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Background Lithium is one of the most effective medications for bipolar disorder episode prevention, but its mechanism of action is still largely unknown. The hippocampus is a subcortical cerebral structure involved in the formation of emotional responses, cognition and various primitive functions, altered during affective episodes. Deviations in the anatomy or physiology of the hippocampus would partially explain the symptomatology of bipolar subjects, and restoration may reflect treatment response. Methods In this mini review, we summarize the studies which have investigated the effect of lithium intake on the volume of hippocampus, measured using magnetic resonance imaging (MRI). We performed a bibliographic search on PubMed, using the terms terms “hippocampus”, “lithium”, “bipolar disorder”, “volume” and “MRI”. Only original studies were considered. Results Thirteen studies met the inclusion criteria. Nine studies demonstrated increased total hippocampal volume or hippocampal subfield volumes in BD patients on lithium treatment (Li BD) compared to those not taking lithium (non-Li BD), while four failed to show significant differences between groups. When healthy controls were compared to either the Li subjects or the non-Li ones, the findings were more heterogeneous. Limitations Heterogeneity in the methodology and definition of groups limits the comparison of study results. Conclusions Lithium may be associated with increased hippocampal volume in BD, potentially due to its putative neurotrophic action, but further research is needed better define the morphological alterations of hippocampus in BD and the longitudinal effects of lithium in the short and long-term.

In the last decades, many neuroimaging studies have investigated the brain structural deficits associated with the pathophysiology of Bipolar Disorder (BD). Notably, the available literature identified the presence of selective brain structural abnormalities, in terms of both gray matter (GM) and white matter, known to be involved in emotion regulation and processing. Nonetheless, the complex and heterogeneous nature of BD has not yet allowed the identification of a clear biological signature of this disorder. In this context, this review aims to a) briefly summarize the evidence from structural Magnetic Resonance Imaging (MRI) studies on BD in order to provide a clearer picture of the neural circuits involved in this disabling mental illness, and b) describe some critical clinical issues that need to be taken into account when studying BD, especially the history of psychosis and the link between BD and behavioral variant fronto-temporal dementia (bvFTD). Evidence from structural MRI studies on BD suggests ventricular, prefrontal and temporal abnormalities but remain largely inconsistent, possibly reflecting the disorder complexity. Moreover, the reviewed evidence showed that psychotic BD might represent a subtype of the disorder with specific and enhanced alterations of GM morphology. Finally, from the available MRI evidence exploring the relationship between BD and bvFTD emerged the need of a differential identification of biological markers to enable the early and accurate detection of the two disorders, ultimately guaranteeing a better prognosis and the employment of more targeted and effective treatments.

Neurodevelopment represents a period of increased opportunity and vulnerability, during which a complex confluence of genetic and environmental factors influences brain growth trajectories, cognitive and mental health outcomes. Recently, magnetic resonance imaging (MRI) studies on twins have increased our knowledge of the extent to which genes, the environment and their interactions shape inter-individual brain variability. The present review draws from highly salient MRI studies in young twin samples to provide a robust assessment of the heritability of structural and functional brain changes during development. The available studies suggest that (as with many other traits), global brain morphology and network organization are highly heritable from early childhood to young adulthood. Conversely, genetic correlations among brain regions exhibit heterogeneous trajectories, and this heterogeneity reflects the progressive, experience-related increase in brain network complexity. Studies also support the key role of environment in mediating brain network differentiation via changes of genetic expression and hormonal levels. Thus, rest- and task-related functional brain circuits seem to result from a contextual and dynamic expression of heritability.

Objective Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects. Methods A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1‐MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a “learning by example” procedure; the features with best performance was then selected by “greedy forward‐feature selection.” Finally, this model underwent a leave‐one‐out cross‐validation approach. Results From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process. Conclusion We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required., Cortical thickness has been proven to be involved in the etiopathogenesis of autism. We use automatic classification to cortical thickness data in order to investigate its use as a biomarker of autism. Five regions of interest reached accuracies of over 70%, and we reached an accuracy of 84.2% concatenating eight regions. Cortical thickness resulted higher in autism compared to controls, suggesting a role in the pathogenesis of autism.

Background Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls. Methods In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty. Results Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD. Limitations The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected. Conclusions The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.

