ICA model order selection of task co-activation networks

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders

A Bayesian approach to the semi-analytic model of galaxy formation: methodology

We believe that a wide range of physical processes conspire to shape the observed galaxy population but we remain unsure of their detailed interactions. The semi-analytic model (SAM) of galaxy formation uses multi-dimensional parameterisations of the physical processes of galaxy formation and provides a tool to constrain these underlying physical interactions. Because of the high dimensionality, the parametric problem of galaxy formation may be profitably tackled with a Bayesian-inference based approach, which allows one to constrain theory with data in a statistically rigorous way. In this paper we develop a SAM in the framework of Bayesian inference. We show that, with a parallel implementation of an advanced Markov-Chain Monte-Carlo algorithm, it is now possible to rigorously sample the posterior distribution of the high-dimensional parameter space of typical SAMs. As an example, we characterise galaxy formation in the current $\Lambda$CDM cosmology using the stellar mass function of galaxies as an observational constraint. We find that the posterior probability distribution is both topologically complex and degenerate in some important model parameters, suggesting that thorough explorations of the parameter space are needed to understand the models. We also demonstrate that because of the model degeneracy, adopting a narrow prior strongly restricts the model. Therefore, the inferences based on SAMs are conditional to the model adopted. Using synthetic data to mimic systematic errors in the stellar mass function, we demonstrate that an accurate observational error model is essential to meaningful inference.Comment: revised version to match published article published in MNRA

Triple Network Resting State Connectivity Predicts Distress Tolerance and Is Associated with Cocaine Use

Distress tolerance (DT), a predictor of substance use treatment retention and post-treatment relapse, is associated with task based neural activation in regions located within the salience (SN), default mode (DMN), and executive control networks (ECN). The impact of network connectivity on DT has yet to be investigated. The aim of the present study was to test within and between network resting-state functional connectivity (rsFC) associations with DT, and the impact of cocaine use on this relationship. Twenty-nine adults reporting regular cocaine use (CU) and 28 matched healthy control individuals (HC), underwent resting-state functional magnetic resonance imaging followed by the completion of two counterbalanced, computerized DT tasks. Dual-regression analysis was used to derive within and between network rsFC of the SN, DMN, and lateralized (left and right) ECN. Cox proportional-hazards survival models were used to test the interactive effect of rsFC and group on DT. The association between cocaine use severity, rsFC, and DT was tested within the CU group. Lower LECN and higher DMN-SN rsFC were associated with DT impairment. Greater amount of cocaine use per using day was associated with greater DMN-SN rsFC. The findings emphasize the role of neural resource allocation within the ECN and between DMN-SN on distress tolerance

Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20–29 to 70–79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging

Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20–29 to 70–79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging

Influence of age, sex and genetic factors on the human brain

We report effects of age, age(2), sex and additive genetic factors on variability in gray matter thickness, surface area and white matter integrity in 1,010 subjects from the Genetics of Brain Structure and Function Study. Age was more strongly associated with gray matter thickness and fractional anisotropy of water diffusion in white matter tracts, while sex was more strongly associated with gray matter surface area. Widespread heritability of neuroanatomic traits was observed, suggesting that brain structure is under strong genetic control. Furthermore, our findings indicate that neuroimaging-based measurements of cerebral variability are sensitive to genetic mediation. Fundamental studies of genetic influence on the brain will help inform gene discovery initiatives in both clinical and normative samples

Multi-region hemispheric specialization differentiates human from nonhuman primate brain function

The human behavioral repertoire greatly exceeds that of nonhuman primates. Anatomical specializations of the human brain include an enlarged neocortex and prefrontal cortex (Semendeferi et al. in Am J Phys Anthropol 114:224?241, 2001), but regional enlargements alone cannot account for these vast functional differences. Hemispheric specialization has long believed to be a major contributing factor to such distinctive human characteristics as motor dominance, attentional control and language. Yet structural cerebral asymmetries, documented in both humans and some nonhuman primate species, are relatively minor compared to behavioral lateralization. Identifying the mechanisms that underlie these functional differences remains a goal of considerable interest. Here, we investigate the intrinsic connectivity networks in four primate species (humans, chimpanzees, baboons, and capuchin monkeys) using resting-state fMRI to evaluate the intra- and inter- hemispheric coherences of spontaneous BOLD fluctuation. All three nonhuman primate species displayed lateralized functional networks that were strikingly similar to those observed in humans. However, only humans had multi-region lateralized networks, which provide fronto-parietal connectivity. Our results indicate that this pattern of within-hemisphere connectivity distinguishes humans from nonhuman primates

