Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity

Antonucci, Linda A.; Penzel, Nora; Pergola, Giulio; Kambeitz-Ilankovic, Lana; Dwyer, Dominic; Kambeitz, Joseph; Haas, Shalaila S.; Passiatore, Roberta; Fazio, Leonardo; Caforio, Grazia; Falkai, Peter; Blasi, Giuseppe; Bertolino, Alessandro; Koutsouleris, Nikolaos

doi:10.1038/s41386-019-0532-3

Cited by 28 publications

(33 citation statements)

References 72 publications

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“…These findings were consistent with previous studies that found neuroimaging combined with machine learning can classify chronic medicated SSD (11, 13, 15, 19, 32–35). Besides, when subgroup analyses were not used, we also found functional connectivity combined with SVM can identify SSD with satisfied classification performances among three machine learning strategy: the 5-fold cross-validation that pooled all datasets, the leave-one-site-out cross-validation and the five-fold cross-validation that only including first episode unmedicated SSD (Supplementary Materials).…”

Section: Discussionsupporting

confidence: 93%

Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application

Wang

Jiang

et al. 2020

Preprint

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An accurate identification of schizophrenia spectrum disorder (SSD) at early stage could potentially allow for treating SSD with appropriate intervention to potentially prevent future deterioration. Despite mounting studies found neuroimaging combined with machine learning can identify chronic medicated SSD, whether or not the classification model identified the trait biomarker of SSD that can be used to identify early stage SSD is largely unknown. The present study aimed to investigate whether or not the classification model trained using chronic medicated SSD identified the trait biomarker of SSD that whether or not the model can be generalized to early stage SSD, by using functional connectivity (FC) combined with support vector machine (SVM) using a large sample from 4 independent sites (n = 1077). We found that the classification model trained using chronic medicated SSD from three sites (dataset 2, 3 and 4) classified SSD from HCs in another site (dataset 1) with 69% accuracy (P= 2.86e-13). Subgroup analysis indicated that this model can identify chronic medicated SSD in dataset 1 with 71% sensitivity (P= 4.63e-05), but cannot be generalized to first episode unmedicated SSD (sensitivity=48%, P=0.68) and first episode medicated SSD (sensitivity=59%, P=0.10). Univariable analysis showed that medication usage had significant effect on FC, but disease duration had no significant effect on FC. These findings suggest that the classification model trained using chronic medicated SSD may mainly identified the pattern of chronic medication usage state, rather than the trait biomarker of SSD. Therefore, we should reconsider the current machine learning studies in chronic medicated SSD more cautiously in term of the clinical application.

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Section: Discussionsupporting

confidence: 93%

Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application

Wang

Jiang

et al. 2020

Preprint

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“…Indeed, when pooling a set of variables clearly associated with the CP experience based on univariate studies [ 4 ], and using them to discriminate CP from HC, we are able to correctly discriminate a CP individual from a HC at the single-subject level in 86.5% of cases within our repeated-nested cross-validation framework. Notably, as in previous studies using the same SVM technique [ 11 , 15 ], we employed a stringent separation of training and test sets, and a robust, repeated-nested CV scheme. These methodological choices are in line with recent recommendations (for a review, see [ 12 ]), which noted that the gold-standard CV scheme ensuring the highest degree of reliability and generalizability of machine learning findings, in the absence of external replication samples, is nested CV.…”

Section: Discussionmentioning

confidence: 99%

“…So far, studies have characterized CP and HC differences in terms of group differences through univariate statistics, which are limited in terms of generalizability assessments [ 12 , 13 ]. Moreover, to understand whether a syndrome-associated characteristic could also be qualified as a marker (i.e., as a measurable feature associated with a certain condition or process [ 14 , 15 ]), one should investigate its sensitivity and specificity in identifying the respective patient population [ 12 ]. A promising way of addressing this question is by employing machine learning, which allows one to quantify the sensitivity, specificity, and generalizability of a disease signature at the single-subject level [ 16 , 17 ], rather than just describing it at the group level.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study

et al. 2020

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Introduction Chronic pain (CP) is a complex multidimensional experience severely affecting individuals’ quality of life. Multiple cognitive, affective, emotional, and interpersonal factors play a major role in CP. Furthermore, the psychological, social, and physical circumstances leading to CP show high inter-individual variability, thus making it difficult to identify core syndrome characteristics. In a biopsychosocial perspective, we aim at identifying a pattern of psycho-physical impairments that can reliably discriminate between CP individuals and healthy controls (HC) with high accuracy and estimated generalizability using machine learning. Methods A total of 118 CP and 86 HC were recruited. All individuals were administered several scales assessing quality of life, physical and mental health, personal functioning, anxiety, depression, beliefs about medical treatments, and cognitive ability. These features were trained to separate CP from HC using support vector classification and repeated nested cross-validation. Results Our psycho-physical classifier was able to discriminate CP from HC with 86.5% balanced accuracy and significance ( p = 0.0001). The most reliable features characterizing CP were anxiety and depression scores, and belief of harm from prolonged pharmacological treatments; for HP, the most reliable features were physical and occupational functioning, and vitality levels. Conclusion Our findings suggest that, using psychological and physical assessments, it is possible to classify CP from HC with high reliability and estimated generalizability via (i) a pattern of psychological symptoms and cognitive beliefs characteristic of CP, and (ii) a pattern of intact physical functioning characteristic of HC. We think that our algorithm enables novel insights into potential individualized targets for CP-related early intervention programs.

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“…Considering that clinical CHR instruments alone detect only about 47% of transitions after 3 years (14), efforts have been made to identify potential risk factors for psychosis in several symptomatological and biological readouts, or biomarkers, of the disorder (15) so that individualized prognostication may be enhanced. The presence of environmental adverse events (16), cognitive impairments (17), neuromorphological (18), and electrophysiological (19) and hematological (20) alterations, as well as resting-state (21) and task-related (22) neural activity and connectivity anomalies, has been consistently reported in people at risk for psychosis compared with healthy individuals. Some of these phenotypes have been associated with both disease course and transition to the overt disease (4).…”

mentioning

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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Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity

Cited by 28 publications

References 72 publications

Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application

Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application

An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study

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

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