High-resolution virtual brain modeling personalizes deep brain stimulation for treatment-resistant depression: Spatiotemporal response characteristics following stimulation of neural fiber pathways.

Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100,000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.

Optimization of surgical intervention outside the epileptogenic zone in the Virtual Epileptic Patient (VEP).

Studies to improve the efficacy of epilepsy surgery have focused on better refining the localization of the epileptogenic zone (EZ) with the aim of effectively resecting it. However, in a considerable number of patients, EZs are distributed across multiple brain regions and may involve eloquent areas that cannot be removed due to the risk of neurological complications. There is a clear need for developing alternative approaches to induce seizure relief, but minimal impact on normal brain functions. Here, we develop a personalized in-silico network approach, that suggests effective and safe surgical interventions for each patient. Based on the clinically identified EZ, we employ modularity analysis to identify target brain regions and fiber tracts involved in seizure propagation. We then construct and simulate a patient-specific brain network model comprising phenomenological neural mass models at the nodes, and patient-specific structural brain connectivity using the neuroinformatics platform The Virtual Brain (TVB), in order to evaluate effectiveness and safety of the target zones (TZs). In particular, we assess safety via electrical stimulation for pre- and post-surgical condition to quantify the impact on the signal transmission properties of the network. We demonstrate the existence of a large repertoire of efficient surgical interventions resulting in reduction of degree of seizure spread, but only a small subset of them proves safe. The identification of novel surgical interventions through modularity analysis and brain network simulations may provide exciting solutions to the treatment of inoperable epilepsies.

Effects of cochlear implantation on cognitive decline in older adults: A systematic review and meta-analysis.

Background: Hearing loss has been reported as the most significant modifiable risk factor for dementia, but it is still unknown whether auditory rehabilitation can practically prevent cognitive decline. We aim to systematically analyze the longitudinal effects of auditory rehabilitation via cochlear implants (CIs). Methods: In this systematic review and meta-analysis, we searched relevant literature published from January 1, 2000 to April 30, 2022, using electronic databases, and selected studies in which CIs were performed mainly on older adults and follow-up assessments were conducted in both domains: speech perception and cognitive function. A random-effects meta-analysis was conducted for each domain and for each timepoint comparison (pre-CI vs. six months post-CI; six months post-CI vs. 12 months post-CI; pre-CI vs. 12 months post-CI), and heterogeneity was assessed using Cochran's Q test. Findings: Of the 1918 retrieved articles, 20 research papers (648 CI subjects) were included. The results demonstrated that speech perception was rapidly enhanced after CI, whereas cognitive function had different speeds of improvement for different subtypes: executive function steadily improved significantly up to 12 months post-CI (g = 0.281, p < 0.001; g = 0.115, p = 0.003; g = 0.260, p < 0.001 in the order of timepoint comparison); verbal memory was significantly enhanced at six months post-CI and was maintained until 12 months post-CI (g = 0.296, p = 0.002; g = 0.095, p = 0.427; g = 0.401, p < 0.001); non-verbal memory showed no considerable progress at six months post-CI, but significant improvement at 12 months post-CI (g = -0.053, p = 0.723; g = 0.112, p = 0.089; g = 0.214, p = 0.023). Interpretation: The outcomes demonstrate that auditory rehabilitation via CIs could have a long-term positive impact on cognitive abilities. Given that older adults' cognitive abilities are on the trajectory of progressive decline with age, these results highlight the need to increase the adoption of CIs among this population.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening.

Introduction: Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease. Methods: The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance. Results: The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures. Discussion: The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

Aging-Related Dissociation of Spatial and Temporal N400 in Sentence-Level Semantic Processing: Evidence From Source Analyses.

Age-related differences in sentence-level lexical-semantic processes have been extensively studied, based on the N400 component of event-related potential (ERP). However, there is still a lack of understanding in this regard at the brain-region level. This study explores aging effects on sentence-level semantic processing by comparing the characteristics of the N400 ERP component and brain engagement patterns within individual N400 time windows for two age groups (16 younger adults aged 24.38 ± 3.88 years and 15 older adults aged 67.00 ± 5.04 years) during sentence processing with different plausibility conditions. Our results demonstrated that the N400 effect according to the plausibility condition occurred in different temporal windows in the two age groups, with a delay in the older group. Moreover, it was identified that there was a distinct difference between the groups in terms of the source location of the condition-dependent N400 effect even though no significant difference was derived in its magnitude itself at the sensor-level. Interestingly, the source analysis results indicated that the two groups involved different functional networks to resolve the same semantic violations: the younger group activated the regions corresponding to the typical lexical-semantic network more, whereas the older group recruited the regions belonging to the multiple-demand network more. The findings of this study could be used as a basis for understanding the aging brain in a linguistic context.

