Altered inter-hemispheric communication of default-mode and visual networks underlie etiology of primary insomnia

Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia

Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.

Sleep deprivation changes frequency-specific functional organization of the resting human brain

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Towards the neurobiology of insomnia: A systematic review of neuroimaging studies

Insomnia disorder signifies a major public health concern. The development of neuroimaging techniques has permitted to investigate brain mechanisms at a structural and functional level. The present systematic review aims at shedding light on functional, structural, and metabolic substrates of insomnia disorder by integrating the available published neuroimaging data. The databases PubMed, PsycARTICLES, PsycINFO, CINAHL and Web of Science were searched for case-control studies comparing neuroimaging data from insomnia patients and healthy controls. 85 articles were judged as eligible. For every observed finding of each study, the effect size was calculated from standardised mean differences, statistic parameters and figures, showing a marked heterogeneity that precluded a comprehensive quantitative analysis. From a qualitative point of view, considering the findings of significant group differences in the reported regions across the articles, this review highlights the major involvement of the anterior cingulate cortex, thalamus, insula, precuneus and middle frontal gyrus, thus supporting some central themes in the debate on the neurobiology of and offering interesting insights into the psychophysiology of sleep in this disorder.

Opposing brain signatures of sleep in task-based and resting-state conditions

Sleep and depression have a complex, bidirectional relationship, with sleep-associated alterations in brain dynamics and structure impacting a range of symptoms and cognitive abilities. Previous work describing these relationships has provided an incomplete picture by investigating only one or two types of sleep measures, depression, or neuroimaging modalities in parallel. We analyze the correlations between brainwide neural signatures of sleep, cognition, and depression in task and resting-state data from over 30,000 individuals from the UK Biobank and Human Connectome Project. Neural signatures of insomnia and depression are negatively correlated with those of sleep duration measured by accelerometer in the task condition but positively correlated in the resting-state condition. Our results show that resting-state neural signatures of insomnia and depression resemble that of rested wakefulness. This is further supported by our finding of hypoconnectivity in task but hyperconnectivity in resting-state data in association with insomnia and depression. These observations dispute conventional assumptions about the neurofunctional manifestations of hyper- and hypo-somnia, and may explain inconsistent findings in the literature.

Aberrant single-subject morphological cerebellar connectome in chronic insomnia

BACKGROUND

To systematically investigate the topological organisation of morphological networks of the cerebellum using structural MRI and examine their clinical relevance in chronic insomnia (CI).

METHODS

One hundred and one patients with CI and 102 healthy controls (HCs) were recruited in this study. Individual morphological networks of the cerebellum were constructed based on regional grey matter volume, and topologically characterised using weighted graph theory-based network approaches. Between-group comparisons were performed using permutation tests, and Spearman's correlation was used to examine the relationships between topological alterations and clinical variables.

RESULTS

Compared with HCs, patients with CI exhibited a lower normalised clustering coefficient. Locally, CI patients exhibited lower nodal efficiency in the cerebellar lobule VIIb and vermis regions, but higher nodal efficiency in the right cerebellar lobule VIIIa regions. No correlations were observed between network alterations and clinical variables.

CONCLUSIONS

Individual morphological network analysis provides a new strategy for investigating cerebellar morphometric changes in CI, and our findings may have important implications in establishing diagnostic and categorical biomarkers.

Alternations of interhemispheric functional connectivity in patients with optic neuritis using voxel-mirrored homotopic connectivity: A resting state fMRI study

BACKGROUND AND PURPOSE

We used the voxel-mirrored homotopic connectivity (VMHC) method to investigate brain interhemispheric functional connectivity changes in patients with optic neuritis (ON).

METHODS

A total of 22 ON patients and 22 healthy controls (HCs) closely matched in age, sex, and weight were enrolled. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI). Functional interaction between the hemispheres was assessed with the VMHC method. Correlation analysis was applied to explore the association between altered VMHC values in different brain areas and cognitive features. Receiver operating characteristic (ROC) curve analysis was applied to distinguish ON patients from HCs.

RESULTS

Compared with HCs, ON patients had obviously reduced VMHC values in the right superior temporal gyrus, left margin superior gyrus, right superior motor cortex, and left middle cingulate gyrus. a negative relationship between best-corrected visual acuity and VMHC values in left margin superior gyrus was found, besides, the VMHC values within the right superior motor cortex and the right superior temporal gyrus were also anti-correlated with the Hamilton Depression Scales. The ROC curve displayed high diagnostic values in those altered regions.

