Progressive alterations in electrophysiological and epileptic network properties during the development of temporal lobe epilepsy in rats.

OBJECTIVE: Refractory temporal lobe epilepsy (TLE) with recurring seizures causing continuing pathological changes in neural reorganization. There is an incomplete understanding of how spatiotemporal electrophysiological characteristics changes during the development of TLE. Long-term multi-site epilepsy patients' data is hard to obtain. Thus, our study relied on animal models to reveal the changes in electrophysiological and epileptic network characteristics systematically. METHODS: Long-term local field potentials (LFPs) were recorded over a period of 1 to 4 months from 6 pilocarpine-treated TLE rats. We compared variations of seizure onset zone (SOZ), seizure onset pattern (SOP), the latency of seizure onsets, and functional connectivity network from 10-channel LFPs between the early and late stages. Moreover, three machine learning classifiers trained by early-stage data were used to test seizure detection performance in the late stage. RESULTS: Compared to the early stage, the earliest seizure onset was more frequently detected in hippocampus areas in the late stage. The latency of seizure onsets between electrodes became shorter. Low-voltage fast activity (LVFA) was the most common SOP and the proportion of it increased in the late stage. Different brain states were observed during seizures using Granger causality (GC). Moreover, seizure detection classifiers trained by early-stage data were less accurate when tested in late-stage data. SIGNIFICANCE: Neuromodulation especially closed-loop deep brain stimulation (DBS) is effective in the treatment of refractory TLE. Although the frequency or amplitude of the stimulation is generally adjusted in existing closed-loop DBS devices in clinical usage, the adjustment rarely considers the pathological progression of chronic TLE. This suggests that an important factor affecting the therapeutic effect of neuromodulation may have been overlooked. The present study reveals time-varying electrophysiological and epileptic network properties in chronic TLE rats and indicates that classifiers of seizure detection and neuromodulation parameters might be designed to adapt to the current state dynamically with the progression of epilepsy.

Schaffer Collateral Inputs to CA1 Excitatory and Inhibitory Neurons Follow Different Connectivity Rules.

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3 and CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks.SIGNIFICANCE STATEMENT Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow different connectivity patterns. Our new evidence for differently structured connectivity at a fine scale in hippocampal excitatory and inhibitory neurons provides a better understanding of hippocampal networks and will guide theoretical and experimental studies.

Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.

Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.

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.

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.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology.

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Improved synapse detection for mGRASP-assisted brain connectivity mapping.

MOTIVATION: A new technique, mammalian green fluorescence protein (GFP) reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy. RESULTS: We developed a fully automatic method for detecting mGRASP-labeled synapse puncta. By modeling each punctum as a Gaussian distribution, our method enables accurate detection even when puncta of varying size and shape partially overlap. The method consists of three stages: blob detection by global thresholding; blob separation by watershed; and punctum modeling by a variational Bayesian Gaussian mixture models. Extensive testing shows that the three-stage method improved detection accuracy markedly, and especially reduces under-segmentation. The method provides a goodness-of-fit score for each detected punctum, allowing efficient error detection. We applied this advantage to also develop an efficient interactive method for correcting errors. AVAILABILITY: The software is available on http://jinny.kist.re.kr.

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.

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals.

Introduction: In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs). Methods: The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands. Results: The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals. Discussion: The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology. Clinical trial registration: https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.

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.

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.

High-Speed Automated Reconstruction of Drosophila Larval Brain from Volumetric EM Data.

To uncover the relationship between neural activity and behavior, it is essential to reconstruct neural circuits. However, methods typically used for neuron reconstruction from volumetric electron microscopy (EM) dataset are often time-consuming and require extensive manual proofreading, making it difficult to reproduce in a typical laboratory setting. To address this challenge, we have developed a set of acceleration techniques that build upon the Flood Filling Network (FFN), significantly reducing the time required for this task. These techniques can be easily adapted to other similar datasets and laboratory settings. To validate our approach, we tested our pipeline on a dataset of Drosophila larval brain serial section EM images at synaptic-resolution level. Our results demonstrate that our pipeline significantly reduces the inference time compared to the FFN baseline method and greatly reduces the time required for reconstructing the 3D morphology of neurons.

An Open-Source Real-Time Motion Correction Plug-In for Single-Photon Calcium Imaging of Head-Mounted Microscopy.

