Prior observational studies have reported conflicting results regarding whether antidepressant treatment reduces long-term dementia risk, likely due to confounding by indication and reverse causation. We aimed to investigate the association between baseline antidepressant use and incident dementia, incorporating cognitive and neuroimaging outcomes.

We conducted a prospective cohort study using UK Biobank participants free of dementia at baseline. Antidepressant use was self-reported at baseline (2006–2010). Incident dementia was identified through linked electronic health records until December 19, 2022. Cox proportional hazards models estimated hazard ratios (HRs) for all-cause dementia, Alzheimer’s disease (AD), and vascular dementia (VD), adjusting for sociodemographic, lifestyle, health-related, antidepressant indication factors, and co-medication of other anticholinergics. In subsamples, cognitive performance (n = 57,330) and structural brain imaging (n = 42,276) were examined as intermediate outcomes.

Among 461,464 participants, 33,721 (7.3%) reported baseline antidepressant use. Over a mean follow-up of 13.4 years, 7,922 (1.7%) developed incident dementia. Baseline antidepressant use was associated with higher risks of all-cause dementia (adjusted HR: 1.47, 95% CI 1.36–1.60), AD (1.53, 1.36–1.73), and VD (1.44, 1.23–1.70). Users performed worse on fluid intelligence and prospective memory tasks and showed lower total and gray matter volume, regional reductions in the hippocampal gray matter and basal nucleus, and greater white matter hyperintensity volume.

Baseline antidepressant use was linked to a higher risk of dementia, poorer cognitive performance, and adverse brain structural changes. These findings underscore the importance of judicious prescribing, regular cognitive monitoring, and consideration of non-pharmacological approaches in clinical care.

The impact of depression on brain aging remains unclear, but both have been linked to stressful life events. Shared biological pathways may underlie structural brain changes. Clarifying these relationships could advance understanding of underlying mechanisms and inform treatment approaches.

Structural MRI scans of 190 participants (controls, n = 110, clinically diagnosed with major depressive disorder [MDD], n = 80), from the REDEEM dataset, were input into three pretrained brain age prediction models: brainageR, DeepBrainNet, and pyment. Prediction accuracy was compared in controls to identify the optimal model. DeepBrainNet demonstrated the highest accuracy and was selected for subsequent analysis. Brain-predicted age difference (brain-PAD) was calculated as predicted age minus chronological age. Linear regression examined the effects of MDD diagnosis, childhood maltreatment, and cortisol awakening response on brain-PAD.

Depressed participants reported greater childhood maltreatment but a similar cortisol awakening response. An Age × Group interaction (β = 0.34, 95% CI: 0.15–0.53, p < 0.001) indicated older adults with MDD exhibited greater positive deviations from normative brain age predictions, suggesting nonuniform brain aging across the lifespan. Cortisol awakening response showed a negative association with brain-PAD (β = −0.01, 95% CI: −0.01 to −0.00, p = 0.041), indicating higher HPA-axis reactivity was linked to younger-appearing brains. Females showed lower brain-PAD than males, reflecting younger-appearing brains.

MDD was associated with age-dependent differences in brain-PAD. The protective association between cortisol awakening response and brain age highlights the importance of integrating stress biomarkers to better understand neural aging mechanisms in depression.

Few studies have examined how differing diagnostic criteria for mild cognitive impairment (MCI) relate to neuroimaging markers of cerebrovascular disease and neurodegeneration in Veterans. We compared three MCI diagnostic schemes on their associations with white matter hyperintensity (WMH) burden, hippocampal volume, and cortical thickness in nondemented Vietnam-era Veterans.

228 Veterans (mean age = 69.65) were classified using: (1) Alzheimer’s Disease Neuroimaging Initiative (ADNI) criteria (subjective memory concerns, impaired Logical Memory, global Clinical Dementia Rating = 0.5); (2) neuropsychological criteria (>1 standard deviation [SD] below norms on two tests within a domain or one test across three domains); and (3) typical criteria (subjective memory concerns and >1.5 SD below norms on one test). Regression models predicting WMH burden adjusted for age and intracranial volume and included MCI status and hippocampal volume; ADNI-based models also included posttraumatic stress disorder symptom severity.

