The research team conducted a secondary analysis of the OpenNeuro ds005713 dataset, combining two dataset versions and removing duplicate records identified by participant identifier. The baseline comparison included 86 people with trigeminal neuralgia and 40 healthy controls. Participants were excluded when essential demographic data or usable baseline MRI were unavailable, when they had conditions that could compromise segmentation, or when the scans showed structural, vascular or space-occupying abnormalities that might affect cerebellar measurements. People with trigeminal autonomic cephalalgias, previous surgery for trigeminal neuralgia or Fazekas grade 3 white-matter lesions were also excluded. The longitudinal component was limited to the 49 patients for whom usable paired preoperative and postoperative MRI could be found.

All scans were acquired with a 3.0-T Philips Ingenia scanner and a 16-channel head coil. The investigators used high-resolution sagittal three-dimensional T1-weighted images, acquired with a fast field echo sequence at approximately 1.0-mm isotropic resolution and reconstructed at approximately 0.5-mm resolution. DeepCERES version 1.1, running on the volBrain platform, processed the images through denoising, registration to the Montreal Neurological Institute 152 standard space, bias-field correction, intensity normalization and intracranial-cavity segmentation. The pipeline generated synthetic T2-weighted images and subject-specific multi-atlas priors before an ensemble model delineated cerebellar gray matter, white matter and bilateral lobules. The preoperative and postoperative scans were processed independently rather than through a joint longitudinal segmentation pipeline.

For each participant, the researchers extracted absolute volume and MRI-derived mean cortical thickness from 12 bilateral cerebellar regions: Lobules I–II, III, IV, V, VI, Crus I, Crus II, VIIb, VIIIa, VIIIb, IX and X. Bilateral volume was the sum of the left and right absolute volumes in cubic centimetres, while bilateral thickness was the arithmetic mean of the two sides in millimetres. The measurements also included intracranial volume and the absolute volumes of the whole cerebellum, cerebellar gray matter and cerebellar white matter. DeepCERES thickness is a macroscopic estimate based on MRI contrast and segmentation boundaries; it is not a histological measurement and cannot establish changes in cells or tissue layers.

The main cross-sectional models included diagnostic group, linear and quadratic age terms and sex, while volume models also included intracranial volume. The investigators treated regional volume and regional thickness as separate families of 12 tests and applied the Benjamini–Hochberg procedure to control the false discovery rate within each family. The adjusted thickness difference was 0.104 millimetres for Lobule IV, with a 95% confidence interval of 0.040 to 0.168 and a corrected q value of 0.020. Lobule VI was 0.071 millimetres thicker, with a 95% confidence interval of 0.021 to 0.121 and q equal to 0.036. Neither the remaining lobules nor the regional, whole-cerebellum, cerebellar gray-matter or cerebellar white-matter volume measures showed a significant group difference after correction. The confidence intervals were not themselves adjusted for multiple comparisons.

Sensitivity analysis indicated that the Lobule IV result was the more dependable of the two cross-sectional findings. It remained significant across three approaches to age, with adjusted differences of 0.104 to 0.105 millimetres, and it also remained significant when intracranial volume or automated image-quality scores were added to the model. Lobule VI was more sensitive to analytical choices: its adjusted difference was 0.057 millimetres under a linear age model and did not meet the corrected significance threshold, although a restricted cubic spline model produced a similar estimate of 0.073 millimetres that did pass the threshold. Neither result reversed direction in leave-one-out analyses, and bootstrap confidence intervals excluded zero. Within the trigeminal neuralgia group, however, Lobule VI thickness was positively correlated with automated quality scores, indicating that image quality and the measured structure were not entirely independent.

Disease duration, pain side and imaging-based Sindou grade were not significantly associated with regional volume or thickness after correction for multiple testing. Exploratory analyses also found no pain-related cerebellar asymmetry that passed the corrected threshold. The longitudinal group contained 33 patients who underwent microvascular decompression and 16 who underwent a percutaneous intervention. Clinical follow-up information was incomplete: among 26 patients with available immediate postoperative pain records, 21 had a recorded 100% reduction in pain. The first follow-up was reported for those 26 patients, and second telephone follow-up pain scores were available for 24. The study did not test whether changes in cerebellar morphology were associated with pain improvement, limiting interpretation of the imaging changes as clinically meaningful.

Within the 49 paired scans, postoperative volume increased significantly in Lobules IV, V, VI, VIIb, VIIIa and VIIIb. Mean absolute increases ranged from 0.044 to 0.103 cubic centimetres, while the mean of individual percentage changes ranged from 0.86% to 1.69%. Thickness increased in Lobules IV, V and VI: Lobule IV rose by 0.039 millimetres, Lobule V by 0.066 millimetres and Lobule VI by 0.037 millimetres, with corrected q values of 0.003, 0.002 and 0.003. The corresponding mean individual percentage changes were 1.82%, 3.00% and 1.38%. Supportive analyses also showed increases of 0.594 cubic centimetres in whole-cerebellum volume and 0.538 cubic centimetres in cerebellar gray-matter volume, although white-matter volume did not change significantly after correction.

These small postoperative signals were generally supported by alternative statistical tests, but their stability varied among regions. Nonparametric paired tests and bootstrap resampling supported all nine primary volume and thickness increases, and none reversed direction when individual patients were omitted. Models that simultaneously accounted for changes in intracranial volume and automated quality scores retained significant estimates for volume in Lobules IV, V and VIIIb and thickness in Lobules IV–VI. The volume increases in Lobules VI, VIIb and VIIIa did not pass the corrected threshold in those models, and the leave-one-out results for VIIb and VIIIa were less stable. The direction of the findings also matters: Lobules IV and VI were already thicker between groups, so their postoperative increases cannot be interpreted as normalization toward healthy-control levels.

Several limitations constrain what can be concluded. The cross-sectional design cannot establish whether cerebellar differences preceded or resulted from trigeminal neuralgia, and the longitudinal cohort lacked repeated scans in controls or matched test–retest measurements from the same software version and scanning conditions. Earlier repeat-scan work cited by the authors reported coefficients of variation ranging from 0.70% to 2.95% for unilateral lobular volume and from 0.57% to 1.60% for unilateral thickness in regions corresponding to some findings here, but coefficients of variation and mean changes measure different aspects and cannot show whether the postoperative shifts exceeded measurement error. Most patients also took medication, usually including carbamazepine, with insufficient information on dose, treatment duration and postoperative changes to separate medication effects from disease effects. Visual quality review was not blinded to diagnosis or scan time, and the two procedures were combined despite their potential differences. The results therefore identify MRI-derived morphometric patterns for further study, not a cellular mechanism, causal treatment effect or demonstrated link between postoperative brain change and pain relief.

Subject of Research: MRI-based cerebellar lobular morphometry in patients with trigeminal neuralgia

Article Title: Cerebellar lobular morphometry in trigeminal neuralgia: cross-sectional and exploratory postoperative analyses

Article References: Li, H., Chen, J., Wang, X., She, J., Gao, W., & Wu, T. (2026). Cerebellar lobular morphometry in trigeminal neuralgia: cross-sectional and exploratory postoperative analyses. Frontiers in Neurology, 17, Article 1964745. https://doi.org/10.3389/fneur.2026.1964745

Image Credits: AI Generated

DOI: 10.3389/fneur.2026.1964745

Keywords: trigeminal neuralgia, cerebellar morphology, cortical thickness, MRI, DeepCERES, postoperative imaging, morphometry, cerebellar lobules, longitudinal analysis, neuroimaging, Cerebellar, lobular

The study used open-access Human Connectome Project data from participants aged 22 to 36 and older. After researchers excluded 21 people with missing analysis variables, the final sample included 743 adults, representing 97.3% retention of the eligible group. Magnetic resonance images had been acquired on a 3 Tesla Siemens Skyra scanner, and the cortex was reconstructed with FreeSurfer 5.3.0-HCP. Using the Desikan–Killiany atlas, the researchers extracted thickness and surface-area measurements from 22 bilateral prefrontal regions, including lateral frontal, orbitofrontal and anterior cingulate areas. The analysis was cross-sectional, so it could identify associations but not determine whether brain structure influences behavior, behavior influences brain structure, or both reflect shared genetic or environmental influences.

