Biology subjects

Nunez, M. D.

Publications and source records attributed to Nunez, M. D..

7 recordsLinked to original sources

bioRxiv2022

Neuro-cognitive models of single-trial EEG measures describe latent effects of spatial attention during perceptual decision making

Visual perceptual decision-making involves multiple components including visual encoding, attention, accumulation of evidence, and motor execution. Recent research suggests that EEG oscillations can identify the time of encoding and the onset of evidence accumulation during perceptual decision-making. Although scientists show that spatial attention improves participant performance in decision making, little is know about how spatial attention influences the individual cognitive components that gives rise to that improvement in performance. We found evidence in this work that both visual encoding time (VET) before evidence accumulation and other non-decision time process after or during evidence accumulation are influenced by spatial top-down attention, but not evidence accumulation itself. Specifically we used an open-source data set in which participants were informed about the location of a target stimulus in the visual field on some trials during a face-car perceptual decision-making task. Fitting neural drift-diffusion models to response time, accuracy, and single-trial N200 latencies (~ 125 to 225 ms post-stimulus) of EEG allowed us to separate the processes of visual encoding and the decision process from other non-decision time processes such as motor execution. These models were fit in a single step in a hierarchical Bayesian framework. Model selection criteria and comparison to model simulations show that spatial attention manipulates both VET and other non-decision time process. We discuss why spatial attention may affect other non-evidence accumulation processes, such as motor execution time (MET), and why this may seem unexpected given the literature. We make recommendations for future work on this topic.

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bioRxiv2020

Embodied Cognition and Decision-Making in the Primate Superior Colliculus

A popular model of decision-making suggests that in primates, including humans, decisions evolve within forebrain structures responsible for preparing voluntary actions; a concert referred to as embodied cognition. Embodied cognition posits that in decision tasks, neuronal activity generally associated with preparing an action, actually reflects the accumulation of evidence for a particular decision. Testing the embodied cognition model causally is challenging because dissociating the evolution of a decision from preparing a motor act is difficult, if the same neuronal activity instantiates both processes. Ideally, one would show that manipulation of neuronal activity thought to be involved in movement preparation actually alters decisions, and not movement preparation. Here, trained monkeys performed a two-choice perceptual decision-making task in which they judged the orientation of a dynamic Glass pattern before and after unilateral, reversible inactivation of a brainstem area involved in preparing eye movements, the superior colliculus (SC). Surprisingly, we found that unilateral SC inactivation produced significant decision biases and changes in reaction times consistent with a role for the SC in evidence accumulation. Fitting signal detection theory and sequential sampling models (drift-diffusion and urgency-gating) to the data revealed that SC inactivation produced a decrease in the relative evidence for contralateral decisions. Control experiments showed that SC inactivation did not result in eye movement biases ruling out interpretations based on motor preparation or spatial attentional impairment. The results provide causal evidence for an embodied cognition model of perceptual decision-making and provide compelling evidence that the SC of primates plays a causal role in how evidence is accumulated for perceptual decisions, a process that is usually attributed to the cerebral cortex.

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bioRxiv2020

Towards automatic classification of pathological epileptic tissue with interictal high frequency oscillations

ObjectiveHigh frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO "rate") is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. "interictal") HFO dynamics both within and outside the seizure onset zone (SOZ). ApproachUsing long-term intracranial EEG (mean duration 10.3 hours) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical Negative Binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves. Main resultsParameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slowwave sleep in the second model improved SOZ prediction compared to the first model for only some patients. SignificanceThis work suggests that delineation of seizure onset zone with interictal data can be improved by the inclusion of time-varying HFO dynamics. 1. Novelty & SignificanceThe rate of high frequency oscillations (HFOs), measured as number per minute, is a biomarker of the seizure onset zone (SOZ) in epilepsy patients. However, the rate changes over time and HFO occurrence can be phase-coupled to slow oscillations. Here we show, through novel application of negative binomial models to HFO count data, that HFO temporal dynamics are a biomarker of the SOZ and are superior to HFO rate. Specifically, more random occurrence of HFOs predicted SOZ, as opposed to events clustered in time. This suggests that consideration of HFO temporal dynamics can improve SOZ localization for epilepsy surgery.

