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A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data.

ABSTRACT: It has been known for decades that head motion/other artifacts affect the blood oxygen level-dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind-source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of event-related functional magnetic resonance imaging (erfMRI) and block-design (bdfMRI) datasets collected with multiband 32- (repetition time [TR] = 460?ms) or older 12-channel (TR = 2,000?ms) head coils. The amount of motion varied across coil designs and task types. Quality control plots indicated small to moderate relationships between head motion estimates and percent signal change in both signal and noise regions. Blind-source denoising strategies eliminated signal as well as noise relative to MP24 regression; however, the undesired effects on signal depended both on algorithm (FIX?>?AROMA) and design (bdfMRI?>?erfMRI). Moreover, in contrast to previous results, there were minimal differences between MP12/24 and MP0 pipelines in both erfMRI and bdfMRI designs. MP12/24 pipelines were detrimental for a task with both longer block length (30 ± 5 s) and higher correlations between head MPs and design matrix. In summary, current results suggest that there does not appear to be a single denoising approach that is appropriate for all fMRI designs. However, even nonaggressive blind-source denoising approaches appear to remove signal as well as noise from task-related data at individual subject and group levels.

SUBMITTER: Mayer AR

PROVIDER: S-EPMC6865567 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data.

Mayer Andrew R AR Ling Josef M JM Dodd Andrew B AB Shaff Nicholas A NA Wertz Christopher J CJ Hanlon Faith M FM

Human brain mapping 20190522 13

It has been known for decades that head motion/other artifacts affect the blood oxygen level-dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind-source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of ...[more]

PMID: 31119818

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Project description:Mesial temporal lobe epilepsy (mTLE) is a chronic disorder with spontaneous seizures recurring for years, or even decades. Many structural and functional changes have been detected in both the seizure focus and distal regions throughout the brain over this duration that may reflect the development of epileptogenic networks. Resting state functional magnetic resonance imaging (fMRI) connectivity mapping has the potential to elucidate and quantify these networks. The network between the left and right hippocampus may very likely be one of the most susceptible to changes due to long-term seizure propagation effects. Therefore, the objective of this study was to quantify cross hippocampal influence in mTLE using high temporal resolution fMRI, and to determine its relationship with disease duration.fMRI images were acquired in the resting (interictal) state with 500 ms temporal resolution across the temporal lobes of 19 mTLE patients (13 left, 6 right). The left and right hippocampi were identified on each subject's images using both structurally defined and functionally defined boundaries. The cross hippocampal influence was quantified in two ways for each pair of regions: (1) the nondirectional hippocampal functional connectivity calculated as the Pearson's correlation between the average time series in the left and the right hippocampus regions, and (2) the Granger causality (GC) laterality measure, which implies directional influence by determining temporal precedence. Each of these measures was correlated with age, age of onset, and disease duration across subjects to investigate relationship to disease progression.The hippocampal connectivity was not significantly different between patients with left and right mTLE using either the structurally or the functionally defined regions. Across all patients, hippocampal connectivity was not correlated significantly with age of onset or duration of disease. However, as duration of disease increased after 10 years (nine patients), the hippocampal connectivity increased linearly. Using the functionally defined regions, the GC laterality was increased in the right mTLE over the left mTLE, indicating that the left hippocampus was influencing the right hippocampus more than the right influencing left. This was also positively correlated with age of onset. Furthermore, like hippocampal connectivity, the relationship between GC laterality and duration of disease changes after 10 years duration of disease. After this duration, the GC laterality was positive in the three of three patients with right mTLE (left influencing right), whereas the GC laterality was negative in five of six patients with left mTLE (right influencing left).This study reveals a relationship between fMRI functional connectivity and causal influence of the left and right hippocampi and duration of disease in mTLE. During the interictal state, the interhemispheric hippocampal connectivity initially is disrupted and then linearly increases as the epilepsy progresses longer than 10 years. This increase in connectivity appears to be due to the hippocampus contralateral to the epileptogenic focus exerting more influence over the ipsilateral hippocampus. These findings may have implications in understanding the functional development of epileptic networks and possibly prediction of surgical outcome of mTLE.

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A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data.

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A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data.

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