Evaluating global brain connectivity as an imaging marker for depression: influence of preprocessing strategies and placebo-controlled ketamine treatment.
Journal
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
ISSN: 1740-634X
Titre abrégé: Neuropsychopharmacology
Pays: England
ID NLM: 8904907
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
15
10
2019
accepted:
15
01
2020
revised:
07
01
2020
pubmed:
30
1
2020
medline:
24
6
2021
entrez:
30
1
2020
Statut:
ppublish
Résumé
Major depressive disorder (MDD) is associated with altered global brain connectivity (GBC), as assessed via resting-state functional magnetic resonance imaging (rsfMRI). Previous studies found that antidepressant treatment with ketamine normalized aberrant GBC changes in the prefrontal and cingulate cortices, warranting further investigations of GBC as a putative imaging marker. These results were obtained via global signal regression (GSR). This study is an independent replication of that analysis using a separate dataset. GBC was analyzed in 28 individuals with MDD and 22 healthy controls (HCs) at baseline, post-placebo, and post-ketamine. To investigate the effects of preprocessing, three distinct pipelines were used: (1) regression of white matter (WM)/cerebrospinal fluid (CSF) signals only (BASE); (2) WM/CSF + GSR (GSR); and (3) WM/CSF + physiological parameter regression (PHYSIO). Reduced GBC was observed in individuals with MDD only at baseline in the anterior and medial cingulate cortices, as well as in the prefrontal cortex only after regressing the global signal. Ketamine had no effect compared to baseline or placebo in either group in any pipeline. PHYSIO did not resemble GBC preprocessed with GSR. These results concur with several studies that used GSR to study GBC. Further investigations are warranted into disease-specific components of global fMRI signals that may drive these results and of GBCr as a potential imaging marker in MDD.
Identifiants
pubmed: 31995812
doi: 10.1038/s41386-020-0624-0
pii: 10.1038/s41386-020-0624-0
pmc: PMC7162890
doi:
Substances chimiques
Antidepressive Agents
0
Ketamine
690G0D6V8H
Banques de données
ClinicalTrials.gov
['NCT00088699']
Types de publication
Journal Article
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
982-989Subventions
Organisme : Intramural NIH HHS
ID : ZIA MH002857
Pays : United States
Références
Hahn A, Lanzenberger R, Kasper S. Making sense of connectivity. Int J Neuropsychopharmacol. 2019;22:194–207.
pubmed: 30544240
Murphy K, Fox MD. Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage. 2017;154:169–73.
pubmed: 27888059
pmcid: 5489207
Caballero-Gaudes C, Reynolds RC. Methods for cleaning the BOLD fMRI signal. Neuroimage. 2017;154:128–49.
pubmed: 27956209
Dutta A, McKie S, Deakin JF. Resting state networks in major depressive disorder. Psychiatry Res. 2014;224:139–51.
pubmed: 25456520
Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry. 2015;72:603–11.
pubmed: 25785575
pmcid: 4456260
Evans JW, Szczepanik J, Brutsche N, Park LT, Nugent AC, Zarate CA Jr. Default mode connectivity in major depressive disorder measured up to 10 days after ketamine administration. Biol Psychiatry. 2018;84:582–90.
pubmed: 29580569
pmcid: 6093808
Schmaal L, Hibar DP, Samann PG, Hall GB, Baune BT, Jahanshad N, et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry. 2017;22:900–09.
pubmed: 27137745
Wise T, Radua J, Via E, Cardoner N, Abe O, Adams TM, et al. Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis. Mol Psychiatry. 2017;22:1455–63.
pubmed: 27217146
Jenkins LM, Barba A, Campbell M, Lamar M, Shankman SA, Leow AD, et al. Shared white matter alterations across emotional disorders: a voxel-based meta-analysis of fractional anisotropy. Neuroimage Clin. 2016;12:1022–34.
pubmed: 27995068
pmcid: 5153602
Cole MW, Pathak S, Schneider W. Identifying the brain’s most globally connected regions. Neuroimage. 2010;49:3132–48.
pubmed: 19909818
Abdallah CG, Averill CL, Salas R, Averill LA, Baldwin PR, Krystal JH, et al. Prefrontal connectivity and glutamate transmission: relevance to depression pathophysiology and ketamine treatment. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2:566–74.
pubmed: 29034354
pmcid: 5635826
Abdallah CG, Averill LA, Collins KA, Geha P, Schwartz J, Averill C, et al. Ketamine treatment and global brain connectivity in major depression. Neuropsychopharmacology. 2017;42:1210–19.
pubmed: 27604566
Afyouni S, Smith SM, Nichols TE. Effective degrees of freedom of the Pearson’s correlation coefficient under autocorrelation. Neuroimage. 2019;199:609–25.
pubmed: 31158478
pmcid: 6693558
Tomasi D, Volkow ND. Functional connectivity hubs in the human brain. Neuroimage. 2011;57:908–17.
pubmed: 21609769
pmcid: 3129362
Murrough JW, Abdallah CG, Anticevic A, Collins KA, Geha P, Averill LA, et al. Reduced global functional connectivity of the medial prefrontal cortex in major depressive disorder. Hum Brain Mapp. 2016;37:3214–23.
pubmed: 27144347
pmcid: 4980239
Anticevic A, Corlett PR, Cole MW, Savic A, Gancsos M, Tang Y, et al. N-methyl-D-aspartate receptor antagonist effects on prefrontal cortical connectivity better model early than chronic schizophrenia. Biol Psychiatry. 2015;77:569–80.
