Single-subject Data Research Articles

A Quantitative Synthesis of Methodology in the Meta-Analysis of Single-Subject Research for Students with Disabilities: 1985–2009

The purpose of this review was to examine the methods used to conduct meta-analyses of single-subject research involving students with and at-risk for disabilities. Specifically, the procedures used for preparing, aggregating, analyzing, and evaluating single-subject data across 68 primary syntheses were examined. In addition to these methodological and reporting issues, the present review also considered various characteristics of syntheses to determine their overall prevalence and focus. Results of the review indicated that the publication rate of single-subject meta-analyses has increased considerably in recent years, focusing equally on students with high- and low-incidence disabilities. This review revealed considerable variability in the methods and procedures used to synthesize single-subject research. Based on these findings, suggestions for future single-subject meta-analyses were made.

Errors and Conflict at the Task Level and the Response Level

In the last decade, research on error and conflict processing has become one of the most influential research areas in the domain of cognitive control. There is now converging evidence that a specific part of the posterior frontomedian cortex (pFMC), the rostral cingulate zone (RCZ), is crucially involved in the processing of errors and conflict. However, error-related research has focused primarily on a specific error type, namely, response errors. The aim of the present study was to investigate whether errors on the task level rely on the same neural and functional mechanisms. Here we report a dissociation of both error types in the pFMC: whereas response errors activate the RCZ, task errors activate the dorsal frontomedian cortex. Although this last region shows an overlap in activation for task and response errors on the group level, a closer inspection of the single-subject data is more in accordance with a functional anatomical dissociation. When investigating brain areas related to conflict on the task and response levels, a clear dissociation was perceived between areas associated with response conflict and with task conflict. Overall, our data support a dissociation between response and task levels of processing in the pFMC. In addition, we provide additional evidence for a dissociation between conflict and errors both at the response level and at the task level.

Realigning Student and Teacher Perceptions of School Rules: A Behavior Management Strategy for Students with Challenging Behaviors

This article describes a data-informed intervention to close the gap between student and teacher perceptions of school rules and improve student behavior. The student and teacher agreement realignment strategy was pilot tested over 36 weeks with 10 middle school students receiving services for special education in the eligibility category of emotional disturbance. Students and teachers used similar assessment tools to record student compliance with five classroom behavior norms or rules. Student and teacher reports of student behavior were compared in weekly student-led conferences. Data were used by students, teachers, and the school social worker to evaluate students' progress, identify goals, and develop individualized interventions for improving behavior. In the current study, single-subject data on the 10 students were analyzed for clinical and visual significance and statistical significance using the proportion-frequency procedure and the two standard deviations statistical test. Significant improvement in student behavior was revealed. Furthermore, nine out of 10 students demonstrated a significant reduction in teacher-generated office disciplinary referrals.

A non-parametric approach for fMRI analysis: application for single subject data analysis

