Neuronal population activity in the olivocerebellum encodes the frequency of essential tremor in mice and patients.

Essential tremor (ET) is the most prevalent movement disorder, characterized primarily by action tremor, an involuntary rhythmic movement with a specific frequency. However, the neuronal mechanism underlying the coding of tremor frequency remains unexplored. Here, we used in vivo electrophysiology, optogenetics, and simultaneous motion tracking in the Grid2dupE3 mouse model to investigate whether and how neuronal activity in the olivocerebellum determines the frequency of essential tremor. We report that tremor frequency was encoded by the temporal coherence of population neuronal firing within the olivocerebellums of these mice, leading to frequency-dependent cerebellar oscillations and tremors. This mechanism was precise and generalizable, enabling us to use optogenetic stimulation of the deep cerebellar nuclei to induce frequency-specific tremors in wild-type mice or alter tremor frequencies in tremor mice. In patients with ET, we showed that deep brain stimulation of the thalamus suppressed tremor symptoms but did not eliminate cerebellar oscillations measured by electroencephalgraphy, indicating that tremor-related oscillations in the cerebellum do not require the reciprocal interactions with the thalamus. Frequency-disrupting transcranial alternating current stimulation of the cerebellum could suppress tremor amplitudes, confirming the frequency modulatory role of the cerebellum in patients with ET. These findings offer a neurodynamic basis for the frequency-dependent stimulation of the cerebellum to treat essential tremor.

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.

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.

A Scalable ECG Identification System Based on Locality-Sensitive Hashing.

Electrocardiograms (ECGs) have the inherent property of being intrinsic and dynamic and are shown to be unique among individuals, making them promising as a biometric trait. Although many ECG biometric recognition approaches have demonstrated accurate recognition results in small enrollment sets, they can suffer from performance degradation when many subjects are enrolled. This study proposes an ECG biometric identification system based on locality-sensitive hashing (LSH) that can accommodate a large number of registrants while maintaining satisfactory identification accuracy. By incorporating the concept of LSH, the identity of an unknown subject can be recognized without performing vector comparisons for all registered subjects. Moreover, a kernel density estimator-based method is used to exclude unregistered subjects. The ECGs of 285 subjects from the PTB dataset were used to evaluate the proposed scheme's performance. Experimental results demonstrated an IR and EER of 99% and 4%, respectively, when Nen/Nid = 15/3.

Para-toluenesulfonamide, a novel potent carbonic anhydrase inhibitor, improves hypoxia-induced metastatic breast cancer cell viability and prevents resistance to αPD-1 therapy in triple-negative breast cancer.

Overexpression of the hypoxia-induced transmembrane enzyme carbonic anhydrase IX (CA9) has been associated with poor prognosis and chemoresistance in aggressive breast cancer. This study aimed to investigate the involvement of CA9 in the anti-tumor activity of para-toluenesulfonamide (PTS) and elucidate its mechanism of action against breast cancer both in vitro and in vivo. MCF-7 and MDA-MB-231 breast cancer cells were treated with PTS or subjected to hypoxic conditions using cobalt chloride (CoCl2), with acetazolamide serving as a positive control. Additionally, 4T1 breast cancer cell allograft mice were co-treated with PTS and α-programmed cell death 1 (αPD-1) monoclonal antibody for one month. The results demonstrated that PTS effectively reduced cell viability and reversed migration ability in MCF-7 and MDA-MB-231 cells under CoCl2-induced hypoxia. Furthermore, PTS upregulated the expression of apoptosis-related proteins and downregulated CA9, hypoxia-inducible factor-1α (HIF-1α), and vascular endothelial growth factor (VEGF) proteins, possibly through modulation of p38 MAPK and ERK1/2 phosphorylated proteins. In the animal model, PTS100 inhibited tumor growth and lung metastasis in mammary tumor allograft mice, exhibiting synergistic effects when combined with αPD-1 therapy. Collectively, our findings suggest that PTS inhibits breast cancer growth and metastasis through the p38 MAPK/ERK1/2 pathway. Moreover, PTS may have the potential to prevent the development of resistance to αPD-1 therapy in breast cancer.

