Increased brain entropy of resting-state fMRI mediates the relationship between depression severity and mental health-related quality of life in late-life depressed elderly

Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial

BACKGROUND

Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD.

METHODS

A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes.

RESULTS

A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels.

LIMITATIONS

Sample size is small with only marginal clinical effect.

CONCLUSION

These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression.

Changed brain entropy and functional connectivity patterns induced by electroconvulsive therapy in majoy depression disorder

OBJECTIVE

Our objective is to innovatively integrate both linear and nonlinear characteristics of brain signals in Electroconvulsive Therapy (ECT) research, with the goal of uncovering deeper insights into the pathogenesis of Major Depressive Disorder (MDD) and identifying novel targets for other physical intervention therapies.

METHODS

We measured brain entropy (BEN) in 42 MDD patients and 42 matched healthy controls (HC) using rs-fMRI data. Brain regions that differed significantly in patients with MDD before and after ECT were extracted. Then, we use these brain regions as seed points to investigate the differences in whole-brain resting-state functional connectivity (RSFC) patterns before and after ECT.

RESULTS

Compared to HCs, patients had higher BEN levels in the right precuneus (PCUN.R) and right angular gyrus (ANG.R). After ECT, patients had lower BEN levels in the PCUN.R and ANG.R. Compared with before ECT, patients showed significantly increased RSFC after ECT between the PCUN.R and right middle temporal gyrus and ANG.R. Significantly increased RSFC was observed between the ANG.R and right middle frontal gyrus and right supramarginal gyrus after ECT.

CONCLUSION

Combining the linear and nonlinear characteristics of brain signals can effectively explore the pathogenesis of depression and provide new targets for ECT.

Brain network entropy, depression, and quality of life in people with traumatic brain injury and seizure disorders

OBJECTIVE

Traumatic brain injury (TBI) often precedes the onset of epileptic (ES) or psychogenic nonepileptic seizures (PNES) with depression being a common comorbidity. The relationship between depression severity and quality of life (QOL) may be related to resting-state network complexity. We investigated these relationships in adults with TBI-only, TBI + ES, or TBI + PNES using Sample Entropy (SampEn), a measure of physiologic signals complexity.

METHODS

Adults with TBI-only (n = 60), TBI + ES (n = 21), or TBI + PNES (n = 56) completed the Beck Depression Inventory-II (BDI-II; depression symptom severity) and QOL in Epilepsy (QOLIE-31) assessments and underwent resting-state functional magnetic resonance imaging (rs-fMRI). SampEn values derived from six resting state functional networks were calculated per participant. Effects of group, network, and group-by-network-interactions for SampEn were investigated with a mixed-effects model. We examined relationships between BDI-II, QOL, and SampEn of each of the networks.

RESULTS

Groups did not differ in age, but there was a higher proportion of women with TBI + PNES (p = 0.040). TBI + ES and TBI-only groups did not differ in BDI-II or QOLIE-31 scores, while the TBI + PNES group scored worse on both measures. The fixed effects of the model revealed significant differences in SampEn values across networks (lower SampEn for the frontoparietal network compared to other networks). The likelihood ratio test for group-by-network-interactions was significant (p = 0.033). BDI-II was significantly negatively associated with Overall QOL scale scores in all groups, and significantly negatively associated with network SampEn values only in the TBI + PNES group.

SIGNIFICANCE

Only TBI + PNES had significant relationships between depression symptom severity and network SampEn values indicating that the resting state network complexity is related to depression severity in this group but not in TBI + ES or TBI-only.

PLAIN LANGUAGE SUMMARY

The brain has a complex network of internal connections. How well these connections work may be affected by TBI and seizures and may underlie mental health symptoms including depression; the worse the depression, the worse the quality of life. Our study compared brain organization in people with TBI, people with epilepsy after TBI, and people with nonepileptic seizures after TBI. Only people with nonepileptic seizures after TBI showed a relationship between how organized their brain connections were and how bad was their depression. We need to better understand these relationships to develop more impactful, effective treatments.

Task-induced changes in brain entropy

Entropy indicates irregularity of a dynamic system, with higher entropy indicating higher irregularity and more transit states. In the human brain, regional brain entropy (BEN) has been increasingly assessed using resting state fMRI (rs-fMRI), while changes of regional BEN during task-based fMRI have been scarcely studied. The purpose of this study is to characterize task-induced regional BEN alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only (task BEN) and then compared to BEN of rs-fMRI (resting BEN). Moreover, BEN was separately calculated from the control blocks of the task-fMRI runs (control BEN) and compared to task BEN. Finally, control BEN was compared to resting BEN to test for residual task effects in the control condition. With respect to resting state, task performance unanimously induced BEN reduction in the peripheral cortical area and BEN increase in the centric part of the sensorimotor and perception networks. Control compared to resting BEN showed similar entropy alterations, suggesting large residual task effects. Task compared to control BEN was characterized by reduced entropy in occipital, orbitofrontal, and parietal regions.

Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI

BACKGROUND

Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD).

METHODS

We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation.

RESULTS

We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide.

CONCLUSION

Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.

EEG Network Analysis of Depressive Emotion Interference Spatial Cognition Based on a Simulated Robotic Arm Docking Task

Depressive emotion (DE) refers to clinically relevant depressive symptoms without meeting the diagnostic criteria for depression. Studies have demonstrated that DE can cause spatial cognition impairment. However, the brain network mechanisms underlying DE interference spatial cognition remain unclear. This study aimed to reveal the differences in brain network connections between DE and healthy control (HC) groups during resting state and a spatial cognition task. The longer operation time of the DE group during spatial cognition task indicated DE interference spatial cognition. In the resting state stage, the DE group had weaker network connections in theta and alpha bands than the HC group had. Specifically, the electrodes in parietal regions were hubs of the differential networks, which are related to spatial attention. Moreover, in docking task stages, the left frontoparietal network connections in delta, beta, and gamma bands were stronger in the DE group than those of the HC group. The enhanced left frontoparietal connections in the DE group may be related to brain resource reorganization to compensate for spatial cognition decline and ensure the completion of spatial cognition tasks. Thus, these findings might provide new insights into the neural mechanisms of depressive emotion interference spatial cognition.

Common hyper-entropy patterns identified in nicotine smoking, marijuana use, and alcohol use based on uni-drug dependence cohorts

Substance use disorders present similar behaviors and psychopathologies related to impaired decision making/inhibition control and information processing, suggesting common alterations in frontal and limbic brain areas. To test this hypothesis, we identified three uni-substance use cohorts with dependence to only one type of substance from the Human Connectome Project: marijuana dependence, nicotine dependence, and alcohol dependence. Fifty-nine marijuana uses, 34 nicotine smokers, 35 alcohol drinkers, and their age and sex-matched non-substance use controls were identified. We used brain entropy mapping to probe brain alterations in substance use disorders. Compared to non-substance use individuals, all three substance use disorder cohorts had increased brain entropy. Marijuana dependence and nicotine dependence showed overlapped hyper-brain entropy in bilateral dorso-lateral prefrontal cortex, anterior cingulate cortex, and right insula. Hyper-brain entropy in marijuana dependence and alcohol dependence overlap in left insula, left doso-lateral prefrontal cortex, and posterior cingulate. Hyper-brain entropy in nicotine dependence and alcohol dependence overlap only in left dorso-lateral prefrontal cortex. Hyper-brain entropy in those areas was correlated with increased impulsivity or reduced inhibition control in substance use disorder but not in controls. Drug dependence is associated with hyper-brain entropy in the prefrontal cortex and the meso-limbic system, independent of a specific addictive drug. Brain entropy in this circuit provides a sensitive marker to detect brain and behavioral alterations in substance user disorders.

Neural complexity EEG biomarkers of rapid and post-rapid ketamine effects in late-life treatment-resistant depression: a randomized control trial

Ketamine is an effective intervention for treatment-resistant depression (TRD), including late-in-life (LL-TRD). The proposed mechanism of antidepressant effects of ketamine is a glutamatergic surge, which can be measured by electroencephalogram (EEG) gamma oscillations. Yet, non-linear EEG biomarkers of ketamine effects such as neural complexity are needed to capture broader systemic effects, represent the level of organization of synaptic communication, and elucidate mechanisms of action for treatment responders. In a secondary analysis of a randomized control trial, we investigated two EEG neural complexity markers (Lempel-Ziv complexity [LZC] and multiscale entropy [MSE]) of rapid (baseline to 240 min) and post-rapid ketamine (24 h and 7 days) effects after one 40-min infusion of IV ketamine or midazolam (active control) in 33 military veterans with LL-TRD. We also studied the relationship between complexity and Montgomery-Åsberg Depression Rating Scale score change at 7 days post-infusion. We found that LZC and MSE both increased 30 min post-infusion, with effects not localized to a single timescale for MSE. Post-rapid effects of reduced complexity with ketamine were observed for MSE. No relationship was observed between complexity and reduction in depressive symptoms. Our findings support the hypothesis that a single sub-anesthetic ketamine infusion has time-varying effects on system-wide contributions to the evoked glutamatergic surge in LL-TRD. Further, changes to complexity were observable outside the time-window previously shown for effects on gamma oscillations. These preliminary results have clinical implications in providing a functional marker of ketamine that is non-linear, amplitude-independent, and represents larger dynamic properties, providing strong advantages over linear measures in highlighting ketamine's effects.

