Krzysztof Gorgolewski PhD

Krzysztof J. Gorgolewski


Research Interests

I am working on combining functional magnetic resonance imaging and diffusion tensor imaging to provide information that would improve the accuracy of tumour extraction procedures. Our goal is to map areas of patients brain that are involved in certain cognitive skills (like speech, movement, attention) and present this information to neurosurgeons.

Publications:
2014
  Test-retest reliability of structural brain networks from diffusion MRI
Buchanan, C, Pernet, CR, Gorgolewski, KJ, Storkey, AJ & Bastin, ME 2014, 'Test-retest reliability of structural brain networks from diffusion MRI' NeuroImage, vol 86, pp. 231-243. DOI: 10.1016/j.neuroimage.2013.09.054
Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test-retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Buchanan, Colin, Pernet, Cyril R, Gorgolewski, Krzysztof J., Storkey, Amos J & Bastin, Mark E.
Keywords: (Connectome , Diffusion MRI , Human brain , Network , Test–retest , Tractography. )
Pages: 231-243
Publication Date: 1 Feb 2014
Publication Information
Category: Article
Journal: NeuroImage
Volume: 86
ISSN: 1053-8119
Original Language: English
DOIs: 10.1016/j.neuroimage.2013.09.054
2013
  The test-retest reliability of structural brain networks obtained from diffusion MRI
Buchanan, CR, Pernet, C, Gorgolewski, KJ, Storkey, A & Bastin, M 2013, 'The test-retest reliability of structural brain networks obtained from diffusion MRI' 19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, Washington, United States, 16/06/13 - 20/06/13, .
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Buchanan, Colin R., Pernet, Cyril, Gorgolewski, Krzysztof J., Storkey, Amos & Bastin, Mark.
Publication Date: 2013
Publication Information
Category: Poster
Original Language: English
  A test-retest fMRI dataset for motor, language and spatial attention functions
Gorgolewski, KJ, Storkey, A, Bastin, ME, Whittle, IR, Wardlaw, JM & Pernet, CR 2013, 'A test-retest fMRI dataset for motor, language and spatial attention functions' GigaScience, vol 2, no. 1, 6. DOI: 10.1186/2047-217X-2-6
Since its inception over twenty years ago, functional magnetic resonance imaging (fMRI) has been used in numerous studies probing neural underpinnings of human cognition. However, the between session variance of many tasks used in fMRI remains understudied. Such information is especially important in context of clinical applications. A test-retest dataset was acquired to validate fMRI tasks used in pre-surgical planning. In particular, five task-related fMRI time series (finger, foot and lip movement, overt verb generation, covert verb generation, overt word repetition, and landmark tasks) were used to investigate which protocols gave reliable single-subject results. Ten healthy participants in their fifties were scanned twice using an identical protocol 2-3 days apart. In addition to the fMRI sessions, high-angular resolution diffusion tensor MRI (DTI), and high-resolution 3D T1-weighted volume scans were acquired.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Gorgolewski, Krzysztof J., Storkey, Amos, Bastin, Mark E, Whittle, Ian R, Wardlaw, Joanna M & Pernet, Cyril R.
Publication Date: 2013
Publication Information
Category: Article
Journal: GigaScience
Volume: 2
Issue number: 1
Original Language: English
DOIs: 10.1186/2047-217X-2-6
  Single subject fMRI test-retest reliability metrics and confounding factors
Gorgolewski, KJ, Storkey, AJ, Bastin, ME, Whittle, I & Pernet, C 2013, 'Single subject fMRI test-retest reliability metrics and confounding factors' NeuroImage, vol 69, pp. 231-43. DOI: 10.1016/j.neuroimage.2012.10.085
While the fMRI test-retest reliability has been mainly investigated from the point of view of group level studies, here we present analyses and results for single-subject test-retest reliability. One important aspect of group level reliability is that not only does it depend on between-session variance (test-retest), but also on between-subject variance. This has partly led to a debate regarding which reliability metric to use and how different sources of noise contribute to between-session variance. Focusing on single subject reliability allows considering between-session only. In this study, we measured test-retest reliability in four behavioural tasks (motor mapping, covert verb generation, overt word repetition, and a landmark identification task) to ensure generalisation of the results and at three levels of data processing (time-series correlation, t value variance, and overlap of thresholded maps) to understand how each step influences the other and how confounding factors influence reliability at each of these steps. The contributions of confounding factors (scanner noise, subject motion, and coregistration) were investigated using multiple regression and relative importance analyses at each step. Finally, to achieve a fuller picture of what constitutes a reliable task, we introduced a bootstrap technique of within- vs. between-subject variance. Our results show that (i) scanner noise and coregistration errors have little contribution to between-session variance (ii) subject motion (especially correlated with the stimuli) can have detrimental effects on reliability (iii) different tasks lead to different reliability results. This suggests that between-session variance in fMRI is mostly caused by the variability of underlying cognitive processes and motion correlated with the stimuli rather than technical limitations of data processing.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Gorgolewski, Krzysztof J., Storkey, Amos J, Bastin, Mark E, Whittle, Ian & Pernet, Cyril.
