Complex Neural Activity Research Articles

Sapphire-Based Optrode for Low Noise Neural Recording and Optogenetic Manipulation.

Electrophysiological recordings of neurons in deep brain regions using optogenetic stimulation are essential to understanding and regulating the role of complex neural activity in biological behavior and cognitive function. Optogenetic techniques have significantly advanced neuroscience research by enabling the optical manipulation of neural activities. Because of the significance of the technique, constant advancements in implantable optrodes that integrate optical stimulation with low-noise, large-scale electrophysiological recording are in demand to improve the spatiotemporal resolution for various experimental designs and future clinical applications. However, robust and easy-to-use neural optrodes that integrate neural recording arrays with high-intensity light emitting diodes (LEDs) are still lacking. Here, we propose a neural optrode based on Gallium Nitride (GaN) on sapphire technology, which integrates a high-intensity blue LED with a 5x2 recording array monolithically for simultaneous neural recording and optogenetic manipulation. To reduce the noise interference between the recording electrodes and the LED, which is in close physical proximity, three metal grounding interlayers were incorporated within the optrode, and their ability to reduce LED-induced artifacts during neural recording was confirmed through both electromagnetic simulations and experimental demonstrations. The capability of the sapphire optrode to record action potentials has been demonstrated by recording the firing of mitral/tuft cells in the olfactory bulbs of mice in vivo. Additionally, the elevation of action potential firing due to optogenetic stimulation observed using the sapphire probe in medial superior olive (MSO) neurons of the gerbil auditory brainstem confirms the capability of this sapphire optrode to precisely access neural activities in deep brain regions under complex experimental designs.

Finding emergence in data by maximizing effective information

Abstract Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to emergent behaviors cannot be directly captured by micro-level observational data. Thus, it’s crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from fMRI data, to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data.

An Integrated Neural Optrode with Modification of Polymer-Carbon Composite Films for Suppression of the Photoelectric Artifacts.

Optogenetics-based integrated photoelectrodes with high spatiotemporal resolution play an important role in studying complex neural activities. However, the photostimulation artifacts caused by the high level of integration and the high impedance of metal recording electrodes still hinder the application of photoelectrodes for optogenetic studies of neural circuits. In this study, a neural optrode fabricated on sapphire GaN material was proposed, and 4 μLEDs and 14 recording microelectrodes were monolithically integrated on a shank. Poly(3,4-ethylenedioxythiophene)/polystyrenesulfonate and multiwalled carbon nanotubes (PEDOT:PSS-MWCNT) and poly(3,4-ethylenedioxythiophene) and graphene oxide (PEDOT-GO) composite films were deposited on the surface of the recording microelectrode by electrochemical deposition. The results demonstrate that compared with the gold microelectrode, the impedances of both composite films reduced by more than 98%, and the noise amplitudes decreased by 70.73 and 87.15%, respectively, when exposed to light stimulation. Adjusting the high and low levels, we further reduced the noise amplitude by 48.3%. These results indicate that modifying the electrode surface by a polymer composite film can effectively enhance the performance of the microelectrode and further promote the application of the optrode in the field of neuroscience.

STUDY OF fMRI BRAIN ACTIVITY DURING NON-HANDWRITING AND HANDWRITING TASKS ON ESSENTIAL HAND TREMOR

Essential hand tremor (EHT) is a prevalent neurological condition affecting geriatric populations, yet its underlying mechanisms remain poorly understood. This study aims to investigate the neural substrates associated with motor tasks in EHT patients, illuminating the complex neural activity characterizing this condition. Twenty participants underwent a thorough evaluation to ensure eligibility, excluding factors such as mental illness, drug dependency, or Parkinson’s disease (PD). Functional magnetic resonance imaging (fMRI) was utilized to examine brain activation patterns during non-handwriting tasks (NHWT) and handwriting tasks (HWT). Participants received training to standardize hand and forearm movements for effective fMRI assessment. fMRI data preprocessing included motion correction, filtering, removal of linear trends, normalization to Montreal Neurological Institute (MNI) space, and spatial smoothing. Distinctive patterns of brain activation were observed during motor tasks in individuals with EHT compared to controls. During NHWT, the EHT group showed significantly increased activation in the precentral gyrus, supplementary motor area, thalamus, and posterior cerebellar lobe, highlighting their role in mediating motor challenges in EHT. Similarly, during HWT, the EHT group exhibited heightened activation in the precentral gyrus and supplementary motor areas. In contrast, reduced activation was noted in the caudate nucleus, inferior temporal gyrus, and precuneus during HWT in the EHT group compared to controls. These findings align with previous research on involuntary movement disorders, such as early-stage PD, emphasizing the importance of the caudate nucleus and related regions in EHT. In conclusion, this study sheds light on the intricate neural activity underlying motor tasks in EHT patients. The identified neural regions and their functions offer insights into the neurophysiological basis of EHT-related motor impairments. These findings have the potential to enhance the understanding of EHT beyond its surface-level effects. This study has identified the brain regions involved in motor tasks affected by EHT. This sets a foundation for future research to better understand the complexities of this neurological condition. These discoveries may lead to novel therapeutic interventions tailored to address the unique challenges faced by individuals with EHT, representing a significant milestone in understanding and managing EHT.

