Haloperidol for the Treatment of Delirium in ICU Patients.

BACKGROUND: Haloperidol is frequently used to treat delirium in patients in the intensive care unit (ICU), but evidence of its effect is limited. METHODS: In this multicenter, blinded, placebo-controlled trial, we randomly assigned adult patients with delirium who had been admitted to the ICU for an acute condition to receive intravenous haloperidol (2.5 mg 3 times daily plus 2.5 mg as needed up to a total maximum daily dose of 20 mg) or placebo. Haloperidol or placebo was administered in the ICU for as long as delirium continued and as needed for recurrences. The primary outcome was the number of days alive and out of the hospital at 90 days after randomization. RESULTS: A total of 1000 patients underwent randomization; 510 were assigned to the haloperidol group and 490 to the placebo group. Among these patients, 987 (98.7%) were included in the final analyses (501 in the haloperidol group and 486 in the placebo group). Primary outcome data were available for 963 patients (97.6%). At 90 days, the mean number of days alive and out of the hospital was 35.8 (95% confidence interval [CI], 32.9 to 38.6) in the haloperidol group and 32.9 (95% CI, 29.9 to 35.8) in the placebo group, with an adjusted mean difference of 2.9 days (95% CI, -1.2 to 7.0) (P = 0.22). Mortality at 90 days was 36.3% in the haloperidol group and 43.3% in the placebo group (adjusted absolute difference, -6.9 percentage points [95% CI, -13.0 to -0.6]). Serious adverse reactions occurred in 11 patients in the haloperidol group and in 9 patients in the placebo group. CONCLUSIONS: Among patients in the ICU with delirium, treatment with haloperidol did not lead to a significantly greater number of days alive and out of the hospital at 90 days than placebo. (Funded by Innovation Fund Denmark and others; AID-ICU ClinicalTrials.gov number, NCT03392376; EudraCT number, 2017-003829-15.).

Different inflammatory signatures based on CSF biomarkers relate to preserved or diminished brain structure and cognition.

Neuroinflammation is a hallmark of Alzheimer's disease (AD) and both positive and negative associations of individual inflammation-related markers with brain structure and cognitive function have been described. We aimed to identify inflammatory signatures of CSF immune-related markers that relate to changes of brain structure and cognition across the clinical spectrum ranging from normal aging to AD. A panel of 16 inflammatory markers, Aß42/40 and p-tau181 were measured in CSF at baseline in the DZNE DELCODE cohort (n = 295); a longitudinal observational study focusing on at-risk stages of AD. Volumetric maps of gray and white matter (GM/WM; n = 261) and white matter hyperintensities (WMHs, n = 249) were derived from baseline MRIs. Cognitive decline (n = 204) and the rate of change in GM volume was measured in subjects with at least 3 visits (n = 175). A principal component analysis on the CSF markers revealed four inflammatory components (PCs). Of these, the first component PC1 (highly loading on sTyro3, sAXL, sTREM2, YKL-40, and C1q) was associated with older age and higher p-tau levels, but with less pathological Aß when controlling for p-tau. PC2 (highly loading on CRP, IL-18, complement factor F/H and C4) was related to male gender, higher body mass index and greater vascular risk. PC1 levels, adjusted for AD markers, were related to higher GM and WM volumes, less WMHs, better baseline memory, and to slower atrophy rates in AD-related areas and less cognitive decline. In contrast, PC2 related to less GM and WM volumes and worse memory at baseline. Similar inflammatory signatures and associations were identified in the independent F.ACE cohort. Our data suggest that there are beneficial and detrimental signatures of inflammatory CSF biomarkers. While higher levels of TAM receptors (sTyro/sAXL) or sTREM2 might reflect a protective glia response to degeneration related to phagocytic clearance, other markers might rather reflect proinflammatory states that have detrimental impact on brain integrity.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Single-value brain activity scores reflect both severity and risk across the Alzheimer's continuum.

