Cognitive training has shown promise for improving cognition in older adults. Aging involves a variety of neuroanatomical changes that may affect response to cognitive training. White matter hyperintensities (WMH) are one common age-related brain change, as evidenced by T2-weighted and Fluid Attenuated Inversion Recovery (FLAIR) MRI. WMH are associated with older age, suggestive of cerebral small vessel disease, and reflect decreased white matter integrity. Higher WMH load associates with reduced threshold for clinical expression of cognitive impairment and dementia. The effects of WMH on response to cognitive training interventions are relatively unknown. The current study assessed (a) proximal cognitive training performance following a 3-month randomized control trial and (b) the contribution of baseline whole-brain WMH load, defined as total lesion volume (TLV), on pre-post proximal training change.

Sixty-two healthy older adults ages 65-84 completed either adaptive cognitive training (CT; n=31) or educational training control (ET; n=31) interventions. Participants assigned to CT completed 20 hours of attention/processing speed training and 20 hours of working memory training delivered through commercially-available Posit Science BrainHQ. ET participants completed 40 hours of educational videos. All participants also underwent sham or active transcranial direct current stimulation (tDCS) as an adjunctive intervention, although not a variable of interest in the current study. Multimodal MRI scans were acquired during the baseline visit. T1- and T2-weighted FLAIR images were processed using the Lesion Segmentation Tool (LST) for SPM12. The Lesion Prediction Algorithm of LST automatically segmented brain tissue and calculated lesion maps. A lesion threshold of 0.30 was applied to calculate TLV. A log transformation was applied to TLV to normalize the distribution of WMH. Repeated-measures analysis of covariance (RM-ANCOVA) assessed pre/post change in proximal composite (Total Training Composite) and sub-composite (Processing Speed Training Composite, Working Memory Training Composite) measures in the CT group compared to their ET counterparts, controlling for age, sex, years of education and tDCS group. Linear regression assessed the effect of TLV on post-intervention proximal composite and sub-composite, controlling for baseline performance, intervention assignment, age, sex, years of education, multisite scanner differences, estimated total intracranial volume, and binarized cardiovascular disease risk.

RM-ANCOVA revealed two-way group*time interactions such that those assigned cognitive training demonstrated greater improvement on proximal composite (Total Training Composite) and sub-composite (Processing Speed Training Composite, Working Memory Training Composite) measures compared to their ET counterparts. Multiple linear regression showed higher baseline TLV associated with lower pre-post change on Processing Speed Training sub-composite (ß = -0.19, p = 0.04) but not other composite measures.

These findings demonstrate the utility of cognitive training for improving postintervention proximal performance in older adults. Additionally, pre-post proximal processing speed training change appear to be particularly sensitive to white matter hyperintensity load versus working memory training change. These data suggest that TLV may serve as an important factor for consideration when planning processing speed-based cognitive training interventions for remediation of cognitive decline in older adults.

Aging is associated with disruptions in functional connectivity within the default mode (DMN), frontoparietal control (FPCN), and cingulo-opercular (CON) resting-state networks. Greater within-network connectivity predicts better cognitive performance in older adults. Therefore, strengthening network connectivity, through targeted intervention strategies, may help prevent age-related cognitive decline or progression to dementia. Small studies have demonstrated synergistic effects of combining transcranial direct current stimulation (tDCS) and cognitive training (CT) on strengthening network connectivity; however, this association has yet to be rigorously tested on a large scale. The current study leverages longitudinal data from the first-ever Phase III clinical trial for tDCS to examine the efficacy of an adjunctive tDCS and CT intervention on modulating network connectivity in older adults.

This sample included 209 older adults (mean age = 71.6) from the Augmenting Cognitive Training in Older Adults multisite trial. Participants completed 40 hours of CT over 12 weeks, which included 8 attention, processing speed, and working memory tasks. Participants were randomized into active or sham stimulation groups, and tDCS was administered during CT daily for two weeks then weekly for 10 weeks. For both stimulation groups, two electrodes in saline-soaked 5x7 cm2 sponges were placed at F3 (cathode) and F4 (anode) using the 10-20 measurement system. The active group received 2mA of current for 20 minutes. The sham group received 2mA for 30 seconds, then no current for the remaining 20 minutes.

