@article{RN167,
   author = {Gupta, L. and Besseling, R. M. and Overvliet, G. M. and Hofman, P. A. and de Louw, A. and Vaessen, M. J. and Aldenkamp, A. P. and Ulman, S. and Jansen, J. F. and Backes, W. H.},
   title = {Spatial heterogeneity analysis of brain activation in fMRI},
   journal = {Neuroimage Clin},
   volume = {5},
   pages = {266-76},
   note = {Gupta, Lalit
Besseling, Rene M H
Overvliet, Geke M
Hofman, Paul A M
de Louw, Anton
Vaessen, Maarten J
Aldenkamp, Albert P
Ulman, Shrutin
Jansen, Jacobus F A
Backes, Walter H
eng
Netherlands
2014/08/28 06:00
Neuroimage Clin. 2014 Jul 6;5:266-76. doi: 10.1016/j.nicl.2014.06.013. eCollection 2014.},
   abstract = {In many brain diseases it can be qualitatively observed that spatial patterns in blood oxygenation level dependent (BOLD) activation maps appear more (diffusively) distributed than in healthy controls. However, measures that can quantitatively characterize this spatial distributiveness in individual subjects are lacking. In this study, we propose a number of spatial heterogeneity measures to characterize brain activation maps. The proposed methods focus on different aspects of heterogeneity, including the shape (compactness), complexity in the distribution of activated regions (fractal dimension and co-occurrence matrix), and gappiness between activated regions (lacunarity). To this end, functional MRI derived activation maps of a language and a motor task were obtained in language impaired children with (Rolandic) epilepsy and compared to age-matched healthy controls. Group analysis of the activation maps revealed no significant differences between patients and controls for both tasks. However, for the language task the activation maps in patients appeared more heterogeneous than in controls. Lacunarity was the best measure to discriminate activation patterns of patients from controls (sensitivity 74%, specificity 70%) and illustrates the increased irregularity of gaps between activated regions in patients. The combination of heterogeneity measures and a support vector machine approach yielded further increase in sensitivity and specificity to 78% and 80%, respectively. This illustrates that activation distributions in impaired brains can be complex and more heterogeneous than in normal brains and cannot be captured fully by a single quantity. In conclusion, heterogeneity analysis has potential to robustly characterize the increased distributiveness of brain activation in individual patients.},
   keywords = {Adolescent
Algorithms
Brain/*physiopathology
Brain Mapping/*methods
Child
Epilepsy, Rolandic/physiopathology
Female
Humans
Image Interpretation, Computer-Assisted/*methods
Magnetic Resonance Imaging/*methods
Male
Spatial Analysis
Activation patterns
BOLD activation maps
Co-occurrence Matrix
Fractal dimensions
Functional magnetic resonance imaging
Lacunarity
Spatial heterogeneity},
   ISSN = {2213-1582 (Print)
2213-1582 (Linking)},
   DOI = {10.1016/j.nicl.2014.06.013},
   url = {http://www.ncbi.nlm.nih.gov/pubmed/25161893
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4141984/pdf/main.pdf},
   year = {2014},
   type = {Journal Article}
}

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H."],"year":2014,"bibtype":"article","biburl":"https://raw.githubusercontent.com/jansenjfa1/bibbase.github.io/master/jansenjfa.bib","bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Gupta"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Besseling"],"firstnames":["R.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Overvliet"],"firstnames":["G.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Hofman"],"firstnames":["P.","A."],"suffixes":[]},{"propositions":["de"],"lastnames":["Louw"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Vaessen"],"firstnames":["M.","J."],"suffixes":[]},{"propositions":[],"lastnames":["Aldenkamp"],"firstnames":["A.","P."],"suffixes":[]},{"propositions":[],"lastnames":["Ulman"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Jansen"],"firstnames":["J.","F."],"suffixes":[]},{"propositions":[],"lastnames":["Backes"],"firstnames":["W.","H."],"suffixes":[]}],"title":"Spatial heterogeneity analysis of brain activation in fMRI","journal":"Neuroimage Clin","volume":"5","pages":"266-76","note":"Gupta, Lalit Besseling, Rene M H Overvliet, Geke M Hofman, Paul A M de Louw, Anton Vaessen, Maarten J Aldenkamp, Albert P Ulman, Shrutin Jansen, Jacobus F A Backes, Walter H eng Netherlands 2014/08/28 06:00 Neuroimage Clin. 2014 Jul 6;5:266-76. doi: 10.1016/j.nicl.2014.06.013. eCollection 2014.","abstract":"In many brain diseases it can be qualitatively observed that spatial patterns in blood oxygenation level dependent (BOLD) activation maps