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.

Multimodal Connectivity-based Individual Parcellation and Analysis for Humans and Rhesus Monkeys.

Individual brains vary greatly in morphology, connectivity and organization. Individualized brain parcellation is capable of precisely localizing subject-specific functional regions. However, most individualization approaches examined single modality of data and have not generalized to nonhuman primates. The present study proposed a novel multimodal connectivity-based individual parcellation (MCIP) method, which optimizes within-region homogeneity, spatial continuity and similarity to a reference atlas with the fusion of personal functional and anatomical connectivity. Comprehensive evaluation demonstrated that MCIP outperformed state-of-the-art multimodal individualization methods in terms of functional and anatomical homogeneity, predictability of cognitive measures, heritability, reproducibility and generalizability across species. Comparative investigation showed a higher topographic variability in humans than that in macaques. Therefore, MCIP provides improved accurate and reliable mapping of brain functional regions over existing methods at an individual level across species, and could facilitate comparative and translational neuroscience research.

Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus.

Objective. The development of electrical pulse stimulations in brain, including deep brain stimulation, is promising for treating various brain diseases. However, the mechanisms of brain stimulations are not yet fully understood. Previous studies have shown that the commonly used high-frequency stimulation (HFS) can increase the firing of neurons and modulate the pattern of neuronal firing. Because the generation of neuronal firing in brain is a nonlinear process, investigating the characteristics of nonlinear dynamics induced by HFS could be helpful to reveal more mechanisms of brain stimulations. The aim of present study is to investigate the fractal properties in the neuronal firing generated by HFS.Approach. HFS pulse sequences with a constant frequency 100 Hz were applied in the afferent fiber tracts of rat hippocampal CA1 region. Unit spikes of both the pyramidal cells and the interneurons in the downstream area of stimulations were recorded. Two fractal indexes-the Fano factor and Hurst exponent were calculated to evaluate the changes of long-range temporal correlations (LRTCs), a typical characteristic of fractal process, in spike sequences of neuronal firing.Mainresults. Neuronal firing at both baseline and during HFS exhibited LRTCs over multiple time scales. In addition, the LRTCs significantly increased during HFS, which was confirmed by simulation data of both randomly shuffled sequences and surrogate sequences.Conclusion. The purely periodic stimulation of HFS pulses, a non-fractal process without LRTCs, can increase rather than decrease the LRTCs in neuronal firing.Significance. The finding provides new nonlinear mechanisms of brain stimulation and suggests that LRTCs could be a new biomarker to evaluate the nonlinear effects of HFS.

Face-Specific Activity in the Ventral Stream Visual Cortex Linked to Conscious Face Perception.

When presented with visual stimuli of face images, the ventral stream visual cortex of the human brain exhibits face-specific activity that is modulated by the physical properties of the input images. However, it is still unclear whether this activity relates to conscious face perception. We explored this issue by using the human intracranial electroencephalography technique. Our results showed that face-specific activity in the ventral stream visual cortex was significantly higher when the subjects subjectively saw faces than when they did not, even when face stimuli were presented in both conditions. In addition, the face-specific neural activity exhibited a more reliable neural response and increased posterior-anterior direction information transfer in the "seen" condition than the "unseen" condition. Furthermore, the face-specific neural activity was significantly correlated with performance. These findings support the view that face-specific activity in the ventral stream visual cortex is linked to conscious face perception.

Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability.

Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks.

FFCM-MRF: An accurate and generalizable cerebrovascular segmentation pipeline for humans and rhesus monkeys based on TOF-MRA.

PURPOSE: Cerebrovascular segmentation and quantification of vascular morphological features in humans and rhesus monkeys are essential for prevention, diagnosis, and treatment of brain diseases. However, current automated whole-brain vessel segmentation methods are often not generalizable to independent datasets, limiting their usefulness in real-world environments with their heterogeneity in participants, scanners, and species. MATERIALS AND METHODS: In this study, we proposed an automated, accurate and generalizable segmentation method for magnetic resonance angiography images called FFCM-MRF. This method integrated fast fuzzy c-means clustering and Markov random field optimization by vessel shape priors and spatial constraints. We used a total of 123 human and 44 macaque MRA images scanned at 1.5 T, 3 T, and 7 T MRI from 9 datasets to develop and validate the method. RESULTS: FFCM-MRF achieved average Dice similarity coefficients ranging from 69.16 % to 89.63 % across multiple independent datasets, with improvements ranging from 3.24 % to 7.3 % compared to state-of-the-art methods. Quantitative analysis showed that FFCM-MRF can accurately segment major arteries in the Circle of Willis at the base of the brain and small distal pial arteries while effectively reducing noise. Test-retest analysis showed that the model yielded high vascular volume and diameter reliability. CONCLUSIONS: Our results have demonstrated that FFCM-MRF is highly accurate and reliable and largely independent of variations in field strength, scanner platforms, acquisition parameters, and species. The macaque MRA data and user-friendly open-source toolbox are freely available at OpenNeuro and GitHub to facilitate studies of imaging biomarkers for cerebrovascular and neurodegenerative diseases.