Neuropsychological criteria were associated with greater WMH burden (β = 0.45, p = .024), whereas typical and ADNI criteria were not. Sensitivity analyses found that meeting neuropsychological criteria for amnestic MCI interacted with lower hippocampal volume to predict greater WMH burden (β = −0.001, p = .028). No criteria were associated with cortical thickness.

Neuropsychological criteria more sensitively identified Veterans with greater WMH burden and demonstrated a small, hippocampal volume-dependent effect in amnestic MCI. These findings support the clinical utility of multi-test neuropsychological approaches for detecting complex brain changes in high-comorbidity populations, with implications for risk stratification and targeted intervention in aging Veterans.

Selective serotonin reuptake inhibitors (SSRIs) are limited by inadequate response in a significant proportion of patients, slow onset, minimal cognitive benefit and side-effects. Preclinical studies suggest selective serotonin 4 receptor (5-HT4R) agonists may produce faster antidepressant effects via distinct mechanisms; however, there has been no experimental research in clinical populations to date.

To test whether the novel 5-HT4R partial agonist PF-04995274 produces early behavioural and neural changes in emotional cognition similar to SSRIs in patients with unmedicated major depressive disorder (MDD).

In a double-blind, placebo-controlled trial, 90 participants with MDD were randomised to 7 days of PF-04995274 (15 mg), citalopram (20 mg) or placebo. Emotional processing was assessed using a behavioural facial expression recognition task and functional magnetic resonance imaging (fMRI) of implicit emotional face processing (days 6–9). Observer- and self-reported symptoms of depression were also measured at baseline and study end.

As anticipated, citalopram reduced relative accuracy and increased relative reaction time to identify negative faces, with corresponding changes in neural activity (reduced left amygdala activation to emotional faces and valence-specific shifts in cortical regions). In contrast, PF-04995274 produced no change in behavioural negative bias or amygdala activity but increased medial-frontal cortex activation across valences. While this was not a clinical trial, both active treatments demonstrated an early treatment response with reduced observer-rated depression severity relative to placebo; PF-04995274 also reduced self-reported depression, state anxiety and negative affect.

PF-04995274 did not show the typical antidepressant profile of negative bias reductions observed with citalopram. Instead, it was associated with distinct increased medial-frontal activation during an emotional faces task, coupled with preliminary evidence of early clinical improvement, suggesting a potential alternative pathway for antidepressant effects. Findings support further clinical trials of 5-HT4R agonists and investigation of pro-cognitive and mood effects.

NCT03516604.

Individual responses to stress are highly heterogeneous, resulting in diverse psychopathological outcomes. This variability poses challenges for traditional diagnostic frameworks and underscores the need for a transdiagnostic approach to guide interventions. This study aimed to identify distinct phenotypes within a stress-exposed population and to characterize their biological profiles using a multimodal machine learning framework.

A total of 809 stress-exposed adults (mean age 40.5 ± 8.74 years; 53.7% female) underwent clinical, laboratory, and structural MRI assessments. Data-driven clustering of clinical variables identified phenotypes, followed by machine learning classifiers trained on neuroimaging and laboratory data to predict phenotype membership. SHapley Additive exPlanations (SHAP) analysis was used to identify key biological features distinguishing each phenotype.

Three phenotypes were identified: a multi-risk group (n = 321) characterized by prominent depression, anxiety, and sleep disturbances; an alcohol-related risk group (n = 226) with high alcohol misuse and minimal comorbidity; and a resilient low-risk group (n = 262). Machine learning models accurately classified these phenotypes, indicating distinct biological profiles. SHAP analysis revealed phenotype-specific signatures: the multi-risk phenotype was associated with frontal-subcortical structural alterations and dysregulated cortisol, whereas the alcohol-related risk phenotype was characterized by frontal-insular structural alterations and metabolic abnormalities.