Participants underwent resting-state fMRI at baseline and post-intervention. CONN toolbox was used to preprocess imaging data and conduct region of interest (ROI-ROI) connectivity analyses. The Artifact Detection Toolbox, using intermediate settings, identified outlier volumes. Two participants were excluded for having greater than 50% of volumes flagged as outliers. ROI-ROI analyses modeled the interaction between tDCS group (active versus sham) and occasion (baseline connectivity versus postintervention connectivity) for the DMN, FPCN, and CON controlling for age, sex, education, site, and adherence.

Compared to sham, the active group demonstrated ROI-ROI increases in functional connectivity within the DMN following intervention (left temporal to right temporal [T(202) = 2.78, pFDR < 0.05] and left temporal to right dorsal medial prefrontal cortex [T(202) = 2.74, pFDR < 0.05]. In contrast, compared to sham, the active group demonstrated ROI-ROI decreases in functional connectivity within the FPCN following intervention (left dorsal prefrontal cortex to left temporal [T(202) = -2.96, pFDR < 0.05] and left dorsal prefrontal cortex to left lateral prefrontal cortex [T(202) = -2.77, pFDR < 0.05]). There were no significant interactions detected for CON regions.

These findings (a) demonstrate the feasibility of modulating network connectivity using tDCS and CT and (b) provide important information regarding the pattern of connectivity changes occurring at these intervention parameters in older adults. Importantly, the active stimulation group showed increases in connectivity within the DMN (a network particularly vulnerable to aging and implicated in Alzheimer’s disease) but decreases in connectivity between left frontal and temporal FPCN regions. Future analyses from this trial will evaluate the association between these changes in connectivity and cognitive performance post-intervention and at a one-year timepoint.

Awareness of risk factors associated with any form of impairment is critical for formulating optimal prevention and treatment planning. Millions worldwide suffer from some form of cognitive impairment, with the highest rates amongst Black and Hispanic populations. The latter have also been found to achieve lower scores on standardized neurocognitive testing than other racial/ethnic groups. Understanding the socio-demographic risk factors that lead to this discrepancy in neurocognitive functioning across racial groups is crucial. Adverse childhood experiences (ACEs), are one aspect of social determinants of health. ACES have been linked to a greater risk of future memory impairment, such as dementia. Moreover, higher instances of ACEs have been found amongst racial minorities. Considering the current literature, the purpose of this exploratory research is to better understand how social determinants, more specifically, ACEs, may play a role in the development of cognitive impairment.

This cross-sectional study included data from an urban, public Midwestern academic medical center. There was a total of 64 adult clinical patients that were referred for a neuropsychological evaluation. All patients were administered a standardized neurocognitive battery that included the Montreal Cognitive Assessment (MoCA) as well as a 10-item ACE questionnaire, which measures levels of adverse childhood experiences. The sample was 73% Black and 27% White. The average age was 66 (SD=8.6) and average education was 12.6 years (SD=3.4). A two-way ANOVA was conducted to evaluate the interaction of racial identity (White; Black) and ACE score on MoCA total score. An ACE score >4 was categorized as “high”; ACE <4 was categorized as “low.”

There was not a significant interaction of race and ACE group on MoCA score (p=.929) nor a significant main effect of ACE score (p=.541). Interestingly, there was a significant main effect of Race on MoCA (p=.029). White patients had an average MoCA score of 21.82 (sd=4.77). Black patients had an average MoCA score of 17.54 (sd=5.91).

Overall, Black patients demonstrated statistically lower scores on the MoCA than White patients. There was no significant difference on MoCA score between races when also accounting for ACE scores. Given this study’s findings, one’s level of adverse childhood experiences does not appear to impact one’s cognitive ability later in life. There is a significant difference in cognitive ability between races, specifically Black and White people, which suggests there may be social determinants other than childhood experiences to be explored that influence cognitive impairment.