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bioRxiv2018

Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative

A biophysical framework needed to interpret electrophysiological data recorded at multiple spatial scales of brain tissue is developed. Micro current sources at membrane surfaces produce local field potentials (LFP), electrocorticography (ECoG), and electroencephalography (EEG). We categorize multi-scale sources as genuine, equivalent, or representative. Genuine sources occur at the micro scale of cell surfaces. Equivalent sources provide identical experimental outcomes over a range of scales and applications. In contrast, each representative source distribution is just one of many possible source distributions that yield similar experimental outcomes. Macro sources (\"dipoles\") may be defined at the macrocolumn (mm) scale and depend on several features of the micro sources--magnitudes, micro synchrony within columns, and distribution through the cortical depths. These micro source properties are determined by brain dynamics and the columnar structure of cortical tissue. The number of representative sources underlying EEG data depends on the spatial scale of neural tissue under study. EEG inverse solutions (e.g. dipole localization) and high resolution estimates (e.g. Laplacian, dura imaging) have both strengths and limitations that depend on experimental conditions. The proposed theoretical framework informs studies of EEG source localization, source characterization, and low passes filtering. It also facilitates interpretations of brain dynamics and cognition, including measures of synchrony, functional connections between cortical locations, and other aspects of brain complexity.

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bioRxiv2018

Individual differences in cortical processing speed predict cognitive abilities: a model-based cognitive neuroscience account

Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information-processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing speed and cognitive abilities. We found that a higher neural speed predicted both the velocity of evidence accumulation across behavioral tasks as well as cognitive ability test scores. However, only a small part of the association between neural processing speed and cognitive abilities was mediated by individual differences in the velocity of evidence accumulation. The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and behavioral data. Our results suggest that individual differences in neural processing speed might affect a plethora of higher-order cognitive processes, that only in concert explain the large association between neural processing speed and cognitive abilities, instead of the effect being entirely explained by differences in evidence accumulation speeds.

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bioRxiv2018

Timing of readiness potentials reflect a decision-making process in the human brain

Decision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perceptual categorization and provide evidence linking brain signals in parietal cortex to the evidence accumulation process. In this exploratory study, we use a task where the dominant contribution to response time is response selection and model the response time data with the drift-diffusion model. EEG measurement during the task show that the Readiness Potential (RP) recorded over motor areas has timing consistent with the evidence accumulation process. The duration of the RP predicts decision-making time, the duration of evidence accumulation, suggesting that the RP partly reflects an evidence accumulation process for response selection in the motor system. Thus, evidence accumulation may be a neural implementation of decision-making processes in both perceptual and motor systems. The contributions of perceptual categorization and response selection to evidence accumulation processes in decision-making tasks can be potentially evaluated by examining the timing of perceptual and motor EEG signals.

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bioRxiv2018

Visual encoding signals around 200 milliseconds track onset of evidence accumulation during human decision making

Encoding of a sensory stimulus is believed to be the first step in perceptual decision making. Previous research has shown that electrical signals recorded from the human brain track evidence accumulation during perceptual decision making (Gold and Shadlen, 2007; OConnell et al., 2012; Philiastides et al., 2014). In this study we directly tested the hypothesis that the latency of the N200 recorded by EEG (a negative peak occurring between 150 and 275 ms after stimulus presentation in human participants) reflects the visual encoding time (VET) required for completion of figure-ground segregation before evidence accumulation. We show that N200 latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds, reflecting a linear regression slope of 1. Simulations of cognitive decision-making theory show that variation in human response times not related to evidence accumulation (including VET) are tracked by the fastest response times. A relationship with a slope of 1 between N200 latencies and VET was found by fitting a linear model between trial-averaged N200 latencies and the 10th percentiles of response times. A slope of 1 was also found between single-trial N200 latencies and response times. Fitting a novel neuro-cognitive model of decision-making also yielded a slope of 1 between N200 latency and non-decision time in multiple visual noise conditions, indicating that N200 latencies track the completion of visual encoding and the onset of evidence accumulation. The N200 waveforms were localized to the cortical surface at distributed temporal and extrastriate locations, consistent with a distributed network engaged in figure-ground segregation of the target stimulus.\n\nSignificance StatementEncoding of a sensory stimulus is believed to be the first step in perceptual decision making. In this study, we report evidence that visual evoked potentials (EPs) around 200 ms after stimulus presentation track the time of visual figure-ground segregation before the onset of evidence accumulation during decision making. These EP latencies vary across individuals, are modulated by external visual noise, and increase response time by x milliseconds when they increase by x milliseconds. Hierarchical Bayesian model-fitting was also used to relate these EPs to a specific cognitive parameter that tracks time related to visual encoding in a decision-making model of response time. This work adds to the growing literature that suggests that EEG signals can track the component cognitive processes of decision making.

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