Single-value scores reflecting the deviation from (FADE score) or similarity with (SAME score) prototypical novelty-related and memory-related functional magnetic resonance imaging (fMRI) activation patterns in young adults have been proposed as imaging biomarkers of healthy neurocognitive aging. Here, we tested the utility of these scores as potential diagnostic and prognostic markers in Alzheimer's disease (AD) and risk states like mild cognitive impairment (MCI) or subjective cognitive decline (SCD). To this end, we analyzed subsequent memory fMRI data from individuals with SCD, MCI, and AD dementia as well as healthy controls (HC) and first-degree relatives of AD dementia patients (AD-rel) who participated in the multi-center DELCODE study (N = 468). Based on the individual participants' whole-brain fMRI novelty and subsequent memory responses, we calculated the FADE and SAME scores and assessed their association with AD risk stage, neuropsychological test scores, CSF amyloid positivity, and ApoE genotype. Memory-based FADE and SAME scores showed a considerably larger deviation from a reference sample of young adults in the MCI and AD dementia groups compared to HC, SCD and AD-rel. In addition, novelty-based scores significantly differed between the MCI and AD dementia groups. Across the entire sample, single-value scores correlated with neuropsychological test performance. The novelty-based SAME score further differed between Aß-positive and Aß-negative individuals in SCD and AD-rel, and between ApoE ε4 carriers and non-carriers in AD-rel. Hence, FADE and SAME scores are associated with both cognitive performance and individual risk factors for AD. Their potential utility as diagnostic and prognostic biomarkers warrants further exploration, particularly in individuals with SCD and healthy relatives of AD dementia patients.

Relevance of genetic testing in the gene-targeted trial era: the Rostock Parkinson's disease study.

Estimates of the spectrum and frequency of pathogenic variants in Parkinson's disease (PD) in different populations are currently limited and biased. Furthermore, although therapeutic modification of several genetic targets has reached the clinical trial stage, a major obstacle in conducting these trials is that PD patients are largely unaware of their genetic status and, therefore, cannot be recruited. Expanding the number of investigated PD-related genes and including genes related to disorders with overlapping clinical features in large, well-phenotyped PD patient groups is a prerequisite for capturing the full variant spectrum underlying PD and for stratifying and prioritizing patients for gene-targeted clinical trials. The Rostock Parkinson's disease (ROPAD) study is an observational clinical study aiming to determine the frequency and spectrum of genetic variants contributing to PD in a large international cohort. We investigated variants in 50 genes with either an established relevance for PD or possible phenotypic overlap in a group of 12 580 PD patients from 16 countries [62.3% male; 92.0% White; 27.0% positive family history (FH+), median age at onset (AAO) 59 years] using a next-generation sequencing panel. Altogether, in 1864 (14.8%) ROPAD participants (58.1% male; 91.0% White, 35.5% FH+, median AAO 55 years), a PD-relevant genetic test (PDGT) was positive based on GBA1 risk variants (10.4%) or pathogenic/likely pathogenic variants in LRRK2 (2.9%), PRKN (0.9%), SNCA (0.2%) or PINK1 (0.1%) or a combination of two genetic findings in two genes (∼0.2%). Of note, the adjusted positive PDGT fraction, i.e. the fraction of positive PDGTs per country weighted by the fraction of the population of the world that they represent, was 14.5%. Positive PDGTs were identified in 19.9% of patients with an AAO ≤ 50 years, in 19.5% of patients with FH+ and in 26.9% with an AAO ≤ 50 years and FH+. In comparison to the idiopathic PD group (6846 patients with benign variants), the positive PDGT group had a significantly lower AAO (4 years, P = 9 × 10-34). The probability of a positive PDGT decreased by 3% with every additional AAO year (P = 1 × 10-35). Female patients were 22% more likely to have a positive PDGT (P = 3 × 10-4), and for individuals with FH+ this likelihood was 55% higher (P = 1 × 10-14). About 0.8% of the ROPAD participants had positive genetic testing findings in parkinsonism-, dystonia/dyskinesia- or dementia-related genes. In the emerging era of gene-targeted PD clinical trials, our finding that ∼15% of patients harbour potentially actionable genetic variants offers an important prospect to affected individuals and their families and underlines the need for genetic testing in PD patients. Thus, the insights from the ROPAD study allow for data-driven, differential genetic counselling across the spectrum of different AAOs and family histories and promote a possible policy change in the application of genetic testing as a routine part of patient evaluation and care in PD.

A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.

FARS-ADL across Ataxias: Construct Validity, Sensitivity to Change, and Minimal Important Change.

