Increased Dynamic Amplitude of Low Frequency Fluctuation in Primary Insomnia.
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ABSTRACT:
SUBMITTER: Meng X
PROVIDER: S-EPMC7344192 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Increased Dynamic Amplitude of Low Frequency Fluctuation in Primary Insomnia.
Meng Xianyun X Zheng Jianjun J Liu Yingpeng Y Yin Yi Y Hua Kelei K Fu Shishun S Wu Yunfan Y Jiang Guihua G
Frontiers in neurology 20200630
The physiological mechanism underlying primary insomnia (PI) is poorly understood. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a powerful tool to explore PI. However, previous studies ignore the dynamics of the brain activity. In the current study, we aimed to explore altered dynamic intrinsic brain activity in PI. Fifty-nine patients with PI and 47 matched healthy controls (HCs) were recruited and underwent resting-state fMRI. The variance of dynamic amplitude of l ...[more]
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Project description:BackgroundPrevious neuroimaging studies have mostly focused on changes in static functional connectivity in patients with chronic insomnia (CI) . Features of dynamic brain activity in patients with CI have rarely been described in detail. The present study investigated changes in dynamic intrinsic brain activity in patients with CI by dynamic fractional amplitude of low-frequency fluctuation (dfALFF) analysis.Materials and methodsA total of 30 patients with CI and 27 healthy controls (HCs) were enrolled. We compared dfALFF between these two groups, and examined the correlation between changes in dfALFF and clinical symptoms of CI. Multivariate pattern analysis was performed to differentiate patients with CI from HCs.ResultsCompared with HC subjects, patients with CI showed significantly increased dfALFF in the left insula, right superior temporal gyrus, left parahippocampal gyrus, right amygdala, and bilateral posterior lobes of the cerebellum. Moreover, dfALFF values in the left insula and left parahippocampal gyrus showed a positive correlation with Pittsburgh Sleep Quality Index scores. A logistic regression model was constructed that had 96.7% sensitivity, 80.0% specificity, and 83.0% overall accuracy for distinguishing patients with CI from HCs.ConclusionDynamic local brain activity showed increased instability in patients with CI. The variability in dfALFF in the limbic system and brain areas related to sleep/wakefulness was associated with insomnia symptoms. These findings may provide insight into the neuropathologic basis of CI.
| S-EPMC9744813 | biostudies-literature
Project description:The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.
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Project description:Background and purposePrevious studies have focused on the changes of dynamic and static functional connections in cerebral small vessel disease (CSVD). However, the dynamic characteristics of local brain activity are poorly understood. The purpose of this study was to investigate the dynamic cerebral activity changes in patients with CSVD using the dynamic amplitude of low-frequency fluctuation (d-ALFF).MethodsA total of 104 CSVD patients with cognitive impairment (CSVD-CI, n = 52) or normal cognition (CSVD-NC, n = 52) and 63 matched healthy controls (HCs) were included in this study. Every participant underwent magnetic resonance imaging scans and a battery of neuropsychological examinations. The dynamics of spontaneous brain activity were assessed using dynamic changes in the amplitude of low-frequency fluctuation (ALFF) with the sliding-window method. We used voxel-wise one-way analysis of variance (ANOVA) to compare dynamic ALFF variability among the three groups. Post-hoc t-tests were used to evaluate differences between each group pair. Finally, the brain regions with d-ALFF values with differences between CSVD subgroups were taken as regions of interest (ROI), and the d-ALFF values corresponding to the ROI were extracted for partial correlation analysis with memory.Results(1) There was no significant difference in age (p = 0.120), sex (p = 0.673) and education (p = 0.067) among CSVD-CI, CSVD-NC and HC groups, but there were significant differences Prevalence of hypertension and diabetes mellitus among the three groups (p < 10-3). There were significant differences in scores of several neuropsychological scales among the three groups (p < 10-3). (2) ANOVA and post-hoc t-test showed that there were dynamic abnormalities of spontaneous activity in several brain regions in three groups, mainly located in bilateral parahippocampal gyrus and bilateral hippocampus, bilateral insular and frontal lobes, and the static activity abnormalities in bilateral parahippocampal gyrus and bilateral hippocampal regions were observed at the same time, suggesting that bilateral parahippocampal gyrus and bilateral hippocampus may be the key brain regions for cognitive impairment caused by CSVD. (3) The correlation showed that d-ALFF in the bilateral insular was slightly correlated with the Mini-Mental State Examination (MMSE) score and disease progression rate. The d-ALFF value of the left postcentral gyrus was negatively correlated with the Clock Drawing Test (CDT) score (r = -0.416, p = 0.004), and the d-ALFF value of the right postcentral gyrus was negatively correlated with the Rey's Auditory Verbal Learning Test (RAVLT) word recognition (r = -0.320, p = 0.028).ConclusionThere is a wide range of dynamic abnormalities of spontaneous brain activity in patients with CSVD, in which the abnormalities of this activity in specific brain regions are related to memory and execution or emotion.
