Biology subjects

Afyouni, S.

Publications and source records attributed to Afyouni, S..

3 recordsLinked to original sources

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↗