57 research outputs found
Supplemental Material, supplement20190916 - Brain-Wide Functional Dysconnectivity in Schizophrenia: Parsing Diathesis, Resilience, and the Effects of Clinical Expression
No full textSupplemental Material, supplement20190916 for Brain-Wide Functional Dysconnectivity in Schizophrenia: Parsing Diathesis, Resilience, and the Effects of Clinical Expression by Shuixia Guo, Ningning He, Zhening Liu, Zeqiang Linli, Haojuan Tao and Lena Palaniyappan in The Canadian Journal of Psychiatry</p
Supplemental Material, 19037-c-CJP-2019-178-OR.R2-Abstract__for_translation - Brain-Wide Functional Dysconnectivity in Schizophrenia: Parsing Diathesis, Resilience, and the Effects of Clinical Expression
No full textSupplemental Material, 19037-c-CJP-2019-178-OR.R2-Abstract__for_translation for Brain-Wide Functional Dysconnectivity in Schizophrenia: Parsing Diathesis, Resilience, and the Effects of Clinical Expression by Shuixia Guo, Ningning He, Zhening Liu, Zeqiang Linli, Haojuan Tao and Lena Palaniyappan in The Canadian Journal of Psychiatry</p
A reversal coarse-grained analysis with application to an altered functional circuit in depression
Get PDFIntroduction:
When studying brain function using functional magnetic resonance imaging (fMRI) data containing tens of thousands of voxels, a coarse-grained approach – dividing the whole brain into regions of interest – is applied frequently to investigate the organization of the functional network on a relatively coarse scale. However, a coarse-grained scheme may average out the fine details over small spatial scales, thus rendering it difficult to identify the exact locations of functional abnormalities.
Methods:
A novel and general approach to reverse the coarse-grained approach by locating the exact sources of the functional abnormalities is proposed.
Results:
Thirty-nine patients with major depressive disorder (MDD) and 37 matched healthy controls are studied. A circuit comprising the left superior frontal gyrus (SFGdor), right insula (INS), and right putamen (PUT) exhibit the greatest changes between the patients with MDD and controls. A reversal coarse-grained analysis is applied to this circuit to determine the exact location of functional abnormalities.
Conclusions:
The voxel-wise time series extracted from the reversal coarse-grained analysis (source) had several advantages over the original coarse-grained approach: (1) presence of a larger and detectable amplitude of fluctuations, which indicates that neuronal activities in the source are more synchronized; (2) identification of more significant differences between patients and controls in terms of the functional connectivity associated with the sources; and (3) marked improvement in performing discrimination tasks. A software package for pattern classification between controls and patients is available in Supporting Information
Key functional circuitry altered in schizophrenia involves parietal regions associated with sense of self
No full textThere is still no clear consensus as to which of the many functional and structural changes in the brain in schizophrenia are of most importance, although the main focus to date has been on those in the frontal and cingulate cortices. In the present study, we have used a novel holistic approach to identify brain-wide functional connectivity changes in medicated schizophrenia patients, and functional connectivity changes were analyzed using resting-state fMRI data from 69 medicated schizophrenia patients and 62 healthy controls. As far as we are aware, this is the largest population reported in the literature for a resting-state study. Voxel-based morphometry was also used to investigate gray and white matter volume changes. Changes were correlated with illness duration/symptom severity and a support vector machine analysis assessed predictive validity. A network involving the inferior parietal lobule, superior parietal gyrus, precuneus, superior marginal, and angular gyri was by far the most affected (68% predictive validity compared with 82% using all connections) and different components correlated with illness duration and positive and negative symptom severity. Smaller changes occurred in emotional memory and sensory and motor processing networks along with weakened interhemispheric connections. Our findings identify the key functional circuitry altered in schizophrenia involving the default network midline cortical system and the cortical mirror neuron system, both playing important roles in sensory and cognitive processing and particularly self-processing, all of which are affected in this disorder. Interestingly, the functional connectivity changes with the strongest links to schizophrenia involved parietal rather than frontal regions. Hum Brain Mapp, 2012
Identifying interactions in the time and frequency domains in local and global networks : a Granger causality approach
Get PDFBackground
Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.
Results
Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.
Conclusions
The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data
Beyond element-wise interactions: identifying complex interactions in biological processes
Get PDFBackground: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations.
Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction.
Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem
Listen to genes : dealing with microarray data in the frequency domain
Get PDFBackground: We present a novel and systematic approach to analyze temporal microarray data. The approach includes
normalization, clustering and network analysis of genes.
Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and
estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The
normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger
causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger
causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting
networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000
genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new
global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.
Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum
analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency
domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray
data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by
step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of
potential interest to Arabidopsis researchers
The instability of functional connectivity in patients with schizophrenia and their siblings: A dynamic connectivity study
No full textA Partial Granger Causality Approach to Explore Causal Networks Derived From Multi-parameter Data
Get PDFBackground: Inference and understanding of gene networks from experimental data is an important but complex problem in molecular biology. Mapping of gene pathways typically involves inferences arising from various studies performed on individual pathway components. Although pathways are often conceptualized as distinct entities; it is often understood that inter-pathway cross-talk and other properties of networks reflect underlying complexities that cannot by explained by consideration of individual pathways in isolation. In order to consider interaction between individual paths, a global multivariate approach is required. In this paper, we propose an extended form of Granger causality can be used to infer interactions between sets of time series data.
Results: We successfully tested our method on several artificial datasets, each one depicting various possibilities of connections among the participating entities. We also demonstrate the ability of our method to deal with latent and exogenous variables present in the system. We then applied this method to a highly replicated gene expression microarray time series data to infer causal influences between gene expression events involved. in activation of human T-cells. The application of our method to the T-cell dataset revealed a set of strong causal links between the participating genes, with many links already experimentally verified and reported in the biological literature.
Conclusions: We have proposed a novel form of Granger causality to reverse-engineer a causal network structure from a time series dataset involving multiple entities. We have extensively and successfully tested our method on synthesized as well as real time series microarray data
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