Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia

Subregional thalamic functional connectivity abnormalities and cognitive impairments in first-episode schizophrenia

BACKGROUND

Previous studies have documented thalamic functional connectivity (FC) abnormalities in schizophrenia, typically examining the thalamus as a whole. The specific link between subregional thalamic FC and cognitive deficits in first-episode schizophrenia (FES) remains unexplored.

METHODS

Using data from resting-state functional magnetic resonance imaging, we compared whole-brain FC with thalamic subregions between patients and HCs, and analyzed FC changes in drug-naïve patients separately. We then examined correlations between FC abnormalities with both cognitive impairment and clinical symptoms.

RESULTS

A total of 33 FES patients (20 drug-naïve) and 32 age- and sex-matched healthy controls (HCs) were included. Compared to HCs, FES patients exhibited increased FC between specific thalamic subregions and cortical regions, particularly bilateral middle temporal lobe and cuneus gyrus, left medial superior frontal gyrus, and right inferior/superior occipital gyrus. Decreased FC was observed between certain thalamic subregions and the left inferior frontal triangle. These findings were largely consistent in drug-naïve patients. Notably, deficits in social cognition and visual learning in FES patients correlated with increased FC between certain thalamic subregions and cortical regions involving the right superior occipital gyrus and cuneus gyrus. The severity of negative symptoms was associated with increased FC between a thalamic subregion and the left middle temporal gyrus.

CONCLUSION

Our findings suggest FC abnormalities between thalamic subregions and cortical areas in FES patients. Increased FC correlated with cognitive deficits and negative symptoms, highlighting the importance of thalamo-cortical connectivity in the pathophysiology of schizophrenia.

Variable cardiac responses in rhesus macaque monkeys after discrete mediodorsal thalamus manipulations

The control of some physiological parameters, such as the heart rate, is known to have a role in cognitive and emotional processes. Cardiac changes are also linked to mental health issues and neurodegeneration. Thus, it is not surprising that many of the brain structures typically associated with cognition and emotion also comprise a circuit-the central automatic network-responsible for the modulation of cardiovascular output. The mediodorsal thalamus (MD) is involved in higher cognitive processes and is also known to be connected to some of the key neural structures that regulate cardiovascular function. However, it is unclear whether the MD has any role in this circuitry. Here, we show that discrete manipulations (microstimulation during anaesthetized functional neuroimaging or localized cytotoxin infusions) to either the magnocellular or the parvocellular MD subdivisions led to observable and variable changes in the heart rate of female and male rhesus macaque monkeys. Considering the central positions that these two MD subdivisions have in frontal cortico-thalamocortical circuits, our findings suggest that MD contributions to autonomic regulation may interact with its identified role in higher cognitive processes, representing an important physiological link between cognition and emotion.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Thalamocortical Development: A Neurodevelopmental Framework for Schizophrenia

Adolescence is a period of increased vulnerability for the development of psychiatric disorders, including schizophrenia. The prefrontal cortex (PFC) undergoes substantial maturation during this period, and PFC dysfunction is central to cognitive impairments in schizophrenia. As a result, impaired adolescent maturation of the PFC has been proposed as a mechanism in the etiology of the disorder and its cognitive symptoms. In adulthood, PFC function is tightly linked to its reciprocal connections with the thalamus, and acutely inhibiting thalamic inputs to the PFC produces impairments in PFC function and cognitive deficits. Here, we propose that thalamic activity is equally important during adolescence because it is required for proper PFC circuit development. Because thalamic abnormalities have been observed early in the progression of schizophrenia, we further postulate that adolescent thalamic dysfunction can have long-lasting consequences for PFC function and cognition in patients with schizophrenia.

Longitudinal thalamic white and grey matter changes associated with visual hallucinations in Parkinson's disease

OBJECTIVE

Visual hallucinations are common in Parkinson's disease (PD) and associated with worse outcomes. Large-scale network imbalance is seen in PD-associated hallucinations, but mechanisms remain unclear. As the thalamus is critical in controlling cortical networks, structural thalamic changes could underlie network dysfunction in PD hallucinations.

METHODS

We used whole-brain fixel-based analysis and cortical thickness measures to examine longitudinal white and grey matter changes in 76 patients with PD (15 hallucinators, 61 non-hallucinators) and 26 controls at baseline, and after 18 months. We compared white matter and cortical thickness, adjusting for age, gender, time-between-scans and intracranial volume. To assess thalamic changes, we extracted volumes for 50 thalamic subnuclei (25 each hemisphere) and mean fibre cross-section (FC) for white matter tracts originating in each subnucleus and examined longitudinal change in PD-hallucinators versus non-hallucinators.

