Supervoxel-based statistical analysis of diffusion tensor imaging in schizotypal personality disorder

A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis

BACKGROUND AND OBJECTIVE

The schizophrenia diagnosis represents a difficult task because of the confusing descriptions of symptoms given by the patient, their similarity among several disorders, the lower familiarity with genetic predisposition, and the probably inadequate response to the treatment. Neuro-biological markers of schizophrenia, as a quantitative relationship between the psychiatrist's reports and the biology of the brain, could be used. Functional Magnetic Resonance Imaging (fMRI) obtain the subject's performance in cognitive tasks and may find significant differences between the patient's data and controls. The input data of classifiers may imply alterations in diagnosis; therefore, it is essential to ensure an adequate representation to describe the entire dataset classified.

METHODS

We propose a supervoxels-based representation calculated by two main steps: the short-range connectivity, supervoxels' generation using a Fuzzy Iterative Clustering algorithm, and the long-range connectivity, employing Detrended Cross-Correlation Analysis among supervoxels. The unrelated supervoxels, through a statistical test based on critical points calculated empirically, are removed. The remainder supervoxels are the input for feature selectors to extract the discriminative supervoxels. We implement support vector machine classifiers using the correlation coefficient of the significant supervoxels. The dataset of 1.5 Tesla was downloaded from the SchizConnect site, where the fMRI data, during an auditory oddball task, was acquired. We calculate the performance of the classifiers using a leave-one-out cross-validation and compute the area under the Receiver Operating Characteristic curve and a permutation test to ensure no bias in the classifiers.

RESULTS

According to the permutation test, with p-values less than the significance level of 0.05, the classifiers extract discriminative class structure from data where no bias is shown. Our supervoxels-based representation gets the maximum values of sensitivity, specificity, and accuracy of 92.9%, 100%, and 96.4%, respectively. The discriminative brain regions, to discern among patients and controls, are extracted; these regions also are mentioned by the related works.

CONCLUSIONS

The proposed representation, based on supervoxels, is a data-driven model that does not use predefined models of the signal nor pre-relocated brain regions of interest. The results are competitive against the related works, and the relevant supervoxels are related to the schizophrenia diagnosis.

Neural Correlates of Schizotypal Personality Disorder: a Systematic Review of Neuroimaging and EEG Studies

BACKGROUND

Schizotypal personality disorder (SPD) is a cluster A personality disorder affecting 1.0% of general population, characterised by disturbances in cognition and reality testing dimensions, affect regulation, and interpersonal function. SPD shares similar but attenuated phenomenological, genetic, and neurobiological abnormalities with schizophrenia (SCZ) and is described as part of schizophrenia spectrum disorders.

OBJECTIVE

Aim of this work was to identify the major neural correlates of SPD.

METHODS

This is a systematic review conducted according to PRISMA statement. The protocol was prospectively registered in PROSPERO - International prospective register of systematic reviews. The review was performed to summarise the most comprehensive and updated evidence on functional neuroimaging and neurophysiology findings obtained through different techniques (DW-MRI, DTI, PET, SPECT, fMRI, MRS, EEG) in individuals with SPD.

RESULTS

Of the 52 studies included in this review, 9 were on DW-MRI and DTI, 11 were on PET and SPECT, 11 were on fMRI and MRS, and 21 were on EEG. It was complex to synthesise all the functional abnormalities found into a single, unified, pathogenetic pathway, but a common theme emerged: the dysfunction of brain circuits including striatal, frontal, temporal, limbic regions (and their networks) together with a dysregulation along the dopaminergic pathways.

CONCLUSION

Brain abnormalities in SPD are similar, but less marked, than those found in SCZ. Furthermore, different patterns of functional abnormalities in SPD and SCZ have been found, confirming the previous literature on the 'presence' of possible compensatory factors, protecting individuals with SPD from frank psychosis and providing diagnostic specificity.