Behavioral components of chromosome 22q11.2 deletion syndrome (22q), caused by the most common human microdeletion, include cognitive and adaptive functioning impairments, heightened anxiety, and an elevated risk of schizophrenia. We investigated how interactions between executive function and the largely overlooked factor of emotion regulation might relate to the incidence of symptoms of psychotic thinking in youth with 22q. We measured neural activity with event-related potentials (ERPs) in variants of an inhibitory function (Go/No-Go) experimental paradigm that

ObjectivesOur ability to generate mental representation of magnitude from sensory information affects how we perceive and experience the world. Reduced resolution of the mental representations formed from sensory inputs may generate impairment in the proximal and distal information processes that utilize these representations. Impairment of spatial and temporal information processing likely underpins the non-verbal cognitive impairments observed in 22q11.2 deletion syndrome (22q11DS). The present study builds on prior research by seeking to quantify the resolution of spatial and temporal representation in children with 22q11DS, sex chromosome aneuploidy (SCA), and a typically developing (TD) control group.Participants and methodsChildren (22q11DS = 70, SCA = 49, TD = 46) responded to visual or auditory stimuli with varying difference ratios. The participant’s task was to identify which of two sequentially presented stimuli was of larger magnitude in terms of, size, duration, or auditory frequency. Detection threshold was calculated as the minimum difference ratio between the “standard” and the “target” stimuli required to achieve 75% accuracy in detecting that the two stimuli were different.ResultsChildren with 22q11DS required larger magnitude difference between spatial stimuli for accurate identification compared with both the SCA and TD groups (% difference from standard: 22q11DS = 14; SCA = 8; TD: 7; F  = 8.42, p < 0.001). Temporal detection threshold was also higher for the 22q11DS group to both visual (% difference from standard: 22q11DS = 14; SCA = 8; TD = 7; F  = 8.33, p < 0.001) and auditory (% difference from standard: 22q11DS = 23; SCA = 12; TD: 8; F  = 8.99, p < 0.001) stimuli compared with both the SCA and TD groups, while the SCA and TD groups displayed equivalent performance on these measures (p's > 0.05). Pitch detection threshold did not differ among the groups (p's > 0.05).ConclusionsThe observation of higher detection thresholds to spatial and temporal stimuli indicates further evidence for reduced resolution in both spatial and temporal magnitude representation in 22q11DS, that does not extend to frequency magnitude representation (pitch detection), and which is not explained by generalized cognitive impairment alone. These findings generate further support for the hypothesis that spatiotemporal hypergranularity of mental representations contributes to the non-verbal cognitive impairment seen in 22q11DS.

Individuals with 22q11.2 deletion syndrome (22q11DS) show high rates of anxiety associated with their increased risk of developing schizophrenia. Biased attention is associated with anxiety and is important to investigate in those with 22q11DS given this association. We analyzed attention bias to emotional faces in 7- to 17-year olds with 22q11DS and typically developing controls (TD) using a dot probe threat bias paradigm. We measured response time, eye tracking, and pupilometry. Those with 22q11DS showed no significant changes in early versus late trials, whereas those who were TD showed differing patterns in both gaze and pupilometry over time. The patterns in those who are TD may indicate adaptation that is lacking or slower in individuals with 22q11DS.

No abstract

Population analyses of functional connectivity have provided a rich understanding of how brain function differs across time, individual, and cognitive task. An important but challenging task in such population analyses is the identification of reliable features that describe the function of the brain, while accounting for individual heterogeneity. Our work is motivated by two particularly important challenges in this area: first, how can one analyze functional connectivity data over populations of individuals, and second, how can one use these analyses to infer group similarities and differences. Motivated by these challenges, we model population connectivity data as a multilayer network and develop the multi-node2vec algorithm, an efficient and scalable embedding method that automatically learns continuous node feature representations from multilayer networks. We use multi-node2vec to analyze resting state fMRI scans over a group of 74 healthy individuals and 60 patients with schizophrenia. We demonstrate how multilayer network embeddings can be used to visualize, cluster, and classify functional regions of the brain for these individuals. We furthermore compare the multilayer network embeddings of the two groups. We identify significant differences between the groups in the default mode network and salience network—findings that are supported by the triple network model theory of cognitive organization. Our findings reveal that multi-node2vec is a powerful and reliable method for analyzing multilayer networks. Data and publicly available code are available at https://github.com/jdwilson4/multi-node2vec.

Plenary S27(Occ), and MRS was performed with the point-resolved spectroscopy sequence (PRESS). Voxel data were analyzed for MEGA-PRESS spectroscopy with Gaussian curve fitting to the GABA peaks and LCModel software to determine peak concentrations of GABA+ (including signal from macromolecules) and creatinine (Cr), yielding GABA+/Cr ratios for analysis. All subjects completed the 9-item Psychological Stress Index (PSI-9), a validated measure of stress sensitivity and NA in psychosis. Results:We have completed a preliminary analysis of 28 subjects (FEP: n = 11, 21.8 ± 2.7 years; HC: n = 11, 20.5 ± 3.2 years; APS: n = 6, 20.2 ± 3.9 years). Of the patient subjects, 6 APS subjects and 1 FEP subject were off medications. Analysis with ANCOVA, with group as a factor and PSI-9 scores as a covariate, yielding a significant inverse relationship of PSI-9 scores with mPFC GABA concentration (F[1,24] = 6.61, P = .02), but no effect of group (F[2,24] = 1.74, P = .20). There were no effects of group or relationships with PSI-9 scores in the Occ voxel (Ps > 0.2). We compared all subjects based on whether they were taking antipsychotic medications and found no differences for either voxel (Ps > 0.5). There were no other significant relationships between GABA concentrations and clinical symptoms, cognitive variables (MCCB) or functional level. Conclusion:The data provide support for a relationship between GABA levels and NA, measured by the PSI-9 such that lower GABA concentrations in the mPFC are associated with higher levels of NA. The small sample size and preliminary nature of the data warrant caution. Nevertheless, given that potentiation of GABA activity with benzodiazepines reduces NA, the findings are of potential clinical relevance in understanding the role of GABA systems in affect regulation in psychosis. Background: Identification of valid schizophrenia risk biomarkers, prior to the onset of psychosis, is critical for early treatment intervention and for understanding the progression from prodromal symptoms into full psychosis. Occipital alpha power is known to be impaired in schizophrenia patients, during both the resting state and during cognitive task activation. However, the status of this measure during the pre-clinical risk period is unknown. We investigated occipital lobe alpha power during resting state EEG as a potential biomarker of clinical risk and schizophrenia, and explored correlations with cognitive functioning and clinical symptoms. Methods: Participants included 23 patients with schizophrenia (SZ), 31 clinical high-risk individuals (CR), and 30 healthy controls (HC). Participants underwent a structured clinical interview to assess symptoms using the Structured Interview for Prodromal Syndromes (SIPS) and completed a computerized battery to assess major domains of neurocognitive functioning. Resting state EEG was recorded for 2 minutes each in eyes-closed and eyesopen conditions. Data were segmented into 2-second artifact-free epochs and Fast Fourier Transformed into the frequency domain. Mean occipi...

No abstract

Researchers using Electroencephalograms ("EEGs") to diagnose clinical outcomes often run into computational complexity problems. In particular, extracting complex, sometimes nonlinear, features from a large number of time-series often require large amounts of processing time. In this paper we describe a distributed system that leverages modern cloud-based technologies and tools and demonstrate that it can effectively, and efficiently, undertake clinical research. Specifically we compare three types of clusters, showing their relative costs (in both time and money) to develop a distributed machine learning pipeline for predicting gestation time based on features extracted from these EEGs.