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Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. IMPACT STATEMENT: Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.

Auditory mismatch processing is accompanied by activation of a distributed brain network which can be detected by fMRI. However, the impact of different experimental designs such as event-related or block designs and different stimulus characteristics on the auditory mismatch response and the activity of this network remains controversial. In the present study, we applied five auditory mismatch paradigms with standard experimental designs and recorded fMRI in 31 healthy participants. Brain activity was analyzed using general linear models as well as classification approaches. The results stress a greater role of the type of the applied deviant stimulus compared to the experimental design. Moreover, the absolute number of the deviants as well as the length of the experimental run seems to play a greater role than the experimental design. The present study promotes optimization of experimental paradigms in the context of mismatch research. In particular, our findings contribute to designing auditory mismatch paradigms for application in clinical settings.

Recent fMRI research shows that perceptual and cognitive representations are instantiated in high-dimensional multivoxel patterns in the brain. However, the methods for detecting these representations are limited. Topological data analysis (TDA) is a new approach, based on the mathematical field of topology, that can detect unique types of geometric features in patterns of data. Several recent studies have successfully applied TDA to study various forms of neural data; however, to our knowledge, TDA has not been successfully applied to data from event-related fMRI designs. Event-related fMRI is very common but limited in terms of the number of events that can be run within a practical time frame and the effect size that can be expected. Here, we investigate whether persistent homology—a popular TDA tool that identifies topological features in data and quantifies their robustness—can identify known signals given these constraints. We use fmrisim, a Python-based simulator of realistic fMRI data, to assess the plausibility of recovering a simple topological representation under a variety of conditions. Our results suggest that persistent homology can be used under certain circumstances to recover topological structure embedded in realistic fMRI data simulations., Author Summary How do we represent the world? In cognitive neuroscience it is typical to think representations are points in high-dimensional space. In order to study these kinds of spaces it is necessary to have tools that capture the organization of high-dimensional data. Topological data analysis (TDA) holds promise for detecting unique types of geometric features in patterns of data. Although potentially useful, TDA has not been applied to event-related fMRI data. Here we utilized a popular tool from TDA, persistent homology, to recover topological signals from event-related fMRI data. We simulated realistic fMRI data and explored the parameters under which persistent homology can successfully extract signal. We also provided extensive code and recommendations for how to make the most out of TDA for fMRI analysis.

This paper provides an overview of optimal design for functional magnetic resonance imaging (fMRI) studies. We present the main types of fMRI designs, namely blocked and event-related designs, and common objectives of fMRI experiments, for example, localization of task-related activity in the human brain. Furthermore, we present an introduction into the methodology for analysis and optimization of fMRI experiments, for instance common analysis models and applied optimality criteria. We outline some of the problems encountered when optimizing fMRI experiments, for example, the temporal autocorrelation between measurements in fMRI data. The most important results for optimization of blocked and event-related designs with regard to the different design objectives are presented and explained. Finally, we conclude with future perspectives and challenges for optimization of fMRI experiments.

Neuroimaging studies began using block design and event-related design experiments. While providing many insights into brain functions, these fMRI design types ignore components of the BOLD signal that can teach us additional elements. The development of the mixed block/event-related fMRI design allowed for a fuller characterization of nonlinear and time-sensitive neuronal responses: for example, the interaction between block and event related factors and the simultaneous extraction of transient activity related to trials and block transitions and sustained activity related to task-level processing. This review traces the origins of the mixed block/event-related design from conceptual precursors to a seminal paper and on to subsequent studies using the method. The review also comments on aspects of the experimental design that must be considered when attempting to use the mixed block/event-related design. When taking into account these considerations, the mixed block/event-related design allows fuller utilization of the BOLD signal allowing deeper interpretation of how regions of the brain function on multiple timescales.

This paper documents a proposal to create a Smarter City which is event-driven and designed to enable both intra-agency and inter-agency interactions implicit in day-to-day operations in a complex city environment. This paper highlights technologies and event related design that address key points of integration which create a foundation for a citywide management cycle. A Smarter City also delivers additional functionality such as prediction based on analysis of historical data, real time event monitoring, and response activation which are critical to operational efficiency.

Targets presented outside the focus of attention trigger stimulus-driven spatial reorienting and activation of the right temporal-parietal junction (rTPJ). However, event-related functional resonance imaging (fMRI) studies that used task-irrelevant non-predictive cues systematically failed to activate rTPJ, suggesting that this region controls reorienting only when attention is shifted between two task-relevant locations. Here we challenge this view showing that non-predictive peripheral cues can affect activity in rTPJ, but only when they share a feature with the target: i.e. when they are set-relevant. Trials including a set-relevant cue plus a target on the uncued/unattended side produced the slowest reaction times and selective activation of the rTPJ. These findings demonstrate that rTPJ is not involved only in reorienting between two task-relevant locations, but engages also when non-predictive cues are set-relevant, thereby, irrespective of voluntary attention and breaches of task-related expectations.

Functional imaging of human trigemino-nociceptive processing provides meaningful insights into altered pain processing in head and face pain diseases. Although functional magnetic resonance imaging (fMRI) offers high temporal and spatial resolution, most studies available were done with radioligand-positron emission tomography, as fMRI requires non-magnetic stimulus equipment and fast on–off conditions. We developed a new approach for painful stimulation of the trigeminal nerve that can be implemented within an event-related design using fMRI and aimed to detect increased blood-oxygen-level-dependent (BOLD) signals as surrogate markers of trigeminal pain processing. Using an olfactometer, 20 healthy volunteers received intranasally standardized trigeminal nociceptive stimuli (ammonia gas) as well as olfactory (rose odour) and odourless control stimuli (air puffs). Imaging revealed robust BOLD responses to the trigeminal nociceptive stimulation in cortical and subcortical brain areas known to be involved in pain processing. Focusing on the trigeminal pain pathway, significant activations were observed bilaterally in brainstem areas at the trigeminal nerve entry zone, which are agreeable with the principal trigeminal nuclei. Furthermore, increased signal changes could be detected ipsilaterally at anatomical localization of the trigeminal ganglion and bilaterally in the rostral medulla, which probably represents the spinal trigeminal nuclei. However, brainstem areas involved in the endogenous pain control system that are close to this anatomical localization, such as raphe nuclei, have to be discussed. Our findings suggest that mapping trigeminal pain processing using fMRI with this non-invasive experimental design is feasible and capable of evoking specific activations in the trigeminal nociceptive system. This method will provide an ideal opportunity to study the trigeminal pain system in both health and pathological conditions such as idiopathic headache disorders.