Spatiotemporal Rhythmic Seizure Sources Can be Imaged by means of Biophysically Constrained Deep Neural Networks.

Noninvasive dynamic brain imaging of neural oscillations provides valuable insights into both physiological and pathological brain states. Yet, challenges remain due to the ill-posed nature of the problem and high complexity of the solution space, which can be alleviated by advanced computational models. Here, we investigated the capability of a novel deep learning-based source imaging framework (DeepSIF) for imaging ictal activities from high-density electroencephalogram (EEG) recordings in drug-resistant focal epilepsy patients. The neural mass model of ictal oscillations was adopted to generate synthetic training data with spatio-temporal-spectra features similar to ictal dynamics. We rigorously validated the trained DeepSIF model using computer simulations and in a cohort of 33 drug-resistant focal epilepsy patients. The DeepSIF ictal source imaging was compared with interictal source imaging and three conventional imaging methods as benchmark comparisons. Our findings show that the trained DeepSIF model outperforms other methods in estimating the spatial and temporal information of ictal sources. It achieves a high spatial specificity of 96% and a low spatial dispersion of 3.80 ± 5.74 mm when compared to the resection region. The noninvasive source imaging results also demonstrate good coverage of seizure-onset-zone (SOZ), with an average distance of 10.89 ± 10.14 mm (from the SOZ to the reconstruction). These promising results suggest that DeepSIF has significant potential for advancing noninvasive imaging of ictal activities in patients with focal epilepsy. By providing valuable insights into the spatiotemporal dynamics of seizure activity, DeepSIF promises to help guide clinical decisions and improve treatment outcomes for epilepsy patients.

Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.

Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF's capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.

Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements.

Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients' interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.

High-Frequency Hubs of the Ictal Cross-Frequency Coupling Network Predict Surgical Outcome in Epilepsy Patients.

Seizure generation is thought to be a process driven by epileptogenic networks; thus, network analysis tools can help determine the efficacy of epilepsy treatment. Studies have suggested that low-frequency (LF) to high-frequency (HF) cross-frequency coupling (CFC) is a useful biomarker for localizing epileptogenic tissues. However, it remains unclear whether the LF or HF coordinated CFC network hubs are more critical in determining the treatment outcome. We hypothesize that HF hubs are primarily responsible for seizure dynamics. Stereo-electroencephalography (SEEG) recordings of 36 seizures from 16 intractable epilepsy patients were analyzed. We propose a new approach to model the temporal-spatial-spectral dynamics of CFC networks. Graph measures are then used to characterize the HF and LF hubs. In the patient group with Engel Class (EC) I outcome, the strength of HF hubs was significantly higher inside the resected regions during the early and middle stages of seizure, while such a significant difference was not observed in the EC III group and only for the early stage in the EC II group. For the LF hubs, a significant difference was identified at the late stage and only in the EC I group. Our findings suggest that HF hubs increase at early and middle stages of the ictal interval while LF hubs increase activity at the late stages. In addition, HF hubs can predict treatment outcomes more precisely, compared to the LF hubs of the CFC network. The proposed method promises to identify more accurate targets for surgical interventions or neuromodulation therapies.

Noninvasive high-frequency oscillations riding spikes delineates epileptogenic sources.

High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic brain and guiding successful neurosurgery. However, the utility and translation of noninvasive HFOs, although highly desirable, is impeded by the difficulty in differentiating pathological HFOs from nonepileptiform high-frequency activities and localizing the epileptic tissue using noninvasive scalp recordings, which are typically contaminated with high noise levels. Here, we show that the consistent concurrence of HFOs with epileptiform spikes (pHFOs) provides a tractable means to identify pathological HFOs automatically, and this in turn demarks an epileptiform spike subgroup with higher epileptic relevance than the other spikes in a cohort of 25 temporal epilepsy patients (including a total of 2,967 interictal spikes and 1,477 HFO events). We found significant morphological distinctions of HFOs and spikes in the presence/absence of this concurrent status. We also demonstrated that the proposed pHFO source imaging enhanced localization of epileptogenic tissue by 162% (∼5.36 mm) for concordance with surgical resection and by 186% (∼12.48 mm) with seizure-onset zone determined by invasive studies, compared to conventional spike imaging, and demonstrated superior congruence with the surgical outcomes. Strikingly, the performance of spike imaging was selectively boosted by the presence of spikes with pHFOs, especially in patients with multitype spikes. Our findings suggest that concurrent HFOs and spikes reciprocally discriminate pathological activities, providing a translational tool for noninvasive presurgical diagnosis and postsurgical evaluation in vulnerable patients.

Exploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging.

Electrophysiological source imaging (ESI) has been successfully employed in many brain imaging applications during the last 20 years. ESI estimates of underlying brain networks provide millisecond resolution of dynamic brain processes; yet, it remains to be a challenge to further improve the spatial resolution of ESI modality, in particular on its capability of imaging the extent of underlying brain sources. In this review, we discuss the recent developments in signal processing and machine learning that have made it possible to image the extent, i.e. size, of underlying brain sources noninvasively, using scalp electromagnetic measurements from electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings.

Contribution of Ictal Source Imaging for Localizing Seizure Onset Zone in Patients With Focal Epilepsy.

OBJECTIVE: To determine whether seizure onset zone (SOZ) can be localized accurately prior to surgical planning in patients with focal epilepsy, we performed noninvasive EEG recordings and source localization analyses on 39 patients. METHODS: In 39 patients with focal epilepsy, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We investigated a novel approach for directly imaging sources of seizures and interictal spikes from high-density EEG recordings, and rigorously validated it for noninvasive localization of SOZ determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison. RESULTS: Ictal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients. CONCLUSION: Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.

Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources.

Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks.

Electromagnetic Brain Source Imaging by Means of a Robust Minimum Variance Beamformer.

OBJECTIVE: Adaptive beamformer methods that have been extensively used for functional brain imaging using EEG/MEG (magnetoencephalography) signals are sensitive to model mismatches. We propose a robust minimum variance beamformer (RMVB) technique, which explicitly incorporates the uncertainty of the lead field matrix into the estimation of spatial-filter weights that are subsequently used to perform the imaging. METHODS: The uncertainty of the lead field is modeled by ellipsoids in the RMVB method; these hyperellipsoids (ellipsoids in higher dimensions) define regions of uncertainty for a given nominal lead field vector. These ellipsoids are estimated empirically by sampling lead field vectors surrounding each point of the source space, or more generally by building several forward models for the source space. Once these uncertainty regions (ellipsoids) are estimated, they are used to perform the source-imaging task. Computer simulations are conducted to evaluate the performance of the proposed RMVB technique. RESULTS: Our results show that robust beamformers can outperform conventional beamformers in terms of localization error, recovering source dynamics, and estimation of the underlying source extents when uncertainty in the lead field matrix is properly determined and modeled. CONCLUSION: The RMVB can be substituted for conventional beamformers, especially in applications where source imaging is performed off-line, and computational speed and complexity are not of major concern. SIGNIFICANCE: A high-quality source imaging can be utilized in various applications, such as determining the epileptogenic zone in medically intractable epilepsy patients or estimating the time course of activity, which is a required step for computing the functional connectivity of brain networks.

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks.

OBJECTIVE: The goal of this study is to investigate the performance, merits and limitations of source imaging using intracranial EEG (iEEG) recordings and to compare its accuracy to the results of EEG source imaging. Accuracy in this study, is measured both by determining the location and inter-nodal connectivity of underlying brain networks. METHODS: Systematic computer simulation studies are conducted to evaluate iEEG-based source imaging vs. EEG-based source imaging, and source imaging using both EEG and iEEG. To test the source imaging models, networks of inter-connected nodes (in terms of activity) are simulated. The location of the network nodes is randomly selected within a realistic geometry head model and a connectivity link is created among these nodes based on a multi-variate auto-regressive (MVAR) model. Then the forward problem is solved to calculate the potentials at the electrodes and noise (white and correlated) is added to these simulated potentials to simulate realistic measurements. Subsequently, the inverse problem is solved and an algorithm based on principle component analysis is performed on the estimated source activities to determine the location of the simulated network nodes. The activity of these nodes (over time), is then extracted, and used to estimate the connectivity links among the mentioned nodes using Granger causality analysis. RESULTS: Source imaging based on iEEG recordings may or may not improve the accuracy in localization, depending on the number and location of active nodes relative to iEEG electrodes and to other nodes within the network. However, our simulation results suggest that combining EEG and iEEG modalities (simultaneous scalp and intracranial recordings) can improve the imaging accuracy significantly. CONCLUSIONS: While iEEG source imaging is useful in estimating the exact location of sources near the iEEG electrodes, combining EEG and iEEG recordings can achieve a more accurate imaging due to the high spatial coverage of the scalp electrodes and the added near field information provided by the iEEG electrodes. SIGNIFICANCE: The present results suggest the feasibility of localizing brain electrical sources from iEEG recordings and improving EEG source localization using simultaneous EEG and iEEG recordings to cover the whole brain. The hybrid EEG and iEEG source imaging can assist the clinicians when unequivocal decisions about determining the epileptogenic zone cannot be reached using a single modality.

