P. Mickle Fox

Correspondence of the brain's functional architecture during activation and rest.

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.”

ALE meta-analysis: Controlling the false discovery rate and performing statistical contrasts

Activation likelihood estimation (ALE) has greatly advanced voxel‐based meta‐analysis research in the field of functional neuroimaging. We present two improvements to the ALE method. First, we evaluate the feasibility of two techniques for correcting for multiple comparisons: the single threshold test and a procedure that controls the false discovery rate (FDR). To test these techniques, foci from four different topics within the literature were analyzed: overt speech in stuttering subjects, the color‐word Stroop task, picture‐naming tasks, and painful stimulation. In addition, the performance of each thresholding method was tested on randomly generated foci. We found that the FDR method more effectively controls the rate of false positives in meta‐analyses of small or large numbers of foci. Second, we propose a technique for making statistical comparisons of ALE meta‐analyses and investigate its efficacy on different groups of foci divided by task or response type and random groups of similarly obtained foci. We then give an example of how comparisons of this sort may lead to advanced designs in future meta‐analytic research. Hum Brain Mapp 25:155–164, 2005.

Behavioral interpretations of intrinsic connectivity networks

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

BrainMap taxonomy of experimental design: Description and evaluation

Coordinate‐based, voxel‐wise meta‐analysis is an exciting recent addition to the human functional brain mapping literature. In view of the critical importance of selection criteria for any valid meta‐analysis, a taxonomy of experimental design should be an important tool for aiding in the design of rigorous meta‐analyses. The coding scheme of experimental designs developed for and implemented within the BrainMap database provides a candidate taxonomy. In this study, the BrainMap experimental‐design taxonomy is described and evaluated by comparing taxonomy fields to data‐filtering choices made by subject‐matter experts carrying out meta‐analyses of the functional imaging literature. Fifteen publications reporting a total of 46 voxel‐wise meta‐analyses were included in this assessment. Collectively these 46 meta‐analyses pooled data from 351 publications, selected for experimental similarity within each meta‐analysis. Filter implementations within BrainMap were graded by ease‐of‐use (A–C) and by stage‐of‐use (1–3). Quality filters and content filters were tabulated separately. Quality filters required for data entry into BrainMap were classed as mandatory (five filters), being above the use grading system. All authors spontaneously adopted the five mandatory filters in constructing their meta‐analysis, indicating excellent agreement on data quality among authors and between authors and the BrainMap development team. Two non‐mandatory quality filters (group size and imaging modality) were applied by all authors; both were Stage 1, Grade A filters. Field‐of‐view filters were the least‐accessible quality filters (Stage 3, Grade C); two field‐of‐view filters were applied by six and four authors, respectively. Authors made a total of 115 content‐filter choices. Of these, 78 (68%) were Stage 1, Grade A filters; 16 (14%) were Stage 2, Grade A; and 21 (18%) were Stage 2, Grade C. No author‐applied filter was absent from the taxonomy. Hum Brain Mapp 25:185–198, 2005.

The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data.

BackgroundNeuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.FindingsIn this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.ConclusionsThe BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.

The functional connectivity of the human caudate: An application of meta-analytic connectivity modeling with behavioral filtering

Meta-analysis based techniques are emerging as powerful, robust tools for developing models of connectivity in functional neuroimaging. Here, we apply meta-analytic connectivity modeling to the human caudate to 1) develop a model of functional connectivity, 2) determine if meta-analytic methods are sufficiently sensitive to detect behavioral domain specificity within region-specific functional connectivity networks, and 3) compare meta-analytic driven segmentation to structural connectivity parcellation using diffusion tensor imaging. Results demonstrate strong coherence between meta-analytic and data-driven methods. Specifically, we found that behavioral filtering resulted in cognition and emotion related structures and networks primarily localized to the head of the caudate nucleus, while perceptual and action specific regions localized to the body of the caudate, consistent with early models of nonhuman primate histological studies and postmortem studies in humans. Diffusion tensor imaging (DTI) revealed support for meta-analytic connectivity modelings (MACM) utility in identifying both direct and indirect connectivity. Our results provide further validation of meta-analytic connectivity modeling, while also highlighting an additional potential, namely the extraction of behavioral domain specific functional connectivity.

Implementation errors in the GingerALE Software: Description and recommendations.

Neuroscience imaging is a burgeoning, highly sophisticated field the growth of which has been fostered by grant‐funded, freely distributed software libraries that perform voxel‐wise analyses in anatomically standardized three‐dimensional space on multi‐subject, whole‐brain, primary datasets. Despite the ongoing advances made using these non‐commercial computational tools, the replicability of individual studies is an acknowledged limitation. Coordinate‐based meta‐analysis offers a practical solution to this limitation and, consequently, plays an important role in filtering and consolidating the enormous corpus of functional and structural neuroimaging results reported in the peer‐reviewed literature. In both primary data and meta‐analytic neuroimaging analyses, correction for multiple comparisons is a complex but critical step for ensuring statistical rigor. Reports of errors in multiple‐comparison corrections in primary‐data analyses have recently appeared. Here, we report two such errors in GingerALE, a widely used, US National Institutes of Health (NIH)‐funded, freely distributed software package for coordinate‐based meta‐analysis. These errors have given rise to published reports with more liberal statistical inferences than were specified by the authors. The intent of this technical report is threefold. First, we inform authors who used GingerALE of these errors so that they can take appropriate actions including re‐analyses and corrective publications. Second, we seek to exemplify and promote an open approach to error management. Third, we discuss the implications of these and similar errors in a scientific environment dependent on third‐party software. Hum Brain Mapp 38:7–11, 2017.