BACKGROUND: Patient-focused outcomes present a central need for trial-readiness across all ataxias. The Activities of Daily Living part of the Friedreich Ataxia Rating Scale (FARS-ADL) captures functional impairment and longitudinal change but is only validated in Friedreich Ataxia. OBJECTIVE: Validation of FARS-ADL regarding disease severity and patient-meaningful impairment, and its sensitivity to change across genetic ataxias. METHODS: Real-world registry data of FARS-ADL in 298 ataxia patients across genotypes were analyzed, including (1) cross-correlation with FARS-stage, Scale for the Assessment and Rating of Ataxia (SARA), Patient-Reported Outcome Measure (PROM)-ataxia, and European Quality of Life 5 Dimensions visual analogue scale (EQ5D-VAS); (2) sensitivity to change within a trial-relevant 1-year median follow-up, anchored in Patient Global Impression of Change (PGI-C); and (3) general linear modeling of factors age, sex, and depression (nine-item Patient Health Questionnaire [PHQ-9]). RESULTS: FARS-ADL correlated with overall disability (rhoFARS-stage = 0.79), clinical disease severity (rhoSARA = 0.80), and patient-reported impairment (rhoPROM-ataxia = 0.69, rhoEQ5D-VAS = -0.37), indicating comprehensive construct validity. Also at item level, and validated within genotype (SCA3, RFC1), FARS-ADL correlated with the corresponding SARA effector domains; and all items correlated to EQ5D-VAS quality of life. FARS-ADL was sensitive to change at a 1-year interval, progressing only in patients with worsening PGI-C. Minimal important change was 1.1. points based on intraindividual variability in patients with stable PGI-C. Depression was captured using FARS-ADL (+0.3 points/PHQ-9 count) and EQ5D-VAS, but not FARS-stage or SARA. CONCLUSION: FARS-ADL reflects both disease severity and patient-meaningful impairment across genetic ataxias, with sensitivity to change in trial-relevant timescales in patients perceiving change. It thus presents a promising patient-focused outcome for upcoming ataxia trials. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Maternal inflammation during pregnancy is associated with risk of ADHD in children at age 10.

INTRODUCTION: Maternal inflammation during pregnancy may affect early neurodevelopment in offspring as suggested by preclinical and register data. However, clinical evidence for risk of aberrant neurodevelopment later in childhood is scarce. In the population-based COPSAC2010 mother-child cohort, we investigated associations between maternal inflammation levels during pregnancy and the risk of a diagnosis of ADHD as well as the load of ADHD symptoms in the children at age 10. METHODS: The COPSAC2010 cohort consists of 700 mother-child pairs followed prospectively since pregnancy week 24.Maternal high-sensitivity C-Reactive Protein (hs-CRP) level at week 24 of gestation was investigated in relation to child neurodevelopment by age 10 using logistic and linear regression models with extensive confounder adjustment, including socioeconomic status and maternal polygenic risk of ADHD. The children completed a comprehensive examination of neurodevelopment including categorical (i.e., diagnostic) and dimensional (i.e., symptom load) psychopathology using the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version (K-SADS-PL) and parental rated ADHD-Rating Scale (ADHD-RS). RESULTS: A total of 604 (86 %) of the 700 children in the COPSAC2010 cohort participated in the COPSYCH visit at age 10. Sixty-five (10.8 %) fulfilled a research diagnosis of ADHD (16 girls and 49 boys). Higher maternal hs-CRP level in pregnancy at week 24 (median 5.4 mg/L) was significantly associated with increased risk for a diagnosis of ADHD, adjusted OR 1.40, 95 %CI (1.16-1.70), p = 0.001. Additionally, higher maternal hs-CRP was associated with increased ADHD symptom load in the entire cohort, reflected by ADHD-RS raw scores. DISCUSSION: These clinical data demonstrated a robust association of prenatal maternal inflammation assessed by hs-CRP with a diagnosis of ADHD by age 10. Moreover, maternal inflammation was associated with ADHD symptom load in the complete cohort. Identifying inflammation as an important marker will provide a potential target for future increased awareness and prevention during pregnancy thereby ultimately improving neurodevelopmental outcomes in children.

Using computer vision of facial expressions to assess symptom domains and treatment response in antipsychotic-naïve patients with first-episode psychosis.

