Depression is associated with dimensional and categorical effects on white matter pathways

Dillon, Daniel G.; Gönenç, A.; Belleau, Emily L.; Pizzagalli, Diego A.

doi:10.1002/da.22734

Cited by 36 publications

(18 citation statements)

References 51 publications

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“…Since it transmits cognitive, sensory and motor information across the hemispheres, and de cits in this area could affect complex motor tasks, such as freezing of gait (Fling, Dale et al 2016), and cognitive impairment (Bledsoe, Stebbins et al 2018) in PD patients. The neurodegeneration of corpus callosum in PD may re ect its role in motor, cognitive and emotion features (Dillon, Gonenc et al 2018), which is in accordance with our nding that the de cits of genu of corpus callosum had positive association trends with HDRS score.…”

Section: The Most Discriminative Wm Connections/regions: the Association Berssupporting

confidence: 91%

Alterations of white matter abnormalities in Parkinson’s disease: a machine learning approach

Qian¹,

Xiao²,

Liu³

et al. 2021

Preprint

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The inter-tract/region dependencies of white-matter in Parkinson’s disease are usually ignored by standard statistical tests. Moreover, it remains unclear whether the disruption of white-matter tracts/regions suffices to identify Parkinson’s disease patients from healthy controls. A machine learning approach was applied to capture the interdependencies between white-matter tracts/regions and to differentiate PD patients from healthy controls. First, the mean regional white-matter profiles, including white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were extracted as features in Parkinson’s disease patients (N = 78) and in healthy controls (N = 91). Then, the feature selection and classification were performed using t-test and linear support vector machine, respectively. Last, the relationships between clinical variables and regional magnetic resonance indices were estimated. Our results showed the combined features (white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) had the best performance with an accuracy of 75.15% and area under curve of 0.8171, respectively. The most discriminative white-matter features were centered on the association fibers, commissural fibers, projection fibers, and striatal fibers. The discriminative regions of right anterior limb of internal capsule had positive association trends with the Unified Parkinson Disease Rating Scale III score; while the genu of corpus callosum and right retrolenticular part of internal capsule had positively association trends with the Hamilton Depression Rating Scale score. Our finding showed the multivariate machine learning approach is a promising tool to detect abnormal white-matter tracts/regions in Parkinson’s disease, and provides us a multidimensional means for neuroimaging classification.

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Section: The Most Discriminative Wm Connections/regions: the Association Berssupporting

confidence: 91%

Alterations of white matter abnormalities in Parkinson’s disease: a machine learning approach

Qian¹,

Xiao²,

Liu³

et al. 2021

Preprint

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“…23 In MDD, anhedonia (closely related to apathy) is associated with fractional anisotropy in the anterior cingulum and the internal capsule. 28 Using tractography, we could not find a positive association between AES scores and the number of tracts starting from the left superior frontal gyrus and the left rostral ACC (r = -0.17, p = 0.4) among participants with MDD, failing to reproduce our results for functional connectivity. We found increased fractional anisotropy in the posterior cerebellum, also involved in apathy in Parkinson disease.…”

Section: Discussioncontrasting

confidence: 79%

“…[25][26][27] Similarly, we expected to find apathy-related anatomic connectivity features in the ACC based on findings from recent literature. 28,29…”

Section: Introductionmentioning

confidence: 99%

Multimodal brain imaging connectivity analyses of emotional and motivational deficits in depression among women