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Project description:Background Insomnia is considered one of the manifestations of sleep disorders, and its intensity is linked to the treatment effect or suicidal thoughts. Major depressive disorder (MDD) is classified into various subtypes due to heterogeneous symptoms. Melancholic MDD has been considered one of the most common subtypes with special sleep features. However, the brain functional mechanisms in melancholic MDD with insomnia remain unclear. Materials and methods Melancholic MDD and healthy controls (HCs, n = 46) were recruited for the study. Patients were divided into patients with melancholic MDD with low insomnia (mMDD-LI, n = 23) and patients with melancholic MDD with high insomnia (mMDD-HI, n = 30), according to the sleep disturbance subscale of the 17-item Hamilton Depression Rating Scale. The dynamic amplitude of low-frequency fluctuation was employed to investigate the alterations of brain activity among the three groups. Then, the correlations between abnormal dALFF values of brain regions and the severity of symptoms were investigated. Results Lower dALFF values were found in the mMDD-HI group in the right middle temporal gyrus (MTG)/superior temporal gyrus (STG) than in the mMDD-LI (p = 0.014) and HC groups (p < 0.001). Melancholic MDD groups showed decreased dALFF values than HC in the right middle occipital gyri (MOG)/superior occipital gyri (SOG), the right cuneus, the bilateral lingual gyrus, and the bilateral calcarine (p < 0.05). Lower dALFF values than HC in the left MOG/SOG and the left cuneus in melancholic MDD groups were found, but no significant difference was found between the mMDD-LI group and HC group (p = 0.079). Positive correlations between the dALFF values in the right MTG/STG and HAMD-SD scores (the sleep disturbance subscale of the HAMD-17) in the mMDD-HI group (r = 0.41, p = 0.042) were found. In the pooled melancholic MDD, the dALFF values in the right MOG/SOG and the right cuneus (r = 0.338, p = 0.019), the left MOG/SOG and the left cuneus (r = 0.299, p = 0.039), and the bilateral lingual gyrus and the bilateral calcarine (r = 0.288, p = 0.047) were positively correlated with adjusted HAMD scores. Conclusion The occipital cortex may be related to depressive symptoms in melancholic MDD. Importantly, the right MTG/STG may play a critical role in patients with melancholic MDD with more severe insomnia.
| S-EPMC9441487 | biostudies-literature
Project description:BackgroundStatic changes in local brain activity in patients suffering from amyotrophic lateral sclerosis (ALS) have been studied. However, the dynamic characteristics of local brain activity are poorly understood. Whether dynamic alterations could differentiate patients with ALS from healthy controls (HCs) remains unclear.MethodsA total of 54 patients with ALS (mean age = 48.71 years, male/female = 36/18) and 54 (mean age = 48.30 years, male/female = 36/18) HCs underwent magnetic resonance imaging scans. To depict static alterations in cortical activity, amplitude of low-frequency fluctuations (ALFF) which measures the total power of regional activity was computed. Dynamic ALFF (d-ALFF) from all subjects was calculated using a sliding-window approach. Statistical differences in ALFF and d-ALFF between both groups were used as features to explore whether they could differentiate ALS from HC through support vector machine method.ResultsIn contrast with HCs, patients with ALS displayed increased ALFF in the right inferior temporal gyrus and bilateral frontal gyrus and decreased ALFF in the left middle occipital gyrus and left precentral gyrus. Furthermore, patients with ALS demonstrated lower d-ALFF in widespread regions, including the right lingual gyrus, left superior temporal gyrus, bilateral precentral gyrus, and left paracentral lobule by comparison with HCs. In addition, the ALFF in the left superior orbitofrontal gyrus had a tendency of correlation with ALSFRS-R score and disease progression rate. The classification performance in distinguishing ALS was higher with both features of ALFF and d-ALFF than that with a single approach.ConclusionsDecreased dynamic brain activity in the precentral gyrus, paracentral gyrus, lingual gyrus, and temporal regions was found in the ALS group. The combined ALFF and d-ALFF could distinguish ALS from HCs with a higher accuracy than ALFF and d-ALFF alone. These findings may provide important evidence for understanding the neuropathology underlying ALS.