Rather than examining each brain region separately, the researchers used principal component analysis to identify recurring patterns in which prefrontal measurements covaried across people. This structural covariance approach describes how morphology varies together in a population; a person’s component score represents how strongly that individual expresses a group-wide pattern, not the strength of that person’s direct anatomical connections. Before the analysis, age, sex and intracranial volume were removed from the regional measurements. An oblique rotation was then used because neighboring prefrontal regions may share anatomical features and can legitimately covary. The investigators also examined whether the component solutions remained stable when the sample was repeatedly resampled.

The retained cortical-thickness solution contained three components and accounted for 53.7% of the variance. The largest, RC1, grouped the superior and middle frontal regions with the pars opercularis and was labeled global or lateral prefrontal thickness. RC2 combined the lateral and medial orbitofrontal cortex with the pars orbitalis, while RC3 was centered on the anterior and posterior portions of the anterior cingulate cortex. This three-part anatomy broadly resembled established lateral, orbital and medial divisions of the prefrontal cortex, supporting the study’s first hypothesis only partly. Parallel analysis actually favored a two-component solution, so the three-component arrangement was retained because it had been specified in advance. Bootstrapping also revealed uneven stability: congruence fell below 0.85 in 19.4% of resamples for RC1 and 28.0% for RC2, compared with 0.7% for RC3.

The thickness analysis initially produced two associations with cognition. People expressing more of the lateral-prefrontal thickness pattern RC1 obtained slightly lower scores on the NIH Toolbox Flanker test of inhibitory control and attention: the standardized coefficient was β = −0.103, with p = 0.027 and partial eta-squared of 0.007. The direction was opposite to the prediction that greater lateral-prefrontal expression would accompany better performance, and the result did not survive false discovery rate correction across the four directional hypothesis tests, where q = 0.054. It also had a straightforward alternative: RC1 correlated strongly with whole-cortex mean thickness, at r = 0.85. Mean thickness alone predicted lower Flanker scores, and after global thickness was regressed out of the regional data before repeating the principal component analysis, the RC1 association was no longer significant. The evidence therefore favored a global rather than specifically prefrontal relationship, although these sensitivity analyses were added after review and were not prespecified.

A second thickness result concerned the Gambling RT Difference, calculated as the median time to make a “larger” guess in reward blocks minus the corresponding time in punishment blocks. Because block context was not cued before each guess, the measure was interpreted as the carryover of recent outcomes into response speed, not as a direct measure of whether participants preferred larger rewards to larger punishments. Greater expression of the orbitofrontal thickness component RC2 was associated with a smaller difference, with β = −0.103, p = 0.025 and partial eta-squared of 0.007. Like the Flanker result, it failed false discovery rate correction at q = 0.054 across the prespecified family. It also disappeared under robust regression, after influential cases were removed, and when global mean thickness was regressed out before the component analysis. The corresponding relationship for “smaller” guesses pointed in the same direction but was not significant, so the data did not establish a reliable orbitofrontal effect.

The most notable behavioral result came from a separate analysis of surface area, which produced a three-component solution accounting for 39.9% of the variance. Its topology only partly matched the thickness solution: the largest Tucker’s congruence between corresponding components was 0.81, indicating that the two measures captured related but distinguishable patterns. The orbitofrontal surface-area component AC2 was associated with faster 2-Back reaction time, the speed of correctly processing working-memory information presented two trials earlier. The standardized coefficient was β = −0.125, p = 0.003 and partial eta-squared of 0.012. It survived false discovery rate correction across 12 surface-area component tests, where q = 0.032, but not across all 24 thickness and surface-area component tests, where q = 0.064. The association was not predicted in the original hypotheses and was therefore exploratory.

Most robustness checks supported the AC2 association. It remained significant with heteroskedasticity-consistent standard errors, robust regression, removal of influential cases, and adjustment for intracranial volume. It also was not a simple speed–accuracy tradeoff: greater AC2 expression was associated with higher 2-Back accuracy, and including accuracy as a covariate reduced but did not eliminate the reaction-time relationship. Yet the regional specificity of the finding remained uncertain. AC2 correlated with total cortical surface area at r = 0.38, and total surface area alone was associated with faster 2-Back reaction time before adjustment for intracranial volume. When total surface area was regressed out of the regional measurements before repeating the principal component analysis, the reconstructed AC2 closely resembled the original component, with Tucker’s congruence of 0.91, but its reaction-time coefficient was cut roughly in half to β = −0.065 and became non-significant at p = 0.078. The result could therefore not be cleanly separated from global brain size.

Several limitations constrain the interpretation. The HCP sample includes monozygotic and dizygotic twins and non-twin siblings, but the open-access release did not contain family identifiers, so related participants could not be modeled with mixed-effects analyses. Treating them as independent likely makes the reported p and q values anti-conservative. The sample also excluded education data available only in the restricted release, limiting adjustment for a factor known to influence cognitive performance. Some behavioral measures had limited reliability or could not test their intended hypotheses: for example, the block-level gambling choice score lacked the trial-level information needed to assess win-stay and lose-switch behavior. In addition, FreeSurfer thickness is an indirect morphometric measure influenced by myelination, neuronal structure, partial-volume effects and surface reconstruction, while the atlas groups some cytoarchitecturally distinct areas into coarse parcels. All behavioral models explained no more than 2.7% of variance, and the magnitude of the reported effects was small by conventional standards. Replication with independent samples, explicit family-structure modeling, education adjustment, finer anatomical parcellation and complementary diffusion or myelin imaging would be needed before the findings could be treated as evidence of distinct structure–function relationships in the prefrontal cortex.

Subject of Research: Exploratory analysis of prefrontal cortical morphometric covariance and cognitive performance in healthy young adults

Article Title: Prefrontal cortical morphometric covariance and cognitive task performance in healthy young adults: an exploratory analysis of Human Connectome Project data

Article References: Chivers, R. (2026). Prefrontal cortical morphometric covariance and cognitive task performance in healthy young adults: an exploratory analysis of Human Connectome Project data. Frontiers in Human Neuroscience, 20, Article 1976371. https://doi.org/10.3389/fnhum.2026.1976371

Image Credits: AI Generated

DOI: 10.3389/fnhum.2026.1976371

Keywords: Human Connectome Project, prefrontal cortex, structural MRI, cortical thickness, brain surface area, working memory, inhibitory control, orbitofrontal cortex, morphometric covariance, cognitive neuroscience, Prefrontal, cortical

Researchers built single-subject cortical morphological networks using six measures: cortical thickness, surface area, cortical volume, mean curvature, sulcal depth and local gyrification index. General linear models were used to compare local cortical morphology and network topology between the generalized anxiety disorder and healthy-control groups. The analysis covered 144 participants in total, all of whom were evaluated in the same study, while the network construction allowed each person’s cortical measurements to be represented as a distinct network.

At the regional level, the generalized anxiety disorder group had increased cortical thickness in the left middle temporal gyrus and a decreased local gyrification index in the left superior frontal gyrus. The clearest network-level findings were reported for cortical volume-based networks. Within those networks, local efficiency and normalized clustering coefficient increased, characteristic path length decreased and small-worldness increased. Together, the findings show both focal differences in cortical structure and broader changes in the modeled organization of cortical networks. However, they do not demonstrate that the observed structural differences cause generalized anxiety disorder or its associated symptoms.

Partial correlation analyses within the patient group connected local morphological abnormalities and network properties with illness duration, age of onset, anxiety severity and personality traits. These analyses controlled for age, sex, years of education and intracranial volume. The reported relationships therefore link particular brain features with clinical characteristics, but the group comparison and correlation analyses do not establish temporal sequence, direction of effect or a causal mechanism. The study’s scope is also limited to the measured population, morphological characteristics and network metrics: it does not establish that the differences are unique to generalized anxiety disorder, that cortical volume networks can be used diagnostically, or that changing the network organization would alter symptoms. Published in Translational Psychiatry in 2026, the accepted research offers a detailed account of cortical structural-network abnormalities and their associations with multiple clinical features of the disorder.

