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Mild Traumatic Brain Injury Disrupts Functional Dynamic Attractors of Healthy Mental States

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Mild Traumatic Brain Injury Disrupts Functional Dynamic Attractors of Healthy Mental States medRxiv preprint doi: https://doi.org/10.1101/19007906; this version posted October 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 1 mTBI Disrupts Brain Connectivity Dynamics VM Vergara, HJ van der Horn, AR Mayer, FA Espinoza, J van der Naalt,VD Calhoun Mild Traumatic Brain Injury Disrupts Functional Dynamic Attractors of Healthy Mental States 1Victor M. Vergara, PhD, 2Harm J. van der Horn, MD, PhD, 3Andrew R. Mayer, PhD, 1Flor A. Espinoza, PhD, 2Joukje van der Naalt, MD, PhD 1Vince D Calhoun, PhD 1Tri-institutional center for Translational Research in Neuroimaging and Data Science (TRenDS), [Georgia State University, Georgia Institute of Technology, Emory University], 25 Park Place, Atlanta GA 30303 2Dept of Neurology, University of Groningen, University Medical Center Groningen, The Netherlands 3The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, New Mexico, 87106 Abstract The human brain has the ability of changing its wiring configuration by increasing or decreasing functional connectivity strength between specific areas. Variable but recurring configuration patterns in dynamic functional connectivity have been observed during resting fMRI experiments, patterns which are defined as dynamic brain states. The question arises whether in a regular healthy brain these states evolve in a random fashion or in a specific sequential order. The current work reveals both the specific state sequence in healthy brains, as well as the set of disruptions in this sequence produced by traumatic brain injury. The healthy sequence consists of oscillatory dynamic connectivity patterns that orbit an attractor state in a high dimensional space. Using discovery (96 subjects) and replication (74 subjects) cohorts, this study demonstrated that mild traumatic brain injury results in immediate orbital disruptions that recover over time. Brain dynamics enter a status of disrupted orbits right after injury, with partial recovery at 4 weeks, and full recovery at 3 months post-injury. In summary, our results describe an aspect of neuronal dysfunction in mild traumatic brain injury that is fully based on brain state dynamics, and different from traditional brain connectivity strength measures. Keywords: Traumatic brain injury, functional magnetic resonance imaging, dynamic functional network connectivity, transition probabilities, dynamic attractors. 1. Introduction Symptoms resulting from mild traumatic brain injury (mTBI) produce deleterious effects on cognitive and social functioning, and these deficits may last a lifetime for a minority of patients. Commonly reported symptoms include dizziness, vertigo, irritability, chronic headaches, difficulty concentrating, depression, and impulsiveness (DeKosky et al., 2010). The fact that these symptoms are notoriously difficult to objectify using current clinical measures provides a strong rationale for the ongoing research that tries to uncover the different aspects of this disease (Levin and Diaz-Arrastia, 2015). Previous studies suggested that functional magnetic resonance imaging (fMRI) is a promising technique to provide biomarker features (Vergara et al., 2017). Specifically, we showed that mTBI patients can be individually differentiatedNOTE: This preprint reportsfrom new healthy research that controls has not been certified(HC) byparticipants peer review and should with not behigh used toaccuracy guide clinical practice.using medRxiv preprint doi: https://doi.org/10.1101/19007906; this version posted October 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 2 mTBI Disrupts Brain Connectivity Dynamics VM Vergara, HJ van der Horn, AR Mayer, FA Espinoza, J van der Naalt,VD Calhoun dynamic functional network connectivity (dFNC), a measurement obtained from fMRI imaging (Vergara et al., 2018). However, previous analyses did not make full use of the temporal information in dFNC, limiting the method to extract features based on assessments of connectivity strength. Studies of patterns of functional connectivity transitions occurring in the brain are still an underexplored area. We predict that observed connectivity differences in mTBI compared to HC are rooted in patterns of dynamic transitions. The varied accelerations of the head during a mTBI event affect the microstructure of axons, resulting in varying patterns of deficient anatomical connectivity (Huisman et al., 2004; Holli et al., 2010; Ling et al., 2012; Arenth et al., 2014). In accordance with white matter damage, abnormal patterns of functional connectivity have been found in several regions of the brain (Hillary et al., 2014; Sharp et al., 2014; Mayer et al., 2015a). Functional connectivity exists when there is temporal synchronicity of neuronal activation between two brain areas. Assessments of functional connectivity throughout the literature indicate a consistent map of functional connectivity abnormalities in mTBI. Resting state studies points towards the default mode network (DMN) as one of the main affected brain networks displaying a common pattern of weaker connectivity with other brain networks (Sours et al., 2013; Vakhtin et al., 2013; Sharp et al., 2014; Palacios et al., 2017). In contrast, increased functional connectivity within the DMN has been observed in some cases (Nathan et al., 2015; Vergara et al., 2017). Another consistent observation is increased functional connectivity that involves the cerebellum (Pagulayan et al., 2018), and especially between the cerebellum and the supplementary motor area (Nathan et al., 2015; Vergara et al., 2017; Vergara et al., 2018). However, effects in other networks may seem to differ. In the executive control network (ECN) increases (Borich et al., 2015) and decreases (Palacios et al., 2017) of functional connectivity has been reported. While reduced subcortical connectivity has been reported in mTBI (Vakhtin et al., 2013), thalamic connectivity has also been observed to increase (Tang et al., 2011). All of these observations have been made by considering that functional connectivity within a specific time lapse can be described by a single summary assessment. In such cases functional connectivity is assumed to have a quasi-static behavior with small variations that can be averaged (Allen et al., 2014). However, challenging this assumption might lead to an improvement of our understanding of brain mechanisms linked to TBI Considering functional connectivity as a time evolving brain feature leads to several hypotheses that need to be described. As a preamble, we must view the brain as a dynamic system which is not always functionally connected in the same way in spite of a time invariant anatomical wiring. It is known that the human brain iterates among several patterns, also known as states, that can be identified using a dynamic functional connectivity method (Allen et al., 2014). Figure 1a shows an example of two different states and the two assumptions (Sharp and Smooth dynamic transitions) focus of this discussion. The simplest model describes the connectivity as unchanged through the duration of a state followed by a jump to the next state. While it is known that time variations of connectivity occur, no assessment of temporal nature is made within the state (Miller et al., 2016). We believe a more realistic assumption is a time-related variation of connectivity strength medRxiv preprint doi: https://doi.org/10.1101/19007906; this version posted October 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 3 mTBI Disrupts Brain Connectivity Dynamics VM Vergara, HJ van der Horn, AR Mayer, FA Espinoza, J van der Naalt,VD Calhoun within the state, which is our first hypothesis. Figure 1a presents this alternative concept in a simplified manner. Second, we hypothesize the existence of oscillations of weak and strong connectivity within particular states as exemplified in Figure 1b. We propose to model these oscillations as a brain connectivity state that orbits around a given attractor connectivity pattern. The center of such an orbit is related to the state centroids as some examples have been previously identified in mTBI (Vergara et al., 2018). To represent the attractor and orbit within a state, we have chosen to display a double arrow from weak to strong and back. The third hypothesis is that attractor states are part of the normal brain behavior belonging to healthy controls as shown in Figure 1c. States not exhibiting oscillations may also be present, representing an intermediate transition between attractor states. Finally, we hypothesize that mTBI can disrupt the attractor states in such a way that no oscillation is observed. This abnormality is illustrated in Figure 1d. Different from previous assessments, these disruptions may not be directly related to connectivity strength and may only be observed when using the more dynamic attractor model. This study examined the existence of attractor states in the healthy
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