pubmed: 25281999
Weissenbacher A, Kasess C, Gerstl F, Lanzenberger R, Moser E, Windischberger C. Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. Neuroimage. 2009;47:1408–16.
pubmed: 19442749
Nalci A, Rao BD, Liu TT. Global signal regression acts as a temporal downweighting process in resting-state fMRI. Neuroimage. 2017;152:602–18.
pubmed: 28089677
Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, Martin A, et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2012;2:25–32.
pubmed: 22432927
pmcid: 3484684
Gotts SJ, Saad ZS, Jo HJ, Wallace GL, Cox RW, Martin A. The perils of global signal regression for group comparisons: a case study of autism spectrum disorders. Front Hum Neurosci. 2013;7:356.
pubmed: 23874279
pmcid: 3709423
Nugent AC, Ballard ED, Gould TD, Park LT, Moaddel R, Brutsche NE, et al. Ketamine has distinct electrophysiological and behavioral effects in depressed and healthy subjects. Mol Psychiatry. 2019;24:1040–52.
pubmed: 29487402
Evans JW, Lally N, An L, Li N, Nugent AC, Banerjee D, et al. 7T (1)H-MRS in major depressive disorder: a ketamine treatment study. Neuropsychopharmacology. 2018;43:1908–14.
pubmed: 29748628
pmcid: 6046051
Reed JL, Nugent AC, Furey ML, Szczepanik JE, Evans JW, Zarate CA Jr. Effects of ketamine on brain activity during emotional processing: differential findings in depressed versus healthy control participants. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:610–18.
pubmed: 30826253
Nugent AC, Farmer C, Evans JW, Snider SL, Banerjee D, Zarate CA Jr. Multimodal imaging reveals a complex pattern of dysfunction in corticolimbic pathways in major depressive disorder. Hum Brain Mapp. 2019;40:3940–50.
pubmed: 31179620
Sackeim HA. The definition and meaning of treatment-resistant depression. J Clin Psychiatry. 2001;62(Suppl. 16):10–7.
pubmed: 11480879
Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29:162–73.
pubmed: 8812068
Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage. 2010;52:571–82.
pubmed: 20420926
pmcid: 2897154
Anticevic A, Brumbaugh MS, Winkler AM, Lombardo LE, Barrett J, Corlett PR, et al. Global prefrontal and fronto-amygdala dysconnectivity in bipolar I disorder with psychosis history. Biol Psychiatry. 2013;73:565–73.
pubmed: 22980587
Falahpour M, Refai H, Bodurka J. Subject specific BOLD fMRI respiratory and cardiac response functions obtained from global signal. Neuroimage. 2013;72:252–64.
pubmed: 23376493
Preller KH, Burt JB, Ji JL, Schleifer CH, Adkinson BD, Stampfli P, et al. Changes in global and thalamic brain connectivity in LSD-induced altered states of consciousness are attributable to the 5-HT2A receptor. Elife. 2018;7:e35082.
pubmed: 30355445
pmcid: 6202055
Abdallah CG, Dutta A, Averill CL, McKie S, Akiki TJ, Averill LA, et al. Ketamine, but not the NMDAR antagonist lanicemine, increases prefrontal global connectivity in depressed patients. Chronic Stress (Thousand Oaks). 2018;2. https://doi.org/10.1177/2470547018796102 .
Rajkowska G, Miguel-Hidalgo JJ, Wei J, Dilley G, Pittman SD, Meltzer HY, et al. Morphometric evidence for neuronal and glial prefrontal cell pathology in major depression. Biol Psychiatry. 1999;45:1085–98.
pubmed: 10331101
Duman RS, Aghajanian GK, Sanacora G, Krystal JH. Synaptic plasticity and depression: new insights from stress and rapid-acting antidepressants. Nat Med. 2016;22:238–49.
pubmed: 26937618
pmcid: 5405628
Noble S, Scheinost D, Finn ES, Shen X, Papademetris X, McEwen SC, et al. Multisite reliability of MR-based functional connectivity. Neuroimage. 2017;146:959–70.
pubmed: 27746386
Agcaoglu O, Wilson TW, Wang YP, Stephen J, Calhoun VD. Resting state connectivity differences in eyes open versus eyes closed conditions. Hum Brain Mapp. 2019;40:2488–98.
pubmed: 30720907
Bowring A, Maumet C, Nichols TE. Exploring the impact of analysis software on task fMRI results. Hum Brain Mapp. 2019;40:3362–84.
pubmed: 31050106
pmcid: 6618324
Chang C, Glover GH. Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. Neuroimage. 2009;47:1448–59.
pubmed: 19446646
pmcid: 2995588
Tong Y, Yao JF, Chen JJ, Frederick BD. The resting-state fMRI arterial signal predicts differential blood transit time through the brain. J Cereb Blood Flow Metab. 2019;39:1148–60.
pubmed: 29333912
Scholvinck ML, Maier A, Ye FQ, Duyn JH, Leopold DA. Neural basis of global resting-state fMRI activity. Proc Natl Acad Sci USA. 2010;107:10238–43.
pubmed: 20439733
Wong CW, DeYoung PN, Liu TT. Differences in the resting-state fMRI global signal amplitude between the eyes open and eyes closed states are related to changes in EEG vigilance. Neuroimage. 2016;124(Part A):24–31.
pubmed: 26327245
Yang GJ, Murray JD, Glasser M, Pearlson GD, Krystal JH, Schleifer C, et al. Altered global signal topography in schizophrenia. Cereb Cortex. 2017;27:5156–69.
pubmed: 27702810
Li J, Kong R, Liegeois R, Orban C, Tan Y, Sun N, et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage. 2019;196:126–41.
pubmed: 30974241