Now showing items 1-7 of 7

    • Addressing indirect frequency coupling via partial generalized coherence 

      Young, Joseph; Homma, Ryota; Aazhang, Behnaam (2021)
      Distinguishing between direct and indirect frequency coupling is an important aspect of functional connectivity analyses because this distinction can determine if two brain regions are directly connected. Although partial coherence quantifies partial frequency coupling in the linear Gaussian case, we introduce a general framework that can address ...
    • Inferring functional connectivity through graphical directed information 

      Young, Joseph; Neveu, Curtis L.; Byrne, John H.; Aazhang, Behnaam (2021)
      Objective. Accurate inference of functional connectivity is critical for understanding brain function. Previous methods have limited ability distinguishing between direct and indirect connections because of inadequate scaling with dimensionality. This poor scaling performance reduces the number of nodes that can be included in conditioning. Our goal ...
    • Investigating irregularly patterned deep brain stimulation signal design using biophysical models 

      Summerson, Samantha R.; Aazhang, Behnaam; Kemere, Caleb (2015-06)
      Parkinson's disease (PD) is a neurodegenerative disorder which follows from cell loss of dopaminergic neurons in the substantia nigra pars compacta (SNc), a nucleus in the basal ganglia (BG). Deep brain stimulation (DBS) is an electrical therapy that modulates the pathological activity to treat the motor symptoms of PD. Although this therapy is ...
    • Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy 

      Malladi, Rakesh; Johnson, Don H.; Kalamangalam, Giridhar P.; Tandon, Nitin; Aazhang, Behnaam (2018)
      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 ...
    • NetDI: Methodology Elucidating the Role of Power and Dynamical Brain Network Features That Underpin Word Production 

      Yellapantula, Sudha; Forseth, Kiefer; Tandon, Nitin; Aazhang, Behnaam (2021)
      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 ...
    • Precise measurement of correlations between frequency coupling and visual task performance 

      Young, Joseph; Dragoi, Valentin; Aazhang, Behnaam (2020)
      Functional connectivity analyses focused on frequency-domain relationships, i.e. frequency coupling, powerfully reveal neurophysiology. Coherence is commonly used but neural activity does not follow its Gaussian assumption. The recently introduced mutual information in frequency (MIF) technique makes no model assumptions and measures non-Gaussian and ...
    • Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries 

      Schmid, William; Fan, Yingying; Chi, Taiyun; Golanov, Eugene; Regnier-Golanov, Angelique S.; (2021)
      Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making ‘go/no-go’ decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- ...