Directional Absolute Coherence: a phase-based measure of effective connectivity for neurophysiology data

Abstract

Communication between neural structures is a topic of much clinical and scientific interest and has been linked to a variety of behavioural, cognitive, and psychiatric measures. Here, we introduce a novel effective connectivity measure, termed the directional absolute coherence (DAC). Combining aspects of magnitude squared coherence, imaginary coherence, and phase slope index, DAC provides an estimate of connectivity that is resistant to volume conduction, encapsulates the directionality of neural communication, and is bound to the interval of -1 and 1. To highlight the properties of this newly proposed method, we compare DAC to a number of established connectivity methods using data recorded from the subthalamic nucleus of patients with Parkinson's disease with deep brain stimulation electrodes. By applying a combination of real and simulated data, we demonstrate that DAC provides a reliable estimate of the magnitude and direction of connectivity, independent of the phase difference between brain signals. As such, DAC facilitates a reliable investigation of inter-regional neural communication, rendering it a valuable tool for gaining a deeper understanding of the functional architecture of the brain and its relationship to behaviour and cognition. A Python implementation of DAC is freely available at https://github.com/neurophysiological-analysis/FiNN.

FiNN: A toolbox for neurophysiological network analysis

“…FiNN implements both established and novel metrics for the evaluation of the same frequency coupling (Fries, 2005) and cross frequency coupling (Canolty & Knight, 2010) analyses used to investigate network level information flows. In particular, FiNN offers implementations for the newly proposed connectivity metrics directional absolute coherence (Scherer, Wang, et al, 2022b) and direct modulation index (Scherer, Wang, et al, 2022a).…”

“…The same-frequency coupling package currently implements the following metrics: directionalised absolute coherency (Scherer et al, 2022b), magnitude squared coherence (Carter et al, 1973), imaginary coherence (Nolte et al, 2004), weighted phase lag index (Vinck et al, 2011), and phase slope index (Nolte et al, 2008). A description of the metrics can be found in Table 2.…”

“…Directional absolute coherency (Scherer et al, 2022b) The directional absolute coherency utilizes the complex coherency function to calculate the magnitude squared coherence (Carter et al, 1973), phase slope index (Nolte et al, 2008), and the imaginary coherence (Nolte et al, 2004). These three metrics are combined to create a single reliable measure of coherence drawing from their individual strengths without incorporating the individual weaknesses.…”

FiNN: A toolbox for neurophysiological network analysis

“…Same-frequency coupling package. The sfc package currently implements the following metrics: directionalized absolute coherency (Scherer et al, 2022c), magnitude squared coherence (Carter et al, 1973), imaginary coherence (Nolte et al, 2004), weighted phase lag index (Vinck et al, 2011), and phase slope index (Nolte et al, 2008). A description of the metrics can be found in Table 2.…”

Single-neuron bursts encode pathological oscillations in Parkinson’s disease and essential tremor

“…Briefly, DAC is a conglomerate SFC metric that quantifies direction of the information flow using the phase slope index, volume conductance via imaginary coherency, and magnitude of the relationship via magnitude squared coherence. 24 Based on the results of SFC analysis, each individual recording was subdivided based on whether spiking activity preceded LFP activity or vice versa. PAC analyses were subsequently repeated on these subgroups (Fig.…”