Ethan Solomon
Postdoctoral Medical Fellow, Psychiatry
Resident in Psychiatry and Behavioral Sciences
All Publications
-
TMS provokes target-dependent intracranial rhythms across human cortical and subcortical sites.
Brain stimulation
2024
Abstract
Transcranial magnetic stimulation (TMS) is believed to alter ongoing neural activity and cause circuit-level changes in brain function. While the electrophysiological effects of TMS have been extensively studied with scalp electroencephalography (EEG), this approach generally evaluates low-frequency neural activity at the cortical surface. However, TMS can be safely used in patients with intracranial electrodes (iEEG), allowing for direct assessment of deeper and more localized oscillatory responses across the frequency spectrum.Our study used iEEG to understand the effects of TMS on human neural activity in the spectral domain. We asked (1) which brain regions respond to cortically-targeted TMS, and in what frequency bands, (2) whether deeper brain structures exhibit oscillatory responses, and (3) whether the neural responses to TMS reflect evoked versus induced oscillations.We recruited 17 neurosurgical patients with indwelling electrodes and recorded neural activity while patients underwent repeated trials of single-pulse TMS at either the dorsolateral prefrontal cortex (DLPFC) or parietal cortex. iEEG signals were analyzed using spectral methods to understand the oscillatory responses to TMS.Stimulation to DLPFC drove widespread low-frequency increases (3-8Hz) in frontolimbic cortices and high-frequency decreases (30-110Hz) in frontotemporal areas, including the hippocampus. Stimulation to parietal cortex specifically provoked low-frequency responses in the medial temporal lobe. While most low-frequency activity was consistent with phase-locked evoked responses, anterior frontal regions exhibited induced theta oscillations following DLPFC stimulation.By combining TMS with intracranial EEG recordings, our results suggest that TMS is an effective means to perturb oscillatory neural activity in brain-wide networks, including deeper structures not directly accessed by stimulation itself.
View details for DOI 10.1016/j.brs.2024.05.014
View details for PubMedID 38821396
-
TMS Provokes Target-Dependent Intracranial Rhythms Across Human Cortical and Subcortical Sites
SPRINGERNATURE. 2023: 323-324
View details for Web of Science ID 001126640300199
-
TMS provokes target-dependent intracranial rhythms across human cortical and subcortical sites.
bioRxiv : the preprint server for biology
2023
Abstract
Transcranial magnetic stimulation (TMS) is increasingly deployed in the treatment of neuropsychiatric illness, under the presumption that stimulation of specific cortical targets can alter ongoing neural activity and cause circuit-level changes in brain function. While the electrophysiological effects of TMS have been extensively studied with scalp electroencephalography (EEG), this approach is most useful for evaluating low-frequency neural activity at the cortical surface. As such, little is known about how TMS perturbs rhythmic activity among deeper structures - such as the hippocampus and amygdala - and whether stimulation can alter higher-frequency oscillations. Understanding these effects is necessary to refine clinical stimulation protocols and better use TMS as a neuroscientific tool to investigate causal relationships in the brain. Recent work has established that TMS can be safely used in patients with intracranial electrodes (iEEG), making it possible to collect direct neural recordings at sufficient spatiotemporal resolution to examine oscillatory responses to stimulation. To that end, we recruited 17 neurosurgical patients with indwelling electrodes and recorded neural activity while patients underwent repeated trials of single-pulse TMS at various cortical sites. We found that TMS elicited widespread - but brief - changes in spectral power that markedly differed according to the stimulation target. Stimulation to the dorsolateral prefrontal cortex (DLPFC) drove widespread low-frequency increases (3-8Hz) in frontolimbic cortices, as well as high-frequency decreases (30-110Hz) in frontotemporal areas. Stimulation in parietal cortex specifically provoked low-frequency responses in the medial temporal lobe and hippocampus but not other regions. We also found high inter-trial phase consistency at low frequencies in the early post-stimulation period, suggestive of evoked responses. Taken together, we established that exogenous, non-invasive stimulation can be used to (1) provoke phase-locked theta increases and (2) briefly suppress high-frequency activity in a cortico-subcortical pattern that varies by stimulation target.