Event Abstract Back to Event A non-parametric approach for fMRI analysis: application for single subject data analysis Andor V. Gál1, 2*, Adám Bíró1, 2 and Zoltán Vidnyánszky1, 2, 3 1 Pázmány Péter Catholic University, Faculty of Information Technology, Hungary 2 Semmelweis University, MR Research Center, Szentágothai J. Knowledge Center, Hungary 3 Pázmány Péter Catholic University - Semmelweis University, Neurobionics Research Group, Hungarian Academy of Sciences, Hungary The classical analytical method in functional neuroimaging, Statistical Parametric Mapping is prone to violation of a set of assumptions about homogeneity of variance, normality, and independent errors. Prospective bias in the statistical decision escalates as the number of inspected voxels grow (due to multiple comparisons) and in case of fixed effect (single subject) analysis. In clinical applications (typical single subject studies) the appropriate control for potential statistical errors is primary concern. We developed a new, non-parametric method that requires only minimal assumptions for validity and can be used to assess statistical significance in voxelwise analysis accounting for the multiple comparisons problem. The core concept is to create a null-hypothesis sampling distribution of GLM beta-weights for each experimental condition separately. The sampling distribution is formed using randomly generated alternative temporal condition sequences that meet design optimization constraints. Unlike other nonparametric permutation or surrogate data generation based algorithms, this technique does not require multiple experimental conditions and additional assumptions about the structure of the data are minimal. We validated our non-parametric approach on the data obtained from two single subject fMRI experiments and the results were compared to the results obtained using standard parametric approaches on the same data set. It was found that when the appropriate assumptions hold, the nonparametric permutation approach gives more stable results as compared to those obtained from a parametric analysis: i.e. variability in voxel-activations between different studies of the same subject is lower. Conference: IBRO International Workshop 2010, Pécs, Hungary, 21 Jan - 23 Jan, 2010. Presentation Type: Poster Presentation Topic: Abstracts Citation: Gál AV, Bíró A and Vidnyánszky Z (2010). A non-parametric approach for fMRI analysis: application for single subject data analysis. Front. Neurosci. Conference Abstract: IBRO International Workshop 2010. doi: 10.3389/conf.fnins.2010.10.00228 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 May 2010; Published Online: 04 May 2010. * Correspondence: Andor V Gál, Pázmány Péter Catholic University, Faculty of Information Technology, Budapest, Hungary, gviktor@digitus.itk.ppke.hu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andor V Gál Adám Bíró Zoltán Vidnyánszky Google Andor V Gál Adám Bíró Zoltán Vidnyánszky Google Scholar Andor V Gál Adám Bíró Zoltán Vidnyánszky PubMed Andor V Gál Adám Bíró Zoltán Vidnyánszky Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A Bayesian spatiotemporal model for very large data sets

Functional MRI provides a unique perspective of neuronal organization; however, these data include many complex sources of spatiotemporal variability, which require spatial preprocessing and statistical analysis. For the latter, Bayesian models provide a promising alternative to classical inference, which uses results from Gaussian random field theory to assess the significance of spatially correlated statistic images. A Bayesian approach generalizes the application of these ideas in that (1) random fields are used to model all spatial parameters, not solely observation error, (2) their smoothness is optimized, and (3) a broader class of models can be compared. The main problem, however, is computational, due to the large number of voxels in a brain volume. Sampling methods are time-consuming; however, approximate inference using variational Bayes (VB) offers a principled and transparent way to specify assumptions necessary for computational tractability. Penny et al. (2005b) described such a scheme using a joint spatial prior and approximated the joint posterior density with one that factorized over voxels. However, a further computational bottleneck is encountered when evaluating the log model evidence used to compare models. This has lead to dividing a brain volume into slices and treating each independently. This amounts to approximating the spatial prior over a full volume with stacked 2D priors. That is, smoothness along the z-axis is not included in the model. Here we describe a VB scheme that approximates the zero mean joint spatial prior with a non-zero mean empirical prior that factors over voxels, thereby overcoming this problem. We do this by modifying the original VB algorithm of Penny et al. using the conditional form of a so-called conditional autoregressive (CAR) prior to update a marginal prior over voxels. We refer to this as a spatially-informed voxel-wise prior (SVP) and use them to spatially regularise general linear model (GLM) and autoregressive (AR) coefficients (over time to model serial correlations). This algorithm scales more favourably with the number of voxels providing a truly 3D spatiotemporal model over volumes containing tens of thousands of voxels. We compare the scaling of compute times with the number of voxels and performance with a joint prior applied to synthetic and single-subject data.

Modeling inter-subject variability in FMRI activation location: a Bayesian hierarchical spatial model.

The aim of this article is to develop a spatial model for multi-subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi-subject data, some work on spatial modeling of single-subject data, and some recent work on spatial modeling of multi-subject data. However, there has been no work on spatial models that explicitly account for inter-subject variability in activation locations. In this article, we use the idea of activation centers and model the inter-subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical framework which allows us to draw inferences at all levels: the population level, the individual level, and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question that is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass-univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data.

