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  • 1
    In: Human Brain Mapping, Wiley, Vol. 25, No. 1 ( 2005-05), p. 155-164
    Abstract: 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. © 2005 Wiley‐Liss, Inc.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2005
    detail.hit.zdb_id: 1492703-2
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  • 2
    In: Human Brain Mapping, Wiley, Vol. 25, No. 1 ( 2005-05), p. 185-198
    Abstract: 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. © 2005 Wiley‐Liss, Inc.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2005
    detail.hit.zdb_id: 1492703-2
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  • 3
    In: Alzheimer's & Dementia, Wiley, Vol. 15, No. 7S_Part_26 ( 2019-07)
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2201940-6
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  • 4
    Online Resource
    Online Resource
    Wiley ; 2005
    In:  Human Brain Mapping Vol. 25, No. 1 ( 2005-05), p. 174-184
    In: Human Brain Mapping, Wiley, Vol. 25, No. 1 ( 2005-05), p. 174-184
    Abstract: The high information content in large data sets from voxel‐based meta‐analyses is complex, making it hard to readily resolve details. Using the meta‐analysis network as a standardized data structure, network analysis algorithms can examine complex interrelationships and resolve hidden details. Two new network analysis algorithms have been adapted for use with meta‐analysis networks. The first, called replicator dynamics network analysis (RDNA), analyzes co‐occurrence of activations, whereas the second, called fractional similarity network analysis (FSNA), uses binary pattern matching to form similarity subnets. These two network analysis methods were evaluated using data from activation likelihood estimation (ALE)‐based meta‐analysis of the Stroop paradigm. Two versions of these data were evaluated, one using a more strict ALE threshold ( P 〈 0.01) with a 13‐node meta‐analysis network, and the other a more lax threshold ( P 〈 0.05) with a 22‐node network. Java‐based applications were developed for both RDNA and FSNA. The RDNA algorithm was modified to provide multiple subnets or maximal cliques for meta‐analysis networks. Three different similarity measures were evaluated with FSNA to form subsets of nodes and experiments. RDNA provides a means to gauge importance of metanalysis subnets and complements FSNA, which provides a more comprehensive assessment of node similarity subsets, experiment similarity subsets, and overall node‐to‐factors similarity. The need to use both presence and absence of activations was an important finding in similarity analyses. FSNA revealed details from the pooled Stroop meta‐analysis that would otherwise require separate highly filtered meta‐analyses. These new analysis tools demonstrate how network analysis strategies can simplify greatly and enhance voxel‐based meta‐analyses. Hum Brain Mapp 25:174–184, 2005. © 2005 Wiley‐Liss, Inc.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2005
    detail.hit.zdb_id: 1492703-2
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  • 5
    Online Resource
    Online Resource
    Wiley ; 2017
    In:  Human Brain Mapping Vol. 38, No. 1 ( 2017-01), p. 7-11
    In: Human Brain Mapping, Wiley, Vol. 38, No. 1 ( 2017-01), p. 7-11
    Abstract: 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 . © 2016 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1492703-2
    Location Call Number Limitation Availability
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  • 6
    In: Alzheimer's & Dementia, Wiley, Vol. 14, No. 7S_Part_15 ( 2018-07)
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2201940-6
    Location Call Number Limitation Availability
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