BACKGROUND: Facial expressions are a core aspect of non-verbal communication. Reduced emotional expressiveness of the face is a common negative symptom of schizophrenia, however, quantifying negative symptoms can be clinically challenging and involves a considerable element of rater subjectivity. We used computer vision to investigate if (i) automated assessment of facial expressions captures negative as well as positive and general symptom domains, and (ii) if automated assessments are associated with treatment response in initially antipsychotic-naïve patients with first-episode psychosis. METHOD: We included 46 patients (mean age 25.4 (6.1); 65.2% males). Psychopathology was assessed at baseline and after 6 weeks of monotherapy with amisulpride using the Positive and Negative Syndrome Scale (PANSS). Baseline interview videos were recorded. Seventeen facial action units (AUs), that is, activation of muscles, from the Facial Action Coding System were extracted using OpenFace 2.0. A correlation matrix was calculated for each patient. Facial expressions were identified using spectral clustering at group-level. Associations between facial expressions and psychopathology were investigated using multiple linear regression. RESULTS: Three clusters of facial expressions were identified related to different locations of the face. Cluster 1 was associated with positive and general symptoms at baseline, Cluster 2 was associated with all symptom domains, showing the strongest association with the negative domain, and Cluster 3 was only associated with general symptoms. Cluster 1 was significantly associated with the clinically rated improvement in positive and general symptoms after treatment, and Cluster 2 was significantly associated with clinical improvement in all domains. CONCLUSION: Using automated computer vision of facial expressions during PANSS interviews did not only capture negative symptoms but also combinations of the three overall domains of psychopathology. Moreover, automated assessments of facial expressions at baseline were associated with initial antipsychotic treatment response. The findings underscore the clinical relevance of facial expressions and motivate further investigations of computer vision in clinical psychiatry.

The impact of weight gain on antipsychotic nonadherence or discontinuation: A systematic review and meta-analysis.

BACKGROUND: Nonadherence/discontinuation of antipsychotic (AP) medications represents an important clinical issue in patients across psychiatric disorders, including schizophrenia spectrum disorders (SSDs). While antipsychotic-induced weight gain (AIWG) is a reported contributor to nonadherence, a systematic review of the association between AIWG and medication nonadherence/discontinuation has not been explored previously. METHOD: A systematic search was conducted in MEDLINE, EMBASE, PsychINFO, CINAHL, and CENTRAL databases, among others, to help identify all studies which explored adherence, study dropouts, AP switching and/or discontinuations attributable to AIWG among individuals with severe mental illness. A meta-analysis was also completed where applicable. RESULTS: We identified two categories of studies for the meta-analysis. Category 1 included three studies, which compared measures of AP adherence or discontinuation across BMI classes/degrees of self-reported weight gain. When compared to normal weight individuals receiving APs or those who did not report AIWG, individuals who were either overweight or obese or reported weight gain in relation to AP use had an increased odds of AP nonadherence (OR 2.37; 95% CI 1.51-3.73; p = 0.0002). Category 2 had 14 studies which compared measures of discontinuation related to weight gain reported as an adverse effect across different APs. Olanzapine was associated with a 3.32 times (95% CI 2.32-4.74; p < 0.00001) increased likelihood of nonadherence or discontinuation when compared to other APs with lower weight gain liabilities. Similarly, APs with moderate weight gain liability (paliperidone, risperidone, and quetiapine) increased the odds of nonadherence or discontinuation by 2.25 (95% CI 1.31-3.87; p = 0.003) when compared to APs considered to have lower weight gain liability (i.e. haloperidol and aripiprazole). The qualitative summary also confirmed these findings. CONCLUSION: This review and meta-analysis suggests that AIWG influences medication nonadherence/discontinuation, whereby APs with higher weight gain liability are associated with nonadherence/discontinuation. Additional studies are needed to confirm these findings.

Cholinergic white matter pathways along the Alzheimer's disease continuum.

