Miniaturized Implantable Fluorescence Probes Integrated with Metal-Organic Frameworks for Deep Brain Dopamine Sensing.

Continuously monitoring neurotransmitter dynamics can offer profound insights into neural mechanisms and the etiology of neurological diseases. Here, we present a miniaturized implantable fluorescence probe integrated with metal-organic frameworks (MOFs) for deep brain dopamine sensing. The probe is assembled from physically thinned light-emitting diodes (LEDs) and phototransistors, along with functional surface coatings, resulting in a total thickness of 120 µm. A fluorescent MOF that specifically binds dopamine is introduced, enabling a highly sensitive dopamine measurement with a detection limit of 79.9 nM. A compact wireless circuit weighing only 0.85 g is also developed and interfaced with the probe, which was later applied to continuously monitor real-time dopamine levels during deep brain stimulation in rats, providing critical information on neurotransmitter dynamics. Cytotoxicity tests and immunofluorescence analysis further suggest a favorable biocompatibility of the probe for implantable applications. This work presents fundamental principles and techniques for integrating fluorescent MOFs and flexible electronics for brain-computer interfaces and may provide more customized platforms for applications in neuroscience, disease tracing, and smart diagnostics.

A Generalized Cortico-Muscular-Cortical Network to Evaluate the Effects of Three-Week Brain Stimulation.

OBJECTIVE: Post-stroke transcranial magnetic stimulation (TMS) has gradually become a brain intervention to assist patients in the recovery of motor function. The long lasting regulatory of TMS may involve the coupling changes between cortex and muscles. However, the effects of multi-day TMS on motor recovery after stroke is unclear. METHODS: This study proposed to quantify the effects of three-week TMS on brain activity and muscles movement performance based on a generalized cortico-muscular-cortical network (gCMCN). The gCMCN-based features were further extracted and combined with the partial least squares (PLS) method to predict the Fugl-Meyer of upper extremity (FMUE) in stroke patients, thereby establishing an objective rehabilitation method that can evaluate the positive effects of continuous TMS on motor function. RESULTS: We found that the improvement of motor function after three-week TMS was significantly correlated with the complexity trend of information interaction between hemispheres and the intensity of corticomuscular coupling. In addition, the fitting coefficient ([Formula: see text]) for predicted and actual FMUE before and after TMS were 0.856 and 0.963, respectively, suggesting that the gCMCN-based measurement may be a promising method for evaluating the therapeutic effect of TMS. CONCLUSION: From the perspective of a novel brain-muscles network with dynamic contraction as the entry point, this work quantified TMS-induced connectivity differences while evaluating the potential efficacy of multi-day TMS. SIGNIFICANCE: It provides a unique insight for the further application of intervention therapy in the field of brain diseases.

Brainprint Recognition Based on the Stable SSVEP Space-Frequency Energy Distribution.

Brainprint recognition has received increasing attention in information security. Electroencephalography (EEG) signals measured under task-related or task-free conditions have been exploited as brain biometrics. However, what components make the uniqueness of one's brain signals remains unclear. In this study, we proposed an interpretable biomarker based on steady-state visual evoked potentials (SSVEP) signals for EEG biometric identification. Firstly, we recovered pure SSVEP components from EEG by a point-position equivalent reconstruction (PPER) method. Then, we calculated the distribution properties of SSVEP components in space and frequency. By using the uniform manifold approximation and projection, we reduced the distribution features to 2-dimensions, which shows the separability of the subjects. Lastly, we built a long short-term memory (LSTM) network to perform brainprint recognition on the SSVEP benchmark dataset. The average recognition accuracy can reach up to 98.33%. Our results demonstrate that the space-frequency energy feature of SSVEP is an effective and interpretable biomarker for brainprint recognition. This study provides a further understanding of the uniqueness of individual EEG signal, and facilitates its potential application for personal identification.

A 4.43 TΩ ZIN 0.0128 mm2 Cascaded Instrumentation Amplifier with Input-biased Pseudo Resistor for Implantable Brain Machine Interfaces.