Activated region fitting: A robust high‐power method for fMRI analysis using parameterized regions of activation

An important issue in the analysis of fMRI is how to account for the spatial smoothness of activated regions. In this article a method is proposed to accomplish this by modeling activated regions with Gaussian shapes. Hypothesis tests on the location, spatial extent, and amplitude of these regions are performed instead of hypothesis tests of individual voxels. This increases power and eases interpretation. Simulation studies show robust hypothesis tests under misspecification of the shape model, and increased power over standard techniques especially at low signal-to-noise ratios. An application to real single-subject data also indicates that the method has increased power over standard methods.

Comparison of Overlap Methods for Quantitatively Synthesizing Single-Subject Data

Four overlap methods for quantitatively synthesizing single-subject data were compared to visual analysts’ judgments. The overlap methods were percentage of nonoverlapping data, pairwise data overlap squared, percentage of data exceeding the median, and percentage of data exceeding a median trend. Visual analysts made judgments about 160 A-B data sets selected randomly from the Journal of Applied Behavior Analysis . The four overlap methods were compared for data sets in which all visual analysts agreed a change in data occurred or a change did not occur across conditions. Each overlap method had unacceptably high levels of errors. Given the findings and weaknesses of the overlap methods, their use should be abandoned. The desirable characteristics of a quantitative synthesis method are described.

Modeling sequential context effects in judgment analysis: A time series approach

AbstractIn this article a broad perspective incorporating elements of time series theory is presented for conceptualizing the data obtained in multi-trial judgment experiments. Recent evidence suggests that sequential context effects, assimilation and contrast, commonly found in psychophysical judgment tasks, may be present in judgments of abstract magnitudes. A time series approach for analyzing single-subject data is developed and applied to expert prognostic judgments of risk for heart disease with an emphasis on detecting possible sequential context effects. The results demonstrate that sequential context effects do exist in such expert prognostic judgments. Contrast and assimilation were produced by cue series; the latter occurring more frequently. Experts also showed assimilation of prior responses that was independent of the cue series input. Time series analysis also revealed that abrupt or large trial-by-trial changes in the value of cues that receive the most attention in prognostic judgment tasks can disrupt the accuracy of these judgments. These findings suggest that a time series approach is a useful alternative to ordinary least squares regression, providing additional insights into the cognitive processes operating during multi-cue judgment experiments.

A multilevel meta-analysis of single-subject experimental design studies

A multilevel approach is proposed to synthesize the results of single-subject experimental research by combining either raw data or effect-size measures. A multilevel meta-analysis of single-subject data allows us to make inferences not only about the effects for the participants that are investigated, but also about the generality of the effects. It also permits a systematic exploration of the possible moderating effect of study and participant characteristics. We also discuss the possibility of combining single-subject experimental research and group-comparison research in one single meta-analysis, and the difficulties involved, especially in making effect-size measures comparable for both kinds of studies.

Intersubject variability in the analysis of diffusion tensor images at the group level: fractional anisotropy mapping and fiber tracking techniques

Diffusion tensor imaging (DTI) provides comprehensive information about quantitative diffusion and connectivity in the human brain. Transformation into stereotactic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The objective of the present study was to optimize technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. Different averaging methods for mean diffusion-weighted images containing DTI information were compared, i.e., region of interest-based fractional anisotropy (FA) mapping, fiber tracking (FT) and corresponding tractwise FA statistics (TFAS). The novel technique of intersubject FT that takes into account directional information of single data sets during the FT process was compared to standard FT techniques. Application of the methods was shown in the comparison of normal subjects and subjects with defined white matter pathology (alterations of the corpus callosum). Fiber tracking was applied to averaged data sets and showed similar results compared with FT on single subject data. The application of TFAS to averaged data showed averaged FA values around 0.4 for normal controls. The values were in the range of the standard deviation for averaged FA values for TFAS applied to single subject data. These results were independent of the applied averaging technique. A significant reduction of the averaged FA values was found in comparison to TFAS applied to data from subjects with defined white matter pathology (FA around 0.2). The applicability of FT techniques in the analysis of different subjects at the group level was demonstrated. Group comparisons as well as FT on group averaged data were shown to be feasible. The objective of this work was to identify the most appropriate method for intersubject averaging and group comparison which incorporates intersubject variability of the directional information.