Single-photon-based head-mounted microscopy is widely used to record the brain activities of freely-moving animals. However, during data acquisition, the free movement of animals will cause shaking in the field of view, which deteriorates subsequent neural signal analyses. Existing motion correction methods applied to calcium imaging data either focus on offline analyses or lack sufficient accuracy in real-time processing for single-photon data. In this study, we proposed an open-source real-time motion correction (RTMC) plug-in for single-photon calcium imaging data acquisition. The RTMC plug-in is a real-time subpixel registration algorithm that can run GPUs in UCLA Miniscope data acquisition software. When used with the UCLA Miniscope, the RTMC algorithm satisfies real-time processing requirements in terms of speed, memory, and accuracy. We tested the RTMC algorithm by extending a manual neuron labeling function to extract calcium signals in a real experimental setting. The results demonstrated that the neural calcium dynamics and calcium events can be restored with high accuracy from the calcium data that were collected by the UCLA Miniscope system embedded with our RTMC plug-in. Our method could become an essential component in brain science research, where real-time brain activity is needed for closed-loop experiments.

Topographic connectivity and cellular profiling reveal detailed input pathways and functionally distinct cell types in the subthalamic nucleus.

The subthalamic nucleus (STN) controls psychomotor activity and is an efficient therapeutic deep brain stimulation target in individuals with Parkinson's disease. Despite evidence indicating position-dependent therapeutic effects and distinct functions within the STN, the input circuit and cellular profile in the STN remain largely unclear. Using neuroanatomical techniques, we construct a comprehensive connectivity map of the indirect and hyperdirect pathways in the mouse STN. Our circuit- and cellular-level connectivities reveal a topographically graded organization with three types of indirect and hyperdirect pathways (external globus pallidus only, STN only, and collateral). We confirm consistent pathways into the human STN by 7 T MRI-based tractography. We identify two functional types of topographically distinct glutamatergic STN neurons (parvalbumin [PV+/-]) with synaptic connectivity from indirect and hyperdirect pathways. Glutamatergic PV+ STN neurons contribute to burst firing. These data suggest a complex interplay of information integration within the basal ganglia underlying coordinated movement control and therapeutic effects.

Multiple dense particle tracking in fluorescence microscopy images based on multidimensional assignment.

Multiple particle tracking (MPT) has seen numerous applications in live-cell imaging studies of subcellular dynamics. Establishing correspondence between particles in a sequence of frames with high particle density, particles merging and splitting, particles entering and exiting the frame, temporary particle disappearance, and an ill-performing detection algorithm is the most challenging part of MPT. Here we propose a tracking method based on multidimensional assignment to address these problems. We combine an Interacting Multiple Model (IMM) filter, multidimensional assignment, particle occlusion handling, and merge-split event detection in a single software analysis package. The main advantage of a multidimensional assignment is that both spatial and temporal information can be used by using several later frames as reference. The IMM filter, which is used to maintain and predict the state of each track, contains several models which correspond to different types of biologically realistic movements. It works especially well with multidimensional assignment, because there tends to be a higher probability of correct particle association over time. First the method generates many particle-correspondence hypotheses, merge-split hypotheses and misdetection hypotheses within the framework of a sliding window over the frames of the image sequence. Then it builds a multidimensional assignment problem (MAP) accordingly. The particle is tracked with gap-filling, and merging and splitting events are then detected using the MAP solution. The tracking method is validated on both simulated tracks and microscopy image sequences. The results of these experiments show that the method is more accurate and robust than other "tracking from detected features" methods in dense particle situations.

Diets and Cellular-Derived Microparticles: Weighing a Plausible Link With Cerebral Small Vessel Disease.

