Double-wavelet transform for multisubject task-induced functional magnetic resonance imaging data.
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ABSTRACT:
SUBMITTER: Zhou M
PROVIDER: S-EPMC6771256 | biostudies-literature | 2019 Sep
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC6865567 | biostudies-literature
Project description:Introduction: The selection of an appropriate window size, window function, and functional connectivity (FC) metric in the sliding window method is not straightforward due to the absence of ground truth. Methods: A previously proposed wavelet-based method was accordingly adjusted for estimating time-varying FC (TVFC) and was applied to a large high-quality, low-motion dataset of 400 resting-state functional magnetic resonance imaging data. Specifically, the wavelet coherence magnitude and relative phase were averaged across wavelet (frequency) scales to yield TVFC and synchronization patterns. To assess whether the observed fluctuations in TVFC were statistically significant (dynamic FC [dFC]; the distinction between TVFC and dFC is intentional), surrogate data were generated using the multivariate phase randomization (MVPR) and multivariate autoregressive randomization (MVAR) methods to define the null hypothesis of dFC absence. Results: By averaging across all frequencies, core regions of the default mode network (DMN; medial prefrontal and posterior cingulate cortices, inferior parietal lobes, hippocampal formation) were found to exhibit dFC (test-retest reproducibility of 90%) and were also synchronized in activity (-15° ≤ phase ≤15°). When averaging across distinct frequency bands, the same dynamic connections were identified, with the majority of them identified in the frequency range (0.01, 0.198) Hz, though with lower test-retest reproducibility (<66%). Additional analysis suggested that MVPR method better preserved properties (p < 10-10), including time-averaged coherence, of the original data compared with MVAR approach. Conclusions: The wavelet-based approach identified dynamic associations between the core DMN regions with fewer choices in parameters, compared with sliding window method. Impact statement We employed a wavelet-based method, previously used in the literature, and proposed modifications to assess time-varying functional connectivity in resting-state functional magnetic resonance imaging. With this approach, dynamic connections within the default mode network were identified, involving the medial prefrontal and posterior cingulate cortices, inferior parietal lobes, and hippocampal formation, which were also highly consistent in test-retest analysis (test-retest reproducibility of 90%), without the need to select window size, window function, and functional connectivity metric as with the sliding window method, whereby no consensus on the appropriate choices of hyperparameters currently exists in the literature.
| S-EPMC9271336 | biostudies-literature
Project description:UnlabelledBackgroundAutism Spectrum Conditions (ASC) are a set of pervasive neurodevelopmental conditions characterized by a wide range of lifelong signs and symptoms. Recent explanatory models of autism propose abnormal neural connectivity and are supported by studies showing decreased interhemispheric coherence in individuals with ASC. The first aim of this study was to test the hypothesis of reduced interhemispheric coherence in ASC, and secondly to investigate specific effects of task performance on interhemispheric coherence in ASC.MethodsWe analyzed electroencephalography (EEG) data from 15 participants with ASC and 15 typical controls, using Wavelet Transform Coherence (WTC) to calculate interhemispheric coherence during face and chair matching tasks, for EEG frequencies from 5 to 40 Hz and during the first 400 ms post-stimulus onset.ResultsResults demonstrate a reduction of interhemispheric coherence in the ASC group, relative to the control group, in both tasks and for all electrode pairs studied. For both tasks, group differences were generally observed after around 150 ms and at frequencies lower than 13 Hz. Regarding within-group task comparisons, while the control group presented differences in interhemispheric coherence between faces and chairs tasks at various electrode pairs (FT7-FT8, TP7-TP8, P7-P8), such differences were only seen for one electrode pair in the ASC group (T7-T8). No significant differences in EEG power spectra were observed between groups.ConclusionsInterhemispheric coherence is reduced in people with ASC, in a time and frequency specific manner, during visual perception and categorization of both social and inanimate stimuli and this reduction in coherence is widely dispersed across the brain.Results of within-group task comparisons may reflect an impairment in task differentiation in people with ASC relative to typically developing individuals.Overall, the results of this research support the value of WTC in examining the time-frequency microstructure of task-related interhemispheric EEG coherence in people with ASC.
