Immune dysregulation appears involved in affective disorder pathophysiology. Inflammatory biomarkers have been linked with the cognitive impairment observed in people with bipolar disorders and as such are candidate markers that may improve with, and/or predict outcomes to, cognitive remediation therapies (CRT).

Nine candidate biomarkers were examined as putative mediators and/or moderators to improvements following CRT compared with treatment as usual (TAU) from a randomised controlled trial.

Euthymic adults with bipolar disorders who had been randomised to CRT (n = 23) or TAU (n = 21) underwent blood testing before and after a 12 week intervention period. Five cytokines and four growth factor proteins, selected a priori, were examined in association with global cognition and psychosocial functioning outcomes.

CRT attenuated a reduction in the brain-derived neurotrophic factor (BDNF), basic fibroblast growth factor and vascular endothelial growth factor-C compared to TAU. For the BDNF, lower baseline levels predicted better functional outcomes across the sample but was more pronounced in TAU versus CRT participants and indicated larger CRT effects in those with a higher BDNF. A moderation effect was also apparent for tumour necrosis factor-β and interleukin-16, with greater CRT versus TAU effects on functioning for participants with lower baseline levels.

Although preliminary, results suggest that CRT may exert some protective biological effects, and that people with lower levels of neurotrophins or cytokines may benefit more from CRT. We note an absence of associations with cognitive (versus functional) outcomes. These findings require further examination in large well-controlled studies.

Amygdala and dorsal anterior cingulate cortex responses to facial emotions have shown promise in predicting treatment response in medication-free major depressive disorder (MDD). Here, we examined their role in the pathophysiology of clinical outcomes in more chronic, difficult-to-treat forms of MDD.

Forty-five people with current MDD who had not responded to ⩾2 serotonergic antidepressants (n = 42, meeting pre-defined fMRI minimum quality thresholds) were enrolled and followed up over four months of standard primary care. Prior to medication review, subliminal facial emotion fMRI was used to extract blood-oxygen level-dependent effects for sad v. happy faces from two pre-registered a priori defined regions: bilateral amygdala and dorsal/pregenual anterior cingulate cortex. Clinical outcome was the percentage change on the self-reported Quick Inventory of Depressive Symptomatology (16-item).

We corroborated our pre-registered hypothesis (NCT04342299) that lower bilateral amygdala activation for sad v. happy faces predicted favorable clinical outcomes (rs[38] = 0.40, p = 0.01). In contrast, there was no effect for dorsal/pregenual anterior cingulate cortex activation (rs[38] = 0.18, p = 0.29), nor when using voxel-based whole-brain analyses (voxel-based Family-Wise Error-corrected p < 0.05). Predictive effects were mainly driven by the right amygdala whose response to happy faces was reduced in patients with higher anxiety levels.

We confirmed the prediction that a lower amygdala response to negative v. positive facial expressions might be an adaptive neural signature, which predicts subsequent symptom improvement also in difficult-to-treat MDD. Anxiety reduced adaptive amygdala responses.

Major depressive disorder (MDD) is one of the most prevalent and disabling illnesses worldwide. Treatment of MDD typically relies on trial-and-error to find an effective approach. Identifying early response-related biomarkers that predict response to antidepressants would help clinicians to decide, as early as possible, whether a particular treatment might be suitable for a given patient.

Data were from the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial. A whole-brain, voxel-wise, mixed-effects model was applied to identify early-treatment cerebral blood flow (CBF) changes as biomarkers of treatment response. We examined changes in CBF measured with arterial spin labeling 1-week after initiating double-masked sertraline/placebo. We tested whether these early 1-week scans could be used to predict response observed after 8-weeks of treatment.

Response to 8-week placebo treatment was associated with increased cerebral perfusion in temporal cortex and reduced cerebral perfusion in postcentral region captured at 1-week of treatment. Additionally, CBF response in these brain regions was significantly correlated with improvement in Hamilton Depression Rating Scale score in the placebo group. No significant associations were found for selective serotonin reuptake inhibitor treatment.

We conclude that early CBF responses to placebo administration in multiple brain regions represent candidate neural biomarkers of longer-term antidepressant effects.

Proteomics may help discover novel biomarkers and underlying mechanisms for cardiovascular disease. This could be useful for childhood cancer survivors as they show an increased risk of cardiovascular disease. The aim of this study was to investigate circulating cardiovascular proteins in young adult survivors of childhood cancer and their relationship to previously reported subclinical cardiovascular disease.

