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BackgroundThe integrity and function of catecholamine neurotransmitter systems can be assessed using MRI sequences often referred to as neuromelanin-sensitive MRI (NM-MRI). The relevance of this method to neurodegenerative and psychiatric disorders is becoming increasingly evident, and it has potential as a clinical biomarker. To support such future applications, we report here the normative range of NM-MRI signal and volume metrics in cognitively normal older adults.Methods3 Tesla NM-MRI images and demographic and cognitive data were available from 152 cognitively normal older adults aged 53-86 years old at baseline; a subsample of 68 participants also had follow-up NM-MRI data collected around one-year later. NM-MRI images were processed to yield summary measures of volume and signal (contrast-to-noise ratio, CNR) for the substantia nigra (SN) and locus coeruleus (LC) using a recently developed software employing a fully automated algorithm. The extent of annual change in these metrics was quantified and tested for significance using 1-sample t-tests.ResultsBaseline SN signal (CNR) was 10.02% (left SN) and 10.28% (right) and baseline LC signal was 24.71% (left) and 20.42% (right). The only NM-MRI metric to show a significant annual change was a decrease in left SN volume.ConclusionWe report normative values for NM-MRI signal and volume in the SN and LC of cognitively normal older adults and normative values for their change over time. These values may help future efforts to use NM-MRI as a clinical biomarker for adults in this age range by facilitating identification of patients with extreme NM-MRI values.

BackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.ObjectiveUsing ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.MethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).ResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.ConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.