No abstract

Local brain signal variability (standard deviation of the BOLD signal [SDBOLD]) correlates with age and cognitive performance, and recently differentiated Alzheimer's disease (AD) patients from healthy controls. However, it is unknown if changes to SDBOLD precede diagnosis of AD or mild cognitive impairment (MCI). We compared ostensibly healthy older adult humans who scored below the recommended threshold on the Montreal Cognitive Assessment (MoCA) and who showed reduced medial temporal lobe (MTL) volume in a previous study ('at-risk' group, n=20), with healthy older adults who scored within the normal range on the MoCA ('control' group, n=20). Using multivariate partial least squares analysis we assessed the correlations between SDBOLD and age, MoCA score, global fractional anisotropy, global mean diffusivity, and four cognitive factors. Greater SDBOLD in the MTL and occipital cortex positively correlated with performance on cognitive control/speed tasks but negatively correlated with memory scores in the control group. These relations were weaker in the at-risk group. A post-hoc analysis assessed associations between MTL volumes and SDBOLD in both groups. This revealed a negative correlation, most robust in the at-risk group, between MTL SDBOLD and MTL subregion volumetry, particularly the entorhinal and parahippocampal regions. Taken together, these results suggest that the association between SDBOLD and cognition differs between the at-risk and control groups, which may be due to lower MTL volumes in the at-risk group. Our data indicate relations between MTL SDBOLD and cognition may be helpful in understanding brain differences in individuals who may be at risk for further cognitive decline. 3 Significance StatementMoment-to-moment variability in the BOLD signal, once dismissed as nuisance noise, is now understood to be an information-bearing signal. BOLD variability correlates with age and cognitive performance and was recently used to differentiate Alzheimer's disease (AD) patients from controls. As AD is a progressive disease, AD patients may benefit from its early detection. We found that older adults at-risk for cognitive decline showed differences in the relationships between BOLD variability and cognitive performance, relative to healthy controls. Notably, the differences were strongest in medial temporal lobe (MTL), areas where AD is known to begin. Our data suggest correlations between MTL BOLD variability and cognition may be useful for understanding brain differences in individuals at risk for further cognitive decline.

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the micro-scale a neurometabolic cascade alters neurotransmission, while on the macro-scale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of forty-four adult TBI patients recruited from a regional trauma center, scanned at 1-2 weeks post-injury, and with follow-up behavioral outcome assessed six months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semi-acute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6-month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory 4 connection strengths even when traumatic intracranial lesions were not present. Our results suggest large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes. Significance StatementThe variability of clinical outcomes following mild to moderate traumatic brain injury (TBI) is underscored by complex pathophysiological mechanisms that take effect across spatial scales. We used the neuroinformatics platform, The Virtual Brain, to model individualized brain activity and make inferences across these spatial scales. Specifically, this approach allowed us to link macroscopic brain dynamics with mesoscopic biophysical parameters, distinguishing semi-acute mild to moderate TBI patients from comparison participants and predicting the long-term recovery of these patients. Our results demonstrate the sensitivity of our large-scale brain model to pathophysiological changes following TBI and illustrates how computational modeling may be used to advance understanding of chronic TBI outcome.