CONCLUSION

Abnormal VMHC values may reflect the underlying neuropathologic mechanism of ON.

Reduced homotopic interhemispheric connectivity in psychiatric disorders: evidence for both transdiagnostic and disorder specific features

There is considerable interest in the significance of structural and functional connections between the two brain hemispheres in terms of both normal function and in relation to psychiatric disorders. In recent years, many studies have used voxel mirrored homotopic connectivity analysis of resting state data to investigate the importance of connectivity between homotopic regions in the brain hemispheres in a range of neuropsychiatric disorders. The current review summarizes findings from these voxel mirrored homotopic connectivity studies in individuals with autism spectrum disorder, addiction, attention deficit hyperactivity disorder, anxiety and depression disorders, and schizophrenia, as well as disorders such as Alzheimer's disease, mild cognitive impairment, epilepsy, and insomnia. Overall, other than attention deficit hyperactivity disorder, studies across psychiatric disorders report decreased homotopic resting state functional connectivity in the default mode, attention, salience, sensorimotor, social cognition, visual recognition, primary visual processing, and reward networks, which are often associated with symptom severity and/or illness onset/duration. Decreased homotopic resting state functional connectivity may therefore represent a transdiagnostic marker for general psychopathology. In terms of disorder specificity, the extensive decreases in homotopic resting state functional connectivity in autism differ markedly from attention deficit hyperactivity disorder, despite both occurring during early childhood and showing extensive co-morbidity. A pattern of more posterior than anterior regions showing reductions in schizophrenia is also distinctive. Going forward, more studies are needed to elucidate the functions of these homotopic functional connections in both health and disorder and focusing on associations with general psychopathology, and not only on disorder specific symptoms.

Dynamic changes in fractional amplitude of low-frequency fluctuations in patients with chronic insomnia

BACKGROUND

Previous 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 METHODS

A 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.

RESULTS

Compared 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.

CONCLUSION

Dynamic 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.

Alcohol-induced brain deficit in alcohol dependence

Although numerous adverse effects of alcohol addiction on health, behavior, and brain function were widely reported, the neurobiological mechanism of alcohol dependence remains largely unknown. In this study, a total of twenty-nine patients with alcohol dependence and twenty-nine status-matched normal controls (NCs) were recruited. Percent amplitude of fluctuation (PerAF) was applied to identify alcohol-related brain activity deficits. We found that alcohol dependence was associated with widespread differences in the left orbitofrontal cortex, right higher visual cortex, right supramarginal gyrus, right postcentral gyrus, and bilateral cerebellum posterior lobe with decreased PerAF, but no brain areas with increased PerAF differences were found. ROC curve showed that decreased PerAF revealed extremely high discriminatory power with a high AUC value of 0.953, as well as a high degree of sensitivity (96.6%) and specificity (86.2%), in distinguishing patients with alcohol dependence from NCs. In the alcohol dependence group, the amount of daily alcohol consumption showed significant negative correlations with the right cerebellum posterior lobe and right higher visual cortex. These findings suggest that the cerebellar-visual-orbitofrontal circuit was disturbed by alcohol dependence. The proposed new method of PerAF may be served as a potential biomarker to identify the regional brain activity deficits of alcohol dependence.

Hippocampal atrophy in neurofunctional subfields in insomnia individuals

Objective

The aim of this study was to investigate the pattern of volume changes in neurofunctional hippocampal subfields in patients with insomnia and their associations with risk of development of insomnia.

Methods

A total of 120 patients with insomnia (78 females, 42 males; mean age ± standard deviation, 43.74 ± 13.02 years) and 120 good sleepers (67 females, 53 males; mean age, 42.69 ± 12.24 years) were recruited. The left hippocampus was segmented into anterior (L1), middle (L2), and posterior (L3) subregions. The right hippocampus was segmented into top anterior (R1), second top anterior (R2), middle (R3), posterior (R4), and last posterior (R5) subregions. Multivariate logistic regression was used to evaluate the associations of hippocampal volume (HV) of each subfield with the risk of the development of insomnia. Mediation analyses were performed to evaluate mediated associations among post-insomnia negative emotion, insomnia severity, and HV atrophy. A visual easy-to-deploy risk nomogram was used for individual prediction of risk of development of insomnia.