This study demonstrates the stratification of stress-exposed individuals into clinically and biologically distinct phenotypes. By integrating multimodal data with machine learning, we identified phenotype-specific neurobiological and metabolic profiles that extend beyond conventional diagnostic frameworks. These findings support a transdiagnostic, data-driven approach to improve risk stratification and inform personalized interventions in stress-exposed populations.

Prior neuroimaging studies and meta-analyses investigating brain correlates of placebo analgesia (PA) have yielded neuroanatomically heterogeneous findings, which may be reconciled from a connectomics perspective. The objective of this study was to examine network localization of brain functional alterations related to PA.

We initially identified PA-induced brain activation alterations (hyper-activation and hypo-activation separately) during experimental pain from 29 published studies with 674 individuals. By combining these implicated dysfunctional brain regions with large-scale discovery (N = 1113) and validation (N = 1093) resting-state functional magnetic resonance imaging datasets, we then employed a novel functional connectivity network mapping approach to construct PA hyper-activation and hypo-activation networks, respectively.

The PA hyper-activation network manifested as a pattern of circumscribed brain regions mainly involving the limbic, default, and frontoparietal networks. By contrast, the PA hypo-activation network comprised a broadly distributed set of brain regions primarily implicating the ventral attention, somatomotor, and subcortical networks.

Our findings regarding the brain network representations of PA may contribute to a deeper understanding of its action mechanisms and provide a neural framework that may inform future clinical translation.

Emotion regulation relies on the interplay between prefrontal and limbic brain regions, with prefrontal regions implicated in the top-down modulation of the amygdala. In social anxiety disorder, disruptions in these networks have been reported, but most studies used undirected functional connectivity.

Dynamic causal modelling (DCM) was used to assess effective (i.e. directed) connectivity differences during emotion processing and regulation in individuals with social anxiety disorder compared with healthy controls.

A total of 102 participants (61 with social anxiety disorder, 41 healthy controls) performed a functional magnetic resonance imaging emotion regulation task under two conditions: viewing neutral/negative faces, and downregulating emotions using a self-chosen strategy. DCM was applied to model effective connectivity among the amygdala and key prefrontal regions. Connectivity patterns were characterised in healthy controls, and group comparisons tested how social anxiety disorder differed from this baseline model using parametric empirical Bayes. Leave-one-out cross-validation (LOOCV) evaluated whether connectivity differences predicted diagnostic group, symptom severity and emotion regulation difficulties.

In healthy controls, observation of negative faces was characterised by reciprocal influences between the amygdala and prefrontal cortex (PFC), including increased amygdala-to-ventromedial PFC (vmPFC) connectivity and inhibitory vmPFC-to-amygdala connectivity. During emotion regulation, healthy controls showed negative modulation from the amygdala to all prefrontal regions. Patients with social anxiety disorder did not differ from controls in amygdala–prefrontal connectivity; their alterations were confined to prefrontal circuits, with inhibitory connectivity from the pre-supplementary motor area (preSMA) to dorsolateral PFC during observation and bidirectional excitatory connectivity between the preSMA and vmPFC during regulation. LOOCV indicated that connectivity differences predicted diagnostic group.

The results support the idea that emotion processing and regulation influence connectivity between prefrontal areas and the amygdala in a complex, feedback-driven manner. Our findings suggest that aberrant emotion regulation in social anxiety disorder appears to be more closely linked to differences in intra-prefrontal circuits than deficits in amygdala–prefrontal connectivity.

We tested the hypothesis that resuming dietary control in early-treated phenylketonuria (PKU) is associated with improvements in white matter integrity, using data from the ReDAPT study, which previously demonstrated cognitive and psychiatric improvements with reduced phenylalanine (Phe) levels.

We re-initiated dietary control for early-treated patients with PKU and assessed the T1w/T2w ratio from standard T1-and T2-weighted magnetic resonance images, a marker of myelination and microstructural integrity. General linear mixed-effects model (GLMM) analyses were performed to assess change in the T1w/T2w ratio from baseline over twelve months after resumption of dietary control.

Seven participants (mean age 31 years; five female) with neuroimaging were included, with a mean of 16 years off diet and baseline Phe levels of 1157 µmol/L. GLMM analyses showed significant increases in T1w/T2w ratio over time for the whole brain (β = 0.47 [95%CI = 0.28, 0.66]), left hemisphere (β = 0.36 [95%CI = 0.19, 0.54]) and right hemisphere regions of interest (β = 0.52 [95%CI = 0.30, 0.72]). Longer time off diet was also positively associated with greater T1w/T2w changes. There was no evidence for the effects of gender or age at baseline.

This study demonstrated significant increases in the T1w/T2w ratio in PKU patients as they resumed dietary control over a 12-month period. Raw Phe levels were not strongly associated with neuroimaging measures. These findings support the importance of lifelong treatment for PKU and also demonstrate the potential reversibility of white matter changes in the disease.

We need to identify novel, tractable therapeutic targets for anxiety disorders. Converging evidence suggests the endogenous opioid system plays a role in modulating affective processing, but its contribution to regulation of threat processing in humans remains unclear.

We investigated the neural correlates of non-specific opioid antagonism on explicit and implicit regulation of threatening stimuli in healthy volunteers, using functional magnetic resonance imaging (fMRI).

In a randomised, double-blind, placebo-controlled, crossover design, 38 healthy participants received the opioid antagonist naltrexone (50 mg) or placebo before completing two tasks during fMRI: (a) a cognitive emotional reappraisal task probing explicit regulation and (b) a face-viewing task probing implicit processing.

Contrary to our hypothesis, we found naltrexone reduced distress ratings during the reappraisal task (p = 0.044) without impairing regulation success. Explicit regulation in the reappraisal task engaged lateral prefrontal regions similarly across drug conditions. However, naltrexone attenuated ventromedial prefrontal cortex, thalamus and caudate activation when viewing negative images. Naltrexone additionally altered ventromedial prefrontal cortex activity and in task-positive regions including right premotor area and frontal pole compared with placebo when viewing emotional faces. In particular, naltrexone increased left middle frontal gyrus activity when viewing fearful faces.

Our results support a role for opioid signalling in automatic emotional regulation, but not in explicit regulation. Furthermore, naltrexone appeared to diminish activity in task-positive regions in response to emotional faces. These findings are consistent with a model where endogenous opioids ‘fine-tune’ affective responses to both negative and positive stimuli. Future research should explore dose–response effects, kappa-opioid contributions and whether similar results are seen in clinical populations.

People with schizophrenia develop more chronic diseases at a younger age and die younger than people in the general population. It has been hypothesized that this excess morbidity and mortality could be partially due to accelerated aging in schizophrenia. If true, this would motivate the development of ‘gero-protective’ interventions to reduce chronic disease burden in schizophrenia. However, it has been difficult to test this hypothesis, in part, due to the limited ability to measure aging in samples of people with schizophrenia.

We utilized a novel neuroimaging biomarker of the longitudinal pace of aging, DunedinPACNI, to test for accelerated whole-body aging in schizophrenia across four neuroimaging datasets (total N = 2,096, 48% female) accessed through the Lieber Institute for Brain Development, the University of Bari Aldo Moro, and the North American Prodrome Longitudinal Study – 3.

We found consistent evidence of faster DunedinPACNI in schizophrenia compared with controls. In contrast, youth at clinical-high risk for psychosis did not have faster DunedinPACNI compared to controls. Unaffected siblings of patients also did not have faster DunedinPACNI than controls. Faster DunedinPACNI in schizophrenia was not explained by tobacco smoking or antipsychotic medication use.

The results support the hypothesis that schizophrenia is accompanied by accelerated aging. Results were inconsistent with some of the most obvious explanations for accelerated aging in schizophrenia (familial risk, smoking, and iatrogenic medication effects). Research should aim to uncover why people who have schizophrenia age rapidly, as well as the utility of early disease-risk monitoring and anti-aging interventions in schizophrenia.