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.

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.

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.

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 (ES!) of tFUS-induced brain activity from noninvasive scalp EEG recordings. Methods: A single-element ultrasound transducer was used to generate lowintensity tFUS (I _spta <; 1 mW/ cm ² ) 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 ES! 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. ES! 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 ES!-guided tFUS neuromodulation modality.