Review of EEG-based neurofeedback as a therapeutic intervention to treat depression.

Depression, or major depressive disorder, is a common mental disorder that affects individuals' behavior, mood, and physical health, and its prevalence has increased during the lockdowns implemented to curb the COVID-19 pandemic. There is an urgent need to update the treatment recommendations for mental disorders during such crises. Conventional interventions to treat depression include long-term pharmacotherapy and cognitive behavioral therapy. Electroencephalogram-neurofeedback (EEG-NF) training has been suggested as a non-invasive option to treat depression with minimal side effects. In this systematic review, we summarize the recent literature on EEG-NF training for treating depression. The 12 studies included in our final sample reported that despite several issues related to EEG-NF practices, patients with depression showed significant cognitive, clinical, and neural improvements following EEG-NF training. Given its low cost and the low risk of side effects due to its non-invasive nature, we suggest that EEG-NF is worth exploring as an augmented tool for patients who already receive standard medications but remain symptomatic, and that EEG-NF training may be an effective intervention tool that can be utilized as a supplementary treatment for depression. We conclude by providing some suggestions related to experimental designs and standards to improve current EEG-NF training practices for treating depression.

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.

A Scalable Open-Set ECG Identification System Based on Compressed CNNs.

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize "open-set" identification. With the help of its scalable structure and "transfer learning," new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy.

State-of-Charge Estimation for Lithium-Ion Batteries Using Residual Convolutional Neural Networks.

State-of-charge (SOC) is a relative quantity that describes the ratio of the remaining capacity to the present maximum available capacity. Accurate SOC estimation is essential for a battery-management system. In addition to informing the user of the expected usage until the next recharge, it is crucial for improving the utilization efficiency and service life of the battery. This study focuses on applying deep-learning techniques, and specifically convolutional residual networks, to estimate the SOC of lithium-ion batteries. By stacking the values of multiple measurable variables taken at many time instants as the model inputs, the process information for the voltage or current generation, and their interrelations, can be effectively extracted using the proposed convolutional residual blocks, and can simultaneously be exploited to regress for accurate SOCs. The performance of the proposed network model was evaluated using the data obtained from a lithium-ion battery (Panasonic NCR18650PF) under nine different driving schedules at five ambient temperatures. The experimental results demonstrated an average mean absolute error of 1.260%, and an average root-mean-square error of 0.998%. The number of floating-point operations required to complete one SOC estimation was 2.24 × 106. These results indicate the efficacy and performance of the proposed approach.

ECG-based Biometric Recognition without QRS Segmentation: A Deep Learning-Based Approach.

Electrocardiogram (ECG)-based identification systems have been widely studied in the literature. Usually, an ECG trace needs to be segmented according to the detected R peaks to enable feature extraction from the ECGs of duration equal to nearly one cardiac cycle. Beat averaging should also be applied to reduce the influence of inter-beat variation on the extracted features and identification accuracy. Either detecting R peaks or collecting extra heartbeats for averaging will inevitably lead to a delay in the identification process. This paper proposes a deep learning-based ECG biometric identification scheme that allows identity recognition using a random ECG segment without needing R-peak detection and beat averaging. Moreover, the problem of being vulnerable to unregistered subjects in an identification system is also addressed. Experimental results demonstrated that an identification rate of 99.1% for an identification system having 235 enrollees with an equal error rate of 8.08% was achieved.

Effects of Visfatin on Intracellular Mechanics and Catabolism in Human Primary Chondrocytes through Glycogen Synthase Kinase 3ß Inactivation.

Osteoarthritis (OA) is still a recalcitrant musculoskeletal disease on account of its complex biochemistry and mechanical stimulations. Apart from stimulation by external mechanical forces, the regulation of intracellular mechanics in chondrocytes has also been linked to OA development. Recently, visfatin has received significant attention because of the clinical finding of the positive correlation between its serum/synovial level and OA progression. However, the precise mechanism involved is still unclear. This study determined the effect of visfatin on intracellular mechanics and catabolism in human primary chondrocytes isolated from patients. The intracellular stiffness of chondrocytes was analyzed by the particle-tracking microrheology method. It was shown that visfatin damages the microtubule and microfilament networks to influence intracellular mechanics to decrease the intracellular elasticity and viscosity via glycogen synthase kinase 3ß (GSK3ß) inactivation induced by p38 signaling. Further, microtubule network destruction in human primary chondrocytes is predominantly responsible for the catabolic effect of visfatin on the cyclooxygenase 2 upregulation. The present study shows a more comprehensive interpretation of OA development induced by visfatin through biochemical and biophysical perspectives. Finally, the role of GSK3ß inactivation, and subsequent regulation of intracellular mechanics, might be considered as theranostic targets for future drug development for OA.

