A re-defined and generalized percent-overlap-of-activation measure for studies of fMRI reproducibility and its use in identifying outlier activation maps

Functional Magnetic Resonance Imaging (fMRI) is a popular noninvasive modality to investigate activation in the human brain. The end result of most fMRI experiments is an activation map corresponding to the given paradigm. These maps can vary greatly from one study to the next, so quantifying the reliability of identified activation over several fMRI studies is important. The percent overlap of activation (Rombouts et al., 1998 and Machielsen et al., 2000) is a global reliability measure between activation maps drawn from any two fMRI studies. A slightly modified but more intuitive measure is provided by the Jaccard (1901) coefficient of similarity, whose use we study in this paper. A generalization of these measures is also proposed to comprehensively summarize the reliability of multiple fMRI studies. Finally, a testing mechanism to flag potentially anomalous studies is developed. The methodology is illustrated on studies involving left- and right-hand motor task paradigms performed by a right-hand dominant male subject several times over a period of two months, with excellent results

An Iterative Jackknife Approach for Assessing Reliability and Power of fMRI Group Analyses

For functional magnetic resonance imaging (fMRI) group activation maps, so-called second-level random effect approaches are commonly used, which are intended to be generalizable to the population as a whole. However, reliability of a certain activation focus as a function of group composition or group size cannot directly be deduced from such maps. This question is of particular relevance when examining smaller groups (<20–27 subjects). The approach presented here tries to address this issue by iteratively excluding each subject from a group study and presenting the overlap of the resulting (reduced) second-level maps in a group percent overlap map. This allows to judge where activation is reliable even upon excluding one, two, or three (or more) subjects, thereby also demonstrating the inherent variability that is still present in second-level analyses. Moreover, when progressively decreasing group size, foci of activation will become smaller and/or disappear; hence, the group size at which a given activation disappears can be considered to reflect the power necessary to detect this particular activation. Systematically exploiting this effect allows to rank clusters according to their observable effect size. The approach is tested using different scenarios from a recent fMRI study (children performing a “dual-use” fMRI task, n = 39), and the implications of this approach are discussed

Complex-valued Time Series Modeling for Improved Activation Detection in fMRI Studies

A complex-valued data-based model with th order autoregressive errors and general real/imaginary error covariance structure is proposed as an alternative to the commonly used magnitude-only data-based autoregressive model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and compared for experimental and simulated data. For a dataset from a right-hand finger-tapping experiment, the activation map obtained using complex-valued modeling more clearly identifies the primary activation region (left functional central sulcus) than the magnitude-only model. Such improved accuracy in mapping the left functional central sulcus has important implications in neurosurgical planning for tumor and epilepsy patients. Additionally, we develop magnitude and phase detrending procedures for complex-valued time series and examine the effect of spatial smoothing. These methods improve the power of complex-valued data-based activation statistics. Our results advocate for the use of the complex-valued data and the modeling of its dependence structures as a more efficient and reliable tool in fMRI experiments over the current practice of using only magnitude-valued datasets

Visualising inter-subject variability in fMRI using threshold-weighted overlap maps

Functional neuroimaging studies are revealing the neural systems sustaining many sensory, motor and cognitive abilities. A proper understanding of these systems requires an appreciation of the degree to which they vary across subjects. Some sources of inter-subject variability might be easy to measure (demographics, behavioural scores, or experimental factors), while others are more difficult (cognitive strategies, learning effects, and other hidden sources). Here, we introduce a simple way of visualising whole-brain consistency and variability in brain responses across subjects using threshold-weighted voxel-based overlap maps. The output quantifies the proportion of subjects activating a particular voxel or region over a wide range of statistical thresholds. The sensitivity of our approach was assessed in 30 healthy adults performing a matching task with their dominant hand. We show how overlap maps revealed many effects that were only present in a subsample of our group; we discuss how overlap maps can provide information that may be missed or misrepresented by standard group analysis, and how this information can help users to understand their data. In particular, we emphasize that functional overlap maps can be particularly useful when it comes to explaining typical (or atypical) compensatory mechanisms used by patients following brain damage

Reliability of the fMRI-based assessment of self-evaluation in individuals with internet gaming disorder

