Resting-state fMRI in depressive and (hypo)manic mood states in bipolar disorders: A systematic review

Claeys, E.H.I.; Mantingh, Tim; Morrens, Manuel; Yalın, Nefize; Stokes, Paul

doi:10.1016/j.pnpbp.2021.110465

Cited by 25 publications

(26 citation statements)

References 91 publications

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“…Rather, the four brain regions work together, and each brain region contains its own, which are responsible for the network architecture and synergistic work among the functional regions. The lack of a common node in the parietal area in this investigation suggests that the parietal region does not influence mood alterations, which is in line with physiological findings [ 43 ]. The parietal lobe is located in the center of the human brain, on the posterior side of the central dorsal plough sulcus, and it is responsible for helping humans grasp spatial relationships, as well as pain and touch.…”

Section: Resultssupporting

confidence: 90%

Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm

et al. 2022

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Emotion analysis has emerged as one of the most prominent study areas in the field of Brain Computer Interface (BCI) due to the critical role that the human brain plays in the creation of human emotions. In this study, a Multi-objective Immunogenetic Community Division Algorithm Based on Memetic Framework (MFMICD) was suggested to study different emotions from the perspective of brain networks. To improve convergence and accuracy, MFMICD incorporates the unique immunity operator based on the traditional genetic algorithm and combines it with the taboo search algorithm. Based on this approach, we examined how the structure of people’s brain networks alters in response to different emotions using the electroencephalographic emotion database. The findings revealed that, in positive emotional states, more brain regions are engaged in emotion dominance, the information exchange between local modules is more frequent, and various emotions cause more varied patterns of brain area interactions than in negative brain states. A brief analysis of the connections between different emotions and brain regions shows that MFMICD is reliable in dividing emotional brain functional networks into communities.

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Section: Resultssupporting

confidence: 90%

Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm

et al. 2022

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“…Functional brain connectivity (FC) is an important aspect of rsfMRI defined as the statistical dependence between cortical and subcortical areas during periods of rest or low cognitive demand [4]. A common application of rsfMRI is for the prediction of cognitive performance [5]–[9], and clinical phenotypes [5], [10]–[13] [14]–[20]. It is usually accomplished by extracting various features from rsfMRI, such as widely used FC measures, and using them for predictive modeling.…”

mentioning

confidence: 99%

Is resting state fMRI better than individual characteristics at predicting cognition?

Omidvarnia

Sasse

Larabi

et al. 2023

Preprint

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Changes in spontaneous brain activity at rest provide rich information about behavior and cognition. The mathematical properties of resting-state functional magnetic resonance imaging (rsfMRI) are a depiction of brain function and are frequently used to predict cognitive phenotypes. Individual characteristics such as age, gender, and total intracranial volume (TIV) play an important role in predictive modeling of rsfMRI (for example, as "confounders" in many cases). It is unclear, however, to what extent rsfMRI carries independent information from the individual characteristics that is able to predict cognitive phenotypes. Here, we used kernel ridge regression modeling to thoroughly examine the predictability of four cognitive phenotypes in 20,000 healthy UK Biobank subjects. We extracted common rsfMRI features of functional brain connectivity (FC) and temporal complexity (TC). We assessed the ability of these features to predict outcomes in the presence and absence of age, gender, and TIV. Additionally, we assessed the predictiveness of age, gender, and TIV only. We find TC and FC features to perform comparably with regard to predicting cognitive phenotypes. As compared to rsfMRI features, individual characteristics provide systematically better predictions with smaller sample sizes and, to some extent, in larger cohorts. It is also consistent across different levels of inherent temporal noise in rsfMRI. Our results suggest that when the objective is to perform cognitive predictions as opposed to understanding the relationship between brain and behavior, individual characteristics outperform rsfMRI features.

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“…The and interepisode phases and shown positive outcomes (Claeys et al, 2022;Siegel-Ramsay et al, 2022;Sunaga et al, 2020). Other research has explored differences in peripheral blood inflammatory cytokines and paraoxonase1 Q192R gene polymorphism between patients with BD and MDD (Bortolasci et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Research endeavors have targeted potential methods for the early diagnosis of BD. Some investigations have employed neuroimaging techniques to examine structural and functional brain anomalies in BD patients during manic and interepisode phases and shown positive outcomes (Claeys et al., 2022; Siegel‐Ramsay et al., 2022; Sunaga et al., 2020). Other research has explored differences in peripheral blood inflammatory cytokines and paraoxonase1 Q192R gene polymorphism between patients with BD and MDD (Bortolasci et al., 2014).…”

Section: Discussionmentioning

confidence: 99%

Study on exosomes for identifying bipolar disorder in early stage: A cross‐sectional and validation study protocol

Wu,

Li,

et al. 2024

Brain and Behavior

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BackgroundThe difficulty is remained to accurately distinguish bipolar disorder (BD) from major depressive disorder (MDD) in early stage, with a delayed diagnosis for 5–10 years. BD patients are often treated with antidepressants systematically due to being diagnosed with MDD, affecting the disease course and clinical outcomes. The current study aims to explore the role of plasma exosomes as biomarker to distinguish BD from MDD in early stage.MethodsTwo stages are included. The first stage is a cross‐sectional study, comparing the concentrations of plasma exosome microRNA and related proteins among BD group, MDD group, and healthy controls (HC) group (n = 40 respectively), to identify target biomarkers preliminarily. The “Latent Class Analysis” and “Receiver Operating Characteristic” analysis will be performed to determine the optimal concentration range for each biomarker. The second stage is to validate target markers in subjects, coming from an ongoing study focusing on patients with a first depressive episode. All target biomarkers will be test in plasma samples reserved at the initial stage to detect whether the diagnosis indicated by biomarker level is consistent with the diagnosis by DSM‐5. Furthermore, the correlation between specific biomarkers and the manic episode, suicidal ideation, and adverse reactions will also be observed.DiscussionExosome‐derived microRNA and related proteins have potential in serving as a good medium for exploring mental disorders because it can pass through the blood‐brain barrier bidirectionally and convey a large amount of information stably. Improving the early diagnosis of BD would help implement appropriate intervention strategy as early as possible and significantly reduce the burden of disease.

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Resting-state fMRI in depressive and (hypo)manic mood states in bipolar disorders: A systematic review

Cited by 25 publications

References 91 publications

Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm

Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm

Is resting state fMRI better than individual characteristics at predicting cognition?

Study on exosomes for identifying bipolar disorder in early stage: A cross‐sectional and validation study protocol

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