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Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting. We obtained T1-weighted and resting-state functional MRI data from 50 patients with episodic migraine and 50 age- and sex-matched healthy controls, with follow-up data collected after one year. Morphological features, including cortical thickness, curvature, and sulcal depth, and functional connectivity features, such as low-dimensional representation of functional connectivity (gradient), degree centrality, and betweenness centrality, were utilized. We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. By testing the models with varying feature combinations, penalty terms, and spatial granularities within a strict cross-validation framework, we found that the combination of curvature, sulcal depth, cortical thickness, and functional gradient achieved a robust classification performance. The model performance was assessed using the test dataset and achieved 87% accuracy and 0.94 area under the curve (AUC) at distinguishing migraine patients from healthy controls, with 85%, 0.97 and 84%, 0.93 during the interictal and ictal/peri-ictal phases, respectively. When validated using follow-up data, which was not included during model training, the model achieved 91%, 94%, 89% accuracies and 0.96, 0.94, 0.98 AUC for the total, interictal, and ictal/peri-ictal phases, respectively, confirming its robustness. Feature importance and clinical association analyses exhibited that the somatomotor, limbic, and default mode regions could be reliable markers of migraine. Our findings, which demonstrate a robust diagnostic performance using multimodal MRI features and a machine-learning framework, may offer a valuable approach for clinical diagnosis across diverse cohorts and help alleviate the decision-making burden for clinicians., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Gangliosides in the brain play a crucial role in modulating the integrity of vertebrate central nervous system in a region-specific manner. However, to date, a comprehensive structural elucidation of complex intact ganglioside isomers has not been achieved, resulting in the elusiveness into related molecular mechanism. Here, we present a glycolipidomic approach for isomer-specific and brain region-specific profiling of the mouse brain. Considerable region-specificity and commonality in specific group of regions are highlighted. Notably, we observe a similarity in the abundance of major isomers, GD1a and GD1b, within certain regions, which provides significant biological implications with interpretation through the lens of a theoretical retrosynthetic state-transition network. Furthermore, A glycocentric-omics approaches using gangliosides and N-glycans reveal a remarkable convergence in spatial dynamics, providing valuable insight into molecular interaction network. Collectively, this study uncovers the spatial dynamics of intact glyco-conjugates in the brain, which are relevant to regional function and accelerates the discovery of potential therapeutic targets for brain diseases., (© 2024. The Author(s).)

Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends - or gradients - in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization., Competing Interests: Declarations. Competing Interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The brain integrates information from pain-predictive cues and noxious inputs to construct the pain experience. Although previous studies have identified neural encodings of individual pain components, how they are integrated remains elusive. Here, using a cue-induced pain task, we examined temporal functional magnetic resonance imaging activities within the state space, where axes represent individual voxel activities. By analyzing the features of these activities at the large-scale network level, we demonstrated that overall brain networks preserve both cue and stimulus information in their respective subspaces within the state space. However, only higher-order brain networks, including limbic and default mode networks, could reconstruct the pattern of participants' reported pain by linear summation of subspace activities, providing evidence for the integration of cue and stimulus information. These results suggest a hierarchical organization of the brain for processing pain components and elucidate the mechanism for their integration underlying our pain perception.

Understanding the brain's mechanisms in individuals with obesity is important for managing body weight. Prior neuroimaging studies extensively investigated alterations in brain structure and function related to body mass index (BMI). However, how the network communication among the large-scale brain networks differs across BMI is underinvestigated. This study used diffusion magnetic resonance imaging of 290 young adults to identify links between BMI and brain network mechanisms. Navigation efficiency, a measure of network routing, was calculated from the structural connectivity computed using diffusion tractography. The sensory and frontoparietal networks indicated positive associations between navigation efficiency and BMI. The neurotransmitter association analysis identified that serotonergic and dopaminergic receptors, as well as opioid and norepinephrine systems, were related to BMI-related alterations in navigation efficiency. The transcriptomic analysis found that genes associated with network routing across BMI overlapped with genes enriched in excitatory and inhibitory neurons, specifically, gene enrichments related to synaptic transmission and neuron projection. Our findings suggest a valuable insight into understanding BMI-related alterations in brain network routing mechanisms and the potential underlying cellular biology, which might be used as a foundation for BMI-based weight management., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Neuromodulation technologies are crucial for investigating neuronal connectivity and brain function. Magnetic neuromodulation offers wireless and remote deep brain stimulations that are lacking in optogenetic- and wired-electrode-based tools. However, due to the limited understanding of working principles and poorly designed magnetic operating systems, earlier magnetic approaches have yet to be utilized. Furthermore, despite its importance in neuroscience research, cell-type-specific magnetic neuromodulation has remained elusive. Here we present a nanomaterials-based magnetogenetic toolbox, in conjunction with Cre-loxP technology, to selectively activate genetically encoded Piezo1 ion channels in targeted neuronal populations via torque generated by the nanomagnetic actuators in vitro and in vivo. We demonstrate this cell-type-targeting magnetic approach for remote and spatiotemporal precise control of deep brain neural activity in multiple behavioural models, such as bidirectional feeding control, long-term neuromodulation for weight control in obese mice and wireless modulation of social behaviours in multiple mice in the same physical space. Our study demonstrates the potential of cell-type-specific magnetogenetics as an effective and reliable research tool for life sciences, especially in wireless, long-term and freely behaving animals., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Synaptically localized NMDA receptors (NMDARs) play a crucial role in important cognitive functions by mediating synaptic transmission and plasticity. In contrast, a tonic NMDAR current, thought to be mediated by extrasynaptic NMDARs, has a less clear function. This review provides a comprehensive overview of tonic NMDAR currents, focusing on their roles in synaptic transmission/plasticity and their impact on cognitive functions and psychiatric disorders. We discuss the roles of 3 endogenous ligands (i.e., glutamate, glycine, and D-serine) and receptors in mediating tonic NMDAR currents and explore the diverse mechanisms that regulate tonic NMDAR currents. In light of recent controversies surrounding the source of D-serine, we highlight the recent findings suggesting that astrocytes release D-serine to modulate tonic NMDAR currents and control cognitive flexibility. Furthermore, we propose distinct roles of neuronal and astrocytic D-serine in different locations and their implications for synaptic regulation and cognitive functions. The potential roles of tonic NMDAR currents in various psychiatric disorders, such as schizophrenia and autism spectrum disorder, are discussed in the context of the NMDAR hypofunction hypothesis. By presenting the mechanisms by which various cells, particularly astrocytes, regulate tonic NMDAR currents, we aim to stimulate future research in NMDAR hypofunction- or hyperfunction-related psychiatric disorders. This review not only provides a better understanding of the complex interplay between tonic NMDAR currents and cognitive functions but also sheds light on its potential therapeutic target for the treatment of various psychiatric disorders., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Pleasure and pain are two fundamental, intertwined aspects of human emotions. Pleasurable sensations can reduce subjective feelings of pain and vice versa, and we often perceive the termination of pain as pleasant and the absence of pleasure as unpleasant. This implies the existence of brain systems that integrate them into modality-general representations of affective experiences. Here, we examined representations of affective valence and intensity in an functional MRI (fMRI) study ( n = 58) of sustained pleasure and pain. We found that the distinct subpopulations of voxels within the ventromedial and lateral prefrontal cortices, the orbitofrontal cortex, the anterior insula, and the amygdala were involved in decoding affective valence versus intensity. Affective valence and intensity predictive models showed significant decoding performance in an independent test dataset ( n = 62). These models were differentially connected to distinct large-scale brain networks-the intensity model to the ventral attention network and the valence model to the limbic and default mode networks. Overall, this study identified the brain representations of affective valence and intensity across pleasure and pain, promoting a systems-level understanding of human affective experiences., Competing Interests: Competing interests statement:The authors declare no competing interest.