Subject of Research: Cortical morphological-network differences associated with symptoms of generalized anxiety disorder

Article Title: Aberrant single-subject cortical morphological networks and their associations with multiple symptoms in generalized anxiety disorder

Article References: Liu, L., Hu, X., Deng, R., Cao, L., Chai, S., Wang, Y., Li, H., Gao, Y., Gong, Q., Qiu, C., & Huang, X. (2026). Aberrant single-subject cortical morphological networks and their associations with multiple symptoms in generalized anxiety disorder. Translational Psychiatry. https://doi.org/10.1038/s41398-026-04434-3

Image Credits: AI Generated

DOI: 10.1038/s41398-026-04434-3

Keywords: generalized anxiety disorder, cortical morphology, morphological networks, cortical volume, neuroimaging, network topology, cortical thickness, gyrification, anxiety severity, personality traits, Aberrant, single-subject

The research was motivated by a persistent frustration in psychiatry: the effectiveness of psychopharmaceutical treatments has largely stagnated in recent decades, and clinicians still have no reliable method for predicting which class of antidepressant, for example, will help a specific patient. The authors argue that the first step toward truly individualized treatment is the ability to predict psychological attributes from biological markers. If brain morphology can forecast cognitive performance at the individual level, the same logic could eventually extend to predicting treatment outcomes, closing a gap that group-level correlations between brain structure and behavior have never been able to bridge.

To capture as much structural information as possible, the team scanned 74 transdiagnostic patients and 41 healthy controls on a 7-Tesla Magnetom Terra scanner at Forschungszentrum Jülich, one of the most powerful clinical MRI systems available. The MP2RAGE sequence produced T1-weighted anatomical images at an isotropic resolution of 0.75 millimeters, far finer than the 1-millimeter standard of conventional 3-Tesla scanners. The patient group was genuinely heterogeneous: 49 participants had major depressive disorder, with the remainder diagnosed with schizophrenia, bipolar disorder, ADHD, alcohol or drug abuse disorders, and non-schizophrenic psychosis. Healthy controls were screened with the Mini International Neuropsychiatric Interview to exclude any lifetime neurological or psychiatric illness.

From each scan, the researchers extracted three families of morphological features: grey matter volume, cortical thickness, and gyrification, the degree of cortical folding. The brain was parcellated into 100 regions using the Schaefer Atlas, which defines areas according to functional connectivity patterns rather than anatomical landmarks alone. In total, 304 features fed into the model. The authors deliberately avoided any a priori hypotheses about which brain areas matter, adopting a strictly data-driven approach in which every atlas region contributed volumetric and surface parameters for every participant.

The classification model was a feed-forward neural network trained with a triple loss function, an objective well suited to small datasets because it embeds information in a compact latent space and carries a relatively low risk of overfitting. Participants were labeled as above- or below-median performers on the Trail-Making Test, with the A and B versions combined to double the number of data points per subject. Training ran for up to 200 epochs using the Adam optimizer with a learning rate of 0.2, a scheduler that reduced the rate when validation loss plateaued, and gradient clipping to stabilize updates. Crucially, the team employed a strict Leave-Subject-Out splitting scheme, ensuring that no individual’s data appeared in both training and validation sets during any iteration.

To guard against overly optimistic results, the entire pipeline was wrapped in an outer hold-out loop executed ten times with different random splits. The mean and median accuracy across all ten iterations was 0.795, with a standard deviation of only 0.068; the worst run scored 0.682 and the best 0.891. Validation loss tracked training loss closely, and accuracy reached a stable plateau by roughly epoch 30 to 40, indicating that the network learned generalizable patterns rather than memorizing noise. The low variance across independent runs was presented as key evidence that the model’s performance reflects true predictive power rather than a lucky data split.

Interpretability came from Shapley Additive Explanations, or SHAP, a technique that quantifies each feature’s contribution to the model’s output. Averaged across all ten hold-out iterations, the SHAP analysis revealed that gyrification was by far the most decisive feature, while cortical thickness did not appear among the important variables at all. Grey matter volume in frontal, parietal, temporal, and occipital regions also contributed meaningfully. The influential regions clustered within four functional networks: the sensorimotor network, the limbic network, the default mode network, and the dorsal attention network.

The anatomical pattern aligns remarkably well with decades of independent neuroscience. Gyrification changes in the sensorimotor network, including the auditory cortex, make intuitive sense because completing the Trail-Making Test requires fine motor coordination and speed control. The limbic contribution came from the left orbitofrontal cortex, a region known for temporal cognition, sequencing, and stimulus-reward processing. The default mode network was represented by the dorsal prefrontal cortex and temporal cortex, while the dorsal attention network contributed through the postcentral gyrus. Previous lesion-mapping studies have shown that damage to the dorsolateral prefrontal cortex and anterior cingulate cortex impairs Trail-Making performance, and functional imaging work has repeatedly implicated these same circuits, lending independent validation to the data-driven findings.

The dominance of gyrification carries a deeper biological message. Unlike grey matter volume and cortical thickness, which change measurably across adulthood in response to stress, aging, and disease, cortical folding is largely fixed during ontogenesis and early childhood development. The model’s heavy reliance on gyrification suggests that predisposing structural patterns shaping cognitive performance are set early in life, while the concurrent relevance of grey matter volume in temporal and parietal-occipital cortex shows that modifiable features still matter. This distinction between static and dynamic morphology may prove important for deciding which biomarkers are useful for tracking change over time versus characterizing stable individual traits.

Perhaps the most consequential implication concerns how psychiatric illness should be classified. A sizable proportion of both patients and controls fell on the slow side of the median test times, meaning that diagnosis alone was a poor predictor of cognitive function. The authors argue this supports a transdiagnostic approach, in which structural brain features, psychological test results, and demographic data inform clinical decisions rather than broad diagnostic categories. Notably, the model drew on the orbitofrontal cortex, a region more often associated with affective disease than with executive function, hinting that psychiatric conditions as different as bipolar disorder, unipolar depression, and the negative symptoms of schizophrenia may share overlapping neurobiological substrates. Symptom prediction from neuroimaging, the study concludes, appears to be an achievable goal, and establishing that brain structure can forecast cognitive performance is a crucial first step toward identifying which biological interventions are most likely to help each individual patient.

Subject of Research: Predicting cognitive test performance from 7-Tesla structural brain MRI using deep learning

Article Title: Predicting trail-making test performance using 7-Tesla structural cerebral MRI

Article References: Schnellbächer, G. J., Rajkumar, R., Veselinović, T., Ramkiran, S., Hagen, J., Collee, M., Shah, N. J., Neuner, I., & Dammers, J. (2026). Predicting trail-making test performance using 7-Tesla structural cerebral MRI. Translational Psychiatry, 16(1), Article 572. https://doi.org/10.1038/s41398-026-04402-x

Image Credits: AI Generated

DOI: 10.1038/s41398-026-04402-x

Keywords: 7-Tesla MRI, deep learning, Trail-Making Test, gyrification, grey matter volume, cortical thickness, transdiagnostic psychiatry, SHAP analysis, executive function, neuroimaging biomarkers, Translational Psychiatry, cognitive prediction

The study, published in BMC Medicine, analyzed structural brain scans from 1,761 individuals with schizophrenia and 1,513 healthy controls, drawn primarily from East Asian cohorts across multiple research centers in China. Led by Lu Jin, Zhaoyun Liu, and Xiao Chang, with corresponding author Yuchao Jiang of Shenzhen University, the research team applied a standardized image-processing pipeline to T1-weighted magnetic resonance imaging data, quantifying regional asymmetry in three distinct anatomical measures: cortical thickness, cortical surface area, and subcortical volume. This multi-metric approach matters because asymmetry is not a single property of the brain; a region can show typical leftward bias in thickness while remaining symmetric in volume, and different measures may capture different developmental or degenerative processes.

To compute asymmetry, the researchers calculated an asymmetry index for each brain region, essentially comparing the value of a metric in the left hemisphere against its counterpart on the right, scaled to the total of the two. When they compared patients against controls across the entire sample, three robust differences survived the strictest statistical correction, Bonferroni correction at p < 0.001. Patients showed reduced leftward asymmetry in the rostral middle frontal gyrus, a region in the frontal lobe involved in executive control and working memory; reduced rightward asymmetry in the medial orbitofrontal cortex, which participates in emotion regulation and decision-making; and enhanced rightward asymmetry in the amygdala, the deep temporal structure central to threat processing and emotional salience.

These three findings are notable because they touch regions that map plausibly onto core symptoms of the illness. The frontal deficits align with the cognitive and negative symptoms that have long been linked to frontal lobe dysfunction, while altered amygdala asymmetry resonates with the emotional and perceptual disturbances characteristic of psychosis. Yet the effect sizes, the researchers emphasize, are small. This is a crucial point for the field: individual asymmetry differences in schizophrenia are subtle enough that small studies, which have dominated the literature for decades, could easily find them, miss them, or find them by chance, producing the inconsistent results that have plagued asymmetry research since the era of postmortem studies.