Aberrant brain entropy in posttraumatic stress disorder comorbid with major depressive disorder during the coronavirus disease 2019 pandemic

Aim

Previously, neuroimaging studies on comorbid Posttraumatic-Major depression disorder (PTSD-MDD) comorbidity found abnormalities in multiple brain regions among patients. Recent neuroimaging studies have revealed dynamic nature on human brain activity during resting state, and entropy as an indicator of dynamic regularity may provide a new perspective for studying abnormalities of brain function among PTSD-MDD patients. During the COVID-19 pandemic, there has been a significant increase in the number of patients with PTSD-MDD. We have decided to conduct research on resting-state brain functional activity of patients who developed PTSD-MDD during this period using entropy.

Methods

Thirty three patients with PTSD-MDD and 36 matched TCs were recruited. PTSD and depression symptoms were assessed using multiple clinical scales. All subjects underwent functional magnetic resonance imaging (fMRI) scans. And the brain entropy (BEN) maps were calculated using the BEN mapping toolbox. A two-sample t-test was used to compare the differences in the brain entropy between the PTSD-MDD comorbidity group and TC group. Furthermore, correlation analysis was conducted between the BEN changes in patients with PTSD-MDD and clinical scales.

Results

Compared to the TCs, PTSD-MDD patients had a reduced BEN in the right middle frontal orbital gyrus (R_MFOG), left putamen, and right inferior frontal gyrus, opercular part (R_IFOG). Furthermore, a higher BEN in the R_MFOG was related to higher CAPS and HAMD-24 scores in the patients with PTSD-MDD.

Conclusion

The results showed that the R_MFOG is a potential marker for showing the symptom severity of PTSD-MDD comorbidity. Consequently, PTSD-MDD may have reduced BEN in frontal and basal ganglia regions which are related to emotional dysregulation and cognitive deficits.

Task-induced changes in brain entropy

Entropy indicates irregularity of a dynamic system with higher entropy indicating higher irregularity and more transit states. In the human brain, regional entropy has been increasingly assessed using resting state fMRI. Response of regional entropy to task has been scarcely studied. The purpose of this study is to characterize task-induced regional brain entropy (BEN) alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block-design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only and then compared to BEN of rsfMRI. Compared to resting state, task-performance unanimously induced BEN reduction in the peripheral cortical area including both the task activated regions and task non-specific regions such as the task negative area and BEN increase in the centric part of the sensorimotor and perception networks. Task control condition showed large residual task effects. After controlling the task non-specific effects using the control BEN vs task BEN comparison, regional BEN showed task specific effects in target regions.

Amygdala-based functional connectivity mediates the relationship between thought control ability and trait anxiety

Thought control ability (TCA) refers to the ability to exclude unwanted thoughts. There has been consistent evidence on the protective effect of TCA on anxiety, that higher TCA is associated with lower anxiety. However, the underlying neural mechanism remains unclear. In this study, with a large sample (N = 495), we investigated how seed-based resting-state functional connectivity (RSFC) mediates the relationship between TCA and anxiety. Our behaviour results replicated previous findings that TCA is negatively associated with trait anxiety after controlling for gender, age, and depression. More importantly, the RSFC results revealed that TCA is negatively associated with the left amygdala - left frontal pole (LA-LFP), left amygdala - left inferior temporal gyrus (LA-LITG), and left hippocampus - left inferior frontal gyrus (LH-LIFG) connectivity. In addition, a mediation analysis demonstrated that the LA-LFP and LA-LITG connectivity in particular mediated the influence of TCA on trait anxiety. Overall, our study extends previous research by revealing the neural bases underlying the protective effect of TCA on anxiety and pinpointing specific mediating RSFC pathways. Future studies could explore whether targeted TCA training (behavioural or neural) can help alleviate anxiety.

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy

Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ.