Understanding cellular architectures and their connectivity is essential for interrogating system function and dysfunction. However, we lack technologies for mapping the multiscale details of individual cells and their connectivity in the human organ-scale system. We developed a platform that simultaneously extracts spatial, molecular, morphological, and connectivity information of individual cells from the same human brain. The platform includes three core elements: a vibrating microtome for ultraprecision slicing of large-scale tissues without losing cellular connectivity (MEGAtome), a polymer hydrogel-based tissue processing technology for multiplexed multiscale imaging of human organ-scale tissues (mELAST), and a computational pipeline for reconstructing three-dimensional connectivity across multiple brain slabs (UNSLICE). We applied this platform for analyzing human Alzheimer's disease pathology at multiple scales and demonstrating scalable neural connectivity mapping in the human brain.

Alzheimer's disease (AD) is a brain network disorder where pathological proteins accumulate through networks and drive cognitive decline. Yet, the role of network connectivity in facilitating this accumulation remains unclear. Using in-vivo multimodal imaging, we show that the distribution of tau and reactive microglia in humans follows spatial patterns of connectivity variation, the so-called gradients of brain organization. Notably, less distinct connectivity patterns ("gradient contraction") are associated with cognitive decline in regions with greater tau, suggesting an interaction between reduced network differentiation and tau on cognition. Furthermore, by modeling tau in subject-specific gradient space, we demonstrate that tau accumulation in the frontoparietal and temporo-occipital cortices is associated with greater baseline tau within their functionally and structurally connected hubs, respectively. Our work unveils a role for both functional and structural brain organization in pathology accumulation in AD, and supports subject-specific gradient space as a promising tool to map disease progression., (© 2024. The Author(s).)

Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. We found the most apparent cortical hierarchical organization differentiating between low-level sensory and higher-order transmodal regions in the middle of the diffusion time, indicating that the hierarchical organization of the brain may reflect the intermediate mechanisms of mono- and polysynaptic communications. Associations between the functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the potential biological underpinnings specifying gene enrichment in the striatum, hypothalamus, and cortical cells. Our findings provide evidence that structure-function correspondence is strongly coupled with BMI when hierarchical organization is the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI., Competing Interests: Declaration of competing interest Bo-yong Park is an Associate Editor for NeuroImage but was not involved in the handling or review process of this manuscript., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Conventional power-integrated wireless neural recording devices suffer from bulky, rigid batteries in head-mounted configurations, hindering the precise interpretation of the subject's natural behaviors. These power sources also pose risks of material leakage and overheating. We present the direct printing of a power-integrated wireless neural recording system that seamlessly conforms to the cranium. A quasi-solid-state Zn-ion microbattery was 3D-printed as a built-in power source geometrically synchronized to the shape of a mouse skull. Soft deep-brain neural probes, interconnections, and auxiliary electronics were also printed using liquid metals on the cranium with high resolutions. In vivo studies using mice demonstrated the reliability and biocompatibility of this wireless neural recording system, enabling the monitoring of neural activities across extensive brain regions without notable heat generation. This all-printed neural interface system revolutionizes brain research, providing bio-conformable, customizable configurations for improved data quality and naturalistic experimentation.

The contents and dynamics of spontaneous thought are important factors for personality traits and mental health. However, assessing spontaneous thoughts is challenging due to their unconstrained nature, and directing participants' attention to report their thoughts may fundamentally alter them. Here, we aimed to decode two key content dimensions of spontaneous thought-self-relevance and valence-directly from brain activity. To train functional MRI-based predictive models, we used individually generated personal stories as stimuli in a story-reading task to mimic narrative-like spontaneous thoughts ( n = 49). We then tested these models on multiple test datasets (total n = 199). The default mode, ventral attention, and frontoparietal networks played key roles in the predictions, with the anterior insula and midcingulate cortex contributing to self-relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex contributing to valence prediction. Overall, this study presents brain models of internal thoughts and emotions, highlighting the potential for the brain decoding of spontaneous thought., Competing Interests: Competing interests statement:The authors declare no competing interest.