The inconsistency problem became even clearer when the team compared their results with previous findings from Western cohorts covering more than 5,000 schizophrenia patients, work associated with the large-scale ENIGMA consortium effort. The comparison revealed only moderate similarity in cortical surface area asymmetry, with a correlation coefficient of r = 0.36 that reached statistical significance at p = 0.02. For cortical thickness and subcortical volume, however, there were no significant correlations at all between the East Asian and Western asymmetry maps. In other words, the brain asymmetry signature of schizophrenia appears to be partly population-specific, a result that carries substantial implications for how neuroimaging biomarkers are developed and generalized across the globe.

Ancestry is not the only source of heterogeneity the study addressed. Schizophrenia is increasingly understood not as a single brain disease but as a spectrum of neurobiologically distinct conditions that happen to share a clinical label. To capture this, the researchers turned to a machine learning algorithm called Subtype and Stage Inference, or SuStaIn, which had previously been used to derive neuroanatomical subtypes of schizophrenia from patterns of brain structural variation. SuStaIn works by modeling how different patterns of brain change can coexist within a dataset, inferring latent subgroups and the stage of progression along each subgroup’s trajectory, using techniques including Markov Chain Monte Carlo estimation to quantify uncertainty. When the schizophrenia patients in this study were stratified according to these previously derived subtypes, new asymmetry differences emerged that were invisible in the pooled case-control analysis.

The subtype comparisons, corrected at p < 0.05 with Bonferroni correction, revealed differences in the temporal and parietal lobes, the precentral gyrus, and the caudate nucleus. The temporal lobe findings are particularly provocative given that temporal structures, including the superior temporal gyrus and auditory association cortex, have been repeatedly implicated in auditory verbal hallucinations, the most emblematic symptom of schizophrenia. The caudate, a key node of the striatal circuitry that modulates dopamine signaling, sits squarely within the dominant neurochemical models of psychosis. That asymmetry in these regions differs between machine-learned subtypes suggests that the neurodevelopmental deviation underlying asymmetry disruption may follow different trajectories in different patients.

Perhaps the most clinically intriguing result concerns the anterior cingulate cortex, a region straddling the medial frontal surface that regulates conflict monitoring, motivation, and error detection. The researchers found that the relationship between asymmetry in anterior cingulate volume and the severity of negative symptoms, the amotivational and socially withdrawn features of schizophrenia that respond poorly to current medications, differed significantly across the neuroanatomical subtypes, as confirmed by a permutation test at p < 0.05. In practical terms, the same degree of cingulate asymmetry may carry different prognostic or symptomatic meaning depending on which subtype a patient belongs to. This kind of interaction between brain structure and clinical presentation, conditioned by latent neurobiological grouping, is exactly what precision psychiatry aims to exploit.

The study’s conclusions extend a hypothesis with deep historical roots. The idea that schizophrenia involves abnormal cerebral asymmetry was championed by researchers who noted links between the illness, handedness, and language lateralization, arguing that schizophrenia might be the price Homo sapiens pays for the lateralized, language-dominant brain. The new findings neither fully vindicate nor demolish that idea; instead, they refine it. Asymmetry alterations in schizophrenia are real, statistically robust in specific frontal, orbitofrontal, and amygdala regions when tested at scale, but their magnitude is modest and their expression is modulated by both neuroanatomical subtype and ancestral background. A single universal asymmetry marker for schizophrenia, the data suggest, does not exist.

For the field, the methodological lessons may be as important as the biological ones. Large, harmonized samples with standardized preprocessing pipelines can resolve effects that smaller studies have argued over for decades. Machine learning subtyping can unmask heterogeneity that pooling conceals, turning apparent contradictions into structured, interpretable variation. And cross-population comparisons, though still rare in psychiatric neuroimaging, are essential if findings from predominantly Western cohorts are to be translated into clinical tools for the rest of the world. As neuroimaging datasets grow and subtyping algorithms mature, the asymmetry of the human brain, once a niche curiosity of neuropsychology, is emerging as a measurable axis along which the neurobiology of schizophrenia, and perhaps of psychiatric illness more broadly, can be mapped with new precision.

Subject of Research: Brain structural asymmetry alterations in schizophrenia across neuroanatomical subtypes identified by machine learning

Article Title: Revisiting brain asymmetry in schizophrenia: insights from machine learning subtypes from 1,761 patients

Article References: Jin, L., Liu, Z., Chang, X., Lin, X., Huang, Z., Hu, H., Yang, R., Zhang, J., Hu, H., Guo, Q., Tang, Y., Zhang, T., Wang, J., Huang, H., Zhou, J., Cui, L.-B., Yi, Q., Luo, C., Yao, D., … Jiang, Y. (2026). Revisiting brain asymmetry in schizophrenia: insights from machine learning subtypes from 1,761 patients. BMC Medicine. https://doi.org/10.1186/s12916-026-05318-5

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05318-5

Keywords: schizophrenia, brain asymmetry, MRI, machine learning, SuStaIn, neuroanatomical subtypes, cortical thickness, amygdala, anterior cingulate cortex, negative symptoms, psychiatry, neuroimaging

The research was led by Junwei Zheng, Taoyu Wu, and colleagues working with Jie Shi and Yan Sun at Peking University’s National Institute on Drug Dependence, together with Sizhi Ai at Guangzhou Medical University. Between June 2023 and October 2024, the team recruited right-handed men aged 18 to 45 who met the diagnostic criteria for etomidate use disorder under the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, alongside age-matched healthy male controls. All participants were assessed in Guangzhou, and the study received ethics approval from Peking University Health Science Center with written informed consent from every participant. By restricting the sample to men within a defined age band, the investigators controlled for the hormonal and anatomical variability that often complicates addiction neuroimaging studies.

The experiment centered on a cue-reactivity paradigm, a standard but powerful technique in addiction science. Participants viewed images associated with the drug alongside neutral pictures, while researchers measured how the brain responded to each category. Because craving unfolds on multiple timescales, from milliseconds to seconds, the team deliberately paired two complementary imaging technologies. Electroencephalography captures the brain’s electrical activity with millisecond precision through electrodes on the scalp, while functional magnetic resonance imaging tracks changes in blood oxygenation that mark where neural activity is concentrated over seconds. Independent cohorts completed each modality, allowing the researchers to test whether the electrical and hemodynamic signatures of craving converged on the same circuitry.

The EEG findings were striking. When people with etomidate use disorder viewed drug-related images, their brains produced markedly amplified late positive potentials, a slow electrical wave that emerges roughly 300 milliseconds after a stimulus and peaks over frontocentral electrode sites. The late positive potential has long been treated as an index of motivated attention: the brain flagging something as salient and worth allocating resources to. In this study, the size of the etomidate-related signal correlated directly with participants’ subjective ratings of craving, measured with a desire-for-drug questionnaire. In other words, the brains of dependent users were not passively registering the pictures; they were actively and involuntarily magnifying their importance, and the degree of that amplification tracked how strongly each individual wanted the drug at that moment.

The fMRI data told a spatially precise story that converged with the electrical one. Drug cues triggered hyperactivation in two regions: the temporoparietal junction, an area at the intersection of temporal and parietal cortex involved in attentional orienting and salience detection, and the ventrolateral prefrontal cortex, a region classically associated with cognitive control and the regulation of impulses. Hyperactivation of the ventrolateral prefrontal cortex might sound like a signature of stronger control, but the connectivity analysis revealed something more troubling about what that activation was actually doing.

To move beyond simple co-activation maps, the researchers applied dynamic causal modeling, a computational framework that estimates the direction and sign of influences between brain regions rather than merely noting which areas light up together. The signed connectivity parameters exposed a critical abnormality: in the etomidate group, the ventrolateral prefrontal cortex exerted abnormal inhibitory modulation over the temporoparietal junction, and crucially, the strength of that inhibition was linked to craving severity. Participants with weaker inhibitory modulation reported higher subjective craving. This finding reframes the role of the prefrontal cortex in the disorder. Rather than simply failing to engage, the control system appears to be miswired, its top-down signal to the attention-grabbing temporoparietal junction weakened precisely when it would be needed most to disengage from drug-related cues.

