Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points.

We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367).

Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals.

Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Trace amine-associated receptor 1 (TAAR1) agonists offer a new approach, but there is uncertainty regarding their effects, exact mechanism of action and potential role in treating psychosis.

To evaluate the available evidence on TAAR1 agonists in psychosis, using triangulation of the output of living systematic reviews (LSRs) of animal and human studies, and provide recommendations for future research prioritisation.

This study is part of GALENOS (Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis). In the triangulation process, a multidisciplinary group of experts, including those with lived experience, met and appraised the first co-produced living systematic reviews from GALENOS, on TAAR1 agonists.

The animal data suggested a potential antipsychotic effect, as TAAR1 agonists reduced locomotor activity induced by pro-psychotic drug treatment. Human studies showed few differences for ulotaront and ralmitaront compared with placebo in improving overall symptoms in adults with acute schizophrenia (four studies, n = 1291 participants, standardised mean difference (SMD) 0.15, 95% CI −0.05 to 0.34). Large placebo responses were seen in ulotaront phase three trials. Ralmitaront was less efficacious than risperidone (one study, n = 156 participants, SMD = −0.53, 95% CI −0.86 to −0.20). The side-effect profile of TAAR1 agonists was favourable compared with existing antipsychotics. Priorities for future studies included (a) using different animal models of psychosis with greater translational validity; (b) animal and human studies with wider outcomes including cognitive and affective symptoms and (c) mechanistic studies and investigations of other potential applications, such as adjunctive treatments and long-term outcomes. Recommendations for future iterations of the LSRs included (a) meta-analysis of individual human participant data, (b) including studies that used different methodologies and (c) assessing other disorders and symptoms.

This co-produced, international triangulation examined the available evidence and developed recommendations for future research and clinical applications for TAAR1 agonists in psychosis. Broader challenges included difficulties in assessing the risk of bias, reproducibility, translation and interpretability of animal models to clinical outcomes, and a lack of individual and clinical characteristics in the human data. The research will inform a separate, independent prioritisation process, led by lived experience experts, to prioritise directions for future research.

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case–control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

In UKB, reductions in network efficiency were observed in MDD cases globally (d = −0.076, pFDR = 0.033), across all tiers (d = −0.069 to −0.079, pFDR = 0.020), and in hubs (d = −0.080 to −0.113, pFDR = 0.013–0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

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.

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.