Screening of new psychoactive substances in human plasma by magnetic solid phase extraction and LC-QTOF-MS.

The emergence of new psychoactive substances (NPSs) is an increasing challenge in forensic toxicology. There are many extraction methods in use to isolate NPSs in biological fluids, including protein precipitation (PPT), liquid-liquid extraction (LLE), and solid phase extraction (SPE). However, there is a need to develop an effective extraction method with a short extraction time and low consumption of solvent. To meet these requirements, magnetic solid phase extraction (m-SPE) was attempted to isolate 40 NPSs in human plasma in this study. Forty NPSs (13 synthetic cannabinoids, 13 phenethylamines, 4 tryptamines, 4 other substances, 3 aminoindanes, 2 piperazines, 1 phencyclidine-type substance) were spiked in plasma and analyzed by m-SPE using COOH-functionalized multi-walled carbon nanotubes with magnetic nanoparticles (COOH-mMWCNTs). A liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) method was used for screening and identification of 40 target compounds. Method validation including limits of detection, recovery, matrix effect, and precision was performed for all 40 -target compounds. The limits of detection (LOD) of the 40 analytes were between 0.002 and 0.084 mg/L. Extraction recovery ranged from 36.9% to 110.6% (average 87%). Matrix effects ranged from -29.0% (ion suppression) to 9.8% (ion enhancement). Both intra- and inter-day precision values were less than 27.5% (RSD%). The accurate mass of QTOF-MS enabled the identification of analytes by exact monoisotopic mass and isotopic pattern. m-SPE was applied to extract 40 NPSs, and revealed less time-consuming and laborious than conventional SPE. This method proved to be an advantageous procedure to extract NPSs from biological fluids.

Artificial Intelligence and Computational Approaches for Epilepsy.

Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient's data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.

Effect of transmembrane pressure on Factor VIII yield in ATF perfusion culture for the production of recombinant human Factor VIII co-expressed with von Willebrand factor.

In this study, we evaluated three cell retention devices, an alternating tangential flow (ATF) system, a spin-filter, and a Centritech Lab III centrifuge, for the production of recombinant human Factor VIII co-expressed with von Willebrand factor. From the results, it was found that the FVIII activity in bioreactor was significantly higher in the ATF perfusion culture than two other perfusion cultures. Moreover, the FVIII activity yield was unexpectedly low in the ATF perfusion culture. We have, therefore, studied the reasons for this low FVIII activity yield. It was revealed that the inactivation and the surface adsorption of FVIII onto the harvest bag were not the main reasons for the low yield in the ATF perfusion culture. The FVIII activity yield was not increased by the use of a hollow fiber filter with 0.5 µm pore size instead of 0.2 µm pore size. Additionally, the retention of FVIII molecules by the hollow fiber filter was a dominant factor in the low FVIII activity yield in the ATF perfusion culture. We demonstrated that FVIII yield was significantly improved by controlling transmembrane pressure (TMP) across the hollow fiber filter membrane. Taken together, these results suggest that TMP control could be an efficient method for the enhancement of FVIII yield in an ATF perfusion culture.

Modified harvest system for enhancing Factor VIII yield in alternating tangential flow perfusion culture.

This study describes the development and experimental verification of a modified harvest system to enhance Factor VIII (FVIII) yield in an alternating tangential flow (ATF) perfusion culture. The main innovation of the modified harvest system is the use of check and pinch valves, eliminating the need of a peristaltic pump for harvest. The system was applied to perfusion cultures of Chinese hamster ovary cells, which co-express both recombinant human FVIII (rhFVIII) and von Willebrand factor (vWF). The modified harvest system showed comparable cell growth with the conventional harvest system using a peristaltic pump. The perfusion rate was successfully controlled using the system. In addition, the modified harvest system achieved an approximately 13.6-fold increase in the final concentration yield of FVIII activity and a 1.47-fold increase in the production yield of FVIII activity compared with a peristaltic pump. Enhancement of the yield of FVIII activity resulted from the reduction of FVIII antigen ( FVIII: Ag) retention. As a result of transmembrane pressure (TMP) measurement, the reduction of the retained FVIII: Ag was due to the increased TMP, which was caused by the characteristic function of a check valve, compared with a peristaltic harvest system. The modified harvest system developed in this study could be useful to enhance the production yield of other recombinant proteins in ATF perfusion culture.