Patients with hematological malignancies are at high risk of infections due to both the disease and the associated treatments. The use of immunoglobulin (Ig) to prevent infections is increasing in this population, but its cost effectiveness is unknown. This trial-based economic evaluation aimed to compare the cost effectiveness of prophylactic Ig with prophylactic antibiotics in patients with hematological malignancies.

The economic evaluation used individual patient data from the RATIONAL feasibility trial, which randomly assigned 63 adults with chronic lymphocytic leukemia, multiple myeloma, or lymphoma to prophylactic Ig or prophylactic antibiotics. The following two analyses were conducted to estimate the cost effectiveness of the two treatments over the 12-month trial period from the perspective of the Australian health system:

(i) a cost-utility analysis (CUA) to assess the incremental cost per quality-adjusted life-year (QALY) gained using data collected with the EuroQol 5D-5L questionnaire; and

(ii) a cost-effectiveness analysis (CEA) to assess the incremental cost per serious infection prevented (grade ≥3) and per infection prevented (any grade).

The total cost per patient was significantly higher in the Ig arm than in the antibiotic arm (difference AUD29,140 [USD19,000]). There were non-significant differences in health outcomes between the treatment arms: patients treated with Ig had fewer QALYs (difference −0.072) and serious infections (difference −0.26) than those given antibiotics, but more overall infections (difference 0.76). The incremental cost-effectiveness from the CUA indicated that Ig was more costly than antibiotics and associated with fewer QALYs. In the CEA, Ig costed an additional AUD111,262 (USD73,000) per serious infection prevented, but it was more costly than antibiotics and associated with more infections when all infections were included.

These results indicate that, on average, Ig prophylactic treatment may not be cost effective compared with prophylactic antibiotics for the group of patients with hematological malignancies recruited to the RATIONAL feasibility trial. Further research is needed to confirm these findings in a larger population and over the longer term.

Women have a greater lifetime risk of developing Alzheimer’s disease (AD) dementia than men, a sex/gender disparity that cannot be explained by female longevity alone. There is substantial evidence for sex differences in the effects of APOE £4 on risk for AD. While APOE e4 increases AD risk in both sexes, women who carry APOE e4 are disproportionately vulnerable to cognitive impairment and AD compared to their counterpart men. In contrast to APOE e4, APOE £2 is associated with slower cognitive decline and a lower risk of AD. Although a less robust literature, APOE e2 may also have sex-specific effects. Because APOE e2 is the rarest major APOE allele, well-powered studies are needed to examine sex-specific effects. The objective of the present study was to examine sex-specific associations of APOE e2 carriage with longitudinal cognitive decline in a large cohort of clinically unimpaired adults.

We used observational data from two sources: the National Alzheimer’s Coordinating Center (NACC) and the Rush Alzheimer’s Disease Center (ROS/MAP/MARS) studies. We included data from clinically unimpaired adults who were >50 years old at baseline who self-identified as non-Hispanic White (NHW) or non-Hispanic Black (NHB). Participants were categorized as APOE £2, £4, or £3/e3 carriers. APOE e2/e4 carriers were excluded. The same battery of neuropsychological tests was used to assess global cognition in participants from both data sources. Linear mixed models examined interactive associations of genotype (£2 or £4 vs. £3/£3), sex, and time on longitudinal cognition in NHW and NHB participants separately. Analyses were first performed in a pooled sample of NACC and ROS/MAP/MARS participants and if significant they were repeated separately in each data source.

Across both data sources, 9,766 NHW (mean (SD) age=73.0(9.00) years, mean (SD) education=16.3(2.83) years, n(%) women=6,344(65.0)) and 2,010 NHB participants (mean(SD) age=71.3(7.59) years, mean(SD) education=14.9(3.10) years, n(%) women=1,583(78.8)) met inclusion criteria. Sex modified the association between APOE £2 and cognitive decline in NHW (ß=0.097, 95% CI: 0.023-0.172, pint=.01) but not NHB participants (ß=-0.011, 95% CI: -0.153-0.131, pint=.9). In sex-stratified analyses of NHW participants, APOE £2 (vs. £3/£3) carriage was associated with attenuated cognitive decline in men (ß=0.096, 95% CI: 0.037-0.155, p=.001), but not women (ß=-0.001, 95% CI: -0.044-0.043, p=.97). In analyses comparing men and women APOE £2 carriers, men exhibited slower cognitive decline than women (ß=0.120, 95% CI: 0.051-0.190, p=.001). Analyses performed separately in NACC and ROS/MAP revealed the same pattern of male-specific APOE £2 protection in NHW participants in both data sources.

In light of the longstanding view that APOE £2 protects against AD and dementia, our results provide evidence that APOE £2 is associated with attenuated cognitive decline in men but not women among NHW adults. This male-specific protection may contribute to sex differences in AD-related cognitive decline. Our findings have important implications for understanding the biological drivers of sex differences in AD risk, which is crucial for developing sex-specific strategies to prevent and treat AD dementia.

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.

Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.

Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.

Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.

We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).

This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.

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.

Obesity is associated with adverse effects on brain health, including increased risk for neurodegenerative diseases. Changes in cerebral metabolism may underlie or precede structural and functional brain changes. While bariatric surgery is known to be effective in inducing weight loss and improving obesity-related medical comorbidities, few studies have examined whether it may be able to improve brain metabolism. In the present study, we examined change in cerebral metabolite concentrations in participants with obesity who underwent bariatric surgery.

35 patients with obesity (BMI > 35 kg/m2) were recruited from a bariatric surgery candidate nutrition class. They completed single voxel 1H-proton magnetic resonance spectroscopy at baseline (pre-surgery) and within one year post-surgery. Spectra were obtained from a large medial frontal brain region. Tissue-corrected absolute concentrations for metabolites including choline-containing compounds (Cho), myo-inositol (mI), N-acetylaspartate (NAA), creatine (Cr), and glutamate and glutamine (Glx) were determined using Osprey. Paired t-tests were used to examine within-subject change in metabolite concentrations, and correlations were used to relate these changes to other health-related outcomes, including weight loss and glycemic control.

Bariatric surgery was associated with a reduction in cerebral Cho (f[34j = -3.79, p < 0.001, d = -0.64) and mI (f[34] = -2.81, p < 0.01, d = -0.47) concentrations. There were no significant changes in NAA, Glx, or Cr concentrations. Reductions in Cho were associated with greater weight loss (r = 0.40, p < 0.05), and reductions in mI were associated with greater reductions in HbA1c (r = 0.44, p < 0.05).

Participants who underwent bariatric surgery exhibited reductions in cerebral Cho and mI concentrations, which were associated with improvements in weight loss and glycemic control. Given that elevated levels of Cho and mI have been implicated in neuroinflammation, reduction in these metabolites after bariatric surgery may reflect amelioration of obesity-related neuroinflammatory processes. As such, our results provide evidence that bariatric surgery may improve brain health and metabolism in individuals with obesity.

The National Institutes of Health-Toolbox cognition battery (NIH-TCB) is widely used in cognitive aging studies and includes measures in cognitive domains evaluated for dimensional structure and psychometric properties in prior research. The present study addresses a current literature gap by demonstrating how NIH-TCB integrates into a battery of traditional clinical neuropsychological measures. The dimensional structure of NIH-TCB measures along with conventional neuropsychological tests is assessed in healthy older adults.

Baseline cognitive data were obtained from 327 older adults. The following measures were collected: NIH-Toolbox cognitive battery, Controlled Oral Word Association (COWA) letter and animals tests, Wechsler Test of Adult Reading (WTAR), Stroop Color-Word Interference Test, Paced Auditory Serial Addition Test (PASAT), Brief Visuospatial Memory Test (BVMT), Letter-Number Sequencing (LNS), Hopkins Verbal Learning Test (HVLT), Trail Making Test A&B, Digit Span. Hmisc, psych, and GPARotation packages for R were used to conduct exploratory factor analyses (EFA). A 5-factor solution was conducted followed by a 6-factor solution. Promax rotation was used for both EFA models.

The 6-factor EFA solution is reported here. Results indicated the following 6 factors: working memory (Digit Span forward, backward, and sequencing, PASAT trials 1 and 2, NIH-Toolbox List Sorting, LNS), speed/executive function (Stroop color naming, word reading, and color-word interference, NIH-Toolbox Flanker, Dimensional Change, and Pattern Comparison, Trail Making Test A&B), verbal fluency (COWA letters F-A-S), crystallized intelligence (WTAR, NIH-Toolbox Oral Recognition and Picture Vocabulary), visual memory (BVMT immediate and delayed), and verbal memory (HVLT immediate and delayed. COWA animals and NIH-Toolbox Picture Sequencing did not adequately load onto any EFA factor and were excluded from the subsequent CFA.

Findings indicate that in a sample of healthy older adults, these collected measures and those obtained through the NIH-Toolbox battery represent 6 domains of cognitive function. Results suggest that in this sample, picture sequencing and COWA animals did not load adequately onto the factors created from the rest of the measures collected. These findings should assist in interpreting future research using combined NIH-TCB and neuropsychological batteries to assess cognition in healthy older adults.

Subthreshold depressive symptoms are both prevalent and associated with negative outcomes in older adults, including conversion to major depressive disorder and other medical conditions. Antidepressants are not recommended as first-line or sole intervention for subthreshold depression; thus, finding other efficacious interventions is important. In depressed adults, transcranial direct current stimulation (tDCS) applied to the frontal lobe has antidepressant properties and pairing tDCS with cognitive training results in additional benefit due to enhancement of frontal cortical activity. However, these studies have primarily targeted depressed adults under age 65 years and less is known about whether this intervention combination is beneficial or affects subthreshold depressive symptoms in older adults.

We are reporting secondary data analyses from Nissim et al. (2019), who recruited 30 non-demented healthy older adults and randomized them to receive active or sham tDCS in combination with cognitive training for 2 weeks. Active tDCS was delivered bifrontally over F3 (cathode) and F4 (anode) for 20-min at 2 mA intensity through two 5x7 cm2 saline saturated sponge electrodes using the Soterix Medical 1x1 tDCS clinical trials device. Sham tDCS had identical set-up with 2 mA stimulation for 30-sec with 30-sec ramp up and down. Cognitive training was administered for 40-min daily using attention/processing speed and working memory modules from BrainHQ. The first 20-min of cognitive training was paired with active or sham tDCS. To allow room for symptom improvement, we only included participants with Beck Depression Inventory, 2nd edition (BDI-II) scores of 5 or greater ("minimal" depression severity). We identified 15 participants who met this cut-off (70.93 ± 5.41 years old, 10 females, 16.4 years ± 2.32 years education, MoCA = 27.27 ± 2.34; 7 active, 8 sham).

tDCS conditions did not significantly differ in age, sex, years of education, MoCA scores, number of completed intervention days, or baseline BDI-II (active: 7.71 ± 2.93, sham: 11.38 ± 6.44). There were no differences in sensation ratings between groups or in confidence ratings for condition received (suggesting successful blinding). Results indicated the combination of active (and not sham) tDCS with cognitive training was associated with reduced depressive symptoms (2.7 vs. 1.4 points, active vs. sham). Including covariates (age, sex, education, MoCA scores, and number of completed intervention days) in the model further strengthened this discrepancy (3.7 vs. 0.51 points, active vs. sham).

While preliminary, these results suggest this intervention combination may be a potential method for improving subthreshold depressive symptoms in older adults via targeting prefrontal neural circuitry and promoting neuroplasticity of the underlying neural network. While baseline BDI-II scores did not significantly differ, the active tDCS group had a lower score than sham, but saw greater improvement in BDI-II scores post-intervention despite having less room for change. Adequate treatment of subthreshold depressive symptoms may prevent or reduce negative outcomes associated with depressive symptoms in at-risk older adults. Larger randomized clinical trials are needed to better understand tDCS plus cognitive training antidepressant effects in this age group.

Exercise elicits a variety of physiological responses in the body. In the brain, exercise can modulate levels of neurotransmitters and other neurochemicals as well as sparking neurogenesis and structural changes. Downstream psychological effects of exercise include changes in mood and cognition. These changes vary depending on the type of exercise conducted (e.g., running versus strength training). While much is known about the effects of exercise in animals and adult humans, literature on the oldest-old (>= 85 years old) is sparse. The present cross-sectional study explores the relationship between exercise and cognition in the oldest-old.

The final sample includes 194 cognitively healthy participants (106 females, MoCA mean score = 24.75) aged 85 to 99 years old (mean = 88.48). Each participant completed the Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire and a cognitive battery comprising of the NIH Toolbox, digit coding, symbol search, verbal fluency, and Stroop task. Three groups (sedentary, cardio, and cardio plus strength training) were derived from responses on CHAMPS.

The cardio plus strength training group performed significantly better on the cognitive measures compared to the sedentary group. For two measures, digit coding and symbol search, the cardio plus strength training group also performed significantly better than the cardio group. Cardio group did not significantly differ from the sedentary group on the cognitive measures. All at p < 0.05 and adjusted for multiple comparisons.

Our findings suggest exercise in the oldest-old is linked with higher fluid intelligence and better performance on cognitive measures of processing speed and that there may be an additive effect of exercise types on cognition.

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 brain is reliant on mitochondria to carry out a host of vital cellular functions (e.g., energy metabolism, respiration, apoptosis) to maintain neuronal integrity. Clinically relevant, dysfunctional mitochondria have been implicated as central to the pathogenesis of Alzheimer’s disease (AD). Phosphorous magnetic resonance spectroscopy (31p MRS) is a non-invasive and powerful method for examining in vivo mitochondrial function via high energy phosphates and phospholipid metabolism ratios. At least one prior 31p MRS study found temporal-frontal differences for high energy phosphates in persons with mild AD. The goal of the current study was to examine regional (i.e., frontal, temporal) 31p MRS ratios of mitochondrial function in a sample of older adults at-risk for AD. Given the high energy consumption in temporal lobes (i.e., hippocampus) and preferential age-related changes in frontal structure-function, we predicted 31p MRS ratios of mitochondrial function would be greater in temporal as compared to frontal regions.

The current study leveraged baseline neuroimaging data from an ongoing multisite study at the University of Florida and University of Arizona. Participants were older adults with memory complaints and a first-degree family history of AD [N = 70; mean [M] age [years] = 70.9, standard deviation [SD] =5.1; M education [years] = 16.2, SD = 2.2; M MoCA = 26.5, SD = 2.4; 61.4% female; 91.5% non-latinx white]. To achieve optimal sensitivity, we used a single voxel method to examine 31p MRS ratios (bilateral prefrontal and left temporal). Mitochondrial function was estimated by computing 5 ratios for each voxel: summed adenosine triphosphate to total pooled phosphorous (ATP/TP; momentary energy), ATP to inorganic phosphate (ATP/Pi; energy consumption), phosphocreatine to ATP (PCr/ATP; energy reserve), phosphocreatine to inorganic phosphate (PCr/Pi; oxidative phosphorylation), and phosphomonoesters to phosphodiesters (PME/PDE; cellular membrane turnover rate). All ratios were corrected for voxel size and cerebrospinal fluid fraction. Separate repeated measures analyses of variance controlling for scanner site differences (RM ANCOVAs) were performed.

