Twin Classification in Resting-State Brain Connectivity

Research Square (Research Square), 2022

The brain intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows a complex inter-individual variability, consolidated during development. Nevertheless, the role of genetic and environmental factors on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (age range 10-30 years) to explore developmental determinants of brain FC. FC features at multiple spatial scales were extracted and tested for applicability of classical ACE and ADE twin design. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the predominant effect on FC link-and node-level features was related to unique environment. Our results showed that developmental age is characterized by complex relationships between genetic and environmental factors and functional brain connections, suggesting a predominant role of unique environment on multi-scale RSN characteristics.

NeuroImage, 2012

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach.

2015

Anatomical brain networks change throughout life and with diseases. Genetic analysis of these networks may help identify processes giving rise to heritable brain disorders, but we do not yet know which network measures are promising for genetic analyses. Many factors affect the downstream results, such as the tractography algorithm used to define structural connectivity. We tested nine different tractography algorithms and four normalization methods to compute brain networks for 853 young healthy adults (twins and their siblings). We fitted genetic structural equation models to all nine network measures, after a normalization step to increase network consistency across tractography algorithms. Probabilistic tractography algorithms with global optimization (such as Probtrackx and Hough) yielded higher heritability statistics than "greedy" algorithms (such as FACT) which process small neighborhoods at each step. Some global network measures (probtrackx-derived GLOB and ST) showed significant genetic effects, making them attractive targets for genome-wide association studies.

eLife, 2017

Patterns of intrinsic human brain activity exhibit a profile of functional connectivity that is associated with behaviour and cognitive performance, and deteriorates with disease. This paper investigates the relative importance of genetic factors and the common environment between twins in determining this functional connectivity profile. Using functional magnetic resonance imaging (fMRI) on 820 subjects from the Human Connectome Project, and magnetoencephalographic (MEG) recordings from a subset, the heritability of connectivity between 39 cortical regions was estimated. On average over all connections, genes account for about 15% of the observed variance in fMRI connectivity (and about 10% in alpha-band and 20% in beta-band oscillatory power synchronisation), which substantially exceeds the contribution from the environment shared between twins. Therefore, insofar as twins share a common upbringing, it appears that genes, rather than the developmental environment, play a dominant ...

2010

Spontaneous brain activity, as observed in functional neuroimaging, has been shown to display reproducible structure that expresses brain architecture and carries markers of brain pathologies. An important view of modern neuroscience is that such large-scale structure of coherent activity reflects modularity properties of brain connectivity graphs. However, to date, there has been no demonstration that the limited and noisy data available in spontaneous activity observations could be used to learn full-brain probabilistic models that generalize to new data. Learning such models entails two main challenges: i) modeling full brain connectivity is a difficult estimation problem that faces the curse of dimensionality and ii) variability between subjects, coupled with the variability of functional signals between experimental runs, makes the use of multiple datasets challenging. We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population. We show that individual models learned from functional Magnetic Resonance Imaging (fMRI) data using this population prior generalize better to unseen data than models based on alternative regularization schemes. To our knowledge, this is the first report of a cross-validated model of spontaneous brain activity. Finally, we use the estimated graphical model to explore the large-scale characteristics of functional architecture and show for the first time that known cognitive networks appear as the integrated communities of functional connectivity graph.

Computational Intelligence and Neuroscience, 2016

Extracting functional connectivity patterns among cortical regions in fMRI datasets is a challenge stimulating the development of effective data-driven or model based techniques. Here, we present a novel data-driven method for the extraction of significantly connected functional ROIs directly from the preprocessed fMRI data without relying on a priori knowledge of the expected activations. This method finds spatially compact groups of voxels which show a homogeneous pattern of significant connectivity with other regions in the brain. The method, called Select and Cluster (S&C), consists of two steps: first, a dimensionality reduction step based on a blind multiresolution pairwise correlation by which the subset of all cortical voxels with significant mutual correlation is selected and the second step in which the selected voxels are grouped into spatially compact and functionally homogeneous ROIs by means of a Support Vector Clustering (SVC) algorithm. The S&C method is described in...

2019

fMRI is a unique non-invasive approach for understanding the functional organization of the human brain, and task-based fMRI promotes identification of functionally relevant brain regions associated with a given task. Here, we use fMRI (using the Poffenberger Paradigm) data collected in mono- and dizygotic twin pairs to propose a novel approach for assessing similarity in functional networks. In particular, we compared network similarity between pairs of twins in task-relevant and task-orthogonal networks. The proposed method measures the similarity between functional networks using a geodesic distance between graph Laplacians. With method we show that networks are more similar in monozygotic twins compared to dizygotic twins. Furthermore, the similarity in monozygotic twins is higher for task-relevant, than task-orthogonal networks.

arXiv (Cornell University), 2019

fMRI is a unique non-invasive approach for understanding the functional organization of the human brain, and taskbased fMRI promotes identification of functionally relevant brain regions associated with a given task. Here, we use fMRI (using the Poffenberger Paradigm) data collected in mono-and dizygotic twin pairs to propose a novel approach for assessing similarity in functional networks. In particular, we compared network similarity between pairs of twins in task-relevant and task-orthogonal networks. The proposed method measures the similarity between functional networks using a geodesic distance between graph Laplacians. With method we show that networks are more similar in monozygotic twins compared to dizygotic twins. Furthermore, the similarity in monozygotic twins is higher for task-relevant, than task-orthogonal networks.

arXiv (Cornell University), 2020

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders. In this paper, we investigate how functional connectivity in males and females differs in an age prediction framework. We first estimate functional connectivity between regions-of-interest (ROIs) using distance correlation instead of Pearson's correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex information of between-ROI interactions. Then, a novel non-convex multi-task learning (NC-MTL) model is proposed to study age-related gender differences in functional connectivity, where age prediction for each gender group is viewed as one task. Specifically, in the proposed NC-MTL model, we introduce a composite regularizer with a combination of non-convex ℓ 2,1-2 and ℓ 1-2 regularization terms for selecting both common and task-specific features. Finally, we validate the proposed NC-MTL model along with distance correlation based functional connectivity on rs-fMRI of the Philadelphia Neurodevelopmental Cohort for predicting ages of both genders. The experimental results demonstrate that the proposed NC-MTL model outperforms other competing MTL models in age prediction, as well as characterizing developmental gender differences in functional connectivity patterns.

Nature Scientific Reports, 2019

The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets. The analysis of brain networks, or connectomes, is a recent and exciting advancement in magnetic resonance imaging (MRI) that promises to identify new phenotypes for healthy, diseased or aging brains 1. A connectome is a comprehensive map of the connections in the brain, which is conceived as a network, where brain areas (nodes) are connected by links (edges) 2 , and connections can be either given by white matter tracts between pairs of brain regions or by an index of the correlation of functional activity 3. This approach allows for analyzing the brain as a complex system of dynamically interacting components without explicitly relying on local activation or brain morphology.