View details for DOI 10.1101/2023.08.09.552524
View details for PubMedID 37645954
View details for PubMedCentralID PMC10461914
-
Theta-burst stimulation entrains frequency-specific oscillatory responses.
Brain stimulation
2021; 14 (5): 1271-1284
Abstract
BACKGROUND: Brain stimulation has emerged as a powerful tool in human neuroscience, becoming integral to next-generation psychiatric and neurologic therapeutics. Theta-burst stimulation (TBS), in which electrical pulses are delivered in rhythmic bouts of 3-8Hz, seeks to recapitulate neural activity seen endogenously during cognitive tasks. A growing literature suggests that TBS can be used to alter or enhance cognitive processes, but little is known about how these stimulation events influence underlying neural activity.OBJECTIVE: Our study sought to investigate the effect of direct electrical TBS on mesoscale neural activity in humans by asking (1) whether TBS evokes persistent theta oscillations in cortical areas, (2) whether these oscillations occur at the stimulated frequency, and (3) whether stimulation events propagate in a manner consistent with underlying functional and structural brain architecture.METHODS: We recruited 20 neurosurgical epilepsy patients with indwelling electrodes and delivered direct cortical TBS at varying locations and frequencies. Simultaneous iEEG was recorded from non-stimulated electrodes and analyzed to understand how TBS influences mesoscale neural activity.RESULTS: We found that TBS rapidly evoked theta rhythms in widespread brain regions, preferentially at the stimulation frequency, and that these oscillations persisted for hundreds of milliseconds post stimulation offset. Furthermore, the functional connectivity between recording and stimulation sites predicted the strength of theta response, suggesting that underlying brain architecture guides the flow of stimulation through the brain.CONCLUSIONS: By demonstrating that cortical TBS induces frequency-specific oscillatory responses, our results suggest this technology can be used to directly and predictably influence the activity of cognitively-relevant brain networks.
View details for DOI 10.1016/j.brs.2021.08.014
View details for PubMedID 34428553
-
Steroid-Responsive Mania Secondary to Pachymeningitis of the Right Frontal Lobe
JOURNAL OF THE ACADEMY OF CONSULTATION-LIAISON PSYCHIATRY
2021; 62 (1): 89-96
View details for Web of Science ID 000672228200012
View details for PubMedID 33183847
-
Biomarkers of memory variability in traumatic brain injury
BRAIN COMMUNICATIONS
2021; 3 (1): fcaa202
Abstract
Traumatic brain injury is a leading cause of cognitive disability and is often associated with significant impairment in episodic memory. In traumatic brain injury survivors, as in healthy controls, there is marked variability between individuals in memory ability. Using recordings from indwelling electrodes, we characterized and compared the oscillatory biomarkers of mnemonic variability in two cohorts of epilepsy patients: a group with a history of moderate-to-severe traumatic brain injury (n = 37) and a group of controls without traumatic brain injury (n = 111) closely matched for demographics and electrode coverage. Analysis of these recordings demonstrated that increased high-frequency power and decreased theta power across a broad set of brain regions mark periods of successful memory formation in both groups. As features in a logistic-regression classifier, spectral power biomarkers effectively predicted recall probability, with little difference between traumatic brain injury patients and controls. The two groups also displayed similar patterns of theta-frequency connectivity during successful encoding periods. These biomarkers of successful memory, highly conserved between traumatic brain injury patients and controls, could serve as the basis for novel therapies that target disordered memory across diverse forms of neurological disease.