Anodal Transcranial Direct Current Stimulation Increases Bilateral Directed Brain Connectivity during Motor-Imagery Based Brain-Computer Interface Control.

Transcranial direct current stimulation (tDCS) has been shown to affect motor and cognitive task performance and learning when applied to brain areas involved in the task. Targeted stimulation has also been found to alter connectivity within the stimulated hemisphere during rest. However, the connectivity effect of the interaction of endogenous task specific activity and targeted stimulation is unclear. This study examined the aftereffects of concurrent anodal high-definition tDCS over the left sensorimotor cortex with motor network connectivity during a one-dimensional EEG based sensorimotor rhythm brain-computer interface (SMR-BCI) task. Directed connectivity following anodal tDCS illustrates altered connections bilaterally between frontal and parietal regions, and these alterations occur in a task specific manner; connections between similar cortical regions are altered differentially during left and right imagination trials. During right-hand imagination following anodal tDCS, there was an increase in outflow from the left premotor cortex (PMC) to multiple regions bilaterally in the motor network and increased inflow to the stimulated sensorimotor cortex from the ipsilateral PMC and contralateral sensorimotor cortex. During left-hand imagination following anodal tDCS, there was increased outflow from the stimulated sensorimotor cortex to regions across the motor network. Significant correlations between connectivity and the behavioral measures of total correct trials and time-to-hit (TTH) correct trials were also found, specifically that the input to the left PMC correlated with decreased right hand imagination performance and that flow from the ipsilateral posterior parietal cortex (PPC) to midline sensorimotor cortex correlated with improved performance for both right and left hand imagination. These results indicate that tDCS interacts with task-specific endogenous activity to alter directed connectivity during SMR-BCI. In order to predict and maximize the targeted effect of tDCS, the interaction of stimulation with the dynamics of endogenous activity needs to be examined comprehensively and understood.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.

OBJECTIVE: Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions. METHODS: Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG). RESULTS: Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. CONCLUSION: Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). SIGNIFICANCE: The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Electrophysiological Source Imaging of Brain Networks Perturbed by Low-Intensity Transcranial Focused Ultrasound.

OBJECTIVE: Transcranial focused ultrasound (tFUS) has been introduced as a noninvasive neuromodulation technique with good spatial selectivity. We report an experimental investigation to detect noninvasive electrophysiological response induced by low-intensity tFUS in an in vivo animal model and perform electrophysiological source imaging (ESI) of tFUS-induced brain activity from noninvasive scalp EEG recordings. METHODS: A single-element ultrasound transducer was used to generate low-intensity tFUS ( ) and induce brain activation at multiple selected sites in an in vivo rat model. Up to 16 scalp electrodes were used to record tFUS-induced EEG. Event-related potentials were analyzed in time, frequency, and spatial domains. Current source distributions were estimated by ESI to reconstruct spatiotemporal distributions of brain activation induced by tFUS. RESULTS: Neuronal activation was observed following low-intensity tFUS, as correlated to tFUS intensity and sonication duration. ESI revealed initial focal activation in cortical area corresponding to tFUS stimulation site and the activation propagating to surrounding areas over time. CONCLUSION: The present results demonstrate the feasibility of noninvasively recording brain electrophysiological response in vivo following low-intensity tFUS stimulation, and the feasibility of imaging spatiotemporal distributions of brain activation as induced by tFUS in vivo. SIGNIFICANCE: The present approach may lead to a new means of imaging brain activity using tFUS perturbation and a closed-loop ESI-guided tFUS neuromodulation modality.

Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy.

