Abstract

Background Severe depression is associated with high morbidity and mortality. Neural network dysfunction may contribute to disease mechanisms underlying different clinical subtypes. Here, we apply resting-state functional magnetic resonance imaging based measures of brain connectivity to investigate network dysfunction in severely depressed in-patients with and without psychotic symptoms. Methods A cohort study was performed at two sites. Older patients with major depressive disorder with or without psychotic symptoms were included (n = 23 at site one, n = 26 at site two). Resting state 3-Tesla functional MRI scans, with eyes closed, were obtained and Montgomery-Åsberg Depression Rating Scales were completed. We denoised data and calculated resting state networks in the two groups separately. We selected five networks of interest (1. bilateral frontoparietal, 2.left lateralized frontoparietal, 3.right lateralized frontoparietal, 4. default mode network (DMN) and 5.bilateral basal ganglia and insula network) and performed regression analyses with severity of depression, as well as presence or absence of psychotic symptoms. Results The functional connectivity (FC) patterns did not correlate with severity of depression. Depressed patients with psychotic symptoms (n = 14, 61%) compared with patients without psychotic symptoms (n = 9, 39%) from site one showed significantly decreased FC in the right part of the bilateral frontoparietal network (p = 0.002). This result was not replicated when comparing patients with (n = 9, 35%) and without (n = 17, 65%) psychotic symptoms from site two. Conclusion Psychotic depression may be associated with decreased FC of the frontoparietal network, which is involved in cognitive control processes, such as attention and emotion regulation. These findings suggest that FC in the frontoparietal network may be related to the subtype of depression, i.e. presence of psychotic symptoms, rather than severity of depression. Since the findings could not be replicated in the 2nd sample, replication is needed before drawing definite conclusions.
Original languageEnglish
Article numbere0209908
JournalPLoS ONE
Volume14
Issue number1
DOIs
Publication statusPublished - 2019

Cite this

@article{c4fda3cb42cb4c089e2b9a9c3787a5f2,
title = "Exploring resting state connectivity in patients with psychotic depression",
abstract = "Background Severe depression is associated with high morbidity and mortality. Neural network dysfunction may contribute to disease mechanisms underlying different clinical subtypes. Here, we apply resting-state functional magnetic resonance imaging based measures of brain connectivity to investigate network dysfunction in severely depressed in-patients with and without psychotic symptoms. Methods A cohort study was performed at two sites. Older patients with major depressive disorder with or without psychotic symptoms were included (n = 23 at site one, n = 26 at site two). Resting state 3-Tesla functional MRI scans, with eyes closed, were obtained and Montgomery-{\AA}sberg Depression Rating Scales were completed. We denoised data and calculated resting state networks in the two groups separately. We selected five networks of interest (1. bilateral frontoparietal, 2.left lateralized frontoparietal, 3.right lateralized frontoparietal, 4. default mode network (DMN) and 5.bilateral basal ganglia and insula network) and performed regression analyses with severity of depression, as well as presence or absence of psychotic symptoms. Results The functional connectivity (FC) patterns did not correlate with severity of depression. Depressed patients with psychotic symptoms (n = 14, 61{\%}) compared with patients without psychotic symptoms (n = 9, 39{\%}) from site one showed significantly decreased FC in the right part of the bilateral frontoparietal network (p = 0.002). This result was not replicated when comparing patients with (n = 9, 35{\%}) and without (n = 17, 65{\%}) psychotic symptoms from site two. Conclusion Psychotic depression may be associated with decreased FC of the frontoparietal network, which is involved in cognitive control processes, such as attention and emotion regulation. These findings suggest that FC in the frontoparietal network may be related to the subtype of depression, i.e. presence of psychotic symptoms, rather than severity of depression. Since the findings could not be replicated in the 2nd sample, replication is needed before drawing definite conclusions.",
author = "Oudega, {Mardien L.} and {van der Werf}, {Ysbrand D.} and Annemieke Dols and Wattjes, {Mike P.} and Frederik Barkhof and Filip Bouckaert and Mathieu Vandenbulcke and {de Winter}, Fran{\cc}ois-Laurent and Pascal Sienaert and Piet Eikelenboom and Stek, {Max L.} and {van den Heuvel}, {Odile A.} and Louise Emsell and Didi Rhebergen and {van Exel}, Eric",
year = "2019",
doi = "10.1371/journal.pone.0209908",
language = "English",
volume = "14",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "1",

}

Exploring resting state connectivity in patients with psychotic depression. / Oudega, Mardien L.; van der Werf, Ysbrand D.; Dols, Annemieke; Wattjes, Mike P.; Barkhof, Frederik; Bouckaert, Filip; Vandenbulcke, Mathieu; de Winter, François-Laurent; Sienaert, Pascal; Eikelenboom, Piet; Stek, Max L.; van den Heuvel, Odile A.; Emsell, Louise; Rhebergen, Didi; van Exel, Eric.