View details for DOI 10.1093/braincomms/fcaa202
View details for Web of Science ID 000645553500007
View details for PubMedID 33543140
View details for PubMedCentralID PMC7850041
-
Theta Oscillations in Human Memory
TRENDS IN COGNITIVE SCIENCES
2020; 24 (3): 208-227
Abstract
Theta frequency (4-8 Hz) fluctuations of the local field potential have long been implicated in learning and memory. Human studies of episodic memory, however, have provided mixed evidence for theta's role in successful learning and remembering. Re-evaluating these conflicting findings leads us to conclude that: (i) successful memory is associated both with increased narrow-band theta oscillations and a broad-band tilt of the power spectrum; (ii) theta oscillations specifically support associative memory, whereas the spectral tilt reflects a general index of activation; and (iii) different cognitive contrasts (generalized versus specific to memory), recording techniques (invasive versus noninvasive), and referencing schemes (local versus global) alter the balance between the two phenomena to make one or the other more easily detectable.
View details for DOI 10.1016/j.tics.2019.12.006
View details for Web of Science ID 000514216700009
View details for PubMedID 32029359
View details for PubMedCentralID PMC8310425
-
Hippocampal theta codes for distances in semantic and temporal spaces
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2019; 116 (48): 24343-24352
Abstract
The medial temporal lobe (MTL) is known to support episodic memory and spatial navigation, raising the possibility that its true function is to form "cognitive maps" of any kind of information. Studies in humans and animals support the idea that the hippocampal theta rhythm (4 to 8 Hz) is key to this mapping function, as it has been repeatedly observed during spatial navigation tasks. If episodic memory and spatial navigation are 2 sides of the same coin, we hypothesized that theta oscillations might reflect relations between explicitly nonspatial items, such as words. We asked 189 neurosurgical patients to perform a verbal free-recall task, of which 96 had indwelling electrodes placed in the MTL. Subjects were instructed to remember short lists of sequentially presented nouns. We found that hippocampal theta power and connectivity during item retrieval coded for semantic distances between words, as measured using word2vec-derived subspaces. Additionally, hippocampal theta indexed temporal distances between words after filtering lists on recall performance, to ensure adequate dynamic range in time. Theta effects were noted only for semantic subspaces of 1 dimension, indicating a substantial compression of the possible semantic feature space. These results lend further support to our growing confidence that the MTL forms cognitive maps of arbitrary representational spaces, helping to reconcile longstanding differences between the spatial and episodic memory literatures.
View details for DOI 10.1073/pnas.1906729116
View details for Web of Science ID 000499101100067
View details for PubMedID 31723043
View details for PubMedCentralID PMC6883851
-
Multivariate stochastic volatility modeling of neural data
ELIFE
2019; 8
Abstract
Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.
View details for DOI 10.7554/eLife.42950
View details for Web of Science ID 000481904900001
View details for PubMedID 31368892
View details for PubMedCentralID PMC6697415
-
Dynamic Theta Networks in the Human Medial Temporal Lobe Support Episodic Memory
CURRENT BIOLOGY
2019; 29 (7): 1100-+
Abstract
The medial temporal lobe (MTL) is a locus of episodic memory in the human brain. It is comprised of cytologically distinct subregions that, in concert, give rise to successful encoding and retrieval of context-dependent memories. However, the functional connections between these subregions are poorly understood. To determine functional connectivity among MTL subregions, we had 131 subjects fitted with indwelling electrodes perform a verbal memory task and asked how encoding or retrieval correlated with inter-regional synchronization. Using phase-based measures of connectivity, we found that synchronous theta (4-8 Hz) activity underlies successful episodic memory. During encoding, we observed a dynamic pattern of connections converging on the left entorhinal cortex, beginning with the perirhinal cortex and shifting through hippocampal subfields. Retrieval-associated networks demonstrated enhanced involvement of the subiculum and CA1, reflecting a substantial reorganization of the encoding network. We posit that coherent theta activity within the MTL marks periods of successful memory, but distinct patterns of connectivity dissociate key stages of memory processing.
