Letizia Squarcina

Cortical diffusion-tensor imaging abnormalities in multiple sclerosis: a 3-year longitudinal study.

PURPOSE To evaluate whether diffusion-tensor imaging can be combined with double inversion recovery to improve the detection of structural changes occurring in the cortex of patients with multiple sclerosis (MS). MATERIALS AND METHODS Once local ethics committee approval and informed consent were obtained, 168 patients with relapsing-remitting MS and 45 sex- and age-matched control subjects were included in a 3-year longitudinal study. Expanded Disability Status Scale (EDSS) and magnetic resonance (MR) imaging examinations were performed at study entry and after 3 years. Number and volume of cortical lesions, T2 white matter lesion volume (WMLV), and fractional anisotropy (FA) and mean diffusivity (MD) of normal-appearing gray matter (NAGM) and cortical lesions were analyzed. Between-group differences in terms of NAGM-FA and NAGM-MD were assessed with analysis of variance followed by Tukey test correction. RESULTS At baseline, NAGM-FA was higher in patients (mean ± standard deviation, 0.149 ± 0.011) than in control subjects (0.125 ± 0.008; P < .001) and higher in patients with cortical lesions (0.154 ± 0.011) than in those without (0.138 ± 0.010; P < .001). Moreover, FA was higher in cortical lesions than in NAGM (P < .001). After 3 years, NAGM-FA was unchanged in control subjects and increased in patients (0.154 ± 0.012; P < .001), especially in patients with worsened EDSS score (0.170 ± 0.011; P < .001). The same behavior was observed for NAGM-MD. At baseline, NAGM-FA significantly correlated with EDSS score (r = 0.75; P < .001) and cortical lesion volume (r = 0.850; P < .001). Multivariate analysis identified NAGM-FA (B = 0.654; P < .001) and T2 WMLV (B = 0.310; P < .001) as independent predictors of EDSS score, while NAGM-FA change (B = 0.523; P < .001) and disease duration (B = 0.342; P < .001) were independent predictors of EDSS change. CONCLUSION Compared with control subjects, patients with RRMS had an increase in FA of NAGM that strongly correlated with cortical lesion volume and clinical disability.

Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques.

ABSTRACT First episode psychosis (FEP) patients are of particular interest for neuroimaging investigations because of the absence of confounding effects due to medications and chronicity. Nonetheless, imaging data are prone to heterogeneity because for example of age, gender or parameter setting differences. With this work, we wanted to take into account possible nuisance effects of age and gender differences across dataset, not correcting the data as a pre‐processing step, but including the effect of nuisance covariates in the classification phase. To this aim, we developed a method which, based on multiple kernel learning (MKL), exploits the effect of these confounding variables with a subject‐depending kernel weighting procedure. We applied this method to a dataset of cortical thickness obtained from structural magnetic resonance images (MRI) of 127 FEP patients and 127 healthy controls, who underwent either a 3 Tesla (T) or a 1.5 T MRI acquisition. We obtained good accuracies, notably better than those obtained with standard SVM or MKL methods, up to more than 80% for frontal and temporal areas. To our best knowledge, this is the largest classification study in FEP population, showing that fronto‐temporal cortical thickness can be used as a potential marker to classify patients with psychosis. HighlightsLargest classification study in FEP populationFronto‐temporal cortical thickness discriminates between psychosis patients and healthy controls.Frontal and temporal cortical thickness are involved in psychosis.Nuisance correction based on age and gender during the training phase improves classification.

Fractal analysis of MRI data for the characterization of patients with schizophrenia and bipolar disorder

Fractal geometry can be used to analyze shape and patterns in brain images. With this study we use fractals to analyze T1 data of patients affected by schizophrenia or bipolar disorder, with the aim of distinguishing between healthy and pathological brains using the complexity of brain structure, in particular of grey matter, as a marker of disease. 39 healthy volunteers, 25 subjects affected by schizophrenia and 11 patients affected by bipolar disorder underwent an MRI session. We evaluated fractal dimension of the brain cortex and its substructures, calculated with an algorithm based on the box-count algorithm. We modified this algorithm, with the aim of avoiding the segmentation processing step and using all the information stored in the image grey levels. Moreover, to increase sensitivity to local structural changes, we computed a value of fractal dimension for each slice of the brain or of the particular structure. To have reference values in comparing healthy subjects with patients, we built a template by averaging fractal dimension values of the healthy volunteers data. Standard deviation was evaluated and used to create a confidence interval. We also performed a slice by slice t-test to assess the difference at slice level between the three groups. Consistent average fractal dimension values were found across all the structures in healthy controls, while in the pathological groups we found consistent differences, indicating a change in brain and structures complexity induced by these disorders.

The use of dynamic susceptibility contrast (DSC) MRI to automatically classify patients with first episode psychosis

Hemodynamic changes in the brain have been reported in major psychosis in respect to healthy controls, and could unveil the basis of structural brain modifications happening in patients. The study of first episode psychosis is of particular interest because the confounding role of chronicity and medication can be excluded. The aim of this work is to automatically discriminate first episode psychosis patients and normal controls on the basis of brain perfusion employing a support vector machine (SVM) classifier. 35 normal controls and 35 first episode psychosis underwent dynamic susceptibility contrast magnetic resonance imaging, and cerebral blood flow and volume, along with mean transit time were obtained. We investigated their behavior in the whole brain and in selected regions of interest, in particular the left and right frontal, parietal, temporal and occipital lobes, insula, caudate and cerebellum. The distribution of values of perfusion indexes were used as features in a support vector machine classifier. Mean values of blood flow and volume were slightly lower in patients, and the difference reached statistical significance in the right caudate, left and right frontal lobes, and in left cerebellum. Linear SVM reached an accuracy of 83% in the classification of patients and normal controls, with the highest accuracy associated with the right frontal lobe and left parietal lobe. In conclusion, we found evidence that brain perfusion could be used as a potential marker to classify patients with psychosis, who show reduced blood flow and volume in respect to normal controls.

Similar white matter changes in schizophrenia and bipolar disorder: A tract-based spatial statistics study

Several strands of evidence reported a significant overlapping, in terms of clinical symptoms, epidemiology and treatment response, between the two major psychotic disorders—Schizophrenia (SCZ) and Bipolar Disorder (BD). Nevertheless, the shared neurobiological correlates of these two disorders are far from conclusive. This study aims toward a better understanding of possible common microstructural brain alterations in SCZ and BD. Magnetic Resonance Diffusion data of 33 patients with BD, 19 with SCZ and 35 healthy controls were acquired. Diffusion indexes were calculated, then analyzed using Tract-Based Spatial Statistics (TBSS). We tested correlations with clinical and psychological variables. In both patient groups mean diffusion (MD), volume ratio (VR) and radial diffusivity (RD) showed a significant increase, while fractional anisotropy (FA) and mode (MO) decreased compared to the healthy group. Changes in diffusion were located, for both diseases, in the fronto-temporal and callosal networks. Finally, no significant differences were identified between patient groups, and a significant correlations between length of disease and FA and VR within the corpus callosum, corona radiata and thalamic radiation were observed in bipolar disorder. To our knowledge, this is the first study applying TBSS on all the DTI indexes at the same time in both patient groups showing that they share similar impairments in microstructural connectivity, with particular regards to fronto-temporal and callosal communication, which are likely to worsen over time. Such features may represent neural common underpinnings characterizing major psychoses and confirm the central role of white matter pathology in schizophrenia and bipolar disorder.

A review of altered biochemistry in the anterior cingulate cortex of first-episode psychosis

Relevant biochemicals of the brain can be quantified in vivo, non-invasively, using proton Magnetic Resonance Spectroscopy (¹H MRS). This includes metabolites associated with neural general functioning, energetics, membrane phospholipid metabolism and neurotransmission. Moreover, there is substantial evidence of implication of the frontal and prefrontal areas in the pathogenesis of psychotic disorders such as schizophrenia. In particular, the anterior cingulate cortex (ACC) plays an important role in cognitive control of emotional and non-emotional processes. Thus the study of its extent of biochemistry dysfunction in the early stages of psychosis is of particular interest in gaining a greater understanding of its aetiology. In this review, we selected ¹H MRS studies focused on the ACC of first-episode psychosis (FEP). Four studies reported increased glutamatergic levels in FEP, while other four showed preserved concentrations. Moreover, findings on FEP do not fully mirror those in chronic patients. Due to conflicting findings, larger longitudinal ¹H MRS studies are expected to further explore glutamatergic neurotransmission in ACC of FEP in order to have a better understanding of the glutamatergic mechanisms underlying psychosis, possibly using ultra high field MR scanners.

