Emerging role of the lateral habenula in conditioned inhibition and depression.

Associating a neutral conditioned stimulus (CS) with the absence of a biologically significant unconditioned stimulus (US) confers conditioned inhibitory properties upon the CS, referred to as conditioned inhibition. Conditioned inhibition and conditioned excitation, an association of a CS with the presence of the US, are fundamental components of associative learning. While the neural substrates of conditioned excitation are well established, those of conditioned inhibition remain poorly understood. Recent research has shed light on the lateral habenula (LHb) engagement in conditioned inhibition, along with the midbrain dopaminergic neurons. This article reviews behavioral tasks conducted to assess conditioned inhibition and how experimental LHb manipulations affect performance in these tasks. These results underscore the critical role of the LHb in conditioned inhibition. Intriguingly, stress increases LHb reactivity and impairs performances in tasks consisting of a component of conditioned inhibition in animals. Dysfunction of the LHb is observed in patients with depression. The ability of an organism to perform conditioned inhibition is closely linked to altered neuronal activity in the LHb, which has implications for mental disorders. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer's disease.

Developing reliable biomarkers is important for screening Alzheimer's disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aß PET scan. We developed EEG-ML algorithm to detect brain Aß pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aß PET. 19-channel resting-state EEG and Aß PET were collected from 311 subjects: 196 SCD(36 Aß +, 160 Aß -), 115 MCI(54 Aß +, 61Aß -). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI + SCD (33 Aß +, 43 Aß -). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aß +, 24 Aß -). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aß +, 19 Aß -). Similar trends of EEG power have been observed from the group comparison between Aß + and Aß -, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended.

Highly efficient thin-film 930 nm VCSEL on PDMS for biomedical applications.

Recently, biocompatible optical sources have been surfacing for new-rising biomedical applications, allowing them to be used for multi-purpose technologies such as biological sensing, optogenetic modulation, and phototherapy. Especially, vertical-cavity surface-emitting laser (VCSEL) is in the spotlight as a prospective candidate for optical sources owing to its low-driving current performance, low-cost, and package easiness in accordance with two-dimensional (2D) arrays structure. In this study, we successfully demonstrated the actualization of biocompatible thin-film 930 nm VCSELs transferred onto a Polydimethylsiloxane (PDMS) carrier. The PDMS feature with biocompatibility as well as biostability makes the thin-film VCSELs well-suited for biomedical applications. In order to integrate the conventional VCSEL onto the PDMS carrier, we utilized a double-transfer technique that transferred the thin-film VCSELs onto foreign substrates twice, enabling it to maintain the p-on-n polarity of the conventional VCSEL. Additionally, we employed a surface modification-assisted bonding (SMB) using an oxygen plasma in conjunction with silane treatment when bonding the PDMS carrier with the substrate-removed conventional VCSELs. The threshold current and maximum output power of the fabricated 930 nm thin-film VCSELs are 1.08 mA and 7.52 mW at an injection current of 13.9 mA, respectively.

SELENBP1 overexpression in the prefrontal cortex underlies negative symptoms of schizophrenia.

The selenium-binding protein 1 (SELENBP1) has been reported to be up-regulated in the prefrontal cortex (PFC) of schizophrenia patients in postmortem reports. However, no causative link between SELENBP1 and schizophrenia has yet been established. Here, we provide evidence linking the upregulation of SELENBP1 in the PFC of mice with the negative symptoms of schizophrenia. We verified the levels of SELENBP1 transcripts in postmortem PFC brain tissues from patients with schizophrenia and matched healthy controls. We also generated transgenic mice expressing human SELENBP1 (hSELENBP1 Tg) and examined their neuropathological features, intrinsic firing properties of PFC 2/3-layer pyramidal neurons, and frontal cortex (FC) electroencephalographic (EEG) responses to auditory stimuli. Schizophrenia-like behaviors in hSELENBP1 Tg mice and mice expressing Selenbp1 in the FC were assessed. SELENBP1 transcript levels were higher in the brains of patients with schizophrenia than in those of matched healthy controls. The hSELENBP1 Tg mice displayed negative endophenotype behaviors, including heterotopias- and ectopias-like anatomical deformities in upper-layer cortical neurons and social withdrawal, deficits in nesting, and anhedonia-like behavior. Additionally, hSELENBP1 Tg mice exhibited reduced excitabilities of PFC 2/3-layer pyramidal neurons and abnormalities in EEG biomarkers observed in schizophrenia. Furthermore, mice overexpressing Selenbp1 in FC showed deficits in sociability. These results suggest that upregulation of SELENBP1 in the PFC causes asociality, a negative symptom of schizophrenia.