Robert

Bannier

Comte

et al. 2021

JPN

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Background: Major depressive disorder (MDD) is characterized by impaired cortical–subcortical functional connectivity. Apathy adds to functional impairment, but its cerebral basis in MDD remains unknown. Our objective was to describe impairments in functional connectivity during emotional processing in MDD (with varying levels of congruency and attention), and to determine their correlation with apathy. Methods: We used the Variable Attention Affective Task during functional MRI, followed by diffusion-weighted MRI, to assess 55 right-handed women (30 with MDD and 25 healthy controls) between September 2012 and February 2015. We estimated functional connectivity using generalized psychophysiologic interaction and anatomic connectivity with tract-based spatial statistics. We measured apathy using the Apathy Evaluation Scale. Results: We found decreased functional connectivity between the left amygdala and the left anterior cingulate cortex (ACC) during negative stimuli in participants with MDD (t54 = 4.2; p = 0.035, family-wise error [FWE]–corrected). During high-attention stimuli, participants with MDD showed reduced functional connectivity between the right dorsolateral prefrontal cortex (dlPFC) and the right ACC (t54 = 4.06, pFWE = 0.02), but greater functional connectivity between the right dlPFC and the right amygdala (t54 = 3.35, p = 0.048). Apathy was associated with increased functional connectivity between the right dlPFC and the right ACC during high-attention stimuli (t28 = 5.2, p = 0.01) and increased fractional anisotropy in the right posterior cerebellum, the anterior and posterior cingulum and the bilateral internal capsule (all pFWE < 0.05). Limitations: Limitations included a moderate sample size, concomitant antidepressant therapy and no directed connectivity. Conclusion: We found that MDD was associated with impairments in cortical–subcortical functional connectivity during negative stimuli that might alter the recruitment of networks engaged in attention. Apathy-related features suggested networks similar to those observed in degenerative disorders, but possible different mechanisms.

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“…Fractional anisotropy (FA) which measures the principal directionality of water diffusion is the commonly used parameter to assess whiter matter integrity in DTI studies. Decreased FA values were detected in the uncinate fasciculus, the superior longitudinal fasciculus, the cingulum, the corpus callosum (12), the genu and UF (13) in adult MDD, indicating abnormalities of white matter fiber integrity. In the recent studies of adolescent MDD, Cullen et al reported lower FA in the tract connecting subgenual ACC to amygdala (14), Bessette et al reported lower FA in corpus callosum, midbrain white matter tracts, and corticospinal tracts (15), while Aghajani et al reported lower FA in the body of the corpus callosum, as well as higher FA in the uncinate fasciculus (16), Taken together, these findings support the hypothesis that white matter deficits of frontal-limbic neural circuits may present in the early stage of depression.…”

Section: Introductionmentioning

confidence: 95%

Abnormal Functional and Structural Connectivity of Amygdala-Prefrontal Circuit in First-Episode Adolescent Depression: A Combined fMRI and DTI Study

Sun

et al. 2020

Front. Psychiatry

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Background: Abnormalities of functional and structural connectivity in the amygdalaprefrontal circuit which involved with emotion processing have been implicated in adults with major depressive disorder (MDD). Adolescent MDD may have severer dysfunction of emotion processing than adult MDD. In this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) to examine the potential functional and structural connectivity abnormalities within amygdala-prefrontal circuit in first-episode medication-naïve adolescents with MDD. Methods: Rs-fMRI and DTI data were acquired from 36 first-episode medication-naïve MDD adolescents and 37 healthy controls (HC). Functional connectivity between amygdala and the prefrontal cortex (PFC) and fractional anisotropy (FA) values of the uncinate fasciculus (UF) which connecting amygdala and PFC were compared between the MDD and HC groups. The correlation between the FA value of UF and the strength of the functional connectivity in the PFC showing significant differences between the two groups was identified. Results: Compared with the HC group, decreased functional connectivity between left amygdala and left ventral PFC was detected in the adolescent MDD group. FA values were significant lower in the left UF within the adolescent MDD group compared to the HC group. There was no significant correlation between the UF and FA, and the strength of functional connectivity within the adolescent MDD group. Conclusions: First-episode medication-naïve adolescent MDD showed decreased functional and structural connectivity in the amygdala-prefrontal circuit. These findings suggest that both functional and structural abnormalities of the amygdala-prefrontal circuit

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Depression is associated with dimensional and categorical effects on white matter pathways

Cited by 36 publications

References 51 publications

Alterations of white matter abnormalities in Parkinson’s disease: a machine learning approach

Alterations of white matter abnormalities in Parkinson’s disease: a machine learning approach

Multimodal brain imaging connectivity analyses of emotional and motivational deficits in depression among women

Abnormal Functional and Structural Connectivity of Amygdala-Prefrontal Circuit in First-Episode Adolescent Depression: A Combined fMRI and DTI Study

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