David Tomecek

Purpose: We examine brain connectivity alterations in early‐stage schizophrenia, with a focus on patterns of hyper‐ and hypoconnectivity. Although altered functional connectivity (FC) is well documented in schizophrenia, substantial inconsistencies remain regarding the location and direction of specific dysconnections. We hypothesize that early FC changes in schizophrenia include both decreases and increases and are correlated with symptoms and medication levels, with insufficient preprocessing potentially masking out some of these effects. Method: Using a large sample of 100 first‐episode schizophrenia patients (42 females) and 90 healthy controls (50 females), we compared FC across 90 brain regions defined by the Automated Anatomical Labeling atlas. We assessed medication effects and examined associations between FC changes and duration of untreated psychosis, duration of antipsychotic (AP) treatment, and symptom severity. In addition, we performed a systematic comparison of four functional magnetic resonance imaging data denoising strategies. Finding: Relative to controls, patients showed increased FC in 150 region pairs and decreased FC in 15 pairs, when using a “stringent” data denoising scheme. Despite this numerical imbalance, the overall distribution of FC changes remained balanced: Median FC was not systematically shifted, indicating no global bias toward hyper‐ or hypoconnectivity. Importantly, several FC alterations were significantly associated with symptom severity and AP medication. Conclusion: These findings indicate localized dysconnections embedded within a globally balanced connectivity profile in early schizophrenia. This balance was disrupted by less stringent denoising, leading to results dominated by hypoconnectivity, highlighting preprocessing as a major source of variability across studies.

Background: Although most individuals with schizophrenia do not show aggressive behavior, rates are higher than in the general population. Identifying the neurobiological correlates of aggression may inform prevention. Methods: We included 2260 individuals with schizophrenia spectrum disorders and 3058 healthy controls (HC) from 26 sites in the ENIGMA-Schizophrenia Working Group. The former were classified as showing at least moderate aggression (SZ+A; N = 231) or not (SZ−A; N = 2029) based on symptom rating scales. Structural covariance networks were constructed from cortical and subcortical volumes. We examined global and nodal network topology, as well as density within six large-scale brain networks and between each network and the rest of the brain. Metrics were summarized as the area under the curve (AUC) across a prespecified range of densities ( K = 0.10–0.25, Δ K = 0.01). Group differences were assessed using pairwise permutation tests with Benjamini–Hochberg correction for multiple comparisons. Results: Global and nodal network topology did not differ significantly between SZ+A and HC. Network–rest density of the salience network (SN) was higher in SZ+A than in HC, although this difference did not survive multiple-comparison correction (ΔAUC = 0.018, p _BH = .083). In exploratory subgroup analyses, higher SN network–rest density was observed only in SZ+A with high delusion severity (ΔAUC = 0.021, p = .007). By contrast, SZ−A demonstrated extensive alterations in global network topology compared with SZ+A and HC, alongside selective alterations in nodal topology and large-scale network density compared with HC. Results were broadly consistent across sensitivity analyses using alternative definitions of aggression and adjusting for current antipsychotic dose. Conclusions: SZ+A was characterized by fewer—not more—abnormalities in structural covariance network organization than SZ−A, which may reflect etiological heterogeneity. In SZ+A with high delusion severity, higher network–rest density of the SN may point to aberrant salience processing in aggressive behavior.

Importance: Average gray matter volume (GMV) loss in early psychosis may conceal clinically relevant differences in brain change. Objective: To identify longitudinal GMV-change patterns, their cross-site correspondence, and clinical associations. Design, Setting, and Participants: Prospective cohort with T1-weighted MRI acquired during 2011–2026 at 2 Czech sites; median follow-up, approximately 13 months. Participants were 222 patients with first-episode schizophrenia-spectrum psychosis and 178 healthy controls frequency-matched by site and baseline age. Exposure: First-episode psychosis vs healthy control status. Main Outcomes and Measures: Diagnosis-blind clustering by site of image-quality-corrected, subsequently annualized GMV change across 333 cortical parcels; longitudinal voxel-based morphometry (VBM); Global Assessment of Functioning and consensus 5-factor Positive and Negative Syndrome Scale negative-symptom scores. Results: Among 400 participants (mean [SD] age, 28.2 [7.5] years), 208 (52.0%) were male. Each site independently yielded 3 clusters: C1 (near-stable GMV), C2 (pronounced decrease), and C3 (increase). Cross-site centroid correlations across 333 parcels were strong for C2 and C3 (Spearman ρ = 0.865 and 0.861); C1 correlation was weak (ρ = 0.138), as expected for near-zero profiles with little regional variation. C2/C3 correspondence supported pooling matched site-specific clusters for post hoc analyses. Pooled clusters contained 77 patients and 125 controls in C1, 66 patients and 16 controls in C2, and 79 patients and 37 controls in C3. Suffixes −P and −HC identify patient and control subgroups. To situate patient changes relative to controls, post hoc VBM revealed an unanticipated gradient of increasingly pronounced GMV reduction across C1-HC → C1-P → C2-P, prominently involving association cortex, and bilateral ventral temporal GMV increase in C3-P (familywise error–corrected P < .05). The opposite contrast along this ordering showed periventricular changes consistent with progressively greater ventricular enlargement. C2-P had the lowest mean follow-up functioning and highest mean negative-symptom severity (adjusted omnibus P = .005 and .02); clinical changes from baseline did not differ significantly among patient groups. Conclusions and Relevance: Early GMV change in psychosis was not uniform but comprised two contrasting directions: a clinically relevant, control-anchored pattern of increasingly pronounced GMV loss involving default mode regions and a distinct, directionally opposing ventral temporal increase. Key Points: Question: What patterns of cortical gray matter volume change emerge over approximately 1 year after first-episode schizophrenia-spectrum psychosis? Findings: In this 2-site cohort of 222 patients and 178 controls, nearly two-thirds of patients lay along a gradient from near-stability in controls to widespread volume decrease, whereas one-third showed an opposing ventral temporal increase. Follow-up functioning and negative symptoms differed significantly among patient groups, with the least favorable scores in those showing the greatest decrease. Meaning: Directions of within-person brain change may capture clinically relevant heterogeneity obscured by average volume loss.

Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing research and informing clinical understanding. However, a major obstacle to clinical translation is the limited ability of neuroimaging-based classification models to generalize across scanning sites. In this study, we first establish best performing within-site models, and then systematically investigate cross-site generalization in first-episode schizophrenia (FES) classification and evaluate strategies for mitigating site-related distribution shifts. Using data from two acquisition sites ( n = 544), we examine ComBat, CovBat, optimal transport, and deep adaptation. Across functional, structural, and diffusion-based features, both traditional machine learning (TML) and neural network (NN) models achieved comparable performance in within-site classification, with resting state fMRI functional connectivity providing the most robust unimodal features. When models are transferred across sites, performance degraded substantially across all approaches, by 5 to 12 percentage points of balanced accuracy. Distribution-alignment methods partially mitigated this degradation. Harmonization recovered 6 to 8 points for functional connectivity in one transfer direction, mainly by correcting a displaced decision threshold. Deep adaptation recovered similar gains but less consistently. Increasing model complexity alone did not result in systematic performance gains. Across the classifiers tested on this pair of sites, multimodal feature fusion did not consistently outperform functional connectivity alone. Overall, our findings indicate that controlling for site effects was more critical than model complexity for achieving generalizable classification in FES. These results come from two sites of a single coordinated study, and whether they hold for more heterogeneous sites remains to be tested.

An increasing number of studies are currently focusing on ‘personality neuroscience’, a term denoting the research aimed at neuroimaging correlates of inter-individual temperament and character variability. Among other methods, a graph theoretical analysis of the functional connectivity in resting-state functional magnetic resonance imaging data was applied in a study by Gao et al. (2013), reporting novel functional connectivity correlates of personality traits. The current paper presents a conceptual replication of the results of this study and discusses the related challenges, including an extension of the original statistical methods in order to illustrate the effect of the multiple comparison problem. Five personality dimensions were obtained using the revised ‘Big Five’ Personality Inventory, including scores of Extraversion and Neuroticism covered in the original paper. Using a larger sample (84 subjects) with adequate statistical power (ranging from 0.75 to 0.95 across analyses), we failed to replicate any of the nine specific neuroimaging correlates of personality presented by Gao et al. While acknowledging differences in the experimental procedures, we discuss that the lack of replication might be caused by the relatively liberal control of false positives in the original study. Indeed, the original testing scheme leads to an expected count of about 10 false positive observations among all tests; applying this scheme to our data we observed a similar number of positive tests, albeit for different relations. No significant correlations were found in our data when standard family-wise error control was applied. These results illustrate the importance of combining exploration with independent validation, use of large datasets, as well as appropriate control of multiple comparison problem in order to prevent false alarms in research into neural substrates of personality differences. Importantly, our findings do not disprove the existence of a link between personality and the brain’s intrinsic functional architecture; but rather suggest that such a link might be even more subtle and elusive than previously reported.

The relationship between personality and brain activity has been an increasingly popular topic of neuroscientific research. Naturalistic viewing has been shown to enhance individual differences and might, therefore, be particularly useful for exploring this relationship. Here, we thus examine neural signatures of personality using naturalistic fMRI of 82 healthy subjects. We implemented a simple dimensionality reduction method to characterize brain activity by its “typicality,” assessed a range of personality traits using widely‐used personality inventories, and tested the relationship between the two. We found that there is, indeed, a relationship between personality and the typicality of brain activity, which appears to be most consistently manifested by lower typicality in subjects with higher Neuroticism/Harm Avoidance. Our results highlight the usefulness of naturalistic viewing data for exploring the relationship between individual differences in personality and brain activity.

