensitive measure than unimodal imaging for disease classification, e.g. multimodal biomarkers, which potentially can be used to support clinical diagnosis based on neuroimaging techniques.

Metabolic module mining based on Independent Component Analysis in Arabidopsis thaliana

Independent Component Analysis (ICA) has been introduced as one of the useful tools for gene-functional discovery in animals. However, this approach has been poorly utilized in the plant sciences. In the present study, we have exploited ICA combined with pathway enrichment analysis to address the statistical challenges associated with genome-wide analysis in plant system. To generate an Arabidopsis metabolic platform, we collected 4,373 Affy-metrix ATH1 microarray datasets. Out of the 3,232 metabolic genes and transcription factors, 99.47% of these genes were identified in at least one component, indicating the coverage of most of the metabolic pathways by the components. During the metabolic pathway enrichment analysis, we found components that indicate an independent regulation between the isoprenoid biosynthesis pathways. We also utilized this analysis tool to investigate some transcription factors involved in secondary cell wall biogenesis. This approach has identified remarkably more transcription factors compared to previously reported analysis tools. A website providing user-friendly searching and downloading of the entire dataset analyzed by ICA is available at http://kimjy.gnu.ac.kr/ICA.files/slide0002.htm . ICA combined with pathway enrichment analysis might provide a powerful approach for the extraction of the components responsible for a biological process of interest in plant systems.

The paradox of syndromic diversity in Alzheimer disease

Variant syndromes of Alzheimer disease (AD), led by deficits that extend beyond memory dysfunction, are of considerable clinical and neurobiological importance. Such syndromes present major challenges for both diagnosis and monitoring of disease, and serve to illustrate the apparent paradox of a clinically diverse group of disorders underpinned by a common histopathological substrate. This Review focuses on the most common variant AD phenotypes: posterior cortical atrophy, logopenic variant primary progressive aphasia and frontal variant AD. The neuroanatomical, molecular and pathological correlates of these phenotypes are highlighted, and the heterogeneous clinical presentations of the syndromes are discussed in the context of the emerging network paradigm of neurodegenerative disease. We argue that these apparently diverse clinical phenotypes reflect the differential involvement of a common core temporoparietofrontal network that is vulnerable to AD. According to this interpretation, the network signatures corresponding to AD variant syndromes are produced by genetic and other modulating factors that have yet to be fully characterized. The clinical and neurobiological implications of this network paradigm in the quest for disease-modifying treatments are also explored.