Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data

Connectional-style-guided contextual representation learning for brain disease diagnosis

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

Speech based natural language profile before, during and after the onset of psychosis: A cluster analysis

BACKGROUND AND HYPOTHESIS

Speech markers are digitally acquired, computationally derived, quantifiable set of measures that reflect the state of neurocognitive processes relevant for social functioning. "Oddities" in language and communication have historically been seen as a core feature of schizophrenia. The application of natural language processing (NLP) to speech samples can elucidate even the most subtle deviations in language. We aim to determine if NLP based profiles that are distinctive of schizophrenia can be observed across the various clinical phases of psychosis.

DESIGN

Our sample consisted of 147 participants and included 39 healthy controls (HC), 72 with first-episode psychosis (FEP), 18 in a clinical high-risk state (CHR), 18 with schizophrenia (SZ). A structured task elicited 3 minutes of speech, which was then transformed into quantitative measures on 12 linguistic variables (lexical, syntactic, and semantic). Cluster analysis that leveraged healthy variations was then applied to determine language-based subgroups.

RESULTS

We observed a three-cluster solution. The largest cluster included most HC and the majority of patients, indicating a 'typical linguistic profile (TLP)'. One of the atypical clusters had notably high semantic similarity in word choices with less perceptual words, lower cohesion and analytical structure; this cluster was almost entirely composed of patients in early stages of psychosis (EPP - early phase profile). The second atypical cluster had more patients with established schizophrenia (SPP - stable phase profile), with more perceptual but less cognitive/emotional word classes, simpler syntactic structure, and a lack of sufficient reference to prior information (reduced givenness).

CONCLUSION

The patterns of speech deviations in early and established stages of schizophrenia are distinguishable from each other and detectable when lexical, semantic and syntactic aspects are assessed in the pursuit of 'formal thought disorder'.

Cortical impoverishment in a stable subgroup of schizophrenia: Validation across various stages of psychosis

BACKGROUND

Cortical thinning is a well-known feature in schizophrenia. The considerable variation in the spatial distribution of thickness changes has been used to parse heterogeneity. A 'cortical impoverishment' subgroup with a generalized reduction in thickness has been reported. However, it is unclear if this subgroup is recoverable irrespective of illness stage, and if it relates to the glutamate hypothesis of schizophrenia.

METHODS

We applied hierarchical cluster analysis to cortical thickness data from magnetic resonance imaging scans of three datasets in different stages of psychosis (n = 288; 160 patients; 128 healthy controls) and studied the cognitive and symptom profiles of the observed subgroups. In one of the samples, we also studied the subgroup differences in 7-Tesla magnetic resonance spectroscopy glutamate concentration in the dorsal anterior cingulate cortex.

RESULTS

Our consensus-based clustering procedure consistently produced 2 subgroups of participants. Patients accounted for 75%-100% of participants in one subgroup that was characterized by significantly lower cortical thickness. Both subgroups were equally symptomatic in clinically unstable stages, but cortical impoverishment indicated a higher symptom burden in a clinically stable sample and higher glutamate levels in the first-episode sample. There were no subgroup differences in cognitive and functional outcome profiles or antipsychotic exposure across all stages.

CONCLUSIONS

Cortical thinning does not vary with functioning or cognitive impairment, but it is more prevalent among patients, especially those with glutamate excess in early stages and higher residual symptom burden at later stages, providing an important mechanistic clue to one of the several possible pathways to the illness.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.

Influence of TMX2-CTNND1 polymorphism on cortical thickness in schizophrenia patients and unaffected siblings: an exploratory study based on target region sequencing

OBJECTIVE

The advancement of neuroimaging and genetic research has revealed the presence of morphological abnormalities and numerous risk genes, along with their associations. We aimed to estimate magnetic resonance imaging-derived cortical thickness across multiple brain regions.

METHODS

The cortical thickness of 129 schizophrenia patients, 42 of their unaffected siblings, and 112 healthy controls was measured and the candidate genes were sequenced. Comparisons were made of cortical thickness (including 68 regions of the Desikan-Killiany Atlas) and genetic variants (in 108 risk genes for schizophrenia) among the three groups, and correlation analyses were performed regarding cortical thickness, clinical symptoms, cognitive tests (such as the N-back task and the logical memory test), and genetic variants.

