Microstructural changes in the reward system are associated with post-stroke depression

Oestreich, Lena; Wright, Paul; O’Sullivan, Michael

doi:10.1016/j.nicl.2020.102360

Cited by 13 publications

(9 citation statements)

References 52 publications

(68 reference statements)

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“…Associations of WMHs with PSD varied according to the type of WMH and the time after stroke, such that early depressive symptoms are associated with periventricular WMHs and delayed severe depression rather with diffuse WMHs [ 172 ]. Poststroke depression (PSD) was also shown to be associated with gray matter volume loss, reduced fractional anisotropy, and increased extracellular free water (FW) in the reward system, similar to features observed in LLD without stroke [ 173 ]. One recent study reported infarcts in the right amygdala and striatum, and disconnection of right limbic and fronto–cortico–basal ganglia–thalamic circuits associated with PSD [ 174 ], while others suggested that generalized degenerative and vascular brain pathology rather than lesion-related pathology may be an important predictor for PSD [ 175 ].…”

Section: Structural Brain Lesions In Vadmentioning

confidence: 99%

Pathomechanisms of Vascular Depression in Older Adults

Jellinger¹

2021

IJMS

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Depression in older individuals is a common complex mood disorder with high comorbidity of both psychiatric and physical diseases, associated with high disability, cognitive decline, and increased mortality The factors predicting the risk of late-life depression (LLD) are incompletely understood. The reciprocal relationship of depressive disorder and age- and disease-related processes has generated pathogenic hypotheses and provided various treatment options. The heterogeneity of depression complicates research into the underlying pathogenic cascade, and factors involved in LLD considerably differ from those involved in early life depression. Evidence suggests that a variety of vascular mechanisms, in particular cerebral small vessel disease, generalized microvascular, and endothelial dysfunction, as well as metabolic risk factors, including diabetes, and inflammation that may induce subcortical white and gray matter lesions by compromising fronto–limbic and other important neuronal networks, may contribute to the development of LLD. The “vascular depression” hypothesis postulates that cerebrovascular disease or vascular risk factors can predispose, precipitate, and perpetuate geriatric depression syndromes, based on their comorbidity with cerebrovascular lesions and the frequent development of depression after stroke. Vascular burden is associated with cognitive deficits and a specific form of LLD, vascular depression, which is marked by decreased white matter integrity, executive dysfunction, functional disability, and poorer response to antidepressive therapy than major depressive disorder without vascular risk factors. Other pathogenic factors of LLD, such as neurodegeneration or neuroimmune regulatory dysmechanisms, are briefly discussed. Treatment planning should consider a modest response of LLD to antidepressants, while vascular and metabolic factors may provide promising targets for its successful prevention and treatment. However, their effectiveness needs further investigation, and intervention studies are needed to assess which interventions are appropriate and effective in clinical practice.

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Section: Structural Brain Lesions In Vadmentioning

confidence: 99%

Pathomechanisms of Vascular Depression in Older Adults

Jellinger¹

2021

IJMS

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“…The current work included data drawn from the STROKDEM (Study of Factors Influencing Posttroke Dementia) 10 and 3 studies from the STROKOG consortium, including the DEDEMAS (Determinants of Dementia After Stroke) cohort from Germany, 11 the SSS (Sydney Stroke Study) from Australia, 12 and STRATEGIC from the United Kingdom. 13 Four other studies from Hong Kong, Korea, and Singapore contributed data; however, the MR images did not pass quality control and were excluded from the analyses. Images were required to have slice thickness <5 mm to allow accurate brain structures segmentation.…”

Section: Methodsmentioning

confidence: 99%

Texture Features of Magnetic Resonance Images Predict Poststroke Cognitive Impairment: Validation in a Multicenter Study

