Continuous Prediction of Cognitive State Using A Marked-Point Process Modeling Framework

Amidi, Yalda; Paulk, Angelique C.; Dougherty, Darin D.; Cash, Sydney S.; Widge, Alik S.; Eden, Uri T.; Yousefi, Ali

doi:10.1109/embc.2019.8856681

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Researchers have also found that spindle features, including spindle duration, frequency, and amplitude, are highly correlated with demographic differences and neurological disorders 1,5,[16][17][18]57,90,91 . We have previously developed a class of marked point process models for neural spiking data, which model the temporal patterns and factors influencing spikes with different waveform structures 92,93 . These marked point process models can be extended to address issues inherent in spindle detection 1,2,48 and target specific scientific questions related to spindle morphology.…”

Section: Extensions and Future Directionsmentioning

confidence: 99%

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

Chen,

He,

Brown

et al. 2024

Preprint

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Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase (~14%) and sleep depth (~16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.

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Section: Extensions and Future Directionsmentioning

confidence: 99%

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

Chen,

He,

Brown

et al. 2024

Preprint

Self Cite

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“…At each time step k, r k = 1 indicates the onset of the participant's response and r k = 0 otherwise. Additionally, to differentiate between conflict and non-conflict tasks, we define another random variable, referred to as a mark, m k ∈ {−1, 1}, in the MPP theory [13,23,25]. The variable m k can take two values, 1 and −1, where 1 represents a conflict decision, and −1 indicates a non-conflict choice.…”

Section: Marked-point Process Observation Model For Modeling Behavior...mentioning

confidence: 99%

“…where {c 0 , d 0 , c x , d x , c H , d H } are the observation process free parameters [25] and H k is the history term of previous events. This leads to the observation likelihood formulated as…”

Section: Marked-point Process Observation Model For Modeling Behavior...mentioning

confidence: 99%

“…In the HI-DGD, we assumed that the discriminative process model, g(s k ; Ω), is a linear function of the neural features with additive normal noise with variance σ n [21]. The generative process model, l(x k ; Γ), is a marked-point process in which its marginal CIF is weighted by the cognitive state, by coefficient c x , and the history of response times, by coefficient c h , with a baseline intensity of c 0 [18,25]. Similarly, the generation of the associated marks to behavioral signals is parameterized by the cognitive state, by coefficient d x , and the history of response time, by coefficient d h , with a baseline of d 0 .…”

Section: Hi-dgd Model Identification In the Verbal Stroop Task Datasetmentioning

confidence: 99%

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Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model

et al. 2023

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The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g., the response time), remains to be identified. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals’ response time) recorded in 10 Parkinson’s disease patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy population. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural- and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.

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Inferring Cognitive State Underlying Conflict Choices in Verbal Stroop Task Using Heterogeneous Input Discriminative-Generative Decoder Model

Rezaei

Jeoung

Gharamani

et al. 2022

Preprint

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The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g., the response time), remains to be identified. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in 10 Parkinson's disease patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy population. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural- and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.

show abstract

Continuous Prediction of Cognitive State Using A Marked-Point Process Modeling Framework

Cited by 4 publications

References 17 publications

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model

Inferring Cognitive State Underlying Conflict Choices in Verbal Stroop Task Using Heterogeneous Input Discriminative-Generative Decoder Model

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