A cascaded instrumentation amplifier (CaIA) with input-biased pseudo resistors (IBPR) is presented for implantable brain machine interfaces (BMI). The gain distribution of two-stage cascaded amplifiers, instead of a single-stage amplifier, helps to achieve an input impedance of 4.43TΩ at 100Hz, and maintain the small active area (0.0128 mm2). The input-biased pseudo resistors contribute to a much lower high-pass corner (fHP=0.00011Hz) compared with the conventional structure, the input-referred noise is only 3.836µVrms integrated from 0.5Hz to 10kHz with 0.98µW power consumption.Clinical Relevance- This establishes an area-efficient amplifier design with ultra-high input impedance (4.43TΩ at 100Hz) and hyper-low high-pass corner frequency (fHP=0.00011Hz), which is suitable for long-term monitoring of neural activities (including slow oscillations) in implantable brain-machine interfaces.

Unsupervised Feature Representation of Sleep EEG Data with Transient Deep Boltzmann Machine.

The supervised sleep staging methods are challenged by their strict requirements of a labelled and large dataset. This study considers an unsupervised dimensionality reduction method, the Deep Boltzmann Machine (DBM), trained to a transient state for binary classification of sleep stages. First, the joint time-frequency domain features from the polysomnographic recordings are extracted. Second, the extracted features are smoothed using 2 min rolling window to include contextual temporal information, and finally, they serve as an input for unsupervised training of DBM_transient. The results show that our method effectively separates the sleep stages in two-dimensional feature space with a large Fisher's discriminant value. The classification performance by the DBM_transient achieves a 96.1% F1 score, which is higher than DBM converged to an equilibrium state (95.2%), Principal Component Analysis (92.5%), Isometric Feature Mapping (95.9%), t-distributed Stochastic Neighbor Embedding (94.9%), and Uniform Manifold Approximation (95.0%) on the widely used sleep-EDF database. Additionally, Fisher's discriminant function demonstrates the superiority of the DBM_transient. The significance of the DBM transient lies in its ease of interpretability in two-dimensional space, and future multi-class implementation of the method may facilitate its usage in clinical applications.

The role of age-related sleep EEG changes in memory decline: experiments and computational modeling.

The slow oscillation (SO) observed during deep sleep is known to facilitate memory consolidation. However, the impact of age-related changes in sleep electroencephalography (EEG) oscillations and memory remains unknown. In this study, we aimed to investigate the contribution of age-related changes in sleep SO and its role in memory decline by combining EEG recordings and computational modeling. Based on the detected SO events, we found that older adults exhibit lower SO density, lower SO frequency, and longer Up and Down state durations during N3 sleep compared to young and middle-aged groups. Using a biophysically detailed thalamocortical network model, we simulated the "aged" brain as a partial loss of synaptic connections between neurons in the cortex. Our simulations showed that the changes in sleep SO properties in the "aged" brain, similar to those observed in older adults, resulting in impaired memory consolidation. Overall, this study provides mechanistic insights into how age-related changes modulate sleep SOs and memory decline.Clinical Relevance- This study contributes towards finding feasible biomarkers and target mechanism for designing therapy in older adults with memory deficits, such as Alzheimer's disease patients.

Associations between in vitro , in vivo and in silico cell classes in mouse primary visual cortex.

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex.

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types.

Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce a computational optimization workflow to generate 9,200 single-neuron models with active conductances. These models are based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that, in contrast to current belief, the generated models are robust representations of individual experiments and cortical cell types as defined via cellular electrophysiology or transcriptomics. Next, we show that differences in specific conductances predicted from the models reflect differences in gene expression supported by single-cell transcriptomics. The differences in model conductances, in turn, explain electrophysiological differences observed between the cortical subclasses. Our computational effort reconciles single-cell modalities that define cell types and enables causal relationships to be examined.

Closed-Loop Construction and Analysis of Cortico-Muscular-Cortical Functional Network After Stroke.