The self-concept—defined as the cognitive representation of beliefs about oneself—determines how individuals view themselves, others, and their actions. A negative self-concept can drive gaming use and internet gaming disorder (IGD). The assessment of the neural correlates of self-evaluation gained popularity to assess the self-concept in individuals with IGD. This attempt, however, seems to critically depend on the reliability of the investigated task-fMRI brain activation. As first study to date, we assessed test–retest reliability of an fMRI self-evaluation task. Test–retest reliability of neural brain activation between two separate fMRI sessions (approximately 12 months apart) was investigated in N = 29 healthy participants and N = 11 individuals with pathological internet gaming. We computed reliability estimates for the different task contrasts (self, a familiar, and an unknown person) and the contrast (self > familiar and unknown person). Data indicated good test–retest reliability of brain activation, captured by the “self”, “familiar person”, and “unknown person” contrasts, in a large network of brain regions in the whole sample (N = 40) and when considering both experimental groups separately. In contrast to that, only a small set of brain regions showed moderate to good reliability, when investigating the contrasts (“self > familiar and unknown person”). The lower reliability of the contrast can be attributed to the fact that the constituting contrast conditions were highly correlated. Future research on self-evaluation should be cautioned by the findings of substantial local reliability differences across the brain and employ methods to overcome these limitations

An efficient kk-means-type algorithm for clustering datasets with incomplete records

The $k$-means algorithm is arguably the most popular nonparametric clustering method but cannot generally be applied to datasets with incomplete records. The usual practice then is to either impute missing values under an assumed missing-completely-at-random mechanism or to ignore the incomplete records, and apply the algorithm on the resulting dataset. We develop an efficient version of the $k$-means algorithm that allows for clustering in the presence of incomplete records. Our extension is called $k_m$-means and reduces to the $k$-means algorithm when all records are complete. We also provide initialization strategies for our algorithm and methods to estimate the number of groups in the dataset. Illustrations and simulations demonstrate the efficacy of our approach in a variety of settings and patterns of missing data. Our methods are also applied to the analysis of activation images obtained from a functional Magnetic Resonance Imaging experiment.Comment: 21 pages, 12 figures, 3 tables, in press, Statistical Analysis and Data Mining -- The ASA Data Science Journal, 201

Quality data assessment and improvement in pre-processing pipeline to minimize impact of spurious signals in functional magnetic imaging (fMRI)

In the recent years, the field of quality data assessment and signal denoising in functional magnetic resonance imaging (fMRI) is rapidly evolving and the identification and reduction of spurious signal with pre-processing pipeline is one of the most discussed topic. In particular, subject motion or physiological signals, such as respiratory or/and cardiac pulsatility, were showed to introduce false-positive activations in subsequent statistical analyses. Different measures for the evaluation of the impact of motion related artefacts, such as frame-wise displacement and root mean square of movement parameters, and the reduction of these artefacts with different approaches, such as linear regression of nuisance signals and scrubbing or censoring procedure, were introduced. However, we identify two main drawbacks: i) the different measures used for the evaluation of motion artefacts were based on user-dependent thresholds, and ii) each study described and applied their own pre-processing pipeline. Few studies analysed the effect of these different pipelines on subsequent analyses methods in task-based fMRI.The first aim of the study is to obtain a tool for motion fMRI data assessment, based on auto-calibrated procedures, to detect outlier subjects and outliers volumes, targeted on each investigated sample to ensure homogeneity of data for motion. The second aim is to compare the impact of different pre-processing pipelines on task-based fMRI using GLM based on recent advances in resting state fMRI preprocessing pipelines. Different output measures based on signal variability and task strength were used for the assessment

Statistical Methods for Functional Magnetic Resonance Imaging Data

Understanding how the brain functions is one of the most important goals in science and medicine today. Functional magnetic resonance imaging (fMRI) is a noninvasive, widely used technology for studying brain function in humans. While fMRI has great potential to shed light on cognitive development, decline and disorders, it also presents statistical and computational challenges due to a myriad of sources of noise and the large size of the data. In this thesis, I propose several methods to improve the analysis of resting-state fMRI, which is used to understand connectivity between different regions of the brain. Specifically, this thesis addresses two primary themes. First, I propose shrinkage estimators for functional connectivity, which improve reliability of subject-level estimates by "borrowing strength" across subjects. Second, I propose a method of identifying artifacts in fMRI data through a novel high-dimensional outlier detection method. The proposed methods can be used together and have the potential to significantly improve our understanding of brain connectivity at the subject level using resting-state fMRI

Behavioral and neural signatures of working memory in childhood

Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during human development, however, are not well understood. Here, we establish associations between working memory, other cognitive abilities, and functional MRI (fMRI) activation in data from over 11,500 9- to 10-year-old children (both sexes) enrolled in the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotiona