Cognitive training is a non-pharmacological intervention aimed at improving cognitive function across a single or multiple domains. Although the underlying mechanisms of cognitive training and transfer effects are not well-characterized, cognitive training has been thought to facilitate neural plasticity to enhance cognitive performance. Indeed, the Scaffolding Theory of Aging and Cognition (STAC) proposes that cognitive training may enhance the ability to engage in compensatory scaffolding to meet task demands and maintain cognitive performance. We therefore evaluated the effects of cognitive training on working memory performance in older adults without dementia. This study will help begin to elucidate non-pharmacological intervention effects on compensatory scaffolding in older adults.

48 participants were recruited for a Phase III randomized clinical trial (Augmenting Cognitive Training in Older Adults [ACT]; NIH R01AG054077) conducted at the University of Florida and University of Arizona. Participants across sites were randomly assigned to complete cognitive training (n=25) or an education training control condition (n=23). Cognitive training and the education training control condition were each completed during 60 sessions over 12 weeks for 40 hours total. The education training control condition involved viewing educational videos produced by the National Geographic Channel. Cognitive training was completed using the Posit Science Brain HQ training program, which included 8 cognitive training paradigms targeting attention/processing speed and working memory. All participants also completed demographic questionnaires, cognitive testing, and an fMRI 2-back task at baseline and at 12-weeks following cognitive training.

Repeated measures analysis of covariance (ANCOVA), adjusted for training adherence, transcranial direct current stimulation (tDCS) condition, age, sex, years of education, and Wechsler Test of Adult Reading (WTAR) raw score, revealed a significant 2-back by training group interaction (F[1,40]=6.201, p=.017, η2=.134). Examination of simple main effects revealed baseline differences in 2-back performance (F[1,40]=.568, p=.455, η2=.014). After controlling for baseline performance, training group differences in 2-back performance was no longer statistically significant (F[1,40]=1.382, p=.247, η2=.034).

After adjusting for baseline performance differences, there were no significant training group differences in 2-back performance, suggesting that the randomization was not sufficient to ensure adequate distribution of participants across groups. Results may indicate that cognitive training alone is not sufficient for significant improvement in working memory performance on a near transfer task. Additional improvement may occur with the next phase of this clinical trial, such that tDCS augments the effects of cognitive training and results in enhanced compensatory scaffolding even within this high performing cohort. Limitations of the study include a highly educated sample with higher literacy levels and the small sample size was not powered for transfer effects analysis. Future analyses will include evaluation of the combined intervention effects of a cognitive training and tDCS on nback performance in a larger sample of older adults without dementia.

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.

Interventions using a cognitive training paradigm called the Useful Field of View (UFOV) task have shown to be efficacious in slowing cognitive decline. However, no studies have looked at the engagement of functional networks during UFOV task completion. The current study aimed to (a) assess if regions activated during the UFOV fMRI task were functionally connected and related to task performance (henceforth called the UFOV network), (b) compare connectivity of the UFOV network to 7 resting-state functional connectivity networks in predicting proximal (UFOV) and near-transfer (Double Decision) performance, and (c) explore the impact of network segregation between higher-order networks and UFOV performance.

336 healthy older adults (mean age=71.6) completed the UFOV fMRI task in a Siemens 3T scanner. UFOV fMRI accuracy was calculated as the number of correct responses divided by 56 total trials. Double Decision performance was calculated as the average presentation time of correct responses in log ms, with lower scores equating to better processing speed. Structural and functional MRI images were processed using the default pre-processing pipeline within the CONN toolbox. The Artifact Rejection Toolbox was set at a motion threshold of 0.9mm and participants were excluded if more than 50% of volumes were flagged as outliers. To assess connectivity of regions associated with the UFOV task, we created 10 spherical regions of interest (ROIs) a priori using the WFU PickAtlas in SPM12. These include the bilateral pars triangularis, supplementary motor area, and inferior temporal gyri, as well as the left pars opercularis, left middle occipital gyrus, right precentral gyrus and right superior parietal lobule. We used a weighted ROI-to-ROI connectivity analysis to model task-based within-network functional connectivity of the UFOV network, and its relationship to UFOV accuracy. We then used weighted ROI-to-ROI connectivity analysis to compare the efficacy of the UFOV network versus 7 resting-state networks in predicting UFOV fMRI task performance and Double Decision performance. Finally, we calculated network segregation among higher order resting state networks to assess its relationship with UFOV accuracy. All functional connectivity analyses were corrected at a false discovery threshold (FDR) at p<0.05.

ROI-to-ROI analysis showed significant within-network functional connectivity among the 10 a priori ROIs (UFOV network) during task completion (all pFDR<.05). After controlling for covariates, greater within-network connectivity of the UFOV network associated with better UFOV fMRI performance (pFDR=.008). Regarding the 7 resting-state networks, greater within-network connectivity of the CON (pFDR<.001) and FPCN (pFDR=. 014) were associated with higher accuracy on the UFOV fMRI task. Furthermore, greater within-network connectivity of only the UFOV network associated with performance on the Double Decision task (pFDR=.034). Finally, we assessed the relationship between higher-order network segregation and UFOV accuracy. After controlling for covariates, no significant relationships between network segregation and UFOV performance remained (all p-uncorrected>0.05).

