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Inati, S. K.

Publications and source records attributed to Inati, S. K..

6 recordsLinked to original sources

Reactivation of reward representations associated with the reinforcement of behavior

Reward-related representations are found distributed throughout many human subcortical and neocortical regions that support different neural processes. These representations get reinstated for different but related tasks. There is rising evidence that representations reactivate during learning to reinforce prior rewarding choices. However, there remains a critical lack of understanding for whether and how reactivations in humans can facilitate behavioral reinforcement. To investigate this, we recorded from the temporal lobe and prefrontal cortex with intracranial electrocorticography while human subjects learnt two-choice decisions across two different scenes. Crucially, subjects were able to straightforwardly reactivate knowledge gained in a prior scene to make decisions in the current scene, but only when the reward contingencies stayed the same between the two scenes. Using a Bayesian learner, we inferred reward expectation from choice behavior, and measured representations of these reward expectations in electrocorticography data. We found that the representations of reward expectation in the medial temporal lobe and orbitofrontal cortex were reactivated, only when the subjects could straightforwardly transfer knowledge between the two scenes. In the anterior temporal lobe, the reactivation of reward representations increased as learning increased, suggesting an area of plasticity during learning. This work presents a novel framework for measuring the reinforcement, or reactivation, of representations from human electrophysiology during the reinforcement of behavior. Significance statementThis study provides a framework for how the brain processes and reinstates reward-related information during the reinforcement of behavior. Using intracranial electrocorticography, the researchers found that reward information is encoded in distant brain regions, the medial temporal lobe and orbitofrontal cortex. These representations are dynamically reinstated across scenes when reward contingencies align and when knowledge can be transferred. Additionally, reward-related representations in the anterior temporal lobe become increasingly reinforced or reinstated as learning progresses, revealing an area of plasticity during learning. These findings offer new insights into the neural mechanisms underlying decision-making and highlight potential avenues for addressing conditions like addiction or depression, where reward processing and decision-making are disrupted.

neuroscience↗

Discrete ripples reflect a spectrum of synchronous spiking activity in human association cortex

Direct brain recordings have provided important insights into how persistent oscillatory activity support human memory retrieval, but the extent to which transient fluctuations in intracranial EEG (iEEG) captures the dynamic coordination of underlying neurons involved in memory processing remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that cortical ripples contribute to broadband high frequency activity and exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human cortex is organized into dynamic, discrete packets of information.

neuroscience↗

Low frequency traveling waves in the human cortex coordinate neural activity across spatial scales

Traveling waves of oscillatory activity are thought to influence neuronal spiking, providing a spatiotemporal frame-work for neural communication. However, no direct link has been established between traveling waves and neuronal spiking in humans. We examined traveling waves in the human lateral temporal lobe by using recordings from intracranial electrodes implanted in twenty participants for seizure monitoring as they performed a paired-associates verbal memory task. We observed ubiquitous low frequency traveling waves across the temporal lobe. While wave occurrence in a broad low frequency range did not differ between successful and unsuccessful memory conditions, in a subset of participants with microelectrode recordings, we found that macro-scale waves co-occurred with micro-scale waves, which in turn were temporally locked to single unit spiking. This temporal coordination between traveling waves at different spatial scales and between waves and neuronal spiking in the human brain suggests a role for traveling waves in neural communication.

neuroscience↗

Using Network Analysis to Localize the Epileptogenic Zone from Invasive EEG Recordings in Intractable Focal Epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.\n\nAUTHOR SUMMARYEpilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patients brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

neuroscience↗

Signal complexity of human intracranial EEG tracks successful associative memory formation across individuals

Memory performance is highly variable between individuals. Most studies examining human memory, however, have largely focused on the neural correlates of successful memory formation within individuals, rather than the differences between them. As such, what gives rise to this variability is poorly understood. Here, we examined intracranial EEG (iEEG) recordings captured from 43 participants (23 male) implanted with subdural electrodes for seizure monitoring as they performed a paired-associates verbal memory task. We identified three separate but related signatures of neural activity that tracked differences in successful memory formation across individuals. High performing individuals consistently exhibited less broadband power, flatter power spectral density (PSD) slopes, and greater complexity in their iEEG signals. Furthermore, within individuals across three separate time scales ranging from seconds to days, successful recall was positively associated with these same metrics. Our data therefore suggest that memory ability across individuals can be indexed by increased neural signal complexity.\n\nSignificance StatementWe show that participants whose intracranial EEG exhibits less low frequency power, flatter power spectrums, and greater sample entropy overall are better able to memorize associations, and that the same metrics track fluctuations in memory performance across time within individuals. These metrics together signify greater neural signal complexity which may index the brains ability to flexibly engage with information and generate separable memory representations. Critically, the current set of results provide a unique window into the neural markers of individual differences in memory performance which have hitherto been underexplored.

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Dual origins of measured phase-amplitude coupling reveal distinct neural mechanisms underlying episodic memory in the human cortex

Phase-amplitude coupling (PAC) is hypothesized to coordinate neural activity, but its role in successful memory formation in the human cortex is unknown. Measures of PAC are difficult to interpret, however. Both increases and decreases in PAC have been linked to memory encoding, and PAC may arise due to different neural mechanisms. Here, we use a waveform analysis to examine PAC in the human cortex as participants with intracranial electrodes performed a paired associates memory task. We found that successful memory formation exhibited significant decreases in left temporal lobe and prefrontal cortical PAC, and these two regions exhibited changes in PAC within different frequency bands. Two underlying neural mechanisms, nested oscillations and sharp waveforms, were responsible for the changes in these regions. Our data therefore suggest that decreases in measured cortical PAC during episodic memory reflect two distinct underlying mechanisms that are anatomically segregated in the human brain.

neuroscience↗