Electrophysiological recording technologies can provide valuable insights into the functioning of the central and peripheral nervous systems. Surface electrode arrays made of soft materials or implantable multi-electrode arrays with high electrode density have been widely utilized as neural probes. However, neither of these probe types can simultaneously achieve minimal invasiveness and robust neural signal detection. Here, we present an ultra-thin, minimally invasive neural probe (the "NeuroWeb") consisting of hexagonal boron nitride and graphene, which leverages the strengths of both surface electrode array and implantable multi-electrode array. The NeuroWeb open lattice structure with a total thickness of 100 nm demonstrates high flexibility and strong adhesion, establishing a conformal and tight interface with the uneven mouse brain surface. In vivo electrophysiological recordings show that NeuroWeb detects stable single-unit activity of neurons with high signal-to-noise ratios. Furthermore, we investigate neural interactions between the somatosensory cortex and the cerebellum using transparent dual NeuroWebs and optical stimulation, and measure the times of neural signal transmission between the brain regions depending on the pathway. Therefore, NeuroWeb can be expected to pave the way for understanding complex brain networks with optical and electrophysiological mapping of the brain., (© 2023. The Author(s).)

Dopamine synapses play a crucial role in volitional movement and reward-related behaviors, while dysfunction of dopamine synapses causes various psychiatric and neurological disorders. Despite this significance, the true biological nature of dopamine synapses remains poorly understood. Here, we show that dopamine transmission is strongly correlated with GABA co-transmission across the brain and dopamine synapses are structured and function like GABAergic synapses with marked regional heterogeneity. In addition, GABAergic-like dopamine synapses are clustered on the dendrites, and GABA transmission at dopamine synapses has distinct physiological properties. Interestingly, the knockdown of neuroligin-2, a key postsynaptic protein at GABAergic synapses, unexpectedly does not weaken GABA co-transmission but instead facilitates it at dopamine synapses in the striatal neurons. More importantly, the attenuation of GABA co-transmission precedes deficits in dopaminergic transmission in animal models of Parkinson's disease. Our findings reveal the spatial and functional nature of GABAergic-like dopamine synapses in health and disease., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Multisensory integration (MSI) occurs in a variety of brain areas, spanning cortical and subcortical regions. In traditional studies on sensory processing, the sensory cortices have been considered for processing sensory information in a modality-specific manner. The sensory cortices, however, send the information to other cortical and subcortical areas, including the higher association cortices and the other sensory cortices, where the multiple modality inputs converge and integrate to generate a meaningful percept. This integration process is neither simple nor fixed because these brain areas interact with each other via complicated circuits, which can be modulated by numerous internal and external conditions. As a result, dynamic MSI makes multisensory decisions flexible and adaptive in behaving animals. Impairments in MSI occur in many psychiatric disorders, which may result in an altered perception of the multisensory stimuli and an abnormal reaction to them. This review discusses the diversity and flexibility of MSI in mammals, including humans, primates and rodents, as well as the brain areas involved. It further explains how such flexibility influences perceptual experiences in behaving animals in both health and disease. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Brain stimulation with ultra-low-intensity ultrasound has rarely been investigated due to the lack of a reliable device to measure small neuronal signal changes made by the ultra-low intensity range. We propose Ultrasonocoverslip, an ultrasound-transducer-integrated-glass-coverslip that determines the minimum intensity for brain cell activation. Brain cells can be cultured directly on Ultrasonocoverslip to simultaneously deliver uniform ultrasonic pressure to hundreds of cells with real-time monitoring of cellular responses using fluorescence microscopy and single-cell electrophysiology. The sensitivity for detecting small responses to ultra-low-intensity ultrasound can be improved by averaging simultaneously obtained responses. Acoustic absorbers can be placed under Ultrasonocoverslip, and stimuli distortions are substantially reduced to precisely deliver user-intended acoustic stimulations. With the proposed device, we discover the lowest acoustic threshold to induce reliable neuronal excitation releasing glutamate. Furthermore, mechanistic studies on the device show that the ultra-low-intensity ultrasound stimulation induces cell type-specific neuromodulation by activating astrocyte-mediated neuronal excitation without direct neuronal involvement. The performance of ultra-low-intensity stimulation is validated by in vivo experiments demonstrating improved safety and specificity in motor modulation of tail movement compared to that with supra-watt-intensity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Visualization of focused ultrasound in high spatial and temporal resolution is crucial for accurately and precisely targeting brain regions noninvasively. Magnetic resonance imaging (MRI) is the most widely used noninvasive tool for whole-brain imaging. However, focused ultrasound studies employing high-resolution (> 9.4 T) MRI in small animals are limited by the small size of the radiofrequency (RF) volume coil and the noise sensitivity of the image to external systems such as bulky ultrasound transducers. This technical note reports a miniaturized ultrasound transducer system packaged directly above a mouse brain for monitoring ultrasound-induced effects using high-resolution 9.4 T MRI. Our miniaturized system integrates MR-compatible materials with electromagnetic (EM) noise reduction techniques to demonstrate echo-planar imaging (EPI) signal changes in the mouse brain at various ultrasound acoustic intensities. The proposed ultrasound-MRI system will enable extensive research in the expanding field of ultrasound therapeutics., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023. Published by Elsevier Inc.)

Real-time monitoring of various neurochemicals with high spatial resolution in multiple brain regions in vivo can elucidate neural circuits related to various brain diseases. However, previous systems for monitoring neurochemicals have limitations in observing multiple neurochemicals without crosstalk in real time, and these methods cannot record electrical activity, which is essential for investigating neural circuits. Here, we present a real-time bimodal (RTBM) neural probe that uses monolithically integrated biosensors and multiple shanks to study the connectivity of neural circuits by measuring multiple neurochemicals and electrical neural activity in real time. Using the RTBM probe, we demonstrate concurrent measurements of four neurochemicals-glucose, lactate, choline, and glutamate without cross-talking each other-and electrical activity in real time in vivo. Additionally, we show the functional connectivity between the medial prefrontal cortex and mediodorsal thalamus through the simultaneous measurement of chemical and electrical signals. We expect that our device will contribute to not only elucidating the role of neurochemicals in neural circuits related to brain functions but also developing drugs for various brain diseases related to neurochemicals.

