Exploring the impact of analysis software on task fMRI results.
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ABSTRACT:
SUBMITTER: Bowring A
PROVIDER: S-EPMC6618324 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
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Project description:INTRODUCTION:In geriatric clinical diagnostics, gait analysis with cognitive-motor dual tasking is used to predict fall risk and cognitive decline. To date, the neural correlates of cognitive-motor dual tasking processes are not fully understood. To investigate these underlying neural mechanisms, we designed an fMRI paradigm to reproduce the gait analysis. METHODS:We tested the fMRI paradigm's feasibility in a substudy with fifteen young adults and assessed 31 healthy older adults in the main study. First, gait speed and variability were quantified using the GAITRite© electronic walkway. Then, participants lying in the MRI-scanner were stepping on pedals of an MRI-compatible stepping device used to imitate gait during functional imaging. In each session, participants performed cognitive and motor single tasks as well as cognitive-motor dual tasks. RESULTS:Behavioral results showed that the parameters of both gait analyses, GAITRite© and fMRI, were significantly positively correlated. FMRI results revealed significantly reduced brain activation during dual task compared to single task conditions. Functional ROI analysis showed that activation in the superior parietal lobe (SPL) decreased less from single to dual task condition than activation in primary motor cortex and in supplementary motor areas. Moreover, SPL activation was increased during dual tasks in subjects exhibiting lower stepping speed and lower executive control. CONCLUSION:We were able to simulate walking during functional imaging with valid results that reproduce those from the GAITRite© gait analysis. On the neural level, SPL seems to play a crucial role in cognitive-motor dual tasking and to be linked to divided attention processes, particularly when motor activity is involved.
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Project description:Task-fMRI researchers have great flexibility as to how they analyze their data, with multiple methodological options to choose from at each stage of the analysis workflow. While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group-level results of a task-fMRI investigation (Bowring et al., 2019, BMN). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in BMN, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages (AFNI, FSL, and SPM) across the remaining steps of the pipeline. We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets (e.g., choice of first-level signal model had the most impact for the balloon analog risk task dataset, while first-level noise model and group-level model were more influential for the false belief and antisaccade task datasets, respectively). We also observe areas of the analysis workflow where changing the software package causes minimal differences in the final results, finding that the group-level results were largely unaffected by which software package was used to model the low-frequency fMRI drifts.
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Project description:Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series is challenging because of the intrinsic complexity. Hypothesis-driven methods, such as the general linear model (GLM), which regress exterior stimulus from voxel-wise functional brain activity, are limited due to overlooking the complexity of brain activities and the diversity of concurrent brain networks. Recently, sparse representation and dictionary learning methods have attracted increasing interests in task fMRI data analysis. The major advantage of this methodology is its promise in reconstructing concurrent brain networks systematically. However, this data-driven strategy is, to some extent, arbitrary and does not sufficiently utilize the prior information of task design and neuroscience knowledge. To bridge this gap, we here propose a novel supervised sparse representation and dictionary learning framework based on stochastic coordinate coding (SCC) algorithm for task fMRI data analysis, in which certain brain networks are learned with known information such as pre-defined temporal patterns and spatial network patterns, and at the same time other networks are learned automatically from data. Our proposed method has been applied to two independent task fMRI datasets, and qualitative and quantitative evaluations have shown that our method provides a new and effective framework for task fMRI data analysis.
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Project description:Scientists are often asked to what extent a simple finding in a laboratory can be generalized to complicated phenomena in our daily lives. The same is equally true of vision science; numerous critical discoveries about our visual system have been made using very simple visual images, such as Gabor patches, but to what extent can these findings be applied to more natural images? Here, we used the fMRI decoding technique and directly tested whether the findings obtained with primitive visual stimuli (Gabor patches) were applicable to natural images. In the fMRI experiments, participants performed depth and resolution tasks with both Gabor patches and natural images. We created a fMRI decoder made from the results of the Gabor patch experiments that classified a brain activity pattern into the depth or resolution task, and then examined how successful the task-dependent decoder could sort a brain activity pattern in the natural image experiment into the depth or resolution task. As a result, we found that the task-dependent decoder constructed from Gabor patch experiments could predict which task (depth or resolution task) a participant was engaged in the natural image experiments, especially in the V3 and middle temporal (MT+) areas of the brain. This is consistent with previous researches on the cortical activation relating to depth perception rather than perceptual processing of display resolution. These results provide firm evidence that fMRI decoding technique possesses the power to evaluate the application of Gabor patch results (laboratory findings) to the natural images (everyday affairs), representing a new approach for studying the mechanism of visual perception.