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Project description:Task-based functional neuroimaging methods are increasingly being used to identify biomarkers of treatment response in psychiatric disorders. To facilitate meaningful interpretation of neural correlates of tasks and their potential changes with treatment over time, understanding the reliability of the blood-oxygen-level dependent (BOLD) signal of such tasks is essential. We assessed test-retest reliability of an emotional conflict task in healthy participants collected as part of the Canadian Biomarker Integration Network in Depression. Data for 36 participants, scanned at three time points (weeks 0, 2, and 8) were analyzed, and intra-class correlation coefficients (ICC) were used to quantify reliability. We observed moderate reliability (median ICC values between 0.5 and 0.6), within occipital, parietal, and temporal regions, specifically for conditions of lower cognitive complexity, that is, face, congruent or incongruent trials. For these conditions, activation was also observed within frontal and sub-cortical regions, however, their reliability was poor (median ICC < 0.2). Clinically relevant prognostic markers based on task-based fMRI require high predictive accuracy at an individual level. For this to be achieved, reliability of BOLD responses needs to be high. We have shown that reliability of the BOLD response to an emotional conflict task in healthy individuals is moderate. Implications of these findings to further inform studies of treatment effects and biomarker discovery are discussed.
| S-EPMC7267954 | biostudies-literature
Project description:IntroductionDecoding brain activities is one of the most popular topics in neuroscience in recent years. And deep learning has shown high performance in fMRI data classification and regression, but its requirement for large amounts of data conflicts with the high cost of acquiring fMRI data.MethodsIn this study, we propose an end-to-end temporal contrastive self-supervised learning algorithm, which learns internal spatiotemporal patterns within fMRI and allows the model to transfer learning to datasets of small size. For a given fMRI signal, we segmented it into three sections: the beginning, middle, and end. We then utilized contrastive learning by taking the end-middle (i.e., neighboring) pair as the positive pair, and the beginning-end (i.e., distant) pair as the negative pair.ResultsWe pretrained the model on 5 out of 7 tasks from the Human Connectome Project (HCP) and applied it in a downstream classification of the remaining two tasks. The pretrained model converged on data from 12 subjects, while a randomly initialized model required 100 subjects. We then transferred the pretrained model to a dataset containing unpreprocessed whole-brain fMRI from 30 participants, achieving an accuracy of 80.2 ± 4.7%, while the randomly initialized model failed to converge. We further validated the model's performance on the Multiple Domain Task Dataset (MDTB), which contains fMRI data of 26 tasks from 24 participants. Thirteen tasks of fMRI were selected as inputs, and the results showed that the pre-trained model succeeded in classifying 11 of the 13 tasks. When using the 7 brain networks as input, variations of the performance were observed, with the visual network performed as well as whole brain inputs, while the limbic network almost failed in all 13 tasks.DiscussionOur results demonstrated the potential of self-supervised learning for fMRI analysis with small datasets and unpreprocessed data, and for analysis of the correlation between regional fMRI activity and cognitive tasks.
| S-EPMC10330812 | biostudies-literature
Project description:To examine the feasibility and test-retest reliability of encoding-task functional magnetic resonance imaging (fMRI) in mild Alzheimer disease (AD).Randomized, double-blind, placebo-controlled study.Memory clinical trials unit.We studied 12 patients with mild AD (mean [SEM] Mini-Mental State Examination score, 24.0 [0.7]; mean Clinical Dementia Rating score, 1.0) who had been taking donepezil hydrochloride for more than 6 months from the placebo arm of a larger 24-week study (n = 24, 4 scans on weeks 0, 6, 12, and 24, respectively).Placebo and 3 face-name, paired-associate encoding, block-design blood oxygenation level-dependent fMRI scans in 12 weeks.We performed whole-brain t maps (P < .001, 5 contiguous voxels) and hippocampal regions-of-interest analyses of extent (percentage of active voxels) and magnitude (percentage of signal change) for novel-greater-than-repeated face-name contrasts. We also calculated intraclass correlation coefficients and power estimates for hippocampal regions of interest.Task tolerability and data yield were high (95 of 96 scans yielded favorable-quality data). Whole-brain maps were stable. Right and left hippocampal regions-of-interest intraclass correlation coefficients were 0.59 to 0.87 and 0.67 to 0.74, respectively. To detect 25.0% to 50.0% changes in week-0 to week-12 hippocampal activity using left-right extent or right magnitude with 80.0% power (2-sided ? = .05) requires 14 to 51 patients. Using left magnitude requires 125 patients because of relatively small signal to variance ratios.Encoding-task fMRI was successfully implemented in a single-site, 24-week, AD randomized controlled trial. Week 0 to 12 whole-brain t maps were stable, and test-retest reliability of hippocampal fMRI measures ranged from moderate to substantial. Right hippocampal magnitude may be the most promising of these candidate measures in a leveraged context. These initial estimates of test-retest reliability and power justify evaluation of encoding-task fMRI as a potential biomarker for signal of effect in exploratory and proof-of-concept trials in mild AD. Validation of these results with larger sample sizes and assessment in multisite studies is warranted.