The human brain supports social cognitive functions, including Theory of Mind, empathy, and compassion, through its intrinsic hierarchical organization. However, it remains unclear how the learning and refinement of social skills shapes brain function and structure. We studied if different types of social mental training induce changes in cortical function and microstructure, investigating 332 healthy adults (197 women, 20-55 years) with repeated multimodal neuroimaging and behavioral testing. Our neuroimaging approach examined longitudinal changes in cortical functional gradients and myelin-sensitive T1 relaxometry, two complementary measures of cortical hierarchical organization. We observed marked changes in intrinsic cortical function and microstructure, which varied as a function of social training content. In particular, cortical function and microstructure changed as a result of attention-mindfulness and socio-cognitive training in regions functionally associated with attention and interoception, including insular and parietal cortices. Conversely, socio-affective and socio-cognitive training resulted in differential microstructural changes in regions classically implicated in interoceptive and emotional processing, including insular and orbitofrontal areas, but did not result in functional reorganization. Notably, longitudinal changes in cortical function and microstructure predicted behavioral change in attention, compassion and perspective-taking. Our work demonstrates functional and microstructural plasticity after the training of social-interoceptive functions, and illustrates the bidirectional relationship between brain organisation and human social skills., Competing Interests: SV, PK, BP, SH, AB, FT, BB, TS No competing interests declared, (© 2023, Valk et al.)

Migraine is a type of headache with multiple neurological symptoms. Prior neuroimaging studies in patients with migraine based on functional magnetic resonance imaging have found regional as well as network-level alterations in brain function. Here, we expand on prior studies by establishing whole-brain functional connectivity patterns in patients with migraine using dimensionality reduction techniques. We studied functional brain connectivity in 50 patients with episodic migraine and sex- and age-matched healthy controls. Using dimensionality reduction techniques that project high-dimensional functional connectivity onto low-dimensional representations (i.e., eigenvectors), we found significant between-group differences in the eigenvectors between patients with migraine and healthy controls, particularly in the sensory/motor and limbic cortices. Furthermore, we assessed between-group differences in subcortical connectivity with subcortical weighted manifolds defined by subcortico-cortical connectivity multiplied by cortical eigenvectors and revealed significant alterations in the amygdala. Finally, leveraging supervised machine learning, we moderately predicted headache frequency using cortical and subcortical functional connectivity features, again indicating that sensory and limbic regions play a particularly important role in predicting migraine frequency. Our study confirmed that migraine is a hierarchical disease of the brain that shows alterations along the sensory-limbic axis, and therefore, the functional connectivity in these areas could be a useful marker to investigate migraine symptomatology., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Brain organoids are self-assembled three-dimensional aggregates with brain-like cell types and structures and have emerged as new model systems that can be used to investigate human neurodevelopment and neurological disorders. However, brain organoids are not as mature and functional as real human brains due to limitations of the culture system with insufficient developmental patterning signals and a lack of components that are important for brain development and function, such as the non-neural population and vasculature. In addition, establishing the desired brain-like environment and monitoring the complex neural networks and physiological functions of the brain organoids remain challenging. The current protocols to generate brain organoids also have problems with heterogeneity and batch variation due to spontaneous self-organization of brain organoids into complex architectures of the brain. To address these limitations of current brain organoid technologies, various engineering platforms, such as extracellular matrices, fluidic devices, three-dimensional bioprinting, bioreactors, polymeric scaffolds, microelectrodes, and biochemical sensors, have been employed to improve neuronal development and maturation, reduce structural heterogeneity, and facilitate functional analysis and monitoring. In this review, we provide an overview of the latest engineering techniques that overcome these limitations in the production and application of brain organoids.

Adolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14 to 25 y ( n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of corticocortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple corticocortical structural networks in youth. We then assessed structure-function coupling using resting-state functional MRI measures in the same participants both via cross-sectional analysis at baseline and by studying longitudinal change between baseline and follow-up scans. At baseline, regions with more similar structural wiring were more likely to be functionally coupled. Moreover, correlating longitudinal structural wiring changes with longitudinal functional connectivity reconfigurations, we found that increased structural differentiation, particularly between sensory/unimodal and default mode networks, was reflected by reduced functional interactions. These findings provide insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.

Characterizing cerebral contributions to individual variability in pain processing is crucial for personalized pain medicine, but has yet to be done. In the present study, we address this problem by identifying brain regions with high versus low interindividual variability in their relationship with pain. We trained idiographic pain-predictive models with 13 single-trial functional MRI datasets (n = 404, discovery set) and quantified voxel-level importance for individualized pain prediction. With 21 regions identified as important pain predictors, we examined the interindividual variability of local pain-predictive weights in these regions. Higher-order transmodal regions, such as ventromedial and ventrolateral prefrontal cortices, showed larger individual variability, whereas unimodal regions, such as somatomotor cortices, showed more stable pain representations across individuals. We replicated this result in an independent dataset (n = 124). Overall, our study identifies cerebral sources of individual differences in pain processing, providing potential targets for personalized assessment and treatment of pain., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The brain contains both generalized and stimulus-type-specific representations of aversive events, but models of how these are integrated and related to subjective experience are lacking. We combined functional magnetic resonance imaging with predictive modeling to identify representations of generalized (common) and stimulus-type-specific negative affect across mechanical pain, thermal pain, aversive sounds and aversive images of four intensity levels each. This allowed us to examine how generalized and stimulus-specific representations jointly contribute to aversive experience. Stimulus-type-specific negative affect was largely encoded in early sensory pathways, whereas generalized negative affect was encoded in a distributed set of midline, forebrain, insular and somatosensory regions. All models specifically predicted negative affect rather than general salience or arousal and accurately predicted negative affect in independent samples, demonstrating robustness and generalizability. Common and stimulus-type-specific models were jointly important for predicting subjective experience. Together, these findings offer an integrated account of how negative affect is constructed in the brain and provide predictive neuromarkers for future studies., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. We compared models based on (a) a single most pain-predictive region or resting-state network; (b) pain-associated cortical-subcortical systems developed from prior literature ("multisystem models"); and (c) a model spanning the full brain. We estimated model accuracy using leave-one-study-out cross-validation (CV; 7 studies) and subsequently validated in 4 independent holdout studies. All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. Full brain models showed no predictive advantage over multisystem models. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains., Competing Interests: The authors have declared that no competing interests exist.