Cerebral small vessel disease (CSVD) represents a spectrum of pathological processes of various etiologies affecting the brain microcirculation that can trigger neuroinflammation and the subsequent neurodegenerative cascade. Prevalent with aging, CSVD is a recognized risk factor for stroke, vascular dementia, Alzheimer disease, and Parkinson disease. Despite being the most common neurodegenerative condition with cerebrocardiovascular axis, understanding about it remains poor. Interestingly, modifiable risk factors such as unhealthy diet including high intake of processed food, high-fat foods, and animal by-products are known to influence the non-neural peripheral events, such as in the gastrointestinal tract and cardiovascular stress through cellular inflammation and oxidation. One key outcome from such events, among others, includes the cellular activations that lead to elevated levels of endogenous cellular-derived circulating microparticles (MPs). MPs can be produced from various cellular origins including leukocytes, platelets, endothelial cells, microbiota, and microglia. MPs could act as microthrombogenic procoagulant that served as a plausible culprit for the vulnerable end-artery microcirculation in the brain as the end-organ leading to CSVD manifestations. However, little attention has been paid on the potential role of MPs in the onset and progression of CSVD spectrum. Corroboratively, the formation of MPs is known to be influenced by diet-induced cellular stress. Thus, this review aims to appraise the body of evidence on the dietary-related impacts on circulating MPs from non-neural peripheral origins that could serve as a plausible microthrombosis in CSVD manifestation as a precursor of neurodegeneration. Here, we elaborate on the pathomechanical features of MPs in health and disease states; relevance of dietary patterns on MP release; preclinical studies pertaining to diet-based MPs contribution to disease; MP level as putative surrogates for early disease biomarkers; and lastly, the potential of MPs manipulation with diet-based approach as a novel preventive measure for CSVD in an aging society worldwide.

Changes in mitochondrial dynamics during amyloid ß-induced PC12 cell apoptosis.

Changes in mitochondrial morphology and dynamics influence mitochondrial function and ultimately damage neurons in Alzheimer's disease (AD). Amyloid ß (Aß) is a major factor in the pathogenesis of AD. Although it has been proved that Aß can affect the dynamics of mitochondria, there is little known on the precise dynamic process. Thus, MTT, Hoechst 33342, and Annexin V/PI analysis were used to study Aß(25-35) neurotoxity on PC12 cells, live cell station and image processing were applied to study the moving parameters and characters of mitochondria. We also studied changes of mitochondrial membrane potential and reactive oxygen species production. The results showed that long-term exposure of PC12 cells to Aß(25-35) resulted in increase of mitochondrial number and decrease of mitochondrial length and size, which presented fluctuated during early time and dramatic changes occurred after 6 h. Low concentration exposure caused little mitochondrial changes before 24 h while short time exposure induced mitochondrial fragmentation that could be recovered to normal. Mitochondrial membrane potential dissipation and reactive oxygen species production were observed, as well as apparent cell apoptosis with significant morphological changes. These data suggest that mitochondrial fission can be reversed during Aß(25-35)-induced PC12 cell apoptosis, depending on the concentration and exposure time of Aß(25-35), which may be helpful in AD prevention and therapy.

Decoding with Calcium Signals from Layer 2/3 Motor Cortex during A Pressing Movement.

Brain-machine interfaces (BMIs) have been promising for not only neuroprosthesis research but also brain function investigation. Electrophysiological recording commonly used in traditional BMIs is spatially sparse and lack of information about neuron types and spatial organization. However, optical imaging methods might avoid these limitations by providing dense, spatially organized and annotated with genetic information over a large field of view. Here, we tried to demonstrate the potential of calcium imaging signals obtained through the one-photon microscope in neural decoding. When mice were trained to perform a lever press task to obtain water as rewards, the calcium signals of neurons in their layer 2/3 motor cortex were recorded by microscope. With the calcium signals, we analyzed the neural activity at both single individual neuron and neuronal population level. We found two typical classes of pressing-related neurons and distinct ensemble activity patterns between a pressing movement and baseline. The decoding results further demonstrated that the movement-related information could be more completely specified by population response structure. Our results suggested that neural signals from more types and a larger amount of neurons, are crucial for accurate decoding in BMI applications.

neuTube 1.0: A New Design for Efficient Neuron Reconstruction Software Based on the SWC Format.

Brain circuit mapping requires digital reconstruction of neuronal morphologies in complicated networks. Despite recent advances in automatic algorithms, reconstruction of neuronal structures is still a bottleneck in circuit mapping due to a lack of appropriate software for both efficient reconstruction and user-friendly editing. Here we present a new software design based on the SWC format, a standardized neuromorphometric format that has been widely used for analyzing neuronal morphologies or sharing neuron reconstructions via online archives such as NeuroMorpho.org. We have also implemented the design in our open-source software called neuTube 1.0. As specified by the design, the software is equipped with parallel 2D and 3D visualization and intuitive neuron tracing/editing functions, allowing the user to efficiently reconstruct neurons from fluorescence image data and edit standard neuron structure files produced by any other reconstruction software. We show the advantages of neuTube 1.0 by comparing it to two other software tools, namely Neuromantic and Neurostudio. The software is available for free at http://www.neutracing.com, which also hosts complete software documentation and video tutorials.