Chronic musculoskeletal pain is associated with neurobiological, physiological, and cellular measures. Importantly, we have previously demonstrated that a biobehavioral and psychosocial resilience index appears to have a protective relationship on the same biomarkers. Less is known regarding the relationships between chronic musculoskeletal pain, protective factors, and brain aging. This study investigates the relationships between clinical pain, a resilience index, and brain age. We hypothesized that higher reported chronic pain would correlate with older appearing brains, and the resilience index will attenuate the strength of the relationship between chronic pain and brain age.

Participants were drawn from an ongoing observational multisite study and included adults with chronic pain who also reported knee pain (N = 135; age = 58.3 ± 8.1; 64% female; 49% non-Hispanic Black, 51% non-Hispanic White; education Mdn = some college; income level Mdn = $30,000 - $40,000; MoCA M = 24.27 ± 3.49). Measures included the Graded Chronic Pain Scale (GCPS), characteristic pain intensity (CPI) and disability, total pain body sites; and a cognitive screening (MoCA). The resilience index consisted of validated biobehavioral (e.g., smoking, waist/hip ratio, and active coping) and psychosocial measures (e.g., optimism, positive affect, negative affect, perceived stress, and social support). T1-weighted MRI data were obtained. Surface area metrics were calculated in FreeSurfer using the Human Connectome Project's multi-modal cortical parcellation scheme. We calculated brain age in R using previously validated and trained machine learning models. Chronological age was subtracted from predicted brain age to generate a brain age gap (BAG). With higher scores of BAG indicating predicated age is older than chronological age. Three parallel hierarchical regression models (each containing one of three pain measures) with three blocks were performed to assess the relationships between chronic pain and the resilience index in relation to BAG, adjusting for covariates. For each model, Block 1 entered the covariates, Block 2 entered a pain score, and Block 3 entered the resilience index.

GCPS CPI (R2 change = .033, p = .027) and GCPS disability (R2 change = 0.038, p = 0.017) significantly predicted BAG beyond the effects of the covariates, but total pain sites (p = 0.865) did not. The resilience index was negatively correlated and a significant predictor of BAG in all three models (p < .05). With the resilience index added in Block 3, both GCPS CPI (p = .067) and GCPS disability (p = .066) measures were no longer significant in their respective models. Additionally, higher education/income (p = 0.016) and study site (p = 0.031) were also significant predictors of BAG.

In this sample, higher reported chronic pain correlated with older appearing brains, and higher resilience attenuated this relationship. The biobehavioral and psychosocial resilience index was associated with younger appearing brains. While our data is cross-sectional, findings are encouraging that interventions targeting both chronic pain and biobehavioral and psychosocial factors (e.g., coping strategies, positive and negative affect, smoking, and social support) might buffer brain aging. Future directions include assessing if chronic pain and resilience factors can predict brain aging over time.

Understanding healthcare information is an important aspect in managing one’s own needs and navigating a complex healthcare system. Health numeracy and literacy reflect the ability to understand and apply information conveyed numerically (i.e., graphs, statistics, proportions, etc.) and written/verbally (i.e., treatment instructions, appointments, diagnostic results) to communicate with healthcare providers, understand one’s medical condition(s) and treatment plan, and participate in informed medical decision-making. Cognitive impairment has been shown to impact one’s ability to understand complex medical information. The purpose of this study is to explore the relationship between the degree of cognitive impairment and one’s ability to perform on measures of health numeracy and literacy.

