Spatial patterns of intrinsic neural activity in depressed patients with vascular risk factors as revealed by the amplitude of low-frequency fluctuation

Zhu, Zhihua; Lu, Qiangbin; Meng, Xia; Jiang, Qitao; Lan, Peng; Wang, Qingguang

doi:10.1016/j.brainres.2012.07.015

Cited by 14 publications

(5 citation statements)

References 32 publications

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“…A number of other disorders and diseases have also been targets for variability and complexity analyses (Guo et al, 2012a,b; Takahashi, 2012; Yu-Feng et al, 2007; Zhu et al, 2012), thus further supporting the use of these measures for studying group differences and clinical outcomes. However, the application of variability/complexity techniques in clinical neuroimaging remains in its infancy, with relatively few studies taking a multi-scale approach.…”

Section: Within-subject Brain Signal Variability Measures Reveal Pmentioning

confidence: 90%

Moment-to-moment brain signal variability: A next frontier in human brain mapping?

Garrett

Samanez-Larkin

MacDonald

et al. 2013

Neuroscience & Biobehavioral Reviews

506

470

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Neuroscientists have long observed that brain activity is naturally variable from moment-to-moment, but neuroimaging research has largely ignored the potential importance of this phenomenon. An emerging research focus on within-person brain signal variability is providing novel insights, and offering highly predictive, complementary, and even orthogonal views of brain function in relation to human lifespan development, cognitive performance, and various clinical conditions. As a result, brain signal variability is evolving as a bona fide signal of interest, and should no longer be dismissed as meaningless noise when mapping the human brain.

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Section: Within-subject Brain Signal Variability Measures Reveal Pmentioning

confidence: 90%

Moment-to-moment brain signal variability: A next frontier in human brain mapping?

Garrett

Samanez-Larkin

MacDonald

et al. 2013

Neuroscience & Biobehavioral Reviews

506

470

View full text Add to dashboard Cite

show abstract

“…The main nodes involved in these significant connections include key limbic regions, such as the HPC, ACC and AMY, as well as the SPL, which is part of the association cortex. Previous studies exploring the neural correlates of symptom severity of depression have found various positive associations: (1) decreased intrinsic FC within the right AI [31], (2) increased FC of the DMN [74], (3) increased amplitude of low-frequency fluctuations (ALFF) values of the left dorsal medial PFC [75], (4) ALFF values of the right superior frontal gyrus [76] (while the same study found a negative correlation with the ALFF values of the left insula), (4) increased FC of bilateral dorsal medial PFC [77] and (5) reduced perfusion in the DMN (the posterior cingulate cortex and the right inferior parietal lobe) [78]. On the other hand, our finding of negative associations between AI self-regulation and the MADRS score is in line with the report of Manoliu et al, who found that the decreased intrinsic FC within the right AI was positively associated with severity of symptoms [31].…”

Section: Discussionmentioning

confidence: 99%

Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls

Kandilarova

Stoyanov

Paunova

et al. 2021

JPM

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This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BD), as well as the severity of these disorders. Toward this end, we performed an effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of five connections, which discriminated patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), and the frontoparietal and dorsal attention network (middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex, and frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, four out of the five connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers.

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“…The main nodes involved in these significant connections include key limbic regions such as HPC, ACC and AMY as well as the SPL which is part of the association cortex. Previous studies exploring the neural correlates of symptom severity of depression have found various positive associations including with: 1) decreased intrinsic FC within the right AI [75], 2) increased FC of the DMN [76], 3) increased amplitude of low-frequency fluctuations (ALFF) values of the left dorsal medial PFC [77], 4) ALFF values of the right superior frontal gyrus [78] (while the same study found a negative correlation with the ALFF values of the left insula), 4) increased FC of bilateral dorsal medial PFC [79], and 5) reduced perfusion in the DMN (the posterior cingulate cortex and the right inferior parietal lobe) [80]. On the other hand, our finding of negative associations between AI self-regulation and the MADRS score is in line with the report of Manoliu et al who found that the decreased intrinsic FC within the right AI was positively associated with severity of symptoms [75].…”

Section: Discussionmentioning

confidence: 99%

Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls

Kandilarova¹,

Stoyanov²,

Paunova³

et al. 2021

Preprint

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This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ), bipolar disorder (BD) and the severity of these disorders. Toward this end, we have performed effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We have used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of 5 connections, which discriminate patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), fronto-parietal and dorsal attention network (middle frontal gyrus (MFG) corresponding to dorsolateral prefrontal cortex, frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, 4 out of the 5 connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers.

show abstract

Spatial patterns of intrinsic neural activity in depressed patients with vascular risk factors as revealed by the amplitude of low-frequency fluctuation

Cited by 14 publications

References 32 publications

Moment-to-moment brain signal variability: A next frontier in human brain mapping?

Moment-to-moment brain signal variability: A next frontier in human brain mapping?

Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls

Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls

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