Betrouni

Jiang

Duering

et al. 2022

Stroke

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BACKGROUND: Imaging features derived from T1-weighted (T1w) images texture analysis were shown to be potential markers of poststroke cognitive impairment, with better sensitivity than atrophy measurement. However, in magnetic resonance images, the signal distribution is subject to variations and can limit transferability of the method between centers. This study examined the reliability of texture features against imaging settings using data from different centers. METHODS: Data were collected from 327 patients within the Stroke and Cognition Consortium from centers in France, Germany, Australia, and the United Kingdom. T1w images were preprocessed to normalize the signal intensities and then texture features, including first- and second-order statistics, were measured in the hippocampus and the entorhinal cortex. Differences between the data led to the use of 2 methods of analysis. First, a machine learning modeling, using random forest, was used to build a poststroke cognitive impairment prediction model using one dataset and this was validated on another dataset as external unseen data. Second, the predictive ability of the texture features was examined in the 2 remaining datasets by ANCOVA with false discovery rate correction for multiple comparisons. RESULTS: The prediction model had a mean accuracy of 90% for individual classification of patients in the learning base while for the validation base it was ≈ 77%. ANCOVA showed significant differences, in all datasets, for the kurtosis and inverse difference moment texture features when measured in patients with cognitive impairment and those without. CONCLUSIONS: These results suggest that texture features obtained from routine clinical MR images are robust early predictors of poststroke cognitive impairment and can be combined with other demographic and clinical predictors to build an accurate prediction model.

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“…Detailed acquisition parameters for these structural sequences have been published previously. 19 Rs-fMRI was acquired with an echo-planar imaging sequence using the following parameters: TR = 2000 ms, TE = 30 ms, flip angle = 75°, slice thickness = 3 mm and gap = 0.3 mm. Images were acquired in the axial plane with FOV = 211 × 211 mm 2 , matrix size = 64 × 64 mm 2 and number of slices = 40.…”

Section: Methodsmentioning

confidence: 99%

Hyperconnectivity and altered interactions of a nucleus accumbens network in post-stroke depression

Oestreich

Wright

O’Sullivan

2022

Brain Communications

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Post-stroke depression is a common complication after stroke. To date, no consistent locus of injury is associated with this complication. Here, we probed network dynamics and structural alterations in post-stroke depression in four functional circuits linked to major depressive disorder and a visual network, which served as a control network. Forty-four participants with recent stroke (mean age = 69.03, standard deviation age = 8.59, age range = 51 - 86; gender: female = 10) and 16 healthy volunteers (mean age = 71.53, standard deviation age = 10.62, age range = 51 - 84; gender: female = 11, male = 5) were imaged with 3-Tesla structural, diffusion and resting-state functional MRI. The Geriatric Depression Scale was administered to measure depression. Associations between depression severity and functional connectivity were investigated within networks seeded from nucleus accumbens, amygdala, dorsolateral prefrontal cortex and primary visual cortex. In addition, the default mode network was identified by connectivity with medial prefrontal cortex and posterior cingulate cortex. Circuits that exhibited altered activity associated with depression severity were further investigated by extracting within-network volumetric and microstructural measures from structural images. In the stroke group, functional connectivity within the nucleus accumbens-seeded network (left hemisphere: p = 0.001, right hemisphere: p = 0.004) and default mode network (cluster one: p < 0.001, cluster two: p < 0.001) correlated positively with depressive symptoms. Normal anticorrelations between these two networks were absent in patients with post-stroke depression. Grey matter volume of the right posterior cingulate cortex (Pearson correlation coefficient = -0.286, p = 0.03), as well as microstructural measures in the posterior cingulate cortex (right: Pearson correlation coefficient = 0.4, p = 0.024, left: Pearson correlation coefficient = 0.3, p = 0.048), right medial prefrontal cortex (Pearson correlation coefficient = 0.312, p = 0.039), and the medial forebrain bundle (Pearson correlation coefficient = 0.450, p = 0.003), a major projection pathway interconnecting the nucleus accumbens-seeded network and linking to medial prefrontal cortex, were associated with depression severity. Depression after stroke is marked by reduced mutual inhibition between functional circuits involving nucleus accumbens and default mode network as well as volumetric and microstructural changes within these networks. Aberrant network dynamics present in patients with post-stroke depression are therefore likely to be influenced by secondary, pervasive alterations in grey and white matter, remote from the site of injury.

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Microstructural changes in the reward system are associated with post-stroke depression

Cited by 13 publications

References 52 publications

Pathomechanisms of Vascular Depression in Older Adults

Pathomechanisms of Vascular Depression in Older Adults

Texture Features of Magnetic Resonance Images Predict Poststroke Cognitive Impairment: Validation in a Multicenter Study

Hyperconnectivity and altered interactions of a nucleus accumbens network in post-stroke depression

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