Previous studies have shown that the cholinergic nucleus basalis of Meynert and its white matter projections are affected in Alzheimer's disease dementia and mild cognitive impairment. However, it is still unknown whether these alterations can be found in individuals with subjective cognitive decline, and whether they are more pronounced than changes found in conventional brain volumetric measurements. To address these questions, we investigated microstructural alterations of two major cholinergic pathways in individuals along the Alzheimer's disease continuum using an in vivo model of the human cholinergic system based on neuroimaging. We included 402 participants (52 Alzheimer's disease, 66 mild cognitive impairment, 172 subjective cognitive decline and 112 healthy controls) from the Deutsches Zentrum für Neurodegenerative Erkrankungen Longitudinal Cognitive Impairment and Dementia Study. We modelled the cholinergic white matter pathways with an enhanced diffusion neuroimaging pipeline that included probabilistic fibre-tracking methods and prior anatomical knowledge. The integrity of the cholinergic white matter pathways was compared between stages of the Alzheimer's disease continuum, in the whole cohort and in a CSF amyloid-beta stratified subsample. The discriminative power of the integrity of the pathways was compared to the conventional volumetric measures of hippocampus and nucleus basalis of Meynert, using a receiver operating characteristics analysis. A multivariate model was used to investigate the role of these pathways in relation to cognitive performance. We found that the integrity of the cholinergic white matter pathways was significantly reduced in all stages of the Alzheimer's disease continuum, including individuals with subjective cognitive decline. The differences involved posterior cholinergic white matter in the subjective cognitive decline stage and extended to anterior frontal white matter in mild cognitive impairment and Alzheimer's disease dementia stages. Both cholinergic pathways and conventional volumetric measures showed higher predictive power in the more advanced stages of the disease, i.e. mild cognitive impairment and Alzheimer's disease dementia. In contrast, the integrity of cholinergic pathways was more informative in distinguishing subjective cognitive decline from healthy controls, as compared with the volumetric measures. The multivariate model revealed a moderate contribution of the cholinergic white matter pathways but not of volumetric measures towards memory tests in the subjective cognitive decline and mild cognitive impairment stages. In conclusion, we demonstrated that cholinergic white matter pathways are altered already in subjective cognitive decline individuals, preceding the more widespread alterations found in mild cognitive impairment and Alzheimer's disease. The integrity of the cholinergic pathways identified the early stages of Alzheimer's disease better than conventional volumetric measures such as hippocampal volume or volume of cholinergic nucleus basalis of Meynert.

Minor tranquillizers for short-term treatment of newly onset symptoms of anxiety and distress: a systematic review with network meta-analysis of randomized trials.

Unspecific symptoms of anxiety and distress are frequently encountered in patients in both general practice and acute psychiatric services. Minor tranquillizers may be a treatment option when non-pharmacological interventions are insufficient or unavailable. We conducted a systematic review with network meta-analysis of the evidence for short-term (1-4 weeks) pharmacological treatment of newly onset symptoms of anxiety and distress. We searched the PsycInfo, MEDLINE, EMBASE and Cochrane Library databases and extracted data following a predefined hierarchy of outcomes. We assessed risk of bias using the Cochrane Risk of Bias tool and the certainty of the evidence using the Grading of Recommendations Assessment, Development and Evaluation framework (GRADE). We included 34 randomized trials comprising a total of 7044 patients with adjustment disorders or anxiety spectrum disorders. The network meta-analysis showed that regarding the critical outcome symptoms of anxiety within 1-4 weeks benzodiazepines (SMD - 0.58, 95% CI - 0.77 to - 0.40), quetiapine (SMD - 0.51, 95% CI - 0.90 to - 0.13) and pregabalin (SMD - 0.58, 95% CI - 0.87 to - 0.28) all performed better than placebo with no statistically significant difference between the drugs. Data on other important outcomes were inconsistently reported. Adverse effects varied, but overall, it was uncertain whether adverse effects differed between interventions. The evidence regarding the risk of dependence was uncertain, but dependence may be a concern in susceptible individuals even with short-term treatment. Overall, the certainty of the evidence according to GRADE was rated as low to very low across outcomes. Despite the limitations in the evidence, the results of this review can inform treatment guidelines, supporting clinicians in the choice of minor tranquillizer in this prevalent and help-seeking, clinically heterogeneous population.

Running in the FAMILY: understanding and predicting the intergenerational transmission of mental illness.