Brain networks allow a topological understanding into the pathophysiology of stroke-induced motor deficits, and have been an influential tool for investigating brain functions. Unfortunately, currently applied methods generally lack in the recognition of the dynamic changes in the cortical networks related to muscle activity, which is crucial to clarify the alterations of the cooperative working patterns in the motor control system after stroke. In this study, we integrate corticomuscular and intermuscular interactions to cortico-cortical network and propose a novel closed-loop construction of cortico-muscular-cortical functional network, named closed-loop network (CLN). Directional characteristic in terms of differentiating causal interactions is endowed on basis of the CLN framework, further expanding the definition of functional connectivity (FC) and effective connectivity (EC) dedicated to CLN. Next, CLN is applied to stroke patients to reveal the underlying after-effects mechanism of low frequency repetitive transcranial magnetic stimulation (rTMS) induced alterations of cortical physiologic functions during movement. Results show that the short-term modulation of rTMS is reflected in the enhancement of information interaction within the interhemispheric primary motor regions and inhibition of the coupling between motor cortex and effector muscles. CLN provides a new perspective for the study of motor-related cortical networks with muscle activities involvement instead of being restricted to brain network analysis of behaviors.

Cellular resolution anatomical and molecular atlases for prenatal human brains.

Increasing interest in studies of prenatal human brain development, particularly using new single-cell genomics and anatomical technologies to create cell atlases, creates a strong need for accurate and detailed anatomical reference atlases. In this study, we present two cellular-resolution digital anatomical atlases for prenatal human brain at postconceptional weeks (PCW) 15 and 21. Both atlases were annotated on sequential Nissl-stained sections covering brain-wide structures on the basis of combined analysis of cytoarchitecture, acetylcholinesterase staining, and an extensive marker gene expression dataset. This high information content dataset allowed reliable and accurate demarcation of developing cortical and subcortical structures and their subdivisions. Furthermore, using the anatomical atlases as a guide, spatial expression of 37 and 5 genes from the brains, respectively, at PCW 15 and 21 was annotated, illustrating reliable marker genes for many developing brain structures. Finally, the present study uncovered several novel developmental features, such as the lack of an outer subventricular zone in the hippocampal formation and entorhinal cortex, and the apparent extension of both cortical (excitatory) and subcortical (inhibitory) progenitors into the prenatal olfactory bulb. These comprehensive atlases provide useful tools for visualization, segmentation, targeting, imaging, and interpretation of brain structures of prenatal human brain, and for guiding and interpreting the next generation of cell census and connectome studies.

A Novel Delay Estimation Method for Improving Corticomuscular Coherence in Continuous Synchronization Events.

OBJECTIVE: While the corticomuscularcoupling between motor cortex and muscle tissue has received considerable attention, which is typically quantitative measure to evaluate neural signals synchronization in the motor control system, little work has been published regarding the effect of underlying delay of two coupled physiological signals on coherence. METHODS: In this study, we developed a novel delay estimation method, named rate of voxels change (RVC), detecting time delay in two coupled physiological signals. Based on RVC framework, delay compensation was used to adjust magnitude squared coherence (MSC) image. To illustrate the effectiveness of the RVC method, we compared the estimated delays and the adjusted MSC results based on RVC method and corticomuscular coherence with time lag (CMCTL) method. RESULTS: The simulation results suggested that RVC method was not only superior to the CMCTL method in estimating different time delays, but also has better optimization effect on MSC image. The experimental results further confirmed that delay estimated by the proposed RVC method was more in line with the underlying physiology (controls: 22.8 ms vs patients: 34.5 ms). Meanwhile, RVC-based delay compensation could significantly optimize the MSC of specific regions. SIGNIFICANCE: This study proved that RVC has remarkably higher reliability in detecting time delay between coupled neurophysiological signals, and the application of RVC was an improvement on the previous studies that mainly focused on biased MSC estimation.

Health seeking behavior and associated factors among individuals with cough in Yiwu, China: a population-based study.