Effects of escitalopram therapy on functional brain controllability in major depressive disorder

Antidepressant drugs are the mainstay of treatment for patients with major depressive disorders (MDD). Given the critical role of the underlying neural control mechanism in the physiopathology of depression, this study aims to investigate the effects of escitalopram, a type of antidepressant drug, on the changes of functional brain controllability throughout the escitalopram treatment for MDD. We collected resting-state functional magnetic resonance imaging data from 20 unmedicated major depressive patients at baseline (visit 1, pre-treatment), one week (visit 2, 1-week after the onset of the treatment) and six weeks (visit 3, after the 6-week escitalopram treatment). Our results revealed that the global average and modal controllability of MDD patients were significantly larger and smaller, respectively, compared to healthy subjects (P < 0.01). Furthermore, the modal controllability rank of the frontoparietal network in depression patients was also significantly smaller than the healthy subjects (P < 0.01). However, throughout the escitalopram treatment, the global average and modal controllability, and the controllability of the default mode network and frontoparietal network of MDD patients were consistently changed to the healthy subjects' level. Our results also showed that the changes of global average and modal controllability measures can predict the improvements of clinical scores of the MDD patients as the escitalopram treatment advanced (P < 0.05). In conclusion, this study reveals promising brain controllability-based biomarkers to mechanistically understand and predict the effects of the escitalopram treatment for depression and maybe extended to predict and understand the effects of other interventions for other neurological and psychiatric diseases.

Optimized Multiscale Entropy Model Based on Resting-State fMRI for Appraising Cognitive Performance in Healthy Elderly

Many studies have indicated that an entropy model can capture the dynamic characteristics of resting-state functional magnetic resonance imaging (rfMRI) signals. However, there are problems of subjectivity and lack of uniform standards in the selection of model parameters relying on experience when using the entropy model to analyze rfMRI. To address this issue, an optimized multiscale entropy (MSE) model was proposed to confirm the parameters objectively. All healthy elderly volunteers were divided into two groups, namely, excellent and poor, by the scores estimated through traditional scale tests before the rfMRI scan. The parameters of the MSE model were optimized with the help of sensitivity parameters such as receiver operating characteristic (ROC) and area under the ROC curve (AUC) in a comparison study between the two groups. The brain regions with significant differences in entropy values were considered biomarkers. Their entropy values were regarded as feature vectors to use as input for the probabilistic neural network in the classification of cognitive scores. Classification accuracy of 80.05% was obtained using machine learning. These results show that the optimized MSE model can accurately select the brain regions sensitive to cognitive performance and objectively select fixed parameters for MSE. This work was expected to provide the basis for entropy to test the cognitive scores of the healthy elderly.

Dynamical Complexity Fingerprints of Occupation-Dependent Brain Functional Networks in Professional Seafarers

The complexity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data has been applied for exploring cognitive states and occupational neuroplasticity. However, there is little information about the influence of occupational factors on dynamic complexity and topological properties of the connectivity networks. In this paper, we proposed a novel dynamical brain complexity analysis (DBCA) framework to explore the changes in dynamical complexity of brain activity at the voxel level and complexity topology for professional seafarers caused by long-term working experience. The proposed DBCA is made up of dynamical brain entropy mapping analysis and complex network analysis based on brain entropy sequences, which generate the dynamical complexity of local brain areas and the topological complexity across brain areas, respectively. First, the transient complexity of voxel-wise brain map was calculated; compared with non-seafarers, seafarers showed decreased dynamic entropy values in the cerebellum and increased values in the left fusiform gyrus (BA20). Further, the complex network analysis based on brain entropy sequences revealed small-worldness in terms of topological complexity in both seafarers and non-seafarers, indicating that it is an inherent attribute of human the brain. In addition, seafarers showed a higher average path length and lower average clustering coefficient than non-seafarers, suggesting that the information processing ability is reduced in seafarers. Moreover, the reduction in efficiency of seafarers suggests that they have a less efficient processing network. To sum up, the proposed DBCA is effective for exploring the dynamic complexity changes in voxel-wise activity and region-wise connectivity, showing that occupational experience can reshape seafarers’ dynamic brain complexity fingerprints.

