Altered dynamic amplitude of low-frequency fluctuation between bipolar type I and type II in the depressive state

Liu, Wen; Jiang, Xiaowei; Deng, Zijing; Jia, Linna; Sun, Qikun; Kong, Lingtao; Wu, Fengpeng; Tang, Yanqing

doi:10.1016/j.nicl.2022.103184

Cited by 7 publications

(3 citation statements)

References 88 publications

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“…According to the Diagnostic and Statistical Manual Disorders, Fifth Edition (DSM-5), BD I and BD II are the major two clinical subtypes of BD, which are characterized by the classification of mania or hypomania. BD II is sometimes considered a milder form of BD I, because these two subtypes of BD share some phenomenological features (Liu et al, 2022). However, with increasing advances in BD Frontiers in Aging 07 frontiersin.org epidemiology, clinical presentation (Zimmerman et al, 2013), and genetic basis (Charney et al, 2017), the important difference between the two subtypes is gradually being recognised.…”

Section: Discussionmentioning

confidence: 99%

Bidirectional Mendelian randomization study of psychiatric disorders and Parkinson’s disease

Liu

Huang

et al. 2023

Front. Aging Neurosci.

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IntroductionAlthough the relationship between psychiatric disorders and Parkinson’s disease (PD) has attracted continuous research attention, the causal linkage between them has not reached a definite conclusion.MethodsTo identify the causal relationship between psychiatric disorders and PD, we used public summary-level data from the most recent and largest genome-wide association studies (GWASs) on psychiatric disorders and PD to conduct a bidirectional two-sample Mendelian randomization (MR). We applied stringent control steps in instrumental variable selection using the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method to rule out pleiotropy. The inverse-variance weighted (IVW) method was used to identify the causal relationship between psychiatric disorders and PD. Multiple MR analysis methods, including MR-Egger, weighted-median, and leave-one-out analyses, were used for sensitivity analysis, followed by heterogeneity tests. Further validation and reverse MR analyses were conducted to strengthen the results of the forward MR analysis.ResultsThe lack of sufficient estimation results could suggest a causal relationship between psychiatric disorders and PD in the forward MR analysis. However, the subsequent reverse MR analysis detected a causal relationship between PD and bipolar disorder (IVW: odds ratios [OR] =1.053, 95% confidence interval [CI] =1.02–1.09, p = 0.001). Further analysis demonstrated a causal relationship between genetically predicted PD and the risk of bipolar disorder subtype. No pleiotropy or heterogeneity was detected in the analyses.DiscussionOur study suggested that while psychiatric disorders and traits might play various roles in the risk of developing PD, PD might also be involved in the risk of developing psychiatric disorders.

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Section: Discussionmentioning

confidence: 99%

Bidirectional Mendelian randomization study of psychiatric disorders and Parkinson’s disease

Liu

Huang

et al. 2023

Front. Aging Neurosci.

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“…A study from 2022 examined the genetic overlap and distinction between BD I and BD II via integrative post-GWAS analyses, and found genetic differences with a set of candidate genes distinguishing BD I from BD II ( 52 ). Similarly, Liu et al used resting state functional magnetic resonance imaging (fMRI) and data preprocessing technology to uncover shared and unique neurobiological mechanisms between BD I and BD II ( 53 ). They reported that the dynamic amplitudes of low-frequency fluctuation (dALFF) values in BD II patients were significantly higher than those in BD I patients in the right superior temporal gyrus, which indicated that activity in this region could act as a potential biomarker for the differential diagnosis of BD subtypes.…”

Section: Discussionmentioning

confidence: 99%

Variations in olfactory function among bipolar disorder patients with different episodes and subtypes