RESULTS

Schizophrenia patients had significantly thinner bilateral frontal, temporal, and parietal gyri than healthy controls and unaffected siblings. Association analyses in target genes showed that four single nucleotide variants (SNVs) were significantly associated with schizophrenia, including thioredoxin-related transmembrane protein 2-catenin, cadherin-associated protein, delta 1 (SNV20673) (positive false discovery rate [PFDR] = 0.008) and centromere protein M (rs35542507, rs41277477, rs73165153) (PFDR = 0.030). Additionally, cortical thickness in the right pars triangularis was lower in carriers of the SNV20673 variant than in non-carriers (PFDR = 0.048). Finally, a positive correlation was found between right pars triangularis cortical thickness and logical memory in schizophrenia patients (r = 0.199, p = 0.032).

CONCLUSIONS

This study identified regional morphological abnormalities in schizophrenia, including the right homologue of Broca's area, which was associated with a risk variant that affected delta-1 catenin and logical memory. These findings suggest a potential association between candidate gene loci, cortical thickness, and schizophrenia.

Early-onset schizophrenia is associated with immune-related rare variants in a Chinese sample

Background

Rare variants are likely to contribute to schizophrenia (SCZ), given the large discrepancy between the heritability estimated from twin and GWAS studies. Furthermore, the nature of the rare-variant contribution to SCZ may vary with the "age-at-onset" (AAO), since early-onset has been suggested as being indicative of neurodevelopment deviance.

Objective

To examine the association of rare deleterious coding variants in early- and adult-onset SCZ in a Chinese sample.

Method

Exome sequencing was performed on DNA from 197 patients with SCZ spectrum disorder and 82 healthy controls (HC) of Chinese ancestry recruited in Hong Kong. We also gathered AAO information in the majority of SCZ samples. Patients were classified into early-onset (EOS, AAO<18) and adult-onset (AOS, AAO>18). We collapsed the rare variants to improve statistical power and examined the overall association of rare variants in SCZ versus HC, EOS versus HC, and AOS versus HC at the gene and gene-set levels by Sequence Kernel Association Test. The quantitative rare-variant association test of AAO was also conducted. We focused on variants which were predicted to have a medium or high impact on the protein-encoding process as defined by Ensembl. We applied a 100000-time permutation test to obtain empirical p-values, with significance threshold set at p < 1e -3 to control family-wise error rates. Moreover, we compared the burden of targeted rare variants in significant risk genes and gene sets in cases and controls.

Results

Based on several binary-trait association tests (i.e., SCZ vs HC, EOS vs HC and AOS vs HC), we identified 7 candidate risk genes and 20 gene ontology biological processes (GOBP) terms, which exhibited higher burdens in SCZ than in controls. Based on quantitative rare-variant association tests, we found that alterations in 5 candidate risk genes and 7 GOBP pathways were significantly correlated with AAO. Based on biological and functional profiles of the candidate risk genes and gene sets, our findings suggested that, in addition to the involvement of perturbations in neural systems in SCZ in general, altered immune responses may be specifically implicated in EOS.

Conclusion

Disrupted immune responses may exacerbate abnormal perturbations during neurodevelopment and trigger the early onset of SCZ. We provided evidence of rare variants increasing SCZ risk in the Chinese population.

Polygenic risk for schizophrenia and the language network: Putative compensatory reorganization in unaffected siblings

Language-related symptoms, such as disorganized, impoverished speech and communicative behaviors, are one of the core features of schizophrenia. These features most strongly correlate with cognitive deficits and polygenic risk among various symptom dimensions of schizophrenia. Nevertheless, unaffected siblings with genetic high-risk fail to show consistent deficits in language network (LN), indicating that either (1) polygenic risk has no notable effect on LN and/or (2) siblings show compensatory changes in opposing direction to patients. To answer this question, we related polygenic risk scores (PRS) to the region-level, tract-level, and systems-level structure (cortical thickness and fiber connectivity) of LN in 182 patients, 48 unaffected siblings and 135 healthy controls. We also studied the relationships between symptoms, language-related cognition, social functioning and LN structure. We observed a significantly lower thickness in LN (especially the Broca's, Wernicke's area and their right homologues) in patients. Siblings had a distinctly higher thickness in parts of the LN and a more pronounced small-world-like structural integration within the LN. Patients with reduced LN thickness had higher PRS, more disorganization and impoverished speech with lower language-related cognition and social functioning. We conclude that the genetic susceptibility and putative compensatory changes for schizophrenia operate, in part, via key regions in the Language Network.

Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia

BACKGROUND

Grey matter (GM) reduction is a consistent observation in established late stages of schizophrenia, but patients in the untreated early stages of illness display an increase as well as a decrease in GM distribution relative to healthy controls (HC). The relative excess of GM may indicate putative compensatory responses, though to date its relevance is unclear.

METHODS

343 first-episode treatment-naïve patients with schizophrenia (FES) and 342 HC were recruited. Multivariate source-based morphometry was performed to identify covarying 'networks' of grey matter concentration (GMC). Neurocognitive scores using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and symptom burden using the Positive and Negative Symptoms Scale (PANSS) were obtained. Bivariate linear relationships between GMC and cognition/symptoms were studied.

RESULTS

Compared to healthy subjects, FES had prominently lower GMC in two components; the first consists of the anterior insula, inferior frontal gyrus, anterior cingulate and the second component with the superior temporal gyrus, precuneus, inferior/superior parietal lobule, cuneus, and lingual gyrus. Higher GMC was seen in adjacent areas of the middle and superior temporal gyrus, middle frontal gyrus, inferior parietal cortex and putamen. Greater GMC of this component was associated with lower duration of untreated psychosis, less severe positive symptoms and better performance on cognitive tests.

CONCLUSIONS

In untreated stages of schizophrenia, both a distributed lower and higher GMC is observable. While the higher GMC is relatively modest, it occurs across frontoparietal, temporal and subcortical regions in association with reduced illness burden suggesting a compensatory role for higher GMC in the early stages of schizophrenia.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.

Cognitive reserve as a moderator of outcomes in five clusters of first episode psychosis patients: a 10-year follow-up study of the PAFIP cohort

BACKGROUND

Cognitive reserve (CR) has been associated with the development and prognosis of psychosis. Different proxies have been used to estimate CR among individuals. A composite score of these proxies could elucidate the role of CR at illness onset on the variability of clinical and neurocognitive outcomes.

METHODS

Premorbid intelligence quotient (IQ), years of education and premorbid adjustment were explored as proxies of CR in a large sample (N = 424) of first-episode psychosis (FEP) non-affective patients. Clusters of patients were identified and compared based on premorbid, clinical and neurocognitive variables at baseline. Additionally, the clusters were compared at 3-year (N = 362) and 10-year (N = 150) follow-ups.

RESULTS

The FEP patients were grouped into five CR clusters: C1 (low premorbid IQ, low education and poor premorbid) 14%; C2 (low premorbid IQ, low education and good premorbid adjustment) 29%; C3 (normal premorbid IQ, low education and poor premorbid adjustment) 17%; C4 (normal premorbid IQ, medium education and good premorbid adjustment) 25%; and C5 (normal premorbid IQ, higher education and good premorbid adjustment) 15%. In general, positive and negative symptoms were more severe in the FEP patients with the lowest CR at baseline and follow-up assessments, while those with high CR presented and maintained higher levels of cognitive functioning.

CONCLUSIONS

CR could be considered a key factor at illness onset and a moderator of outcomes in FEP patients. A high CR could function as a protective factor against cognitive impairment and severe symptomatology. Clinical interventions focused on increasing CR and documenting long-term benefits are interesting and desirable.

The Effect of TNF-alpha rs1800629 Polymorphism on White Matter Structures and Memory Function in Patients With Schizophrenia: A Pilot Study

OBJECTIVE

This study investigated the effect of TNF-α rs1800629 polymorphism on white matter integrity and memory function in patients with schizophrenia.

METHODS

Fifty-five participants with schizophrenia were enrolled in this study. They were genotyped for TNF-α rs1800629 polymorphism and underwent diffusion tensor imaging. Memory function was assessed using the Rey-Kim memory test. Participants with schizophrenia were grouped into GG homozygotes and A-allele carriers.

RESULTS

Compared to GG homozygotes, A-allele carriers had significantly lower scores for immediate and delayed recall and recognition of verbal memory and showed significantly lower fractional anisotropy in extensive brain regions. Lower total scores in immediate and delayed recall of verbal memory, immediate recall of visual memory, and figure copy of visual memory were significantly correlated with decreased mean fractional anisotropy in the white matter tracts of the corresponding brain regions.