Structural MRI added a third and sobering layer of evidence. Using surface-based morphometry to measure cortical thickness across the entire brain, the team identified widespread thinning among individuals with etomidate use disorder, spanning prefrontal, parietal, occipitotemporal, and limbic regions. Cortical thinning in these territories has been reported in other substance use disorders and is generally interpreted as reflecting chronic neuroadaptation, whether through synaptic pruning, glial changes, or loss of dendritic complexity. That etomidate, a drug once considered relatively benign because of its rapid clearance and organ-sparing profile, is associated with structural changes across such a broad cortical expanse underscores that apparent pharmacological safety in the anesthetic context says little about safety under the repeated, escalating exposure patterns of misuse.

Etomidate’s pharmacology makes the findings biologically coherent. The drug is a GABA-A receptor positive allosteric modulator, and the GABAergic system is deeply intertwined with the reward and executive circuits implicated in every major addiction. Chronic exposure to substances that enhance inhibitory signaling forces the brain to recalibrate, and those recalibrations plausibly extend to the prefrontal networks that govern attention and impulse regulation. The study’s convergent design strengthens this interpretation: an electrical marker of exaggerated salience attribution, a hemodynamic marker of cue-driven hyperactivation, a connectivity marker of impaired top-down inhibition, and a structural marker of cortical compromise all point toward the same VLPFC-TPJ circuit as the neural seat of etomidate craving.

What elevates the study from descriptive to clinically consequential is the target it nominates. Non-invasive brain stimulation techniques, including transcranial magnetic stimulation and transcranial direct current stimulation, can modulate prefrontal activity with growing precision, and randomized trials in other addictions have already explored the ventrolateral prefrontal cortex as a stimulation site. If weakened VLPFC-to-TPJ inhibition is genuinely causal in maintaining craving, then restoring that inhibitory modulation becomes a testable therapeutic hypothesis, one that could be evaluated with stimulation protocols, neurofeedback, or even pharmacological agents that strengthen prefrontal function. The authors position the VLPFC-TPJ circuit explicitly as a promising neuromodulation target for treating etomidate use disorder, a framing that could accelerate translational work in a field that currently has no approved pharmacological treatment.

The study’s limitations invite caution and future work. The cohort was restricted to right-handed men, so the findings cannot yet be generalized to women or to patterns of use in other populations, and the case-control design cannot establish whether the cortical thinning preceded drug use or resulted from it. Longitudinal follow-up, ideally beginning before dependence develops, will be needed to disentangle cause from consequence. Still, as a first multimodal portrait of an emerging and poorly understood form of addiction, the work delivers something rare: a mechanistically specific, biologically grounded, and clinically actionable account of craving. At a moment when etomidate misuse is escalating globally, that combination may prove exactly what public health responses have been missing.

Subject of Research: Neural mechanisms of cue-induced craving and cortical structural changes in etomidate use disorder

Article Title: Multimodal neuroimaging of cue-induced craving and cortical alterations in etomidate use disorder

Article References: Zheng, J., Wu, T., Yue, J., Zhang, M., Liu, W., Huang, Y., Zhao, S., Jia, T., Bao, Y., Lu, L., Ai, S., Sun, Y., & Shi, J. (2026). Multimodal neuroimaging of cue-induced craving and cortical alterations in etomidate use disorder. BMC Medicine. https://doi.org/10.1186/s12916-026-05253-5

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05253-5

Keywords: etomidate use disorder, drug craving, cue reactivity, EEG, fMRI, late positive potential, ventrolateral prefrontal cortex, temporoparietal junction, dynamic causal modeling, cortical thickness, GABA-A receptor, addiction neuroscience

The research, led by Yufei Chen, Haoran Li, and Fenghua Long of West China Hospital of Sichuan University, together with Robert K. McNamara of the University of Cincinnati and senior author Fei Li, set out to resolve a stubborn inconsistency in the OCD literature. For years, structural MRI studies comparing cortical thickness, the width of the brain’s outer gray matter ribbon, between OCD patients and healthy controls have produced a patchwork of findings: some reported thinning in frontal regions, others in cingulate areas, and still others found no differences at all. Because cortical thickness is a sensitive marker of the health of neurons, synapses, and supporting glial cells within each cortical column, such inconsistency has made it difficult to build a coherent biological story about what the disorder does to the cortex.

To cut through the noise, the team performed a coordinate-based meta-analysis pooling cortical thickness comparisons across many independent studies of OCD patients and matched healthy controls. The approach treats each published brain map as a data point, allowing researchers to detect regions where structural differences reliably replicate across cohorts rather than appearing in only one or two samples. The results pointed to three regions where people with OCD consistently showed thinner cortex: the left posterior cingulate cortex, the left anterior cingulate cortex, and the right pars opercularis, a portion of the inferior frontal gyrus on the right side of the brain that plays a central role in response inhibition, the ability to stop an action already in progress.

The anatomical pattern is telling. The anterior and posterior cingulate cortices are core nodes of the default mode network, the set of regions most active when the mind wanders inward, ruminates, and generates self-referential thought, while the pars opercularis belongs to the cingulo-opercular network, which supports cognitive control and the suppression of inappropriate responses. Both networks have been repeatedly implicated in the characteristic symptoms of OCD, from persistent unwanted thoughts to compulsive behaviors that resist inhibition. The finding that cortical thinning concentrates precisely at the junction of these networks suggests that the structural substrate of the disorder may lie in the brain’s machinery for regulating internally directed thought and behavioral control.

Crucially, the researchers did not stop at the group-level map. They ran subgroup analyses and meta-regressions to ask whether clinical and methodological variables, factors that often differ between studies, were quietly shaping the results. Two stood out. In samples of medicated patients, and in samples with comorbid psychiatric conditions, additional cortical thinning emerged in the left inferior parietal lobule and the right posterior cingulate cortex. This is an important caveat for the field: medication status and comorbidity are not merely statistical nuisances but appear to leave their own signatures on cortical structure, meaning that studies enrolling different kinds of patient populations may legitimately reach different conclusions about where and how severely the cortex is affected in OCD.

The authors also flagged a note of scientific caution that adds credibility to the work. One of the three main findings, the thinning in the left anterior cingulate cortex, showed significant evidence of publication bias, the tendency of journals to publish positive results more readily than null ones. By transparently reporting this, the team signals that the left ACC finding should be interpreted with more caution than the others, while the posterior cingulate and pars opercularis results survived the standard robustness checks. In a literature notorious for small samples and inconsistent replication, this kind of self-scrutiny is exactly what meta-analytic science is supposed to provide.

The most ambitious part of the study, however, went beyond anatomy into molecular territory. Using spatial correlation methods, the researchers asked whether the brain regions showing cortical thinning in OCD are also the regions where particular genes are most highly expressed, drawing on transcriptomic atlases that map gene activity across the human cortex. The logic is straightforward: if a structural abnormality clusters in areas enriched for a specific set of genes, those genes become candidate molecular players in the disorder. The analysis revealed that the cortical thickness alterations in OCD were spatially associated with genes enriched in synaptic signaling and synaptic organization, the processes by which neurons form, maintain, and tune their connections with one another.

Zooming in further at the cellular level, the implicated genes were found to be highly expressed in excitatory neurons, inhibitory neurons, and glial cells, the three major cell classes that together build and support cortical circuits. This cellular profile points toward a disruption of excitatory-inhibitory balance, the delicate equilibrium between neurons that speed signals up and neurons that damp them down, as well as toward changes in signaling transmission efficiency, how effectively information moves through cortical microcircuits. Excitatory-inhibitory imbalance has emerged in recent years as a leading hypothesis for several psychiatric conditions, and this study provides structural and transcriptomic evidence that the same principle may apply to OCD, linking the macroscopic thinning seen on MRI to the microscopic choreography of synapses and cell types.

The molecular story extended to neurochemistry as well. When the team overlaid the cortical thinning map with atlases of neurotransmitter receptor and transporter density, they found associations with multiple neurotransmitter systems, principally glutamate, serotonin, dopamine, and gamma-aminobutyric acid, or GABA. The prominence of glutamate is particularly striking, because glutamate is the brain’s primary excitatory transmitter and has been repeatedly implicated in OCD by genetic, pharmacological, and imaging studies, including reports that glutamate-modulating agents can help some patients. The convergence of structural, transcriptomic, and receptor-level evidence on glutamatergic and synaptic pathways suggests that these systems may represent the most promising molecular targets for understanding, and eventually treating, the cortical abnormalities associated with the disorder.