Results

Hippocampal volume atrophy was identified in the L1, R1, and R2 subregions. L1 and R2 volume atrophy each predisposed to an ~3-fold higher risk of insomnia (L1, odds ratio: 2.90, 95% confidence intervals: [1.24, 6.76], p = 0.014; R2, 2.72 [1.19, 6.20], p = 0.018). Anxiety fully mediates the causal path of insomnia severity leading to R1 volume atrophy with a positive effect. We developed a practical and visual competing risk-nomogram tool for individual prediction of insomnia risk, which stratifies individuals into different levels of insomnia risk with the highest prediction accuracy of 97.4% and an average C-statistic of 0.83.

Conclusion

Hippocampal atrophy in specific neurofunctional subfields was not only found to be associated with insomnia but also a significant risk factor predicting development of insomnia.

Graph-Theory-Based Degree Centrality Combined with Machine Learning Algorithms Can Predict Response to Treatment with Antipsychotic Medications in Patients with First-Episode Schizophrenia

Objectives

Schizophrenia (SCZ) is associated with disrupted functional brain connectivity, and antipsychotic medications are the primary and most commonly used treatment for schizophrenia. However, not all patients respond to antipsychotic medications.

Methods

The study is aimed at investigating whether the graph-theory-based degree centrality (DC), derived from resting-state functional MRI (rs-fMRI), can predict the treatment outcomes. rs-fMRI data from 38 SCZ patients were collected and compared with findings from 38 age- and gender-matched healthy controls (HCs). The patients were treated with antipsychotic medications for 16 weeks before undergoing a second rs-fMRI scan. DC data were processed using DPABI and SPM12 software.

Results

SCZ patients at baseline showed increased DC in the frontal and temporal gyrus, anterior cingulate cortex, and precuneus and reduced DC in bilateral subcortical gray matter structures. However, those abnormalities showed a clear renormalization after antipsychotic medication treatments. Support vector machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.2% (sensitivity 78.9%, specificity 89.5%, and area under the receiver operating characteristic curve (AUC) 0.925) for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the bilateral putamen, left inferior frontal gyrus, left middle occipital cortex, bilateral middle frontal gyrus, left cerebellum, left medial frontal gyrus, left inferior temporal gyrus, and left angular. Furthermore, the DC change within the bilateral putamen is negatively correlated with the symptom improvements after treatment.

Conclusions

Our study confirmed that graph-theory-based measures, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of antipsychotic medications.

The role of ascending arousal network in patients with chronic insomnia disorder

The ascending arousal system plays a crucial role in individuals' consciousness. Recently, advanced functional magnetic resonance imaging (fMRI) has made it possible to investigate the ascending arousal network (AAN) in vivo. However, the role of AAN in the neuropathology of human insomnia remains unclear. Our study aimed to explore alterations in AAN and its connections with cortical networks in chronic insomnia disorder (CID). Resting-state fMRI data were acquired from 60 patients with CID and 60 good sleeper controls (GSCs). Changes in the brain's functional connectivity (FC) between the AAN and eight cortical networks were detected in patients with CID and GSCs. Multivariate pattern analysis (MVPA) was employed to differentiate CID patients from GSCs and predict clinical symptoms in patients with CID. Finally, these MVPA findings were further verified using an external data set (32 patients with CID and 33 GSCs). Compared to GSCs, patients with CID exhibited increased FC within the AAN, as well as increased FC between the AAN and default mode, cerebellar, sensorimotor, and dorsal attention networks. These AAN-related FC patterns and the MVPA classification model could be used to differentiate CID patients from GSCs with 88% accuracy in the first cohort and 77% accuracy in the validation cohort. Moreover, the MVPA prediction models could separately predict insomnia (data set 1, R²  = .34; data set 2, R²  = .15) and anxiety symptoms (data set 1, R²  = .35; data set 2, R²  = .34) in the two independent cohorts of patients. Our findings indicated that AAN contributed to the neurobiological mechanism of insomnia and highlighted that fMRI-based markers and machine learning techniques might facilitate the evaluation of insomnia and its comorbid mental symptoms.

Connectomic disturbances underlying insomnia disorder and predictors of treatment response

Objective

Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy.

Materials and methods

51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up.

Results

Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change.

Conclusion

Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.

