Biology subjects

Golestani, A. M.

Publications and source records attributed to Golestani, A. M..

3 recordsLinked to original sources

bioRxiv2023

Single voxel autocorrelation reflects hippocampal function in temporal lobe epilepsy

We have previously shown that autocorrelation analyses of BOLD signal applied to single voxels in healthy controls can identify behaviorally-relevant gradients of temporal dynamics throughout the hippocampal long-axis. A question that remains is how changes in the brains functional and structural integrity affect single voxel autocorrelation. In this study we investigate how hippocampal autocorrelation is affected by hippocampal dysfunction by investigating a population of patients with unilateral temporal lobe epilepsy (TLE). Many patients with TLE have mesial temporal sclerosis (MTS), characterized by scarring and neuronal loss particularly in the anterior hippocampus. Here we compared patients with left and right TLE, some with and without MTS, to healthy controls. We applied our single voxel autocorrelation method and data-driven clustering approach to segment the hippocampus based on the autocorrelation of resting state fMRI. We found that patients with left TLE had longer intrinsic timescales (i.e., higher autocorrelation) compared to controls, particularly in the anterior-medial portion of the hippocampus. This was true for both the epileptogenic and non-epileptogenic hemispheres. We also evaluated the extent of cluster preservation (i.e., spatial overlap with controls) of patient autocorrelation clusters and the relationship to verbal and visuospatial memory. We found that patients with greater cluster preservation in the anterior-medial hippocampus had better memory performance. Surprisingly, we did not find any effect of MTS on single voxel autocorrelation, despite the structural changes associated with the condition. These results suggest that spatiotemporal dynamics of activity can be informative regarding the functional integrity of the hippocampus in TLE.

neuroscience↗

bioRxiv2023

Comparing data-driven physiological denoising approaches for resting-state fMRI: Implications for the study of aging

Physiological nuisance contributions by cardiac and respiratory signals has a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution. In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on each methods ability to remove cardiac and respiratory signal power, as well as its ability to preserve low-frequency signals and age-related functional connectivity (fcMRI) differences. Our findings revealed that ICA-AROMA and GSR consistently remove more heart-beat and respiratory frequencies, but also the most low-frequency signals. Our results confirm that the ICA-AROMA and GSR removed the most physiological noise at the expense of meaningful age-related fcMRI differences. On the other hand, aCompCor and tCompCor seem to provide a good balance between removing physiological signals and preserving fcMRI information. Lastly, methods differ in performance on young- and older-adult data sets. While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also informs the choice of denoising method for broader fcMRI applications.

neuroscience↗

bioRxiv2020

Controlling for the effect of arterial-CO2 fluctuations in resting-state fMRI: Comparing end-tidal CO2 clamping and retroactive CO2 correction

The BOLD signal, as the basis of functional MRI, arises from both neuronal and vascular factors, with their respective contributions to resting state-fMRI still unknown. Among the factors contributing to "physiological noise", dynamic arterial CO2 fluctuations constitutes the strongest and the most widespread modulator of the grey-matter rs-fMRI signal. Some important questions are: (1) if we were able to clamp arterial CO2 such that fluctuations are removed, what would happen to rs-fMRI measures? (2) falling short of that, is it possible to retroactively correct for CO2 effects with equivalent outcome? In this study 13 healthy subjects underwent two rs-fMRI acquisition: During the "clamped" run, end-tidal CO2 (PETCO2) is clamped to the average PETCO2 level of each participant, while during the "free-breathing" run, the PETCO2 level is passively monitored but not controlled. PETCO2 correction was applied to the free-breathing data by convolving PETCO2 with its BOLD response function, and then regressing out the result. We computed the BOLD resting-state fluctuation amplitude (RSFA), as well as seed-independent mean functional connectivity (FC) as the weighted global brain connectivity (wGBC). Furthermore, connectivity between conditions were compared using coupled intrinsic-connectivity distribution (ICD) method. We ensured that PETCO2 clamping did not significantly alter heart-beat and respiratory variation. We found that neither PETCO2 clamping nor correction produced significant change in RSFA and wGBC. In terms of the ICD, PETCO2 clamping and correction both reduced FC strength in the majority of grey matter regions, although the effect of PETCO2 correction is considerably smaller than the effect of PETCO2 clamping. Furthermore, while PETCO2 clamping reduced inter-subject variability in FC, PETCO2 correction increased the variability. Overall PETCO2 correction is not the equivalent of PETCO2 clamping, although it shifts FC values towards the same direction as clamping does.

neuroscience↗