Biology subjects

Afyouni, S.

Publications and source records attributed to Afyouni, S..

7 recordsLinked to original sources

bioRxiv2020

Confound modelling in UK Biobank brain imaging

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and nonimaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions) that researchers may wish to consider for their studies using such data. We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

neuroscience↗

bioRxiv2020

How does group differences in motion scrubbing affect false positives in functional connectivity studies?

Analyzing resting state fMRI data is difficult due to a weak signal and several noise sources. Head motion is also a major problem and it is common to apply motion scrubbing, i.e. to remove time points where a subject has moved more than some pre-defined motion threshold. A problem arises if one cohort on average moves more than another, since the remaining temporal degrees of freedom are then different for the two groups. The effect of this is that the uncertainty of the functional connectivity estimates (e.g. Pearson correlations) are different for the two groups, but this is seldom modelled in resting state fMRI. We demonstrate that group differences in motion scrubbing can result in inflated false positives, depending on how the temporal auto correlation is modelled when performing the Fisher r-to-z transform.

neuroscience↗

bioRxiv2019

Multi-Subject Stochastic Blockmodels for Adaptive Analysis of Individual Differences in Human Brain Network Cluster Structure.

There is great interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of a group average network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two novel extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects on cluster structure of individual differences on subject-level covariates. Multi-subject Stochastic Blockmodels (MS-SBM) can flexibly account for between-subject variability in terms of a homogenous or heterogeneous effect on connectivity of covariates such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on Wald, likelihood ratio and Monte Carlo permutation tests. We show that multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition. Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; N = 268 brain regions), we show that the Heterogeneous Stochastic Blockmodel estimates core-on-modules architecture. The intra-block and inter-block connection weights vary between individual participants and can be modelled as a logistic function of subject-level covariates like age or diagnostic status. Multi-subject Stochastic Blockmodels are likely to be useful tools for statistical analysis of individual differences in human brain graphs and other networks whose prior cluster structure needs to be estimated from the data.

neuroscience↗

bioRxiv2018

Effective Degrees of Freedom of the Pearson’s Correlation Coefficient under Serial Correlation

The dependence between pairs of time series is commonly quantified by Pearsons correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fishers transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fishers transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardized Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical \"xDF\" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

neuroscience↗

bioRxiv2018

Functional connectivity alterations of the temporal lobe and hippocampus in semantic dementia and Alzheimer’s disease

The severe semantic memory impairments in semantic dementia have been attributed to a pronounced atrophy and functional disruption of the anterior temporal lobes. In contrast, the medial and posterior temporal lobe damage predominantly found in patients with Alzheimers disease has been associated with episodic memory disturbance. However, the two dementia subtypes share hippocampal deterioration, despite a relatively spared episodic memory in semantic dementia. To gain more insight into the mutual and divergent functional alterations seen in Alzheimers disease and semantic dementia, we assessed the differences in intrinsic functional connectivity between temporal lobe regions in patients with Alzheimers disease (n = 16), semantic dementia patients from two international sites (n = 23), and healthy controls (n = 17). In an exploratory study, we used a functional parcellation of the temporal cortex to extract time series. The Alzheimers disease group showed a single connection with reduced functional connectivity as compared to the controls. This connection was located between the right orbitofrontal cortex and the right anterior temporal lobe. In contrast, functional connectivity was decreased in the semantic dementia group in six connections, mainly involving the hippocampus, lingual gyrus, temporal pole, and orbitofrontal cortex. We identified a common pathway with semantic dementia, since the functional connectivity between the right anterior temporal lobe and the right orbitofrontal cortex was reduced in both types of dementia. This might be related to social knowledge deficits as part of semantic memory decline. However, such interpretations are preferably made in the context of all disease-specific semantic impairments and functional connectivity changes. Despite some limitations owed to the two database sites, this study provides a first preliminary picture of the brains functional dysconnectivity in Alzheimers disease and semantic dementia. Future studies are needed to replicate findings of such a common pathway with matched diagnosis, neuropsychological, and data MRI acquisition procedures.

neuroscience↗

bioRxiv2017

Directed functional connectivity using dynamic graphical models

There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations, human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 seconds offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, we find the DMN has consistent influence on the cerebellar, the limbic and the auditory/temporal network, as well a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.

neuroscience↗

bioRxiv2017

Insight And Inference For DVARS

Estimates of functional connectivity using resting state functional Magnetic Resonance Imaging (rs-fMRI) are acutely sensitive to artifacts and large scale nuisance variation. As a result much effort is dedicated to preprocessing rs-fMRI data and using diagnostic measure to identify bad scans. One such diagnostic measure is DVARS, the spatial standard deviation of the data after temporal differencing. A limitation of DVARS however is the lack of concrete interpretation of the absolute values of DVARS, and finding a threshold to distinguish bad scans from good. In this work we describe a variance decomposition of the entire 4D dataset that shows DVARS to be just one of three sources of variation we refer to as D-var (closely linked to DVARS), S-var and E-var. D-var and S-var partition the average variance between adjacent time points, while E-var accounts for edge effects, and each can be used to make spatial and temporal summary diagnostic measures. Extending the partitioning to global (and non-global) signal leads to a rs-fMRI DSE ANOVA table, which decomposes the total and global variance into fast (D-var), slow (S-var) and edge (E-var) components. We find expected values for each variance component under nominal models, showing how D-var (and thus DVARS) scales with overall variance and is diminished by temporal autocorrelation. Finally we propose a sampling distribution for squared DVARS (a multiple of D-var) and robust methods to estimate this null model, allowing computations of DVARS p-values. We propose that these diagnostic time series, images, p-values and ANOVA table will provide a succinct summary of the quality of a rs-fMRI dataset that will support comparisons of datasets over preprocessing steps and between subjects.

neuroscience↗