SummaryData analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed.
Fatigue is one of the most pervasive symptoms of multiple sclerosis (MS), and has engendered hundreds of investigations on the topic. While there is a growing literature using various methods to study fatigue, a unified theory of fatigue in MS is yet to emerge. In the current review, we synthesize findings from neuroimaging, pharmacological, neuropsychological, and immunological studies of fatigue in MS, which point to a specific hypothesis of fatigue in MS: the dopamine imbalance hypothesis. The communication between the striatum and prefrontal cortex is reliant on dopamine, a modulatory neurotransmitter. Neuroimaging findings suggest that fatigue results from the disruption of communication between these regions. Supporting the dopamine imbalance hypothesis, structural and functional neuroimaging studies show abnormalities in the frontal and striatal regions that are heavily innervated by dopamine neurons. Further, dopaminergic psychostimulant medication has been shown to alleviate fatigue in individuals with traumatic brain injury, chronic fatigue syndrome, and in cancer patients, also indicating that dopamine might play an important role in fatigue perception. This paper reviews the structural and functional neuroimaging evidence as well as pharmacological studies that suggest that dopamine plays a critical role in the phenomenon of fatigue. We conclude with how specific aspects of the dopamine imbalance hypothesis can be tested in future research.
Recently, there has been renewed interest in the study of cognitive fatigue. It is known that fatigue is one of the most disabling symptoms in numerous neurological populations, including stroke, multiple sclerosis, Parkinson’s disease, and traumatic brain injury. Behavioral studies of cognitive fatigue are hampered by lack of correlation of self-report measures with objective performance. Neuroimaging studies provide new insight about cognitive fatigue and its neural correlates.Impairment within the cortico-striatal network, involved in effort–reward calculation, has been suggested to be critically related to fatigue. The current review surveys the recent neuroimaging literature, and suggests promising avenues for future research.
We investigated differences in brain activation associated with cognitive fatigue between persons with traumatic brain injury (TBI) and healthy controls (HCs). Twenty-two participants with moderate-severe TBI and 20 HCs performed four blocks of a difficult working memory task and four blocks of a control task during fMRI imaging. Cognitive fatigue, assessed before and after each block, was used as a covariate to assess fatigue-related brain activation. The TBI group reported more fatigue than the HCs, though their performance was comparable. Regarding brain activation, the TBI group showed a Task X Fatigue interaction in the caudate tail resulting from a positive correlation between fatigue and brain activation for the difficult task and a negative relationship for the control task. The HC group showed the same Task X Fatigue interaction in the caudate head. Because we had prior hypotheses about the caudate, we performed a confirmatory analysis of a separate dataset in which the same subjects performed a processing speed task. A relationship between Fatigue and brain activation was evident in the caudate for this task as well. These results underscore the importance of the caudate nucleus in relation to cognitive fatigue.
Objective: The objective of this paper is to investigate the interrelationship between subjective and objective cognitive fatigue, information processing domain [processing speed (PS) vs. working memory (WM)], cognitive load (high vs. low), and time on task in Multiple Sclerosis (MS).Methods: Thirty-two MS participants and 24 healthy controls completed experimental tasks in both the PS and WM domains with different levels of cognitive load. Subjective cognitive fatigue was measured using a visual analog scale at baseline and at multiple time points throughout the experiment.Results: A mixed model ANOVA revealed that subjective cognitive fatigue was higher for the PS task, increased across time, and was higher in the MS group. These findings were qualified by an interaction demonstrating that the MS group showed a steeper increase in subjective cognitive fatigue over time than the healthy control group. Subjective and objective (i.e., performance) cognitive fatigue were not correlated.Conclusion: In this study, subjective and objective cognitive fatigue appears to be independent and cognitive fatigue does not depend on cognitive load. Subjective cognitive fatigue increased with time on task and subjective cognitive fatigue increased more steeply for the MS group. These data suggest that cognitive fatigue in MS is a function of time, that is, the longer participants were engaged in a cognitive task, the more likely it was for them to report increases in cognitive fatigue.
The striatum has been shown to play an important role in learning from performance-related feedback that is presented shortly after each response. However, less is known about the neural mechanisms supporting learning from feedback that is substantially delayed from the original response. Since the consequences of one’s actions often do not become known until after a delay, it is important to understand whether delayed feedback can produce neural responses similar to those elicited by immediate feedback presentation. We investigated this issue by using functional magnetic resonance imaging (fMRI) as participants performed a paired-associate learning task with 180 distinct trials. Feedback indicating response accuracy was presented immediately, after a delay of 25 minutes, or not at all. Both immediate and delayed feedback led to significant gains in accuracy on a post-test, relative to no feedback. Replicating previous work, we found that the caudate nuclei showed greater activation for positive feedback than negative feedback when the feedback was presented immediately. In addition, delayed feedback also led to differential caudate activity to positive versus negative feedback. Delayed negative feedback also produced significant activation of the putamen and globus pallidus (the lentiform nucleus), relative to no feedback and delayed positive feedback. This suggests that the caudate nucleus is sensitive to the affective nature of feedback, across different timescales, while the lentiform nucleus may be particularly involved in processing the information carried by negative feedback after a substantial delay.
The Stroop interference task is a cognitively demanding task of executive control, a cognitive ability that is often impaired in patients with multiple sclerosis (MS). The aim of this study was to compare effective connectivity patterns within a network of brain regions involved in the Stroop task performance between MS patients with three disease clinical phenotypes [relapsing-remitting (RRMS), benign (BMS), and secondary progressive (SPMS)] and healthy subjects. Effective connectivity analysis was performed on Stroop task data using a novel method based on causal Bayes networks. Compared with controls, MS phenotypes were slower at performing the task and had reduced performance accuracy during incongruent trials that required increased cognitive control. MS phenotypes also exhibited connectivity abnormalities reflected as weaker shared connections, presence of extra connections (i.e., connections absent in the HC connectivity pattern), connection reversal, and loss. In SPMS and the BMS groups but not in the RRMS group, extra connections were associated with deficits in the Stroop task performance. In the BMS group, the response time associated with correct responses during the congruent condition showed a positive correlation with the left posterior parietal → dorsal anterior cingulate connection. In the SPMS group, performance accuracy during the congruent condition showed a negative correlation with the right insula → left insula connection. No associations between extra connections and behavioral performance measures were observed in the RRMS group. These results suggest that, depending on the phenotype, patients with MS use different strategies when cognitive control demands are high and rely on different network connections. Hum Brain Mapp, 37:2293-2304, 2016. © 2016 Wiley Periodicals, Inc.
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