Hippocampal and orbitofrontal neurons contribute to complementary aspects of associative structure

The ability to establish associations between environmental stimuli is fundamental for higher-order brain functions like state inference and generalization. Both the hippocampus and orbitofrontal cortex (OFC) play pivotal roles in this, demonstrating complex neural activity changes after associative learning. However, how precisely they contribute to representing learned associations remains unclear. Here, we train head-restrained mice to learn four ‘odor-outcome’ sequence pairs composed of several task variables—the past and current odor cues, sequence structure of ‘cue-outcome’ arrangement, and the expected outcome; and perform calcium imaging from these mice throughout learning. Sequence-splitting signals that distinguish between paired sequences are detected in both brain regions, reflecting associative memory formation. Critically, we uncover differential contents in represented associations by examining, in each area, how these task variables affect splitting signal generalization between sequence pairs. Specifically, the hippocampal splitting signals are influenced by the combination of past and current cues that define a particular sensory experience. In contrast, the OFC splitting signals are similar between sequence pairs that share the same sequence structure and expected outcome. These findings suggest that the hippocampus and OFC uniquely and complementarily organize the acquired associative structure.

Using camera-guided electrode microdrive navigation for precise 3D targeting of macaque brain sites.

Spatial accuracy in electrophysiological investigations is paramount, as precise localization and reliable access to specific brain regions help the advancement of our understanding of the brain's complex neural activity. Here, we introduce a novel, multi camera-based, frameless neuronavigation technique for precise, 3-dimensional electrode positioning in awake monkeys. The investigation of neural functions in awake primates often requires stable access to the brain with thin and delicate recording electrodes. This is usually realized by implanting a chronic recording chamber onto the skull of the animal that allows direct access to the dura. Most recording and positioning techniques utilize this implanted recording chamber as a holder of the microdrive or to hold a grid. This in turn reduces the degrees of freedom in positioning. To solve this problem, we require innovative, flexible, but precise tools for neuronal recordings. We instead mount the electrode microdrive above the animal on an arch, equipped with a series of translational and rotational micromanipulators, allowing movements in all axes. Here, the positioning is controlled by infrared cameras tracking the location of the microdrive and the monkey, allowing precise and flexible trajectories. To verify the accuracy of this technique, we created iron deposits in the tissue that could be detected by MRI. Our results demonstrate a remarkable precision with the confirmed physical location of these deposits averaging less than 0.5 mm from their planned position. Pilot electrophysiological recordings additionally demonstrate the accuracy and flexibility of this method. Our innovative approach could significantly enhance the accuracy and flexibility of neural recordings, potentially catalyzing further advancements in neuroscientific research.

Brain Model Technology and Its Implications.

The complexity of the human brain creates a spectrum of sophisticated behavioral repertoires, such as language, tool use, self-awareness, symbolic thought, cultural learning, and consciousness. Understanding how the human brain achieves that has been a longstanding challenge for neuroscientists and may bring insights into the evolution of human cognition and disease states. Human pluripotent stem cells could differentiate into specialized cell types and tissues in vitro. From this pluripotent state, it is possible to generate models of the human brain, such as brain organoids. The recent observation that brain organoids can spontaneously develop complex neural network activity in a dish can help one understand how neural network oscillations evolve and vary between normal and disease states. Moreover, this finding can be leveraged to other applications outside medicine, including engineering and artificial intelligence. However, as the brain model technology becomes more complex, it raises a series of ethical and moral dilemmas. This article discusses the status of this technology, some of its current limitations, and a vision of the future.

Stable Neural Population Dynamics in the Regression Subspace for Continuous and Categorical Task Parameters in Monkeys.