The authors are careful to frame their molecular findings as hypothesis-generating rather than definitive. Spatial correlations between imaging maps and gene expression do not prove causation, and the cortical thickness differences observed in OCD could be causes, consequences, or compensatory responses related to the illness. Still, the study delivers something the field has lacked: a unified framework in which the inconsistent structural imaging literature is distilled into a small set of reliably affected regions, those regions are shown to be modulated by clinically meaningful factors like medication and comorbidity, and the whole pattern is anchored to a plausible molecular substrate centered on synaptic function and excitatory-inhibitory signaling. For a disorder that affects millions of people worldwide and often resists standard treatments, that framework offers a roadmap for the next generation of research, from cell-type-specific investigations of cingulate and frontal cortex to clinical trials aimed at the glutamate system. The work was supported by the Sichuan Science and Technology Program and conducted entirely on previously published, publicly available data, an approach that demonstrates how secondary analyses, done rigorously, can extract new biological insight from decades of accumulated brain imaging.

Subject of Research: Cortical thickness alterations and their molecular correlates in obsessive-compulsive disorder

Article Title: Reduced cortical thickness in obsessive-compulsive disorder and candidate molecular correlates

Article References: Chen, Y., Li, H., Long, F., Zhao, Y., McNamara, R. K., & Li, F. (2026). Reduced cortical thickness in obsessive-compulsive disorder and candidate molecular correlates. Translational Psychiatry. https://doi.org/10.1038/s41398-026-04510-8

Image Credits: AI Generated

DOI: 10.1038/s41398-026-04510-8

Keywords: obsessive-compulsive disorder, cortical thickness, meta-analysis, posterior cingulate cortex, anterior cingulate cortex, pars opercularis, synaptic signaling, excitatory-inhibitory balance, glutamate, serotonin, dopamine, GABA

The methodological heart of the study lies in its commitment to data-driven phenotyping. Rather than assuming in advance which symptom clusters matter, the researchers applied K-means clustering to 174 individual clinical items drawn from validated instruments, including the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Young Mania Rating Scale, the Hypomania Checklist-32, the Snaith-Hamilton Pleasure Scale, the Insomnia Severity Index, the Dysfunctional Attitudes Scale, and measures of non-suicidal self-injury and psychotic symptoms rated on the Brief Psychiatric Rating Scale. Clustering at the level of individual questionnaire items, rather than summed scale scores, allowed the algorithm to detect combinations of symptoms that cut across conventional diagnostic boundaries. The procedure produced two stable, non-overlapping transdiagnostic phenotypes, and the cluster solution held up under internal validation using the adjusted Rand index.

The first group was labeled the Negative Affect-Anhedonic phenotype. Its defining features were self-injurious tendencies together with depressive and anhedonic symptoms — the withdrawal, joylessness, and despair that most people associate with severe depression, but which in this sample appeared in patients carrying either diagnosis. The second group, the Manic-Activation phenotype, was characterized by manic and hypomanic activation: elevated mood, heightened drive, reduced need for sleep, and accelerated thinking and behavior. Crucially, each phenotype drew members from both diagnostic camps. A patient with a bipolar diagnosis could belong to the negative affect cluster, and a patient diagnosed with unipolar depression could show prominent manic-activation features. The researchers report that phenotype dominance only partially aligned with categorical diagnosis, revealing substantial cross-diagnostic overlap and equally substantial heterogeneity within each diagnosis.

With the symptom phenotypes established, the team turned to the brain. All participants underwent high-resolution 3 Tesla structural magnetic resonance imaging, and the scans were processed with FreeSurfer, the widely used reconstruction pipeline, to extract three complementary measures of neuroanatomy: cortical thickness, cortical surface area, and subcortical volumes. These metrics are biologically distinct. Cortical thickness reflects the width of the folded gray matter ribbon and is thought to relate to dendritic arborization and synaptic density, whereas surface area reflects the degree of cortical folding and the expansion of functional columns; the two show largely independent genetic determinants. Subcortical volumes capture structures such as the hippocampus, amygdala, and basal ganglia, regions repeatedly implicated in mood regulation. To relate these anatomical measures to the symptom phenotypes, the researchers used sparse canonical correlation analysis, a multivariate technique that identifies weighted patterns of variables on both sides of the equation that maximally covary with one another — an approach well suited to the complexity of brain-wide morphology paired with multi-item symptom profiles.

The results delivered a clear modality split. Both the Negative Affect-Anhedonic and Manic-Activation phenotypes showed statistically significant multivariate associations with cortical thickness and with cortical surface area, but neither was linked to subcortical volumes in the full sample. Statistical significance was established through permutation testing, and the stability of individual features was verified with bootstrapping, giving the team confidence that the brain-symptom relationships were not artifacts of a single analytic pass. The absence of subcortical findings is itself notable: despite decades of attention to the hippocampus and amygdala in mood disorders, the strongest transdiagnostic signals in this cohort lived in the cortex.

Perhaps the most striking technical finding concerns the spatial organization of the two cortical metrics. The associations involving cortical thickness were focal, concentrated in higher-order association regions — the areas of the cortex that integrate information across sensory modalities and support complex cognition, self-referential processing, and emotion regulation. By contrast, the associations involving cortical surface area were spatially distributed across much of the cortical sheet but appeared to be network-organized, suggesting coordinated maturational or structural variation along the lines of large-scale neural networks rather than isolated regional differences. This thickness-versus-area dissociation matters because the two metrics index different cellular and developmental processes, and the study’s authors interpret the pattern as evidence that transdiagnostic symptom phenotypes map onto partially dissociable, modality-specific structural brain signatures rather than a single undifferentiated anomaly.

The study also tackled a developmental question. Because the sample spanned adolescence and emerging adulthood — a window during which the brain undergoes dramatic cortical thinning and network refinement — the researchers tested whether the strength of brain-symptom associations varied continuously with age in 960 participants aged 10 to 29 years. Six models were examined, crossing each symptom phenotype with each structural modality. The verdict after correction for multiple comparisons was uniformly negative: no age-by-symptom interaction survived false discovery rate correction across the six tested models. The smallest nominal effect appeared in the Negative Affect-Anhedonic model with subcortical volumes, but the authors were explicit that this effect failed to survive correction and should be treated as exploratory only. In a field where developmental moderation claims are often made on nominal p-values, this restrained reporting is a methodological lesson in itself.

The clinical implications are layered. At a practical level, the findings lend empirical weight to dimensional frameworks such as the Research Domain Criteria initiative advanced by the United States National Institute of Mental Health, which encourages researchers and clinicians to study psychopathology along measurable dimensions rather than relying solely on DSM-5 categories. The Wuhan team’s data show that symptom-based phenotypes capture clinically meaningful heterogeneity that categorical diagnoses obscure — two patients with the same diagnostic label can sit in opposite symptom clusters, and two patients with different labels can share a cluster and, potentially, a common cortical profile. That said, the researchers caution that the phenotypes only partially aligned with diagnosis, so the categorical system has not been rendered obsolete; rather, it appears to be an incomplete lens. A multimetric framework that tracks both cortical thickness and surface area, alongside dimensional symptom measures, may better capture the shared and distinct biology of major mood disorders.

There are also important limits to keep in view. The study is cross-sectional, so it can demonstrate that symptom phenotypes and cortical anatomy covary, but not that one causes the other — cortical differences could predispose to particular symptom patterns, chronic illness could reshape the cortex, medications could contribute, or a third factor could drive both. The clustering solution, while internally stable, represents one data-driven cut through a vast symptom space, and alternative algorithms or item sets might yield different groupings. And the null developmental findings mean only that no moderation was detectable in this cohort with these methods, not that age is irrelevant to brain-symptom relationships. The study was registered prospectively at the Chinese Clinical Trial Registry (ChiCTR2500106094), received ethics approval from Renmin Hospital of Wuhan University, and was funded by the National Key R&D Program of China and the National Natural Science Foundation of China, among other sources.