Keywords: (, , . )
Number of pages: 13
Pages: 231-43
Publication Date: 2013
Publication Information
Category: Article
Journal: NeuroImage
Volume: 69
Original Language: English
DOIs: 10.1016/j.neuroimage.2012.10.085
2012
  Nipype 2012: more packages, reusable workflows and reproducible Science
Gorgolewski, KJ, Ghosh, S, Notter, M, Varoquaux, G, Waskom, M & Ziegler, E 2012, 'Nipype 2012: more packages, reusable workflows and reproducible Science' 18th Annual Meeting of the Organization for Human Brain Mapping Meeting, Beijing, China, 10/06/12 - 14/06/12, .
General Information
Organisations: Neuroinformatics DTC.
Authors: Gorgolewski, Krzysztof J., Ghosh, Satrajit, Notter, Michael, Varoquaux, Gael, Waskom, Michael & Ziegler, Erik.
Publication Date: 2012
Publication Information
Category: Abstract
Original Language: English
  Reliability of single subject fMRI in the context of presurgical planning
Gorgolewski, KJ, Storkey, A, Bastin, M & Pernet, C 2012, Reliability of single subject fMRI in the context of presurgical planning. in 18th Annual Meeting of the Organization for Human Brain Mapping.
Test-retest reliability of fMRI paradigms has been assessed in many studies [1], however only few have looked at the problem from the context of presurgical planning [2]. fMRI has been used to map the cortex of patients with intracranial tumour in order to improve surgical planning and safety (minimizing chances of neurological deficits). Most neurosurgeons use threshold maps to identify regions of activation. Therefore we have focused our study on reproducibility of a thresholded single subject t map acquired twice three days apart in normal volunteers undergoing paradigms designed to identify eloquent cortical regions around tumours.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Gorgolewski, Krzysztof J., Storkey, Amos, Bastin, Mark & Pernet, Cyril.
Publication Date: 2012
Publication Information
Category: Conference contribution
Original Language: English
  Data sharing in neuroimaging research
Poline, J-B, Breeze, JL, Ghosh, SS, Gorgolewski, KJ, Halchenko, YO, Hanke, M, Helmer, KG, Marcus, DS, Poldrack, RA, Schwartz, Y, Ashburner, J & Kennedy, DN 2012, 'Data sharing in neuroimaging research' Frontiers in Neuroinformatics, vol 6, no. 9. DOI: 10.3389/fninf.2012.00009
Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.
General Information
Organisations: Neuroinformatics DTC.
Authors: Poline, Jean-Baptiste, Breeze, Janis L, Ghosh, Satrajit S, Gorgolewski, Krzysztof J., Halchenko, Yaroslav O, Hanke, Michael, Helmer, Karl G, Marcus, Daniel S, Poldrack, Russell A, Schwartz, Yannick, Ashburner, John & Kennedy, David N.