Centrality and interhemispheric coordination are related to different clinical/behavioral factors in attention deficit/hyperactivity disorder: a resting-state fMRI study

Eigenvector-Centrality (EC) has shown promising results in the field of Psychiatry, with early results also pertaining to ADHD. Parallel efforts have focused on the description of aberrant interhemispheric coordination in ADHD, as measured by Voxel-Mirrored-Homotopic-Connectivity (VMHC), with early evidence of altered Resting-State fMRI. A sample was collected from the ADHD200-NYU initiative: 86 neurotypicals and 89 participants with ADHD between 7 and 18 years old were included after quality control for motion. After preprocessing, voxel-wise EC and VMHC values between diagnostic groups were compared, and network-level values from 15 functional networks extracted. Age, ADHD severity (Connor's Parent Rating-Scale), IQ (Wechsler-Abbreviated-Scale), and right-hand dominance were correlated with EC/VMHC values in the whole sample and within groups, both at the voxel-wise and network-level. Motion was controlled by censoring time-points with Framewise-Displacement > 0.5 mm, as well as controlling for group differences in mean Framewise-Displacement values. EC was significantly higher in ADHD compared to neurotypicals in the left inferior Frontal lobe, Lingual gyri, Peri-Calcarine cortex, superior and middle Occipital lobes, right inferior Occipital lobe, right middle Temporal gyrus, Fusiform gyri, bilateral Cuneus, right Precuneus, and Cerebellum (FDR-corrected-p = 0.05). No differences were observed between groups in voxel-wise VMHC. EC was positively correlated with ADHD severity scores at the network level (at p-value < 0.01, Inattentive: Cerebellum rho = 0.273; Hyper/Impulsive: High-Visual Network rho = 0.242, Cerebellum rho = 0.273; Global Index Severity: High-Visual Network rho = 0.241, Cerebellum rho = 0.293). No differences were observed between groups for motion (p = 0.443). While EC was more related to ADHD psychopathology, VMHC was consistently and negatively correlated with age across all networks.

Real-Time fMRI Neurofeedback Training Changes Brain Degree Centrality and Improves Sleep in Chronic Insomnia Disorder: A Resting-State fMRI Study

Background

Chronic insomnia disorder (CID) is considered a major public health problem worldwide. Therefore, innovative and effective technical methods for studying the pathogenesis and clinical comprehensive treatment of CID are urgently needed.

Methods

Real-time fMRI neurofeedback (rtfMRI-NF), a new intervention, was used to train 28 patients with CID to regulate their amygdala activity for three sessions in 6 weeks. Resting-state fMRI data were collected before and after training. Then, voxel-based degree centrality (DC) method was used to explore the effect of rtfMRI-NF training. For regions with altered DC, we determined the specific connections to other regions that most strongly contributed to altered functional networks based on DC. Furthermore, the relationships between the DC value of the altered regions and changes in clinical variables were determined.

Results

Patients with CID showed increased DC in the right postcentral gyrus, Rolandic operculum, insula, and superior parietal gyrus and decreased DC in the right supramarginal gyrus, inferior parietal gyrus, angular gyrus, middle occipital gyrus, and middle temporal gyrus. Seed-based functional connectivity analyses based on the altered DC regions showed more details about the altered functional networks. Clinical scores in Pittsburgh sleep quality index, insomnia severity index (ISI), Beck depression inventory, and Hamilton anxiety scale decreased. Furthermore, a remarkable positive correlation was found between the changed ISI score and DC values of the right insula.

Conclusions

This study confirmed that amygdala-based rtfMRI-NF training altered the intrinsic functional hubs, which reshaped the abnormal functional connections caused by insomnia and improved the sleep of patients with CID. These findings contribute to our understanding of the neurobiological mechanism of rtfMRI-NF in insomnia treatment. However, additional double-blinded controlled clinical trials with larger sample sizes need to be conducted to confirm the effect of rtfMRI-NF from this initial study.

