TORONTO: A trial-oriented multidimensional psychometric testing algorithm.

Bayesian adaptive methods for sensory threshold determination were conceived originally to track a single threshold. When applied to the testing of vision, they do not exploit the spatial patterns that underlie thresholds at different locations in the visual field. Exploiting these patterns has been recognized as key to further improving visual field test efficiency. We present a new approach (TORONTO) that outperforms other existing methods in terms of speed and accuracy. TORONTO generalizes the QUEST/ZEST algorithm to estimate simultaneously multiple thresholds. After each trial, without waiting for a fully determined threshold, the trial-oriented approach updates not only the location currently tested but also all other locations based on patterns in a reference data set. Since the availability of reference data can be limited, techniques are developed to overcome this limitation. TORONTO was evaluated using computer-simulated visual field tests: In the reliable condition (false positive [FP] = false negative [FN] = 3%), the median termination and root mean square error (RMSE) of TORONTO was 153 trials and 2.0 dB, twice as fast with equal accuracy as ZEST. In the FP = FN = 15% condition, TORONTO terminated in 151 trials and was 2.2 times faster than ZEST with better RMSE (2.6 vs. 3.7 dB). In the FP = FN = 30% condition, TORONTO achieved 4.2 dB RMSE in 148 trials, while all other techniques had > 6.5 dB RMSE and terminated much slower. In conclusion, TORONTO is a fast and accurate algorithm for determining multiple thresholds under a wide range of reliability and subject conditions.

Improving the robustness of the Sequentially Optimized Reconstruction Strategy (SORS) for visual field testing.

Perimetry, or visual field test, estimates differential light sensitivity thresholds across many locations in the visual field (e.g., 54 locations in the 24-2 grid). Recent developments have shown that an entire visual field may be relatively accurately reconstructed from measurements of a subset of these locations using a linear regression model. Here, we show that incorporating a dimensionality reduction layer can improve the robustness of this reconstruction. Specifically, we propose to use principal component analysis to transform the training dataset to a lower dimensional representation and then use this representation to reconstruct the visual field. We named our new reconstruction method the transformed-target principal component regression (TTPCR). When trained on a large dataset, our new method yielded results comparable with the original linear regression method, demonstrating that there is no underfitting associated with parameter reduction. However, when trained on a small dataset, our new method used on average 22% fewer trials to reach the same error. Our results suggest that dimensionality reduction techniques can improve the robustness of visual field testing reconstruction algorithms.

Using Fused Data from Perimetry and Optical Coherence Tomography to Improve the Detection of Visual Field Progression in Glaucoma.

Perimetry and optical coherence tomography (OCT) are both used to monitor glaucoma progression. However, combining these modalities can be a challenge due to differences in data types. To overcome this, we have developed an autoencoder data fusion (AEDF) model to learn compact encoding (AE-fused data) from both perimetry and OCT. The AEDF model, optimized specifically for visual field (VF) progression detection, incorporates an encoding loss to ensure the interpretation of the AE-fused data is similar to VF data while capturing key features from OCT measurements. For model training and evaluation, our study included 2504 longitudinal VF and OCT tests from 140 glaucoma patients. VF progression was determined from linear regression slopes of longitudinal mean deviations. Progression detection with AE-fused data was compared to VF-only data (standard clinical method) as well as data from a Bayesian linear regression (BLR) model. In the initial 2-year follow-up period, AE-fused data achieved a detection F1 score of 0.60 (95% CI: 0.57 to 0.62), significantly outperforming (p < 0.001) the clinical method (0.45, 95% CI: 0.43 to 0.47) and the BLR model (0.48, 95% CI: 0.45 to 0.51). The capacity of the AEDF model to generate clinically interpretable fused data that improves VF progression detection makes it a promising data integration tool in glaucoma management.

A Fundamental Inequality Governing the Rate Coding Response of Sensory Neurons.

A fundamental inequality governing the spike activity of peripheral neurons is derived and tested against auditory data. This inequality states that the steady-state firing rate must lie between the arithmetic and geometric means of the spontaneous and peak activities during adaptation. Implications towards the development of auditory mechanistic models are explored.

A Data-Driven Model for Simulating Longitudinal Visual Field Tests in Glaucoma.

