Edge event organization across temporal categories
Poster No:
1381
Submission Type:
Abstract Submission
Authors:
Joshua Faskowitz1, Javier Gonzalez-Castillo1, Peter Bandettini1
Institutions:
1National Institute of Mental Health, Bethesda, MD
First Author:
Co-Author(s):
Introduction:
Functional connectivity patterns are thought to reflect cognition, mental traits, or even brain dysfunction [2]. Brain connectivity, traditionally expressed, is obtained by taking the similarity of two regions' activity over time. However, if we opt to look at how this similarity changes in a time-resolved manner, we can reveal how regions are similar or dissimilar, depending on temporal context.
By using a simple method to temporally unwrap functional connectivity into a frame-by-frame form, we observe how it unfolds as large periods of low-amplitude spontaneous activity, punctuated with high-amplitude peaks [3,9]. These peaks, or events, carry signatures of canonical systems commonly identified in fMRI [1,4]. Evidently, connectivity information can be simplified by only retaining these punctate events-an analytical approach known as point process analysis [8].
When we identify a high amplitude event, are there any features that would delineate it from other events? Previous point-processes fMRI studies treat events equally. Here, we classify events based on the duration above a threshold: short, medium and long durations. With this added information, we can probe for functional organization at each timescale. Additionally, we can ask how edges with similar correlation magnitudes might exhibit different event patterns. Overall, we seek to add information beyond what's provided by Pearson correlation, to assess the various functional relationships of the cortex.
By using a simple method to temporally unwrap functional connectivity into a frame-by-frame form, we observe how it unfolds as large periods of low-amplitude spontaneous activity, punctuated with high-amplitude peaks [3,9]. These peaks, or events, carry signatures of canonical systems commonly identified in fMRI [1,4]. Evidently, connectivity information can be simplified by only retaining these punctate events-an analytical approach known as point process analysis [8].
When we identify a high amplitude event, are there any features that would delineate it from other events? Previous point-processes fMRI studies treat events equally. Here, we classify events based on the duration above a threshold: short, medium and long durations. With this added information, we can probe for functional organization at each timescale. Additionally, we can ask how edges with similar correlation magnitudes might exhibit different event patterns. Overall, we seek to add information beyond what's provided by Pearson correlation, to assess the various functional relationships of the cortex.
Methods:
Resting-state fMRI data (14.4 minutes; 0.72 TR) was obtained from 176 low-motion subjects from the HCP [7]. Minimally preprocessed data was further band-pass filtered and had noised removed via ICA-FIX and orthogonalization to the mean traces of CSF and WM. Node time series were obtained for 200 nodes [6]. Edge time series were constructed by taking the element-wise product between two z-score node time series (Fig. 1A). For each time series, supra-threshold moments were defined as edge time series magnitudes exceeding 2.25. This threshold was determined as the level yielding the highest correlation between event count and correlation, on average, across subjects. The number of time points spent supra-threshold contiguously was recorded as the duration. Next, event duration variability was calculated as the mean-absolute deviation of durations at each edge; reflecting temporal consistency between regions (e.g., is connectivity unfolding with the same event lengths or a range of lengths over time?).
Results:
Brain organization formed by counting the short (0.72 sec), intermediate (1.4-2.2 sec), and long (>=2.9 sec) events was assessed as an adjacency matrix (Fig 1B), a weighted degree map (Fig 1C), and as loadings onto canonical functional system (Fig 1D). Short spikes appear to preferentially include limbic nodes, including the orbitofrontal and temporal poles, as well as along the midline cingulate. Intermediate spikes are the most common, and load heavily onto limbic and ventral attention networks. The long spikes involve nodes of the visual cortex and generally involve areas on the primary sensory end of the macroscale gradient of functional organization [5]. Further, we compare edge time series mean with event duration variability (Fig 2A), to map a set of edges that have relatively low and high variability despite sharing a similar static functional connectivity (Fig 2B,C).
Conclusions:
By focusing our attention on event duration, we demonstrate that edges of similar average correlation can have diverse patterns of short-term communication. Furthermore, we find that longer, and more variable events primarily involve somatosensory areas, and the primary visual system. These regions' primary role is processing of external stimuli. That they exhibit highly variable communication patterns during rest (i.e., lacking external stimulation) suggest they also play a role in spontaneous brain process, of yet unknown cognitive/biological purposes that unfold during rest.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Keywords:
FUNCTIONAL MRI
Other - connectivity; network; edge time series;
1|2Indicates the priority used for review
Abstract Information
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7. Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., ... & WU-Minn HCP Consortium. (2013). Resting-state fMRI in the human connectome project. Neuroimage, 80, 144-168.
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9. Zamani Esfahlani, F. (2020). High-amplitude cofluctuations in cortical activity drive functional connectivity. Proceedings of the National Academy of Sciences, 117(45), 28393-28401.