Judged by your neighbors: Brain structural normativity profiles for large and heterogeneous samples.

The detection of norm deviations is fundamental to clinical decision making and impacts our ability to diagnose and treat diseases effectively. Current normative modeling approaches rely on generic comparisons and quantify deviations in relation to the population average. However, generic models interpolate subtle nuances and risk the loss of critical information, thereby compromising effective personalization of health care strategies.

Electroconvulsive therapy and cognitive performance from the Global ECT MRI Research Collaboration.

The Global ECT MRI Research Collaboration (GEMRIC) has collected clinical and neuroimaging data of patients treated with electroconvulsive therapy (ECT) from around the world. Results to date have focused on neuroimaging correlates of antidepressant response. GEMRIC sites have also collected longitudinal cognitive data.

Brain functional effects of cognitive behavioral therapy for depression: A systematic review of task-based fMRI studies.

Background: Depressive disorders are associated with alterations in brain function, affecting processes such as affective and reward processing and emotion regulation. However, the influence of Cognitive Behavioral Therapy (CBT) on the neuronal patterns remains inadequately understood. Therefore, this review systematically summarizes longitudinal fMRI brain activity changes in depressive patients treated with CBT and their association with symptom remission.

Comparing the performance of beamformer algorithms in estimating orientations of neural sources.

The efficacy of transcranial electric stimulation (tES) to effectively modulate neuronal activity depends critically on the spatial orientation of the targeted neuronal population. Therefore, precise estimation of target orientation is of utmost importance. Different beamforming algorithms provide orientation estimates; however, a systematic analysis of their performance is still lacking.

Canonical Correlation Analysis and Partial Least Squares for Identifying Brain-Behavior Associations: A Tutorial and a Comparative Study.

Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior).

Local molecular and global connectomic contributions to cross-disorder cortical abnormalities.

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol.

AgRP neurons control feeding behaviour at cortical synapses via peripherally derived lysophospholipids.

Phospholipid levels are influenced by peripheral metabolism. Within the central nervous system, synaptic phospholipids regulate glutamatergic transmission and cortical excitability. Whether changes in peripheral metabolism affect brain lipid levels and cortical excitability remains unknown.

Mind the gap: Performance metric evaluation in brain-age prediction.

Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing three latent symptom dimensions within the 17-item Hamilton Depression Rating Scale (HDRS) and use data-driven methods to relate multivariate patterns of patient clinical, demographic, and brain structural changes over electroconvulsive therapy (ECT) to dimensional changes in depressive symptoms.

Saccadic suppression in schizophrenia.

About 40% of schizophrenia patients report discrete visual disturbances which could occur if saccadic suppression, the decrease of visual sensitivity around saccade onset, is impaired. Two mechanisms contribute to saccadic suppression: efference copy processing and backwards masking. Both are reportedly altered in schizophrenia.

Genome-wide association study accounting for anticholinergic burden to examine cognitive dysfunction in psychotic disorders.

Identifying genetic contributors to cognitive impairments in psychosis-spectrum disorders can advance understanding of disease pathophysiology. Although CNS medications are known to affect cognitive performance, they are often not accounted for in genetic association studies. In this study, we performed a genome-wide association study (GWAS) of global cognitive performance, measured as composite z-scores from the Brief Assessment of Cognition in Schizophrenia (BACS), in persons with psychotic disorders and controls (N = 817; 682 cases and 135 controls) from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study.

Therapygenetic effects of 5-HTTLPR on cognitive-behavioral therapy in anxiety disorders: A meta-analysis.

There is a recurring debate on the role of the serotonin transporter gene linked polymorphic region (5-HTTLPR) in the moderation of response to cognitive behavioral therapy (CBT) in anxiety disorders. Results, however, are still inconclusive. We here aim to perform a meta-analysis on the role of 5-HTTLPR in the moderation of CBT outcome in anxiety disorders.

Serotonin Transporter Gene Promoter Hypomethylation as a Predictor of Antidepressant Treatment Response in Major Depression: A Replication Study.

Background: The serotonin transporter gene (SLC6A4; 5-HTT; SERT) is considered a prime candidate in pharmacogenetic research in major depressive disorder (MDD). Besides genetic variation, recent advances have spotlighted the involvement of epigenetic mechanisms such as DNA methylation in predicting antidepressant treatment response in "pharmaco-epigenetic" approaches. In MDD, lower SLC6A4 promoter methylation has been suggested to predict impaired response to serotonergic antidepressants.

Predicting intelligence from brain gray matter volume.

A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume.

Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks.

Electroencephalogram (EEG) microstates that represent quasi-stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non-Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states.

Recommendations and future directions for supervised machine learning in psychiatry.

Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learning methods have risen, with different data modalities, mathematically distinct models, and samples of varying size being used to train and test models with the promise of clinical translation. Consequently, and in part due to the preliminary nature of such works, many studies have reported largely varying degrees of accuracy, raising concerns over systematic overestimation and methodological inconsistencies.