| S-EPMC6987206 | biostudies-literature
Project description:Test-retest reliability of individual functional magnetic resonance imaging (fMRI) results is of importance in clinical practice and longitudinal experiments. While several studies have investigated reliability of task-induced motor network activation, less is known about the reliability of the task-free motor network. Here, we investigate the reproducibility of task-free fMRI, and compare it to motor task activity. Sixteen healthy subjects participated in this study with a test-retest interval of seven weeks. The task-free motor network was assessed with a univariate, seed-voxel-based correlation analysis. Reproducibility was tested by means of intraclass correlation (ICC) values and ratio of overlap. Higher ICC values and a better overlap were found for task fMRI as compared to task-free fMRI. Furthermore, ratio of overlap improved for task fMRI at higher thresholds, while it decreased for task-free fMRI, suggesting a less focal spatial pattern of the motor network during resting state. However, for both techniques the most active voxels were located in the primary motor cortex. This indicates that, just like task fMRI, task-free fMRI can properly identify critical brain areas for motor task performance. Although both fMRI techniques are able to detect the motor network, resting-state fMRI is less reliable than task fMRI.
| S-EPMC6869575 | biostudies-literature
Project description:ObjectiveChildhood irritability, operationalized as disproportionate and frequent temper tantrums and low frustration tolerance relative to peers, is a transdiagnostic symptom across many pediatric disorders. Studies using task-dependent functional magnetic resonance imaging (fMRI) to probe neural dysfunction in irritability have increased. However, an integrated review summarizing the published methods and synthesized fMRI results remains lacking.MethodWe conducted a systematic search using irritability terms and task functional neuroimaging in key databases in March 2021, and identified 30 studies for our systematic review. Sample characteristics and fMRI methods were summarized. A subset of 28 studies met the criteria for extracting coordinate-based data for quantitative meta-analysis. Ten activation-likelihood estimations were performed to examine neural convergence across irritability measures and fMRI task domains.ResultsSystematic review revealed small sample sizes (median = 58, mean age range = 8-16 years) with heterogeneous sample characteristics, irritability measures, tasks, and analytical procedures. Meta-analyses found no evidence for neural activation convergence of irritability across neurocognitive functions related to emotional reactivity, cognitive control, and reward processing, or within each domain. Sensitivity analyses partialing out variances driven by heterogeneous tasks, irritability measures, stimulus types, and developmental ages all yielded null findings. Results were compared with a review on irritability-related structural anomalies from 11 studies.ConclusionThe lack of neural convergence suggests a need for common, standardized irritability assessments and more homogeneous fMRI tasks. Thoughtfully designed fMRI studies probing commonly defined neurocognitive functions may be more fruitful to elucidate the neural mechanisms of irritability. Open science practices, data mining in large neuroscience databases, and standardized analytical methods promote meaningful collaboration in irritability research.
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Project description:Response inhibition is a critical cognitive ability underlying executive control over reactions to external cues, or inner requirements. Previous studies suggest that high arousal negative emotions (e.g., anger or fear) could impair response inhibition in implicit emotional stop signal tasks (eSSTs). However, studies exploring how low arousal negative emotions (e.g., sadness) influence response inhibition remain sparse. In the current study, 20 female college students performed an explicit eSST to explore the influence of sadness on response inhibition and its neural mechanism. Participants are instructed to press a button to sad or neutral facial stimuli while inhibiting their response during the presentation of a stop signal. Results showed that compared with neutral stimuli, sad stimuli were related to increased stop signal reaction time (SSRT) (i.e., worse response inhibition). Compared with neutral condition, higher activation during sad condition was found within the right superior frontal gyrus (SFG), right insula, right middle cingulate cortex (MCC), bilateral superior temporal gyrus (STG), left lingual gyrus, and right motor cortex. These findings indicated that sadness, like other negative emotions, may impair response inhibition in an explicit way and highlight the explicit eSST as a new paradigm to investigate the subtle interaction between negative emotion processing and cognitive control.
