There is growing evidence from human intracranial electrocorticography (ECoG) studies that intera... more There is growing evidence from human intracranial electrocorticography (ECoG) studies that interactions between cortical frequencies are important for sensory perception, cognition and inter-regional neuronal communication. Recent studies have focused mainly on the strength of phase-amplitude coupling in cross-frequency interactions. Here, we introduce a complex modulation method based on measures of coherence to investigate cross-frequency coupling in the neural time series. This novel approach uses complex demodulation transform and coherence measures from the transformed signals. We used this method to quantify power coupling between two cortical frequency bands: theta (47 Hz) and high gamma (70-150 Hz) in ECoG signals recorded during an auditory task. We compared complex modulation results with traditional phase-amplitude coupling measures (PAC) derived from the same ECoG dataset. Our results suggest that cross-frequency coupling may involve changes in both phase-amplitude and power relationships between frequencies, reflecting the complexity of neuronal oscillatory interactions.
Epileptic seizures are brief, transient events characterized by rapidly changing dynamics. Partia... more Epileptic seizures are brief, transient events characterized by rapidly changing dynamics. Partial seizures, the most common seizure type, and one often refractory to medications, originate from focal brain regions and have variable patterns of propagation. In the presurgical evaluations of some patients, intracranial recording arrays are needed to better define the seizure focus. Digital processing, high frequency sampling rates, and more extensive or customized electrode arrays can provide a more complete assessment of patterns of seizure onset and propagation. These advanced analyses can supplement visual analyses of ictal recordings and provide new insights. Gabor atom density (a composite measure of signal complexity) and short-time direct transfer function are two examples of new methods that can provide new clues about seizure onset and propagation.
IntroductionDetrended fluctuation analysis (DFA) is a well‐established method to evaluate scaling... more IntroductionDetrended fluctuation analysis (DFA) is a well‐established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number.MethodsHerein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go–NoGo shooting task that was performed by 30 participants under low and high time‐stress conditions in each of six repeated sessions over a 3‐week period.ResultsThis new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time‐stress conditions and (2) predicting task performance outcomes.ConclusionWe show that by changing from op...
Detrended Fluctuation Analysis (DFA) is a well-known method to evaluate scaling indices of time s... more Detrended Fluctuation Analysis (DFA) is a well-known method to evaluate scaling indices of time series, categorizing the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time RT(n) time series, where n is the trial number. Herein we propose treating each RT(n) as a duration time that changes the representation from operational (trial) time n to chronological (temporal) time t, or RT(t). To do this, we fill each time interval of RT(n) with fixed noise of magnitude 1 and with a randomly determined sign. The fixed noise represents the rigidity (order) and the random change of sign represents the flexibility (randomness) of the generated time series RT(t). Then the DFA algorithm was applied to RT(t) time series to evaluate scaling indices. We show that this new perspective leads to better results in: 1) differentiating scaling indices between low vs. high time-stress conditions and 2) predicting task performance outcomes. The datase...
Neural activity emerges and propagates swiftly between brain areas. Investigation of these transi... more Neural activity emerges and propagates swiftly between brain areas. Investigation of these transient large-scale flows requires sophisticated statistical models. We present a method for assessing the statistical confidence of event-related neural propagation. Furthermore, we propose a criterion for statistical model selection, based on both goodness of fit and width of confidence intervals. We show that event-related causality (ERC) with two-dimensional (2D) moving average, is an efficient estimator of task-related neural propagation and that it can be used to determine how different cognitive task demands affect the strength and directionality of neural propagation across human cortical networks. Using electrodes surgically implanted on the surface of the brain for clinical testing prior to epilepsy surgery, we recorded electrocorticographic (ECoG) signals as subjects performed three naming tasks: naming of ambiguous and unambiguous visual objects, and as a contrast, naming to auditory description. ERC revealed robust and statistically significant patterns of high gamma activity propagation, consistent with models of visually and auditorily cued word production. Interestingly, ambiguous visual stimuli elicited more robust propagation from visual to auditory cortices relative to unambiguous stimuli, whereas naming to auditory description elicited propagation in the opposite direction, consistent with recruitment of modalities other than those of the stimulus during object recognition and naming. The new method introduced here is uniquely suitable to both research and clinical applications and can be used to estimate the statistical significance of neural propagation for both cognitive neuroscientific studies and functional brain mapping prior to resective surgery for epilepsy and brain tumors.
