RSS 1.0Welcome to the SUNY Open Access Repository
The SUNY Open Access Repository (SOAR) is a centrally managed online digital repository that stores, indexes, and makes available scholarly and creative works of SUNY faculty, students, and staff across SUNY campuses. SOAR serves as an open access platform for those SUNY campuses that do not have their own open access repository environments.
Access to SUNY campus communities in SOAR are available below under SUNY sectors and also listed alphabetically under the Campus Communities in SOAR on the navigation bar on the left.
Additional information includes
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Communities in SUNY Open Access Repository
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Recently Added
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Recovery and Recovering in Older Adults with Schizophrenia: A 5-Tier Model.Rationale: There are little recent data on clinical recovery in older adults with schizophrenia. This exploratory study uses an empirically measurable construct to address this issue. Methods: From an original sample of 248 community-dwelling persons aged 55 and over with early-onset schizophrenia spectrum disorder, a subsample of 102 persons was reassessed at a mean of 52 months. Clinical recovery required meeting criteria for its two components: clinical remission and community integration. Results: Prospective analysis generated a 5-tier taxonomy of recovery in which 12% remained persistently in clinical recovery at both baseline and follow-up (Tier 1) and 18% never met criteria of clinical recovery (Tier 5). The remaining 70% exhibited a variety of components of clinical recovery at baseline and follow-up (Tiers 2, 3, and 4). Conclusion: The findings generated a dynamic picture of recovery, with most persons being in varying states of "recovering." The 5-tier taxonomy of recovery adumbrated potential treatment strategies for each tier. Keywords: Recovery; community integration; elderly; older adults; outcome; remission; schizophrenia.
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Effects of Mild Traumatic Brain Injury (mTBI) on Retinal Structure, Function, and Pupillary Light ResponsesPurpose: Evaluate the sensitivity and light adaptation characteristics of ipRGC-mediated PLR and how they are altered in the dark and under different backgrounds with direct pupil stimulation in patients with mTBI. Methods: Direct pupillary light reflex (PLR) to blue light (peak λ = 440nm, FWHM = 20nm) was measured from the dominant eye, the other eye was fully occluded, of 12 control adult subjects (ages 42.2 ± 17.0 years) and 12 chronic mTBI patients (ages 35.4 ± 12.8 years) using LiveTrack pupilometer module and an infrared camera (30Hz) inside a LED-driven Ganzfeld system (Espion V6 ColorDome, Diagnosys LLC, Lowell, MA). The study consisted of two protocols: (1) The intensity series included 19 steps of increasing intensity ranging from 0.001 to 198 cd/m2, was completed first in sequence after 5 minutes of initial dark adaptation, and 2 minutes between test flashes. A test blue flash stimulus with a duration of 1 second was used, and the pupil response were recorded for 7.5 seconds. Between each successive step, 2 minutes of dark adaption was allowed. (2) The background intensity series, consisted of 7 steps, ranging from 0 to 10 cd/m2, was completed thereafter with test flash of 120 cd/m2 on top of the background. Pupil diameter measurements were made following 5 minutes of initial dark-adaptation, and first on a dark background with a 120 cd/m2 test flash for a duration of 1 second. The subjects adapted for 2 minutes to each subsequent background intensity. The same bright, blue test flash of 120 cd/m2 was used on top of each background intensity. The 6-second post-illumination pupil response (PIPR) amplitudes were extracted at 6 seconds after stimulus offset, and averaged over a 100ms window (i.e., between 6950ms and 7050ms). The peak or maximal pupil constriction amplitude was measured at the trough from baseline. A stimulus intensity response was plotted for the PIPR and peak percent reduction from baseline across all 19 intensities. The intensity series PIPR and peak data was fitted to the Naka-Rushton equation of the form V(I) = (Vmax * In) / (In + Kn) to derive the saturated amplitude (Vmax), slope (n) and semisaturation constant (K). The values of the fit parameters were compared between control and mTBI groups. For the background series, the baseline pupil diameter, PIPR, and peak parameters were extracted from the 7 steps and compared between controls and mTBI patients. Wilcoxin rank sum test was used to compare the corresponding parameters between mTBI and controls. P values less than 0.05 were considered statistically significant. Results: The PIPR was significantly reduced in mTBI patients relative to controls through both the intensity and background series, indicating a reduction in luminance gain of ipRGCs. In addition, the baseline pupil diameter following 2 or 5-minutes of dark-adaptation and at the end of 2-minutes of light adaptation over a 5-log unit range of background intensities (0.0001-10 cd/m2) was larger (i.e., less constriction) for mTBI patients, suggesting an underlying pathophysiology of the ipRGCs, reaffirming the dysfunction of the luminance gain control. Conclusions: The reduction in the PIPR and the larger baseline pupil responses in patients with mTBI insinuated an underlying pathophysiology that may reflect a dysfunction of ipRGC and its luminance gain control mechanism especially when exposed to long duration light stimuli. Therefore, evaluating the baseline diameter at 2 minutes following light exposure on a series of background intensities may prove to be clinically more useful for identifying retinal abnormalities in mTBI.
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Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity.Background and purpose: The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain. Methods: We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. Results: We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters. Conclusion: The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.
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The association of polygenic risk for schizophrenia, bipolar disorder, and depression with neural connectivity in adolescents and young adults: examining developmental and sex differences.Neurodevelopmental abnormalities in neural connectivity have been long implicated in the etiology of schizophrenia (SCZ); however, it remains unclear whether these neural connectivity patterns are associated with genetic risk for SCZ in unaffected individuals (i.e., an absence of clinical features of SCZ or a family history of SCZ). We examine whether polygenic risk scores (PRS) for SCZ are associated with functional neural connectivity in adolescents and young adults without SCZ, whether this association is moderated by sex and age, and if similar associations are observed for genetically related neuropsychiatric PRS. One-thousand four-hundred twenty-six offspring from 913 families, unaffected with SCZ, were drawn from the Collaborative Study of the Genetics of Alcoholism (COGA) prospective cohort (median age at first interview = 15.6 (12-26), 51.6% female, 98.1% European American, 41% with a family history of alcohol dependence). Participants were followed longitudinally with resting-state EEG connectivity (i.e., coherence) assessed every two years. Higher SCZ PRS were associated with elevated theta (3-7 Hz) and alpha (7-12 Hz) EEG coherence. Associations differed by sex and age; the most robust associations were observed between PRS and parietal-occipital, central-parietal, and frontal-parietal alpha coherence among males between ages 15-19 (B: 0.15-0.21, p < 10). Significant associations among EEG coherence and Bipolar and Depression PRS were observed, but differed from SCZ PRS in terms of sex, age, and topography. Findings reveal that polygenic risk for SCZ is robustly associated with increased functional neural connectivity among young adults without a SCZ diagnosis. Striking differences were observed between men and women throughout development, mapping onto key periods of risk for the onset of psychotic illness and underlining the critical importance of examining sex differences in associations with neuropsychiatric PRS across development.
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Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach.Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.