Shape-changing electrode array for minimally invasive large-scale intracranial brain activity mapping.

Large-scale brain activity mapping is important for understanding the neural basis of behaviour. Electrocorticograms (ECoGs) have high spatiotemporal resolution, bandwidth, and signal quality. However, the invasiveness and surgical risks of electrode array implantation limit its application scope. We developed an ultrathin, flexible shape-changing electrode array (SCEA) for large-scale ECoG mapping with minimal invasiveness. SCEAs were inserted into cortical surfaces in compressed states through small openings in the skull or dura and fully expanded to cover large cortical areas. MRI and histological studies on rats proved the minimal invasiveness of the implantation process and the high chronic biocompatibility of the SCEAs. High-quality micro-ECoG activities mapped with SCEAs from male rodent brains during seizures and canine brains during the emergence period revealed the spatiotemporal organization of different brain states with resolution and bandwidth that cannot be achieved using existing noninvasive techniques. The biocompatibility and ability to map large-scale physiological and pathological cortical activities with high spatiotemporal resolution, bandwidth, and signal quality in a minimally invasive manner offer SCEAs as a superior tool for applications ranging from fundamental brain research to brain-machine interfaces.

Common Sequential Organization of Face Processing in the Human Brain and Convolutional Neural Networks.

Face processing includes two crucial processing levels - face detection and face recognition. However, it remains unclear how human brains organize the two processing levels sequentially. While some studies found that faces are recognized as fast as they are detected, others have reported that faces are detected first, followed by recognition. We discriminated the two processing levels on a fine time scale by combining human intracranial EEG (two females, three males, and three subjects without reported sex information) and representation similarity analysis. Our results demonstrate that the human brain exhibits a "detection-first, recognition-later" pattern during face processing. In addition, we used convolutional neural networks to test the hypothesis that the sequential organization of the two face processing levels in the brain reflects computational optimization. Our findings showed that the networks trained on face recognition also exhibited the "detection-first, recognition-later" pattern. Moreover, this sequential organization mechanism developed gradually during the training of the networks and was observed only for correctly predicted images. These findings collectively support the computational account as to why the brain organizes them in this way.

Theta-Alpha Connectivity in the Hippocampal-Entorhinal Circuit Predicts Working Memory Load.

Working memory (WM) maintenance relies on multiple brain regions and inter-regional communications. The hippocampus and entorhinal cortex (EC) are thought to support this operation. Besides, EC is the main gateway for information between the hippocampus and neocortex. However, the circuit-level mechanism of this interaction during WM maintenance remains unclear in humans. To address these questions, we recorded the intracranial electroencephalography from the hippocampus and EC while patients (N = 13, six females) performed WM tasks. We found that WM maintenance was accompanied by enhanced theta/alpha band (2-12 Hz) phase synchronization between the hippocampus to the EC. The Granger causality and phase slope index analyses consistently showed that WM maintenance was associated with theta/alpha band-coordinated unidirectional influence from the hippocampus to the EC. Besides, this unidirectional inter-regional communication increased with WM load and predicted WM load during memory maintenance. These findings demonstrate that WM maintenance in humans engages the hippocampal-entorhinal circuit, with the hippocampus influencing the EC in a load-dependent manner.

Fast and Adaptive Construction of Gyral Morphological Networks Based on Morphometric Features of Cortical Surface.

