Article

Elucidating a Magnetic Resonance Imaging-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms

Department of Psychology, University of California at Los Angeles, 90095, USA.
Biological psychiatry (Impact Factor: 10.26). 10/2009; 66(11):1055-60. DOI: 10.1016/j.biopsych.2009.07.019
Source: PubMed

ABSTRACT

No objective diagnostic biomarkers or laboratory tests have yet been developed for psychotic illness. Magnetic resonance imaging (MRI) studies consistently find significant abnormalities in multiple brain structures in psychotic patients relative to healthy control subjects, but these abnormalities show substantial overlap with anatomic variation that is in the normal range and therefore nondiagnostic. Recently, efforts have been made to discriminate psychotic patients from healthy individuals using machine-learning-based pattern classification methods on MRI data.
Three-dimensional cortical gray matter density (GMD) maps were generated for 36 patients with recent-onset psychosis and 36 sex- and age-matched control subjects using a cortical pattern matching method. Between-group differences in GMD were evaluated. Second, the sparse multinomial logistic regression classifier included in the Multivariate Pattern Analysis in Python machine-learning package was applied to the cortical GMD maps to discriminate psychotic patients from control subjects.
Patients showed significantly lower GMD, particularly in prefrontal, cingulate, and lateral temporal brain regions. Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation.
These results suggest that even at the early stage of illness, psychotic patients present distinct patterns of regional cortical gray matter changes that can be discriminated from the normal pattern. These findings indicate that we can detect complex patterns of brain abnormality in early stages of psychotic illness, which has critical implications for early identification and intervention in individuals at ultra-high risk for developing psychosis/schizophrenia.

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    • "Regarding patients at psychosis onset, promising results have been achieved with accuracies up to 90% when considering deformations in respect to an atlas representing healthy subjects (Pohl and Sabuncu 2009). Other studies (Takayanagi et al., 2011, Borgwardt et al. 2013, Zanetti et al. 2013, Sun et al. 2009) demonstrated how patients can be separated from healthy controls on the basis or gray matter thickness, regional volumes or gray matter density, with accuracies which reached around 85%. In a previous work of our group, we showed how FEP can be differentiated from HC when considering blood perfusion in the brain (Squarcina et al., 2015). "
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    • "As several classification models have become more and more prevalent , accuracy in distinguishing groups of individuals has increased. Models discriminating healthy subjects from patients with severe mental illnesses have demonstrated promise, including schizophrenia (Arbabshirani et al., 2013; Schnack et al., 2014; Silva et al., 2014; Sui et al., 2009; H. Yang et al., 2010), bipolar disorder (Schnack et al., 2014), psychosis (Arribas et al., 2010; Calhoun et al., 2008; Sun et al., 2009), and Huntington's disease (Rizk-Jackson et al., 2011). Also, models have been used to predict brain maturation (Dosenbach et al., 2010), substance use (Fan et al., 2006; Pariyadath et al., 2014; Zhang et al., 2005), and substance use outcomes (Marhe et al., 2013; Steele et al., 2014). "
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    • "Après un entraînement adéquat, ces classificateurs peuvent servir d'outil diagnostique, prédictif ou pronostique, et quelques applications notables sont à dénombrer dans la détection de sujets malades, à risque ou dans la prédiction de la réponse au traitement pharmacologique ou psychothérapeutique [84] . Dans le domaine spécifique de la transition psychotique, des algorithmes de détection des sujets à haut risque présentant un risque de conversion ont été développés sur la base de mesures d'épaisseur corticale [85] [86] , ou de patterns fonctionnels lors de tâches émotionnelles [87] . "
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