Ninety-two cardiovascular proteins were measured in 57 childhood cancer survivors and in 52 controls. For proteins that were significantly different between childhood cancer survivors and controls, we performed correlations between protein levels and measures of peripheral arterial stiffness (carotid distensibility and stiffness index, and augmentation index) and endothelial dysfunction (reactive hyperemia index).

Leptin was significantly higher in childhood cancer survivors compared to controls (normalized protein expression units: childhood cancer survivors 6.4 (1.5) versus 5.1 (1.7), p < 0.0000001) after taking multiple tests into account. Kidney injury molecule-1, MER proto-oncogene tyrosine kinase, selectin P ligand, decorin, alpha-1-microglobulin/bikunin precursor protein, and pentraxin 3 showed a trend towards group differences (p < 0.05). Among childhood cancer survivors, leptin was associated with anthracycline treatment after adjustment for age, sex, and body mass index (p < 0.0001). Higher leptin correlated with lower carotid distensibility after adjustment for age, sex, body mass index, and treatments with radiotherapy and anthracyclines (p = 0.005).

This proteomics approach identified that leptin is higher in young asymptomatic adult survivors of childhood cancer than in healthy controls and is associated with adverse vascular changes. This could indicate a role for leptin in driving the cardiovascular disease burden in this population.

Neuropsychological criteria for mild cognitive impairment (MCI) more accurately predict progression to Alzheimer’s disease (AD) and are more strongly associated with AD biomarkers and neuroimaging profiles than ADNI criteria. However, research to date has been conducted in relatively healthy samples with few comorbidities. Given that history of traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) are risk factors for AD and common in Veterans, we compared neuropsychological, typical (Petersen/Winblad), and ADNI criteria for MCI in Vietnam-era Veterans with histories of TBI or PTSD.

267 Veterans (mean age = 69.8) from the DOD-ADNI study were evaluated for MCI using neuropsychological, typical, and ADNI criteria. Linear regressions adjusting for age and education assessed associations between MCI status and AD biomarker levels (cerebrospinal fluid [CSF] p-tau181, t-tau, and Aβ42) by diagnostic criteria. Logistic regressions adjusting for age and education assessed the effects of TBI severity and PTSD symptom severity simultaneously on MCI classification by each criteria.

Agreement between criteria was poor. Neuropsychological criteria identified more Veterans with MCI than typical or ADNI criteria, and were associated with higher CSF p-tau181 and t-tau. Typical and ADNI criteria were not associated with CSF biomarkers. PTSD symptom severity predicted MCI diagnosis by neuropsychological and ADNI criteria. History of moderate/severe TBI predicted MCI by typical and ADNI criteria.

MCI diagnosis using sensitive neuropsychological criteria is more strongly associated with AD biomarkers than conventional diagnostic methods. MCI diagnostics in Veterans would benefit from incorporation of comprehensive neuropsychological methods and consideration of the impact of PTSD.

Eating disorders (ED) are serious psychiatric disorders, taking a life every 52 minutes, with high relapse. There are currently no support or effective intervention therapeutics for individuals with an ED in their everyday life. The aim of this study is to build idiographic machine learning (ML) models to evaluate the performance of physiological recordings to detect individual ED behaviors in naturalistic settings.

From an ongoing study (Final N = 120), we piloted the ability for ML to detect an individual's ED behavioral episodes (e.g. purging) from physiological data in six individuals diagnosed with an ED, all of whom endorsed purging. Participants wore an ambulatory monitor for 30 days and tapped a button to denote ED behavioral episodes. We built idiographic (N = 1) logistic regression classifiers (LRC) ML trained models to identify onset of episodes (~600 windows) v. baseline (~571 windows) physiology (Heart Rate, Electrodermal Activity, and Temperature).

Using physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity.

This evidence suggests the ability to build idiographic ML models that detect ED behaviors from physiological indices within everyday life with a high level of accuracy. The novel use of ML with wearable sensors to detect physiological patterns of ED behavior pre-onset can lead to just-in-time clinical interventions to disrupt problematic behaviors and promote ED recovery.

Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting.

Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier.

Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82–0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70–0.73 AUC).

These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown.

We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other.

Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone.

CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.