Purpose: To develop a simulation model for glaucomatous longitudinal visual field (VF) tests with controlled progression rates. Methods: Longitudinal VF tests of 1008 eyes from 755 patients with glaucoma were used to learn the statistical characteristics of VF progression. The learned statistics and known anatomic correlations between VF test points were used to automatically generate progression patterns for baseline fields of patients with glaucoma. VF sequences were constructed by adding spatially correlated noise templates to the generated progression patterns. The two one-sided test (TOST) procedure was used to analyze the equivalence between simulated data and data from patients with glaucoma. VF progression detection rates in the simulated VF data were compared to those in patients with glaucoma using mean deviation (MD), cluster, and pointwise trend analysis. Results: VF indices (MD, pattern standard deviation), MD linear regression slopes, and progression detection rates for the simulated and patients' data were practically equivalent (TOST P < 0.01). In patients with glaucoma, the detection rates in 7 years using MD, cluster, and pointwise trend analysis were 24.4%, 26.2%, and 38.4%, respectively. In the simulated data, the mean detection rates (95% confidence interval) for MD, cluster, and pointwise trend analysis were 24.7% (24.1%-25.2%), 24.9% (24.2%-25.5%), and 35.7% (34.9%-36.5%), respectively. Conclusions: A novel simulation model generates glaucomatous VF sequences that are practically equivalent to longitudinal VFs from patients with glaucoma. Translational Relevance: Simulated VF sequences with controlled progression rates can support the evaluation and optimization of methods to detect VF progression and can provide guidance for the interpretation of longitudinal VFs.

Detection of increased serum miR-122-5p and miR-455-3p levels before the clinical diagnosis of liver cancer in people with type 2 diabetes.

People with type 2 diabetes (T2D) have increased cancer risk. Liver cancer (LC) has a high prevalence in East Asia and is one of the leading causes of cancer death globally. Diagnosis of LC at early stage carries good prognosis. We used stored serum from patients of Hong Kong Diabetes Register before cancer diagnosis to extract RNA to screen for microRNA markers for early detection of LC in T2D. After screening with Affymetrix GeneChip microarray with serum RNA from 19 incident T2D LC (T2D-LC), 20 T2D cancer free (T2D-CF) and 20 non-T2D non-cancer patients, top signals were validated in a 3-group comparison including 1888 T2D-CF, 127 T2D-LC, and 487 T2D patients with non-liver cancer patients using qPCR. We detected 2.55-fold increase in miR-122-5p and 9.21-fold increase in miR-455-3p in the T2D-LC group. Using ROC analysis, miR-122-5p and miR-455-3p jointly predicted LC with an area under the curve of 0.770. After adjustment for confounders, each unit increase of miR-455-3p increased the odds ratio for liver cancer by 1.022. Increased serum levels of miR-122-5p and miR-455-3p were independently associated with increased risk of incident LC in T2D and may serve as potential biomarkers for early detection of LC in T2D.

Consilience in the Peripheral Sensory Adaptation Response.

Measurements of the peripheral sensory adaptation response were compared to a simple mathematical relationship involving the spontaneous, peak, and steady-state activities. This relationship is based on the geometric mean and is found to be obeyed to good approximation in peripheral sensory units showing a sustained response to prolonged stimulation. From an extensive review of past studies, the geometric mean relationship is shown to be independent of modality and is satisfied in a wide range of animal species. The consilience of evidence, from nearly 100 years of experiments beginning with the work of Edgar Adrian, suggests that this is a fundamental result of neurophysiology.

Modelling the visual response to an OUReP retinal prosthesis with photoelectric dye coupled to polyethylene film.

Objective.Retinal prostheses have been developed to restore vision in blind patients suffering from diseases like retinitis pigmentosa.Approach.A new type of retinal prosthesis called the Okayama University-type retinal prosthesis (OUReP) was developed by chemically coupling photoelectric dyes to a polyethylene film surface. The prosthesis works by passively generating an electric potential when stimulated by light. However, the neurophysiological mechanism of how OUReP stimulates the degenerated retina is unknown.Main results.Here, we explore how the OUReP affects retinal tissues using a finite element model to solve for the potential inside the tissue and an active Hodgkin-Huxley model based on rat vision to predict the corresponding retinal bipolar response.Significance.We show that the OUReP is likely capable of eliciting responses in retinal bipolar cells necessary to generate vision under most ambient conditions.

Pancreatic Sirtuin 3 Deficiency Promotes Hepatic Steatosis by Enhancing 5-Hydroxytryptamine Synthesis in Mice With Diet-Induced Obesity.