RESULTS

PD hallucinators showed white matter changes within the corpus callosum at baseline and extensive posterior tract involvement over time. Less extensive cortical thickness changes were only seen after follow-up. White matter connections from the right medial mediodorsal magnocellular thalamic nucleus showed reduced FC in PD hallucinators at baseline followed by volume reductions longitudinally. After follow-up, almost all thalamic subnuclei showed tract losses in PD hallucinators compared with non-hallucinators.

INTERPRETATION

PD hallucinators show white matter loss particularly in posterior connections and in thalamic nuclei, over time with relatively preserved cortical thickness. The right medial mediodorsal thalamic nucleus shows both connectivity and volume loss in PD hallucinations. Our findings provide mechanistic insights into the drivers of network imbalance in PD hallucinations and potential therapeutic targets.

Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia

OBJECTIVE

Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ.

METHODS

We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level.

RESULTS

A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (p_FDR = 0.005). The same measure correlated positively with both social and occupational functioning (both p_FDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC.

CONCLUSIONS

Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.

Thalamic connectivity system across psychiatric disorders: Current status and clinical implications

The thalamic connectivity system, with the thalamus as the central node, enables transmission of the brain’s neural computations via extensive connections to cortical, subcortical, and cerebellar regions. Emerging reports suggest deficits in this system across multiple psychiatric disorders, making it a unique network of high translational and transdiagnostic utility in mapping neural alterations that potentially contribute to symptoms and disturbances in psychiatric patients. However, despite considerable research effort, it is still debated how this system contributes to psychiatric disorders. This review characterizes current knowledge regarding thalamic connectivity system deficits in psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder, across multiple levels of the system. We identify the presence of common and distinct patterns of deficits in the thalamic connectivity system in major psychiatric disorders and assess their nature and characteristics. Specifically, this review assembles evidence for the hypotheses of 1) thalamic microstructure, particularly in the mediodorsal nucleus, as a state marker of psychosis; 2) thalamo-prefrontal connectivity as a trait marker of psychosis; and 3) thalamo-somatosensory/parietal connectivity as a possible marker of general psychiatric illness. Furthermore, possible mechanisms contributing to thalamocortical dysconnectivity are explored. We discuss current views on the contributions of cerebellar-thalamic connectivity to the thalamic connectivity system and propose future studies to examine its effects at multiple levels, from the molecular (e.g., glutamatergic) to the behavioral (e.g., cognition), to gain a deeper understanding of the mechanisms that underlie the disturbances observed in psychiatric disorders.

Accelerated aging signatures in subjects with schizophrenia and their unaffected siblings

Schizophrenia (SZ) is a chronic debilitating disease. Subjects with SZ have significant shorter life expectancy. Growing evidence suggests that a process of pathological accelerated aging occurs in SZ, leading to early development of severe clinical diseases and worse morbimortality. Furthermore, unaffected relatives can share certain endophenotypes with subjects with SZ. We aim to characterize accelerated aging as a possible endophenotype of schizophrenia by using a machine learning (ML) model of peripheral biomarkers to accurately differentiate subjects with SZ (n = 35), their unaffected siblings (SB, n = 36) and healthy controls (HC, n = 47). We used a random forest algorithm that included biomarkers related to aging: eotaxins CCL-11 and CCL-24; the oxidative stress markers thiobarbituric acid-reactive substances (TBARS), protein carbonyl content (PCC), glutathione peroxidase (GPx); and telomere length (TL). The ML algorithm of biomarkers was able to distinguish individuals with SZ from HC with prediction accuracy of 79.7%, SZ from SB with 62.5% accuracy and SB from HC with 75.5% accuracy. These results support the hypothesis that a pathological accelerated aging might occur in SZ, and this pathological aging could be an endophenotype of the disease, once this profile was also observed in SB, suggesting that SB might suffer from an accelerated aging in some level.

