A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects

Antonucci, Linda A.; Pergola, Giulio; Pigoni, Alessandro; Dwyer, Dominic; Kambeitz-Ilankovic, Lana; Penzel, Nora; Romano, Raffaella; Franke, Barbara; Torretta, Silvia; Rampino, Antonio; Trojano, María; Caforio, Grazia; Falkai, Peter; Blasi, Giuseppe; Koutsouleris, Nikolaos; Bertolino, Alessandro

doi:10.1016/j.biopsych.2019.11.007

Cited by 36 publications

(44 citation statements)

References 75 publications

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“…First, we specifically test for an effect of age of cannabis initiation in ROP patients, in contrast to previous studies potentially detecting general effects of cannabis use in psychosis. Second, specific effects of cannabis use during adolescent brain maturation might differ in vulnerable individuals later presenting with ROP, due to genetic vulnerability or additional early environmental risk factors [ 60 ]. Third, the use of univariate statistics in previous studies hindered exploring the highly interconnected nature of the brain.…”

Section: Discussionmentioning

confidence: 99%

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Penzel

Antonucci

Betz

et al. 2021

Neuropsychopharmacol.

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Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life.

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Section: Discussionmentioning

confidence: 99%

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Penzel

Antonucci

Betz

et al. 2021

Neuropsychopharmacol.

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“…Powerful new methodologies able to combine multiple sources of data, such as similarity network fusion (85), might be suitable for this purpose. Indeed, research has shown that a combination of clinical variables and structural brain imaging data might represent a promising multimodal framework for psychosis prediction (10,23,31). Along these lines, Schmidt et al (29) devised a 3-stage sequential testing paradigm, which in theory reaches nearly perfect positive predictive value when individuals are tested on one multimodal modality (i.e., clinical and electroencephalography) and two biological data modalities (i.e., structural MRI and blood based).…”

Section: Type Of Data Modalitymentioning

confidence: 99%

“…Some of these phenotypes have been associated with both disease course and transition to the overt disease (4). Therefore, the identification of reliable markers able to distinguish between atrisk and healthy populations may be potentially useful in clinical practice to monitor disease development and treatment outcome (23) and to obviate time-consuming CHR assessments. The two prevailing statistical approaches to address the challenge of single-subject prediction are machine learning (ML) methods (e.g., support vector machine, LASSO [least absolute shrinkage and selection operator] regression, random forest), which can handle large databases and different data domains (24,25), and Cox proportional hazard regression, a form of multivariate survival analysis (26) able to investigate time-toconversion trajectories.…”

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confidence: 99%

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

Sanfelici

Dwyer

Antonucci

et al. 2020

Biological Psychiatry

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BACKGROUND: The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied. METHODS: We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality. RESULTS: A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects. CONCLUSIONS: Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.

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“…Impaired cognitive function is viewed as a fundamental feature of schizophrenia (SCZ) 1 – 3 . Cognitive declines exist in the prodromal stage of psychosis and persist throughout the course of illness regardless of how symptoms change 4 – 6 .…”

Section: Introductionmentioning

confidence: 99%

Cognitive deficits and white matter abnormalities in never-treated first-episode schizophrenia

2020

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Cognitive impairment is viewed as a core symptom of schizophrenia (SCZ), but its pathophysiological mechanism remains unclear. White matter (WM) disruption is considered to be a central abnormality that may contribute to cognitive impairment in SCZ patients. However, few studies have addressed the association between cognition and WM integrity in never-treated first-episode (NTFE) patients with SCZ. In this study, we used the MATRICS Consensus Cognitive Battery (MCCB) to evaluate cognitive function in NTFE patients (n = 39) and healthy controls (n = 30), and associated it with whole-brain fractional anisotropy (FA) values obtained via voxel-based diffusion tensor imaging. We found that FA was lower in five brain areas of SCZ patients, including the cingulate gyrus, internal capsule, corpus callosum, cerebellum, and brainstem. Compared with the healthy control group, the MCCB’s total score and 8 out of 10 subscores were significantly lower in NTFE patients (all p < 0.001). Moreover, in patients but not healthy controls, the performance in the Trail Making Test was negatively correlated with the FA value in the left cingulate. Our findings provide evidence that WM disconnection is involved in some cognitive impairment in the early course of SCZ.

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A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects

Cited by 36 publications

References 75 publications

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

Cognitive deficits and white matter abnormalities in never-treated first-episode schizophrenia

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