Contains fulltext : 244253.pdf (Publisher’s version ) (Open Access) BACKGROUND: Brain structure abnormalities throughout the course of Parkinson's disease have yet to be fully elucidated. OBJECTIVE: Using a multicenter approach and harmonized analysis methods, we aimed to shed light on Parkinson's disease stage-specific profiles of pathology, as suggested by in vivo neuroimaging. METHODS: Individual brain MRI and clinical data from 2357 Parkinson's disease patients and 1182 healthy controls were collected from 19 sources. We analyzed regional cortical thickness, cortical surface area, and subcortical volume using mixed-effects models. Patients grouped according to Hoehn and Yahr stage were compared with age- and sex-matched controls. Within the patient sample, we investigated associations with Montreal Cognitive Assessment score. RESULTS: Overall, patients showed a thinner cortex in 38 of 68 regions compared with controls (d(max) = -0.20, d(min) = -0.09). The bilateral putamen (d(left) = -0.14, d(right) = -0.14) and left amygdala (d = -0.13) were smaller in patients, whereas the left thalamus was larger (d = 0.13). Analysis of staging demonstrated an initial presentation of thinner occipital, parietal, and temporal cortices, extending toward rostrally located cortical regions with increased disease severity. From stage 2 and onward, the bilateral putamen and amygdala were consistently smaller with larger differences denoting each increment. Poorer cognition was associated with widespread cortical thinning and lower volumes of core limbic structures. CONCLUSIONS: Our findings offer robust and novel imaging signatures that are generally incremental across but in certain regions specific to disease stages. Our findings highlight the importance of adequately powered multicenter collaborations. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between predictions and clinical variables. 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20,484 vertices) were extracted. Linear Support Vector Machine was used for classification within a repeated nested cross-validation framework. Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy of 66.2% and an Area Under the Curve of 72%. By stratifying our sample for MRI scanner, we increased generalizability across sites. Temporal brain areas resulted as the most influential in the classification. The predictive decision scores significantly correlated with age at onset, duration of treatment, and positive symptoms. In conclusion, although far from the threshold of clinical relevance, temporal cortical thickness proved to classify between FEP subjects and healthy individuals. The assessment of site-dependent variables permitted an increase in the across-site generalizability, thus attempting to address an important machine learning limitation.

Background Patterns of coordinated variations of gray matter (GM) morphology across individuals are promising indicators of disease. However, it remains unclear if they can help characterize first-episode psychosis (FEP) and symptoms’ severity. Methods Sixty-seven FEP and 67 matched healthy controls (HC) were assessed with structural MRI to evaluate the existence of distributed GM structural covariance patterns associated to brain areas belonging to salience network. Voxel-based morphometry (VBM) and structural covariance differences, investigated with salience network seed-based Partial Least Square, were applied to explore differences between groups. GM density associations with Raven's intelligent quotient (IQ) and Positive and Negative Syndrome Scale (PANSS) scores were investigated. Results Univariate VBM results gave trend without significant GM differences across groups. GM and IQ correlated positively in both groups: in FEP, mostly in hippocampus, insula, and fronto-temporal structures, while in HC mostly in amygdala, thalamus and fronto-temporal regions. GM and PANSS scores correlated negatively in FEP, with widespread clusters located in limbic regions. Multivariate analysis showed strong and opposite structural GM covariance with salience network for FEP and HC. Moreover, structural covariance of the salience network in FEP correlated negatively with severity of clinical symptoms. Conclusion Our study provides evidence supporting the insular dysfunction model of psychosis. Reduced structural GM covariance of the salience network, with its association to symptom's severity, appears a promising morphometry feature for FEP detection.

BackgroundBrain structure abnormalities throughout the course of Parkinson’s disease (PD) have yet to be fully elucidated. Inconsistent findings across studies may be partly due to small sample sizes and heterogeneous analysis methods. Using a multicenter approach and harmonized analysis methods, we aimed to overcome these limitations and shed light on disease stage-specific profiles of PD pathology as suggested by in vivo neuroimaging.MethodsIndividual brain MRI and clinical data from 2,367 PD patients and 1,183 healthy controls were collected from 19 sites, deriving from 20 countries. We analyzed regional cortical thickness, cortical surface area, and subcortical volume using mixed-effect linear models. Patients were grouped according to the Hoehn & Yahr (HY) disease stages and compared to age- and sex-matched controls. Within the PD sample, we investigated associations between Montreal Cognitive Assessment (MoCA) scores and brain morphology.FindingsThe main analysis showed a thinner cortex in 38 of 68 regions in PD patients compared to controls (dmax = −0·25, dmin = −0·13). The bilateral putamen (left: d = −0·16, right: d = −0·16) and left amygdala (d = −0·15) were smaller in patients, while the left thalamus was larger (d = 0·17). HY staging indicated that a thinner cortex initially presents in the occipital, parietal and temporal cortex, and extends towards caudally located brain regions with increased disease severity. From HY stage 2 and onwards the bilateral putamen and amygdala were consistently smaller with larger effects denoting each increment. Finally, we found that poorer cognitive performance was associated with widespread cortical thinning as well as lower volumes of core limbic structures.InterpretationOur findings offer robust and novel imaging signatures that are specific to the disease severity stages and in line with an ongoing neurodegenerative process, highlighting the importance of such multicenter collaborations.FundingNIH Big Data to Knowledge program, ENIGMA World Aging Center, and ENIGMA Sex Differences Initiative, and other international agencies (listed in full in the Acknowledgments).