It is hypothesised that structural brain abnormalities in individuals with schizophrenia are associated with aggressive behaviour, but this has not been tested directly. We pooled magnetic resonance imaging and clinical data from 2095 patients and 2861 healthy control subjects across 20 sites of the ENIGMA-Schizophrenia Working Group. Using normative modelling, we quantified individual-level deviations from controls (z-scores) for global and regional grey matter volume and white matter microstructural integrity. Ordinal regression models were used to estimate the associations between these deviations and concurrent aggression (odds ratios [ORs] with 99% confidence intervals [CIs]). In mediation analyses, we examined the roles of positive symptoms – delusions, hallucinations and disorganised thinking – impulsivity and illness insight. Aggression was significantly associated with reductions in total cortical volume (OR [99% CI] = 0.88 [0.78, 0.98], p = 0.003), global white matter integrity (OR [99% CI] = 0.72 [0.59, 0.88], p = 3.50 × 10⁻⁵), dorsolateral prefrontal cortex volume (OR [99% CI] = 0.85 [0.74, 0.97], p = 0.002), inferior parietal lobule volume (OR [99% CI] = 0.76 [0.66, 0.87], p = 2.20 × 10⁻⁷) and internal capsule integrity (OR [99% CI] = 0.76 [0.63, 0.92], p = 2.90 × 10⁻⁴). These associations were robust to adjustment for relevant covariates and partly mediated by positive symptoms and impulsivity. Our findings suggest that the co-occurrence of positive symptoms, impulsivity and aggression in schizophrenia has a neurobiological basis, providing potential targets for therapeutic interventions.

Understanding how spontaneous, rather than experimentally induced, thoughts relate to brain activity remains a major challenge. We combined simultaneous fMRI and EEG recordings with Descriptive Experience Sampling (DES) to link momentary, naturally occurring experiences to their neural signatures during rest. Using machine-learning classification of 240 time-locked samples from eight participants—each completing nine 25-minute resting-state sessions—we reliably distinguished internally from externally oriented experiences (fMRI accuracy = 65.4%, EEG = 62.5%). Externally oriented states showed greater fMRI activity in salience, auditory, and visuospatial networks and lower occipital alpha power in EEG, whereas internally oriented states exhibited the opposite pattern, extending prior DMN-focused accounts of internally directed states. Across modalities, integrated resting-state alpha power correlated negatively with BOLD fluctuations in parietal and occipital regions. These multimodal findings reveal distinct neural signatures of spontaneous experience and demonstrate that coordinated large-scale network dynamics and alpha-band oscillations track the natural alternation between inward and outward focus in the human mind.

Background: Disturbances in the sense of agency are a key marker of schizophrenia and are closely linked to core symptoms such as hallucinations and delusions of influence. Neuroimaging studies have implicated cortical midline structures and the defaul t mode network (DMN) associated with self-related processing, but replication across sites and larger samples is needed. Methods: We examined neural correlates of self versus other-agency judgments in two independent cohorts of first-episode schizophrenia patients ( n = 177) and controls ( n = 123) recruited at separate MRI centers. During fMRI, participants performed an agency-related task. Data was analyzed using independent component analysis and statistical parametric mapping, focusing on networks associated with self-referential processing. Results: Across both sites, self-agency consistently engaged the anterior and posterior cingulate cortex and the precuneus, key regions of the DMN. Schizophrenia patients exhibited significantly reduced DMN activation duri ng self-referential processing compared to controls. While site-related differences were noted in the magnitude of activation, the core pattern of DMN disruption was robust across analytic approaches and datasets. Conclusions: This study replicates and extends prior findings of altered self-agency processing in schizophrenia, demonstrating consistent DMN hypo-activation in first-episode patients across two independent cohorts. These results validate the agency paradigm as a reliable probe of self-related neural mechanisms and support models of schizophrenia as a disorder of altered self-perception.

While schizophrenia diagnosis relies on clinical interviews, there is growing interest in neuroimaging-based computational tools to aid classification. In particular, resting-state fMRI-derived functional connectivity has been explored as a potential biomarker, with applications not only in supporting clinical assessment but also in research contexts such as patient stratification and probing disease mechanisms. Here, we compare two common approaches to computing functional connectivity - region of interest (ROI)-level and brain network-level - and evaluate their predictive power for classifying first-episode schizophrenia patients, in contrast to most prior work focusing on chronic patients. We show that ROI-level features consistently outperform network-level features. Despite the simplicity of our classification models, we achieved accuracies up to 83.15% using the AAL90 atlas. We also found that non-lagged functional connectivity generally outperforms lagged variants, suggesting that added temporal complexity may introduce noise rather than improve predictive power. Overall, our findings highlight region-based connectivity from a medium-resolution atlas as a promising representation for early-stage schizophrenia classification, while emphasising the need for validation on independent datasets to confirm generalisability.