Molecular cell identities in the mediodorsal thalamus of infant mice and marmoset

The mediodorsal thalamus (MD) is a higher-order nucleus located within the central thalamus in many mammalian species. Emerging evidence from MD lesions and tracer injections suggests that the MD is reciprocally connected to the prefrontal cortex (PFC) and plays an essential role in specific cognitive processes and tasks. MD subdivisions (medial, central, and lateral) are poorly segregated at the molecular level in rodents, leading to a lack of MD subdivision-specific Cre driver mice. Moreover, this lack of molecular identifiers hinders MD subdivision- and cell-type-specific circuit formation and function analysis. Therefore, using publicly available databases, we explored molecules separately expressed in MD subdivisions. In addition to MD subdivision markers, we identified several genes expressed in a subdivision-specific combination and classified them. Furthermore, after developing medial MD (MDm) or central MD (MDc) region-specific Cre mouse lines, we identified diverse region- and layer-specific PFC projection patterns. Comparison between classified MD marker genes in mice and common marmosets, a nonhuman primate model, revealed diverging gene expression patterns. These results highlight the species-specific organization of cell types and their projections in the MD thalamus.

Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity

At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.

Intact thalamic microstructure in asymptomatic relatives of schizophrenia patients with high genetic loading

BACKGROUND

Disrupted thalamic connectivity system, which encompasses the deficits in the thalamus and thalamocortical connectivity, is regarded to contribute to the pathophysiology of schizophrenia. Recent reports suggest the possible genetic contribution to the disrupted thalamo-prefrontal connectivity, however, research on elucidating thalamic connectivity system components, specifically the thalamic nuclei, associated with the genetic predisposition to schizophrenia has been limited. Here, we investigated the genetic aspects of thalamic nuclei-specific microstructural integrities in schizophrenia.

METHODS

A total of 34 asymptomatic relatives of schizophrenia patients with high genetic loading and 33 healthy control subjects underwent diffusion tensor imaging, diffusion kurtosis imaging, and T1-weighted magnetic resonance imaging. The thalamus was segmented via a connectivity-based segmentation method using the region-of-interest masks. The microstructural integrity of each thalamic nucleus, measured by averages of the diffusion kurtosis values, was then compared between the groups.

RESULTS

The volumetric and mean kurtosis values of the thalamic nuclei were intact in asymptomatic relatives of schizophrenia patients with high genetic loading.

CONCLUSIONS

Our results revealed that, in the thalamic connectivity system, the genetics may hold different weights of effects on different components, and that more is given on the thalamo-prefrontal connectivity than on the thalamus. Further, the current results may add further evidence to the current literature that thalamic nuclei microstructural abnormalities present in psychosis may have state marker characteristics.

Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

Background

Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance.

Methods

Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities.

Results

The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03).

Conclusion

Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-021-00552-3.

Flexible and specific contributions of thalamic subdivisions to human cognition

The thalamus participates in multiple functional brain networks supporting different cognitive abilities. How thalamo-cortical connections map onto the architecture of human cognition remains an outstanding question. The aim of this meta-analysis is to map co-activation between thalamic and extra-thalamic brain regions onto separate cognitive domains and to assess thalamic subdivision specificity within each of the cognitive domains considered. We parsed 93 fMRI studies into twelve cognitive domains. Signed Differential Mapping served to obtain co-activation maps. We then projected the contribution of thalamic subdivisions onto a thalamic atlas to assess cognitive domain specificity. A set of brain regions was flexibly involved with thalamus in several cognitive domains. Thalamic subdivisions showed ample cognitive heterogeneity. Our proposed model represents thalamic involvement in cognition as an "ensemble" of functional subdivisions with common cell properties embedded in separate cortical circuits rather than a homogeneous functional unit.

Thalamus-related anomalies as candidate mechanism-based biomarkers for psychosis

Identification of reliable biomarkers of prognosis in subjects with high risk to psychosis is an essential step to improve care and treatment of this population of help-seekers. Longitudinal studies highlight some clinical criteria, cognitive deficits, patterns of gray matter alterations and profiles of blood metabolites that provide some levels of prediction regarding the conversion to psychosis. Further effort is warranted to validate these results and implement these types of approaches in clinical settings. Such biomarkers may however fall short in entangling the biological mechanisms underlying the disease progression, an essential step in the development of novel therapies. Circuit-based approaches, which map on well-identified cerebral functions, could meet these needs. Converging evidence indicates that thalamus abnormalities are central to schizophrenia pathophysiology, contributing to clinical symptoms, cognitive and sensory deficits. This review highlights the various thalamus-related anomalies reported in individuals with genetic risks and in the different phases of the disorder, from prodromal to chronic stages. Several anomalies are potent endophenotypes, while others exist in clinical high-risk subjects and worsen in those who convert to full psychosis. Aberrant functional coupling between thalamus and cortex, low glutamate content and readouts from resting EEG carry predictive values for transition to psychosis or functional outcome. In this context, thalamus-related anomalies represent a valuable entry point to tackle circuit-based alterations associated with the emergence of psychosis. This review also proposes that longitudinal surveys of neuroimaging, EEG readouts associated with circuits encompassing the mediodorsal, pulvinar in high-risk individuals could unveil biological mechanisms contributing to this psychiatric disorder.