Sirtuin 3 (SIRT3) is a protein deacetylase regulating ß-cell function through inhibiting oxidative stress in obese and diabetic mice, but the detailed mechanism and potential effect of ß-cell-specific SIRT3 on metabolic homeostasis, and its potential effect on other metabolic organs, are unknown. We found that glucose tolerance and glucose-stimulated insulin secretion were impaired in high-fat diet (HFD)-fed ß-cell-selective Sirt3 knockout (Sirt3 f/f;Cre/+) mice. In addition, Sirt3 f/f;Cre/+ mice had more severe hepatic steatosis than Sirt3 f/f mice upon HFD feeding. RNA sequencing of islets suggested that Sirt3 deficiency overactivated 5-hydroxytryptamine (5-HT) synthesis as evidenced by upregulation of tryptophan hydroxylase 1 (TPH1). 5-HT concentration was increased in both islets and serum of Sirt3 f/f;Cre/+ mice. 5-HT also facilitated the effect of palmitate to increase lipid deposition. Treatment with TPH1 inhibitor ameliorated hepatic steatosis and reduced weight gain in HFD-fed Sirt3 f/f;Cre/+ mice. These data suggested that under HFD feeding, SIRT3 deficiency in ß-cells not only regulates insulin secretion but also modulates hepatic lipid metabolism via the release of 5-HT.

On the rate coding response of peripheral sensory neurons.

The rate coding response of a single peripheral sensory neuron in the asymptotic, near-equilibrium limit can be derived using information theory, asymptotic Bayesian statistics and a theory of complex systems. Almost no biological knowledge is required. The theoretical expression shows good agreement with spike-frequency adaptation data across different sensory modalities and animal species. The approach permits the discovery of a new neurophysiological equation and shares similarities with statistical physics.

Circulating branched-chain amino acids and incident heart failure in type 2 diabetes: The Hong Kong Diabetes Register.

AIM: Levels of branched-chain amino acids (BCAAs, namely, isoleucine, leucine, and valine) are modulated by dietary intake and metabolic/genetic factors. BCAAs are associated with insulin resistance and increased risk of type 2 diabetes (T2D). Although insulin resistance predicts heart failure (HF), the relationship between BCAAs and HF in T2D remains unknown. METHODS: In this prospective observational study, we measured BCAAs in fasting serum samples collected at inception from 2139 T2D patients free of cardiovascular-renal diseases. The study outcome was the first hospitalization for HF. RESULTS: During 29 103 person-years of follow-up, 115 primary events occurred (age: 54.8 ± 11.2 years, 48.2% men, median [interquartile range] diabetes duration: 5 years [1-10]). Patients with incident HF had 5.6% higher serum BCAAs than those without HF (median 639.3 [561.3-756.3] vs 605.2 [524.8-708.7] µmol/L; P = .01). Serum BCAAs had a positive linear association with incident HF (per-SD increase in logarithmically transformed BCAAs: hazard ratio [HR] 1.22 [95% CI 1.07-1.39]), adjusting for age, sex, and diabetes duration. The HR remained significant after sequential adjustment of risk factors including incident coronary heart disease (1.24, 1.09-1.41); blood pressure, low-density lipoprotein cholesterol, and baseline use of related medications (1.31, 1.14-1.50); HbA1c , waist circumference, triglyceride, and baseline use of related medications (1.28, 1.11-1.48); albuminuria and estimated glomerular filtration rate (1.28, 1.11-1.48). The competing risk of death analyses showed similar results. CONCLUSIONS: Circulating levels of BCAAs are independently associated with incident HF in patients with T2D. Prospective cohort analysis and randomized trials are needed to evaluate the long-term safety and efficacy of using different interventions to optimize BCAAs levels in these patients.

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

Importance: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression. Objective: To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression. Design, Setting, and Participants: This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment. Interventions: All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment. Main Outcomes and Measures: The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%. Results: Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%). Conclusions and Relevance: These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

Magnetic seizure therapy reduces suicidal ideation and produces neuroplasticity in treatment-resistant depression.

Therapeutic seizures may work for treatment-resistant depression (TRD) by producing neuroplasticity. We evaluated whether magnetic seizure therapy (MST) produces changes in suicidal ideation and neuroplasticity as indexed through transcranial magnetic stimulation and electroencephalography (TMS-EEG) of the dorsolateral prefrontal cortex (DLPFC). Twenty-three patients with TRD were treated with MST. Changes in suicidal ideation was assessed through the Scale for Suicidal Ideation (SSI). Before and after the treatment course, neuroplasticity in excitatory and inhibitory circuits was assessed with TMS-EEG measures of cortical-evoked activity (CEA) and long-interval cortical inhibition (LICI) from the left DLPFC, and the left motor cortex as a control condition. As in our previous report, the relationship between TMS-EEG measures and suicidal ideation was examined with the SSI. Results show that 44.4% of patients experienced resolution of suicidal ideation. Based on DLPFC assessment, MST produced significant CEA increase over the frontal central electrodes (cluster p < 0.05), but did not change LICI on a group level. MST also reduced the SSI scores (p < 0.005) and the amount of reduction correlated with the decrease in LICI over the right frontal central electrodes (cluster p < 0.05; rho = 0.73 for Cz). LICI change identified patients who were resolved of suicidal ideation with 90% sensitivity and 88% specificity (AUC = 0.9, p = 0.004). There was no significant finding with motor cortex assessment. Overall, MST produced significant rates of resolution of suicidal ideation. MST also produced neuroplasticity in the frontal cortex, likely through long-term potentiation (LTP)-like mechanisms. The largest reduction in suicidal ideation was demonstrated in patients showing concomitant decreases in cortical inhibition-a mechanism linked to enhanced LTP-like plasticity. These findings provide insights into the mechanisms through which patients experience resolution of suicidal ideation following seizure treatments in depression.

Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression.

BACKGROUND: Electroconvulsive therapy (ECT) is highly effective for treatment-resistant depression, yet its mechanism of action is still unclear. Understanding the mechanism of action of ECT can advance the optimization of magnetic seizure therapy (MST) towards higher efficacy and less cognitive impairment. Given the neuroimaging evidence for disrupted resting-state network dynamics in depression, we investigated whether seizure therapy (ECT and MST) selectively modifies brain network dynamics for therapeutic efficacy. METHODS: EEG microstate analysis was used to evaluate resting-state network dynamics in patients at baseline and following seizure therapy, and in healthy controls. Microstate analysis defined four classes of brain states (labelled A, B, C, D). Source localization identified the brain regions associated with these states. RESULTS: An increase in duration and decrease in frequency of microstates was specific to responders of seizure therapy. Significant changes in the dynamics of States A, C and D were observed and predicted seizure therapy outcome (specifically ECT). Relative change in the duration of States C and D was shown to be a strong predictor of ECT response. Source localization partly associated C and D to the salience and frontoparietal networks, argued to be impaired in depression. An increase in duration and decrease in frequency of microstates was also observed following MST, however it was not specific to responders. CONCLUSION: This study presents the first evidence for the modulation of global brain network dynamics by seizure therapy. Successful seizure therapy was shown to selectively modulate network dynamics for therapeutic efficacy.

Auditory gap detection: psychometric functions and insights into the underlying neural activity.

The detection of a silent interval or gap provides important insight into temporal processing by the auditory system. Previous research has uncovered a multitude of empirical findings leaving the mechanism of gap detection poorly understood and key issues unresolved. Here, we expand the findings by measuring psychometric functions for a number of conditions including both across-frequency and across-intensity gap detection as a first study of its kind. A model is presented which not only accounts for our findings in a quantitative manner, but also helps frame the body of work on auditory gap research. The model is based on the peripheral response and postulates that the identification of gap requires the detection of activity associated with silence.

Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.

In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%. An adaptive selection of frequency bands was used for movement classification and prediction. This demonstrates the efficacy of developing a real-time brain-machine interface for arm movements with as few as eight ECoG electrodes.

Probability Map Viewer: near real-time probability map generator of serial block electron microscopy collections.

SUMMARY: To expedite the review of semi-automated probability maps of organelles and other features from 3D electron microscopy data we have developed Probability Map Viewer, a Java-based web application that enables the computation and visualization of probability map generation results in near real-time as the data are being collected from the microscope. Probability Map Viewer allows the user to select one or more voxel classifiers, apply them on a sub-region of an active collection, and visualize the results as overlays on the raw data via any web browser using a personal computer or mobile device. Thus, Probability Map Viewer accelerates and informs the image analysis workflow by providing a tool for experimenting with and optimizing dataset-specific segmentation strategies during imaging. AVAILABILITY AND IMPLEMENTATION: https://github.com/crbs/probabilitymapviewer. CONTACT: mellisman@ucsd.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression.

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.

Time-course of coherence in the human basal ganglia during voluntary movements.

We are interested in characterizing how brain networks interact and communicate with each other during voluntary movements. We recorded electrical activities from the globus pallidus pars interna (GPi), subthalamic nucleus (STN) and the motor cortex during voluntary wrist movements. Seven patients with dystonia and six patients with Parkinson's disease underwent bilateral deep brain stimulation (DBS) electrode placement. Local field potentials from the DBS electrodes and scalp EEG from the electrodes placed over the motor cortices were recorded while the patients performed externally triggered and self-initiated movements. The coherence calculated between the motor cortex and STN or GPi was found to be coupled to its power in both the beta and the gamma bands. The association of coherence with power suggests that a coupling in neural activity between the basal ganglia and the motor cortex is required for the execution of voluntary movements. Finally, we propose a mathematical model involving coupled neural oscillators which provides a possible explanation for how inter-regional coupling takes place.

TMSEEG: A MATLAB-Based Graphical User Interface for Processing Electrophysiological Signals during Transcranial Magnetic Stimulation.

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research.