Neural population dynamics provide a key computational framework for understanding information processing in the sensory, cognitive, and motor functions of the brain. They systematically depict complex neural population activity, dominated by strong temporal dynamics as trajectory geometry in a low-dimensional neural space. However, neural population dynamics are poorly related to the conventional analytical framework of single-neuron activity, the rate-coding regime that analyzes firing rate modulations using task parameters. To link the rate-coding and dynamic models, we developed a variant of state-space analysis in the regression subspace, which describes the temporal structures of neural modulations using continuous and categorical task parameters. In macaque monkeys, using two neural population datasets containing either of two standard task parameters, continuous and categorical, we revealed that neural modulation structures are reliably captured by these task parameters in the regression subspace as trajectory geometry in a lower dimension. Furthermore, we combined the classical optimal-stimulus response analysis (usually used in rate-coding analysis) with the dynamic model and found that the most prominent modulation dynamics in the lower dimension were derived from these optimal responses. Using those analyses, we successfully extracted geometries for both task parameters that formed a straight geometry, suggesting that their functional relevance is characterized as a unidimensional feature in their neural modulation dynamics. Collectively, our approach bridges neural modulation in the rate-coding model and the dynamic system, and provides researchers with a significant advantage in exploring the temporal structure of neural modulations for pre-existing datasets.

When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns.

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG.

Mental Resilience and Coping With Stress: A Comprehensive, Multi-level Model of Cognitive Processing, Decision Making, and Behavior.

Aversive events can evoke strong emotions that trigger cerebral neuroactivity to facilitate behavioral and cognitive shifts to secure physiological stability. However, upon intense and/or chronic exposure to such events, the neural coping processes can be maladaptive and disrupt mental well-being. This maladaptation denotes a pivotal point when psychological stress occurs, which can trigger subconscious, “automatic” neuroreactivity as a defence mechanism to protect the individual from potential danger including overwhelming unpleasant feelings and disturbing or threatening thoughts.The outcomes of maladaptive neural activity are cognitive dysfunctions such as altered memory, decision making, and behavior that impose a risk for mental disorders. Although the neurocognitive phenomena associated with psychological stress are well documented, the complex neural activity and pathways related to stressor detection and stress coping have not been outlined in detail. Accordingly, we define acute and chronic stress-induced pathways, phases, and stages in relation to novel/unpredicted, uncontrollable, and ambiguous stressors. We offer a comprehensive model of the stress-induced alterations associated with multifaceted pathophysiology related to cognitive appraisal and executive functioning in stress.

Multiplexed Neurotransmission Emulated by a p–n Cross Nanowire Synaptic Transistor for Satiety, Depression, and Drug Withdrawal

AbstractThis paper presents the first synaptic transistor (ST) that combines crossed p‐type and n‐type semiconductor nanowires (NWs) to form a bipolar conductive channels. The bipolar channel with both electron and hole transport paths enables the emulation of multiplexed neurotransmission of different neurotransmitters in a brain to realize complex neural functions using nanowires. Except for basic synaptic functions, the device demonstrates the potential to distinguish reward‐processing and aversive responses. The opposite polarities of NWs enable emulation of a series of complex neural activities using a single electronic device, such as satiety, depression, and drug withdrawal. The device is applicable to deep convolutional neural networks for electrocardiograms. The new strategy of versatile use of electro‐hydrodynamically printed NWs (e‐NWs) is expected to facilitate emulation of complex neural behaviors, and the realization of future neuromorphic electronics that are capable of these behaviors.

Hopf Bifurcation in Mean Field Explains Critical Avalanches in Excitation-Inhibition Balanced Neuronal Networks: A Mechanism for Multiscale Variability.

Cortical neural circuits display highly irregular spiking in individual neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional importance for information processing. Theoretically, the balance of excitation and inhibition inputs is thought to account for spiking irregularity and critical avalanches may originate from an underlying phase transition. However, the theoretical reconciliation of these multilevel dynamic aspects in neural circuits remains an open question. Herein, we study excitation-inhibition (E-I) balanced neuronal network with biologically realistic synaptic kinetics. It can maintain irregular spiking dynamics with different levels of synchrony and critical avalanches emerge near the synchronous transition point. We propose a novel semi-analytical mean-field theory to derive the field equations governing the network macroscopic dynamics. It reveals that the E-I balanced state of the network manifesting irregular individual spiking is characterized by a macroscopic stable state, which can be either a fixed point or a periodic motion and the transition is predicted by a Hopf bifurcation in the macroscopic field. Furthermore, by analyzing public data, we find the coexistence of irregular spiking and critical avalanches in the spontaneous spiking activities of mouse cortical slice in vitro, indicating the universality of the observed phenomena. Our theory unveils the mechanism that permits complex neural activities in different spatiotemporal scales to coexist and elucidates a possible origin of the criticality of neural systems. It also provides a novel tool for analyzing the macroscopic dynamics of E-I balanced networks and its relationship to the microscopic counterparts, which can be useful for large-scale modeling and computation of cortical dynamics.