A Survey Evaluation of the Reliability of Visual Inspection and Functional Analysis Graphs

Visual inspection is the primary method used to analyze graphed behavioral data produced by functional analyses of problem behavior. The purpose of this study was to examine rater reliability of functional analysis graphs using visual inspection. Forty-three participants responded to a one-time anonymous survey (N = 454) mailed to graduate programs accredited in applied behavior analysis (N = 11). Respondents were instructed to classify single-subject data arrays depicted in multielement graphs from published studies. Classification was based on function categories by Hagopian, Fisher, Thompson, and Owen-DeSchryver. Three indices based on overall inter-rater agreement, the consistency of individual raters across graphs, and the aggregate performance of raters per graph were calculated and compared. Results for all three indices of rater performance were moderate to low. Results confirm prior preliminary work indicating relatively low levels of reliability and are discussed in relation to research, training, and practice issues.

A geometric flow for segmenting vasculature in proton-density weighted MRI

Modern neurosurgery takes advantage of magnetic resonance images (MRI) of a patient's cerebral anatomy and vasculature for planning before surgery and guidance during the procedure. Dual echo acquisitions are often performed that yield proton-density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. In this paper we develop a novel geometric flow for segmenting vasculature in PD images, which can also be applied to the easier cases of MR angiography data or Gadolinium enhanced MRI. Obtaining vasculature from PD data is of clinical interest since the acquisition of such images is widespread, the scanning process is non-invasive, and the availability of vessel segmentation methods could obviate the need for an additional angiographic or contrast-based sequence during preoperative imaging. The key idea is to first apply Frangi's vesselness measure [Frangi, A., Niessen, W., Vincken, K.L., Viergever, M.A., 1998. Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer Assisted Intervention, vol. 1496 of Lecture Notes in Computer Science, pp. 130-137] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which allows the flux maximizing flow algorithm of Vasilevskiy and Siddiqi [Vasilevskiy, A., Siddiqi, K., 2002. Flux maximizing geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (12), 1565-1578] to be applied to recover vessel boundaries. We carry out a qualitative validation of the approach on PD, MR angiography and Gadolinium enhanced MRI volumes and suggest a new way to visualize the segmentations in 2D with masked projections. We validate the approach quantitatively on a single-subject data set consisting of PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, with an expert segmented version of the TOF volume viewed as the ground truth. We then validate the approach quantitatively on 19 PD data sets from a new digital brain phantom, with semi-automatically obtained labels from the corresponding angiography volumes viewed as ground truth. A significant finding is that both for the single-subject and multi-subject studies, 90% or more of the vasculature in the ground truth segmentation is recovered from the automatic segmentation of the other volumes.

(Effect) size matters: And so does the calculation.

