Cortico-limbic network abnormalities in individuals with current and past major depressive disorder

Klauser, Paul; Fornito, Alex; Lorenzetti, Valentina; Davey, Christopher G.; Dwyer, Dominic; Allen, Nicholas B.; Yücel, Murat

doi:10.1016/j.jad.2014.10.041

Cited by 41 publications

(26 citation statements)

References 67 publications

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“…We did not have information about anxiety symptomatology or history of depression for all subjects. Given the common co-occurrence of depressive and anxiety symptoms (Kvaal et al, 2008; Lenze et al, 2000), and the impact of history of depression on neuroimaging measures (Elbejjani et al, 2015; Klauser et al, 2015), future research would benefit from including these variables in analyses. Additionally, given the relatively small sample size, we were unable to examine potentially interesting interactions between depressive symptoms and demographic variables such as age.…”

Section: Discussionmentioning

confidence: 99%

Vertex-wise examination of depressive symptom dimensions and brain volumes in older adults

McLaren

Szymkowicz

O’Shea

et al. 2017

Psychiatry Research: Neuroimaging

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Differences in brain volumes have commonly been reported in older adults with both subthreshold and major depression. However, few studies have examined the association between specific symptom dimensions of depression and brain volumes. This study used vertex-wise analyses to examine the association between specific symptom dimensions of depression and brain volumes in older adults with subthreshold levels of depressive symptoms. Forty-three community-dwelling adults between the ages of 55 and 81 years underwent a structural Magnetic Resonance Imaging scan and completed the Center for Epidemiologic Studies Depression Scale (CES-D). Vertex-wise analyses were conducted using Freesurfer Imaging Suite to examine the relationship between CES-D subscale scores and gray matter volumes while controlling for sex, age, and education. We found distinct associations between depressed mood, somatic symptoms, and lack of positive affect subscales with regional volumes, including primarily positive relationships in temporal regions and a negative association with the lingual gyrus. The relationship between higher depressed mood subscale scores and larger volumes in the left inferior temporal lobe withstood Monte-Carlo correction for multiple comparisons. Results from this preliminary study highlight the importance of examining depression on a symptom dimension level and identify brain regions that may be important in larger studies of depression.

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Section: Discussionmentioning

confidence: 99%

Vertex-wise examination of depressive symptom dimensions and brain volumes in older adults

McLaren

Szymkowicz

O’Shea

et al. 2017

Psychiatry Research: Neuroimaging

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“…Resulting spatially normalized (unmodulated) gray and white matter scans - which we refer to as gray or white matter density were resampled into a 3 × 3 × 3 voxel size. The ‘modulation’ step (30) – which entails scaling the spatially normalized scans using a ‘jacobian matrix’ obtained from the registration step was avoided as recent studies have reported empirical evidence pointing to loss of sensitivity following the ‘modulation’ operation (33, 34). Lastly, gray matter density and white matter density data were used as predictors in the ensuing machine learning analyses with 59,598 and 37,436 features (predictor variables) respectively.…”

Section: Methodsmentioning

confidence: 99%

Individualized Prediction and Clinical Staging of Bipolar Disorders Using Neuroanatomical Biomarkers

Mwangi

Cao

et al. 2016

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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Background Neuroanatomical abnormalities in Bipolar disorder (BD) have previously been reported. However, the utility of these abnormalities in distinguishing individual BD patients from Healthy controls and stratify patients based on overall illness burden has not been investigated in a large cohort. Methods In this study, we examined whether structural neuroimaging scans coupled with a machine learning algorithm are able to distinguish individual BD patients from Healthy controls in a large cohort of 256 subjects. Additionally, we investigated the relationship between machine learning predicted probability scores and subjects’ clinical characteristics such as illness duration and clinical stages. Neuroimaging scans were acquired from 128 BD patients and 128 Healthy controls. Gray and white matter density maps were obtained and used to ‘train’ a relevance vector machine (RVM) learning algorithm which was used to distinguish individual patients from Healthy controls. Results The RVM algorithm distinguished patients from Healthy controls with 70.3 % accuracy (74.2 % specificity, 66.4 % sensitivity, chi-square p<0.005) using white matter density data and 64.9 % accuracy (71.1 % specificity, 58.6 % sensitivity, chi-square p<0.005) with gray matter density. Multiple brain regions – largely covering the fronto – limbic system were identified as ‘most relevant’ in distinguishing both groups. Patients identified by the algorithm with high certainty (a high probability score) – belonged to a subgroup with more than ten total lifetime manic episodes including hospitalizations (late stage). Conclusions These results indicate the presence of widespread structural brain abnormalities in BD which are associated with higher illness burden – which points to neuroprogression.

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“…55 Disentangling emerging psychosis with concomitant mood disturbances from depression or anxiety with attenuated psychotic symptoms is challenging from both a clinical and neuroanatomical point of view. Similarly to psychosis, affective disorders may also show neuroanatomical features within medial prefrontal and medial temporal structures 71 and this could represent an important source of confound for neurostructural findings in ARMS. Accordingly, a recent study showed that comorbid depression and anxiety may contribute to GMV reduction in the anterior cingulate cortex in ARMS.…”

Section: Affective Comorbiditymentioning

confidence: 99%

Lack of Evidence for Regional Brain Volume or Cortical Thickness Abnormalities in Youths at Clinical High Risk for Psychosis: Findings From the Longitudinal Youth at Risk Study: Table 1.

Klauser

Zhou

Lim

et al. 2015

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There is cumulative evidence that young people in an "atrisk mental state" (ARMS) for psychosis show structural brain abnormalities in frontolimbic areas, comparable to, but less extensive than those reported in established schizophrenia. However, most available data come from ARMS samples from Australia, Europe, and North America while large studies from other populations are missing. We conducted a structural brain magnetic resonance imaging study from a relatively large sample of 69 ARMS individuals and 32 matched healthy controls (HC) recruited from Singapore as part of the Longitudinal Youth At-Risk Study (LYRIKS). We used 2 complementary approaches: a voxel-based morphometry and a surface-based morphometry analysis to extract regional gray and white matter volumes (GMV and WMV) and cortical thickness (CT). At the whole-brain level, we did not find any statistically significant difference between ARMS and HC groups concerning total GMV and WMV or regional GMV, WMV, and CT. The additional comparison of 2 regions of interest, hippocampal, and ventricular volumes, did not return any significant difference either. Several characteristics of the LYRIKS sample like Asian origins or the absence of current illicit drug use could explain, alone or in conjunction, the negative findings and suggest that there may be no dramatic volumetric or CT abnormalities in ARMS.

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Cortico-limbic network abnormalities in individuals with current and past major depressive disorder

Cited by 41 publications

References 67 publications

Vertex-wise examination of depressive symptom dimensions and brain volumes in older adults

Vertex-wise examination of depressive symptom dimensions and brain volumes in older adults

Individualized Prediction and Clinical Staging of Bipolar Disorders Using Neuroanatomical Biomarkers

Lack of Evidence for Regional Brain Volume or Cortical Thickness Abnormalities in Youths at Clinical High Risk for Psychosis: Findings From the Longitudinal Youth at Risk Study: Table 1.

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