Deterministic characteristics of spontaneous activity detected by multi-fractal analysis in a spiking neural network with long-tailed distributions of synaptic weights.

Cortical neural networks maintain autonomous electrical activity called spontaneous activity that represents the brain's dynamic internal state even in the absence of sensory stimuli. The spatio-temporal complexity of spontaneous activity is strongly related to perceptual, learning, and cognitive brain functions; multi-fractal analysis can be utilized to evaluate the complexity of spontaneous activity. Recent studies have shown that the deterministic dynamic behavior of spontaneous activity especially reflects the topological neural network characteristics and changes of neural network structures. However, it remains unclear whether multi-fractal analysis, recently widely utilized for neural activity, is effective for detecting the complexity of the deterministic dynamic process. To verify this point, we focused on the log-normal distribution of excitatory postsynaptic potentials (EPSPs) to evaluate the multi-fractality of spontaneous activity in a spiking neural network with a log-normal distribution of EPSPs. We found that the spiking activities exhibited multi-fractal characteristics. Moreover, to investigate the presence of a deterministic process in the spiking activity, we conducted a surrogate data analysis against the time-series of spiking activity. The results showed that the spontaneous spiking activity included the deterministic dynamic behavior. Overall, the combination of multi-fractal analysis and surrogate data analysis can detect deterministic complex neural activity. The multi-fractal analysis of neural activity used in this study could be widely utilized for brain modeling and evaluation methods for signals obtained by neuroimaging modalities.

C-Myc controls the fate of neural progenitor cells during cerebral cortex development.

The anatomical structure of the mammalian cerebral cortex is the essential foundation for its complex neural activity. This structure is developed by proliferation, differentiation, and migration of neural progenitor cells (NPCs), the fate of which is spatially and temporally regulated by the proper gene. This study was used in utero electroporation and found that the well-known oncogene c-Myc mainly promoted NPCs' proliferation and their transformation into intermediate precursor cells. Furthermore, the obtained results also showed that c-Myc blocked the differentiation of NPCs to postmitotic neurons, and the expression of telomere reverse transcriptase was controlled by c-Myc in the neocortex. These findings indicated c-Myc as a key regulator of the fate of NPCs during the development of the cerebral cortex.

Temporal-specific complexity of spiking patterns in spontaneous activity induced by a dual complex network structure

Temporal fluctuation of neural activity in the brain has an important function in optimal information processing. Spontaneous activity is a source of such fluctuation. The distribution of excitatory postsynaptic potentials (EPSPs) between cortical pyramidal neurons can follow a log-normal distribution. Recent studies have shown that networks connected by weak synapses exhibit characteristics of a random network, whereas networks connected by strong synapses have small-world characteristics of small path lengths and large cluster coefficients. To investigate the relationship between temporal complexity spontaneous activity and structural network duality in synaptic connections, we executed a simulation study using the leaky integrate-and-fire spiking neural network with log-normal synaptic weight distribution for the EPSPs and duality of synaptic connectivity, depending on synaptic weight. We conducted multiscale entropy analysis of the temporal spiking activity. Our simulation demonstrated that, when strong synaptic connections approach a small-world network, specific spiking patterns arise during irregular spatio-temporal spiking activity, and the complexity at the large temporal scale (i.e., slow frequency) is enhanced. Moreover, we confirmed through a surrogate data analysis that slow temporal dynamics reflect a deterministic process in the spiking neural networks. This modelling approach may improve the understanding of the spatio-temporal complex neural activity in the brain.

A Method of Flexible Micro-Wire Electrode Insertion in Rodent for Chronic Neural Recording and a Device for Electrode Insertion.