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity., Competing Interests: The authors have declared that no competing interests exist.

Soft, transparent poly(dimethyl siloxane) (PDMS)-based cranial windows in animal models have created many opportunities to investigate brain functions with multiple in vivo imaging modalities. However, due to the hydrophobic nature of PDMS, the wettability by cerebrospinal fluid (CSF) is poor, which may cause air bubble trapping beneath the window during implantation surgery, and favorable heterogeneous bubble nucleation at the interface between hydrophobic PDMS and CSF. This may result in excessive growth of the entrapped bubble under the soft cranial window. Herein, to yield biocompatibility-enhanced, trapped bubble-minimized, and soft cranial windows, this report introduces a CSF-philic PDMS window coated with hydroxyl-enriched poly(vinyl alcohol) (PVA) for long-term in vivo imaging. The PVA-coated PDMS (PVA/PDMS) film exhibits a low contact angle θ ACA (33.7 ± 1.9°) with artificial CSF solution and maintains sustained CSF-philicity. The presence of the PVA layer achieves air bubble-free implantation of the soft cranial window, as well as induces the formation of a thin wetting film that shows anti-biofouling performance through abundant water molecules on the surface, leading to long-term optical clarity. In vivo studies on the mice cortex verify that the soft and CSF-philic features of the PVA/PDMS film provide minimal damage to neuronal tissues and attenuate immune response. These advantages of the PVA/PDMS window are strongly correlated with the enhancement of cortical hemodynamic changes and the local field potential recorded through the PVA/PDMS film, respectively. This collection of results demonstrates the potential for future microfluidic platforms for minimally invasive CSF extraction utilizing a CSF-philic fluidic passage.

It is clear that humans can extract statistical information from streams of visual input, yet how our brain processes sequential images into the abstract representation of the mean feature value remains poorly explored. Using multivariate pattern analyses of electroencephalography recorded while human observers viewed 10 sequentially presented Gabors of different orientations to estimate their mean orientation at the end, we investigated sequential averaging mechanism by tracking the quality of individual and mean orientation as a function of sequential position. Critically, we varied the sequential variance of Gabor orientations to understand the neural basis of perceptual mean errors occurring during a sequential averaging task. We found that the mean-orientation representation emerged at specific delays from each sequential stimulus onset and became increasingly accurate as additional Gabors were viewed. Especially in frontocentral electrodes, the neural representation of mean orientation improved more rapidly and to a greater degree in less volatile environments, whereas individual orientation information was encoded precisely regardless of environmental volatility. The computational analysis of behavioral data also showed that perceptual mean errors arise from the cumulative construction of the mean orientation rather than the low-level encoding of individual stimulus orientation. Thus, our findings provide neural mechanisms to differentially accumulate increasingly abstract features from a concrete piece of information across the cortical hierarchy depending on environmental volatility. SIGNIFICANCE STATEMENT The visual system extracts behaviorally relevant summary statistical representation by exploiting statistical regularity of the visual stream over time. However, how the neural representation of the abstract mean feature value develops in a temporally changing environment remains poorly identified. Here, we directly recover the mean orientation information of sequentially delivered Gabor stimuli with different orientations as a function of their positions in time. The mean orientation representation, which is regularly updated, becomes increasingly accurate with increasing sequential position especially in the frontocentral region. Further, perceptual mean errors arise from the cumulative process rather than the low-level stimulus encoding. Overall, our study reveals a role of higher cortical areas in integrating stimulus-specific information into increasingly abstract task-oriented information., (Copyright © 2022 the authors.)

Functional hierarchy establishes core axes of the brain, and overweight individuals show alterations in the networks anchored on these axes, particularly in those involved in sensory and cognitive control systems. However, quantitative assessments of hierarchical brain organization in overweight individuals are lacking. Capitalizing stepwise functional connectivity analysis, we assess altered functional connectivity in overweight individuals relative to healthy weight controls along the brain hierarchy. Seeding from the brain regions associated with obesity phenotypes, we conduct stepwise connectivity analysis at different step distances and compare functional degrees between the groups. We find strong functional connectivity in the somatomotor and prefrontal cortices in both groups, and both converge to transmodal systems, including frontoparietal and default-mode networks, as the number of steps increased. Conversely, compared with the healthy weight group, overweight individuals show a marked decrease in functional degree in somatosensory and attention networks across the steps, whereas visual and limbic networks show an increasing trend. Associating functional degree with eating behaviors, we observe negative associations between functional degrees in sensory networks and hunger and disinhibition-related behaviors. Our findings suggest that overweight individuals show disrupted functional network organization along the hierarchical axis of the brain and these results provide insights for behavioral associations., (© 2022. The Author(s).)

Prepulse inhibition (PPI) is a neurophysiological finding that is decreased in schizophrenia patients and has been used in pathophysiology studies of schizophrenia and the development of antipsychotic drugs. PPI is affected by several drugs including amphetamine, ketamine, and nicotinic agents, and it is reported that several brain regions and modulatory neurotransmitters are involved in PPI. Here we showed that mice with IRSp53 deletion in each dopaminergic, cholinergic, oxytocinergic, and serotoninergic modulatory neurons showed a decrease in PPI. Other than PPI, there were no other behavioral changes among IRSp53 deletion mice. Through this study, we could reconfirm that dysfunction of each modulatory neuron such as dopamine, acetylcholine, oxytocin, and serotonin can result in PPI impairment, and it should be considered that PPI could be broadly affected by changes in one of a certain kind of modulatory neurons., Competing Interests: Declaration of competing interest The authors declare that they have no competing financial interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Autism spectrum disorder (ASD) is commonly understood as an alteration of brain networks, yet case-control analyses against typically-developing controls (TD) have yielded inconsistent results. Here, we devised a novel approach to profile the inter-individual variability in functional network organization and tested whether such idiosyncrasy contributes to connectivity alterations in ASD. Studying a multi-centric dataset with 157 ASD and 172 TD, we obtained robust evidence for increased idiosyncrasy in ASD relative to TD in default mode, somatomotor and attention networks, but also reduced idiosyncrasy in lateral temporal cortices. Idiosyncrasy increased with age and significantly correlated with symptom severity in ASD. Furthermore, while patterns of functional idiosyncrasy were not correlated with ASD-related cortical thickness alterations, they co-localized with the expression patterns of ASD risk genes. Notably, we could demonstrate that patterns of atypical idiosyncrasy in ASD closely overlapped with connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between inter-individual heterogeneity in autism and functional signatures. Our findings provide novel biomarkers to study atypical brain development and may consolidate prior research findings on the variable nature of connectome level anomalies in autism., (© 2021. The Author(s).)