In 2001, the American Psychological Association (APA) noted in its publication manual that effect size calculations should be included in manuscripts submitted for publication. However, researchers utilizing single subject designs have not typically embraced the approach of any analyses beyond that of the traditional visual analysis (Marascuilo & Busk, 1988; Parsonson & Baer, 1977). In visual analysis of single subject data, researchers have examined data for three changes in the data: trend, variability, and level. Using trend analysis, researchers have examined the direction of the data for an increasing (i.e., upward) or decreasing (i.e., downward) trend. Researchers have also inspected for change in data variability or bounce. Finally, researchers have noted changes in level or mean performance. Recent trends in the field of education have resulted in an increased need to synthesize data sets from single subject studies. For example, the No Child Left Behind Act (NCLBA; 2001) brought considerable attention to the term evidence-based practice. As Odom and colleagues described (2005), some have claimed that only randomized experimental group designs are appropriate for demonstrating scientific evidence. This precluded single subject studies from being included in contributions of scientific evidence on effective intervention methods. However, others have noted that rigorous single subject research has much to contribute when determining scientific knowledge within the field (Horner, et al., 2005). In order to support the use of single subject research as evidence-based, a process of synthesizing single subject data is needed. Additionally, the Individuals with Disabilities Education Act (2004) mandated that teachers use strategies based on evidence based research. It would be tragic for teachers to utilize only teaching strategies proven with group design research; hence a second need to summarize data from single subject studies. Finally, researchers conducting meta-analyses or research syntheses have needed a method for interpreting and comparing intervention effectiveness of single subject studies. Researchers and practitioners in the field have tried to synthesize intervention research and effect sizes have been calculated on single subject data (e.g., Ma, 2006; Parker, Hagan-Burke, & Vannest, 2007; Wanzek, et al., 2006).Therefore, the purpose of this paper is to present the types of measures that may be used to describe intervention effects of single subject research designs. Strengths and limitations of each method will be described. Finally, a recommendation will be made to assist in determining which method should be used with which types of single subject data. Regression Approaches Allison and Gorman described the use of regression models to calculate effect sizes with single subject data (Allison & Gorman, 1993; Faith, Allison, & Gorman, 1996). In doing so, the dependent measure in the study (e.g., reading fluency or out of seat behavior) served as the dependent measure in the analysis while the intervention sessions serve as the independent variable. A separate regression equation was then obtained for the baseline and intervention data resulting in two regression equations. Finally, the intervention was subtracted from the baseline and divided by the standard deviation of baseline (Hershberger, Wallace, Green, & Marquis, 1999). It should be noted that data portrayed in single subject graphs are not independent of one another. Often in single subject research, experimenters visually analyze intervention data following each intervention session. This visual analysis might result in modifications to intervention procedures during the subsequent session resulting in data that are dependent on preceding data. For example, if a child was being taught to exchange a graphic symbol for a preferred item, the dependent variable might be rate of independent exchanges. …

Bayesian M/EEG source reconstruction with spatio-temporal priors

This article proposes a Bayesian spatio-temporal model for source reconstruction of M/EEG data. The usual two-level probabilistic model implicit in most distributed source solutions is extended by adding a third level which describes the temporal evolution of neuronal current sources using time-domain General Linear Models (GLMs). These comprise a set of temporal basis functions which are used to describe event-related M/EEG responses. This places M/EEG analysis in a statistical framework that is very similar to that used for PET and fMRI. The experimental design can be coded in a design matrix, effects of interest characterized using contrasts and inferences made using posterior probability maps. Importantly, as is the case for single-subject fMRI analysis, trials are treated as fixed effects and the approach takes into account between-trial variance, allowing valid inferences to be made on single-subject data. The proposed probabilistic model is efficiently inverted by using the Variational Bayes framework under a convenient mean-field approximation (VB-GLM). The new method is tested with biophysically realistic simulated data and the results are compared to those obtained with traditional spatial approaches like the popular Low Resolution Electromagnetic TomogrAphy (LORETA) and minimum variance Beamformer. Finally, the VB-GLM approach is used to analyze an EEG data set from a face processing experiment.

Guidelines for Graphing Data with Microsoft® PowerPoint™

Graphs are vital components for analyzing data in the experimental analysis of behavior using single subject research methods. This paper extends the previous literature on the construction of single subject graphs by providing instructions for using Microsoft® Power PointTM and Microsoft® PowerPoint for Mac®, and describes improved reporting practices for visual presentation of single subject experimental data. Detailed instructions are provided for constructing line graphs for displaying single subject data, specialized graphs (i. e., semi-logarithmic, dual ordinate, and line and bar combination graphs), and electronically submitting graphs to journals for publication.