View details for DOI 10.1016/j.cub.2019.02.020
View details for Web of Science ID 000462931400018
View details for PubMedID 30905609
View details for PubMedCentralID PMC6445741
-
Functional control of electrophysiological network architecture using direct neurostimulation in humans
NETWORK NEUROSCIENCE
2019; 3 (3): 848-877
Abstract
Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.
View details for DOI 10.1162/netn_a_00089
View details for Web of Science ID 000477902000013
View details for PubMedID 31410383
View details for PubMedCentralID PMC6663306
-
Medial temporal lobe functional connectivity predicts stimulation-induced theta power
NATURE COMMUNICATIONS
2018; 9: 4437
Abstract
Focal electrical stimulation of the brain incites a cascade of neural activity that propagates from the stimulated region to both nearby and remote areas, offering the potential to control the activity of brain networks. Understanding how exogenous electrical signals perturb such networks in humans is key to its clinical translation. To investigate this, we applied electrical stimulation to subregions of the medial temporal lobe in 26 neurosurgical patients fitted with indwelling electrodes. Networks of low-frequency (5-13 Hz) spectral coherence predicted stimulation-evoked increases in theta (5-8 Hz) power, particularly when stimulation was applied in or adjacent to white matter. Stimulation tended to decrease power in the high-frequency broadband (HFB; 50-200 Hz) range, and these modulations were correlated with HFB-based networks in a subset of subjects. Our results demonstrate that functional connectivity is predictive of causal changes in the brain, capturing evoked activity across brain regions and frequency bands.
View details for DOI 10.1038/s41467-018-06876-w
View details for Web of Science ID 000448264600002
View details for PubMedID 30361627
View details for PubMedCentralID PMC6202342
-
Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition
NATURE COMMUNICATIONS
2017; 8: 1704
Abstract
The idea that synchronous neural activity underlies cognition has driven an extensive body of research in human and animal neuroscience. Yet, insufficient data on intracranial electrical connectivity has precluded a direct test of this hypothesis in a whole-brain setting. Through the lens of memory encoding and retrieval processes, we construct whole-brain connectivity maps of fast gamma (30-100 Hz) and slow theta (3-8 Hz) spectral neural activity, based on data from 294 neurosurgical patients fitted with indwelling electrodes. Here we report that gamma networks desynchronize and theta networks synchronize during encoding and retrieval. Furthermore, for nearly all brain regions we studied, gamma power rises as that region desynchronizes with gamma activity elsewhere in the brain, establishing gamma as a largely asynchronous phenomenon. The abundant phenomenon of theta synchrony is positively correlated with a brain region's gamma power, suggesting a predominant low-frequency mechanism for inter-regional communication.
View details for DOI 10.1038/s41467-017-01763-2
View details for Web of Science ID 000416227300001
View details for PubMedID 29167419
View details for PubMedCentralID PMC5700170
-
Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance
JOURNAL OF NEUROSCIENCE
2015; 35 (39): 13402-13418
Abstract
To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior.
View details for DOI 10.1523/JNEUROSCI.5181-14.2015
View details for Web of Science ID 000364104700013
View details for PubMedID 26424887
View details for PubMedCentralID PMC4588611
-
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
PLOS COMPUTATIONAL BIOLOGY
2014; 10 (12): e1003963
Abstract
The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.
View details for DOI 10.1371/journal.pcbi.1003963
View details for Web of Science ID 000346656700023
View details for PubMedID 25521294
View details for PubMedCentralID PMC4270441
-
Performance-optimized hierarchical models predict neural responses in higher visual cortex
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2014; 111 (23): 8619-8624
Abstract
The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model's categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model's intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization--applied in a biologically appropriate model class--can be used to build quantitative predictive models of neural processing.
View details for DOI 10.1073/pnas.1403112111
View details for Web of Science ID 000336976000076
View details for PubMedID 24812127
View details for PubMedCentralID PMC4060707