Background Evaluation of the glymphatic system with intrathecal contrast material injection has limited clinical use. Purpose To investigate the feasibility of using serial intravenous contrast-enhanced T1 mapping in the quantitative evaluation of putative dynamic glymphatic activity in various brain regions and to demonstrate the effect of sleep on glymphatic activity in humans. Materials and Methods In this prospective study from May 2019 to February 2020, 25 healthy participants (mean age, 25 years ± 2 [standard deviation]; 15 men) underwent two cycles of MRI (day and night cycles). For each cycle, T1 maps were acquired at baseline and 0.5, 1, 1.5, 2, and 12 hours after intravenous contrast material injection. For the night cycle, participants had a normal night of sleep between 2 and 12 hours. The time ( t min ) to reach the minimum T1 value (T1 min ), the absolute difference between baseline T1 and T1 min (peak ΔT1), and the slope between two measurements at 2 and 12 hours (slope [2h-12h] ) were determined from T1 value-time curves in cerebral gray matter (GM), cerebral white matter (WM), cerebellar GM, cerebellar WM, and putamen. Mixed-model analysis of variance (ANOVA), Friedman test, and repeated-measures ANOVA were used to assess the effect of sleep on slope (2h-12h) and to compare t min and peak ΔT1 among different regions. Results The slope (2h-12h) increased from the day to night cycles in cerebral GM, cerebellar GM, and putamen (geometric mean ratio [night/day] = 1.4 [95% CI: 1.2, 1.7], 1.3 [95% CI: 1.1, 1.4], and 2.4 [95% CI: 1.6, 3.6], respectively; P = .001, P values were 0.5 hour in cerebral and cerebellar GM and putamen for both cycles. Cerebellar GM had the highest mean peak ΔT1, followed by cerebral GM and putamen in both day (159 msec ± 6, 99 msec ± 4, and 62 msec ± 5, respectively) and night (152 msec ± 6, 104 msec ± 6, and 58 msec ± 4, respectively) cycles. Conclusion Clearance of a gadolinium-based contrast agent was greater after sleep compared with daytime wakefulness. These results suggest that sleep was associated with greater glymphatic clearance compared with wakefulness. © RSNA, 2021 P < .001, respectively). Median t min values were 0.5 hour in cerebral and cerebellar GM and putamen for both cycles. Cerebellar GM had the highest mean peak ΔT1, followed by cerebral GM and putamen in both day (159 msec ± 6, 99 msec ± 4, and 62 msec ± 5, respectively) and night (152 msec ± 6, 104 msec ± 6, and 58 msec ± 4, respectively) cycles. Conclusion Clearance of a gadolinium-based contrast agent was greater after sleep compared with daytime wakefulness. These results suggest that sleep was associated with greater glymphatic clearance compared with wakefulness. © RSNA, 2021 Online supplemental material is available for this article . See also the editorial by Anzai and Minoshima in this issue.

Brain organoids derived from human pluripotent stem cells provide a highly valuable in vitro model to recapitulate human brain development and neurological diseases. However, the current systems for brain organoid culture require further improvement for the reliable production of high-quality organoids. Here, we demonstrate two engineering elements to improve human brain organoid culture, (1) a human brain extracellular matrix to provide brain-specific cues and (2) a microfluidic device with periodic flow to improve the survival and reduce the variability of organoids. A three-dimensional culture modified with brain extracellular matrix significantly enhanced neurogenesis in developing brain organoids from human induced pluripotent stem cells. Cortical layer development, volumetric augmentation, and electrophysiological function of human brain organoids were further improved in a reproducible manner by dynamic culture in microfluidic chamber devices. Our engineering concept of reconstituting brain-mimetic microenvironments facilitates the development of a reliable culture platform for brain organoids, enabling effective modeling and drug development for human brain diseases., (© 2021. The Author(s).)

Mouse fMRI under anesthesia has become increasingly popular due to improvement in obtaining brain-wide BOLD response. Medetomidine with isoflurane has become well-accepted for resting-state fMRI, but whether this combination allows for stable, expected, and robust brain-wide evoked response in mice has yet to be validated. We thus utilized intravenous infusion of dexmedetomidine with inhaled isoflurane and intravenous infusion of ketamine/xylazine to elucidate whether stable mouse physiology and BOLD response are obtainable in response to simultaneous forepaw and whisker-pad stimulation throughout 8 h. We found both anesthetics result in hypercapnia with depressed heart rate and respiration due to self-breathing, but these values were stable throughout 8 h. Regardless of the mouse condition, brain-wide, robust, and stable BOLD response throughout the somatosensory axis was observed with differences in sensitivity and dynamics. Dexmedetomidine/isoflurane resulted in fast, boxcar-like, BOLD response with consistent hemodynamic shapes throughout the brain. Ketamine/xylazine response showed higher sensitivity, prolonged BOLD response, and evidence for cortical disinhibition as significant bilateral cortical response was observed. In addition, differing hemodynamic shapes were observed between cortical and subcortical areas. Overall, we found both anesthetics are applicable for evoked mouse fMRI studies.

Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined associations between functional connectivity and inter-individual BMI variations. We utilized non-linear connectome manifold learning techniques to represent macroscale functional organization along continuous hierarchical axes that dissociate low level and higher order brain systems. We observed an increased differentiation between unimodal and heteromodal association networks in individuals with higher BMI, indicative of a disrupted modular architecture and hierarchy of the brain. Transcriptomic decoding and gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings illustrate functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.