In: PLoS ONE, Vol. 14, No. 1, e0209908, 2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Exploring resting state connectivity in patients with psychotic depression

AU - Oudega, Mardien L.

AU - van der Werf, Ysbrand D.

AU - Dols, Annemieke

AU - Wattjes, Mike P.

AU - Barkhof, Frederik

AU - Bouckaert, Filip

AU - Vandenbulcke, Mathieu

AU - de Winter, François-Laurent

AU - Sienaert, Pascal

AU - Eikelenboom, Piet

AU - Stek, Max L.

AU - van den Heuvel, Odile A.

AU - Emsell, Louise

AU - Rhebergen, Didi

AU - van Exel, Eric

PY - 2019

Y1 - 2019

N2 - Background Severe depression is associated with high morbidity and mortality. Neural network dysfunction may contribute to disease mechanisms underlying different clinical subtypes. Here, we apply resting-state functional magnetic resonance imaging based measures of brain connectivity to investigate network dysfunction in severely depressed in-patients with and without psychotic symptoms. Methods A cohort study was performed at two sites. Older patients with major depressive disorder with or without psychotic symptoms were included (n = 23 at site one, n = 26 at site two). Resting state 3-Tesla functional MRI scans, with eyes closed, were obtained and Montgomery-Åsberg Depression Rating Scales were completed. We denoised data and calculated resting state networks in the two groups separately. We selected five networks of interest (1. bilateral frontoparietal, 2.left lateralized frontoparietal, 3.right lateralized frontoparietal, 4. default mode network (DMN) and 5.bilateral basal ganglia and insula network) and performed regression analyses with severity of depression, as well as presence or absence of psychotic symptoms. Results The functional connectivity (FC) patterns did not correlate with severity of depression. Depressed patients with psychotic symptoms (n = 14, 61%) compared with patients without psychotic symptoms (n = 9, 39%) from site one showed significantly decreased FC in the right part of the bilateral frontoparietal network (p = 0.002). This result was not replicated when comparing patients with (n = 9, 35%) and without (n = 17, 65%) psychotic symptoms from site two. Conclusion Psychotic depression may be associated with decreased FC of the frontoparietal network, which is involved in cognitive control processes, such as attention and emotion regulation. These findings suggest that FC in the frontoparietal network may be related to the subtype of depression, i.e. presence of psychotic symptoms, rather than severity of depression. Since the findings could not be replicated in the 2nd sample, replication is needed before drawing definite conclusions.

AB - Background Severe depression is associated with high morbidity and mortality. Neural network dysfunction may contribute to disease mechanisms underlying different clinical subtypes. Here, we apply resting-state functional magnetic resonance imaging based measures of brain connectivity to investigate network dysfunction in severely depressed in-patients with and without psychotic symptoms. Methods A cohort study was performed at two sites. Older patients with major depressive disorder with or without psychotic symptoms were included (n = 23 at site one, n = 26 at site two). Resting state 3-Tesla functional MRI scans, with eyes closed, were obtained and Montgomery-Åsberg Depression Rating Scales were completed. We denoised data and calculated resting state networks in the two groups separately. We selected five networks of interest (1. bilateral frontoparietal, 2.left lateralized frontoparietal, 3.right lateralized frontoparietal, 4. default mode network (DMN) and 5.bilateral basal ganglia and insula network) and performed regression analyses with severity of depression, as well as presence or absence of psychotic symptoms. Results The functional connectivity (FC) patterns did not correlate with severity of depression. Depressed patients with psychotic symptoms (n = 14, 61%) compared with patients without psychotic symptoms (n = 9, 39%) from site one showed significantly decreased FC in the right part of the bilateral frontoparietal network (p = 0.002). This result was not replicated when comparing patients with (n = 9, 35%) and without (n = 17, 65%) psychotic symptoms from site two. Conclusion Psychotic depression may be associated with decreased FC of the frontoparietal network, which is involved in cognitive control processes, such as attention and emotion regulation. These findings suggest that FC in the frontoparietal network may be related to the subtype of depression, i.e. presence of psychotic symptoms, rather than severity of depression. Since the findings could not be replicated in the 2nd sample, replication is needed before drawing definite conclusions.

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UR - https://www.ncbi.nlm.nih.gov/pubmed/30653516

U2 - 10.1371/journal.pone.0209908

DO - 10.1371/journal.pone.0209908

M3 - Article

VL - 14

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 1

M1 - e0209908

ER -