Reliable chronic neural recording from focal deep brain structures is impeded by insertion injury and foreign body response, the magnitude of which is correlated with the mechanical mismatch between the electrode and tissue. Thin and flexible electrodes cause less glial scarring and record longer than stiff electrodes. However, the insertion of flexible microelectrodes in brain has been a challenge. Here, a novel insertion method is proposed, and demonstrated, for precise targeting deep brain structures using flexible micro-wire electrodes. The microelectrode is spun and slowly inserted in brain through an appropriate electrode guide. The electrode guide does not penetrate into cortex. Based on two new mechanisms, namely spinning and guided insertion, we have demonstrated successful insertion of 25-micron platinum flexible electrodes about 10-mm deep in rat brains without buckling. We present an electrode insertion device based on the proposed method and demonstrate its use to implant flexible microelectrodes in rat brains. The step-by-step insertion process is described. Microelectrodes were inserted in the Bötzinger complex and respiratory neural activity was recorded acutely in nine rats and chronically in two rats for 50 days.

Hierarchical Structured Sparse Learning for Schizophrenia Identification

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured sparse learning method to sufficiently utilize the specificity and complementary structure information across four different frequency bands (from 0.01Hz to 0.25Hz) for SZ diagnosis. The proposed method can help preserve the partial group structures among multiple frequency bands and the specific characters in each frequency band. We further develop an efficient optimization algorithm to solve the proposed objective function. We validate the efficacy of our proposed method on a real SZ dataset. Also, to demonstrate the generality of the method, we apply our proposed method on a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results on both datasets demonstrate that our proposed method achieves promising performance in brain disease classification, compared with several state-of-the-art methods.

Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity.

Recently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of electrophysiology and fMRI signals and their relations to functional connectivity (FC). MSE and FC analyses were performed on simulated data using neural mass model based brain network model with the Brain Dynamics Toolbox, on animal models with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations, and on resting-state fMRI data from the Human Connectome Project. Our results show that the complexity of regional electrophysiology and fMRI signals is positively correlated with network FC. The associations between MSE and FC are dependent on the temporal scales or frequencies, with higher associations between MSE and FC at lower temporal frequencies. Our results from theoretical modeling, animal experiment and human fMRI indicate that (1) Regional neural complexity and network FC may be two related aspects of brain's information processing: the more complex regional neural activity, the higher FC this region has with other brain regions; (2) MSE at high and low frequencies may represent local and distributed information processing across brain regions. Based on literature and our data, we propose that the complexity of regional neural signals may serve as an index of the brain's capacity of information processing—increased complexity may indicate greater transition or exploration between different states of brain networks, thereby a greater propensity for information processing.

Precise multimodal optical control of neural ensemble activity.

Understanding brain function requires technologies that can control the activity of large populations of neurons with high fidelity in space and time. We developed a new multiphoton holographic approach to activate or suppress the activity of ensembles of cortical neurons with cellular resolution and sub-millisecond precision. Since existing opsins were inadequate, we engineered new soma-targeted (ST) optogenetic tools, ST-ChroME and IRES-ST-eGtACR1, optimized for multiphoton activation and suppression. Employing a three-dimensional all-optical read/write interface, we demonstrate the ability to photo-stimulate up to 50 neurons simultaneously distributed in three dimensions in a 550 × 550 × 100 μm volume of brain tissue. This new approach allows the synthesis and editing of complex neural activity patterns needed to gain insight into the principles of neural codes.

Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging

Objective. Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error. The dynamic brain transition network (DBTN) approach, a spatiotemporal fMRI constrained EEG source imaging method, has recently been developed to address these issues by solving the EEG inverse problem in a Bayesian framework, utilizing fMRI priors in a spatial and temporal variant manner. This paper presents a computer simulation study to provide a detailed characterization of the spatial and temporal accuracy of the DBTN method. Approach. Synthetic EEG data were generated in a series of computer simulations, designed to represent realistic and complex brain activity at superficial and deep sources with highly dynamical activity time-courses. The source reconstruction performance of the DBTN method was tested against the fMRI-constrained minimum norm estimates algorithm (fMRIMNE). The performances of the two inverse methods were evaluated both in terms of spatial and temporal accuracy. Main results. In comparison with the commonly used fMRIMNE method, results showed that the DBTN method produces results with increased spatial and temporal accuracy. The DBTN method also demonstrated the capability to reduce crosstalk in the reconstructed cortical time-course(s) induced by neighboring regions, mitigate depth bias and improve overall localization accuracy. Significance. The improved spatiotemporal accuracy of the reconstruction allows for an improved characterization of complex neural activity. This improvement can be extended to any subsequent brain connectivity analyses used to construct the associated dynamic brain networks.