Distribution of cells and proteins in whole organs, such as the brain, can provide another level of insight into molecular and cellular functions. Here, we describe a whole-brain immunostaining method using the turquoise killifish, an emerging model for aging research. We optimized a protocol for tissue clearing and whole-brain immunostaining to the turquoise killifish brain. This protocol provides a comprehensive procedure from brain dissection to whole-brain imaging and image processing. For complete details on the use and execution of this protocol, please refer to Eunjeong Do (2020) and Lee et al. (2021)., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)

mTOR signaling, involving mTORC1 and mTORC2 complexes, critically regulates neural development and is implicated in various brain disorders. However, we do not fully understand all of the upstream signaling components that can regulate mTOR signaling, especially in neurons. Here, we show a direct, regulated inhibition of mTOR by Tanc2, an adaptor/scaffolding protein with strong neurodevelopmental and psychiatric implications. While Tanc2-null mice show embryonic lethality, Tanc2-haploinsufficient mice survive but display mTORC1/2 hyperactivity accompanying synaptic and behavioral deficits reversed by mTOR-inhibiting rapamycin. Tanc2 interacts with and inhibits mTOR, which is suppressed by mTOR-activating serum or ketamine, a fast-acting antidepressant. Tanc2 and Deptor, also known to inhibit mTORC1/2 minimally affecting neurodevelopment, distinctly inhibit mTOR in early- and late-stage neurons. Lastly, Tanc2 inhibits mTORC1/2 in human neural progenitor cells and neurons. In summary, our findings show that Tanc2 is a mTORC1/2 inhibitor affecting neurodevelopment.

Studies in rats, monkeys, and humans have found action-value signals in multiple regions of the brain. These findings suggest that action-value signals encoded in these brain structures bias choices toward higher expected rewards. However, previous estimates of action-value signals might have been inflated by serial correlations in neural activity and also by activity related to other decision variables. Here, we applied several statistical tests based on permutation and surrogate data to analyze neural activity recorded from the striatum, frontal cortex, and hippocampus. The results show that previously identified action-value signals in these brain areas cannot be entirely accounted for by concurrent serial correlations in neural activity and action value. We also found that neural activity related to action value is intermixed with signals related to other decision variables. Our findings provide strong evidence for broadly distributed neural signals related to action value throughout the brain., Competing Interests: ES, YJ, SK, HK, XC, HL, JS, SL, YC, MJ No competing interests declared, DL Reviewing editor, eLife, (© 2021, Shin et al.)

Neuroimaging studies for human participants have shown that the activity in the multiple-demand (MD) network is associated with various kinds of cognitive demand. However, surprisingly, it remains unclear how this MD network is related to a core component of cognition, the process of searching for a target among distractors. This was because previous neuroimaging studies of visual search were confounded by task difficulty or time on task. To circumvent these limitations, we examined human brain activity while participants perform two different visual search tasks. The performance of a task was limited by increased attentional demand, while the other task was primarily limited by poor quality of input data or neural noise. Throughout the MD network, increased activity and strengthened functional connectivity among the MD regions were observed under the search task recruiting capacity-limited attentional resources. The present findings provide unequivocal evidence that the MD network mediates visual search, as well as other capacity-limited cognitive processes., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interests., (Copyright © 2021. Published by Elsevier Inc.)

Childhood experiences play a profound role in conferring risk and resilience for brain and behavioral development. However, how different facets of the environment shape neurodevelopment remains largely unknown. Here we sought to decompose heterogeneous relationships between environmental factors and brain structure in 989 school-aged children from the Adolescent Brain Cognitive Development Study. We applied a cross-modal integration and clustering approach called 'Similarity Network Fusion', which combined two brain morphometrics (i.e., cortical thickness and myelin-surrogate markers), and key environmental factors (i.e., trauma exposure, neighborhood safety, school environment, and family environment) to identify homogeneous subtypes. Depending on the subtyping resolution, results identified two or five subgroups, each characterized by distinct brain structure-environment profiles. Notably, more supportive caregiving and school environments were associated with greater myelination, whereas less supportive caregiving, higher family conflict and psychopathology, and higher perceived neighborhood safety were observed with greater cortical thickness. These subtypes were highly reproducible and predicted externalizing symptoms and overall mental health problems. Our findings support the theory that distinct environmental exposures are differentially associated with alterations in structural neurodevelopment. Delineating more precise associations between risk factors, protective factors, and brain development may inform approaches to enhance risk identification and optimize interventions targeting specific experiences., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Reactive astrocytes are astrocytes undergoing morphological, molecular, and functional remodeling in response to injury, disease, or infection of the CNS. Although this remodeling was first described over a century ago, uncertainties and controversies remain regarding the contribution of reactive astrocytes to CNS diseases, repair, and aging. It is also unclear whether fixed categories of reactive astrocytes exist and, if so, how to identify them. We point out the shortcomings of binary divisions of reactive astrocytes into good-vs-bad, neurotoxic-vs-neuroprotective or A1-vs-A2. We advocate, instead, that research on reactive astrocytes include assessment of multiple molecular and functional parameters-preferably in vivo-plus multivariate statistics and determination of impact on pathological hallmarks in relevant models. These guidelines may spur the discovery of astrocyte-based biomarkers as well as astrocyte-targeting therapies that abrogate detrimental actions of reactive astrocytes, potentiate their neuro- and glioprotective actions, and restore or augment their homeostatic, modulatory, and defensive functions.