Shared increased entropy of brain signals across patients with different mental illnesses: A coordinate-based activation likelihood estimation meta-analysis

Entropy is a measurement of brain signal complexity. Studies have found increased/decreased entropy of brain signals in psychiatric patients. There is no consistent conclusion regarding the relationship between the entropy of brain signals and mental illness. Therefore, this meta-analysis aimed to identify consistent abnormalities in the brain signal entropy in patients with different mental illnesses. We conducted a systematic search to collect resting-state functional magnetic resonance imaging (rs-fMRI) studies in patients with psychiatric disorders. This work identified 9 eligible rs-fMRI studies, which included a total of 14 experiments, 67 activation foci, and 1383 subjects. We tested the convergence across their findings by using the activation likelihood estimation method. P-value maps were corrected by using cluster-level family-wise error p < 0.05 and permuting 2000 times. Results showed that patients with different psychiatric disorders shared commonly increased entropy of brain signals in the left inferior and middle frontal gyri, and the right fusiform gyrus, cuneus, precuneus. No shared alterations were found in the subcortical regions and cerebellum in the patient group. Our findings suggested that the increased entropy of brain signals in the cortex, not subcortical regions and cerebellum, might have associations with the pathophysiology across mental illnesses. This meta-analysis study provided the first comprehensive understanding of the abnormality in brain signal complexity across patients with different psychiatric disorders.

Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI

The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus (p < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the optimized MSE model with the machine learning model can accurately confirm the epileptogenic hemisphere by rfMRI. With the powerful information interaction capabilities of 5G communication, the epilepsy side-fixing algorithm that requires computing power can be integrated into a cloud platform. The demand side only needs to upload patient data to the service platform to realize the preoperative assessment of epilepsy.

Psilocybin therapy increases cognitive and neural flexibility in patients with major depressive disorder

Psilocybin has shown promise for the treatment of mood disorders, which are often accompanied by cognitive dysfunction including cognitive rigidity. Recent studies have proposed neuropsychoplastogenic effects as mechanisms underlying the enduring therapeutic effects of psilocybin. In an open-label study of 24 patients with major depressive disorder, we tested the enduring effects of psilocybin therapy on cognitive flexibility (perseverative errors on a set-shifting task), neural flexibility (dynamics of functional connectivity or dFC via functional magnetic resonance imaging), and neurometabolite concentrations (via magnetic resonance spectroscopy) in brain regions supporting cognitive flexibility and implicated in acute psilocybin effects (e.g., the anterior cingulate cortex, or ACC). Psilocybin therapy increased cognitive flexibility for at least 4 weeks post-treatment, though these improvements were not correlated with the previously reported antidepressant effects. One week after psilocybin therapy, glutamate and N-acetylaspartate concentrations were decreased in the ACC, and dFC was increased between the ACC and the posterior cingulate cortex (PCC). Surprisingly, greater increases in dFC between the ACC and PCC were associated with less improvement in cognitive flexibility after psilocybin therapy. Connectome-based predictive modeling demonstrated that baseline dFC emanating from the ACC predicted improvements in cognitive flexibility. In these models, greater baseline dFC was associated with better baseline cognitive flexibility but less improvement in cognitive flexibility. These findings suggest a nuanced relationship between cognitive and neural flexibility. Whereas some enduring increases in neural dynamics may allow for shifting out of a maladaptively rigid state, larger persisting increases in neural dynamics may be of less benefit to psilocybin therapy.

The Application of Complexity Analysis in Brain Blood-Oxygen Signal

One of the daunting features of the brain is its physiology complexity, which arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various cognitive functions. As a reflection of the complexity of brain physiology, the complexity of brain blood-oxygen signal has been frequently studied in recent years. This paper reviews previous literature regarding the following three aspects: (1) whether the complexity of the brain blood-oxygen signal can serve as a reliable biomarker for distinguishing different patient populations; (2) which is the best algorithm for complexity measure? And (3) how to select the optimal parameters for complexity measures. We then discuss future directions for blood-oxygen signal complexity analysis, including improving complexity measurement based on the characteristics of both spatial patterns of brain blood-oxygen signal and latency of complexity itself. In conclusion, the current review helps to better understand complexity analysis in brain blood-oxygen signal analysis and provide useful information for future studies.

Interindividual Signatures of fMRI Temporal Fluctuations

The complexity and variability of human brain activity, such as quantified from Functional Magnetic Resonance Imaging (fMRI) time series, have been widely studied as potential markers of healthy and pathological states. However, the extent to which fMRI temporal features exhibit stable markers of inter-individual differences in brain function across healthy young adults is currently an open question. In this study, we draw upon two widely used time-series measures-a nonlinear complexity measure (sample entropy; SampEn) and a spectral measure of low-frequency content (fALFF)-to capture dynamic properties of resting-state fMRI in a large sample of young adults from the Human Connectome Project. We observe that these two measures are closely related, and that both generate reproducible patterns across brain regions over four different fMRI runs, with intra-class correlations of up to 0.8. Moreover, we find that both metrics can uniquely differentiate subjects with high identification rates (ca. 89%). Canonical correlation analysis revealed a significant relationship between multivariate brain temporal features and behavioral measures. Overall, these findings suggest that regional profiles of fMRI temporal characteristics may provide stable markers of individual differences, and motivate future studies to further probe relationships between fMRI time series metrics and behavior.

