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61 Robustness of Attention Networks Across Multiple Sessions: Behavioral and ERP Findings
- Catherine Tocci, Elena Polejaeva, Andreas Keil, Sarah Long, Jennifer Ly, Alexia Brown, Christopher N Sozda, William M Perlstein
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 469-470
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Objective:
Attention is the backbone of cognitive systems and is requisite for many cognitive processes vital to everyday functioning, including memory, problem solving, and the cognitive control of behavior. Attention is commonly impaired following traumatic brain injury and is a critical focus of rehabilitation efforts. The development of reliable methods to assess rehabilitation-related changes are paramount. The Attention Network Test (ANT) has been used previously to identify 3 independent, yet interactive attention networks—alerting, orienting, and executive control (EC). We examined the behavioral and neurophysiological robustness and temporal stability of these networks across multiple sessions to assess the ANT’s potential utility as an effective measure of change during attention rehabilitative interventions.
Participants and Methods:15 healthy young adults completed 4 sessions of the ANT (1 session/7-day period). ANT networks were assessed within the task by contrasting opposing stimulus conditions: cued vs. non-cued trials probed alerting, valid vs. invalid spatial cues probed orienting, and congruent vs. incongruent targets probed EC. Differences in median correct-trial reaction times (RTs) and error rates (ERs) between the condition pairs were assessed to determine attention network scores; robustness of networks effects, as determined by one-sample t-tests at each session, against a mean of 0, determining the presence of significant network effects at each session. Sixty-four-channel electroencephalography (EEG) data were acquired concurrently and processed using Matlab to create condition-related event-related potentials (ERPs)—particularly the cue- and probe-related P1, N1, and P3 deflection amplitudes, measured by using signed-area calculation in regions of interest (ROIs) determined by observation of spherical-spline voltages. This enabled us to examine the robustness of cue- and probe-attention-network ERPs.
Results:All three attention networks showed robust effects. However, only the EC RT and ER network scores remained significantly robust [t(14)s>13.9,ps<.001] across all sessions, indicating that EC is robust in the face of repeated exposure. Session 1 showed the greatest EC-RT robustness effect which became smaller during the subsequent sessions per ANOVAs on Session x Congruency [F(3,42)=10.21,p<.0001], reflecting persistence despite practice effects. RT robustness of the other networks varied across sessions. Alerting and EC ERs were similarly robust across all 4 sessions, but were more variable for the orienting network. ERP results: The cue-locked P1-orienting (valid vs. invalid) was generally larger to valid- than invalid-cues, but the robustness across sessions was variable (significant in only sessions 1 and 4 [t(14)s>2.13,ps<.04], as reflected in a significant main effect of session [p=.0042]. Next, target-locked EC P3s were generally smaller to congruent than incongruent targets [F(1,14)=9.40,p=.0084], showing robust effects only in sessions 3 and 4 [ps<.005].
Conclusions:The EC network RT and ER scores were consistently robust across all sessions, suggesting that this network may be less vulnerable to practice effects across session than the other networks and may be the most reliable probe of attentional rehabilitation. ERP measures were more variable across attention networks with respect to robustness. Behavioral measures of EC-network may be most reliable for assessing progress related to attentional-rehabilitation efforts.
Prevalence and Epidemiology of Healthcare-Associated Infections (HAI) in US Nursing Homes (NH), 2017
- Nicola Thompson, Nimalie Stone, Cedric Brown, Taniece Eure, Austin Penna, Grant Barney, Devra Barter, Paula Clogher, Ghinwa Dumyati, Erin Epson, Christina B. Felsen, Linda Frank, Deborah Godine, Lourdes Irizarry, Helen Johnston, Marion Kainer, Linda Li, Ruth Lynfield, J.P. Mahoehney, Joelle Nadle, Valerie Ocampo, Susan Ray, Monika Samper, Sarah Shrum, Marla Sievers, Srinivasan Krithika, Lucy E. Wilson, Alexia Zhang, Shelley Magill
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s45-s46
- Print publication:
- October 2020
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Background: With an aging population, increasingly complex care, and frequent re-admissions, prevention of healthcare-associated infections (HAIs) in nursing homes (NHs) is a federal priority. However, few contemporary sources of HAI data exist to inform surveillance, prevention, and policy. Prevalence surveys (PSs) are an efficient approach to generating data to measure the burden and describe the types of HAI. In 2017, the Centers for Disease Control and Prevention (CDC) performed its first large-scale HAI PS through the Emerging Infections Program (EIP) to measure the prevalence and describe the epidemiology of HAI in NH residents. Methods: NHs from several states (CA, CO, CT, GA, MD, MN, NM, NY, OR, & TN) were randomly selected and asked to participate in a 1-day HAI PS between April and October 2017; participation was voluntary. EIP staff reviewed available medical records for NH residents present on the survey date to collect demographic and basic clinical information and infection signs and symptoms. HAIs with onset on or after NH day 3 were identified using revised McGeer infection definitions applied to data collected by EIP staff and were reported to the CDC through a web-based system. Data were reviewed by CDC staff for potential errors and to validate HAI classifications prior to analysis. HAI prevalence, number of residents with >1 HAI per number of surveyed residents ×100, and 95% CIs were calculated overall (pooled mean) and for selected resident characteristics. Data were analyzed using SAS v9.4 software. Results: Among 15,296 residents in 161 NHs, 358 residents with 375 HAIs were identified. The most common HAI sites were skin (32%), respiratory tract (29%), and urinary tract (20%). Cellulitis, soft-tissue or wound infection, symptomatic UTI, and cold or pharyngitis were the most common individual HAIs (Fig. 1). Overall HAI prevalence was 2.3 per 100 residents (95% CI, 2.1–2.6); at the NH level, the median HAI prevalence was 1.8 and ranged from 0 to 14.3 (interquartile range, 0–3.1). At the resident level (Fig. 2), HAI prevalence was significantly higher in persons admitted for postacute care with diabetes, with a pressure ulcer, receiving wound care, or with a device. Conclusions: In this large-scale survey, 1 in 43 NH residents had an HAI on a given day. Three HAI types comprised >80% of infections. In addition to identifying characteristics that place residents at higher risk for HAIs, these findings provide important data on HAI epidemiology in NHs that can be used to expand HAI surveillance and inform prevention policies and practices.
Funding: None
Disclosures: None