The organization of cortical folding patterns are related to brain function, cognition and behaviors. Due to the enormous complexity and high inter-subject variability in cortical morphology, it has been a challenging task to effectively and efficiently quantify the gyrification patterns of cerebral cortex. To tackle these issues, the gyral net approach used a graph-based representation of cortical architecture by segmenting the gyral crests from the cortical meshes based on its morphological metrics. However, current morphology-based approaches are very time-consuming and not applicable for large-scale dataset. In this study, we develop a fast and adaptive method to automatically construct the gyral morphological graph within 10 seconds. Our method is robust to low contrast conditions and more computationally efficient, approximately 5 times faster than classical approaches. We evaluated the proposed method on 1081 young adults acquired from the HCP dataset and uncovered significant differences among functional brain networks from the perspective of morphological networks.

MosaicNet: A deep-learning-based multi-tile biomedical image stitching method.

Multi-tile image stitching aims to merge multiple natural or biomedical images into a single mosaic. This is an essential step in whole-slide imaging and large-scale pathological imaging systems. To tackle this task, a multi-step framework is usually used by first estimating the optimal transformation for each image and then fusing them into a whole image. However, the traditional approaches are usually time-consuming and require manual adjustments. Advances in deep learning techniques provide an end-to-end solution to register and fuse information of multiple tile images. In this paper, we present a deep learning model for multi-tile biomedical image stitching, namely MosaicNet, consisting of an aligning network and a fusion network. We trained the MosaicNet network on a large simulation dataset based on the VOC2012 dataset and evaluated the model on multiple types of datasets, including simulated natural images, mouse brain T2-weighted Magnetic Resonance Imaging (T2w-MRI) data, and mouse brain polarization sensitive-optical coherence tomography (PS-OCT) data. Our method outperformed traditional approaches on both natural images and brain imaging data. The proposed method is robust to different settings of hyper-parameters and shows high computational efficiency, up to approximately 32 times faster than the conventional methods.

Benchmarking EEG-based Cross-dataset Driver Drowsiness Recognition with Deep Transfer Learning.

It usually takes a long time to collect data for calibration when using electroencephalography (EEG) for driver drowsiness monitoring. Cross-dataset recognition is desirable since it can significantly save the calibration time when an existing dataset is used. However, the recognition accuracy is affected by the distribution drift problem caused by different experimental environments when building different datasets. In order to solve the problem, we propose a deep transfer learning model named Entropy-Driven Joint Adaptation Network (EDJAN), which can learn useful information from source and target domains simultaneously. An entropy-driven loss function is used to promote clustering of target-domain representations and an individual-level domain adaptation technique is proposed to alleviate the distribution discrepancy problem of test subjects. We use two public driving datasets SEEG-VIG and SADT to test the model on the cross-dataset setting. The proposed model achieved an accuracy of 83.3% when SADT is used as source domain and SEED-VIG is used as target domain and 76.7% accuracy on the reverse setting, which is higher than the other SOTA methods. The results are further analyzed with both global and local interpretation methods. Our work illuminates a promising direction of using EEG for calibration-free driver drowsiness recognition.

In-vivoverified anatomically aware deep learning for real-time electric field simulation.

Objective.Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement.Approach.We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset.In-vivoTMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes.Significance.The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments.

Natural brain state change with E/I balance shifting toward inhibition is associated with vigilance impairment.

The delicate balance between cortical excitation and inhibition (E/I) plays a pivotal role in brain state changes. While previous studies have associated cortical hyperexcitability with brain state changes induced by sleep deprivation, whether cortical hypoexcitability is also linked to brain state changes and, if so, how it could affect cognitive performance remain unknown. Here, we address these questions by examining the brain state change occurring after meals, i.e., postprandial somnolence, and comparing it with that induced by sleep deprivation. By analyzing features representing network excitability based on electroencephalogram (EEG) signals, we confirmed cortical hyperexcitability under sleep deprivation but revealed hypoexcitability under postprandial somnolence. In addition, we found that both sleep deprivation and postprandial somnolence adversely affected the level of vigilance. These results indicate that cortical E/I balance toward inhibition is associated with brain state changes, and deviation from the balanced state, regardless of its direction, could impair cognitive performance.

Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.

Introduction: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. Methods: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI. Results: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. Discussion: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.

MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification.