Publication Date: 2012
Publication Information
Category: Article
Journal: Frontiers in Neuroinformatics
Volume: 6
Issue number: 9
ISSN: 1662-5196
Original Language: English
DOIs: 10.3389/fninf.2012.00009
  Quantifying the intra- and inter-subject variability of whole-brain structural networks from diffusion MRI
Buchanan, C, Gorgolewski, KJ, Pernet, C, Storkey, A & Bastin, M 2012, 'Quantifying the intra- and inter-subject variability of whole-brain structural networks from diffusion MRI' ISMRM 20th Annual Meeting & Exhibition, Melbourne, Australia, 5/05/12 - 11/05/12, .
Connectomics is a recent development in neuroscience that combines diffusion MRI (dMRI) and tractography with the analytical tools of network theory to investigate whole-brain connectivity [1]. Under this paradigm, segmented cortical areas (e.g. Brodmann areas) form the nodes of a network and tractography is used to construct a set of white matters tracts which form the connections of the network. Graph-theoretic measures may then be used to characterize topological patterns of connectivity [2]. Recent studies have demonstrated whole-brain network analysis in healthy volunteers [3,4]. However, whether the connectome approach can consistently reconstruct structural white matter networks and produce robust clinically useful metrics remains an open question. Here we measure the reproducibility of basic graph-theoretic measures obtained from dMRI data using a percentile bootstrap technique. Since these measures are an essential prerequisite for more complex analyses, such as ““small-world”” measures or the identification of network hubs, their reliability is crucial to the whole connectome approach.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Buchanan, Colin, Gorgolewski, Krzysztof J., Pernet, Cyril, Storkey, Amos & Bastin, Mark.
Publication Date: May 2012
Publication Information
Category: Poster
Original Language: English
  Adaptive thresholding for reliable topological inference in single subject fMRI analysis
Gorgolewski, KJ, Storkey, AJ, Bastin, ME & Pernet, CR 2012, 'Adaptive thresholding for reliable topological inference in single subject fMRI analysis' Frontiers in Human Neuroscience, vol 6, 245. DOI: 10.3389/fnhum.2012.00245
Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumor resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyzes. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modeling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of total number of errors but also in terms of the trade-off between false negative and positive cluster error rates. Similarly, simulations show that adaptive thresholding performs better than fixed thresholding in terms of over and underestimation of the true activation border (i.e., higher spatial accuracy). Finally, through simulations and a motor test-retest study on 10 volunteer subjects, we show that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined offering an automatic yet flexible way to threshold single subject fMRI maps.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Gorgolewski, Krzysztof J., Storkey, Amos J, Bastin, Mark E & Pernet, Cyril R.
Keywords: (, , . )
Publication Date: 25 Aug 2012
Publication Information
Category: Article
Journal: Frontiers in Human Neuroscience
Volume: 6
Original Language: English
DOIs: 10.3389/fnhum.2012.00245
2011
  Pitfalls of Thresholding Statistical Maps in Presurgical fMRI Mapping
Gorgolewski, KJ, Bastin, M, Rigolo, L, Soleiman, HA, Pernet, C, Storkey, A & Golby, A 2011, 'Pitfalls of Thresholding Statistical Maps in Presurgical fMRI Mapping' ISMRM 19th Annual Meeting & Exhibition, Montreal, Quebec, Canada, 7/05/11 - 13/05/11, .
There is significant variability in the selection of methods used to threshold fMRI activation maps acquired for brain tumour presurgical planning. In a review of 50 recent papers, only 12% of studies claimed to use some kind of Family Wise Error (FWE) correction for multiple comparison testing. 42% of these studies used a p-value threshold lower than the standard 0.05 in an attempt to minimize the number of false positives, while 22% did not use the same threshold for all of the subjects, with the threshold being manually adjusted on per subject basis. What is more, most of the studies used simple thresholds without taking the spatial properties of the statistical maps into account; only 4% used cluster size as an additional threshold. Although thresholding methods used in neuroscience fMRI studies have greatly improved in the last few years, there still remains a lack of consensus in the clinical literature about how to identify activation boundaries accurately and objectively. As a first step towards developing robust frameworks for assessing thresholding methods for tumour resection, we investigated how several automated thresholding methods affect the distance between the activation areas determined from fMRI experiments and the tumour boundary defined on structural MRI, and how this data might potentially change surgical practice.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Gorgolewski, Krzysztof J., Bastin, Mark, Rigolo, Laura, Soleiman, H. A., Pernet, Cyril, Storkey, Amos & Golby, Alexandra.
