ABSTRACT
Schizophrenia (Sz) is a severe mental disorder that affects ~.5% of the population worldwide and is associated
with significant increases in both disability and mortality. Disability is attributable in part to the effects of
cognitive impairments and experiential negative symptoms, while excess mortality is attributable in part to the
effects of increased suicidality early in the disorder and of negative modifiable health risk factors later in the
illness. EEG- and fMRI-based methods have been used extensively to investigate neural mechanisms, but
paradigms and methods differ extensively across groups. Small-scale studies implicate increased urgency as a
risk factor for both aggression and suicidality in schizophrenia, and of loneliness as a driver of increased health
risk but await replication in larger studies. The lack of availability of large scale, open databases that
incorporate both brain imaging and health & wellness information is thus a major barrier to scientific advance in
the field of Sz. The present study leverages the technology and data of the ongoing NIMH-funded (MH124045)
Rockland Sample II (NKI-RSII) study of normal development to create an open data set of multi-modal brain,
physiology and behavior for Sz. As the NKI-RSII, both raw and processed data will be shared online along with
analysis routines and will represent an open resource for clinical investigation. The NKI-RSI (n=1400) and RSII
(n=600) studies collect and share extensive neuroimaging, neurophysiological, physiological and deep
phenotyping data from a community-ascertained, cross-sectional, lifespan sample (ages 6–85 years old).
Design of NKI-RSII was guided by three major themes: 1) multimodal measurement and integration across
functional domains, 2) ecological sampling (e.g., wearables, sensors, smartphone apps), and 3) enhanced
physiological phenotyping for cardiovascular fitness and obesity. Specific multi-modal measures include task-,
resting-state (R-), and natural vision (NV-) fMRI, diffusion kurtosis imaging (DKI), and naturalistic EEG (NV-
EEG) implemented through the recently developed Mobile Body/Brain Imaging (MoBI)-EEG platform. Relative
brain age (RBA) is calculated using novel multimodal algorithms. Behavioral assessments are drawn from the
NIH Toolbox library and include measures of Companionship, Life Satisfaction and Social Distress. Impulsivity
is evaluated using the UPPS-P. Disability is evaluated using the WHO Disability Assessment Schedule.
Ecological sampling is used to supplement behavioral assessments. Consistent with NKI-RSII procedures, all
data will be shared prospectively via the International Neuroimaging Data-sharing Initiative (INDI) and the
NIMH Data Archive (NDA). Despite the extensive investment in the NKI-RSI and RSII initiatives and the goals
of eventual neuropsychiatric use, no patient data sets are presently collected. This project leverages these
resources to collect a rich data set capable of supporting integrated investigation across neural-, behavioral-
and health-related levels, and will address key hypotheses related to causes and treatment target identification
for the extensive disability and excess mortality associated with Sz.