appear more (diffusively) distributed than in healthy controls. However, measures that can quantitatively characterize this spatial distributiveness in individual subjects are lacking. In this study, we propose a number of spatial heterogeneity measures to characterize brain activation maps. The proposed methods focus on different aspects of heterogeneity, including the shape (compactness), complexity in the distribution of activated regions (fractal dimension and co-occurrence matrix), and gappiness between activated regions (lacunarity). To this end, functional MRI derived activation maps of a language and a motor task were obtained in language impaired children with (Rolandic) epilepsy and compared to age-matched healthy controls. Group analysis of the activation maps revealed no significant differences between patients and controls for both tasks. However, for the language task the activation maps in patients appeared more heterogeneous than in controls. Lacunarity was the best measure to discriminate activation patterns of patients from controls (sensitivity 74%, specificity 70%) and illustrates the increased irregularity of gaps between activated regions in patients. The combination of heterogeneity measures and a support vector machine approach yielded further increase in sensitivity and specificity to 78% and 80%, respectively. This illustrates that activation distributions in impaired brains can be complex and more heterogeneous than in normal brains and cannot be captured fully by a single quantity. In conclusion, heterogeneity analysis has potential to robustly characterize the increased distributiveness of brain activation in individual patients.","keywords":"Adolescent Algorithms Brain/*physiopathology Brain Mapping/*methods Child Epilepsy, Rolandic/physiopathology Female Humans Image Interpretation, Computer-Assisted/*methods Magnetic Resonance Imaging/*methods Male Spatial Analysis Activation patterns BOLD activation maps Co-occurrence Matrix Fractal dimensions Functional magnetic resonance imaging Lacunarity Spatial heterogeneity","issn":"2213-1582 (Print) 2213-1582 (Linking)","doi":"10.1016/j.nicl.2014.06.013","url":"http://www.ncbi.nlm.nih.gov/pubmed/25161893 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4141984/pdf/main.pdf","year":"2014","bibtex":"@article{RN167,\n author = {Gupta, L. and Besseling, R. M. and Overvliet, G. M. and Hofman, P. A. and de Louw, A. and Vaessen, M. J. and Aldenkamp, A. P. and Ulman, S. and Jansen, J. F. and Backes, W. H.},\n title = {Spatial heterogeneity analysis of brain activation in fMRI},\n journal = {Neuroimage Clin},\n volume = {5},\n pages = {266-76},\n note = {Gupta, Lalit\nBesseling, Rene M H\nOvervliet, Geke M\nHofman, Paul A M\nde Louw, Anton\nVaessen, Maarten J\nAldenkamp, Albert P\nUlman, Shrutin\nJansen, Jacobus F A\nBackes, Walter H\neng\nNetherlands\n2014/08/28 06:00\nNeuroimage Clin. 2014 Jul 6;5:266-76. doi: 10.1016/j.nicl.2014.06.013. eCollection 2014.},\n abstract = {In many brain diseases it can be qualitatively observed that spatial patterns in blood oxygenation level dependent (BOLD) activation maps appear more (diffusively) distributed than in healthy controls. However, measures that can quantitatively characterize this spatial distributiveness in individual subjects are lacking. In this study, we propose a number of spatial heterogeneity measures to characterize brain activation maps. The proposed methods focus on different aspects of heterogeneity, including the shape (compactness), complexity in the distribution of activated regions (fractal dimension and co-occurrence matrix), and gappiness between activated regions (lacunarity). To this end, functional MRI derived activation maps of a language and a motor task were obtained in language impaired children with (Rolandic) epilepsy and compared to age-matched healthy controls. Group analysis of the activation maps revealed no significant differences between patients and controls for both tasks. However, for the language task the activation maps in patients appeared more heterogeneous than in controls. Lacunarity was the best measure to discriminate activation patterns of patients from controls (sensitivity 74%, specificity 70%) and illustrates the increased irregularity of gaps between activated regions in patients. The combination of heterogeneity measures and a support vector machine approach yielded further increase in sensitivity and specificity to 78% and 80%, respectively. This illustrates that activation distributions in impaired brains can be complex and more heterogeneous than in normal brains and cannot be captured fully by a single quantity. 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