: A multidisciplinary research project was initiated to enhance the fundamental understanding of ... more : A multidisciplinary research project was initiated to enhance the fundamental understanding of brain structural and functional injury mechanisms associated with mild blast events on neuronal cell cultures. Two foundational infrastructure capabilities were realized, but more critically, in-house expertise was developed to execute the research and enable the future US Army capability. The first accomplishment centers on cellular experimentation. A novel experimental research platform was developed to examine the effects of explosive pressure impacts on neurons. This indoor in-vitro experimental research program uses real explosives to study the impact of blast on dissociated neurons. The experimental results found transient damage to the neuronal cell membranes that modulate neurotransmitter release that is necessary for brain signals to generate and carry information between brain regions. The second accomplishment focuses on a multiscale computational brain model implemented in a ...
We propose ToFU, a new trainable neural network unit with a persistence diagram dissimilarity fun... more We propose ToFU, a new trainable neural network unit with a persistence diagram dissimilarity function as its activation. Since persistence diagrams are topological summaries of structures, this new activation measures and learns the topology of data to leverage it in machine learning tasks. We showcase the utility of ToFU in two experiments: one involving the classification of discrete-time autoregressive signals, and another involving a variational autoencoder. In the former, ToFU yields competitive results with networks that use spectral features while outperforming CNN architectures. In the latter, ToFU produces topologically-interpretable latent space representations of inputs without sacrificing reconstruction fidelity.
There is growing evidence from human intracranial electrocorticography (ECoG) studies that intera... more There is growing evidence from human intracranial electrocorticography (ECoG) studies that interactions between cortical frequencies are important for sensory perception, cognition and inter-regional neuronal communication. Recent studies have focused mainly on the strength of phase-amplitude coupling in cross-frequency interactions. Here, we introduce a complex modulation method based on measures of coherence to investigate cross-frequency coupling in the neural time series. This novel approach uses complex demodulation transform and coherence measures from the transformed signals. We used this method to quantify power coupling between two cortical frequency bands: theta (47 Hz) and high gamma (70-150 Hz) in ECoG signals recorded during an auditory task. We compared complex modulation results with traditional phase-amplitude coupling measures (PAC) derived from the same ECoG dataset. Our results suggest that cross-frequency coupling may involve changes in both phase-amplitude and power relationships between frequencies, reflecting the complexity of neuronal oscillatory interactions.
Epileptic seizures are brief, transient events characterized by rapidly changing dynamics. Partia... more Epileptic seizures are brief, transient events characterized by rapidly changing dynamics. Partial seizures, the most common seizure type, and one often refractory to medications, originate from focal brain regions and have variable patterns of propagation. In the presurgical evaluations of some patients, intracranial recording arrays are needed to better define the seizure focus. Digital processing, high frequency sampling rates, and more extensive or customized electrode arrays can provide a more complete assessment of patterns of seizure onset and propagation. These advanced analyses can supplement visual analyses of ictal recordings and provide new insights. Gabor atom density (a composite measure of signal complexity) and short-time direct transfer function are two examples of new methods that can provide new clues about seizure onset and propagation.
IntroductionDetrended fluctuation analysis (DFA) is a well‐established method to evaluate scaling... more IntroductionDetrended fluctuation analysis (DFA) is a well‐established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number.MethodsHerein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go–NoGo shooting task that was performed by 30 participants under low and high time‐stress conditions in each of six repeated sessions over a 3‐week period.ResultsThis new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time‐stress conditions and (2) predicting task performance outcomes.ConclusionWe show that by changing from op...