CONCLUSION

Our findings suggest that the A-allele, which is associated with higher levels of TNF-α expression, correlates with lower connectivity of the fronto-temporal white matter compared to that in GG homozygotes. Impaired fronto-temporal connectivity may be associated with genetic vulnerability to schizophrenia, leading to verbal and visual memory deficits in patients with schizophrenia.

Prognostic associations of cortical gyrification in minimally medicated schizophrenia in an early intervention setting

The aberration in cortical gyrification seen in schizophrenia likely originates in the earliest phase of life, as gyrification begins in utero and reaches its peak in infancy. However, emerging observations have indicated a later reduction in gyrification, especially in early adulthood, may also occur in schizophrenia. At present, it is unclear whether the baseline and later gyrification reduction has any prognostic importance in schizophrenia. We address this question in a longitudinal design in patients minimally medicated at inception. About 108 minimally medicated (duration of medication = <14 days of antipsychotics) patients and 106 healthy controls underwent structural magnetic resonance imaging, with 34 patients being selectively re-scanned when clinically stable following antipsychotic treatment. The cortical surface from each structural image was reconstructed, and the local gyrification index and cortical thickness were computed for each vertex on the surface. We found minimally medicated schizophrenia patients during the first episode had a relatively higher gyrification in bilateral supramarginal, left superior temporal, and right posterior cingulate and paracentral regions. However, poor prognostic features were more likely in patients with lower baseline gyrification. Longitudinal reductions in left superior parietal and right precentral gyrification were associated with lower improvements in both positive and negative symptoms over time. The spatial pattern of longitudinal changes in gyrification was distinct from the changes in cortical thickness. These results indicated that schizophrenia is characterized by a relative hypergyrification in parieto-temporal and medial cortical areas at a group level at first presentation, but poor outcomes relate to lower-gyrification elsewhere both at the onset and during the early course. The early post-onset reduction of gyrification is rather limited in space and magnitude, but occurs unrelated to the progressive thinning, representing a distinct, prognostically important structural trajectory.

Widespread cortical thinning, excessive glutamate and impaired linguistic functioning in schizophrenia: A cluster analytic approach

Introduction

Symptoms of schizophrenia are closely related to aberrant language comprehension and production. Macroscopic brain changes seen in some patients with schizophrenia are suspected to relate to impaired language production, but this is yet to be reliably characterized. Since heterogeneity in language dysfunctions, as well as brain structure, is suspected in schizophrenia, we aimed to first seek patient subgroups with different neurobiological signatures and then quantify linguistic indices that capture the symptoms of "negative formal thought disorder" (i.e., fluency, cohesion, and complexity of language production).

Methods

Atlas-based cortical thickness values (obtained with a 7T MRI scanner) of 66 patients with first-episode psychosis and 36 healthy controls were analyzed with hierarchical clustering algorithms to produce neuroanatomical subtypes. We then examined the generated subtypes and investigated the quantitative differences in MRS-based glutamate levels [in the dorsal anterior cingulate cortex (dACC)] as well as in three aspects of language production features: fluency, syntactic complexity, and lexical cohesion.

Results

Two neuroanatomical subtypes among patients were observed, one with near-normal cortical thickness patterns while the other with widespread cortical thinning. Compared to the subgroup of patients with relatively normal cortical thickness patterns, the subgroup with widespread cortical thinning was older, with higher glutamate concentration in dACC and produced speech with reduced mean length of T-units (complexity) and lower repeats of content words (lexical cohesion), despite being equally fluent (number of words).

Conclusion

We characterized a patient subgroup with thinner cortex in first-episode psychosis. This subgroup, identifiable through macroscopic changes, is also distinguishable in terms of neurochemistry (frontal glutamate) and language behavior (complexity and cohesion of speech). This study supports the hypothesis that glutamate-mediated cortical thinning may contribute to a phenotype that is detectable using the tools of computational linguistics in schizophrenia.

Effects of Antipsychotic Medications and Illness Duration on Brain Features That Distinguish Schizophrenia Patients

BACKGROUND AND HYPOTHESIS

Previous studies have reported effects of antipsychotic treatment and illness duration on brain features. This study used a machine learning approach to examine whether these factors in aggregate impacted the utility of MRI features for differentiating individual schizophrenia patients from healthy controls.