Orbitofrontal Cortex Functional Connectivity-Based Classification for Chronic Insomnia Disorder Patients With Depression Symptoms

Depression is a common comorbid symptom in patients with chronic insomnia disorder (CID). Previous neuroimaging studies found that the orbital frontal cortex (OFC) might be the core brain region linking insomnia and depression. Here, we used a machine learning approach to differentiate CID patients with depressive symptoms from CID patients without depressive symptoms based on OFC functional connectivity. Seventy patients with CID were recruited and subdivided into CID with high depressive symptom (CID-HD) and low depressive symptom (CID-LD) groups. The OFC functional connectivity (FC) network was constructed using the altered structure of the OFC region as a seed. A linear kernel SVM-based machine learning approach was carried out to classify the CID-HD and CID-LD groups based on OFC FC features. The predict model was further verified in a new cohort of CID group (n = 68). The classification model based on the OFC FC pattern showed a total accuracy of 76.92% (p = 0.0009). The area under the receiver operating characteristic curve of the classification model was 0.84. The OFC functional connectivity with reward network, salience network and default mode network contributed the highest weights to the prediction model. These results were further validated in an independent CID group with high and low depressive symptom (accuracy = 67.9%). These findings provide a potential biomarker for early diagnosis and intervention in CID patients comorbid with depression based on an OFC FC-based machine learning approach.

Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis

Objectives

To investigate the classification performance of support vector machine in mild traumatic brain injury (mTBI) from normal controls.

Methods

Twenty-four mTBI patients (15 males and 9 females; mean age, 38.88 ± 13.33 years) and 24 age and sex-matched normal controls (13 males and 11 females; mean age, 40.46 ± 11.4 years) underwent resting-state functional MRI examination. Seven imaging parameters, including amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), long-range functional connectivity density (FCD), and short-range FCD, were entered into the classification model to distinguish the mTBI from normal controls.

Results

The ability for any single imaging parameters to distinguish the two groups is lower than multiparameter combinations. The combination of ALFF, fALFF, DC, VMHC, and short-range FCD showed the best classification performance for distinguishing the two groups with optimal AUC value of 0.778, accuracy rate of 81.11%, sensitivity of 88%, and specificity of 75%. The brain regions with the highest contributions to this classification mainly include bilateral cerebellum, left orbitofrontal cortex, left cuneus, left temporal pole, right inferior occipital cortex, bilateral parietal lobe, and left supplementary motor area.

Conclusions

Multiparameter combinations could improve the classification performance of mTBI from normal controls by using the brain regions associated with emotion and cognition.

Abnormal corpus callosum induced by overt hepatic encephalopathy impairs interhemispheric functional coordination in cirrhosis patients

Background

Although overt hepatic encephalopathy (OHE) patients were shown to have bilaterally symmetrical structural and functional abnormalities in the whole brain, few studies have focused on the bilateral cerebral hemisphere commissural fibers and measured functional coordination between bilateral hemispheres. This study aimed to investigate the structural changes of the corpus callosum (CC) and interhemispheric functional coordination in patients with OHE and to test the hypothesis that abnormal CC induced by OHE impairs interhemispheric functional coordination in cirrhosis patients.

Methods

The microstructural integrity and the volumes of each subregion of the CC were analyzed by diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging. Voxel-mirrored homotopic connectivity (VMHC) was derived from resting-state functional magnetic resonance imaging (MRI).

Results

Compared with the healthy controls (HCs) and patients without hepatic encephalopathy (noHE), the OHE group showed decreased volumes in all subregions of the CC. In OHE patients, the decreased fractional anisotropy (FA) of CC-5 correlated with decreased VMHC in the middle occipital gyrus (MOG) and precuneus. The value of FA in CC-5 and the volumes of CC-3, CC-4, and CC-5 showed correlations with neuropsychological performance in patients with OHE.

Conclusions

These findings suggest that impairment of interhemispheric white matter pathways may disturb the functional connectivity associated with coordination and neurocognitive performance.

Brain network excitatory/inhibitory imbalance is a biomarker for drug-naive Rolandic epilepsy: A radiomics strategy

OBJECTIVE

Seizure occurs when the balance between excitatory and inhibitory (E/I) inputs to neurons is perturbed, resulting in abnormal electrical activity. This study investigated whether an existing E/I imbalance in neural networks is a useful diagnostic biomarker for Rolandic epilepsy by a resting-state dynamic causal modeling-based support vector machine (rs-DCM-SVM) algorithm.

METHODS

This multicenter study enrolled a discovery cohort (76 children with Rolandic epilepsy and 76 normal controls [NCs]) and a replication cohort (59 children with Rolandic epilepsy and 60 NCs). Spatial independent component analysis was used to seven canonical neural networks, and a total of 25 regions of interest were selected from these networks. The rs-DCM-SVM classifier was used for individual classification, consensus feature selection, and feature ranking.