Thalamic Nuclei Volumes in Psychotic Disorders and in Youths With Psychosis Spectrum Symptoms

OBJECTIVE

Thalamus models of psychosis implicate association nuclei in the pathogenesis of psychosis and mechanisms of cognitive impairment. Studies to date have provided conflicting findings for structural deficits specific to these nuclei. The authors sought to characterize thalamic structural abnormalities in psychosis and a neurodevelopmental cohort, and to determine whether nuclear volumes were associated with cognitive function.

METHODS

Thalamic nuclei volumes were tested in a cross-sectional sample of 472 adults (293 with psychosis) and the Philadelphia Neurodevelopmental Cohort (PNC), consisting of 1,393 youths (398 with psychosis spectrum symptoms and 609 with other psychopathologies), using a recently developed, validated method for segmenting thalamic nuclei and complementary voxel-based morphometry. Cognitive function was measured with the Screen for Cognitive Impairment in Psychiatry in the psychosis cohort and the Penn Computerized Neurocognitive Battery in the PNC.

RESULTS

The psychosis group had smaller pulvinar, mediodorsal, and, to a lesser extent, ventrolateral nuclei volumes compared with the healthy control group. Youths with psychosis spectrum symptoms also had smaller pulvinar volumes, compared with both typically developing youths and youths with other psychopathologies. Pulvinar volumes were positively correlated with general cognitive function.

CONCLUSIONS

The study findings demonstrate that smaller thalamic association nuclei represent a neurodevelopmental abnormality associated with psychosis, risk for psychosis in youths, and cognitive impairment. Identifying specific thalamic nuclei abnormalities in psychosis has implications for early detection of psychosis risk and treatment of cognitive impairment in psychosis.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.

Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals

BACKGROUND

Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case-control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities.

METHODS

Following a previous report, we used Random Forests to predict diagnosis in a case-control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire.

RESULTS

Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features.

CONCLUSIONS

These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease.

Brain structural correlates of familial risk for mental illness: a meta-analysis of voxel-based morphometry studies in relatives of patients with psychotic or mood disorders

Schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) are heritable psychiatric disorders with partially overlapping genetic liability. Shared and disorder-specific neurobiological abnormalities associated with familial risk for developing mental illnesses are largely unknown. We performed a meta-analysis of structural brain imaging studies in relatives of patients with SCZ, BD, and MDD to identify overlapping and discrete brain structural correlates of familial risk for mental disorders. Search for voxel-based morphometry studies in relatives of patients with SCZ, BD, and MDD in PubMed and Embase identified 33 studies with 2292 relatives and 2052 healthy controls (HC). Seed-based d Mapping software was used to investigate global differences in gray matter volumes between relatives as a group versus HC, and between those of each psychiatric disorder and HC. As a group, relatives exhibited gray matter abnormalities in left supramarginal gyrus, right striatum, right inferior frontal gyrus, left thalamus, bilateral insula, right cerebellum, and right superior frontal gyrus, compared with HC. Decreased right cerebellar gray matter was the only abnormality common to relatives of all three conditions. Subgroup analyses showed disorder-specific gray matter abnormalities in left thalamus and bilateral insula associated with risk for SCZ, in left supramarginal gyrus and right frontal regions with risk for BD, and in right striatum with risk for MDD. While decreased gray matter in right cerebellum might be a common brain structural abnormality associated with shared risk for SCZ, BD, and MDD, regional gray matter abnormalities in neocortex, thalamus, and striatum appear to be disorder-specific.

Abnormal Development and Dysconnectivity of Distinct Thalamic Nuclei in Patients With 22q11.2 Deletion Syndrome Experiencing Auditory Hallucinations

BACKGROUND

Several studies in patients with schizophrenia have demonstrated an abnormal thalamic volume and thalamocortical connectivity. Specifically, hyperconnectivity with somatosensory areas has been related to the presence of auditory hallucinations (AHs). The 22q11.2 deletion syndrome is a neurogenetic disorder conferring proneness to develop schizophrenia, and deletion carriers (22qdel carriers) experience hallucinations to a greater extent than the general population.