Mental health and music engagement: review, framework, and guidelines for future studies

Is engaging with music good for your mental health? This question has long been the topic of empirical clinical and nonclinical investigations, with studies indicating positive associations between music engagement and quality of life, reduced depression or anxiety symptoms, and less frequent substance use. However, many earlier investigations were limited by small populations and methodological limitations, and it has also been suggested that aspects of music engagement may even be associated with worse mental health outcomes. The purpose of this scoping review is first to summarize the existing state of music engagement and mental health studies, identifying their strengths and weaknesses. We focus on broad domains of mental health diagnoses including internalizing psychopathology (e.g., depression and anxiety symptoms and diagnoses), externalizing psychopathology (e.g., substance use), and thought disorders (e.g., schizophrenia). Second, we propose a theoretical model to inform future work that describes the importance of simultaneously considering music-mental health associations at the levels of (1) correlated genetic and/or environmental influences vs. (bi)directional associations, (2) interactions with genetic risk factors, (3) treatment efficacy, and (4) mediation through brain structure and function. Finally, we describe how recent advances in large-scale data collection, including genetic, neuroimaging, and electronic health record studies, allow for a more rigorous examination of these associations that can also elucidate their neurobiological substrates.

Cognitive remediation therapy modulates intrinsic neural activity in patients with major depression

BACKGROUND

Cognitive impairment is a core feature of major depressive disorder (MDD). Cognitive remediation may improve cognition in MDD, yet so far, the underlying neural mechanisms are unclear. This study investigated changes in intrinsic neural activity in MDD after a cognitive remediation trial.

METHODS

In a longitudinal design, 20 patients with MDD and pronounced cognitive deficits and 18 healthy controls (HC) were examined using resting-state functional magnetic resonance imaging. MDD patients received structured cognitive remediation therapy (CRT) over 5 weeks. The whole-brain fractional amplitude of low-frequency fluctuations was computed before the first and after the last training session. Univariate methods were used to address regionally-specific effects, and a multivariate data analysis strategy was employed to investigate functional network strength (FNS).

RESULTS

MDD patients significantly improved in cognitive function after CRT. Baseline comparisons revealed increased right caudate activity and reduced activity in the left frontal cortex, parietal lobule, insula, and precuneus in MDD compared to HC. In patients, reduced FNS was found in a bilateral prefrontal system at baseline (p < 0.05, uncorrected). In MDD, intrinsic neural activity increased in right inferior frontal gyrus after CRT (p < 0.05, small volume corrected). Left inferior parietal lobule, left insula, left precuneus, and right caudate activity showed associations with cognitive improvement (p < 0.05, uncorrected). Prefrontal network strength increased in patients after CRT, but this increase was not associated with improved cognitive performance.

CONCLUSIONS

Our findings support the role of intrinsic neural activity of the prefrontal cortex as a possible mediator of cognitive improvement following CRT in MDD.

Acute Alcohol Intake Produces Widespread Decreases in Cortical Resting Signal Variability in Healthy Social Drinkers

BACKGROUND

Acute alcohol intoxication has wide-ranging neurobehavioral effects on psychomotor, attentional, inhibitory, and memory-related cognitive processes. These effects are mirrored in disruption of neural metabolism, functional activation, and functional network coherence. Metrics of intraregional neural dynamics such as regional signal variability (RSV) and brain entropy (BEN) may capture unique aspects of neural functional capacity in healthy and clinical populations; however, alcohol's influence on these metrics is unclear. The present study aimed to elucidate the influence of acute alcohol intoxication on RSV and to clarify these effects with subsequent BEN analyses.

METHODS

26 healthy adults between 25 and 45 years of age (65.4% women) participated in 2 counterbalanced sessions. In one, participants consumed a beverage containing alcohol sufficient to produce a breath alcohol concentration of 0.08 g/dl. In the other, they consumed a placebo beverage. Approximately 35 minutes after beverage consumption, participants completed a 9-minute resting-state fMRI scan. Whole-brain, voxel-wise standard deviation was used to assess RSV, which was compared between sessions. Within clusters displaying alterations in RSV, sample entropy was calculated to assess BEN.