Even with those caveats, the scale and rigor of the work make it a landmark in the effort to connect the messy, overlapping reality of mood disorder symptoms to measurable brain structure. By showing that data-derived symptom phenotypes — not diagnostic labels — align with reproducible, modality-specific patterns of cortical morphology in more than a thousand patients, the study offers a template for how large clinical cohorts, item-level machine learning, and multivariate neuroimaging statistics can be combined to move psychiatry beyond its century-old categories. The next step, the researchers and their colleagues in the field will likely agree, is longitudinal: following patients over time to see whether these cortical signatures predict symptom trajectories, treatment response, or the emergence of manic episodes in those who today carry only a depression diagnosis.

Subject of Research: Neuroanatomical correlates of transdiagnostic symptom phenotypes in major depressive and bipolar disorders

Article Title: Neuroanatomical correlates of transdiagnostic clinical phenotypes in major depressive and bipolar disorders

Article References: Lu, X., Hu, M., Luo, D., Hou, H., Feng, M., He, J., Wang, W., Yang, B. X., & Zong, X. (2026). Neuroanatomical correlates of transdiagnostic clinical phenotypes in major depressive and bipolar disorders. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08602-z

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08602-z

Keywords: major depressive disorder, bipolar disorder, transdiagnostic psychiatry, structural MRI, cortical thickness, cortical surface area, K-means clustering, sparse canonical correlation analysis, FreeSurfer, Research Domain Criteria, anhedonia, mania

The research team, led by John P. Hegarty II of the Department of Psychiatry and Behavioral Sciences, recruited 62 participants and ultimately obtained high-quality structural MRI data from 13 autistic children, 16 neurotypical control children, and a smaller exploratory group of seven autistic adolescents. Crucially, the pediatric sample was designed to be representative of the spectrum rather than a filtered subset. It included children with profound autism, intellectual disability, and significant language delays, populations that are routinely excluded from neuroimaging research and whose absence has long been suspected of masking biologically meaningful subgroups.

Scanning young autistic children without sedation is a formidable technical challenge, and the team relied on validated sleep-scanning protocols for children roughly six and under, with sufficient head and neck padding to minimize motion. Older children and adolescents were scanned awake after behavioral training on an MRI simulator, during which a head-mounted motion sensor provided real-time feedback paired with access to a preferred video until participants could hold still within two millimeters for at least five minutes. Similar proportions of autistic and control children completed sleep versus awake scans, reducing the risk that arousal state confounded group comparisons.

To segment the cerebellar folds with precision, the researchers used DeepCERES, a newly developed artificial intelligence-based tool from the volBrain platform that delineates individual cerebellar lobules from ultra-high-resolution multimodal MRI. Trained raters visually inspected every scan before and after segmentation to confirm accurate separation of cerebellar tissue from surrounding cerebrospinal fluid. The results were striking: Crus I and Crus II were significantly thinner in autistic children than in neurotypical controls, with the effect most pronounced in the left hemisphere of Crus I and the right hemisphere of Crus II. Autistic children also showed reduced asymmetry in Crus II between hemispheres and marginally thinner left Crus I cortex compared with autistic adolescents, hinting at developmental trajectories that shift with age.

Structure was only half of the story. The study’s most technically ambitious component was the use of a novel proton magnetic resonance spectroscopy sequence, called MEGA-SPECIAL, to measure the brain’s principal excitatory and inhibitory neurotransmitters, glutamate and GABA, directly within the cerebellar hemispheres. The standard clinical sequence, MEGA-PRESS, suffers from a well-known weakness: co-edited macromolecule signals can contaminate GABA quantification by 40 to 60 percent. MEGA-SPECIAL employs more selective editing pulses, set at 1.9 and 7.5 parts per million, to suppress these macromolecule signals and improve localization. Single-voxel data were acquired from two-by-four-by-two-centimeter regions in the left and right cerebellar hemispheres over ten-minute acquisitions, with glutamate quantified using LCModel and GABA measured from the edited 3 ppm peak.

Quality control was rigorous. Spectra were included only if the full-width at half-maximum line width remained below 15 hertz, Cramer-Rao lower bounds stayed under 20 percent, and visual inspection confirmed reliable model fitting. Even so, spectroscopy proved harder to obtain than anatomy: of 42 children who attempted imaging, high-quality neurometabolite data were available from only 10 autistic children, nine neurotypical controls, and four autistic adolescents. Within that reduced subset, the researchers found no significant group differences in glutamate or GABA concentrations, a null result they interpret cautiously given the small numbers.

The correlations, however, were compelling. Across all participants regardless of diagnosis, thinner Crus I grey matter and smaller Crus II volume were associated with a higher ratio of excitatory glutamate to inhibitory GABA, an index of the excitation-inhibition balance that many theorists place at the heart of autism neurophysiology. The strongest relationships appeared in left Crus I, with a correlation of negative 0.70, and right Crus II, at negative 0.61, both surviving correction for multiple comparisons. Glutamate drove most of the effect, correlating at negative 0.78 with left Crus I thickness. In other words, the smaller the cerebellar fold, the more its chemistry tilted toward excitation.

When the team linked these brain measures to behavior, one domain dominated: restricted and repetitive behaviors, or RRB. Crus I volume correlated with self-injurious behavior in both hemispheres, reaching a correlation of 0.53 on the left that survived statistical correction. Within the autistic sample alone, the associations strengthened, with left Crus I volume correlating at 0.71 with self-injurious behavior. Neurochemistry told a parallel story. Right-hemisphere GABA levels tracked with self-injurious and restricted behaviors, and the single strongest spectroscopy finding, a correlation of 0.81 between glutamate and compulsive behaviors, survived correction for multiple comparisons. By contrast, relationships with social communication and sensory processing were modest, a pattern the authors attribute partly to the limited sensitivity of standard screening instruments to the social subdomains most influenced by cerebro-cerebellar circuitry.

These findings converge with a substantial body of post-mortem and translational work. Autopsy studies have repeatedly documented reduced Purkinje cell density, diminished dendritic arborization, and altered cellular size and shape in the posterolateral cerebellar hemispheres, some of the most consistent neuropathological findings in autism. Mouse models reinforce the circuit-level logic: genetic knockouts of autism-associated genes such as SHANK2 and PTEN produce Purkinje cell loss, reduced neuroplasticity, and the emergence of social and repetitive behavioral traits. The cerebellum also develops earlier than the cortex, with explosive growth in the late second and third trimesters, a window in which cerebellar hemorrhage in premature infants is associated with markedly elevated rates of autism, language delay, and intellectual disability. The authors propose that early cellular alterations disrupt cerebellar modulation of cortical development, derailing the feedback loops that optimize neural circuit dynamics for social, cognitive, and behavioral skill acquisition.

The study is explicitly preliminary, and the authors are candid about its limits. The spectroscopy samples are small, the adolescent group lacked age-matched neurotypical controls, and neurometabolite concentrations are an indirect proxy for excitation-inhibition neurophysiology rather than a direct measure of it. Psychoactive medication use was not specifically excluded, and the novel MRS sequence, while suppressing macromolecule contamination, still requires cross-validation against standard sequences by independent laboratories. Yet the implications are tantalizing. If a cerebellar phenotype, defined by structure and excitation-inhibition balance, characterizes a identifiable subgroup of autistic children, non-invasive biomarkers could eventually guide patient stratification and even inform neuromodulation therapies targeting cerebro-cerebellar circuits. For a condition affecting roughly one in 31 children in the United States and still lacking any biological markers for clinical care, the idea that answers might lie in a structure long dismissed as the brain’s afterthought is precisely the kind of paradigm shift the field has been waiting for.

Subject of Research: Cerebellar structure and excitation-inhibition balance in autism spectrum disorder

Article Title: Cerebellar Crus I and Crus II Structure and Excitation/Inhibition Balance in Autism Spectrum Disorder

Article References: Hegarty, J. P., II, Komaki, H., Gu, M., Young, K., Ogbonnaya, N., Spielman, D. M., & Hardan, A. Y. (2026). Cerebellar Crus I and Crus II Structure and Excitation/Inhibition Balance in Autism Spectrum Disorder. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07529-4

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07529-4

Keywords: autism spectrum disorder, cerebellum, Crus I, Crus II, glutamate, GABA, excitation-inhibition balance, MR spectroscopy, cortical thickness, restricted and repetitive behaviors, neurodevelopment, MEGA-SPECIAL

The research, published in the Journal of Neurology, focused on a measurable feature of the brain’s outer mantle known as cortical thickness. The cerebral cortex, the wrinkled sheet of gray matter responsible for memory, language, and reasoning, gradually thins as neurons and their connections are lost in neurodegenerative disease. Previous large-scale studies, including work from the Longitudinal Early-Onset Alzheimer’s Disease Study consortium, had established that patients with dementia from early-onset Alzheimer’s show a characteristic pattern of thinning concentrated in the posterior parietal and lateral temporal regions of the brain, including the precuneus, the inferior and superior parietal lobules, and the supramarginal gyrus, while relatively sparing the medial temporal areas typically affected first in older-onset disease. What remained unknown was when this signature emerges along the clinical trajectory, particularly in people who do not carry rare genetic mutations but instead develop the disease sporadically.