Real-time functional magnetic resonance imaging: methods and applications

Functional magnetic resonance imaging (fMRI) has been limited by time-consuming data analysis and a low signal-to-noise ratio, impeding online analysis. Recent advances in acquisition techniques, computational power and algorithms increased the sensitivity and speed of fMRI significantly, making real-time analysis and display of fMRI data feasible. So far, most reports have focused on the technical aspects of real-time fMRI (rtfMRI). Here, we provide an overview of the different major areas of applications that became possible with rtfMRI: online analysis of single-subject data provides immediate quality assurance and functional localizers guiding the main fMRI experiment or surgical interventions. In teaching, rtfMRI naturally combines all essential parts of a neuroimaging experiment, such as experimental design, data acquisition and analysis, while adding a high level of interactivity. Thus, the learning of essential knowledge required to conduct functional imaging experiments is facilitated. rtfMRI allows for brain-computer interfaces (BCI) with a high spatial and temporal resolution and whole-brain coverage. Recent studies have shown that such BCI can be used to provide online feedback of the blood-oxygen-level-dependent signal and to learn the self-regulation of local brain activity. Preliminary evidence suggests that this local self-regulation can be used as a new paradigm in cognitive neuroscience to study brain plasticity and the functional relevance of brain areas, even being potentially applicable for psychophysiological treatment.

The spatial distribution and temporal dynamics of brain regions activated during the perception of object and non-object patterns

Both animal and human studies suggest that the efficiency with which we are able to grasp objects is attributable to a repertoire of motor signals derived directly from vision. This is in general agreement with the long-held belief that the automatic generation of motor signals by the perception of objects is based on the actions they afford. In this study, we used magnetoencephalography (MEG) to determine the spatial distribution and temporal dynamics of brain regions activated during passive viewing of object and non-object targets that varied in the extent to which they afforded a grasping action. Synthetic Aperture Magnetometry (SAM) was used to localize task-related oscillatory power changes within specific frequency bands, and the time course of activity within given regions-of-interest was determined by calculating time–frequency plots using a Morlet wavelet transform. Both single subject and group-averaged data on the spatial distribution of brain activity are presented. We show that: (i) significant reductions in 10–25 Hz activity within extrastriate cortex, occipito-temporal cortex, sensori-motor cortex and cerebellum were evident with passive viewing of both objects and non-objects; and (ii) reductions in oscillatory activity within the posterior part of the superior parietal cortex (area Ba7) were only evident with the perception of objects. Assuming that focal reductions in low-frequency oscillations (< 30 Hz) reflect areas of heightened neural activity, we conclude that: (i) activity within a network of brain areas, including the sensori-motor cortex, is not critically dependent on stimulus type and may reflect general changes in visual attention; and (ii) the posterior part of the superior parietal cortex, area Ba7, is activated preferentially by objects and may play a role in computations related to grasping.

Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event-related beamforming approach.

We describe a novel spatial filtering approach to the localization of cortical activity accompanying voluntary movements. The synthetic aperture magnetometry (SAM) minimum-variance beamformer algorithm was used to compute spatial filters three-dimensionally over the entire brain from single trial neuromagnetic recordings of subjects performing self-paced index finger movements. Images of instantaneous source power ("event-related SAM") computed at selected latencies revealed activation of multiple cortical motor areas prior to and following left and right index finger movements in individual subjects, even in the presence of low-frequency noise (e.g., eye movements). A slow premovement motor field (MF) reaching maximal amplitude approximately 50 ms prior to movement onset was localized to the hand area of contralateral precentral gyrus, followed by activity in the contralateral postcentral gyrus at 40 ms, corresponding to the first movement-evoked field (MEFI). A novel finding was a second activation of the precentral gyrus at a latency of approximately 150 ms, corresponding to the second movement-evoked field (MEFII). Group averaging of spatially normalized images indicated additional premovement activity in the ipsilateral precentral gyrus and the left inferior parietal cortex for both left and right finger movements. Weaker activations were also observed in bilateral premotor areas and the supplementary motor area. These results show that event-related beamforming provides a robust method for studying complex patterns of time-locked cortical activity accompanying voluntary movements, and offers a new approach for the localization of multiple cortical sources derived from neuromagnetic recordings in single subject and group data.

Effect size calculations and single subject designs

This study compared visual analyses with five alternative methods for assessing the magnitude of effect with single subject designs. Each method was successful in detecting intervention effect. When rank ordered, each method was consistent in identifying the participants with the largest effect. We recommend the use of the standard mean difference (SMDall) formula and we believe the use of a regression equation for calculating effect size in single subject data should be avoided.