FOXM1 is required for small cell lung cancer tumorigenesis and associated with poor clinical prognosis.

Small cell lung cancer (SCLC) continues to cause poor clinical outcomes due to limited advances in sustained treatments for rapid cancer cell proliferation and progression. The transcriptional factor Forkhead Box M1 (FOXM1) regulates cell proliferation, tumor initiation, and progression in multiple cancer types. However, its biological function and clinical significance in SCLC remain unestablished. Analysis of the Cancer Cell Line Encyclopedia and SCLC datasets in the present study disclosed significant upregulation of FOXM1 mRNA in SCLC cell lines and tissues. Gene set enrichment analysis (GSEA) revealed that FOXM1 is positively correlated with pathways regulating cell proliferation and DNA damage repair, as evident from sensitization of FOXM1-depleted SCLC cells to chemotherapy. Furthermore, Foxm1 knockout inhibited SCLC formation in the Rb1fl/flTrp53fl/flMycLSL/LSL (RPM) mouse model associated with increased levels of neuroendocrine markers, Ascl1 and Cgrp, and decrease in Yap1. Consistently, FOXM1 depletion in NCI-H1688 SCLC cells reduced migration and enhanced apoptosis and sensitivity to cisplatin and etoposide. SCLC with high FOXM1 expression (N = 30, 57.7%) was significantly correlated with advanced clinical stage, extrathoracic metastases, and decrease in overall survival (OS), compared with the low-FOXM1 group (7.90 vs. 12.46 months). Moreover, the high-FOXM1 group showed shorter progression-free survival after standard chemotherapy, compared with the low-FOXM1 group (3.90 vs. 8.69 months). Our collective findings support the utility of FOXM1 as a prognostic biomarker and potential molecular target for SCLC.

Depression Scale Prediction with Cross-Sample Entropy and Deep Learning.

A two-stage deep learning-based scheme is presented to predict the Hamilton Depression Scale (HAM-D) in this study. First, the cross-sample entropy (CSE) that allows assessing the degree of similarity of two data series are evaluated for the 90 brain regions of interest partitioned according to Automated Anatomical Labeling. The obtained CSE maps are then converted to 3D CSE volumes to serve as the inputs to the deep learning network models for the HAM-D scale level classification and prediction. The efficacy of the proposed scheme was illustrated by the resting-state functional magnetic resonance imaging data from 38 patients. From the results, the root mean square errors for the HAM-D scale prediction obtained during training, validation, and testing were 2.73, 2.66, and 2.18, which were less than those of a scheme having only a regression stage.

Drosophila Brain Functional Data Analysis: A Unified Framework.

A unified framework for the analysis of fluorescence data taken by a two-photon imaging system is presented. As in the processing of blood-oxygen-level-dependent signals of functional magnetic resonance imaging, the acquired functional images have to be co-registered with a structural brain atlas before delineating the regions activated by a given stimulus. The voxels whose calcium traces are highly correlated with the predicted responses are demarcated without the need for subjective reasoning. Experimental data acquired while presenting olfactory stimuli are used to demonstrate the efficacy of the proposed schemes. The results indicate that the functional images of a Drosophila individual can be normalized into a standard stereotactic space, and the expected brain regions can be delineated adequately. This framework provides an opportunity to enable the development of a Drosophila functional connectome database.

All-Optical Volumetric Physiology for Connectomics in Dense Neuronal Structures.