This cross-sectional study included data from 38 adult clinical patients referred for neuropsychological evaluation for primary memory complaints at an urban, public Midwestern academic medical center. All patients were administered a standardized neurocognitive battery that included the Montreal Cognitive Assessment (MoCA), as well as measures of both health numeracy (Numeracy Understanding of Medicine Instrument-Short Version [NUMI-SF]) and health literacy (Short Assessment of Health Literacy-English [SAHL-E]). The sample was 58% female and 60% Black/40% White. Mean age was 65 (SD=9.4) and mean education was 14.4 years (SD=2.5). The sample was further split into three groups based on cognitive diagnosis determined by comprehensive neuropsychological assessment (i.e., No Diagnosis [34%]; Mild Cognitive Impairment [MCI; 29%]; Dementia [34%]).Groups were well matched and did not statistically differ in premorbid intellectual functioning (F=1.96, p=.157; No Diagnosis, M=100, SD=7.92; MCI, M=99, SD=8.87; Dementia, M=94, SD=7.72) ANOVAs were conducted to evaluate differences between clinical groups on the MoCA, NUMI-SF, and SAHL-E. Multiple regressions were then conducted to determine the association of MoCA scores with NUMI-SF and SAHL-E performance.

As expected, the Dementia group performed significantly below both the No Diagnosis and MCI groups on the MoCA (F=19.92, p<.001) with a large effect (ηp2=.540). Significant differences were also found on the NUM-SF (F=5.90, p>.05) and on the SAHL-E (F=6.20, p>.05) with large effects (ηp2=.258 and ηp2=.267, respectively). Regression found that MoCA performance did not predict performance on the NUMI-SF and SAHL-E in the No Diagnosis group (F=2.30, p=.809) or the MCI group (F=1.31, p=.321). Conversely, the MoCA significantly predicted performance on the NUMI-SF and SAHL-E for the Dementia (F=15.59, p=.001) group.

Degree of cognitive impairment is associated with understanding of health numeracy and literacy information, with patients diagnosed with dementia performing most poorly on these measures. Patients with normal cognitive functioning demonstrated a significantly better understanding of health numeracy and health literacy. This study supports the notion that as cognitive functioning diminishes, incremental support is necessary for patients to understand medical information pertaining to their continued care and medical decision-making, particularly as it relates to both numerical and written information.

Nonpathological aging has been linked to decline in both verbal and visuospatial memory abilities in older adults. Disruptions in resting-state functional connectivity within well-characterized, higherorder cognitive brain networks have also been coupled with poorer memory functioning in healthy older adults and in older adults with dementia. However, there is a paucity of research on the association between higherorder functional connectivity and verbal and visuospatial memory performance in the older adult population. The current study examines the association between resting-state functional connectivity within the cingulo-opercular network (CON), frontoparietal control network (FPCN), and default mode network (DMN) and verbal and visuospatial learning and memory in a large sample of healthy older adults. We hypothesized that greater within-network CON and FPCN functional connectivity would be associated with better immediate verbal and visuospatial memory recall. Additionally, we predicted that within-network DMN functional connectivity would be associated with improvements in delayed verbal and visuospatial memory recall. This study helps to glean insight into whether within-network CON, FPCN, or DMN functional connectivity is associated with verbal and visuospatial memory abilities in later life.

330 healthy older adults between 65 and 89 years old (mean age = 71.6 ± 5.2) were recruited at the University of Florida (n = 222) and the University of Arizona (n = 108). Participants underwent resting-state fMRI and completed verbal memory (Hopkins Verbal Learning Test - Revised [HVLT-R]) and visuospatial memory (Brief Visuospatial Memory Test - Revised [BVMT-R]) measures. Immediate (total) and delayed recall scores on the HVLT-R and BVMT-R were calculated using each test manual’s scoring criteria. Learning ratios on the HVLT-R and BVMT-R were quantified by dividing the number of stimuli (verbal or visuospatial) learned between the first and third trials by the number of stimuli not recalled after the first learning trial. CONN Toolbox was used to extract average within-network connectivity values for CON, FPCN, and DMN. Hierarchical regressions were conducted, controlling for sex, race, ethnicity, years of education, number of invalid scans, and scanner site.