Objectives Bipolar disorder (BD) is a psychiatric condition causing shifts in mood, energy and activity levels severely altering the quality of life of the patients even in the euthymic phase. Although widely accepted, the neurobiological bases of the disorder in the euthymic phase remain elusive. This study aims at characterizing resting state functional activity of the BD euthymic phase in order to better understand the pathogenesis of the disease and build future neurobiological models. Methods Fifteen euthymic BD patients (10 females; mean age 40.2; standard deviation 13.5; range 20-61) and 27 healthy controls (HC) (21 females; mean age 37; standard deviation 10.6; range 22-60) underwent a 3T functional MRI scan at rest. Resting state activity was extracted through independent component analysis (ICA) run with automatic dimensionality estimation. Results ICA identified 22 resting state networks (RSNs). Within-network analysis revealed decreased connectivity in the visual, temporal, motor and cerebellar RSNs of BD patients vs HC. Between-network analysis showed increased connectivity between motor area and the default mode network (DMN) partially overlapping with the fronto-parietal network (FPN) in BD patients. Conclusion Within-network analysis confirmed existing evidence of altered cerebellar, temporal, motor and visual networks in BD. Increased connectivity between the DMN and the motor area network suggests the presence of alterations of the fronto-parietal regions, precuneus and cingulate cortex in the euthymic condition. These findings indicate that specific connectivity alterations might persist even in the euthymic state suggesting the importance of examining both within and between-network connectivity to achieve a global understanding of the BD euthymic condition.

Despite various advances in the study of the neurobiological underpinnings of personality traits, the specific neural correlates associated with character and temperament traits are not yet fully understood. Therefore, this study aims to fill this gap by exploring the biochemical basis of personality, which is explored with the temperament and character inventory (TCI), during brain development in a sample of adolescents. Twenty-six healthy adolescents (aged between 13 and 21 years; 17 males and 9 females) with behavioral and emotional problems underwent a TCI evaluation and a 3T single-voxel proton magnetic resonance spectroscopy (1H MRS) acquisition of the anterior cingulate cortex (ACC). Absolute metabolite levels were estimated using LCModel: significant correlations between metabolite levels and selective TCI scales were identified. Specifically, phosphocreatine plus creatine (PCr+Cre) significantly correlated with self-directedness, positively, and with a self-transcendence (ST), negatively, while glycerophosphocholine plus phosphocholine (GPC+PC) and myo-inositol negatively correlated with ST. To the best of our knowledge, this is the first study reporting associations of brain metabolites with personality traits in adolescents. Therefore, our results represent a step forward for personality neuroscience within the study of biochemical systems and brain structures.

Gender differences in mood and anxiety disorders are well-established. However, the neural basis of these differences is not clear yet, especially in terms of brain metabolism. Indeed, although several proton Magnetic Resonance Spectroscopy (¹H MRS) investigations reported different metabolic levels in both depression and anxiety disorders, which have been also linked to symptoms severity and response to treatment, the role of gender on these differences have not been explored yet. Therefore, this study aims at investigating the role of sex in neurometabolic alterations associated with both mood and anxiety disorders. A 3T single-voxel ¹H MRS acquisition of the dorsolateral prefrontal cortex was acquired from 14 Major Depressive Disorder, 10 Generalized Anxiety Disorder (GAD), 11 Panic Disorder (PD), patients and 16 healthy controls (HC). Among males, PD patients showed significantly lower GPC+PC (also observed in GAD+PD) and Glu levels compared to HC. Finally, a significant group x sex interaction effect was observed in the GPC+PC and Glu levels. We proved the presence of an association between sex and brain metabolites in anxiety spectrum.

