Open data allows researchers to explore pre-existing datasets in new ways. However, if many researchers reuse the same dataset, multiple statistical testing may increase false positives. Here we demonstrate that sequential hypothesis testing on the same dataset by multiple researchers can inflate error rates. We go on to discuss a number of correction procedures that can reduce the number of false positives, and the challenges associated with these correction procedures.

Arguments in support of open science tend to focus on confirmatory research practices. Here we argue that exploratory research should also be encouraged within the framework of open science. We lay out the benefits of 'open exploration' and propose two complementary ways to implement this with little infrastructural change.

In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behaviour. Here we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient) in time-varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occuring. Further, we present a temporal extension to the participation coefficient measure (temporal participation coefficient) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node’s integration through time while adjusting for the possible changes in the community structure of the overall network.

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

Data must be manipulated for their evidential import to be assessed. However, data analysis is regarded as a source of inferential errors by scientists and critics of neuroscience alike. In this chapter I argue that of data analysis is epistemically challenged in part because data are causally separated from the events that they are intended to provide evidence for claims about. Experimental manipulations place researchers in epistemically advantageous positions by making contact with the objects and phenomena of interest. Data manipulations, on the other hand, are applied to material objects that are not in causal contact with the events they are used to learn about. I then propose that some of the inferential liabilities that go along with data manipulation are partly overcome through the occurrence of epistemic friction. I consider two forthcoming contributions to network neuroscience to illustrate the benefits, and risks, of the data analyst's reliance on epistemic friction.

The current neuroimaging workflow has matured into a large chain of processing and analysis steps involving a large number of experts, across imaging modalities and applications. The development and fast adoption of fMRIPrep [1] have revealed that neuroscientists need tools that simplify their research workflow, provide visual reports and checkpoints, and engender trust in the tool itself. Here we present the NiPreps (NeuroImaging Preprocessing toolS) framework, which extends fMRIPrep's approach and principles to new imaging modalities. The vision for NiPreps is to provide end-users (i.e., researchers) with applications that allow them to perform quality control smoothly and to prepare their data for modeling and statistical analysis.

The argument from inductive risk (AIR) is perhaps the most common argument against the value-free ideal of science. Brian MacGillivray rejects the AIR (at least as it would apply to risk assessment) and embraces the value-free ideal. We clarify the issues at stake and argue that MacGillivray's criticisms, although effective against some formulations of the AIR, fail to overcome the essential concerns that motivate the AIR. There are inevitable trade-offs in scientific enquiry that cannot be resolved with any formal methods or general rules. Choices must be made, and values will be involved. It is best to recognize this explicitly. Even so, there is more work to be done developing methods and institutional support for these choices.

Open data has two principal uses: (i) to reproduce original findings and (ii) to allow researchers to ask new questions with existing data. The latter enables discoveries by allowing a more diverse set of viewpoints and hypotheses to approach the data, which is self-evidently advantageous for the progress of science. However, if many researchers reuse the same dataset, multiple statistical testing may increase false positives in the literature. Current practice suggests that the number of tests to be corrected is the number of simultaneous tests performed by a researcher. Here we demonstrate that sequential hypothesis testing on the same dataset by multiple researchers can inflate error rates. This finding is troubling because, as more researchers embrace an open dataset, the likelihood of false positives (i.e. type I errors) will increase. Thus, we should expect a dataset’s utility for discovering new true relations between variables to decay. We consider several sequential correction procedures. These solutions can reduce the number of false positives but, at the same time, can prompt undesired challenges to open data (e.g. incentivising restricted access).

Interacting sets of nodes and fluctuations in their interaction are important properties of a dynamic network system. In some cases the edges reflecting these interactions are directly quantifiable from the data collected. However, in many cases (such as functional magnetic resonance imaging (fMRI) data), the edges must be inferred from statistical relations between the nodes. Here we present a new method, Temporal Communities through Trajectory Clustering (TCTC), that derives time-varying communities directly from time-series data collected from the nodes in a network. First, we verify TCTC on resting and task fMRI data by showing that time-averaged results correspond with expected static connectivity results. We then show that the time-varying communities correlate and predict single-trial behaviour. This new perspective on temporal community detection of node-collected data identifies robust communities revealing ongoing spatiotemporal community configurations during task performance.