To date, this is the first study to assess task-based functional connectivity during completion of the UFOV task. We observed that coherence within 10 a priori ROIs significantly predicted UFOV performance. Additionally, enhanced within-network connectivity of the UFOV network predicted better performance on the Double Decision task, while conventional resting-state networks did not. These findings provide potential targets to optimize efficacy of UFOV interventions.

Cognitive training using a visual speed-of-processing task, called the Useful Field of View (UFOV) task, reduced dementia risk and reduced decline in activities of daily living at a 10-year follow-up in older adults. However, there is variability in the level of cognitive gains after cognitive training across studies. One potential explanation for this variability could be moderating factors. Prior studies suggest variables moderating cognitive training gains share features of the training task. Learning trials of the Hopkins Verbal Learning Test-Revised (HVLT-R) and Brief Visuospatial Memory Test-Revised (BVMT-R) recruit similar cognitive abilities and have overlapping neural correlates with the UFOV task and speed-ofprocessing/working memory tasks and therefore could serve as potential moderators. Exploring moderating factors of cognitive training gains may boost the efficacy of interventions, improve rigor in the cognitive training literature, and eventually help provide tailored treatment recommendations. This study explored the association between the HVLT-R and BVMT-R learning and the UFOV task, and assessed the moderation of HVLT-R and BVMT-R learning on UFOV improvement after a 3-month speed-ofprocessing/attention and working memory cognitive training intervention in cognitively healthy older adults.

75 healthy older adults (M age = 71.11, SD = 4.61) were recruited as part of a larger clinical trial through the Universities of Florida and Arizona. Participants were randomized into a cognitive training (n=36) or education control (n=39) group and underwent a 40-hour, 12-week intervention. Cognitive training intervention consisted of practicing 4 attention/speed-of-processing (including the UFOV task) and 4 working memory tasks. Education control intervention consisted of watching 40-minute educational videos. The HVLT-R and BVMT-R were administered at the pre-intervention timepoint as part of a larger neurocognitive battery. The learning ratio was calculated as: trial 3 total - trial 1 total/12 - trial 1 total. UFOV performance was measured at pre- and post-intervention time points via the POSIT Brain HQ Double Decision Assessment. Multiple linear regressions predicted baseline Double Decision performance from HVLT-R and BVMT-R learning ratios controlling for study site, age, sex, and education. A repeated measures moderation analysis assessed the moderation of HVLT-R and BVMT-R learning ratio on Double Decision change from pre- to post-intervention for cognitive training and education control groups.

Baseline Double Decision performance significantly associated with BVMT-R learning ratio (β=-.303, p=.008), but not HVLT-R learning ratio (β=-.142, p=.238). BVMT-R learning ratio moderated gains in Double Decision performance (p<.01); for each unit increase in BVMT-R learning ratio, there was a .6173 unit decrease in training gains. The HVLT-R learning ratio did not moderate gains in Double Decision performance (p>.05). There were no significant moderations in the education control group.

Better visuospatial learning was associated with faster Double Decision performance at baseline. Those with poorer visuospatial learning improved most on the Double Decision task after training, suggesting that healthy older adults who perform below expectations may show the greatest training gains. Future cognitive training research studying visual speed-of-processing interventions should account for differing levels of visuospatial learning at baseline, as this could impact the magnitude of training outcomes.

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.

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.

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.

During the COVID-19 pandemic, care for the elderly in the community was greatly limited. Accordingly, the demand for alternative community care have increased to cope with changing situations.

In this study, we tried to find out whether the companion robot improved mood state and related problem in depressive or isolated community dwelling elderly

For 186 community dwelling elderly who have received social welfare service due to depression or social isolation, we provided companion robot that could support their daily living. The robot was equipped with special program that could recognize and respond to the participant’s own emotion. It was part of behavioral activation techniques which is one of powerful treatment for depression. The self-report questionnaires were used to measure changes in cognitive function, depression, suicidality, loneliness, resilience and satisfaction of life. Outcomes were measured before using companion robot and after 3 months, and we compared them.

The elderly using companion robot for 3 months showed improved cognitive function (45.7% to 30.1%), depression (p<0.001), suicidality(p<0.001), and loneliness (p=0.033) in the self-report questionnaire. Resilience(p=0.749) and satisfaction of life (p=0.246) were also improved but not reached significance.

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These findings showed that the use of companion robot with emotional recognition coaching program could help improve depression, cognitive function, loneliness and suicidal ideation. In particular, this effect was also useful for those who were diagnosed with depression. Also if we can put more techniques of behavioral activation programs into robot, it could be useful in community care for depressive and isolated elderly.

None Declared

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.