METHODS

We acquired 442 consecutive magnetic resonance imaging scans from 120 22qdel carriers and 110 control subjects every 3 years (age range: 8-35 years). The volume of thalamic nuclei was obtained with FreeSurfer and was compared between 22qdel carriers and control subjects and between 22qdel carriers with and without AHs. In a subgroup of 76 22qdel carriers, we evaluated the functional connectivity between thalamic nuclei affected in patients experiencing AHs and cortical regions.

RESULTS

As compared with control subjects, 22qdel carriers had lower and higher volumes of nuclei involved in sensory processing and cognitive functions, respectively. 22qdel carriers with AHs had a smaller volume of the medial geniculate nucleus, with deviant trajectories showing a steeper volume decrease from childhood with respect to those without AHs. Moreover, we showed an aberrant development of nuclei intercalated between the prefrontal cortex and hippocampus (the anteroventral and medioventral reuniens nuclei) and hyperconnectivity of the medial geniculate nucleus and anteroventral nucleus with the auditory cortex and Wernicke's area.

CONCLUSIONS

The increased connectivity of the medial geniculate nucleus and anteroventral nucleus to the auditory cortex might be interpreted as a lack of maturation of thalamocortical connectivity. Overall, our findings point toward an aberrant development of thalamic nuclei and an immature pattern of connectivity with temporal regions in relation to AHs.

Thalamic Volume Is Related to Increased Anterior Thalamic Radiations in Children with Reactive Attachment Disorder

Reactive attachment disorder (RAD) is associated with childhood maltreatment and affects approximately 1% of the general population. Recent data suggest that childhood maltreatment is associated with brain alterations in white and gray matter. However, the neural mechanisms of RAD-related brain alterations remain unknown. Herein, we evaluated the white matter pathways and gray matter volumes in 31 and 41 age-matched children with RAD and typical development (TD), respectively, by analyzing T1- and diffusion-weighted images. An increased fractional anisotropy (FA) and axial diffusivity in the anterior thalamic radiations (ATR) and an increased volume in the bilateral pallidum and right thalamus were observed in children with RAD compared with those with TD. Moreover, the volume of the thalamus was associated with increased ATR FA in children with RAD. Our study confirmed the existence of atypical neurodevelopment processes in the thalamus, pallidum, and ATR in children with RAD and highlighted an interdependent relationship between the alterations in the thalamus and ATR. These findings may help to improve our understanding of the comprehensive neural mechanisms of RAD.

Normative ultrasound data of the fetal transverse thalamic diameter derived from 18 to 22 weeks of gestation in routine second-trimester morphology examinations

Introduction

The thalamus is important for a wide range of sensorimotor and neuropsychiatric functions. Departure from normal reference values of the thalamus may be a biomarker for differences in neurodevelopment outcomes and brain anomalies perinatally. Antenatal measurement of thalamus is not currently included in routine fetal ultrasound as differentiation of thalamic borders is difficult. The aim of this work was to present a method to standardise the thalamus measure and provide normative data of the fetal transverse thalamic diameter between 18 and 22 weeks of gestational age.

Methods

Transverse thalamic diameter was measured by two sonographers on 1,111 stored ultrasound images at the standard transcerebellar plane. A 'guitar' shape representative structure is presented to demarcate the thalamic diameter. The relationship of the transverse thalamic diameter with gestational age, head circumference and transcerebellar diameter using linear regression modelling was assessed, and the mean of the thalamic diameter was calculated and plotted as a reference chart.

Results

Transverse thalamic diameter increased significantly with increasing gestational age, head circumference, and transcerebellar diameter linearly, and normal range thalamic charts are presented. The guitar shape provided good reproducibility of thalamic diameter measures.

Conclusion

Measuring thalamus size in antenatal ultrasound examinations with reference to normative charts could be used to assess midline brain structures and predict neurodevelopment disorders and potentially brain anomalies.

The psychosis risk timeline: can we improve our preventive strategies? Part 3: primary common pathways and preventive strategies

Psychosis is a recognised feature of several psychiatric disorders and it causes patients significant distress and morbidity. It is therefore important to keep knowledge of possible risk factors for psychosis up to date and to have an overview model on which further learning can be structured. This article concludes a three-part series. It gives a review of evidence regarding common pathways by which many risk factors come together to influence the development of psychosis and finalises our suggested overview model, a psychosis risk timeline. The three primary pathways considered are based on the major themes identified in this narrative review of recent literature and they focus on neurological, neurochemical and inflammatory changes. We link each back to the factors discussed in the first and second parts of this series that alter psychosis risk through different mechanisms and at different stages throughout life. We then consider and summarise key aspects of this complex topic with the aim of providing current and future clinicians with a model on which to build their knowledge and begin to access and understand current psychosis research and implications for future preventive work.