RESULTS

The rs-DCM-SVM classifier showed that the E/I imbalance in brain networks is a useful neuroimaging biomarker for Rolandic epilepsy, with an accuracy of 88.2% and 81.5% and an area under curve of .92 and .83 in the discovery and the replication cohorts, respectively. Consensus brain regions with the highest contributions to the classification were located within the epilepsy-related networks, indicating that this classifier was suitable. Consensus functional connection pairs with the highest contributions to the classification were associated with an excitation network loop and an inhibition network loop. The excitation loop mediated the integration of advanced cognitive networks (subcortex, dorsal attention, default mode, executive control, and salience networks), whereas the inhibition loop was involved in the segregation of sensorimotor and language networks. The two loops showed functional segregation.

SIGNIFICANCE

Brain E/I imbalance has potential to serve as a biomarker for individual classification in children with Rolandic epilepsy, and might be an important mechanism for causing seizures and cognitive impairment in children with Rolandic epilepsy.

Decreased modulation of segregated SEEKING and selective attention systems in chronic insomnia

Sleep-related attentional bias and instinctual craving-sleep status may be associated with value-driven selective attention network and SEEKING system. We hypothesized that the two networks might be important components and underlie etiology of inability to initiate or/and maintain sleep in patients with chronic insomnia (PIs). Our aim is to investigate whether frequency-frequency couplings(temporal and spatial coupling, and differences of a set of imaging parameters) could elevate the sensibility to characterize the two insomnia-related networks in studying their relationships with sleep parameters and post-insomnia emotions. Forty-eight PIs and 48 status-matched good sleepers were requested to complete sleep and emotion-related questionnaires. Receiver operating characteristic curve was used to calculate the discriminatory power of a set of parameters. Granger causality and mediating causality analysis were used to address the causal relationships between the two networks and sleep/emotion-related parameters. Frequency-frequency couplings could characterize the two networks with high discriminatory power (AUC, 0.951; sensitivity, 87.5%; specificity, 95.8%), which suggested that the frequency-frequency couplings could be served as a useful biomarker to address the insomnia-related brain networks. Functional deficits of the SEEKING system played decreased mediator acting in post-insomnia negative emotions (decreased frequency-frequency coupling). Functional hyperarousal of the value-driven attention network played decreased mediator acting in sleep regulation (increased frequency-frequency coupling). Granger causality analysis showed decreased causal effect connectivity between and within the two networks. The between-network causal effect connectivity segregation played decreased mediator acting in sleep regulation (decreased connectivity). These findings suggest that the functional deficits and segregation of the two systems may underlie etiology of PIs.

[Small-world network of patients with primary insomnia: a resting-state functional magnetic resonance imaging study]

OBJECTIVE

To explore the changes of small-world network properties in patients with primary insomnia based on resting-state functional magnetic resonance imaging (rs-fMRI).

OBJECTIVE

The rs-MRI data and neurological scale data of 65 patients and 60 matched healthy controls were collected. The brain network was constructed using GRENTA software. SPSS software and network-based statistical analysis methods were used for statistical analysis.

OBJECTIVE

There was no significant difference between the two groups in terms of age, gender or education level (P > 0.05), but PSQI, HAMA and HAMD scale scores differed significantly between the two groups (P < 0.05). Both of the groups showed attributes of the small-world network. Compared with the control group, the patients with insomnia showed lower Cp, γ, Eloc, λ, connectivity, and σ of the small world network (P < 0.05).

OBJECTIVE

Patients with primary insomnia have lower global and local efficiencies than healthy individuals, and their ability to transmit information on the surface topology is impaired. Our data provide objective imaging evidences for the neuropathological mechanism of patients with primary insomnia.

Altered Percent Amplitude of Fluctuation in Healthy Subjects After 36 h Sleep Deprivation