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Project description:Various functional imaging tools have been used to detect epileptic activity in the neural network underlying mesial temporal lobe epilepsy (mTLE). In the present fMRI study, a data-driven approach was employed to map interictal epileptic activity in mTLE patients by measuring the amplitude of low-frequency fluctuation (ALFF) of the blood oxygen level-dependent (BOLD) signal. Twenty-four left mTLE patients and 26 right mTLE patients were investigated by comparing with 25 healthy subjects. In the patients, the regions showing increased ALFF were consistently distributed in the mesial temporal lobe, thalamus, and a few of other cortical and subcortical structures composing a mesial temporal epilepsy network proposed previously, while the regions showing decreased ALFF were mostly located in the areas of so-called default-mode network. Data of simultaneous EEG-fMRI from a portion of the patients suggested that the increases in ALFF might be associated with the interictal epileptic activity. Individual analyses based on statistic parametric mapping revealed a moderate sensitivity and a fairly high specificity for the lateralization of unilateral mTLE. We conclude that the ALFF analysis may provide a useful tool in fMRI study of epilepsy.
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Project description:Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.
| S-EPMC10025115 | biostudies-literature
Project description:New neuroimaging techniques have led to significant advancements in our understanding of cerebral mechanisms of primary insomnia. However, the neuronal low-frequency oscillation remains largely uncharacterized in chronic primary insomnia (CPI). In this study, the amplitude of low-frequency fluctuation (ALFF), a data-driven method based on resting-state functional MRI, was used to examine local intrinsic activity in 27 patients with CPI and 27 age-, sex-, and education-matched healthy controls. We examined neural activity in two frequency bands, slow-4 (between 0.027 and 0.073 Hz) and slow-5 (0.010-0.027 Hz), because blood-oxygen level dependent (BOLD) fluctuations in different low-frequency bands may present different neurophysiological manifestations that pertain to a spatiotemporal organization. The ALFF associated with the primary disease effect was widely distributed in the cerebellum posterior lobe (CPL), dorsal and ventral prefrontal cortex, anterior cingulate cortex, precuneus, somatosensory cortex, and several default-mode sub-regions. Several brain regions (i.e., the right cerebellum, anterior lobe, and left putamen) exhibited an interaction between the frequency band and patient group. In the slow-5 band, increased ALFF of the right postcentral gyrus/inferior parietal lobule (PoCG/IPL) was enhanced in association with the sleep quality (? = 0.414, P = 0.044) and anxiety index (? = 0.406, P = 0.049) of the CPI patients. These findings suggest that during chronic insomnia, the intrinsic functional plasticity primarily responds to the hyperarousal state, which is the loss of inhibition in sensory-informational processing. Our findings regarding an abnormal sensory input and intrinsic processing mechanism might provide novel insight into the pathophysiology of CPI. Furthermore, the frequency factor should be taken into consideration when exploring ALFF-related clinical manifestations.
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Project description:BackgroundGeneralized tonic-clonic seizures (GTCS) are associated with significant disability and sudden unexpected death when they cannot be controlled. We aimed to explore the underlying neural substrate of the different responses to antiseizure drugs between the seizure-free (SF) and non-seizure-free (NSF) patients with GTCS through the amplitude of low-frequency fluctuation (ALFF) method.MethodsWe calculated ALFF among the SF group, NSF group, and healthy controls (HCs) by collecting resting-state functional magnetic resonance imaging (rs-fMRI) data. One-way ANOVA was used to compare the ALFF of the three groups, and post-hoc analysis was done at the same time. Pearson's correlation analysis between ALFF in the discrepant brain areas and the clinical characteristics (disease course and age of onset of GTCS) was calculated after then.ResultsA significant group effect was found in the right fusiform gyrus (R.FG), left fusiform gyrus (L.FG), left middle occipital gyrus (L.MOG), right inferior frontal gyrus (R.IFG), right precentral gyrus (R.PreG), right postcentral gyrus (R.PostG), and left calcarine sulcus (L.CS). The SF and NSF groups both showed increased ALFF in all discrepant brain areas compared to HCs except the R.IFG in the NSF group. Significantly higher ALFF in the bilateral FG and lower ALFF in the R.IFG were found in the NSF group compared to the SF group.ConclusionsHigher ALFF in the bilateral FG were found in the NSF group compared to the SF and HC groups. Our findings indicate that abnormal brain activity in the FG may be one potential neural substrate to interpret the failure of seizure control in patients with GTCS.
| S-EPMC9010667 | biostudies-literature