Over 50% of children with a parent with severe mental illness will develop mental illness by early adulthood. However, intergenerational transmission of risk for mental illness in one's children is insufficiently considered in clinical practice, nor is it sufficiently utilised into diagnostics and care for children of ill parents. This leads to delays in diagnosing young offspring and missed opportunities for protective actions and resilience strengthening. Prior twin, family, and adoption studies suggest that the aetiology of mental illness is governed by a complex interplay of genetic and environmental factors, potentially mediated by changes in epigenetic programming and brain development. However, how these factors ultimately materialise into mental disorders remains unclear. Here, we present the FAMILY consortium, an interdisciplinary, multimodal (e.g., (epi)genetics, neuroimaging, environment, behaviour), multilevel (e.g., individual-level, family-level), and multisite study funded by a European Union Horizon-Staying-Healthy-2021 grant. FAMILY focuses on understanding and prediction of intergenerational transmission of mental illness, using genetically informed causal inference, multimodal normative prediction, and animal modelling. Moreover, FAMILY applies methods from social sciences to map social and ethical consequences of risk prediction to prepare clinical practice for future implementation. FAMILY aims to deliver: (i) new discoveries clarifying the aetiology of mental illness and the process of resilience, thereby providing new targets for prevention and intervention studies; (ii) a risk prediction model within a normative modelling framework to predict who is at risk for developing mental illness; and (iii) insight into social and ethical issues related to risk prediction to inform clinical guidelines.

Dimensional profiling of psychopathology in children and adolescents based on the K-SADS-PL and an analysis of the construct validity of two ADHD symptom dimensions.

OBJECTIVES: The traditional view on psychiatric disorders as categorical and distinct is being challenged by perspectives emphasizing the relevance of dimensional and transdiagnostic assessment. However, most diagnostic instruments are based on a categorical view with a threshold-approach to disease classification. METHODS: We here describe algorithms for dimensionalizing the psychopathological ratings of the widely used diagnostic interview for children and adolescents, the Kiddie-Schedule for Affective Disorders and Schizophrenia - Present and Lifetime Version (K-SADS-PL). We further evaluate the criterion-related construct validity of the dimensionalized attention-deficit/hyperactivity disorder (ADHD) scales using Rasch models in a sample of 590 children (mean age 10.29 (.36), 49% girls). RESULTS: The algorithms generate scores of current symptom load, i.e., the sum of clinician-rated symptoms within each disorder assessed with the interview. We found support for counting symptoms of inattention and hyperactivity/impulsivity, respectively, but not for a single combined ADHD scale. CONCLUSIONS: The algorithms constitute an initial step in creating a framework for clinician-rated dimensional analyses of symptoms derived from the K-SADS-PL, but future studies are needed to further evaluate the construct validity of the remaining scales and the reliability and clinical utility of the method. We believe that our proposed algorithms offer a novel method of dimensional psychopathological assessment, which can be applied in multiple branches of child and adolescent psychiatry.

The relationship between resting-state amplitude fluctuations and memory-related deactivations of the default mode network in young and older adults.

The default mode network (DMN) typically exhibits deactivations during demanding tasks compared to periods of relative rest. In functional magnetic resonance imaging (fMRI) studies of episodic memory encoding, increased activity in DMN regions even predicts later forgetting in young healthy adults. This association is attenuated in older adults and, in some instances, increased DMN activity even predicts remembering rather than forgetting. It is yet unclear whether this phenomenon is due to a compensatory mechanism, such as self-referential or schema-dependent encoding, or whether it reflects overall reduced DMN activity modulation in older age. We approached this question by systematically comparing DMN activity during successful encoding and tonic, task-independent, DMN activity at rest in a sample of 106 young (18-35 years) and 111 older (60-80 years) healthy participants. Using voxel-wise multimodal analyses, we assessed the age-dependent relationship between DMN resting-state amplitude (mean percent amplitude of fluctuation, mPerAF) and DMN fMRI signals related to successful memory encoding, as well as their modulation by age-related hippocampal volume loss, while controlling for regional grey matter volume. Older adults showed lower resting-state DMN amplitudes and lower task-related deactivations. However, a negative relationship between resting-state mPerAF and subsequent memory effect within the precuneus was observed only in young, but not older adults. Hippocampal volumes showed no relationship with the DMN subsequent memory effect or mPerAF. Lastly, older adults with higher mPerAF in the DMN at rest tend to show higher memory performance, pointing towards the importance of a maintained ability to modulate DMN activity in old age.

Single-value scores of memory-related brain activity reflect dissociable neuropsychological and anatomical signatures of neurocognitive aging.