Pleiotropic locus for emotion recognition and amygdala volume identified using univariate and bivariate linkage

Objective: The role of the amygdala in emotion recognition is well established, and amygdala volume and emotion recognition performance have each been shown separately to be highly heritable traits, but the potential role of common genetic influences on both traits has not been explored. The authors investigated the pleiotropic influences of amygdala volume and emotion recognition performance. Method: In a sample of randomly selected extended pedigrees (N=858), the authors used a combination of univariate and bivariate linkage to investigate pleiotropy between amygdala volume and emotion recognition performance and followed up with association analysis. Results: The authors found a pleiotropic region for amygdala volume and emotion recognition performance on chromosome 4q26 (LOD score=4.40). Association analysis conducted in the region underlying the bivariate linkage peak revealed a variant meeting the corrected significance level (Bonferroni-corrected p=5.01×10-5) within an intron of PDE5A (rs2622497, p=4.4×10-5) as being jointly influential on both traits. PDE5A has been implicated previously in recognition-memory deficits and is expressed in subcortical structures that are thought to underlie memory ability, including the amygdala. Conclusions: This study extends our understanding of the shared etiology between the amygdala and emotion recognition by showing that the overlap between amygdala volume and emotion recognition performance is due at least in part to common genetic influences. Moreover, this study identifies a pleiotropic locus for the two traits and an associated variant, which localizes the genetic signal even more precisely. These results, when taken in the context of previous research, highlight the potential utility of PDE5 inhibitors for ameliorating emotion recognition deficits in individuals suffering from mental or neurodegenerative illness

Genome-wide Linkage on Chromosome 10q26 for a Dimensional Scale of Major Depression

Major depressive disorder (MDD) is a common and potentially life-threatening mood disorder. Identifying genetic markers for depression might provide reliable indicators of depression risk, which would, in turn, substantially improve detection, enabling earlier and more effective treatment. The aim of this study was to identify rare variants for depression, modeled as a continuous trait, using linkage and post-hoc association analysis. The sample comprised 1221 Mexican–American individuals from extended pedigrees. A single dimensional scale of MDD was derived using confirmatory factor analysis applied to all items from the Past Major Depressive Episode section of the Mini-International Neuropsychiatric Interview. Scores on this scale of depression were subjected to linkage analysis followed by QTL region-specific association analysis. Linkage analysis revealed a single genome-wide significant QTL (LOD=3.43) on 10q26.13, QTL-specific association analysis conducted in the entire sample revealed a suggestive variant within an intron of the gene LHPP (rs11245316, p=7.8×10−04; LD-adjusted Bonferroni-corrected p=8.6×10−05). This region of the genome has previously been implicated in the etiology of MDD; the present study extends our understanding of the involvement of this region by highlighting a putative gene of interest (LHPP)

Genome-wide significant linkage of schizophrenia-related neuroanatomical trait to 12q24

The insula and medial prefrontal cortex (mPFC) share functional, histological, transcriptional and developmental characteristics and they serve higher cognitive functions of theoretical relevance to schizophrenia and related disorders. Meta-analyses and multivariate analysis of structural magnetic resonance imaging (MRI) scans indicate that gray matter density and volume reductions in schizophrenia are the most consistent and pronounced in a network primarily composed of the insula and mPFC. We used source-based morphometry, a multivariate technique optimized for structural MRI, in a large sample of randomly ascertained pedigrees (N = 887) to derive an insula-mPFC component and to investigate its genetic determinants. Firstly, we replicated the insula-mPFC gray matter component as an independent source of gray matter variation in the general population, and verified its relevance to schizophrenia in an independent case-control sample. Secondly, we showed that the neuroanatomical variation defined by this component is largely determined by additive genetic variation (h2 = 0.59), and genome-wide linkage analysis resulted in a significant linkage peak at 12q24 (LOD = 3.76). This region has been of significant interest to psychiatric genetics as it contains the Darier’s disease locus and other proposed susceptibility genes (e.g. DAO, NOS1), and it has been linked to affective disorders and schizophrenia in multiple populations. Thus, in conjunction with previous clinical studies, our data imply that one or more psychiatric risk variants at 12q24 are co-inherited with reductions in mPFC and insula gray matter concentration