Preprocessing strategy influences graph-based exploration of altered functional networks in major depression
Resting‐state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease‐related variations in many neuropsychiatric disorders including major depression (MDD)....
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| Published in | Human brain mapping Vol. 37; no. 4; pp. 1422 - 1442 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Blackwell Publishing Ltd
01.04.2016
John Wiley & Sons, Inc John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1065-9471 1097-0193 1097-0193 |
| DOI | 10.1002/hbm.23111 |
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| Abstract | Resting‐state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease‐related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting‐state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph‐theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean‐signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Hum Brain Mapp 37:1422‐1442, 2016. © 2016 Wiley Periodicals, Inc.
Highlights
Groups can be differentiated based on local graph metrics
Group differences are influenced by preprocessing method
Finding group differences depends strongly on the network density
Conclusions regarding functional connectomic differences in major depression should be mindful of the influence of preprocessing strategies. |
|---|---|
| AbstractList | Resting‐state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease‐related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting‐state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph‐theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean‐signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field.
Hum Brain Mapp 37:1422‐1442, 2016
. © 2016 Wiley Periodicals, Inc.
Groups can be differentiated based on local graph metrics
Group differences are influenced by preprocessing method
Finding group differences depends strongly on the network density
Conclusions regarding functional connectomic differences in major depression should be mindful of the influence of preprocessing strategies. Resting‐state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease‐related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting‐state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph‐theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean‐signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Hum Brain Mapp 37:1422‐1442, 2016. © 2016 Wiley Periodicals, Inc. Highlights Groups can be differentiated based on local graph metrics Group differences are influenced by preprocessing method Finding group differences depends strongly on the network density Conclusions regarding functional connectomic differences in major depression should be mindful of the influence of preprocessing strategies. Resting-state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease-related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting-state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph-theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean-signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Hum Brain Mapp 37:1422-1442, 2016. © 2016 Wiley Periodicals, Inc. Highlights Groups can be differentiated based on local graph metrics Group differences are influenced by preprocessing method Finding group differences depends strongly on the network density Conclusions regarding functional connectomic differences in major depression should be mindful of the influence of preprocessing strategies. Resting-state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease-related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting-state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph-theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean-signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field.Resting-state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease-related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting-state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph-theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean-signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Resting-state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease-related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting-state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph-theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean-signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Hum Brain Mapp 37:1422-1442, 2016. Highlights * Groups can be differentiated based on local graph metrics * Group differences are influenced by preprocessing method * Finding group differences depends strongly on the network density * Conclusions regarding functional connectomic differences in major depression should be mindful of the influence of preprocessing strategies. Resting-state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease-related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting-state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph-theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean-signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. |
| Author | Lord, Anton Richard Borchardt, Viola van der Meer, Johan Li, Meng Walter, Martin Bogerts, Bernhard Breakspear, Michael Heinze, Hans-Jochen |