Memory-related functional magnetic resonance imaging (fMRI) activations show age-related differences across multiple brain regions that can be captured in summary statistics like single-value scores. Recently, we described two single-value scores reflecting deviations from prototypical whole-brain fMRI activity of young adults during novelty processing and successful encoding. Here, we investigate the brain-behavior associations of these scores with age-related neurocognitive changes in 153 healthy middle-aged and older adults. All scores were associated with episodic recall performance. The memory network scores, but not the novelty network scores, additionally correlated with medial temporal gray matter and other neuropsychological measures including flexibility. Our results thus suggest that novelty-network-based fMRI scores show high brain-behavior associations with episodic memory and that encoding-network-based fMRI scores additionally capture individual differences in other aging-related functions. More generally, our results suggest that single-value scores of memory-related fMRI provide a comprehensive measure of individual differences in network dysfunction that may contribute to age-related cognitive decline.

Negative symptoms in First-Episode Schizophrenia related to morphometric alterations in orbitofrontal and superior temporal cortex: the OPTiMiSE study.

BACKGROUND: Negative symptoms are one of the most incapacitating features of Schizophrenia but their pathophysiology remains unclear. They have been linked to alterations in grey matter in several brain regions, but findings have been inconsistent. This may reflect the investigation of relatively small patient samples, and the confounding effects of chronic illness and exposure to antipsychotic medication. We sought to address these issues by investigating concurrently grey matter volumes (GMV) and cortical thickness (CTh) in a large sample of antipsychotic-naïve or minimally treated patients with First-Episode Schizophrenia (FES). METHODS: T1-weighted structural MRI brain scans were acquired from 180 antipsychotic-naïve or minimally treated patients recruited as part of the OPTiMiSE study. The sample was stratified into subgroups with (N = 88) or without (N = 92) Prominent Negative Symptoms (PMN), based on PANSS ratings at presentation. Regional GMV and CTh in the two groups were compared using Voxel-Based Morphometry (VBM) and FreeSurfer (FS). Between-group differences were corrected for multiple comparisons via Family-Wise Error (FWE) and Monte Carlo z-field simulation respectively at p < 0.05 (2-tailed). RESULTS: The presence of PMN symptoms was associated with larger left inferior orbitofrontal volume (p = 0.03) and greater CTh in the left lateral orbitofrontal gyrus (p = 0.007), but reduced CTh in the left superior temporal gyrus (p = 0.009). CONCLUSIONS: The findings highlight the role of orbitofrontal and temporal cortices in the pathogenesis of negative symptoms of Schizophrenia. As they were evident in generally untreated FEP patients, the results are unlikely to be related to effects of previous treatment or illness chronicity.

Plasma concentrations of anti-inflammatory cytokine TGF-ß are associated with hippocampal structure related to explicit memory performance in older adults.

Human cognitive abilities, and particularly hippocampus-dependent memory performance typically decline with increasing age. Immunosenescence, the age-related disintegration of the immune system, is increasingly coming into the focus of research as a considerable factor contributing to cognitive decline. In the present study, we investigated potential associations between plasma levels of pro- and anti-inflammatory cytokines and learning and memory performance as well as hippocampal anatomy in young and older adults. Plasma concentrations of the inflammation marker CRP as well as the pro-inflammatory cytokines IL-6 and TNF-α and the anti-inflammatory cytokine TGF-ß1 were measured in 142 healthy adults (57 young, 24.47 ± 4.48 years; 85 older, 63.66 ± 7.32 years) who performed tests of explicit memory (Verbal Learning and Memory Test, VLMT; Wechsler Memory Scale, Logical Memory, WMS) with an additional delayed recall test after 24 h. Hippocampal volumetry and hippocampal subfield segmentation were performed using FreeSurfer, based on T1-weighted and high-resolution T2-weighted MR images. When investigating the relationship between memory performance, hippocampal structure, and plasma cytokine levels, we found that TGF-ß1 concentrations were positively correlated with the volumes of the hippocampal CA4-dentate gyrus region in older adults. These volumes were in turn positively associated with better performance in the WMS, particularly in the delayed memory test. Our results support the notion that endogenous anti-inflammatory mechanisms may act as protective factors in neurocognitive aging.

Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression.

BACKGROUND: Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages. METHODS: In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aß42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). RESULTS: Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets. CONCLUSION: Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.