885 research outputs found
Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood
There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention
Bayesian estimation of clustered dependence structures in functional neuroconnectivity
Motivated by the need to model joint dependence between regions of interest
in functional neuroconnectivity for efficient inference, we propose a new
sampling-based Bayesian clustering approach for covariance structures of
high-dimensional Gaussian outcomes. The key technique is based on a Dirichlet
process that clusters covariance sub-matrices into independent groups of
outcomes, thereby naturally inducing sparsity in the whole brain connectivity
matrix. A new split-merge algorithm is employed to improve the mixing of the
Markov chain sampling that is shown empirically to recover both uniform and
Dirichlet partitions with high accuracy. We investigate the empirical
performance of the proposed method through extensive simulations. Finally, the
proposed approach is used to group regions of interest into functionally
independent groups in the Autism Brain Imaging Data Exchange participants with
autism spectrum disorder and attention-deficit/hyperactivity disorder.Comment: 31 pages, 7 figures, 2 table
Using personâspecific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones
ObjectiveEmotional eating has been linked to ovarian hormone functioning, but no studies toâdate have considered the role of brain function. This knowledge gap may stem from methodological challenges: Data are heterogeneous, violating assumptions of homogeneity made by betweenâsubjects analyses. The primary aim of this paper is to describe an innovative withinâsubjects analysis that models heterogeneity and has potential for filling knowledge gaps in eating disorder research. We illustrate its utility in an application to pilot neuroimaging, hormone, and emotional eating data across the menstrual cycle.MethodGroup iterative multiple model estimation (GIMME) is a personâspecific network approach for estimating sampleâ, subgroupâ, and individualâlevel connections between brain regions. To illustrate its potential for eating disorder research, we apply it to pilot data from 10 female twins (Nâ=â5 pairs) discordant for emotional eating and/or anxiety, who provided two resting state fMRI scans and hormone assays. We then demonstrate how the multimodal data can be linked in multilevel models.ResultsGIMME generated personâspecific neural networks that contained connections common across the sample, shared between coâtwins, and unique to individuals. Illustrative analyses revealed positive relations between hormones and default mode connectivity strength for control twins, but no relations for their coâtwins who engage in emotional eating or who had anxiety.DiscussionThis paper showcases the value of personâspecific neuroimaging network analysis and its multimodal associations in the study of heterogeneous biopsychosocial phenomena, such as eating behavior.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146371/1/eat22902.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146371/2/eat22902_am.pd
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
From Correlation to Causation: Estimation of Effective Connectivity from Continuous Brain Signals based on Zero-Lag Covariance
Knowing brain connectivity is of great importance both in basic research and
for clinical applications. We are proposing a method to infer directed
connectivity from zero-lag covariances of neuronal activity recorded at
multiple sites. This allows us to identify causal relations that are reflected
in neuronal population activity. To derive our strategy, we assume a generic
linear model of interacting continuous variables, the components of which
represent the activity of local neuronal populations. The suggested method for
inferring connectivity from recorded signals exploits the fact that the
covariance matrix derived from the observed activity contains information about
the existence, the direction and the sign of connections. Assuming a sparsely
coupled network, we disambiguate the underlying causal structure via
-minimization. In general, this method is suited to infer effective
connectivity from resting state data of various types. We show that our method
is applicable over a broad range of structural parameters regarding network
size and connection probability of the network. We also explored parameters
affecting its activity dynamics, like the eigenvalue spectrum. Also, based on
the simulation of suitable Ornstein-Uhlenbeck processes to model BOLD dynamics,
we show that with our method it is possible to estimate directed connectivity
from zero-lag covariances derived from such signals. In this study, we consider
measurement noise and unobserved nodes as additional confounding factors.
Furthermore, we investigate the amount of data required for a reliable
estimate. Additionally, we apply the proposed method on a fMRI dataset. The
resulting network exhibits a tendency for close-by areas being connected as
well as inter-hemispheric connections between corresponding areas. Also, we
found that a large fraction of identified connections were inhibitory.Comment: 18 pages, 10 figure
Changes in Alcohol-Related Brain Networks Across the First Year of College: A Prospective Pilot Study Using fMRI Effective Connectivity Mapping
The upsurge in alcohol use that often occurs during the first year of college has been convincingly linked to a number of negative psychosocial consequences and may negatively affect brain development. In this longitudinal functional magnetic resonance imaging (fMRI) pilot study, we examined changes in neural responses to alcohol cues across the first year of college in a normative sample of late adolescents. Participants (N=11) were scanned three times across their first year of college (summer, first semester, second semester), while completing a go/no-go task in which images of alcoholic and non-alcoholic beverages were the response cues. A state-of-the-art effective connectivity mapping technique was used to capture spatiotemporal relations among brain regions of interest (ROIs) at the level of the group and the individual. Effective connections among ROIs implicated in cognitive control were greatest at the second assessment (when negative consequences of alcohol use increased), and effective connections among ROIs implicated in emotion processing were lower (and response times were slower) when participants were instructed to respond to alcohol cues compared to non-alcohol cues. These preliminary findings demonstrate the value of a prospective effective connectivity approach for understanding adolescent changes in alcohol-related neural processes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/123049/1/Changes in Alcohol-Related Brain Networks Across the First Year of College_A Prospective Pilot Study Using fMRI Effective Connectivity Mapping.pd
Characterizing the role of the preâSMA in the control of speed/accuracy tradeâoff with directed functional connectivity mapping and multiple solution reduction
Several plausible theories of the neural implementation of speed/accuracy tradeâoff (SAT), the phenomenon in which individuals may alternately emphasize speed or accuracy during the performance of cognitive tasks, have been proposed, and multiple lines of evidence point to the involvement of the preâsupplemental motor area (preâSMA). However, as the nature and directionality of the preâSMAâs functional connections to other regions involved in cognitive control and task processing are not known, its precise role in the topâdown control of SAT remains unclear. Although recent advances in crossâsectional path modeling provide a promising way of characterizing these connections, such models are limited by their tendency to produce multiple equivalent solutions. In a sample of healthy adults (Nâ=â18), the current study uses the novel approach of Group Iterative Multiple Model Estimation for Multiple Solutions (GIMMEâMS) to assess directed functional connections between the preâSMA, other regions previously linked to control of SAT, and regions putatively involved in evidence accumulation for the decision task. Results reveal a primary role of the preâSMA for modulating activity in regions involved in the decision process but suggest that this region receives topâdown input from the DLPFC. Findings also demonstrate the utility of GIMMEâMS and solutionâreduction methods for obtaining valid directional inferences from connectivity path models.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149347/1/hbm24493.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149347/2/hbm24493_am.pd
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
Recommended from our members
BRAIN Initiative: Cutting-Edge Tools and Resources for the Community.
The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources
State space modeling of time-varying contemporaneous and lagged relations in connectivity maps
Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample
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