Liu

Zou

et al. 2023

Front. Psychiatry

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PurposeMost studies on olfactory function in individuals with bipolar disorder (BD) have not distinguished between the different subtypes or between the acute phase (mania or depression) and euthymic state. In this study, we compared olfactory function among BD patients with different subtypes and episodes to explore the potential use of olfactory function as a biomarker for the early identification of BD.Patients and methodsThe study sample consisted of 117 BD patients who were hospitalized between April 2019 and June 2019, and 47 healthy volunteers as controls. The BD patients were divided into a bipolar I disorder (BD I) (n = 86) and bipolar II disorder (BD II) group (n = 31) according to the different subtypes, and divided into depressive BD (n = 36), manic BD (n = 44), or euthymic BD (n = 37) groups according to the types of episodes they experienced. We assessed olfactory sensitivity (OS) and olfactory identification (OI) via the Sniffin’ Sticks test and used the Hamilton Depression Rating Scale (HAMD) and Young Manic Rating Scale (YMRS) to evaluate BD characteristics among all subjects.ResultsCompared with controls, the participants with BD showed decreased OS and OI. We found statistically significant differences in OS and OI between the BD I group and controls, as well as differences in OS between the BD I and BD II group. Least-significant difference multiple comparisons revealed statistically significant differences in OS between the depressive BD group, manic BD group and controls and also between the manic BD and euthymic BD group. OI was positively correlated with the YMRS score in the BD I group and OS was negatively correlated with the HAMD score in the BD II group.ConclusionThis may be the first study to compare olfactory function in patients with BD I vs. BD II via pairwise comparisons. Our findings suggest that OS may have potential as a biomarker for distinguishing the different subtypes of BD and as a state-related biomarker for differentiating the acute phase from the euthymic state of BD. However, further prospective research is warranted.

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“…Abnormal dALFF has been explored in various brain diseases (Cui et al, 2020;Zhou et al, 2021;Liu et al, 2022); however, in patients with PSD, the dynamic abnormalities in regional neural activity have rarely been evaluated, and the relationship between the alterations of temporal variability in local neural activity and depressive manifestations is unclear.…”

Section: Introductionmentioning

confidence: 99%

Characteristic alterations of dynamic amplitude of low-frequency fluctuation in patients with post-stroke depression

Lu,

Wang

et al. 2024

Preprint

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The abnormalities in static brain activity in patients with post-stroke depression (PSD) have been widely reported. However, the dynamics of neural activity in PSD remain enigmatic. We investigated the abnormalities in dynamic neural activity in patients with PSD with the dynamic amplitude of low-frequency fluctuation (dALFF) method. Imaging and clinical data from 25 patients with PSD and 28 without PSD collected from April 2020 to June 2023 were evaluated. The between-group comparisons of dALFF variance and state properties were conducted using a combined approach of sliding window and clustering analysis. The correlation coefficients between abnormal dALFF variance and ALFF in the distinguished regions (DRs) and depression severity of PSD were calculated. Compared with that in patients without PSD, decreased dALFF variance in patients with PSD was mainly located in lower dALFF variance in multiple emotion-related brain regions. The dALFF was clustered into four states in all patients. In particular, state 3 was the remarkably distinct state that dwelled for more time in the PSD group. There was a negative correlation between decreased dALFF variance in the right parietal cortex and Hamilton Rating Scale for Depression (HAMD) scores in patients with PSD. ALFF values in the DRs showed no correlation with the HAMD score. Our results suggest that patients with PSD showed characteristic alterations in dynamic regional neural activity, which might be regarded as potential neural mechanisms of PSD. This study demonstrates the dynamic features of neural activity in PSD, which may advance future neuroimaging studies.

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Altered dynamic amplitude of low-frequency fluctuation between bipolar type I and type II in the depressive state

Cited by 7 publications

References 88 publications

Bidirectional Mendelian randomization study of psychiatric disorders and Parkinson’s disease

Bidirectional Mendelian randomization study of psychiatric disorders and Parkinson’s disease

Variations in olfactory function among bipolar disorder patients with different episodes and subtypes

Characteristic alterations of dynamic amplitude of low-frequency fluctuation in patients with post-stroke depression

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