On the spatial organization of sound processing in the human temporal lobe: A meta-analysis

In analogy to visual object recognition, proposals have been made that auditory object recognition is organized by sound class (e.g., vocal/non-vocal, linguistic/non-linguistic) and linked to several pathways or processing streams with specific functions. To test these proposals, we analyzed temporal lobe activations from 297 neuroimaging studies on vocal, musical and environmental sound processing. We found that all sound classes elicited activations anteriorly, posteriorly and ventrally of primary auditory cortex. However, rather than being sound class (e.g., voice) or attribute (e.g., complexity) specific, these processing streams correlated with sound knowledge or experience. Specifically, an anterior stream seemed to support general, sound class independent sound recognition and discourse-level semantic processing. A posterior stream could be best explained as supporting the embodiment of sound associated actions and a ventral stream as supporting multimodal conceptual representations. Vocalizations and music engaged these streams evenly in the left and right hemispheres, whereas environmental sounds produced a left-lateralized pattern. Together, these results both challenge and confirm existing proposal of temporal lobe specialization. Moreover, they suggest that the temporal lobe maintains the neuroanatomical building blocks for an all-purpose sound comprehension system that, instead of being preset for a particular sound class, is shaped in interaction with an individuals sonic environment.

Thalamic medial dorsal nucleus atrophy in medial temporal lobe epilepsy: A VBM meta-analysis

Purpose Medial temporal lobe epilepsy (MTLE) is associated with MTLE network pathology within and beyond the hippocampus. The purpose of this meta-analysis was to identify consistent MTLE structural change to guide subsequent targeted analyses of these areas. Methods We performed an anatomic likelihood estimation (ALE) meta-analysis of 22 whole-brain voxel-based morphometry experiments from 11 published studies. We grouped these experiments in three ways. We then constructed a meta-analytic connectivity model (MACM) for regions of consistent MTLE structural change as reported by the ALE analysis. Key findings ALE reported spatially consistent structural change across VBM studies only in the epileptogenic hippocampus and the bilateral thalamus; within the thalamus, the medial dorsal nucleus of the thalamus (MDN thalamus) represented the greatest convergence (P < 0.05 corrected for multiple comparisons). The subsequent MACM for the hippocampus and ipsilateral MDN thalamus demonstrated that the hippocampus and ipsilateral MDN thalamus functionally co-activate and are nodes within the same network, suggesting that MDN thalamic damage could result from MTLE network excitotoxicity. Significance Notwithstanding our large sample of studies, these findings are more restrictive than previous reports and demonstrate the utility of our inclusion filters and of recently modified meta-analytic methods in approximating clinical relevance. Thalamic pathology is commonly observed in animal and human studies, suggesting it could be a clinically useful indicator. Thalamus-specific research as a clinical marker awaits further investigation.

Automated regional behavioral analysis for human brain images

Behavioral categories of functional imaging experiments along with standardized brain coordinates of associated activations were used to develop a method to automate regional behavioral analysis of human brain images. Behavioral and coordinate data were taken from the BrainMap database (http://www.brainmap.org/), which documents over 20 years of published functional brain imaging studies. A brain region of interest (ROI) for behavioral analysis can be defined in functional images, anatomical images or brain atlases, if images are spatially normalized to MNI or Talairach standards. Results of behavioral analysis are presented for each of BrainMaps 51 behavioral sub-domains spanning five behavioral domains (Action, Cognition, Emotion, Interoception, and Perception). For each behavioral sub-domain the fraction of coordinates falling within the ROI was computed and compared with the fraction expected if coordinates for the behavior were not clustered, i.e., uniformly distributed. When the difference between these fractions is large behavioral association is indicated. A z-score ≥ 3.0 was used to designate statistically significant behavioral association. The left-right symmetry of ~100K activation foci was evaluated by hemisphere, lobe, and by behavioral sub-domain. Results highlighted the classic left-side dominance for language while asymmetry for most sub-domains (~75%) was not statistically significant. Use scenarios were presented for anatomical ROIs from the Harvard-Oxford cortical (HOC) brain atlas, functional ROIs from statistical parametric maps in a TMS-PET study, a task-based fMRI study, and ROIs from the ten “major representative” functional networks in a previously published resting state fMRI study. Statistically significant behavioral findings for these use scenarios were consistent with published behaviors for associated anatomical and functional regions.