STUDY DESIGN

This case-control study used patients with never-treated first-episode schizophrenia (FES, n = 179) and long-term ill schizophrenia (LTSZ, n = 30), with follow-up of the FES group after treatment (n = 71), a group of patients who had received long-term antipsychotic treatment (n = 93) and age and sex-matched healthy controls (n = 373) for each patient group. A multiple kernel learning classifier combining both structural and functional brain features was used to discriminate individual patients from controls.

STUDY RESULTS

MRI features differentiated untreated FES (0.73) and LTSZ (0.83) patients from healthy controls with moderate accuracy, but accuracy was significantly higher in antipsychotic-treated FES (0.94) and LTSZ (0.98) patients. Treatment was associated with significantly increased accuracy of case identification in both early course and long-term ill patients (both p < .001). Effects of illness duration, examined separately in treated and untreated patients, were less robust.

CONCLUSIONS

Our results demonstrate that initiation of antipsychotic treatment alters brain features in ways that further distinguish individual schizophrenia patients from healthy individuals, and have a modest effect of illness duration. Intrinsic illness-related brain alterations in untreated patients, regardless of illness duration, are not sufficiently robust for accurate identification of schizophrenia patients.

Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages

Importance

Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures.

Objective

To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages.

Design, Setting, and Participants

A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022.

Main Outcomes and Measures

A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample.

Results

There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample.

Conclusions and Relevance

The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

Clustering of cognitive subtypes in schizophrenia patients and their siblings: relationship with regional brain volumes

Schizophrenia patients (SZH) often show impaired cognition and reduced brain structural volumes; these deficits are also detectable in healthy relatives of SZH. However, there is considerable heterogeneity: a sizable percentage of SZH are relatively cognitively intact; clustering strategies have proved useful for categorising into cognitive subgroups. We used a clustering strategy to investigate relationships between subgroup assignment and brain volumes, in 102 SZH (N = 102) and 32 siblings of SZH (SZH-SIB), alongside 92 controls (CON) and 48 of their siblings. SZH had poorer performance in all cognitive domains, and smaller brain volumes within prefrontal and temporal regions compared to controls. We identified three distinct cognitive clusters (‘neuropsychologically normal’, ‘intermediate’, ‘cognitively impaired’) based on age- and gender-adjusted cognitive domain scores. The majority of SZH (60.8%) were assigned to the cognitively impaired cluster, while the majority of SZH-SIB (65.6%) were placed in the intermediate cluster. Greater right middle temporal volume distinguished the normal cluster from the more impaired clusters. Importantly, the observed brain volume differences between SZH and controls disappeared after adjustment for cluster assignment. This suggests an intimate link between cognitive performance levels and regional brain volume differences in SZH. This highlights the importance of accounting for heterogeneity in cognitive performance within SZH populations when attempting to characterise the brain structural abnormalities associated with the disease.

Clusters, lines and webs-so does my patient have psychosis? reflections on the use of psychiatric conceptual frameworks from a clinical vantage point

Mental health professionals working in hospitals or community clinics inevitably face the realisation that we possess imperfect conceptual means to understand mental disorders. In this paper the authors bring together ideas from the fields of Philosophy, Psychiatry, Cognitive Psychology and Linguistics to reflect on the ways we represent phenomena of high practical importance that we often take for granted, but are nevertheless difficult to define in ontological terms. The paper follows through the development of the concept of psychosis over the last two centuries in the interplay of three different conceptual orientations: the categorical, dimensional and network approaches. Each of these represent the available knowledge and dominant thinking styles of the era in which they emerged and take markedly different stances regarding the nature of mental phenomena. Without particular commitment to any ontological positions or models described, the authors invite the reader into a thinking process about the strengths and weaknesses of these models, and how they can be reconciled in multidisciplinary settings to benefit the process of patient care.

Transdiagnostic phenotypes of compulsive behavior and associations with psychological, cognitive, and neurobiological affective processing

Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18-45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled "Compulsive Non-Avoidant", "Compulsive Reactive" and "Compulsive Stressed". They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.

Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomical or functional scale (e.g., atlas-based regions of interest). Herein we propose a novel method, "Multi-scAle heteroGeneity analysIs and Clustering" (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to effectively drive identification of inter-scale-consistent disease subtypes. More importantly, to understand the conditions under which the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N = 4403). We then applied MAGIC to imaging data from Alzheimer's disease (ADNI, N = 1728) and schizophrenia (PHENOM, N = 1166) patients to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of common brain diseases. Taken together, we aim to provide guidance regarding when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC.

