Browsing by Author "Tandon, Nitin"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Applying the Short-Time Direct Directed Transfer Function to Human Electrocorticographic Recordings from a Language Task
    (2013-06-28) Whaley, Meagan; Cox, Steven J.; Embree, Mark; Tandon, Nitin; Dabaghian, Yuri
    This thesis applied the short-time direct directed transfer function (SdDTF) to time series data recordings from intracranial electrodes that measure the brain's electrical activity to determine the causal influences that occurred between brain regions during a speech production task. The combination of high temporal and spatial resolution of the electrocorticography (ECoG) recordings directly from the cortex render these measurements of brain activity desirable, particularly when analyzing the fine cognitive dynamics involved in word generation. This research applied a new method to characterize the SdDTF results by compressing across time and high gamma frequencies, generating adjacency matrices, and graphing them to visualize the influences between anatomical regions over the duration of the entire task. This consolidated SdDTF analysis technique allowed for data from a total of seven patients to be combined, generating results which were consistent with current speech production models. The results from this thesis contribute to the expansion of language research by identifying areas relevant to word generation, providing information that will help surgeons avoid irreparable damage to crucial cortex during brain surgery.
  • Thumbnail Image
    Item
    Dynamics of brain networks during reading
    (2015-10-05) Whaley, Meagan; Cox, Steven J; Dabaghian, Yuri; Kemere, Caleb; Tandon, Nitin
    We recorded electrocorticographic (ECoG) data from 15 patients with intractable epilepsy during a word completion task to precisely describe the spatiotemporal brain dynamics underlying word reading. Using a novel technique of analyzing grouped ECoG, cortical regions distributed throughout the left hemisphere were identified as significantly active versus baseline during our word stem completion task. Regions of activity spread from fusiform to frontal regions, including pars opercularis, pars triangularis, and pre, post, and subcentral gyri during the time period approaching articulation onset. The ECoG data recorded from electrodes within these regions were fit into linear multivariate autoregressive models, which precisely reveal the time, frequency, and magnitude of information flow between localized brain regions. Grouped network dynamics were quantified with two metrics of evaluating statistical significance of post-stimulus interactions compared to baseline. Results from both methods reveal bidirectional exchanges between frontal regions with fusiform, supporting theories which incorporate top-down and bottom-up processing during single word reading.
  • Thumbnail Image
    Item
    Endogenous fluctuations in cortical state selectively enhance different modes of sensory processing in human temporal lobe
    (Springer Nature, 2023) Parajuli, Arun; Gutnisky, Diego; Tandon, Nitin; Dragoi, Valentin
    The degree of synchronized fluctuations in neocortical network activity can vary widely during alertness. One influential idea that has emerged over the past few decades is that perceptual decisions are more accurate when the state of population activity is desynchronized. This suggests that optimal task performance may occur during a particular cortical state – the desynchronized state. Here we show that, contrary to this view, cortical state can both facilitate and suppress perceptual performance in a task-dependent manner. We performed electrical recordings from surface-implanted grid electrodes in the temporal lobe while human subjects completed two perceptual tasks. We found that when local population activity is in a synchronized state, network and perceptual performance are enhanced in a detection task and impaired in a discrimination task, but these modulatory effects are reversed when population activity is desynchronized. These findings indicate that the brain has adapted to take advantage of endogenous fluctuations in the state of neural populations in temporal cortex to selectively enhance different modes of sensory processing during perception in a state-dependent manner.
  • Thumbnail Image
    Item
    Modulation of Orthographic Decoding by Frontal Cortex
    (Society for Neuroscience, 2016) Whaley, Meagan Lee; Kadipasaoglu, Cihan Mehmet; Cox, Steven James; Tandon, Nitin
    Opinions are divided on whether word reading processes occur in a hierarchical, feedforward fashion or within an interactive framework. To critically evaluate these competing theories, we recorded electrocorticographic (ECoG) data from 15 human patients with intractable epilepsy during a word completion task and evaluated brain network dynamics across individuals. We used a novel technique of analyzing multihuman ECoG recordings to identify cortical regions most relevant to processing lexical information. The mid fusiform gyrus showed the strongest, earliest response after stimulus onset, whereas activity was maximal in frontal, dorsal lateral prefrontal, and sensorimotor regions toward articulation onset. To evaluate interregional functional connectivity, ECoG data from electrodes situated over specific cortical regions of interest were fit into linear multivariate autoregressive (MVAR) models. Spectral characteristics of the MVAR models were used to precisely reveal the timing and the magnitude of information flow between localized brain regions. This is the first application of MVAR for developing a comprehensive account of interregional interactions from a word reading ECoG dataset. Our comprehensive findings revealed both top-down and bottom-up influences between higher-level language areas and the mid fusiform gyrus. Our findings thus challenge strictly hierarchical, feedforward views of word reading and suggest that orthographic processes are modulated by prefrontal and sensorimotor regions via an interactive framework.
  • Thumbnail Image
    Item
    Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy
    (IEEE, 2018) Malladi, Rakesh; Johnson, Don H.; Kalamangalam, Giridhar P.; Tandon, Nitin; Aazhang, Behnaam
    We define a metric, mutual information in frequency (MI-in-frequency), to detect and quantify the statistical dependence between different frequency components in the data, referred to as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer cross-frequency coupling. The current metrics used to quantify the cross-frequency coupling in neuroscience cannot detect if two frequency components in non-Gaussian brain recordings are statistically independent or not. Our MI-in-frequency metric, based on Shannon's mutual information between the Cramér's representation of stochastic processes, overcomes this shortcoming and can detect statistical dependence in frequency between non-Gaussian signals. We then describe two data-driven estimators of MI-in-frequency: One based on kernel density estimation and the other based on the nearest neighbor algorithm and validate their performance on simulated data. We then use MI-in-frequency to estimate mutual information between two data streams that are dependent across time, without making any parametric model assumptions. Finally, we use the MI-in-frequency metric to investigate the cross-frequency coupling in seizure onset zone from electrocorticographic recordings during seizures. The inferred cross-frequency coupling characteristics are essential to optimize the spatial and spectral parameters of electrical stimulation based treatments of epilepsy.
  • Thumbnail Image
    Item
    NetDI: Methodology Elucidating the Role of Power and Dynamical Brain Network Features That Underpin Word Production
    (Society for Neuroscience, 2021) Yellapantula, Sudha; Forseth, Kiefer; Tandon, Nitin; Aazhang, Behnaam
    Canonical language models describe eloquent function as the product of a series of cognitive processes, typically characterized by the independent activation profiles of focal brain regions. In contrast, more recent work has suggested that the interactions between these regions, the cortical networks of language, are critical for understanding speech production. We investigated the cortical basis of picture naming (PN) with human intracranial electrocorticography (ECoG) recordings and direct cortical stimulation (DCS), adjudicating between two competing hypotheses: are task-specific cognitive functions discretely computed within well-localized brain regions or rather by distributed networks? The time resolution of ECoG allows direct comparison of intraregional activation measures [high gamma (hγ) power] with graph theoretic measures of interregional dynamics. We developed an analysis framework, network dynamics using directed information (NetDI), using information and graph theoretic tools to reveal spatiotemporal dynamics at multiple scales: coarse, intermediate, and fine. Our analysis found novel relationships between the power profiles and network measures during the task. Furthermore, validation using DCS indicates that such network parameters combined with hγ power are more predictive than hγ power alone, for identifying critical language regions in the brain. NetDI reveals a high-dimensional space of network dynamics supporting cortical language function, and to account for disruptions to language function observed after neurosurgical resection, traumatic injury, and degenerative disease.