Objective: To investigate regional brain activity alteration in healthy subjects in a sleep deprivation (SD) status relative to a rested wakefulness status using a percent amplitude of fluctuation (PerAF) method. Methods: A total of 20 healthy participants (12 males, 8 females; age, 22.25 ± 1.12 years) were recruited. All participants underwent attention tests and resting-state functional MRI scans during rested wakefulness before SD and after 36 h SD, respectively. The PerAF method was applied to identify SD-related regional brain activity alteration. A ROC curve was conducted to evaluate the ability of the PerAF method in distinguishing different sleep statuses. The relationships between SD-induced brain alterations and attention deficits were determined by Pearson correlation analysis. Results: SD resulted in a 2.23% decrease in accuracy rate and an 8.82% increase in reaction time. SD was associated with increased PerAF differences in the bilateral visual cortex and bilateral sensorimotor cortex, and was associated with decreased PerAF differences in bilateral dorsolateral prefrontal cortex and bilateral cerebellum posterior lobe. These SD-induced brain alterations exhibited a high discriminatory power of extremely high AUC values (0.993-1) in distinguishing the two statuses. The accuracy rate positively correlated with the bilateral cerebellum posterior lobe, and bilateral dorsolateral prefrontal cortex, and negatively correlated with the bilateral sensorimotor cortex. Conclusions: Acute SD could lead to an ~8% attention deficit, which was associated with regional brain activity deficits. The PerAF method might work as a potential sensitivity biomarker for identifying different sleep statuses.

Gradually Increased Interhemispheric Functional Connectivity During One Night of Sleep Deprivation

BACKGROUND

It is well known that circadian rhythms and sleep homeostasis contribute to a pronounced trough in sleepiness and behavioral performance at night. However, the underlying neuroimaging mechanisms remain unclear. How brain-function connectivity is modulated during sleep deprivation (SD) has been rarely examined.

METHODS

By increasing the number of scanning sessions during SD, the current study used voxel-mirrored homotopic connectivity (VMHC) to investigate dynamic changes in interhemispheric communication during one night of SD. Every 2 hours from 10 pm to 06 am (session 1, 10 pm; session 2, 12 am; session 3, 2 am; session 4, 4 am; session 5, 6 am), functional magnetic resonance-imaging data and Stanford Sleepiness Scale (SSS) scores were collected from 36 healthy participants with intermediate chronotype. Dynamic changes in SSS scores and VMHC were determined using one-way repeated-measure ANOVA with the false discovery-rate method to correct for multiple comparisons.

RESULTS

Significant time effects for VMHC were found mainly in the bilateral thalamus, bilateral superior temporal gyrus, and bilateral precentral gyrus. SSS scores and VMHC in these areas were both found to be monotonously increased during SD. Furthermore, significant positive associations were found between SSS valu and VMHC values in the left superior temporal and right superior gyri.

CONCLUSION

These findings might represent the dynamic modulation of circadian rhythm merely or the interaction effects of both circadian rhythm and sleep homeostasis on interhemispheric connectivity within the thalamus, default-mode network, and sensorimotor network. Our study provides more comprehensive information on how SD regulates brain connectivity between hemispheres and adds new evidence of neuroimaging correlates of increased sleepiness after SD.

Relapsing-Remitting Multiple Sclerosis Is Associated With Regional Brain Activity Deficits in Motor- and Cognitive-Related Brain Areas

Objective: To identify the abnormal regional spontaneous brain activity associated with relapsing-remitting multiple sclerosis (RRMS) using fractional amplitude of low-frequency fluctuation (fALFF) analysis and their relationships with clinical features.

Methods: A total of 26 RRMS (11 males, 15 females; age, 36.58 ± 10.82 years) and 27 status-matched healthy group (HGs; 12 males, 15 females; age, 35.85 ± 12.05 years) underwent an Expanded Disability Status Scale (EDSS) examination. fALFF was applied to evaluate the abnormal regional brain activity associated with RRMS. Pearson's correlation analysis was applied to calculate the correlations between the signal values of brain areas that exhibited abnormal fALFF values and clinical features. Receiver operating characteristic (ROC) curve was performed to evaluate the sensitivity and specificity of those altered brain areas to distinguish between RRMS and HGs.

Results: Compared with HGs, RRMS exhibited higher fALFF in the right cerebellum posterior lobe, left orbitofrontal cortex, left dorsolateral prefrontal cortex, bilateral supplementary motor area, and right fusiform gyrus and lower fALFF values in the left hippocampus and right precuneus. ROC revealed that these areas showed two good and five fair AUC values (0.77 ± 0.03, 0.729~0.822). However, four combinations with more than five brain regions received the same best discriminatory power with a sensitivity of 96.3% and a specificity of 88.5%. EDSS revealed a negative correlation with supplementary motor area (r = −0.395, p = 0.046).

Conclusions: RRMS is associated with abnormal regional brain activity deficits of motor- and cognitive-related areas. The fALFF parameter may serve as a potential biological marker to discriminate between the two groups.