Distinct neuroanatomic subtypes in antipsychotic-treated patients with schizophrenia classified by the predefined classification in a never-treated sample

Background

Distinct neuroanatomic subtypes have been identified in never-treated patients with schizophrenia based on cerebral structural abnormalities, but whether antipsychotic-treated patients would be stratified under the guidance of such previously formed classification remains unclear.

Objective

The present study aimed to investigate alterations of brain structures in antipsychotic-treated patients with schizophrenia based on a predefined morphological classification and their relationships with cognitive performance.

Methods

Cortical thickness, surface area, and subcortical volume were extracted from 147 antipsychotic-treated patients with schizophrenia using structural magnetic resonance imaging for classification. The Brief Assessment of Cognition in Schizophrenia (BACS) and Positive and Negative Syndrome Scale (PANSS) were used to assess cognition and symptoms.

Results

Antipsychotic-treated patients were categorized into three subtypes with distinct patterns of brain morphological alterations. Subtypes 1 and 2 were characterized by widespread deficits in cortical thickness but relatively limited deficits in surface area. In contrast, subtype 3 demonstrated cortical thickening mainly in parietal-occipital regions and widespread deficits in surface area. All three subgroups demonstrated cognitive deficits compared with healthy controls. Significant associations between neuroanatomic and cognitive abnormalities were only observed in subtype 1, where cortical thinning in the left lingual gyrus was conversely related to symbol coding performance.

Conclusions

Similar to drug-naïve patients, neuroanatomic heterogeneity exists in antipsychotic-treated patients, with disparate associations with cognition. These findings promote our understanding of relationships between neuroanatomic abnormalities and cognitive performance in the context of heterogeneity. Moreover, these results suggest that neurobiological heterogeneity needs to be considered in cognitive research in schizophrenia.

Abnormal Network Properties and Fiber Connections of DMN across Major Mental Disorders: A Probability Tracing and Graph Theory Study

The default mode network (DMN) is related to brain functions and its abnormalities were associated with mental disorders' pathophysiology. To further understand the common and distinct DMN alterations across disorders, we capitalized on the probability tracing method and graph theory to analyze the role of DMN across three major mental disorders. A total of 399 participants (156 schizophrenia [SCZ], 90 bipolar disorder [BP], 58 major depression disorder [MDD], and 95 healthy controls [HC]) completed magnetic resonance imaging (MRI)-scanning, clinical, and cognitive assessment. The MRI preprocessing of diffusion-tensor-imaging was conducted in FMRIB Software Library and probabilistic fiber tracking was applied by PANDA. This study had three main findings. First, patient groups showed significantly lower cluster coefficient in whole-brain compared with HC. SCZ showed significantly longer characteristic path compared with HC. Second, patient groups showed inter-group specificity in abnormalities of DMN connections. Third, SCZ was sensitive to left_medial_superior_frontal_gyrus (L_SFGmed)-right_anterior_cingulate_gyrus (R_ACG) connection relating to positive symptoms; left_ACG-right_ACG connection was the mania's antagonistic factor in BP. This trans-diagnostic study found disorder-specific structural abnormalities in the fiber connection of R_SFGmed-L_SFGmed-R_ACG_L_ACG within DMN, where SCZ showed more disconnections compared with other disorders. And these connections are diagnosis-specifically correlated to phenotypes. The current study may provide further evidence of shared and distinct endo-phenotypes across psychopathology.

A Systematic Review of Cognition-Brain Morphology Relationships on the Schizophrenia-Bipolar Disorder Spectrum

The nature of the relationship between cognition and brain morphology in schizophrenia-spectrum disorders (SSD) and bipolar disorder (BD) is uncertain. This review aimed to address this, by providing a comprehensive systematic investigation of links between several cognitive domains and brain volume, cortical thickness, and cortical surface area in SSD and BD patients across early and established illness stages. An initial search of PubMed and Scopus databases resulted in 1486 articles, of which 124 met inclusion criteria and were reviewed in detail. The majority of studies focused on SSD, while those of BD were scarce. Replicated evidence for specific regions associated with indices of cognition was minimal, however for several cognitive domains, the frontal and temporal regions were broadly implicated across both recent-onset and established SSD, and to a lesser extent BD. Collectively, the findings of this review emphasize the significance of both frontal and temporal regions for some domains of cognition in SSD, while highlighting the need for future BD-related studies on this topic.