To address this, the researchers studied 292 patients under the age of 65 who had been referred to the Karolinska memory clinic with cognitive concerns between April 2018 and May 2024. Every participant underwent a 3 Tesla structural MRI scan and a lumbar puncture to obtain cerebrospinal fluid, which was analyzed for two key Alzheimer’s biomarkers: the Aβ42/40 ratio, which signals the accumulation of amyloid-beta plaques in the brain, and phosphorylated tau 181, a protein released when tau pathology begins to damage neurons. Using these markers, the team stratified patients into three biologically defined groups: a reference group of 107 individuals negative for both markers, 76 patients with amyloid positivity alone, and 109 patients positive for both amyloid and p-tau181. Clinically, the cohort spanned the full spectrum of the disease, from subjective cognitive decline, in which patients report symptoms but perform normally on testing, through mild cognitive impairment, to frank dementia.

The first step was to define the early-onset Alzheimer’s cortical signature itself. Comparing patients with biomarker-confirmed Alzheimer’s dementia against the biomarker-negative reference group, using a vertex-wise analysis across the entire cortex, the researchers identified a robust and spatially coherent pattern of thinning. The most severely affected areas lay in the bilateral posterior parietal and lateral association cortices, particularly the precuneus, the inferior and superior parietal lobules, and the supramarginal gyrus. Additional atrophic clusters extended into the banks of the superior temporal sulcus, the superior frontal gyrus, the middle temporal gyrus, and the fusiform gyrus, with a notable predominance in the left hemisphere. This left-sided emphasis echoes earlier reports in younger populations and reinforces the idea that early-onset Alzheimer’s is not merely early-arriving late-onset disease, but a distinct neurodegenerative phenotype with its own spatial and possibly molecular underpinnings.

With the signature established, the team then traced it backward through the earlier clinical stages, and this is where the findings become striking. Among patients with mild cognitive impairment and Alzheimer’s biomarkers, cortical thinning was already present across most signature regions, accompanied by significant hippocampal atrophy. But when the researchers separated the amyloid-positive patients according to their tau status, a clear gradient emerged. Patients with amyloid positivity alone showed focal thinning confined to a handful of regions, including the signature composite, the banks of the superior temporal sulcus, the middle temporal gyrus, and the supramarginal gyrus, while their hippocampal volumes remained statistically intact. Patients with combined amyloid and p-tau181 positivity, by contrast, displayed far more extensive thinning that encompassed the precuneus, fusiform gyrus, inferior parietal lobule, superior parietal lobule, and superior frontal gyrus, together with marked hippocampal volume loss that was significantly greater than in the amyloid-only group.

Even more remarkable were the results at the earliest stage, subjective cognitive decline, when patients are aware something is wrong but still score normally on cognitive tests. Among these individuals, those with amyloid positivity alone showed no detectable structural brain changes whatsoever compared with the reference group. But those with both amyloid and p-tau181 positivity exhibited subtle yet statistically significant thinning in the early-onset signature regions, the precuneus, and the banks of the superior temporal sulcus. In other words, the structural footprint of Alzheimer’s disease is already present in the brains of some younger patients who appear, by every conventional clinical measure, to be functioning normally. This finding parallels evidence from studies of autosomal dominant Alzheimer’s, in which mutation carriers show cortical thinning years before expected symptom onset, but it extends that principle to the predominantly sporadic form of the disease seen in real-world memory clinics.

The study also connected these structural changes to actual cognitive performance, and here a threshold effect became apparent. In the dementia group, thinner cortex within the signature regions correlated with lower scores on global screening measures, the Mini-Mental State Examination and the Montreal Cognitive Assessment. At the mild cognitive impairment stage, the correlations became more specific: thinner inferior parietal cortex and precuneus were associated with poorer episodic memory on verbal learning tests, while thinning in the fusiform gyrus and the banks of the superior temporal sulcus tracked impaired visuospatial memory on complex figure recall tasks. Crucially, no such brain-behavior relationships were observed in the subjective cognitive decline group, suggesting that structural and cognitive changes become coupled only once neurodegeneration crosses a critical threshold, even though the structural changes themselves are already underway.

Why should amyloid positivity alone be so structurally silent? The authors offer two complementary interpretations. The first is temporal: amyloid accumulation precedes tau-related neurodegeneration, and patients with isolated amyloid positivity may simply be at an earlier point on the continuum, destined to convert to tau positivity later, a pattern supported by longitudinal biomarker studies. Consistent with this, amyloid-only patients were proportionally more represented in the earlier clinical stages of the cohort. The second interpretation is more provocative: amyloid-positive, tau-negative and amyloid-positive, tau-positive individuals may represent biologically distinct subtypes of Alzheimer’s disease, with the tau-negative group following a more indolent course and the tau-positive group facing a substantially higher risk of near-term progression. A further speculative possibility raised by the authors is that early amyloid-related processes may transiently increase cortical thickness, potentially masking subtle atrophy in the amyloid-only group, a phenomenon previously reported in presymptomatic carriers of PSEN1 mutations. Notably, the two biomarker groups did not differ in their frequency of the APOE ε4 allele, the major genetic risk factor for sporadic Alzheimer’s, indicating that the structural differences were not simply explained by genetic loading.

The translational implications are considerable. As disease-modifying therapies targeting amyloid move into clinical practice, identifying which patients have biologically active neurodegeneration, rather than amyloid deposition alone, becomes essential for timing intervention and selecting trial participants. The findings suggest that regional cortical thickness measured on a standard structural MRI could serve as a staging marker in biologically defined early-onset Alzheimer’s, complementing fluid biomarkers. Perhaps most importantly, the identification of focal cortical thinning in amyloid- and tau-positive patients who still have only subjective complaints defines a clinically silent but biologically progressive phase of the disease, a window in which closer surveillance and enrollment in secondary prevention trials could alter the trajectory before irreversible damage accumulates.

The authors are careful to note the limitations of their work. The cross-sectional design cannot establish individual disease trajectories, comprehensive neuropsychological testing was available for only a subset of participants, and the cohort lacked systematic genetic testing for monogenic Alzheimer’s genes such as PSEN1, PSEN2, and APP. The cortical signature was also derived and tested within the same clinical cohort, meaning that external replication in independent samples will be required before the pattern can be considered diagnostically applicable. Nevertheless, as one of the largest biomarker-stratified studies of early-onset Alzheimer’s conducted in a real-world memory clinic setting, the study provides a rigorous template for how imaging and fluid biomarkers can be combined to stage the disease in younger patients. It demonstrates that the atrophy signature of early-onset Alzheimer’s dementia is not a late-breaking phenomenon but a process that unfolds along a biomarker-defined gradient, becoming visible to the MRI scanner precisely when amyloid pathology is joined by the tau response that signals the brain’s descent into active neurodegeneration.

Subject of Research: Cortical thickness changes across clinical stages and amyloid/tau biomarker profiles in early-onset Alzheimer's disease

Article Title: Cortical thickness signature of early-onset Alzheimer’s disease across clinical stages and A/T1 biomarker profiles

Article References: Zilioli, A., Mohanty, R., Rosenberg, A., Matton, A., Granberg, T., Hagman, G., Spallazzi, M., Kivipelto, M., & Westman, E. (2026). Cortical thickness signature of early-onset Alzheimer’s disease across clinical stages and A/T1 biomarker profiles. Journal of Neurology, 273(10), Article 646. https://doi.org/10.1007/s00415-026-14187-0

Image Credits: AI Generated

DOI: 10.1007/s00415-026-14187-0

Keywords: early-onset Alzheimer's disease, cortical thickness, p-tau181, amyloid biomarkers, cerebrospinal fluid, MRI, mild cognitive impairment, subjective cognitive decline, hippocampal atrophy, precuneus, biomarker staging, memory clinic