All-optical physiology (AOP) manipulates and reports neuronal activities with light, allowing for interrogation of neuronal functional connections with high spatiotemporal resolution. However, contemporary high-speed AOP platforms are limited to single-depth or discrete multi-plane recordings that are not suitable for studying functional connections among densely packed small neurons, such as neurons in Drosophila brains. Here, we constructed a 3D AOP platform by incorporating single-photon point stimulation and two-photon high-speed volumetric recordings with a tunable acoustic gradient-index (TAG) lens. We demonstrated the platform effectiveness by studying the anterior visual pathway (AVP) of Drosophila. We achieved functional observation of spatiotemporal coding and the strengths of calcium-sensitive connections between anterior optic tubercle (AOTU) sub-compartments and >70 tightly assembled 2-µm bulb (BU) microglomeruli in 3D coordinates with a single trial. Our work aids the establishment of in vivo 3D functional connectomes in neuron-dense brain areas.

Millisecond two-photon optical ribbon imaging for small-animal functional connectome study.

We developed a high-speed two-photon optical ribbon imaging system, which combines galvo-mirrors for an arbitrary curve scan on a lateral plane and a tunable acoustic gradient-index lens for a 100 kHz-1 MHz axial scan. The system provides micrometer/millisecond spatiotemporal resolutions, which enable continuous readout of functional dynamics from small and densely packed neurons in a living adult Drosophila brain. Compared to sparse sampling techniques, the ribbon imaging modality avoids motion artifacts. Combined with a Drosophila anatomical connectome database, which is the most complete among all model animals, this technique paves the way toward establishing whole-brain functional connectome.

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.

BACKGROUND: Entropy analysis is a computational method used to quantify the complexity in a system, and loss of brain complexity is hypothesized to be related to mental disorders. Here, we applied entropy analysis to the resting-state functional magnetic resonance imaging (rs-fMRI) signal in subjects with late-life depression (LLD), an illness combined with emotion dysregulation and aging effect. METHODS: A total of 35 unremitted depressed elderly and 22 control subjects were recruited. Multiscale entropy (MSE) analysis was performed in the entire brain, 90 automated anatomical labeling-parcellated ROIs, and five resting networks in each study participant. LIMITATIONS: Due to ethical concerns, all the participants were under medication during the study. RESULTS: Regionally, subjects with LLD showed decreased entropy only in the right posterior cingulate gyrus but had universally increased entropy in affective processing (putamen and thalamus), sensory, motor, and temporal nodes across different time scales. We also found higher entropy in the left frontoparietal network (FPN), which partially mediated the negative correlation between depression severity and mental components of the quality of life, reflecting the possible neural compensation during depression treatment. CONCLUSION: MSE provides a novel and complementary approach in rs-fMRI analysis. The temporal-spatial complexity in the resting brain may provide the adaptive variability beneficial for the elderly with depression.

ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning.

Biometric technologies offer much convenience over the conventional approaches to identity recognition, but security and privacy concerns also accompany their applications. In this paper, an electrocardiogram (ECG)-based identification scheme is proposed to relieve such concerns. With the help of a deep learning (DL) technique, the identity of an unknown beat bundle can be determined without the need for biometric template construction. Thus, the disclosure of the physiological and pathological condition of an individual from his/her stolen templates will no longer be possible. Furthermore, the problem of being vulnerable to unregistered subjects in this DL-based recognition system is also addressed. Experiments with real and synthesized ECGs are used to illustrate the efficacy of the proposed scheme. An identification rate of 97.84% for the 200 registered subjects with a false-positive identification rate of 0.69% under the attack of 1,000 synthesized single-lead ECGs was achieved.

Sample Entropy of High Frequency Oscillations for Epileptogenic Zone Localization.

For epileptic patients whose seizures are poorly controlled with medication, removing the brain region responsible for seizure onset is a treatment option. This requires the epileptogenic zone (EZ) to be accurately delineated. In this paper, a two-stage approach for EZ delineation is proposed. The algorithm starts by detecting events of high-frequency oscillations (HFOs) directly from the multi-channel intracranial electroencephalograms (iEEGs). The sample entropy is then computed for each of their channels that will be used for determining the channels correlated with the EZ. The performance of our proposed method is evaluated using the receiver operating characteristic curve analysis, and the results indicate that our proposed approach can provide an accurate estimation of the EZ.