Biology subjects

Sivaraju, A.

Publications and source records attributed to Sivaraju, A..

4 recordsLinked to original sources

bioRxiv2025

Beta bursts mediate amygdala gating of hippocampal emotional encoding

The amygdala and hippocampus are key structures in emotional processing, yet the transient neural dynamics coordinating their interaction remain poorly understood. We simultaneously recorded single-neuron activity and local field potentials from these regions in epilepsy patients performing an emotional image-rating task. Neurons in both areas exhibited firing rate changes predictive of subjective emotional valence. While conventional spectral analysis revealed no valence-specific patterns, beta bursts (13-30 Hz)--transient, high-power events-were uniquely modified by task features. In both regions, beta bursts were associated with increased gamma amplitude and enhanced phase coherence, with beta-gamma phase-amplitude coupling capturing emotion-related dynamics. Critically, amygdala beta bursts were associated with strong suppression of hippocampal firing, coinciding with interneuron activation, during negative valence/highest arousal processing, whereas hippocampal bursts showed no reciprocal influence. These findings suggest that beta bursts may represent a temporally precise neural correlate of emotional appraisal and a candidate mechanism for targeted neuromodulation in mood disorders.

neuroscience↗

bioRxiv2024

Widespread brain activity increases in frontal lobe seizures with impaired consciousness

Impaired consciousness is a serious clinical manifestation of epilepsy with negative consequences on quality of life. Little work has investigated impaired consciousness in frontal lobe seizures, a common form of focal epilepsy. In temporal lobe seizures, previous studies showed widespread cortical slow waves associated with depressed subcortical arousal and impaired consciousness. However, in frontal lobe epilepsy, it is not known whether cortical slow waves are present, or whether a very different cortical activity pattern may be related to impaired consciousness. We used intracranial EEG recordings of 65 frontal lobe seizures in 30 patients for quantitative analysis of ictal cortical activity and its relationship to impaired consciousness. Behavioral changes based on blinded review of seizure videos were used to classify focal aware, focal impaired awareness, and focal to bilateral tonic-clonic seizures. Changes in intracranial EEG power from preictal baseline were analyzed in different cortical regions and across frequency ranges in these three categories. We found that frontal lobe focal aware seizures showed approximately 40% increases in intracranial EEG power localized to the frontal lobe of seizure onset across frequency ranges, with relatively smaller changes in other cortical regions. Frontal lobe focal impaired awareness seizures showed approximately 50% increases in intracranial EEG power, not significantly different from focal aware seizures in the frontal lobe of seizure onset (P = 1.038), but significantly greater than focal aware seizures in other broad cortical regions (P < 0.001). Importantly, the widespread cortical increases in EEG power observed in focal impaired awareness versus focal aware seizures were seen not just in the frequency range of slow waves, but were also observed across other frequencies including fast activity. However, the widespread cortical increases in focal impaired awareness seizures differed from focal to bilateral tonic-clonic seizures where intracranial EEG power increased to a much higher level by approximately 600%. The large power increases in focal to bilateral tonic-clonic were significantly greater than in focal impaired awareness seizures both in the frontal lobe of seizure onset and in other cortical regions (P < 0.001). Our findings contrast with focal temporal lobe epilepsy, where impaired consciousness is associated with cortical slow waves. We can speculate that different focal seizure types produce impaired consciousness by impacting widespread cortical regions but through different physiological mechanisms. Insights gained by studying mechanisms of impaired consciousness may be the first step towards developing novel treatments to prevent this important negative consequence of epilepsy.

neuroscience↗

bioRxiv2023

Statistical learning in epilepsy: Behavioral, anatomical, and causal mechanisms in the human brain

Statistical learning, the fundamental cognitive ability of humans to extract regularities across experiences over time, engages the medial temporal lobe in the healthy brain. This leads to the hypothesis that statistical learning may be impaired in epilepsy patients, and that this impairment could contribute to their varied memory deficits. In turn, epilepsy patients provide a platform to advance basic understanding of statistical learning by helping to evaluate the necessity of medial temporal lobe circuitry through disease and causal perturbations. We implemented behavioral testing, volumetric analysis of the medial temporal lobe substructures, and direct electrical brain stimulation to examine statistical learning across a cohort of 61 epilepsy patients and 28 healthy controls. Behavioral performance in a statistical learning task was negatively associated with seizure frequency, irrespective of where seizures originated in the brain. The volume of hippocampal subfields CA1 and CA2/3 correlated with statistical learning performance, suggesting a more specific role of the hippocampus. Indeed, transient direct electrical stimulation of the hippocampus disrupted statistical learning. Furthermore, the relationship between statistical learning and seizure frequency was selective: behavioral performance in an episodic memory task was impacted by structural lesions in the medial temporal lobe and by antiseizure medications, but not by seizure frequency. Overall, these results suggest that statistical learning may be hippocampally dependent and that this task could serve as a clinically useful behavioral assay of seizure frequency distinct from existing neuropsychological tests. Simple and short statistical learning tasks may thus provide patient-centered endpoints for evaluating the efficacy of novel treatments in epilepsy.

neuroscience↗

bioRxiv2023

Online abstraction during statistical learning revealed by neural entrainment from intracranial recordings

We encounter the same people, places, and objects in predictable sequences and configurations. These regularities are learned efficiently by humans via statistical learning. Importantly, statistical learning creates knowledge not only of specific regularities, but also of more abstract, generalizable regularities. However, prior evidence of such abstract learning comes from post-learning behavioral tests, leaving open the question of whether abstraction occurs online during initial exposure. We address this question by measuring neural entrainment during statistical learning with intracranial recordings. Neurosurgical patients viewed a stream of scene photographs with regularities at one of two levels: In the Exemplar-level Structured condition, the same photographs appeared repeatedly in pairs. In the Category-level Structured condition, the photographs were trial-unique but their categories were paired across repetitions. In a baseline Random condition, the same photographs repeated but in a scrambled order. We measured entrainment at the frequency of individual photographs, which was expected in all conditions, but critically also at half of that frequency -- the rate at which to-be-learned pairs appeared in the two structured conditions (but not the random condition). Neural entrainment to both exemplar and category pairs emerged within minutes throughout visual cortex and in frontal and temporal brain regions. Many electrode contacts were sensitive to only one level of structure, but a significant number encoded both exemplar and category regularities. These findings suggest that abstraction occurs spontaneously during statistical learning, providing insight into the brains unsupervised mechanisms for building flexible and robust knowledge that generalizes across input variation and conceptual hierarchies.

neuroscience↗