scholarly journals Sharing brain mapping statistical results with the neuroimaging data model

2016 ◽  
Author(s):  
Camille Maumet ◽  
Tibor Auer ◽  
Alexander Bowring ◽  
Gang Chen ◽  
Samir Das ◽  
...  
Keyword(s):  
Data Model ◽  
Image Data ◽  
Software Packages ◽  
Fmri Study ◽  
Neuroimaging Data ◽  
Neuroimaging Software ◽  
Meta Analyses ◽  
Machine Readable ◽  
Univariate Analyses ◽  
Statistical Results

AbstractOnly a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html.

scholarly journals Exploring the Impact of Analysis Software on Task fMRI Results

2018 ◽  
Author(s):  
Alexander Bowring ◽  
Camille Maumet ◽  
Thomas E. Nichols
Keyword(s):  
Software Package ◽  
Similarity Coefficients ◽  
Analysis Software ◽  
Shared Data ◽  
Software Packages ◽  
Fmri Study ◽  
Task Variation ◽  
Scientific Results ◽  
Neuroimaging Software ◽  
The Impact

AbstractA wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this ‘methodological plurality’ comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software packages, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. Neuroimaging is currently experiencing a surge in initiatives to improve research practices and ensure that all conclusions inferred from an fMRI study are replicable.In this work, our goal is to understand how choice of software package impacts on analysis results. We use publically shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyze, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. While qualitatively we find broad similarities between packages, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 - 0.743 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our own efforts to make this research reproducible.

scholarly journals Sharing brain mapping statistical results with the neuroimaging data model

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Camille Maumet ◽  
Tibor Auer ◽  
Alexander Bowring ◽  
Gang Chen ◽  
Samir Das ◽  
...  
Keyword(s):  
Brain Mapping ◽  
Data Model ◽  
Neuroimaging Data ◽  
Statistical Results

scholarly journals Neurolinguistics

2007 ◽  
Vol 40 (1) ◽  
pp. 86-88
Keyword(s):  
Los Angeles ◽  
Brain Research ◽  
Event Related Potential ◽  
Cortical Activation ◽  
Fmri Study ◽  
Neuroimaging Data ◽  
Interlingual Homographs ◽  
L1 And L2 ◽  
Phonological Complexity ◽  
Cortical Activation Pattern

07–165Crinion, J., R. Turner, A. Grogan, T. Hanakawa, U. Noppeney, J. T. Devlin, T. Aso, S. Urayama, H. Fukuyama, K. Stockton, K. Usui, D. W. Green & C. J. Price (U College, London, UK; [email protected]), Language control in the bilingual brain. Science (American Association for the Advancement of Science) 312.5779 (2006), 1537–1540.07–166Desai, Rutvik (U Trier, Germany), Lisa L. Conant, Eric Waldron & Jeffrey R. Binder, fMRI of past tense processing: The effects of phonological complexity and task difficulty. Journal of Cognitive Neuroscience (MIT Press) 18.2 (2006), 278–297.07–167Kerkhofs, Roel (Radboud U, the Netherlands; [email protected]), Ton Dijkstra, Dorothee J. Chwilla & Ellen R.A. de Bruijn, Testing a model for bilingual semantic priming with interlingual homographs: RT and N400 effects. Brain Research (Elsevier) 1068. 1 (2006), 170–183.07–168Kyung Hwan, Kim & Kim Ja Hyun (U Yonsei, South Korea), Comparison of spatiotemporal cortical activation pattern during visual perception of Korean, English, Chinese words: An event-related potential study. Neuroscience Letters (Elsevier) 394.3 (2006), 227–232.07–169Paradis, Michel (McGill U, Canada; [email protected]), More belles infidels – or why do so many bilingual studies speak with forked tongue?Journal of Neurolinguistics (Elsevier) 19. 3 (2006), 195–208.07–170Poldrack, Russell, A. (U California, Los Angeles, USA; [email protected]), Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Science (Elsevier) 10.2 (2006), 59–63.07–171Ylinen, Sari (U Helsinki, Finland; [email protected]), Anna Shestakova, Minna Huotilainen, Paavo Alku & Risto Näätänen, Mismatch negativity (MMN) elicited by changes in phoneme length: A cross-linguistic study. Brain Research (Elsevier) 1072.1 (2006), 175–185.07–172Yokoyama Satoru (U Tohoku, Japan),Hideyuki Okamoto, Tadao Miyamoto, Kei Yoshimoto, Jungho Kim, Kazuki Iwata, Hyeonjeong Jeong, Shinya Uchida, Naho Ikuta, Yuko Sassa, Wataru Nakamura, Kaoru Horie, Shigeru Sato & Ryuta Kawashima, Cortical activation in the processing of passive sentences in L1 and L2: An fMRI study. NeuroImage (Elsevier) 30. 2 (2006), 570–579.

scholarly journals Remote measurement methods for 3-D modeling purposes using BAE Systems’ Software

2015 ◽  
Vol 64 (1) ◽  
pp. 113-124 ◽  
Author(s):  
Stewart Walker ◽  
Arleta Pietrzak
Keyword(s):  
Feature Extraction ◽  
Data Collection ◽  
Image Data ◽  
Data Sources ◽  
Remote Measurement ◽  
Software Packages ◽  
Systems Software ◽  
Digital Elevation ◽  
Sensor Models ◽  
Elevation Model

Abstract Efficient, accurate data collection from imagery is the key to an economical generation of useful geospatial products. Incremental developments of traditional geospatial data collection and the arrival of new image data sources cause new software packages to be created and existing ones to be adjusted to enable such data to be processed. In the past, BAE Systems’ digital photogrammetric workstation, SOCET SET®, met fin de siècle expectations in data processing and feature extraction. Its successor, SOCET GXP®, addresses today’s photogrammetric requirements and new data sources. SOCET GXP is an advanced workstation for mapping and photogrammetric tasks, with automated functionality for triangulation, Digital Elevation Model (DEM) extraction, orthorectification and mosaicking, feature extraction and creation of 3-D models with texturing. BAE Systems continues to add sensor models to accommodate new image sources, in response to customer demand. New capabilities added in the latest version of SOCET GXP facilitate modeling, visualization and analysis of 3-D features.