Publication Date: 2011
Publication Information
Category: Poster
Original Language: English
  Comparison Between FWE and FDR Corrections for Threshold Free Cluster Enhancement Maps
Gorgolewski, KJ, Storkey, A, Bastin, M & Pernet, C 2011, 'Comparison Between FWE and FDR Corrections for Threshold Free Cluster Enhancement Maps' 17th Annual Meeting of the Organization for Human Brain Mapping, Québec, Canada, 26/06/11 - 30/06/11, .
Threshold Free Cluster Enhancement (TFCE) (Smith 2009) is a successful attempt to avoid the problem of selecting a cluster forming threshold in topological inference procedures. In this transformation, excursions sets are integrated over all the possible thresholds resulting in a new set of values reflecting both cluster height and support. In the original paper, Smith et al. proposed permutation based Family Wise Error (FWE) correction as a thresholding method. This, however, controls for the probability of making at least one false positive error, an assumption which is known to be conservative. A different approach is to control for the proportion of all the false positive errors among all the discoveries - False Discovery Rate (FDR). In this paper we investigate the power and false positive error rates achieved by both methods. Additionally we focus on a randomisation method suitable for single subject application, namely time points instead of group labels shuffling.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Gorgolewski, Krzysztof J., Storkey, Amos, Bastin, Mark & Pernet, Cyril.
Publication Date: 2011
Publication Information
Category: Poster
Original Language: English
  Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python
Gorgolewski, KJ, Burns, C, Madison, C, Clark, D, Halchenko, Y, Waskom, M & Ghosh, S 2011, 'Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python' Frontiers in Neuroinformatics, vol 5.
Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
General Information
Organisations: Neuroinformatics DTC.
Authors: Gorgolewski, Krzysztof J., Burns, Chris, Madison, Cindee, Clark, Dav, Halchenko, Yaroslav, Waskom, Michael & Ghosh, Satrajit.
Publication Date: 2011
Publication Information
Category: Article
Journal: Frontiers in Neuroinformatics
Volume: 5
ISSN: 1662-5196
Original Language: English
  Using a Combination of a Mixture Model and Topological FDR in the Context of Presurgical Planning
Gorgolewski, KJ, Storkey, A, Bastin, M & Pernet, C 2011, 'Using a Combination of a Mixture Model and Topological FDR in the Context of Presurgical Planning' 17th Annual Meeting of the Organization for Human Brain Mapping, Québec, Canada, 26/06/11 - 30/06/11, .
Functional Magnetic Resonance Imaging (fMRI) can be used in presurgical planning to establish which parts of the brain are functionally eloquent so that they can be avoided in surgical procedures such as tumour resection. In contrast to cognitive neuroscience where fMRI scans are averaged over multiple healthy controls, in clinical use only a single scan is available for analysis. This poses challenges, since data obtained from single subjects can be very noisy and the signal strength can vary between patients due to their anatomy, time of scanning and degree of cooperation. Because of this variation some labs use different thresholds selected manually for every patient. To avoid this potentially subjective approach, we propose a new method that adapts to changing signal strength while still maintaining sound statistical properties.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Gorgolewski, Krzysztof J., Storkey, Amos, Bastin, Mark & Pernet, Cyril.
Publication Date: 2011
Publication Information
Category: Poster
Original Language: English
2010
  Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools
Ghosh, S, Burns, C, Clark, D, Gorgolewski, KJ, Halchenko, Y, Madison, C, Tungaraza, R & Millman, J 2010, 'Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools' 16th Annual Meeting of the Organization for Human Brain Mapping, Barcelona, Spain, 6/06/10 - 10/06/10, .
Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.
General Information
Organisations: Neuroinformatics DTC.
Authors: Ghosh, Satrajit, Burns, Chris, Clark, Dav, Gorgolewski, Krzysztof J., Halchenko, Yaroslav, Madison, Cindee, Tungaraza, Rosalia & Millman, Jarrod.
Publication Date: 2010
Publication Information
Category: Poster
Original Language: English

Projects:
Using probabilistic models of white and grey matter activation for pre-surgical planning of tumour extraction (PhD)