Subtyping Schizophrenia Patients Based on Patterns of Structural Brain Alterations

Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.

Quantitative Macromolecular Proton Fraction Mapping Reveals Altered Cortical Myelin Profile in Schizophrenia Spectrum Disorders

Myelin abnormalities have been reported in schizophrenia spectrum disorders (SSD) in white matter. However, in vivo examinations of cortical myeloarchitecture in SSD, especially those using quantitative measures, are limited. Here, we employed macromolecular proton fraction (MPF) obtained from quantitative magnetization transfer imaging to characterize intracortical myelin organization in 30 SSD patients versus 34 healthy control (HC) participants. We constructed cortical myelin profiles by extracting MPF values at various cortical depths and quantified their shape using a nonlinearity index (NLI). To delineate the association of illness duration with myelin changes, SSD patients were further divided into 3 duration groups. Between-group comparisons revealed reduced NLI in the SSD group with the longest illness duration (>5.5 years) compared with HC predominantly in bilateral prefrontal areas. Within the SSD group, cortical NLI decreased with disease duration and was positively associated with a measure of spatial working memory capacity as well as with cortical thickness (CT). Layer-specific analyses suggested that NLI decreases in the long-duration SSD group may arise in part from significantly increased MPF values in the midcortical layers. The current study reveals cortical myelin profile changes in SSD with illness progression, which may reflect an abnormal compensatory mechanism of the disorder.

Cognitive impairment in schizophrenia: relationships with cortical thickness in fronto-temporal regions, and dissociability from symptom severity

Cognitive impairments are a core and persistent characteristic of schizophrenia with implications for daily functioning. These show only limited response to antipsychotic treatment and their neural basis is not well characterised. Previous studies point to relationships between cortical thickness and cognitive performance in fronto-temporal brain regions in schizophrenia patients (SZH). There is also evidence that these relationships might be independent of symptom severity, suggesting dissociable disease processes. We set out to explore these possibilities in a sample of 70 SZH and 72 age and gender-matched healthy controls (provided by the Center of Biomedical Research Excellence (COBRE)). Cortical thickness within fronto-temporal regions implicated by previous work was considered in relation to performance across various cognitive domains (from the MATRICS Cognitive Battery). Compared to controls, SZH had thinner cortices across most fronto-temporal regions and significantly lower performance on all cognitive domains. Robust relationships with cortical thickness were found: visual learning and attention performance correlated with bilateral superior and middle frontal thickness in SZH only. Correlations between attention performance and right transverse temporal thickness were also specific to SZH. Findings point to the importance of these regions for cognitive performance in SZH, possibly reflecting compensatory processes and/or aberrant connectivity. No links to symptom severity were observed in these regions, suggesting these relationships are dissociable from underlying psychotic symptomology. Findings enhance understanding of the brain structural underpinnings and possible aetiology of cognitive impairment in SZH.

Anomalous brain gyrification patterns in major psychiatric disorders: a systematic review and transdiagnostic integration

Anomalous patterns of brain gyrification have been reported in major psychiatric disorders, presumably reflecting their neurodevelopmental pathology. However, previous reports presented conflicting results of patients having hyper-, hypo-, or normal gyrification patterns and lacking in transdiagnostic consideration. In this article, we systematically review previous magnetic resonance imaging studies of brain gyrification in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder at varying illness stages, highlighting the gyral pattern trajectory for each disorder. Patients with each psychiatric disorder may exhibit deviated primary gyri formation under neurodevelopmental genetic control in their fetal life and infancy, and then exhibit higher-order gyral changes due to mechanical stress from active brain changes (e.g., progressive reduction of gray matter volume and white matter integrity) thereafter, representing diversely altered pattern trajectories from those of healthy controls. Based on the patterns of local connectivity and changes in neurodevelopmental gene expression in major psychiatric disorders, we propose an overarching model that spans the diagnoses to explain how deviated gyral pattern trajectories map onto clinical manifestations (e.g., psychosis, mood dysregulation, and cognitive impairments), focusing on the common and distinct gyral pattern changes across the disorders in addition to their correlations with specific clinical features. This comprehensive understanding of the role of brain gyrification pattern on the pathophysiology may help to optimize the prediction and diagnosis of psychiatric disorders using objective biomarkers, as well as provide a novel nosology informed by neural circuits beyond the current descriptive diagnostics.