RESULTS

Compared to the placebo, alcohol intake resulted in widespread reductions in RSV in the bilateral middle frontal, right inferior frontal, right superior frontal, bilateral posterior cingulate, bilateral middle temporal, right supramarginal gyri, and bilateral inferior parietal lobule. Within these clusters, significant reductions in BEN were found in the bilateral middle frontal and right superior frontal gyri. No effects were noted in subcortical or cerebellar areas.

CONCLUSIONS

Findings indicate that alcohol intake produces diffuse reductions in RSV among structures associated with attentional processes. Within these structures, signal complexity was also reduced in a subset of frontal regions. Neurobehavioral effects of acute alcohol consumption may be partially driven by disruption of intraregional neural dynamics among regions involved in higher-order cognitive and attentional processes.

Temporal regularity of cerebral activity at rest correlates with slowness of reaction times in intellectual disability

OBJECTIVE

Intellectual disability (ID) is described as a general slowness in behavior and an inadequacy in adaptive skills. The present study examines whether behavioral slowness in ID could originate from abnormal complexity in brain signals.

METHODS

Participants (N = 29) performed a reaction times (RTs) task assessing their individual information processing speeds. Half of the participants had moderate intellectual disability (intelligence quotient (IQ) < 70). Continuous electroencephalogram recording during the resting period was used to quantify brain signal complexity by approximate entropy estimation (ApEn).

RESULTS

For all participants, a negative correlation between RTs and IQ was found, with longer RTs coinciding with lower IQ. This behavioral slowness in ID was associated with increased temporal regularity in electrocortical brain signals.

CONCLUSIONS

Behavioral slowness in ID subjects is closely related to lower brain signal complexity.

SIGNIFICANCE

Brain signal ApEn is shown to correspond with processing speed for the first time: in ID participants, the higher the regularity in brain signals at rest, the slower RTs will be in the active state. ID should be understood as a lack of lability in the cortical transition to the active state, weakening the efficiency of adaptive behavior.

Individual Cortical Entropy Profile: Test-Retest Reliability, Predictive Power for Cognitive Ability, and Neuroanatomical Foundation

The entropy profiles of cortical activity have become novel perspectives to investigate individual differences in behavior. However, previous studies have neglected foundational aspects of individual entropy profiles, that is, the test-retest reliability, the predictive power for cognitive ability in out-of-sample data, and the underlying neuroanatomical basis. We explored these issues in a large young healthy adult dataset (Human Connectome Project, N = 998). We showed the whole cortical entropy profile from resting-state functional magnetic resonance imaging is a robust personalized measure, while subsystem profiles exhibited heterogeneous reliabilities. The limbic network exhibited lowest reliability. We tested the out-of-sample predictive power for general and specific cognitive abilities based on reliable cortical entropy profiles. The default mode and visual networks are most crucial when predicting general cognitive ability. We investigated the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. Cortical thickness and structural connectivity explained spatial variations in the group-averaged entropy profile. Cortical folding and myelination in the attention and frontoparietal networks determined predominantly individual cortical entropy profile. This study lays foundations for brain-entropy-based studies on individual differences to understand cognitive ability and related pathologies. These findings broaden our understanding of the associations between neural structures, functional dynamics, and cognitive ability.

Altered Brain Entropy as a predictor of antidepressant response in major depressive disorder

OBJECTIVE

To explore the alterations and value of brain entropy (BEN) in major depressive disorder (MDD).

METHODS

85 MDD patients and 45 matched normal controls were recruited. MDD was diagnosed based on Diagnostic and Statistical Manual of Mental Disorders, 4th ed (DSM-IV). Symptoms of depression were assessed using the Hamilton depression scale-24 (HAMD-24) at baseline and follow-up (after 8-week treatment). All subjects underwent functional magnetic resonance imaging (fMRI) scans at baseline, and 30 MDD patients completed scans at 8th week. Whole brain BEN maps at each session was calculated using the BEN mapping toolbox.

RESULTS

The 8-week antidepressant treatment improved symptoms for all MDD patients. As compared to normal controls, MDD showed reduced BEN in medial orbitofrontal cortex (MOFC)/subgenual cingulate cortex (sgACC), but increased BEN in motor cortex (MC). In MDD patients, higher baseline BEN in MOFC/sgACC and lower baseline BEN in temporal cortex (TC) were associated with higher baseline HAMD scores; higher baseline BEN in MOFC/sgACC and hippocampus were associated with greater reduction of HAMD scores after 8-week medication; greater reduction of HAMD scores after 8-week medication was correlated with greater BEN reduction in MOFC/sgACC but were correlated with less BEN reduction in MC, TC, fusiform gyrus (FG) and visual cortex (VC).