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

Functional magnetic resonance imaging research is often associated with images of brains overlaid with patterns of color that indicate significant activity. These images are one of the most salient and recognizable pieces of evidence neuroscientists appeal to as justification for claims about the relationship between cognitive processes and human behavior. The strongest critics of neuroimaging research argue that the technology possesses little, if any, scientific value, in part because of the assumptions implicit in the complex analysis procedures used to transform the data into interpretable data patterns. In this chapter, I shift the focus of this debate away from assumptions implicit in the operation of techniques themselves, and toward the role data analysis techniques play as parts of the process of interpreting neuroimaging data. I propose that data analysis techniques can be conceived of as a lens that brings patterns within the data into focus through its selective transformation. This approach recognizes the double-edged nature of data analysis and interpretation: techniques render data interpretable, but their selection and application is often informed by the methodological and theoretical commitments of researchers using them.

The sceptical positions philosophers have adopted with respect to neuroimaging data are based on detailed evaluations of subtraction, which is one of many data analysis techniques used with neuroimaging data. These positions are undermined when the epistemic implications of the use of a diversity of data analysis techniques are taken into account. I argue that different data analysis techniques reveal different patterns in the data. Through the use of multiple data analysis techniques, researchers can produce results that are locally robust. Thus, the epistemology of neuroimaging must take into consideration the details of the different data analysis techniques that are used to evaluate neuroimaging data, and the specific theoretical aims those techniques are deployed towards.

A role of perirhinal cortex (PrC) in recognition memory for objects has been well established. Contributions of parahippocampal cortex (PhC) to this function, while documented, remain less well understood. Here, we used fMRI to examine whether the organization of item-based recognition memory signals across these two structures is shaped by object category, independent of any difference in representing episodic context. Guided by research suggesting that PhC plays a critical role in processing landmarks, we focused on three categories of objects that differ from each other in their landmark suitability as confirmed with behavioral ratings (buildings > trees > aircraft). Participants made item-based recognition-memory decisions for novel and previously studied objects from these categories, which were matched in accuracy. Multi-voxel pattern classification revealed category-specific item-recognition memory signals along the long axis of PrC and PhC, with no sharp functional boundaries between these structures. Memory signals for buildings were observed in the mid to posterior extent of PhC, signals for trees in anterior to posterior segments of PhC, and signals for aircraft in mid to posterior aspects of PrC and the anterior extent of PhC. Notably, item-based memory signals for the category with highest landmark suitability ratings were observed only in those posterior segments of PhC that also allowed for classification of landmark suitability of objects when memory status was held constant. These findings provide new evidence in support of the notion that item-based memory signals for objects are not limited to PrC, and that the organization of these signals along the longitudinal axis that crosses PrC and PhC can be captured with reference to landmark suitability.

The problem of inadequate professional training for graduate students in teaching and pedagogy has recently come into sharp relief. Providing teacher training for philosophy graduate students through for-credit courses has been recommended as a solution to this problem. This paper provides an overview of the problem, identifies several aims such a course should have, and provides a detailed overview of a course satisfying those aims. By providing a detailed outline of the course, this paper can act as a resource for faculty tasked with teaching such a course. Finally, we justify the pedagogical decisions made in the design of this course to prepare facilitators to more effectively teach it, to allow facilitators to make informed and intentional decisions when adapting the course to their program, and as a demonstration of what we take to be some of the best practices in teaching and pedagogy. That is, the design of the course is informed by the very material covered in the course.

Evidence from a large body of research suggests that perirhinal cortex (PrC), which interfaces the medial temporal lobe with the ventral visual pathway for object identification, plays a critical role in item-based recognition memory. The precise manner in which PrC codes for the prior occurrence of objects, however, remains poorly understood. In the present functional magnetic resonance imaging (fMRI) study, we used multivoxel pattern analyses to examine whether the prior occurrence of faces is coded by distributed patterns of PrC activity that consist of voxels with decreases as well as increases in signal. We also investigated whether pertinent voxels are preferentially tuned to the specific object category to which judged stimuli belong. We found that, when no a priori constraints were imposed on the direction of signal change, activity patterns that allowed for successful classification of recognition-memory decisions included some voxels with decreases and others with increases in signal in association with perceived prior occurrence. Moreover, successful classification was obtained in the absence of a mean difference in activity across the set of voxels in these patterns. Critically, we observed a positive relationship between classifier accuracy and behavioral performance across participants. Additional analyses revealed that voxels carrying diagnostic information for classification of memory decisions showed category specificity in their tuning for faces when probed with an independent functional localizer in a nonmnemonic task context. These voxels were spatially distributed in PrC, and extended beyond the contiguous voxel clusters previously described as the anterior temporal face patch. Our findings provide support for proposals, recently raised in the neurophysiological literature, that the prior occurrence of objects is coded by distributed PrC representations. They also suggest that the stimulus category to which an item belongs shapes the organization of these distributed representations.