Characteristic changes in the asymmetric nature of the human brain are associated with neurodevelopmental differences related to autism. In people with autism, these differences are thought to affect brain structure and function, although the structural and functional bases of these defects are yet to be fully characterized.

We applied a comprehensive meta-analysis to resting-state functional and structural magnetic resonance imaging datasets from 370 people with autism and 498 non-autistic controls using seven datasets of the Autism Brain Imaging Data Exchange Project. We studied the meta-effect sizes based on standardized mean differences and standard deviations (s.d.) for lateralization of gray matter volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo). We examined the functional correlates of atypical laterality through an indirect annotation approach followed by a direct correlation analysis with symptom scores.

In people with autism, 85, 51, and 51% of brain regions showed a significant diagnostic effect for lateralization in GMV, fALFF, and ReHo, respectively. Among these regions, 35.7% showed overlapping differences in lateralization in GMV, fALFF, and ReHo, particularly in regions with functional annotations for language, motor, and perceptual functions. These differences were associated with clinical measures of reciprocal social interaction, communication, and repetitive behaviors. A meta-analysis based on s.d. showed that people with autism had lower variability in structural lateralization but higher variability in functional lateralization.

These findings highlight that atypical hemispheric lateralization is a consistent feature in autism across different sites and may be used as a neurobiological marker for autism.

Studying offspring of schizophrenia (SZo) and bipolar disorder patients (BDo) provides important information on the putative neurodevelopmental trajectories underlying development toward severe mental illnesses. We compared intracranial volume (ICV), as a marker for neurodevelopment, and global and local brain measures between SZo or BDo and control offspring (Co) in relation to IQ and psychopathology.

T1-weighted magnetic resonance imaging (MRI) brain scans were obtained from 146 participants (8–19 years; 40 SZo, 66 BDo, 40 Co). Linear mixed models were applied to compare ICV, global, and local brain measures between groups. To investigate the effect of ICV, IQ (four subtests Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale-III) or presence of psychopathology these variables were each added to the model.

SZo and BDo had significantly lower IQ and more often met criteria for a lifetime psychiatric disorder than Co. ICV was significantly smaller in SZo than in BDo (d = −0.56) and Co (d = −0.59), which was largely independent of IQ (respectively, d = −0.54 and d = −0.35). After ICV correction, the cortex was significantly thinner in SZo than in BDo (d = −0.42) and Co (d = −0.75) and lateral ventricles were larger in BDo than in Co (d = 0.55). Correction for IQ or lifetime psychiatric diagnosis did not change these findings.

Despite sharing a lower IQ and a higher prevalence of psychiatric disorders, brain abnormalities in BDo appear less pronounced (but are not absent) than in SZo. Lower ICV in SZo implies that familial risk for schizophrenia has a stronger association with stunted early brain development than familial risk for bipolar disorder.

A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation.

Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission.

Cognitive data significantly classified patients from controls (accuracies = 60–69%; p values = 0.0001–0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission.

In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.

Cortical thickness measurement offers an index of brain development processes. In healthy individuals, cortical thickness is reduced with increasing age and is related to cognitive decline. Cortical thinning has been reported in schizophrenia. Whether cortical thickness changes differently over time in patients and its impact on outcome remain unanswered.

Data were examined from 109 patients and 76 healthy controls drawn from the Santander Longitudinal Study of first-episode schizophrenia for whom adequate structural magnetic resonance imaging (MRI) data were available (n = 555 scans). Clinical and cognitive assessments and MRIs were acquired at three regular time points during a 3-year follow-up period. We investigated likely progressive cortical thickness changes in schizophrenia during the first 3 years after initiating antipsychotic treatment. The effects of cortical thickness changes on cognitive and clinical variables were also examined along with the impact of potential confounding factors.

There were significant diagnoses × scan time interaction main effects for total cortical thickness (F1,309.1 = 4.60, p = 0.033) and frontal cortical thickness (F1,310.6 = 5.30, p = 0.022), reflecting a lesser thinning over time in patients. Clinical and cognitive outcome was not associated with progressive cortical changes during the early years of the illness.