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Project description:BackgroundGeneralized anxiety disorder (GAD) is closely associated with emotional dysregulation. Patients with GAD tend to overreact to emotional stimuli and are impaired in emotional regulation. Using emotional regulation task, studies have found hypo-activation in prefrontal cortex (PFC) of GAD patients and concluded with inadequate top-down control. However, results remain inconsistent concerning PFC and limbic area's reactivity to emotional stimuli. What's more, only a few studies aim to identify how limbic area interacts with PFC in GAD patients. The current study aims to identify the difference in PFC-limbic circuitry response to emotional stimuli between GAD patients and healthy controls (HCs) from the perspective of brain network. Through brain network analysis, it revealed the connectivity between limbic area and PFC, and moreover, the orientation of connectivity, all of which gave a better test of inadequate top-down control hypothesis.MethodsDuring fMRI scanning, participants were required to complete an emotional face identification task (fearful, neutral, happy facial expression). 30 participants (16 GAD patients, 14 HCs) were included in the formal analysis. A Bayesian-network based method was used to identify the brain network consisting of several pre-hypothesized regions of interest (ROIs) under each condition (negative, positive, neutral). In total, six graphs were obtained. Each of them represented the brain network that was common to the group under corresponding condition.ResultsResults revealed that GAD patients showed more bottom-up connection but less top-down connection regardless of condition, relative to HCs. Also, the insula was more connected but the amygdala was less connected regardless of condition, relative to HCs. the results also revealed a very different brain network response between GAD patients and HCs even under neutral condition.ConclusionsMore bottom-up connection but less top-down connection may indicate that GAD patients are insufficient in top-down control, in keeping with inadequate top-down control hypothesis. The more connected insula may indicate GAD patients' abnormality in interoception processing. Relative to HCs, distinct brain network response pattern in GAD patients under neutral condition suggests GAD patients' abnormality in distinguishing safety from threat and intolerance of uncertainty.
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Project description:Declines in processing speed performance occur in aging and are a critical marker of functional independence in older adults. Numerous studies suggest that Useful Field of View (UFOV) training may ameliorate cognitive decline in older adults. Despite its efficacy, little is known about the neural correlates of this task. The current study is the first to investigate the coherence of functional connectivity during UFOV task completion. A total of 336 participants completed the UFOV task while undergoing task-based functional magnetic resonance imaging (fMRI). Ten spherical regions of interest (ROIs), selected a priori, were created based on regions with the greatest peak BOLD activation patterns in the UFOV fMRI task and regions that have been shown to significantly relate to UFOV fMRI task performance. We used a weighted ROI-to-ROI connectivity analysis to model task-specific functional connectivity strength between these a priori selected ROIs. We found that our UFOV fMRI network was functionally connected during task performance and was significantly associated to UFOV fMRI task performance. Within-network connectivity of the UFOV fMRI network showed comparable or better predictive power in accounting for UFOV accuracy compared to 7 resting state networks, delineated by Yeo and colleagues. Finally, we demonstrate that the within-network connectivity of UFOV fMRI task accounted for scores on a measure of "near transfer", the Double Decision task, better than the aforementioned resting state networks. Our data elucidate functional connectivity patterns of the UFOV fMRI task. This may assist in future targeted interventions that aim to improve synchronicity within the UFOV fMRI network.
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Project description:System design is recognized as one of the most critical components of a software system that bridges system requirements and coding. System design also has a significant impact on testing and maintenance activities, and on further improvements during the lifespan of the software system. Software design should reflect all necessary components of the requirements in a clear and understandable manner by all stakeholders of the software system. To distinguish system elements, separation of concerns in software design is suggested. In this respect, identification of the user tasks, i.e., the tasks that need to be performed by the user, is not currently reflected explicitly in system design documents. Our main assumption in this study is that software quality can be improved significantly by clearly identifying the user tasks from those that need to be performed by the computer system itself. Additionally, what we propose has the potential to better reflect the user requirements and main objectives of the system on the software design and thereby to improve software quality. The main aim of this study is to introduce a novel notation for software developers in the frame of UML Activity Diagram (UML-AD) that enables designers to identify the user tasks and define them separately from the system tasks. For this purpose, an extension of UML-AD, named UML-ADE (UML-Activity Diagram Extended) was proposed. Afterwards, it was implemented in a serious game case for which the specification of user tasks is extremely important. Finally, its effectiveness was analyzed and compared to UML-AD experimentally with 72 participants. The defect detection performance of the participants on both diagrams with two real-life serious game scenarios was evaluated. Results show a higher level of understandability for those using UML-ADE, which in turn may indicate a better design and higher software quality. The results encourage researchers to develop specific design representations dedicated to task design to improve system quality and to conduct further evaluations of the impact of these design on each of the above mentioned potential benefits for the software systems.
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