EFFECT OF JOB SKILLS TRAINING ON EMPLOYMENT AND JOB SEEKING BEHAVIORS IN AN AMERICAN INDIAN SUBSTANCE ABUSE TREATMENT SAMPLE.

Employment difficulties are common among American Indian individuals in substance abuse treatment. To address this problem, the Southwest Node of NIDA's Clinical Trials Network conducted a single-site adaptation of its national Job Seekers Workshop study in an American Indian treatment program, Na'Nizhoozhi Center (NCI). 102 (80% men, 100% American Indian) participants who were in residential treatment and currently unemployed were randomized to (1) a three session, manualized program (Job seekers workshop: JSW) or (2) a 40-minute Job Interviewing Video: JIV). Outcomes were assessed at 3-month follow up: 1) number of days to a new taxed job or enrollment in a job-training program, and 2) total hours working or enrolled in a job-training program. No significant differences were found between the two groups for time to a new taxed job or enrollment in a job-training program. There were no significant differences between groups in substance use frequency at 3-month follow-up. These results do not support the use of the costly and time-consuming JSW intervention in this population and setting. Despite of the lack of a demonstrable treatment effect, this study established the feasibility of including a rural American Indian site in a rigorous CTN trial through a community-based participatory research approach.

Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia.

An investigation of differences in dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) was completed, using eyes-open resting state MEG data. The MEG analysis utilized a source-space activity estimate (MNE/dSPM) whose result was the input to a group spatial independent component analysis (ICA), on which the networks of our MEG dFNC analysis were based. We have previously reported that our MEG dFNC revealed that SP change between brain meta-states (repeating patterns of network correlations which are allowed to overlap in time) significantly more often and to states which are more different, relative to HC. Here, we extend our previous work to investigate the relationship between symptomology in SP and four meta-state metrics. We found a significant correlation between positive symptoms and the two meta-state metrics which showed significant differences between HC and SP. These two statistics quantified 1) how often individuals change state and 2) the total distance traveled within the state-space. We additionally found that a clustering of the meta-state metrics divides SP into groups which vary in symptomology. These results indicate specific relationships between symptomology and brain function for SP.

Multimodal Neuroimaging in Schizophrenia: Description and Dissemination.

In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network's collaborative informatics and neuroimaging suite (COINS).