LEARNING OBJECTIVES

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DECLARATION OF INTEREST

None.

Disrupted modulation of thalamus activation and thalamocortical connectivity during dual task performance in schizophrenia

Despite considerable evidence showing thalamus anatomy and connectivity abnormalities in schizophrenia, how these abnormalities are reflected in thalamus function during cognition is relatively understudied. Modulation of thalamic connectivity with the prefrontal cortex (PFC) is required for higher-order cognitive processes, which are often impaired in schizophrenia. To address this gap, we investigated how thalamus function and thalamus-PFC connectivity under different levels of cognitive demand may be disrupted in schizophrenia. Participants underwent fMRI scanning while performing an event-related two-alternative forced choice task under Single and Dual task conditions. In the Single task condition, participants responded either to a visual cue with a well-learned motor response, or an audio cue with a well-learned vocal response. In the Dual task condition, participants performed both tasks. Thalamic connectivity with task relevant regions of the PFC for each condition was measured using beta-series correlation. Individuals with schizophrenia demonstrated less modulation of both mediodorsal thalamus activation and thalamus-PFC connectivity with increased cognitive demand. In contrast, their ability to modulate PFC function during task performance was maintained. These results suggest that the pathophysiology of cognitive impairment in schizophrenia is associated with thalamus-PFC circuitry and suggests that the thalamus, along with the PFC, should be a focus of investigation.

Enhanced Global-Brain Functional Connectivity in the Left Superior Frontal Gyrus as a Possible Endophenotype for Schizophrenia

The notion of dysconnectivity in schizophrenia has been put forward for many years and results in substantial attempts to explore altered functional connectivity (FC) within different networks with inconsistent results. Clinical, demographical, and methodological heterogeneity may contribute to the inconsistency. Forty-four patients with first-episode, drug-naive schizophrenia, 42 unaffected siblings of schizophrenia patients and 44 healthy controls took part in this study. Global-brain FC (GFC) was employed to analyze the imaging data. Compared with healthy controls, patients with schizophrenia and unaffected siblings shared enhanced GFC in the left superior frontal gyrus (SFG). In addition, patients had increased GFC mainly in the thalamo-cortical network, including the bilateral thalamus, bilateral posterior cingulate cortex (PCC)/precuneus, left superior medial prefrontal cortex (MPFC), right angular gyrus, and right SFG/middle frontal gyrus and decreased GFC in the left ITG/cerebellum Crus I. No other altered GFC values were observed in the siblings group relative to the control group. Further ROC analysis showed that increased GFC in the left SFG could separate the patients or the siblings from the controls with acceptable sensitivities. Our findings suggest that increased GFC in the left SFG may serve as a potential endophenotype for schizophrenia.

The Regulatory Role of the Human Mediodorsal Thalamus

The function of the human mediodorsal thalamic nucleus (MD) has so far eluded a clear definition in terms of specific cognitive processes and tasks. Although it was at first proposed to play a role in long-term memory, a set of recent studies in animals and humans has revealed a more complex, and broader, role in several cognitive functions. The MD seems to play a multifaceted role in higher cognitive functions together with the prefrontal cortex and other cortical and subcortical brain areas. Specifically, we propose that the MD is involved in the regulation of cortical networks especially when the maintenance and temporal extension of persistent activity patterns in the frontal lobe areas are required.

Slow Binocular Rivalry as a Potential Endophenotype of Schizophrenia

Objectives: Binocular rivalry is a typical example of bistable perception that arises when two monocular images are simultaneously presented to each eye. Binocular rivalry is a heritable perceptual cognitive function that is impaired in patients with schizophrenia (SZ). Despite its potential suitability as a visual endophenotype, binocular rivalry has hardly been studied in the unaffected siblings of schizophrenia (SIB). There is also little research about whether binocular rivalry is a potential visual endophenotype between SZ and SIB. Methods: In our cross-sectional study, we included 40 SZ and their unaffected SIBs, as well as 40 age- and sex-matched healthy controls (HC). All subjects underwent the binocular rivalry test, the Positive and Negative Syndrome Scale (PANSS) and a battery of cognitive neuropsychological assessments evaluating attention, memory and executive function domains. Results: Our results demonstrate that the switching rate in SZ was significantly slower than in HC (p < 0.001), and compared to the SIB, the mean alternation rates were significantly different (p < 0.01). Moreover, there was a significant difference in mean switching rate between the SIB and the HC (p < 0.001). There was no significant correlation between the alternation rate of binocular rivalry and these cognitive tasks and the PANSS scores. Conclusion: The present study shows that SZ and SIB both exhibit changes in binocular rivalry, with SIB exhibiting intermediate performance compared with that of SZ and the HC. This supports the claim that the switching rate for SZ differs from that of SIB and suggests that binocular rivalry may qualify as a visual endophenotype for SZ.

Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI

Background

Schizophrenia is a clinical syndrome, and its causes have not been well determined. The objective of this study was to investigate the alteration of brain functional connectivity between schizophrenia and healthy control, and present a practical solution for accurately identifying schizophrenia at single-subject level.

Methods

24 schizophrenia patients and 21 matched healthy subjects were recruited to undergo the resting-state functional magnetic resonance imaging (rs-fMRI) scanning. First, we constructed the brain network by calculating the Pearson correlation coefficient between each pair of the brain regions. Then, this study proposed a novel non-negative discriminant functional connectivity selection method, i.e. non-negative elastic-net based method (N2EN), to extract the alteration of brain functional connectivity between schizophrenia and healthy control. It ranks the significance of the connectivity with a uniform criterion by introducing the non-negative constraint. Finally, kernel discriminant analysis (KDA) is exploited to classify the subjects with the selected discriminant brain connectivity features.

Results

The proposed method is applied into schizophrenia classification, and achieves the sensitivity, specificity and accuracy of 100, 90.48 and 95.56%, respectively. Our findings also indicate the alteration of functional network can be used as the biomarks for guiding the schizophrenia diagnosis. The regions of cuneus, superior frontal gyrus, medial, paracentral lobule, calcarine fissure, surrounding cortex, etc. are highly relevant to schizophrenia.

Conclusions

This study provides a method for accurately identifying schizophrenia, which outperforms several state-of-the-art methods, including conventional brain network classification, multi-threshold brain network based classification, frequent sub-graph based brain network classification and support vector machine. Our investigation suggested that the selected discriminant resting-state functional connectivities are meaningful features for classifying schizophrenia and healthy control.

Cognitive Functions and Neurodevelopmental Disorders Involving the Prefrontal Cortex and Mediodorsal Thalamus

The mediodorsal nucleus of the thalamus (MD) has been implicated in executive functions (such as planning, cognitive control, working memory, and decision-making) because of its significant interconnectivity with the prefrontal cortex (PFC). Yet, whilst the roles of the PFC have been extensively studied, how the MD contributes to these cognitive functions remains relatively unclear. Recently, causal evidence in monkeys has demonstrated that in everyday tasks involving rapid updating (e.g., while learning something new, making decisions, or planning the next move), the MD and frontal cortex are working in close partnership. Furthermore, researchers studying the MD in rodents have been able to probe the underlying mechanisms of this relationship to give greater insights into how the frontal cortex and MD might interact during the performance of these essential tasks. This review summarizes the circuitry and known neuromodulators of the MD, and considers the most recent behavioral, cognitive, and neurophysiological studies conducted in monkeys and rodents; in total, this evidence demonstrates that MD makes a critical contribution to cognitive functions. We propose that communication occurs between the MD and the frontal cortex in an ongoing, fluid manner during rapid cognitive operations, via the means of efference copies of messages passed through transthalamic routes; the conductance of these messages may be modulated by other brain structures interconnected to the MD. This is similar to the way in which other thalamic structures have been suggested to carry out forward modeling associated with rapid motor responding and visual processing. Given this, and the marked thalamic pathophysiology now identified in many neuropsychiatric disorders, we suggest that changes in the different subdivisions of the MD and their interconnections with the cortex could plausibly give rise to a number of the otherwise disparate symptoms (including changes to olfaction and cognitive functioning) that are associated with many different neuropsychiatric disorders. In particular, we will focus here on the cognitive symptoms of schizophrenia and suggest testable hypotheses about how changes to MD-frontal cortex interactions may affect cognitive processes in this disorder.