Cortical thickness abnormalities do not unswervingly progress, at least throughout the first years of the illness. Previous studies have suggested that modifiable factors may partly account for cortical thickness abnormalities. Therefore, the importance of implementing practical actions that may modify those factors and improve them over the course of the illness should be highlighted.

Schizophrenia is a chronic brain disorder associated with structural brain abnormalities already present at the onset of the illness. Whether these brain abnormalities might progress over time is still under debate.

The aim of this study was to investigate likely progressive brain volume changes in schizophrenia during the first 3 years after initiating antipsychotic treatment. The study included 109 patients with a schizophrenia spectrum disorder and a control group of 76 healthy subjects. Subjects received detailed clinical and cognitive assessment and structural magnetic resonance imaging (MRI) at regular time points during a 3-year follow-up period. The effects of brain changes on cognitive and clinical variables were examined along with the impact of potential confounding factors.

Overall, patients and healthy controls exhibited a similar pattern of brain volume changes. However, patients showed a significant lower progressive decrease in the volume of the caudate nucleus than control subjects (F1,307.2 = 2.12, p = 0.035), with healthy subjects showing a greater reduction than patients during the follow-up period. Clinical and cognitive outcomes were not associated with progressive brain volume changes during the early years of the illness.

Brain volume abnormalities that have been consistently observed at the onset of non-affective psychosis may not inevitably progress, at least over the first years of the illness. Taking together with clinical and cognitive longitudinal data, our findings, showing a lack of brain deterioration in a substantial number of individuals, suggest a less pessimistic and more reassuring perception of the illness.

Abnormalities in cortical thickness and subcortical structures have been studied in schizophrenia but little is known about corresponding changes in mania and brain structural differences between these two psychiatric conditions, especially early in the stage of the illness. In this study we aimed to compare cortical thickness and shape of the amygdala–hippocampal complex in first-episode schizophrenia (FES) and mania (FEM).

Structural magnetic resonance imaging (MRI) was performed on 28 FES patients, 28 FEM patients and 28 healthy control subjects who were matched for age, gender and handedness.

Overall, the shape of the amygdala was deformed in both patient groups, relative to controls. Compared to FEM patients, FES patients had significant inward shape deformation in the left hippocampal tail, right hippocampal body and a small region in the right amygdala. Cortical thinning was more widespread in FES patients, with significant differences found in the temporal brain regions when compared with FEM and controls.

Significant differences were observed between the two groups of patients with FES and FEM in terms of the hippocampal shape and cortical thickness in the temporal region, highlighting that distinguishable brain structural changes are present early in the course of schizophrenia and mania.

The cerebellum is rich in cannabinoid receptors and implicated in the neuropathology of schizophrenia. Long-term cannabis use is associated with functional and structural brain changes similar to those evident in schizophrenia, yet its impact on cerebellar structure has not been determined. We examined cerebellar grey and white matter in cannabis users with and without schizophrenia.

Seventeen patients with schizophrenia and 31 healthy controls were recruited; 48% of the healthy group and 47% of the patients were long-term heavy cannabis users (mean 19.7 and 17.9 years near daily use respectively). Cerebellar measures were extracted from structural 3-T magnetic resonance imaging (MRI) scans using semi-automated methods, and examined using analysis of covariance (ANCOVA) and correlational analyses.

Cerebellar white-matter volume was reduced in cannabis users with and without schizophrenia compared to healthy non-users, by 29.7% and 23.9% respectively, and by 17.7% in patients without cannabis use. Healthy cannabis users did not differ in white-matter volume from either of the schizophrenia groups. There were no group differences in cerebellar grey matter or total volumes. Total cerebellar volume decreased as a function of duration of cannabis use in the healthy users. Psychotic symptoms and illness duration correlated with cerebellar measures differentially between patients with and without cannabis use.

Long-term heavy cannabis use in healthy individuals is associated with smaller cerebellar white-matter volume similar to that observed in schizophrenia. Reduced volumes were even more pronounced in patients with schizophrenia who use cannabis. Cannabis use may alter the course of brain maturational processes associated with schizophrenia.