Brain structural correlates of irritability: Findings in a large healthy cohort

Irritability and nonviolent aggression are common behavioral features across the population, yet there is limited neurobiological research into subclinical phenotypes representing the lower edge of a symptom continuum ranging from slight irritability to criminal violence. We studied brain structural correlates of irritability in a large healthy cohort to test the hypothesis of associations with fronto‐limbic brain structures implicated in mood regulation. In a large multicenter effort, we recruited 409 mentally healthy adults from the community, who received T1‐weighted high‐resolution 3 T MRI scans. These structural scans were automatically preprocessed for voxel‐ and surface‐based morphometry measurements with the CAT 12 toolbox implemented in SPM 12. Subclinical aggressive symptoms were assessed using the SCL‐90‐R aggression/hostility subscale and then correlated with cortical volume (VBM), and cortical thickness and gyrification. VBM analysis showed significant (P < 0.05, FDR‐corrected at peak‐level) positive correlations of cortical volume with SCL‐90‐R aggression subscale values in large clusters spanning bilateral anterior cingulate and orbitofrontal cortices and left lingual and postcentral gyri. Surface‐based morphometry yielded mostly uncorrected positive correlations with cortical thickness in bilateral precentral gyri and with gyrification in left insula and superior temporal gyrus. Our findings imply an association of subclinical aggressive symptoms with cortical volume in areas important for emotion awareness and regulation, which might also be related to cortical adaptation to mental stress. These results overlap with several findings on impulsive aggression in patients suffering from affective and disruptive behavior disorders. They also suggest a biological symptom continuum manifesting in these brain areas. Hum Brain Mapp 38:6230–6238, 2017.

Twin studies for the investigation of the relationships between genetic factors and brain abnormalities in bipolar disorder.

The pathogenesis of bipolar disorder (BD) is to date not entirely clear. Classical genetic research showed that there is a contribution of genetic factors in BD, with high heritability. Twin studies, thanks to the fact that confounding factors as genetic background or family environment are shared, allow etiological inferences. In this work, we selected twin studies, which focus on the relationship between BD, genetic factors and brain structure, evaluated with magnetic resonance imaging. All the studies found differences in brain structure between BD patients and their co-twins, and also in respect to healthy controls. Genetic effects are predominant in white matter, except corpus callosum, while gray matter resulted more influenced by environment, or by the disease itself. All studies found no interactions between BD and shared environment between twins. Twin studies have been demonstrated to be useful in exploring BD pathogenesis and could be extremely effective at discriminating the neural mechanisms underlying BD.

Learning with Heterogeneous Data for Longitudinal Studies

Longitudinal studies are very important to understand cerebral structural changes especially during the course of pathologies. For instance, in the context of mental health research, it is interesting to evaluate how a certain disease degenerates over time in order to discriminate between pathological and normal time dependent brain deformations. However longitudinal data are not easily available, and very often they are characterized by a large variability in both the age of subjects and time between acquisitions (follow up time). This leads to heterogeneous data that may affect the overall study. In this paper we propose a learning method to deal with this kind of heterogeneous data by exploiting covariate measures in a Multiple Kernel Learning (MKL) framework. Cortical thickness and white matter volume of the left middle temporal region are collected from each subject. Then, a subject-dependent kernel weighting procedure is introduced in order to obtain the correction of covariate effect simultaneously with classification. Experiments are reported for First Episode Psychosis detection by showing very promising results.

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

In certain severe mental diseases, like schizophrenia, structural alterations of the brain are detectable by magnetic resonance imaging (MRI). In this work, we try to automatically distinguish, by using anatomical features obtained from MRI images, schizophrenia patients from healthy controls. We do so by exploiting contextual similarity of imaging data, enhanced with a distance metric learning strategy (DML - by providing “must-be-in-the-same-class” and “must-not-be-in-the-same-class” pairs of subjects). To learn from contextual similarity of the subjects brain anatomy, we use a graph-based semi-supervised label propagation algorithm (graph transduction, GT) and compare it to standard supervised techniques (SVM and K-nearest neighbor, KNN). We performed out tests on a population of 20 schizophrenia patients and 20 healthy controls. DML+GT achieved a statistically significant advantage in classification performance (Accuracy: 0.74, Sensitivity: 0.79, Specificity: 0.69, Ck: 0.48). Enhanced contextual similarity improved performance of GT, SVM and KNN offering promising perspectives for MRI images analysis.