| AuthorAffiliation | 3 QIMR Berghofer Medical Research Institute Brisbane Queensland Australia 9 Metro North Mental Health Service Brisbane Queensland Australia 5 Department of Neurology Otto Von Guericke University Magdeburg Germany 6 Department of Psychiatry and Psychotherapy Otto Von Guericke University Magdeburg Germany 4 University of Queensland St Lucia Queensland Australia 7 Department of Cognition and Emotion Netherlands Institute for Neuroscience, an Institute of the Royal Academy of Arts and Sciences Amsterdam Netherlands 10 Department of Psychiatry University Tübingen 8 Center for Behavioral Brain Sciences (CBBS) Magdeburg Germany 1 Department of Behavioral Neurology Leibniz Institute for Neurobiology Magdeburg Germany 2 Clinical Affective Neuroimaging Laboratory Magdeburg Germany |
| AuthorAffiliation_xml | – name: 3 QIMR Berghofer Medical Research Institute Brisbane Queensland Australia – name: 7 Department of Cognition and Emotion Netherlands Institute for Neuroscience, an Institute of the Royal Academy of Arts and Sciences Amsterdam Netherlands – name: 4 University of Queensland St Lucia Queensland Australia – name: 5 Department of Neurology Otto Von Guericke University Magdeburg Germany – name: 10 Department of Psychiatry University Tübingen – name: 1 Department of Behavioral Neurology Leibniz Institute for Neurobiology Magdeburg Germany – name: 2 Clinical Affective Neuroimaging Laboratory Magdeburg Germany – name: 6 Department of Psychiatry and Psychotherapy Otto Von Guericke University Magdeburg Germany – name: 9 Metro North Mental Health Service Brisbane Queensland Australia – name: 8 Center for Behavioral Brain Sciences (CBBS) Magdeburg Germany |
| Author_xml | – sequence: 1 givenname: Viola surname: Borchardt fullname: Borchardt, Viola organization: Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany – sequence: 2 givenname: Anton Richard surname: Lord fullname: Lord, Anton Richard organization: Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany – sequence: 3 givenname: Meng surname: Li fullname: Li, Meng organization: Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany – sequence: 4 givenname: Johan surname: van der Meer fullname: van der Meer, Johan organization: Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany – sequence: 5 givenname: Hans-Jochen surname: Heinze fullname: Heinze, Hans-Jochen organization: Department of Neurology, Otto Von Guericke University, Magdeburg, Germany – sequence: 6 givenname: Bernhard surname: Bogerts fullname: Bogerts, Bernhard organization: Department of Psychiatry and Psychotherapy, Otto Von Guericke University, Magdeburg, Germany – sequence: 7 givenname: Michael surname: Breakspear fullname: Breakspear, Michael organization: QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia – sequence: 8 givenname: Martin surname: Walter fullname: Walter, Martin email: martin.walter@med.ovgu.de organization: Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26888761$$D View this record in MEDLINE/PubMed |
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Available at: http://w 2012; 61 2009; 44 2009; 47 2002; 15 2010; 107 1995; 34 2000; 44 2013; 70 2012; 59 2010; 182 2013; 7 2005; 26 2005; 25 2013; 15 2013; 10 2011; 70 2014; 16 2008; 21 2013; 110 2014; 8 2010; 4 2014; 169 2012; 142 2015; 16 2002; 296 2012 2013; 149 2013; 42 2009 2010; 121 2012; 36 2015; 9 2014; 111 2011; 6 2004; 304 2012; 33 2011; 8 2014; 44 2012a; 6 2012b; 63 2009; 30 2012; 2 1912; XI 2013; 78 2013; 31 2013; 82 2009; 460 2013 2012; 7 2010; 52 2005; 57 2006; 103 2010; 50 2009; 106 e_1_2_8_28_1 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_26_1 e_1_2_8_49_1 Steiner JB (e_1_2_8_52_1) 2011; 8 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_66_1 e_1_2_8_22_1 e_1_2_8_64_1 e_1_2_8_41_1 e_1_2_8_60_1 e_1_2_8_17_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_59_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_57_1 R Core Team (e_1_2_8_45_1) 2012 Fornito A (e_1_2_8_16_1) 2010; 4 e_1_2_8_32_1 e_1_2_8_55_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_51_1 e_1_2_8_30_1 Horn DI (e_1_2_8_27_1) 2010; 4 e_1_2_8_29_1 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_48_1 e_1_2_8_2_1 Fox MD (e_1_2_8_19_1) 2010; 4 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_23_1 e_1_2_8_44_1 e_1_2_8_65_1 e_1_2_8_63_1 Yan C‐G (e_1_2_8_62_1) 2010; 4 e_1_2_8_40_1 e_1_2_8_61_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_37_1 e_1_2_8_58_1 Cole DM (e_1_2_8_12_1) 2010; 4 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_56_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_50_1 |
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| Snippet | Resting‐state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional... Resting-state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional... |
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| SubjectTerms | Adult Depressive Disorder, Major - diagnostic imaging Depressive Disorder, Major - physiopathology Female functional connectivity functional network analysis graph-theory Humans Magnetic Resonance Imaging - methods major depressive disorder Male Nerve Net - diagnostic imaging Nerve Net - physiopathology resting-state fMRI |
| Title | Preprocessing strategy influences graph-based exploration of altered functional networks in major depression |
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