Altered cerebellar–cerebral resting-state functional connectivity reliably identifies major depressive disorder

Ma, Qingsong; Zeng, Ling‐Li; Shen, Hui; Liu, Li; Hu, Dewen

doi:10.1016/j.brainres.2012.12.002

Cited by 79 publications

(49 citation statements)

References 55 publications

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“…Of note, cerebellar area IX has even been suggested to form a major node of this functional network (Habas et al 2009). Available investigations on altered cerebellar-cerebral functional connectivity in depressed patients have yet employed cerebellar seed regions other than area IX (Alalade et , Liu et al 2012, Guo et al 2013, Ma et al 2013. Also, the techniques for imaging data analyses in these investigations relied on conventional segmentation algoriths and did not employ cerebellum-optimized protocols (Guo et al 2013).…”

Section: Discussionmentioning

confidence: 98%

Abnormal cerebellar volume in acute and remitted major depression

Depping

Wolf

Vasić

et al. 2016

Progress in Neuro-Psychopharmacology and Biological Psychiatry

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Section: Discussionmentioning

confidence: 98%

Abnormal cerebellar volume in acute and remitted major depression

Depping

Wolf

Vasić

et al. 2016

Progress in Neuro-Psychopharmacology and Biological Psychiatry

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“…Simultaneous cerebellar and amygdala reduction in MDD was also reported in prior gray matter density studies (Lee et al 2011; Peng et al 2011). Recent studies have shown that the cerebellum plays a role in emotion regulation and cognition (Baldacara et al 2008; Bugalho et al 2006; Phillips et al 2015), and also have implicated the cerebellum in MDD based on findings of altered structure (Yucel et al 2013; Zhao et al 2016) and function (Liu et al 2012; Ma et al 2013). …”

Section: Discussionmentioning

confidence: 99%

Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders

Sui

et al. 2017

Brain Struct Funct

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Bipolar disorder (BD) and major depressive disorder (MDD) share similar clinical characteristics that often obscure the diagnostic distinctions between these conditions. Both functional and structural brain abnormalities have been reported in these two disorders. However, the direct link between altered functioning and structure in these two diseases is unknown. To elucidate this relationship, we conducted a multimodal fusion analysis on the functional network connectivity (FNC) and gray matter density (GMD) from MRI data from 13 BD, 40 MDD, and 33 matched healthy controls (HC). A data-driven fusion method called mCCA+jICA was used to identify the co-altered FNC and GMD components. We found reduced GMD in the parietal and occipital cortices related to lower FNC in a sensory-motor network as well as stronger interconnection in frontal regions in BD compared to HC. Meanwhile, the MDD group exhibited GMD deficits in the amygdala and cerebellum. Among preliminary classifiers trained using features generated from these group discriminative components, the overall accuracy with resampling and cross-validation reached 91.3% for 3 groups and 99.5% for BD versus MDD. Our findings suggest that both overlapping and unique functional and structural deficits exist in BD and MDD, and the abnormalities may be utilized as potential diagnostic biomarkers.

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“…As an exception, coordinates from one study (Lui et al, 2011) were transformed using tal2mni (Brett et al, 2001) as final coordinates in that study had initially been transformed using this method. Coordinates were assigned to two categories based on directionality of findings in order to avoid that clearly opposed findings in the original studies enhance each other in the ALE-analysis: group A comprises findings of decreased long distance or local connectivity (including lower correlation coefficients or lower regional homogeneity) or lower power of typical low frequency fluctuations representing spontaneous neural activity in depression compared to healthy controls and findings without clearly interpretable directionality information (Greicius et al, 2007; Bluhm et al, 2009; Yao et al, 2009; Liu et al, 2010, 2013a,b; Veer et al, 2010; Zhou et al, 2010; Furman et al, 2011; Guo et al, 2011a,b, 2012a,b, 2013a,b,c; Hamilton et al, 2011; Lui et al, 2011; Peng et al, 2011, 2012; Wu et al, 2011; Ma et al, 2012, 2013; Wang et al, 2012a, 2013a,b; Ye et al, 2012; Zhu et al, 2012; Tang et al, 2013; Zeng et al, 2013). Details of these studies are presented in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression

Sundermann

Beverborg

Pfleiderer

2014

Front. Hum. Neurosci.

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Information derived from functional magnetic resonance imaging (fMRI) during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD). Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD, primarily to serve as feature selection for multivariate pattern analysis techniques (MVPA). Thirty two studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components including the precuneus and neighboring posterior cingulate cortices associated with self-referential processing and the subgenual anterior cingulate and neighboring medial frontal cortices) with lateral prefrontal areas related to externally-directed cognition. Other areas of hyperactivity/hyperconnectivity include the left lateral parietal cortex, right hippocampus and right cerebellum whereas hypoactivity/hypoconnectivity was observed mainly in the left temporal cortex, the insula, precuneus, superior frontal gyrus, lentiform nucleus and thalamus. Results are made available in two different data formats to be used as spatial hypotheses in future studies, particularly for diagnostic classification by MVPA.

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Altered cerebellar–cerebral resting-state functional connectivity reliably identifies major depressive disorder

Cited by 79 publications

References 55 publications

Abnormal cerebellar volume in acute and remitted major depression

Abnormal cerebellar volume in acute and remitted major depression

Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders

Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression

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