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Connectivity patterns of task-specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to molecular architecture

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Background: Despite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods: We investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior molecular imaging in healthy populations. Results: Ten-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. Conclusions: We revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia. ### Competing Interest Statement The authors have declared no competing interest.

Structural Covariance Networks in Post-Traumatic Stress Disorder: A Multisite ENIGMA-PGC Study

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Introduction - Cortical thickness (CT) and surface area (SA) are established biomarkers of brain pathology in posttraumatic stress disorder (PTSD). Structural covariance networks (SCN) constructed from CT and SA may represent developmental associations, or unique interactions between brain regions, possibly influenced by a common causal antecedent. The ENIGMA-PGC PTSD Working Group aggregated PTSD and control subject data from 29 cohorts in five countries (3439). Methods - Using Destrieux Atlas, we built SCNs and compared centrality measures between PTSD subjects and controls. Centrality is a graph theory measure derived using SCN. Results - Notable nodes with higher CT-based centrality in PTSD compared to controls were left fusiform gyrus, left superior temporal gyrus, and right inferior temporal gyrus. We found sex-based centrality differences in bilateral frontal lobe regions, left anterior cingulate, left superior occipital cortex and right ventromedial prefrontal cortex (vmPFC). Comorbid PTSD and MDD showed higher CT-based centrality in the right anterior cingulate gyrus, right parahippocampal gyrus and lower SA-based centrality in left insular gyrus. Conclusion - Unlike previous studies with smaller sample sizes (less than 318), our study found differences in centrality measures using a sample size of 3439 subjects. This is the first cross-sectional study to examine SCN interactions with age, sex, and comorbid MDD. Although limited to group level inferences, centrality measures offer insights into nodal relationship to the entire functional connectome unlike approaches like seed-based connectivity or independent component analysis. Nodes having higher centrality have greater structural or functional connections, lending them invaluable for translational treatments like neuromodulation.

Sociodemographic Characteristics of Missing Data in Digital Phenotyping

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The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphone applications for "digital phenotyping," the collection of phone sensor and log data to study the lived experiences of subjects in their natural environments. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. Here, we show that digital phenotyping presents a viable method of data collection, over long time periods, across diverse study participants with a range of sociodemographic characteristics. We examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression. We found that iOS users had higher rates of accelerometer non-collection but lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection while Asian participants had slightly lower non-collection. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.

Functional connectomics of affective and psychotic pathology

Proceedings of the National Academy of Sciences, 2019-04-15

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Converging evidence indicates that groups of patients with nominally distinct psychiatric diagnoses are not separated by sharp or discontinuous neurobiological boundaries. In healthy populations, individual differences in behavior are reflected in variability across the collective set of functional brain connections (functional connectome). These data suggest that the spectra of transdiagnostic symptom profiles observed in psychiatric patients may map onto detectable patterns of network function. To examine the manner through which neurobiological variation might underlie clinical presentation we obtained functional magnetic resonance imaging (fMRI) data from over 1,000 individuals, including 210 diagnosed with a primary psychotic disorder or affective psychosis (bipolar disorder with psychosis and schizophrenia or schizoaffective disorder), 192 presenting with a primary affective disorder without psychosis (unipolar depression, bipolar disorder without psychosis), and 608 demographically and data-quality matched healthy comparison participants recruited through a large-scale study of brain imaging and genetics. Here, we examine variation in functional connectomes across psychiatric diagnoses, finding striking evidence for disease connectomic 'fingerprints' that are commonly disrupted across distinct forms of pathology and appear to scale as a function of illness severity. Conversely, other properties of network connectivity were preferentially disrupted in patients with psychotic illness, but not patients without psychotic symptoms. This work allows us to establish key biological and clinical features of the functional connectomes of severe mental disease.

DPSleep: Open-Source Longitudinal Sleep Analysis From Accelerometer Data

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Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. Here we introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify relationships between derived sleep metrics and other variables of interest. The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses (a) a stepwise algorithm to detect missing data; (b) within-individual, minute-based, spectral power percentiles of activity; and (c) iterative, forward- and backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of derived sleep features and correction for time zone changes. In this report, we illustrate the pipeline with data from participants studied for more than 200 days each. Actigraphy-based measures of sleep duration are associated with self-report rating of sleep quality. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. We discuss the uses of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (e.g., smartphone, tablet) to measure individual differences across a wide range of behavioral variation in health and disease.

Comparison of stimulus-evoked BOLD responses in human and monkey visual cortex

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We characterized the blood oxygenation level dependent (BOLD) signal in humans and macaque monkeys by comparing the response in visual cortex to a single checkerboard or two checkerboards, spaced 1.5, 3.0, or 4.5 s apart. We found that the magnitude and shape of the BOLD response to a single checkerboard was similar in the two species. In addition, we found that the BOLD responses summed similarly, and that at an inter-stimulus interval (ISI) of 4.5 sec BOLD summation was nearly linear in both species. When comparing the ratio of the amplitude of the response to the second checkerboard at the 4.5 sec ISI with that of the single checkerboard between subjects in both species, the results from both monkey subjects fell within one standard deviation of the mean human results (human mean (n=12): .95 +/- .31 second/single response amplitude; monkey 1: 1.16; monkey 2: .86). At the shorter ISIs, both species demonstrated increased suppression of the BOLD response to the second checkerboard. These findings indicate that the magnitude of the BOLD response to events separated by 4.5 seconds can be accurately measured in and compared between human and monkey visual cortex.