Detrended Fluctuation Analysis (DFA) is a well-known method to evaluate scaling indices of time s... more Detrended Fluctuation Analysis (DFA) is a well-known method to evaluate scaling indices of time series, categorizing the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time RT(n) time series, where n is the trial number. Herein we propose treating each RT(n) as a duration time that changes the representation from operational (trial) time n to chronological (temporal) time t, or RT(t). To do this, we fill each time interval of RT(n) with fixed noise of magnitude 1 and with a randomly determined sign. The fixed noise represents the rigidity (order) and the random change of sign represents the flexibility (randomness) of the generated time series RT(t). Then the DFA algorithm was applied to RT(t) time series to evaluate scaling indices. We show that this new perspective leads to better results in: 1) differentiating scaling indices between low vs. high time-stress conditions and 2) predicting task performance outcomes. The datase...
Neural activity emerges and propagates swiftly between brain areas. Investigation of these transi... more Neural activity emerges and propagates swiftly between brain areas. Investigation of these transient large-scale flows requires sophisticated statistical models. We present a method for assessing the statistical confidence of event-related neural propagation. Furthermore, we propose a criterion for statistical model selection, based on both goodness of fit and width of confidence intervals. We show that event-related causality (ERC) with two-dimensional (2D) moving average, is an efficient estimator of task-related neural propagation and that it can be used to determine how different cognitive task demands affect the strength and directionality of neural propagation across human cortical networks. Using electrodes surgically implanted on the surface of the brain for clinical testing prior to epilepsy surgery, we recorded electrocorticographic (ECoG) signals as subjects performed three naming tasks: naming of ambiguous and unambiguous visual objects, and as a contrast, naming to auditory description. ERC revealed robust and statistically significant patterns of high gamma activity propagation, consistent with models of visually and auditorily cued word production. Interestingly, ambiguous visual stimuli elicited more robust propagation from visual to auditory cortices relative to unambiguous stimuli, whereas naming to auditory description elicited propagation in the opposite direction, consistent with recruitment of modalities other than those of the stimulus during object recognition and naming. The new method introduced here is uniquely suitable to both research and clinical applications and can be used to estimate the statistical significance of neural propagation for both cognitive neuroscientific studies and functional brain mapping prior to resective surgery for epilepsy and brain tumors.
: A multidisciplinary research project was initiated to enhance the fundamental understanding of ... more : A multidisciplinary research project was initiated to enhance the fundamental understanding of brain structural and functional injury mechanisms associated with mild blast events on neuronal cell cultures. Two foundational infrastructure capabilities were realized, but more critically, in-house expertise was developed to execute the research and enable the future US Army capability. The first accomplishment centers on cellular experimentation. A novel experimental research platform was developed to examine the effects of explosive pressure impacts on neurons. This indoor in-vitro experimental research program uses real explosives to study the impact of blast on dissociated neurons. The experimental results found transient damage to the neuronal cell membranes that modulate neurotransmitter release that is necessary for brain signals to generate and carry information between brain regions. The second accomplishment focuses on a multiscale computational brain model implemented in a ...
We propose ToFU, a new trainable neural network unit with a persistence diagram dissimilarity fun... more We propose ToFU, a new trainable neural network unit with a persistence diagram dissimilarity function as its activation. Since persistence diagrams are topological summaries of structures, this new activation measures and learns the topology of data to leverage it in machine learning tasks. We showcase the utility of ToFU in two experiments: one involving the classification of discrete-time autoregressive signals, and another involving a variational autoencoder. In the former, ToFU yields competitive results with networks that use spectral features while outperforming CNN architectures. In the latter, ToFU produces topologically-interpretable latent space representations of inputs without sacrificing reconstruction fidelity.
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