Machine Learning to Predict Brain Amyloid Pathology in Pre-dementia Alzheimer's Disease Using QEEG Features and Genetic Algorithm Heuristic.

The use of positron emission tomography (PET) as the initial or sole biomarker of ß-amyloid (Aß) brain pathology may inhibit Alzheimer's disease (AD) drug development and clinical use due to cost, access, and tolerability. We developed a qEEG-ML algorithm to predict Aß pathology among subjective cognitive decline (SCD) and mild cognitive impairment (MCI) patients, and validated it using Aß PET. We compared QEEG data between patients with MCI and those with SCD with and without PET-confirmed beta-amyloid plaque. We compared resting-state eyes-closed electroencephalograms (EEG) patterns between the amyloid positive and negative groups using relative power measures from 19 channels (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), divided into eight frequency bands, delta (1-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (12-15 Hz), beta 2 (15-20 Hz), beta 3 (20-30 Hz), and gamma (30-45 Hz) calculated by FFT and denoised by iSyncBrain®. The resulting 152 features were analyzed using a genetic algorithm strategy to identify optimal feature combinations and maximize classification accuracy. Guided by gene modeling methods, we treated each channel and frequency band of EEG power as a gene and modeled it with every possible combination within a given dimension. We then collected the models that showed the best performance and identified the genes that appeared most frequently in the superior models. By repeating this process, we converged on a model that approximates the optimum. We found that the average performance increased as this iterative development of the genetic algorithm progressed. We ultimately achieved 85.7% sensitivity, 89.3% specificity, and 88.6% accuracy in SCD amyloid positive/negative classification, and 83.3% sensitivity, 85.7% specificity, and 84.6% accuracy in MCI amyloid positive/negative classification.

Multilayer Transfer Printing for Pixelated, Multicolor Quantum Dot Light-Emitting Diodes.

Here, we report multilayer stacking of films of quantum dots (QDs) for the purpose of tailoring the energy band alignment between charge transport layers and light emitting layers of different color in quantum dot light-emitting diodes (QD LED) for maximum efficiency in full color operation. The performance of QD LEDs formed by transfer printing compares favorably to that of conventional devices fabricated by spin-casting. Results indicate that zinc oxide (ZnO) and titanium dioxide (TiO2) can serve effectively as electron transport layers (ETLs) for red and green/blue QD LEDs, respectively. Optimized selections for each QD layer can be assembled at high yields by transfer printing with sacrificial fluoropolymer thin films to provide low energy surfaces for release, thereby allowing shared common layers for hole injection (HIL) and hole transport (HTL), along with customized ETLs. This strategy allows cointegration of devices with heterogeneous energy band diagrams, in a parallelized scheme that offers potential for high throughput and practical use.

Bioresorbable silicon electronics for transient spatiotemporal mapping of electrical activity from the cerebral cortex.

Bioresorbable silicon electronics technology offers unprecedented opportunities to deploy advanced implantable monitoring systems that eliminate risks, cost and discomfort associated with surgical extraction. Applications include postoperative monitoring and transient physiologic recording after percutaneous or minimally invasive placement of vascular, cardiac, orthopaedic, neural or other devices. We present an embodiment of these materials in both passive and actively addressed arrays of bioresorbable silicon electrodes with multiplexing capabilities, which record in vivo electrophysiological signals from the cortical surface and the subgaleal space. The devices detect normal physiologic and epileptiform activity, both in acute and chronic recordings. Comparative studies show sensor performance comparable to standard clinical systems and reduced tissue reactivity relative to conventional clinical electrocorticography (ECoG) electrodes. This technology offers general applicability in neural interfaces, with additional potential utility in treatment of disorders where transient monitoring and modulation of physiologic function, implant integrity and tissue recovery or regeneration are required.

High-resolution patterns of quantum dots formed by electrohydrodynamic jet printing for light-emitting diodes.

Here we demonstrate materials and operating conditions that allow for high-resolution printing of layers of quantum dots (QDs) with precise control over thickness and submicron lateral resolution and capabilities for use as active layers of QD light-emitting diodes (LEDs). The shapes and thicknesses of the QD patterns exhibit systematic dependence on the dimensions of the printing nozzle and the ink composition in ways that allow nearly arbitrary, systematic control when exploited in a fully automated printing tool. Homogeneous arrays of patterns of QDs serve as the basis for corresponding arrays of QD LEDs that exhibit excellent performance. Sequential printing of different types of QDs in a multilayer stack or in an interdigitated geometry provides strategies for continuous tuning of the effective, overall emission wavelengths of the resulting QD LEDs. This strategy is useful to efficient, additive use of QDs for wide ranging types of electronic and optoelectronic devices.