The functional characteristics of the mouse visual system have not previously been well explored using fMRI. In this research, we examined 9.4 T BOLD fMRI responses to visual stimuli of varying pulse durations (1 - 50 ms) and temporal frequencies (1 - 10 Hz) under ketamine and xylazine anesthesia, and compared fMRI responses of anesthetized and awake mice. Under anesthesia, significant positive BOLD responses were detected bilaterally in the major structures of the visual pathways, including the dorsal lateral geniculate nuclei, superior colliculus, lateral posterior nucleus of thalamus, primary visual area, and higher-order visual area. BOLD responses increased slightly with pulse duration, were maximal at 3 - 5 Hz stimulation, and significantly decreased at 10 Hz, which were all consistent with previous neurophysiological findings. When the mice were awake, the BOLD fMRI response was faster in all active regions and stronger in the subcortical areas compared with the anesthesia condition. In the V1, the BOLD response was biphasic for 5 Hz stimulation and negative for 10 Hz stimulation under wakefulness, whereas prolonged positive BOLD responses were observed at both frequencies under anesthesia. Unexpected activation was detected in the extrastriate postrhinal area and non-visual subiculum complex under anesthesia, but not under wakefulness. Widespread positive BOLD activity under anesthesia likely results from the disinhibition and sensitization of excitatory neurons induced by ketamine. Overall, fMRI can be a viable tool for mapping brain-wide functional networks., Competing Interests: Declarations of Competing Interest None, (Copyright © 2020. Published by Elsevier Inc.)

Functional MRI responses are localized to the synaptic sites of evoked inhibitory neurons, but it is unknown whether, or by what mechanisms, these neurons initiate functional hyperemia. Here, the neuronal origins of these hemodynamic responses were investigated by fMRI or local field potential and blood flow measurements during topical application of pharmacological agents when GABAergic granule cells in the rat olfactory bulb were synaptically targeted. First, to examine if postsynaptic activation of these inhibitory neurons was required for neurovascular coupling, we applied an NMDA receptor antagonist during cerebral blood volume-weighted fMRI acquisition and found that responses below the drug application site (up to ~1.5 mm) significantly decreased within ~30 min. Similarly, large decreases in granule cell postsynaptic activities and blood flow responses were observed when AMPA or NMDA receptor antagonists were applied. Second, inhibition of nitric oxide synthase preferentially decreased the initial, fast component of the blood flow response, while inhibitors of astrocyte-specific glutamate transporters and vasoactive intestinal peptide receptors did not decrease blood flow responses. Third, inhibition of GABA release with a presynaptic GABA B receptor agonist caused less reduction of neuronal and blood flow responses compared to the postsynaptic glutamate receptor antagonists. In conclusion, local hyperemia by synaptically-evoked inhibitory neurons was primarily driven by their postsynaptic activities, possibly through NMDA receptor-dependent calcium signaling that was not wholly dependent on nitric oxide., Competing Interests: Declaration of Competing Interest The authors declare they have no competing financial interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole-brain functional connectivity remains poorly understood. Here, we identified a neuroimaging-based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large-scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole-brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole-brain functional connectivity and have important implications for the obesity neuroimaging community., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent efforts to address this challenge have capitalized on dimensionality reduction techniques applied to resting-state fMRI, identifying principal components of intrinsic connectivity which describe smooth transitions across different cortical systems, so called "connectivity gradients". These gradients recapitulate neurocognitively meaningful organizational principles that are present in both human and primate brains, and also appear to differ among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209 × 2) and the Midnight scan club (n = 9), we tested the following key biomarker traits - reliability, reproducibility and predictive validity - of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (resting-state fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional and multivariate gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment., (Copyright © 2020. Published by Elsevier Inc.)

A mouse skull is a barrier for high-resolution optical imaging because its thick and inhomogeneous internal structures induce complex aberrations varying drastically from position to position. Invasive procedures creating either thinned-skull or open-skull windows are often required for the microscopic imaging of brain tissues underneath. Here, we propose a label-free imaging modality termed laser scanning reflection-matrix microscopy for recording the amplitude and phase maps of reflected waves at non-confocal points as well as confocal points. The proposed method enables us to find and computationally correct up to 10,000 angular modes of aberrations varying at every 10 × 10 µm 2 patch in the sample plane. We realized reflectance imaging of myelinated axons in vivo underneath an intact mouse skull, with an ideal diffraction-limited spatial resolution of 450 nm. Furthermore, we demonstrated through-skull two-photon fluorescence imaging of neuronal dendrites and their spines by physically correcting the aberrations identified from the reflection matrix.

The habenula plays an important role in brain reward circuitry and psychiatric conditions. While much work has been done on the function and structure of the habenula in animal models, in vivo imaging studies of the human habenula have been relatively scarce due to its small size, deep brain location, and lack of clear biomarkers for its heterogeneous substructure. In this paper, we report high-resolution (0.5 × 0.5 × 0.8 mm 3 ) MRI of the human habenula with quantitative susceptibility mapping (QSM) at 3 T. By analyzing 48 scan datasets collected from 21 healthy subjects, we found that magnetic susceptibility contrast is highly non-uniform within the habenula and across the subjects. In particular, we observed high prevalence of elevated susceptibility in the posterior subregion of the habenula. Correlation analysis between the susceptibility and the effective transverse relaxation rate (R2*) indicated that localized susceptibility enhancement in the habenula is more associated with increased paramagnetic (such as iron) rather than decreased diamagnetic (such as myelin) sources. Our results suggest that high-resolution QSM could make a potentially useful tool for substructure-resolved in vivo habenula imaging, and provide a groundwork for the future development of magnetic susceptibility as a quantitative biomarker for human habenula studies.

Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. Although a single connection between the medial prefrontal cortex and nucleus accumbens has been suggested as a powerful marker, how the complex interactions on a large-scale brain network can serve as the markers is underexplored. Here, we aimed to identify a set of functional connections predictive of longitudinal changes in pain intensity using large-scale brain networks. We re-analyzed previously published resting-state functional magnetic resonance imaging data of 49 subacute back pain (SBP) patients. We built a network-level model that predicts changes in pain intensity over one year by combining independent component analysis and a penalized regression framework. Connections involving top-down pain modulation, multisensory integration, and mesocorticolimbic circuits were identified as predictive markers for pain intensity changes. Pearson's correlations between actual and predicted pain scores were r = 0.33-0.72, and group classification results between SBP patients with persisting pain and recovering patients, in terms of area under the curve (AUC), were 0.89/0.75/0.75 for visits four/three/two, thus outperforming the previous work (AUC 0.83/0.73/0.67). This study identified functional connections important for longitudinal changes in pain intensity in SBP patients, providing provisional markers to predict future pain using large-scale brain networks.