Traditional individual identification methods, such as face and fingerprint recognition, carry the risk of personal information leakage. The uniqueness and privacy of electroencephalograms (EEG) and the popularization of EEG acquisition devices have intensified research on EEG-based individual identification in recent years. However, most existing work uses EEG signals from a single session or emotion, ignoring large differences between domains. As EEG signals do not satisfy the traditional deep learning assumption that training and test sets are independently and identically distributed, it is difficult for trained models to maintain good classification performance for new sessions or new emotions. In this article, an individual identification method, called Multi-Loss Domain Adaptor (MLDA), is proposed to deal with the differences between marginal and conditional distributions elicited by different domains. The proposed method consists of four parts: a) Feature extractor, which uses deep neural networks to extract deep features from EEG data; b) Label predictor, which uses full-layer networks to predict subject labels; c) Marginal distribution adaptation, which uses maximum mean discrepancy (MMD) to reduce marginal distribution differences; d) Associative domain adaptation, which adapts to conditional distribution differences. Using the MLDA method, the cross-session and cross-emotion EEG-based individual identification problem is addressed by reducing the influence of time and emotion. Experimental results confirmed that the method outperforms other state-of-the-art approaches.

How does brain geometry influence human brain function?

Recent work by Pang et al. enriches our understanding of how the anatomy of the human brain constrains its function by demonstrating that brain geometry plays a crucial role in predicting neuronal dynamics. We highlight some key findings from this work while also addressing some points of confusion that could potentially cause public misunderstanding.

Abnormality of anxious behaviors and functional connectivity between the amygdala and the frontal lobe in maternally deprived monkeys.

OBJECTIVE: Anxious behaviors often occur in individuals who have experienced early adversity. Anxious behaviors can bring many hazards, such as social withdrawal, eating disorders, negative self-efficacy, self-injurious thoughts and behaviors, anxiety disorders, and even depression. Abnormal behavior are is closely related to changes in corresponding circuit functions in the brain. This study investigated the relationship between brain circuits and anxious behaviors in maternal-deprived rhesus monkey animal model, which mimic early adversity in human. METHODS: Twenty-five rhesus monkeys (Macaca mulatta) were grouped by two different rearing conditions: 11 normal control and mother-reared (MR) monkeys and 14 maternally deprived and peer-reared (MD) monkeys. After obtaining images of the brain areas with significant differences in maternal separation and normal control macaque function, the relationship between functional junction intensity and stereotypical behaviors was determined by correlation analysis. RESULTS: The correlation analysis revealed that stereotypical behaviors were negatively correlated with the coupling between the left lateral amygdala subregion and the left inferior frontal gyrus in both MD and MR macaques. CONCLUSION: This study suggests that early adversity-induced anxious behaviors are associated with changes in the strength of the amygdala-prefrontal connection. The normalization of the regions involved in the functional connection might reverse the behavioral abnormality. It provides a solid foundation for effective intervention in human early adversity. SIGNIFICANCE STATEMENT: This study suggests that early adversity-induced anxious behaviors are associated with changes in the strength of the amygdala-prefrontal connection. The higher the amygdala-prefrontal connection strength, the less stereotyped behaviors exhibited by monkeys experiencing early adversity. Thus, in the future, changing the strength of the amygdala-prefrontal connection may reverse the behavioral abnormalities of individuals who experience early adversity. This study provides a solid foundation for effective intervention in humans' early adversity.

State-specific modulation of mood using intracranial electrical stimulation of the orbitofrontal cortex.

BACKGROUND: Orbitofrontal cortex (OFC) is a promising target for intracranial electrical stimulation (iES) aimed at improving mood states. However, knowledge gaps remain regarding the underlying neural mechanisms of iES effects, such as the effect of the OFC target in comparison with other emotional network targets, the impact of brain state at the time of stimulation, and the neural response induced by iES at both local and network scales. OBJECTIVE: Our study aims to address the neural mechanisms underlying the effects of iES in improving mood states. METHODS: We conducted a study in 24 epilepsy patients who received iES through implanted electrodes in the emotional network and compared the effects of iES on multiple targets in the emotional network. RESULTS: We found that only iES applied to the orbitofrontal cortex (OFC) led to mood improvement and changes in neural activity. We also observed that iES to the OFC suppressed the delta-theta power when the brain was in a low mood state. Moreover, the iES to the OFC decreased delta-theta power and increased gamma power at local regions within the emotional network, and enhanced the information flow through the frequency domain among regions within the emotional network. CONCLUSIONS: These findings provide insight into the neural correlates of iES-induced mood improvement and support the potential of iES as a therapeutic intervention for mood disorders.