BACKGROUND: Previous studies have shown that a certain proportion of the population did not seek medical treatment after coughing, and understanding the potential reasons is crucial for disease prevention and control. METHOD: A population-based study was conducted with the probability proportional to population size sampling in Yiwu, Zhejiang, China. A total of 5855 individuals aged ≥15 years lived in Yiwu for more than 6 months were included. All participants completed a laptop-based questionnaire to collect detailed information by a face-to-face interview. Characteristics of individuals were described by categories of health seeking behavior using frequency and percentage. Univariate and multivariate logistic regression analyses were performed to estimate the associations of social-demographic and cough characteristics with health seeking behavior. RESULTS: 19.3% (1129/5855) of participants had a cough in the past month, 40% (452/1129) had sought medical treatment. Of these, 26.5% (120/452) chose hospitals at county level or above. Individuals aged ≥65 years old (OR = 2.25, 95% CI: 1.23, 4.12), female (OR = 1.57, 95% CI: 1.21, 2.06), living in rural areas (OR = 1.30, 95% CI: 1.003, 1.69), persistent cough for 3-8 weeks (OR = 2.91, 95% CI: 1.72, 4.92) and with more accompanying symptoms (P trend < 0.001) were more likely to seek medical treatment, but those coughed for > 8 weeks were not (p > 0.5). Female (OR = 0.33, 95% CI: 0.21, 0.54) and people living in rural areas (OR = 0.57, 95% CI: 0.36, 0.92) were less likely to choose hospitals at county level or above while the higher educated were more likely to (OR = 3.29, 95% CI: 1.35, 8.02). Those who coughed for more than 2 weeks were more likely to choose hospitals at or above the county level. But the number of accompanying symptoms does not show any significant relationship with the choice of medical facility. CONCLUSION: The present study found that age, sex, living areas and features of cough were associated with health seeking behavior. It is worth noting that those who coughed for too long (e.g. > 8 weeks) were less likely to seek medical treatment. Targeted measures should be developed based on the key factors found in this study to guide persons to seek medical treatment more scientifically.

Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform.

Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species.

Stimulation Augments Spike Sequence Replay and Memory Consolidation during Slow-Wave Sleep.

Newly acquired memory traces are spontaneously reactivated during slow-wave sleep (SWS), leading to the consolidation of recent memories. Empirical studies found that sensory stimulation during SWS can selectively enhance memory consolidation with the effect depending on the phase of stimulation. In this new study, we aimed to understand the mechanisms behind the role of sensory stimulation on memory consolidation using computational models implementing effects of neuromodulators to simulate transitions between awake and SWS sleep, and synaptic plasticity to allow the change of synaptic connections due to the training in awake or replay during sleep. We found that when closed-loop stimulation was applied during the Down states of sleep slow oscillation, particularly right before the transition from Down to Up state, it significantly affected the spatiotemporal pattern of the slow waves and maximized memory replay. In contrast, when the stimulation was presented during the Up states, it did not have a significant impact on the slow waves or memory performance after sleep. For multiple memories trained in awake, presenting stimulation cues associated with specific memory trace could selectively augment replay and enhance consolidation of that memory and interfere with consolidation of the others (particularly weak) memories. Our study proposes a synaptic-level mechanism of how memory consolidation is affected by sensory stimulation during sleep.SIGNIFICANCE STATEMENT Stimulation, such as training-associated cues or auditory stimulation, during sleep can augment consolidation of the newly encoded memories. In this study, we used a computational model of the thalamocortical system to describe the mechanisms behind the role of stimulation in memory consolidation during slow-wave sleep. Our study suggests that stimulation preferentially strengthens memory traces when delivered at a specific phase of the slow oscillation, just before the Down to Up state transition when it makes the largest impact on the spatiotemporal pattern of sleep slow waves. In the presence of multiple memories, presenting sensory cues during sleep could selectively strengthen selected memories. Our study proposes a synaptic-level mechanism of how memory consolidation is affected by sensory stimulation during sleep.

Differential roles of sleep spindles and sleep slow oscillations in memory consolidation.

Sleep plays an important role in the consolidation of recent memories. However, the cellular and synaptic mechanisms of consolidation during sleep remain poorly understood. In this study, using a realistic computational model of the thalamocortical network, we tested the role of Non-Rapid Eye Movement (NREM) sleep in memory consolidation. We found that sleep spindles (the hallmark of N2 stage sleep) and slow oscillations (the hallmark of N3 stage sleep) both promote replay of the spike sequences learned in the awake state and replay was localized at the trained network locations. Memory performance improved after a period of NREM sleep but not after the same time period in awake. When multiple memories were trained, the local nature of the spike sequence replay during spindles allowed replay of the distinct memory traces independently, while slow oscillations promoted competition that could prevent replay of the weak memories in a presence of the stronger memory traces. This could lead to extinction of the weak memories unless when sleep spindles (N2 sleep) preceded slow oscillations (N3 sleep), as observed during the natural sleep cycle. Our study presents a mechanistic explanation for the role of sleep rhythms in memory consolidation and proposes a testable hypothesis how the natural structure of sleep stages provides an optimal environment to consolidate memories.

Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations.

Sleep is critical for regulation of synaptic efficacy, memories, and learning. However, the underlying mechanisms of how sleep rhythms contribute to consolidating memories acquired during wakefulness remain unclear. Here we studied the role of slow oscillations, 0.2-1 Hz rhythmic transitions between Up and Down states during stage 3/4 sleep, on dynamics of synaptic connectivity in the thalamocortical network model implementing spike-timing-dependent synaptic plasticity. We found that the spatiotemporal pattern of Up-state propagation determines the changes of synaptic strengths between neurons. Furthermore, an external input, mimicking hippocampal ripples, delivered to the cortical network results in input-specific changes of synaptic weights, which persisted after stimulation was removed. These synaptic changes promoted replay of specific firing sequences of the cortical neurons. Our study proposes a neuronal mechanism on how an interaction between hippocampal input, such as mediated by sharp wave-ripple events, cortical slow oscillations, and synaptic plasticity, may lead to consolidation of memories through preferential replay of cortical cell spike sequences during slow-wave sleep. SIGNIFICANCE STATEMENT: Sleep is critical for memory and learning. Replay during sleep of temporally ordered spike sequences related to a recent experience was proposed to be a neuronal substrate of memory consolidation. However, specific mechanisms of replay or how spike sequence replay leads to synaptic changes that underlie memory consolidation are still poorly understood. Here we used a detailed computational model of the thalamocortical system to report that interaction between slow cortical oscillations and synaptic plasticity during deep sleep can underlie mapping hippocampal memory traces to persistent cortical representation. This study provided, for the first time, a mechanistic explanation of how slow-wave sleep may promote consolidation of recent memory events.

The Role of Cell Volume in the Dynamics of Seizure, Spreading Depression, and Anoxic Depolarization.

Cell volume changes are ubiquitous in normal and pathological activity of the brain. Nevertheless, we know little of how cell volume affects neuronal dynamics. We here performed the first detailed study of the effects of cell volume on neuronal dynamics. By incorporating cell swelling together with dynamic ion concentrations and oxygen supply into Hodgkin-Huxley type spiking dynamics, we demonstrate the spontaneous transition between epileptic seizure and spreading depression states as the cell swells and contracts in response to changes in osmotic pressure. Our use of volume as an order parameter further revealed a dynamical definition for the experimentally described physiological ceiling that separates seizure from spreading depression, as well as predicted a second ceiling that demarcates spreading depression from anoxic depolarization. Our model highlights the neuroprotective role of glial K buffering against seizures and spreading depression, and provides novel insights into anoxic depolarization and the relevant cell swelling during ischemia. We argue that the dynamics of seizures, spreading depression, and anoxic depolarization lie along a continuum of the repertoire of the neuron membrane that can be understood only when the dynamic ion concentrations, oxygen homeostasis,and cell swelling in response to osmotic pressure are taken into consideration. Our results demonstrate the feasibility of a unified framework for a wide range of neuronal behaviors that may be of substantial importance in the understanding of and potentially developing universal intervention strategies for these pathological states.

Unification of neuronal spikes, seizures, and spreading depression.

The pathological phenomena of seizures and spreading depression have long been considered separate physiological events in the brain. By incorporating conservation of particles and charge, and accounting for the energy required to restore ionic gradients, we extend the classic Hodgkin-Huxley formalism to uncover a unification of neuronal membrane dynamics. By examining the dynamics as a function of potassium and oxygen, we now account for a wide range of neuronal activities, from spikes to seizures, spreading depression (whether high potassium or hypoxia induced), mixed seizure and spreading depression states, and the terminal anoxic "wave of death." Such a unified framework demonstrates that all of these dynamics lie along a continuum of the repertoire of the neuron membrane. Our results demonstrate that unified frameworks for neuronal dynamics are feasible, can be achieved using existing biological structures and universal physical conservation principles, and may be of substantial importance in enabling our understanding of brain activity and in the control of pathological states.