scholarly journals Evaluation of denoising strategies for task‐based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks

2021 ◽  
Vol 42 (6) ◽  
pp. 1805-1828
Author(s):  
Daniele Mascali ◽  
Marta Moraschi ◽  
Mauro DiNuzzo ◽  
Silvia Tommasin ◽  
Michela Fratini ◽  
...  
2012 ◽  
Vol 39 (6Part8) ◽  
pp. 3684-3684
Author(s):  
T Kim ◽  
J Yoon ◽  
S Kang ◽  
T Suh

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Anna Pidnebesna ◽  
David Tomeček ◽  
Jaroslav Tintěra ◽  
Jaroslav Hlinka

AbstractFunctional connectivity analysis of resting state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix, calculated by correlating signals from regions of interest, is a useful approximate representation of the brain’s connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Treatment of such artifacts poses a standing challenge because of their high variability. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.


2021 ◽  
Author(s):  
Igor Peterlik ◽  
Adam Strzelecki ◽  
Mathias Lehmann ◽  
Philippe Messmer ◽  
Peter Munro ◽  
...  

2020 ◽  
Vol 41 (18) ◽  
pp. 5325-5340
Author(s):  
Jakub Kopal ◽  
Anna Pidnebesna ◽  
David Tomeček ◽  
Jaroslav Tintěra ◽  
Jaroslav Hlinka

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S240-S240
Author(s):  
Jose Rubio ◽  
Chrisina Fales ◽  
Anita Barber ◽  
Todd Lencz ◽  
Anil Malhotra ◽  
...  

Abstract Background Most individuals with schizophrenia experience relapse over the course of the illness, yet unfortunately the mechanisms of this phenomenon are poorly understood. This research is often confounded by non-adherence with antipsychotic drugs. We propose to study relapse in individuals treated with long acting injectable antipsychotics (LAIs), for whom treatment adherence is confirmed. Since striatal resting state functional connectivity (RSFC) has been shown to reflect pathophysiological aspects of antipsychotic treatment response, we aim to study striatal RSFC in relapse in individuals treated with LAIs to identify potential mechanisms. In particular, we will compare striatal RSFC between individuals who relapse while treated with LAIs, individuals who are not on LAIs and are non-adherent with antipsychotics at the time of relapse, and healthy controls, to generate a hypothesis about the role of striatal functioning in psychosis relapse. Methods Subjects with a psychotic disorder treated with LAI antipsychotics and history of clinical response to that trial confirmed by collateral, presenting with acute psychotic symptoms at the time of the scan (defined as ≥4 in BPRS in at least one of the psychotic items) (n=16) were compared with subjects also with a psychotic disorder presenting with acute psychotic symptoms who were non-adherent with antipsychotic drugs demonstrated by negative plasma level (n=16), and healthy controls (n=18). Participants were scanned using fMRI and data was pre-processed using the HCP pipeline with the ICA-FIX procedure, removing motion artifacts and nuisance variables. Connectivity maps were generated for 6 bilateral (12 total) striatal regions of interest as in Di Martino et al. 2007, which were compared between groups (cluster threshold p< .05, voxel threshold p<.001 uncorrected). In addition, we calculated striatal connectivity indices (SCI) as in Sarpal et al. 2016, as this metric reflecting RSFC between the striatum and 91 other regions of interest has been shown to have high precision in predicting response to antipsychotics in patients with first episode psychosis. Results We found no significant differences in sex or age between any of the 2 patient groups or the healthy controls, nor of psychopathology between the patient groups. For patients treated with LAIs upon relapse, striatal RSFC was significantly lower in an area in posterior cingulate, whereas it was higher in an area in the middle temporal gyrus, inferior temporal gyrus, and precentral gyrus, compared with healthy controls. When the LAI-treated individuals’ striatal RSFC was compared with that of individuals who were non-adherent with antipsychotic drugs at the time of relapse, it was significantly higher in the posterior parietal cortex, whereas it was lower in the pulvinar (thalamus) and primary and associative cortex. The SCI values for individuals who relapsed despite assured antipsychotic exposure were significantly lower than for non-LAI individuals who had relapsed due to non-adherence (p=0.049), and than healthy controls (p=0.01). Discussion Despite a relatively small sample, these results indicate differences in striatal functional connectivity depending on antipsychotic exposure at the time of relapse. The finding of significantly lower SCI values for LAI treated individuals at the time of relapse compared with non-adherent individuals with relapse and healthy controls suggests the hypothesis that relapse occurring despite assured antipsychotic exposure may result from aberrant striatal functional connectivity which is insufficiently engaged by antipsychotic drugs.


Author(s):  
Athena Taymourtash ◽  
Ernst Schwartz ◽  
Karl-Heinz Nenning ◽  
Daniel Sobotka ◽  
Mariana Diogo ◽  
...  

2019 ◽  
Author(s):  
Caterina Gratton ◽  
Ally Dworetsky ◽  
Rebecca S. Coalson ◽  
Babatunde Adeyemo ◽  
Timothy O. Laumann ◽  
...  

AbstractDenoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (> 0.1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0 – 2.5 s.) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 - 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is relatively trait-like and more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.Highlights-Single-band fMRI motion traces show factitious high-frequency content (HF-motion)-The magnitude of HF-motion relates to age and other demographic factors-HF-motion elevates framewise displacement (FD) and causes data loss-Substantial fMRI data can be recovered from censoring by filtering motion traces-Filtering motion traces reduces motion artifacts in functional connectivity


2008 ◽  
Vol 35 (6Part26) ◽  
pp. 2977-2977
Author(s):  
J Lu ◽  
D Zheng ◽  
P Keall ◽  
E Weiss ◽  
C Bartee ◽  
...  

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