Estimating extended brain sources using EEG/MEG source imaging techniques is challenging. EEG and MEG have excellent temporal resolution at millisecond scale but their spatial resolution is limited due to the volume conduction effect. We have exploited sparse signal processing techniques in this study to impose sparsity on the underlying source and its transformation in other domains (mathematical domains, like spatial gradient). Using an iterative reweighting strategy to penalize locations that are less likely to contain any source, it is shown that the proposed iteratively reweighted edge sparsity minimization (IRES) strategy can provide reasonable information regarding the location and extent of the underlying sources. This approach is unique in the sense that it estimates extended sources without the need of subjectively thresholding the solution. The performance of IRES was evaluated in a series of computer simulations. Different parameters such as source location and signal-to-noise ratio were varied and the estimated results were compared to the targets using metrics such as localization error (LE), area under curve (AUC) and overlap between the estimated and simulated sources. It is shown that IRES provides extended solutions which not only localize the source but also provide estimation for the source extent. The performance of IRES was further tested in epileptic patients undergoing intracranial EEG (iEEG) recording for pre-surgical evaluation. IRES was applied to scalp EEGs during interictal spikes, and results were compared with iEEG and surgical resection outcome in the patients. The pilot clinical study results are promising and demonstrate a good concordance between noninvasive IRES source estimation with iEEG and surgical resection outcomes in the same patients. The proposed algorithm, i.e. IRES, estimates extended source solutions from scalp electromagnetic signals which provide relatively accurate information about the location and extent of the underlying source.

Spectral and spatial changes of brain rhythmic activity in response to the sustained thermal pain stimulation.

The aim of this study was to investigate the neurophysiological correlates of pain caused by sustained thermal stimulation. A group of 21 healthy volunteers was studied. Sixty-four channel continuous electroencephalography (EEG) was recorded while the subject received tonic thermal stimulation. Spectral changes extracted from EEG were quantified and correlated with pain scales reported by subjects, the stimulation intensity, and the time course. Network connectivity was assessed to study the changes in connectivity patterns and strengths among brain regions that have been previously implicated in pain processing. Spectrally, a global reduction in power was observed in the lower spectral range, from delta to alpha, with the most marked changes in the alpha band. Spatially, the contralateral region of the somatosensory cortex, identified using source localization, was most responsive to stimulation status. Maximal desynchrony was observed when stimulation was present. The degree of alpha power reduction was linearly correlated to the pain rating reported by the subjects. Contralateral alpha power changes appeared to be a robust correlate of pain intensity experienced by the subjects. Granger causality analysis showed changes in network level connectivity among pain-related brain regions due to high intensity of pain stimulation versus innocuous warm stimulation. These results imply the possibility of using noninvasive EEG to predict pain intensity and to study the underlying pain processing mechanism in coping with prolonged painful experiences. Once validated in a broader population, the present EEG-based approach may provide an objective measure for better pain management in clinical applications. Hum Brain Mapp 37:2976-2991, 2016. © 2016 Wiley Periodicals, Inc.

Identifying epileptic source location and extent: an iterative sparse electromagnetic source imaging algorithm.

In this paper we have introduced a novel electromagnetic source imaging (ESI) technique and demonstrated its validity and excellent performance in imaging the location and extent of underlying epileptic sources in patients suffering from focal epilepsy. The proposed algorithm employs ideas from sparse signal processing literature and convex optimization theories to improve source imaging results obtained from scalp-recorded electroencephalogram (EEG). EEG source imaging results generally use subjective methods to determine the extent of the underlying brain activity. The proposed technique provides significant improvement in dealing with such shortcomings and eliminates the need for thresholding. The results of our computer simulations and clinical validation study demonstrate the excellent performance of the proposed algorithm and suggest it may become a useful tool for objectively determining the location and extent of focal epileptic activity in a noninvasive fashion.

Estimating underlying neuronal activity from EEG using an iterative sparse technique.

In this paper a novel technique for solving the bio-electromagnetic inverse problem is proposed. This method provides information about the location and extent of underlying neuronal activity. This is essential for the presurgical planning for partial epilepsy patients who are resistant to anti-epileptic drugs. The proposed algorithm takes advantage of the fact that neuronal activity transparent to EEG, arises from a spatially extended brain region. This spatial coherence is modeled within the framework of sparse signal processing techniques and makes better use of the limited number of EEG recordings. An iterative data-driven weighting is also introduced to better the extent estimation as well as eliminating the need to threshold estimated solutions.

Systems Neuroengineering: Understanding and Interacting with the Brain.

In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering-to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.