Closed-loop stimulation of temporal cortex rescues functional networks and improves memory

Ezzyat, Youssef; Wanda, Paul A.; Levy, Deborah F.; Kadel, Allison; Aka, Ada; Pedisich, Isaac; Sperling, Michael R.; Sharan, Ashwini; Lega, Bradley; Burks, Alexis; Gross, Robert E.; Inman, Cory S.; Jobst, Barbara C.; Gorenstein, Mark A.; Davis, Kathryn A.; Worrell, Gregory A.; Kucewicz, Michal T.; Stein, Joel M.; Gorniak, Richard; Das, Sandhitsu R.; Rizzuto, Daniel S.; Kahana, Michael J.

doi:10.1038/s41467-017-02753-0

Cited by 280 publications

(254 citation statements)

References 55 publications

(56 reference statements)

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“…These results, therefore, could provide an informed basis for the use of intermittent and targeted neuromodulation to aid individuals experiencing severe emotion dysregulation at both extremes of the spectrum. Indeed, this approach, applied to other domains and across cognitive and emotional tasks, could allow us to arrive at a more refined view of how to use neural stimulation to therapeutically alter the circuitry underlying important domains of functioning, such as maladaptive emotional processing and decision making, with implications for a wide array of neuropsychiatric diseases (33,34,51,52).…”

Section: Resultsmentioning

confidence: 99%

“…Second, after establishing a subset of behavioral models which were high-performing, in separate task sessions, we tested whether identified hidden states within these behavioral state space models can be driven by stimulation in specific brain networks. We hypothesized that direct electrical intracranial stimulation in different brain regions would have differential, and causal, effects on these behavioral features as has been hinted at with stimulation in other brain regions in learning and memory (50,51), mood (35,52), and OCD (36,53,54). As a final test, we used the state-space model in closed-loop adaptive stimulation to modulate behavior predictably.…”

Section: Introductionmentioning

confidence: 99%

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Bidirectional modulation of human emotional conflict resolution using intracranial stimulation

Yousefi

Ellard

et al. 2019

Preprint

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The ability to regulate emotions in the service of meeting ongoing goals and task demands is a key aspect of adaptive human behavior in our volatile social world. Consequently, difficulties in processing and responding to emotional stimuli underlie many psychiatric diseases ranging from depression to anxiety, the common thread being effects on behavior. Behavior, which is made up of shifting, difficult to measure hidden states such as attention and emotion reactivity, is a product of integrating external input and latent mental processes. Directly measuring, and differentiating, separable hidden cognitive, emotional, and attentional states contributing to emotion conflict resolution, however, is challenging, particularly when only using task-relevant behavioral measures such as reaction time. State-space representations are a powerful method for investigating hidden states underlying complex systems. Using state-space modeling of behavior, we identified relevant hidden cognitive states and predicted behavior in a standardized emotion regulation task. After identifying and validating models which best fit the behavior and narrowing our focus to one model, we used targeted intracranial stimulation of the emotion regulation-relevant neurocircuitry, including prefrontal structures and the amygdala, to causally modulate separable states. Finally, we focused on this one validated state-space model to perform real-time, bidirectional closed-loop adaptive stimulation in a subset of participants. These approaches enable an improved understanding of how to sample and understand emotional processing in a way which could be leveraged in neuromodulatory therapy for disorders of emotional regulation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bidirectional modulation of human emotional conflict resolution using intracranial stimulation

Yousefi

Ellard

et al. 2019

Preprint

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“…In the first study of its kind, applying an integrated IEEG-fMRI method, we have shown that established markers of memory encoding correspond to meaningful network differences captured by rsfMRI. In clinical terms, knowing a region is a hub, playing an important role in multi-regional and multi-functional connectivity, may inform technologies trying to identify the most effective targets to electrically or pharmacologically stimulate for cognitive enhancement in areas such as memory Ezzyat Y et al, 2018;Kucewicz MT et al, 2018), or perhaps be used to identify the areas with sufficient influence over targets to generate effective neuro-feedback loops (Hohenfeld C et al, 2017;Murphy AC et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

Chaitanya

Hinds

Kragel

et al. 2019

Preprint

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High-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFAmemory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding. AbbreviationsHFA -High-frequency activity MEM -Brain regions showing HFA associated with verbal-memory encoding CN -Controls Nodes P.REC -Percentage of words recalled during IEEG memory testing CVLT -California Verbal Learning Test IEEG -Invasive Electroencephalography BC -Betweenness Centrality CC-Clustering Coefficient PC -Participation Coefficient Highlights 1. High frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.2. HFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.3. HFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.4. HFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding. 5. HFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.resting-state fMRI (rsfMRI) data to characterize the network architecture of a task-defined HFAmemory network and understand its relationship to the functional connectivity of a broader set of intrinsic resting-state networks. Graph theory has been a useful tool for mapping functional networks and characterizing their properties during task and at rest. Three important graph indices that are essential in characterizing brain regions and their functionality include: (1) clustering coefficient (CC), which captures network segregation and local information processing and (2) betweenness centrality (BC), which captures hubness and the degree of importance held by a region that connects two or more modules and (3) participation coefficient (PC) which measures the extent to which regions within a network connect to networks other than its own, with a higher participation coefficient indicating that the regions connect with a variety of o...

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“…To benchmark our MSV findings we conducted parallel analyses of wavelet-derived spectral power at frequencies ranging between 3-180 Hz. To aggregate across MTL electrodes within each subject we applied an L2-penalized logistic regression classifier using features extracted during the encoding period to predict subsequent memory performance (Ezzyat et al, 2017(Ezzyat et al, , 2018. To estimate the generalization of the classifier, we utilized a nested cross-validation procedure in which we trained the model on N − 1 sessions using the optimal penalty parameter selected via another inner cross-validation procedure on the same training data (see Appendix 4.6 for details).…”

Section: Classification Of Subsequent Memory Recallmentioning

confidence: 99%

Multivariate Stochastic Volatility Modeling of Neural Data

Phan

Wachter

Kahana

2018

Preprint

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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.

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Closed-loop stimulation of temporal cortex rescues functional networks and improves memory

Cited by 280 publications

References 55 publications

Bidirectional modulation of human emotional conflict resolution using intracranial stimulation

Bidirectional modulation of human emotional conflict resolution using intracranial stimulation

Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

Multivariate Stochastic Volatility Modeling of Neural Data

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