CONCLUSION

The results highlighted MOFC/sgACC BEN as a potential marker for the prediction of MDD diagnosis and treatment effect. MDD might have increased MOFC/sgACC BEN but reduced BEN in visual and sensory-motor circuits corresponding to the imbalanced emotional and sensory-motor information processing. Reversing this unbalanced BEN would improve disease conditions in MDD.

Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis

Background: At present, laboratory blood tests to support major depressive disorder (MDD) diagnosis are not available. This study aimed to screen potential mRNAs for peripheral blood biomarkers and novel pathophysiology of MDD. Methods: The present study utilized public data from two mRNA microarray datasets to analyze the hub genes changes related to MDD. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes (DEGs) were performed. Finally, some potential mRNA quality biomarkers for hub gene expression in blood were identified. Results: A total of 25 significantly co-upregulated DEGs and 98 co-downregulated DEGs were obtained from two datasets. The pathway enrichment analyses showed that co-upregulated genes were significantly enriched in the regulation of cell-matrix adhesion and mitochondrial membrane permeability which were involved in the apoptotic process. Co-downregulated genes were mainly involved in the neutrophil activation which in turn was involved in the immune response, degranulation and cell-mediated immunity, positive regulation of immune response, the Toll-like receptor signaling pathway, and the NOD-like receptor signaling pathway. From the PPI network, 14 hub genes were obtained. Among them, the subnetworks of PLCG1, BCL2A1, TLR8, FADD, and TLR4 screened out from our study have been shown to play a role in immune and inflammation responses. Discussion: The potential molecular mechanisms that have been identified simultaneously include innate immunity, neuroinflammation, and neurotrophic factors for synapse function and development.

Effect of Electroacupuncture and Counseling on Sub-Threshold Depression: A Study Protocol for a Multicenter Randomized Controlled Trial

BACKGROUND

Sub-threshold depression is common and could impair function, as well as increase the risk of developing major depression. Despite evidence of efficacy for electroacupuncture (EA) and counseling in the treatment of sub-threshold depression, the sample size is insufficient and the level of evidence remains low. This study aims to evaluate the effectiveness of sub-threshold depression treatments by comparing the treatment effects among EA, counseling, and combination therapy, as well as to further study their mechanism.

METHODS

This study is a multicenter, randomized, single blind clinical trial that will be conducted in settings at four clinical centers in China. The randomized controlled trial (RCT) will examine the effectiveness of EA intervention, compared with counseling and combination therapy. A total of 138 sub-threshold depression patients (18 to 55 years of age with Beck Depression Inventory (BDI-II) score ≥ 14 points and Hamilton Depression Scale (HAMD-17) score: 7 points ≤ HAMD total score <17 points) will be recruited. The participants will be randomly assigned to receive the above treatments. The interventions will be delivered over a 6-week period (EA: 3 times a week for 6 weeks; 30 min a session. Counseling: once a week for 6 weeks; 50-60 min a session). The primary outcome measure will be the HAMD-17; BDI-II. The secondary outcome measures will be: Self-rating Depression Scale (SDS), Self-rating Anxiety Scale (SAS), and Pittsburgh Sleep Quality Index (PSQI). The assessments will occur at baseline, 2, 4, and 6 weeks and a follow-up period. Recruitment will commence in March 2020 and is anticipated to occur over a 2-year period.

DISCUSSION

This study intends to conduct a multicenter randomized controlled trial to compare the effectiveness among EA, counseling and the combined therapy in the treatment of patients with sub-threshold depression, and to further study the mechanisms of effect.

CHINESE CLINICAL TRIAL REGISTRY REGISTRATION

www.chictr.org.cn/, identifier ChiCTR1900028530.

The rise and fall of MRI studies in major depressive disorder

Structural and functional brain alterations are common in patients with major depressive disorder (MDD). In this review, we assessed the recent literature (1995-2018) on the structural and functional magnetic resonance imaging (MRI) studies of MDD. Despite the growing number of MRI studies on MDD, reverse inference is not possible as MRI scans cannot be used to aid in the diagnosis or treatment planning of patients with MDD. Hence, researchers must develop "bridges" to overcome the reverse inference fallacy in order to build effective tools for MDD diagnostics. From our findings, we proposed that the "bridges" may be built using multidisciplinary technologies, such as artificial intelligence, multimodality imaging, and nanotheranostics, allowing for the further study of MDD at the biological level. In return, the "bridges" will aid in the development of future diagnostics for MDD and other mental disorders.