31p MRS ratios were unrelated to demographic characteristics and were not included as additional covariates in analyses. Results of separate RM ANCOVAs revealed all 31p MRS ratios of mitochondrial function were greater in left temporal relative to bilateral prefrontal voxel: ATP/TP (p < .001), ATP/Pi (p = .001), PCr/ATP (p = .004), PCr/Pi (p = .004), and PME/PDE (p = .017). Effect sizes (partial eta squared) ranged from 0.6-.20.

Consistent and extending one prior study, all 31p MRS ratios of mitochondrial function were greater in temporal as compared to frontal regions in older adults at-risk for AD. This may in part be related to the intrinsically high metabolic rate of the temporal region and preferential age-related changes in frontal structure-function. Alternatively, findings may reflect the influence of unaccounted factors (e.g., hemodynamics, auditory stimulation). Longitudinal study designs may inform whether patterns of mitochondrial function across different brain regions are present early in development, occur across the lifespan, or some combination. In turn, this may inform future studies examining differences in mitochondrial function (as measured using 31p MRS) in AD.

To evaluate the construct validity of the NIH Toolbox Cognitive Battery (NIH TB-CB) in the healthy oldest-old (85+ years old).

Our sample from the McKnight Brain Aging Registry consists of 179 individuals, 85 to 99 years of age, screened for memory, neurological, and psychiatric disorders. Using previous research methods on a sample of 85 + y/o adults, we conducted confirmatory factor analyses on models of NIH TB-CB and same domain standard neuropsychological measures. We hypothesized the five-factor model (Reading, Vocabulary, Memory, Working Memory, and Executive/Speed) would have the best fit, consistent with younger populations. We assessed confirmatory and discriminant validity. We also evaluated demographic and computer use predictors of NIH TB-CB composite scores.

Findings suggest the six-factor model (Vocabulary, Reading, Memory, Working Memory, Executive, and Speed) had a better fit than alternative models. NIH TB-CB tests had good convergent and discriminant validity, though tests in the executive functioning domain had high inter-correlations with other cognitive domains. Computer use was strongly associated with higher NIH TB-CB overall and fluid cognition composite scores.

The NIH TB-CB is a valid assessment for the oldest-old samples, with relatively weak validity in the domain of executive functioning. Computer use’s impact on composite scores could be due to the executive demands of learning to use a tablet. Strong relationships of executive function with other cognitive domains could be due to cognitive dedifferentiation. Overall, the NIH TB-CB could be useful for testing cognition in the oldest-old and the impact of aging on cognition in older populations.

To describe complementary and alternative medicine (CAM) use amongst children with cerebral palsy (CP) in Canada and to identify factors associated with CAM use.

We conducted a cross-sectional study, utilising data from the Canadian CP Registry. We explored the association between CAM use and regional, socioeconomic and CP phenotypic variables, and parental perception of the family-centredness of clinical care using the Measures of Process of Care-56 (MPOC-56). Chi-square analyses were performed, and odds ratios (OR) and 95% confidence intervals (CI) were obtained. Mann–Whitney U tests were used to compare MPOC-56 scores between CAM users and non-CAM users.

The study sample consisted of 313 families of which 27% reported CAM use in the past year. Children with CP using CAM were more likely to reside in Western Canada (OR 3.3, 95% CI 1.6–6.7), live in a two-parent household (OR 3.5, 95% CI 1.5–8.4), have an ataxic/hypotonic or dyskinetic CP subtype (OR 3.0, 95% CI 1.5–6.1) and have a greater motor impairment (OR 2.8, 95% CI 1.7–4.9). MPOC-56 subscale scores were not significantly associated with CAM use.

Physicians need to be aware of existing CAM therapies, the level of evidence supporting their efficacy (beneficence), their associated risks of adverse events (non-maleficence) and enable fair access to care that may be of benefit to each child.