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Project description:Vergence eye movements are the inward and outward rotation of the eyes responsible for binocular coordination. While studies have mapped and investigated the neural substrates of vergence, it is not well understood whether vergence eye movements evoke the blood oxygen level-dependent signal reliably in separate experimental visits. The test-retest reliability of stimulus-induced vergence eye movement tasks during a functional magnetic resonance imaging (fMRI) experiment is important for future randomized clinical trials (RCTs). In this study, we established region of interest (ROI) masks for the vergence neural circuit. Twenty-seven binocularly normal young adults participated in two functional imaging sessions measured on different days on the same 3T Siemens scanner. The fMRI experiments used a block design of sustained visual fixation and rest blocks interleaved between task blocks that stimulated eight or four vergence eye movements. The test-retest reliability of task-activation was assessed using the intraclass correlation coefficient (ICC), and that of spatial extent was assessed using the Dice coefficient. Functional activation during the vergence eye movement task of eight movements compared to rest was repeatable within the primary visual cortex (ICC = 0.8), parietal eye fields (ICC = 0.6), supplementary eye field (ICC = 0.5), frontal eye fields (ICC = 0.5), and oculomotor vermis (ICC = 0.6). The results demonstrate significant test-retest reliability in the ROIs of the vergence neural substrates for functional activation magnitude and spatial extent using the stimulus protocol of a task block stimulating eight vergence eye movements compared to sustained fixation. These ROIs can be used in future longitudinal RCTs to study patient populations with vergence dysfunctions.
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Project description:Upper extremity motor paradigms during spinal cord functional magnetic resonance imaging (fMRI) can provide insight into the functional organization of the cord. Hand-grasping is an important daily function with clinical significance, but previous studies of similar squeezing movements have not reported consistent areas of activity and are limited by sample size and simplistic analysis methods. Here, we study spinal cord fMRI activation using a unimanual isometric hand-grasping task that is calibrated to participant maximum voluntary contraction (MVC). Two task modeling methods were considered: (1) a task regressor derived from an idealized block design (Ideal) and (2) a task regressor based on the recorded force trace normalized to individual MVC (%MVC). Across these two methods, group motor activity was highly lateralized to the hemicord ipsilateral to the side of the task. Activation spanned C5-C8 and was primarily localized to the C7 spinal cord segment. Specific differences in spatial distribution are also observed, such as an increase in C8 and dorsal cord activity when using the %MVC regressor. Furthermore, we explored the impact of data quantity and spatial smoothing on sensitivity to hand-grasp motor task activation. This analysis shows a large increase in number of active voxels associated with the number of fMRI runs, sample size, and spatial smoothing, demonstrating the impact of experimental design choices on motor activation.
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Project description:A technique that utilizes the one-dimensional (1D) continuous wavelet transform (CWT) of linearized fluorescence resonance energy transfer (FRET) microscopic images has been extended to identify signaling macro- and microdomains in cell plasma membranes by incorporating the two-dimensional (2D) CWT of time-lapse fluorescence and/or FRET images. Signaling domains were identified from differences in wavelet coefficient matrices, and there was good agreement between the 1D and 2D methods on examining a), static fluorescent images of COS1 cells expressing calmodulin kinase II fused with enhanced yellow fluorescent protein, and b), time lapse FRET images of reporters of protein kinase C (PKC) (PKC activity reporter) and adenylyl cyclase dynamics (cAMP) activity within COS1 plasma membrane confines after stimulation by phorbol-12,13-dibutyrate or forskolin, respectively. The proposed 2D wavelet-based image analysis effectively detected phosphorylation/dephosphorylation signaling microdomains (PKC) as well as those reflective of cAMP without the limitation of requiring linearized signals imposed by the 1D approach. Illustrating successful application to the analysis of intracellular compartments, the 2D CWT was further used to identify signaling domains of cAMP response element-binding (CREB)-induced transcriptional activation in the nuclei of COS1 cells, which could not be achieved with the 1D approach. This technique may be eventually used to characterize complex cellular signaling and protein-protein interactions within localized cytoplasmic domains.
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Project description:Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has been used to study the effects of anesthetic agents on correlated intrinsic neural activity. Previous studies have focused primarily on intravenous agents. The authors studied the effects of sevoflurane, an inhaled anesthetic.Resting-state BOLD fMRI was acquired from 10 subjects before sedation and from 9 subjects rendered unresponsive by 1.2% sevoflurane. The fMRI data were analyzed taking particular care to minimize the impact of artifact generated by head motion.BOLD correlations were specifically weaker within the default mode network and ventral attention network during sevoflurane-induced unconsciousness, especially between anterior and posterior midline regions. Reduced functional connectivity between these same networks and the thalamus was also spatially localized to the midline frontal regions. The amplitude of BOLD signal fluctuations was substantially reduced across all brain regions. The importance of censoring epochs contaminated by head motion was demonstrated by comparative analyses.Sevoflurane-induced unconsciousness is associated with both globally reduced BOLD signal amplitudes and selectively reduced functional connectivity within cortical networks associated with consciousness (default mode network) and orienting to salient external stimuli (ventral attention network). Scrupulous attention to minimizing the impact of head motion artifact is critical in fMRI studies using anesthetic agents.
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