Greater CON connectivity was significantly associated with better HVLT-R immediate (total) recall (ß = 0.16, p = 0.01), HVLT-R learning ratio (ß = 0.16, p = 0.01), BVMT-R immediate (total) recall (ß = 0.14, p = 0.02), and BVMT-R delayed recall performance (ß = 0.15, p = 0.01). Greater FPCN connectivity was associated with better BVMT-R learning ratio (ß = 0.13, p = 0.04). HVLT-R delayed recall performance was not associated with connectivity in any network, and DMN connectivity was not significantly related to any measure.

Connectivity within CON demonstrated a robust relationship with different components of memory function as well across verbal and visuospatial domains. In contrast, FPCN only evidenced a relationship with visuospatial learning, and DMN was not significantly associated with memory measures. These data suggest that CON may be a valuable target in longitudinal studies of age-related memory changes, but also a possible target in future non-invasive interventions to attenuate memory decline in older adults.

The aim of this study was to evaluate theprevalence of night eating syndrome (NES) and its correlates in schizophrenicoutpatients.

The 14 items of self-reported night eatingquestionnaire (NEQ) was administered to 201 schizophrenic patients in psychiatricoutpatient clinic. We examined demographic and clinical characteristics, bodymass index (BMI), subjective measures of mood, sleep, binge eating, andweight-related quality of life using Beck's Depression Inventory (BDI),Pittsburgh Sleep Quality Index (PSQI), Binge Eating Scale (BES) and Koreanversion of Obesity-Related Quality of Life Scale (KOQoL), respectively.

The prevalence of night eaters in schizophrenicoutpatients was 10.4% (21 of 201). Comparisons between NES group and non-NES grouprevealed no significant differences in sociodemographic characteristics, clinical status and BMI. Compared to non-NES, patients with NES reportedsignificantly greater depressed mood and sleep disturbance, more binge eatingpattern, and decreased weight-related quality of life. While 'morning anorexia'and 'delayed morning meal' (2 of 5 NES core components in NEQ) were notdiffered between groups, 'nocturnal ingestions', 'evening hyperphagia', and'mood/sleep' were more impaired in NES group.

These findings are the first to describe theprevalence and its correlates of night eaters in schizophrenic outpatients. These results suggest that NES has negative mental health implications, although it was not associated with obesity. Further study to generalize theseresults is required.

Thisstudy was to assess the prevalence and its correlates of restless legs syndrome(RLS) in outpatients with bipolar disorder.

A total of 100clinical stabilized bipolar outpatients were examined. The presence of RLS andits severity were assessed using the International Restless Legs Sydrome StudyGroup (IRLSSG) diagnostic criteria. Beck's Depression Inventory (BDI), Spielberg's StateAnxiety Inventory (STAI-X-1), Pittsburgh Sleep Quality Index (PSQI), Koreanversion Drug Attitude Inventory (KDAI-10), Subjective Well-Beings under NeurolepticTreatment Scale-Short Form(SWN-K) and Barnes Akathisia Rating Scale (BARS) wereused to evaluate the depressive symptomatology, level of anxiety, subjectivequality of sleep, subjective feeling of well-being, drug attitude, presence ofakathisia, respectively.

Of the 100 bipolar outpatients,7 (7%) were met to full criteria of IRLSSG and 36 (36%) have at least one ofthe 4 IRLSSG criterion. Because of relatively small sample size, non-parametricanalysis were done to compare the characteristics among 3 groups (full-RLS, 1≥positiveRLS-symptom and Non-RLS). There were no significant differences in sex, age, and other sociodemographic and clinical data among 3 groups. BDI, STAI-X-1 andPSQI are tended to be impaired in RLS and 1≥positive RLS-symptomgroups.

This is the first preliminarystudy for studying the prevalence and its correlates of RLS in bipolardisorder. The results shows that RLS was relatively smaller presentin bipolar disorder than schizophrenia. Sametendencies shown in schizophrenic patients were found that bipolar patientswith RLS had more depressive symptoms, state anxiety and poor subjective sleepquality.