Response to Targeted Cognitive Training Correlates with Change in Thalamic Volume in a Randomized Trial for Early Schizophrenia

Reduced thalamic volume is consistently observed in schizophrenia, and correlates with cognitive impairment. Targeted cognitive training (TCT) of auditory processing in schizophrenia drives improvements in cognition that are believed to result from functional neuroplasticity in prefrontal and auditory cortices. In this study, we sought to determine whether response to TCT is also associated with structural neuroplastic changes in thalamic volume in patients with early schizophrenia (ESZ). Additionally, we examined baseline clinical, cognitive, and neural characteristics predictive of a positive response to TCT. ESZ patients were randomly assigned to undergo either 40 h of TCT (N=22) or a computer games control condition (CG; N=22 s). Participants underwent MRI, clinical, and neurocognitive assessments before and after training (4-month interval). Freesurfer automated segmentation of the subcortical surface was carried out to measure thalamic volume at both time points. Left thalamic volume at baseline correlated with baseline global cognition, while a similar trend was observed in the right thalamus. The relationship between change in cognition and change in left thalamus volume differed between groups, with a significant positive correlation in the TCT group and a negative trend in the CG group. Lower baseline symptoms were related to improvements in cognition and left thalamic volume preservation following TCT. These findings suggest that the cognitive gains induced by TCT in ESZ are associated with structural neuroplasticity in the thalamus. Greater symptom severity at baseline reduced the likelihood of response to TCT both with respect to improved cognition and change in thalamic volume.

Functional analysis of schizophrenia genes using GeneAnalytics program and integrated databases

Schizophrenia (SCZ) is a chronic debilitating neuropsychiatric disorder with multiple risk factors involving numerous complex genetic influences. We examined and updated a master list of clinically relevant and susceptibility genes associated with SCZ reported in the literature and genomic databases dedicated to gene discovery for characterization of SCZ genes. We used the commercially available GeneAnalytics computer-based gene analysis program and integrated genomic databases to create a molecular profile of the updated list of 608 SCZ genes to model their impact in select categories (tissues and cells, diseases, pathways, biological processes, molecular functions, phenotypes and compounds) using specialized GeneAnalytics algorithms. Genes for schizophrenia were predominantly expressed in the cerebellum, cerebral cortex, medulla oblongata, thalamus and hypothalamus. Psychiatric/behavioral disorders incorporating SCZ genes included ADHD, bipolar disorder, autism spectrum disorder and alcohol dependence as well as cancer, Alzheimer's and Parkinson's disease, sleep disturbances and inflammation. Function based analysis of major biological pathways and mechanisms associated with SCZ genes identified glutaminergic receptors (e.g., GRIA1, GRIN2, GRIK4, GRM5), serotonergic receptors (e.g., HTR2A, HTR2C), GABAergic receptors (e.g., GABRA1, GABRB2), dopaminergic receptors (e.g., DRD1, DRD2), calcium-related channels (e.g., CACNA1H, CACNA1B), solute transporters (e.g., SLC1A1, SLC6A2) and for neurodevelopment (e.g., ADCY1, MEF2C, NOTCH2, SHANK3). Biological mechanisms involving synaptic transmission, regulation of membrane potential and transmembrane ion transport were identified as leading molecular functions associated with SCZ genes. Our approach to interrogate SCZ genes and their interactions at various levels has increased our knowledge and insight into the disease process possibly opening new avenues for therapeutic intervention.

Grey Matter Volume Patterns in Thalamic Nuclei are Associated with Schizotypy in Healthy Subjects

Introduction

Schizotypy refers to a set of temporally stable traits that are observed in the general population and that resemble, in attenuated form, the symptoms of schizophrenia. In a previous work, we identified volumetric patterns in thalamic subregions which were associated with disease status, and trained a random forests classifier, accounting for such thalamic volumetric patterns, that discriminated healthy controls (HC) from patients with schizophrenia (SCZ) (81% accuracy) [1].

Objectives

i) to assess performance of random forests classifier developed by Pergola and coworkers [1], in an independent sample of healthy subjects; ii) to test whether false positives (FP), i.e. HC classified as SCZ based on such classifier would be associated with greater schizotypy compared with true negatives (TN), i.e. HC classified as such.

Methods

A total of 167 HC participated in the MRI study and filled the Schizotypal Personality Questionnaire (SPQ). We pre-processed MRI data with SPM8 and DARTEL. Then, we used thalamic grey matter volumes (GMV) as features in the random forests prediction of disease status at the single subject level. Finally, we tested SPQ scores differences between FP and TN with Mann-Whitney test.

Results

The classification accuracy was 71%. FP had greater SPQ scores compared to TN (P = 0.007).

Conclusions

Classification accuracy of our classifier in an independent sample suggests that thalamic GMV patterns are reproducible markers of disease status. Furthermore, the present results also suggest that variability of thalamic GMV patterns in HC may have relevance for subclinical phenotypes related to schizophrenia spectrum.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Topological Measurements of DWI Tractography for Alzheimer's Disease Detection

Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimer's disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%-99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.