Researches on the Object-Oriented Model of Distributed Image Database

2014 ◽  
Vol 543-547 ◽  
pp. 2184-2187
Author(s):  
Ping Zhang Gou ◽  
Yong Zhong Tang
Keyword(s):  
Comparative Analysis ◽  
Data Model ◽  
Object Oriented ◽  
Image Data ◽  
Image Database ◽  
Management Modes

Combined with the characteristics of the image data, this study contrasted four kinds of data model. Then it analyzed the three kinds of realization methods of image database, comparative analysis of management modes of the distributed image database finally.

scholarly journals Improving Transparency, Falsifiability, and Rigour by Making Hypothesis Tests Machine Readable

2020 ◽  
Author(s):  
Daniel Lakens ◽  
Lisa Marie DeBruine
Keyword(s):  
Peer Review ◽  
Hypothesis Test ◽  
Scientific Information ◽  
Real Life ◽  
R Package ◽  
Statistical Prediction ◽  
Hypothesis Tests ◽  
Research Questions ◽  
Meta Analyses ◽  
Machine Readable

Making scientific information machine-readable greatly facilitates its re-use. Many scientific articles have the goal to test a hypothesis, so making the tests of statistical predictions easier to find and access could be very beneficial. We propose an approach that can be used to make hypothesis tests machine readable. We believe there are two benefits to specifying a hypothesis test in a way that a computer can evaluate whether the statistical prediction is corroborated or not. First, hypothesis test will become more transparent, falsifiable, and rigorous. Second, scientists will benefit if information related to hypothesis tests in scientific articles is easily findable and re-usable, for example when performing meta-analyses, during peer review, and when examining meta-scientific research questions. We examine what a machine readable hypothesis test should look like, and demonstrate the feasibility of machine readable hypothesis tests in a real-life example using the fully operational prototype R package scienceverse.

scholarly journals A Meta-analysis of Functional Neuroimaging Studies of Self- and Other Judgments Reveals a Spatial Gradient for Mentalizing in Medial Prefrontal Cortex

2012 ◽  
Vol 24 (8) ◽  
pp. 1742-1752 ◽  
Author(s):  
Bryan T. Denny ◽  
Hedy Kober ◽  
Tor D. Wager ◽  
Kevin N. Ochsner
Keyword(s):  
Functional Organization ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Brain Regions ◽  
The Self ◽  
Spatial Gradient ◽  
Self And Other ◽  
Neuroimaging Data ◽  
Medial Pfc ◽  
Meta Analyses

The distinction between processes used to perceive and understand the self and others has received considerable attention in psychology and neuroscience. Brain findings highlight a role for various regions, in particular the medial PFC (mPFC), in supporting judgments about both the self and others. We performed a meta-analysis of 107 neuroimaging studies of self- and other-related judgments using multilevel kernel density analysis [Kober, H., & Wager, T. D. Meta-analyses of neuroimaging data. Wiley Interdisciplinary Reviews, 1, 293–300, 2010]. We sought to determine what brain regions are reliably involved in each judgment type and, in particular, what the spatial and functional organization of mPFC is with respect to them. Relative to nonmentalizing judgments, both self- and other judgments were associated with activity in mPFC, ranging from ventral to dorsal extents, as well as common activation of the left TPJ and posterior cingulate. A direct comparison between self- and other judgments revealed that ventral mPFC as well as left ventrolateral PFC and left insula were more frequently activated by self-related judgments, whereas dorsal mPFC, in addition to bilateral TPJ and cuneus, was more frequently activated by other-related judgments. Logistic regression analyses revealed that ventral and dorsal mPFC lay at opposite ends of a functional gradient: The z coordinates reported in individual studies predicted whether the study involved self- or other-related judgments, which were associated with increasingly ventral or dorsal portions of mPFC, respectively. These results argue for a distributed rather than localizationist account of mPFC organization and support an emerging view on the functional heterogeneity of mPFC.

scholarly journals Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

2017 ◽  
Author(s):  
Romy Lorenz ◽  
Ines R. Violante ◽  
Ricardo Pio Monti ◽  
Giovanni Montana ◽  
Adam Hampshire ◽  
...  
Keyword(s):  
Deductive Reasoning ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Bayesian Optimization ◽  
Cognitive Tasks ◽  
Optimization Approach ◽  
Real Time Analysis ◽  
Starting Point ◽  
Neuroimaging Data ◽  
Meta Analyses

AbstractUnderstanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because different FPNs spatially overlap and are co-activated for diverse tasks. In order to characterize these networks involves studying how they activate across many different cognitive tasks, which previously has only been possible with meta-analyses. Here, building upon meta-analyses as a starting point, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that dissociate ventral and dorsal FPN activity from a large pool of tasks. We identify and subsequently refine two cognitive tasks (Deductive Reasoning and Tower of London) that are optimal for dissociating the FPNs. The identified cognitive tasks are not those predicted by meta-analysis, highlighting a different mapping between cognitive tasks and FPNs than expected. The optimization approach converged on a similar neural dissociation independently for the two different tasks, suggesting a possible common underlying functional mechanism and the need for neurally-derived cognitive taxonomies.

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