Dynamic network cohesion analysis

The network cohesion analysis (NCA) is an approach developed at the Tel Aviv Center for Brain Functions in collaboration with Yonatan Winetraub, Lavi Shpigelman, and Yael Jacob. This sliding-window approach facilitates the probing of network connectivity dynamics based on fMRI data. It analyzes the set of edges within a network or between networks. Since it relies on t-statistic computation, it is sensitive both to the average and the distribution of the correlations within and between networks (see Figure 1).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NCA was first applied in a hypothesis-driven manner to analyze the connectivity dynamics during emotional cinematic experiences of networks defined based on meta-analytical data or resting-state functional connectivity. It revealed associations between the connectivity of networks implicated in emotional regulation and low-level emotional processing on the one hand and dynamic physiological and self-reported measures of emotional intensity on the other (1), dissociation between networks dynamics of networks implicated in cognitive-related and visceral-based empathic processing (2), and a link between the connectivity of a dorsal salience network and medial amygdala network and the ratings of emotional experience across six different instances of the three emotion categories examined (fear, sadness, and anger) (3).

In a later work (4), we elaborated NCA to facilitate data-driven delineation of networks based on the association between the temporal pattern of their connectivity and a time-series of a continuous measure of interest.  In a specific work, we demarcated networks related to a continuous report on sadness intensity. The demarcated networks have clear common functional denominators. Three of these networks overlap with distinct empathy-related networks, previously identified in distinct sets of studies. The other networks are related to sensorimotor processing, language, attention, and working memory.

Figure 2: Networks delineated by CRNDA whose NCI showed reliable correlation with the sadness intensity rating and their functional profiling. For clarity, we superimposed no more than two networks on each anatomical image. The font size in the word cloud is proportional to the average probability with which the term was assigned to peak voxels in the network by the Neurosynth database. The figure was taken from reference #3.

Portraying Emotions at Their Unfolding: A Multilayered Approach for Probing Dynamics of Neural Networks.

Key Publications

Figure 1: (a) An illustration of the sensitivity of the network cohesion index to phasic coupling of signals. Each of colored lines represents the Blood Oxygen Level Dependent (BOLD) time course of a node in a specific network defined based on prior knowledge (schematically represented as points on a glass brain in (b)). The data presented here were taken from a random representative subject. The upper gray curve indicates the average signal in each time point. The gray curve at the bottom represents the NCI computed for this network. The yellow rectangles mark intervals of increased NCI. Note that during these intervals no global peaks of the mean signal are evident, but rather fluctuations of the signals, which follow a similar temporal trend. This indicates that the NCI is indeed sensitive to the extent to which the fluctuations are homogenous as expected. (b) NCI is computed as the t-statistic for a set of Fisher Z transformed pairwise correlations between the signals of the nodes either within a network (intra-NCI) or between networks (inter-NCI). The figure was taken from reference #3.

Cry for her or cry with her: context-dependent dissociation of two modes of cinematic empathy reflected in network cohesion dynamics.

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences

Psychophysiological whole‐brain network clustering based on connectivity dynamics analysis in naturalistic conditions

Dr. Raz ia a researcher at the Sagol Brain Institute and a Visiting Lecturer at the Sagol School of Neuroscience and the Steve Tisch School of Film and Television at the Tel-Aviv University.  

Dr. Raz leads the #Immersive brain-computer interfaces research team, and an associate investigator of the #Neuropsychiatry & Neuromodulation, the #Consciousness & Psychopharmacology and the #Cognitive Resource and Plasticity research teams at the Sagol Brain Institute. 

DR. GAL RAZ (PhD)