Objectives: To determine the effectiveness of an early intervention program in enhancing visual function in very preterm infants. Methods: We conducted a RCT. We included preterm infants born between 25+0 and 29+6 weeks of gestational age (GA), without severe morbidities, and their families. Infants were randomized to either receive Standard Care (SC) or Early Intervention (EI). SC, according to NICU protocols, included Kangaroo Mother Care and minimal handling. EI included, in addition to routine care, parental training according to the PremieStart program, and multisensory stimulation (infant massage and visual interaction) performed by parents. Visual function was assessed at term equivalent age (TEA) using a prevalidated battery evaluating ocular spontaneous motility, ability to fix and follow a target, reaction to color, stripes discrimination and visual attention at distance. Results: Seventy preterm (EI n = 34, SC n = 36) infants were enrolled. Thirteen were excluded according to protocol. Fifty-seven infants (EI = 27, SC = 30) were assessed at TEA. The two groups were comparable for parental and infant characteristics. In total, 59% of infants in the EI group achieved the highest score in all the nine assessed items compared to 17% in the SC group (p = 0.001): all infants in both groups showed complete maturation in four items, but EI infants showed more mature findings in the other five items (ocular motility both spontaneous and with target, tracking arc, stripes discrimination and attention at distance). Conclusions: Our results suggest that EI has a positive effect on visual function maturation in preterm infants at TEA. Trial Registration: clinicalTrial.gov (NCT02983513).

Parkinson’s disease (PD) is associated with brain mitochondrial dysfunction. High-energy phosphates (HEPs), which rely on mitochondrial functioning, may be considered potential biomarkers for PD. Phosphorus magnetic resonance spectroscopy (31P-MRS) is a suitable tool to explore in vivo cerebral energetics. We considered 10 31P-MRS studies in order to highlight the main findings about brain energetic compounds in patients affected by idiopathic PD and genetic PD. The studies investigated several brain areas such as frontal lobes, occipital lobes, temporoparietal cortex, visual cortex, midbrain, and basal ganglia. Resting-state studies reported contrasting results showing decreased as well as normal or increased HEPs levels in PD patients. Functional studies revealed abnormal PCr + βATP levels in PD subjects during the recovery phase and abnormal values at rest, during activation and recovery in one PD subject with PINK1 gene mutation suggesting that mitochondrial machinery is more impaired in PD patients with PINK1 gene mutation. PD is characterized by energetics impairment both in idiopathic PD as well as in genetic PD, suggesting that mitochondrial dysfunction underlies the disease. Studies are still sparse and sometimes contrasting, maybe due to different methodological approaches. Further studies are needed to better assess the role of mitochondria in the PD development.

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.

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 3Tesla (T) or a 1.5T 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.

Background Lithium is recommended as a first line treatment for bipolar disorders. However, only 30% of patients show an optimal outcome and variability in lithium response and tolerability is poorly understood. It remains difficult for clinicians to reliably predict which patients will benefit without recourse to a lengthy treatment trial. Greater precision in the early identification of individuals who are likely to respond to lithium is a significant unmet clinical need. Structure The H2020-funded Response to Lithium Network (R-LiNK; http://www.r-link.eu.com/) will undertake a prospective cohort study of over 300 individuals with bipolar-I-disorder who have agreed to commence a trial of lithium treatment following a recommendation by their treating clinician. The study aims to examine the early prediction of lithium response, non-response and tolerability by combining systematic clinical syndrome subtyping with examination of multi-modal biomarkers (or biosignatures), including omics, neuroimaging, and actigraphy, etc. Individuals will be followed up for 24 months and an independent panel will assess and classify each participants’ response to lithium according to predefined criteria that consider evidence of relapse, recurrence, remission, changes in illness activity or treatment failure (e.g. stopping lithium; new prescriptions of other mood stabilizers) and exposure to lithium. Novel elements of this study include the recruitment of a large, multinational, clinically representative sample specifically for the purpose of studying candidate biomarkers and biosignatures; the application of lithium-7 magnetic resonance imaging to explore the distribution of lithium in the brain; development of a digital phenotype (using actigraphy and ecological momentary assessment) to monitor daily variability in symptoms; and economic modelling of the cost-effectiveness of introducing biomarker tests for the customisation of lithium treatment into clinical practice. Also, study participants with sub-optimal medication adherence will be offered brief interventions (which can be delivered via a clinician or smartphone app) to enhance treatment engagement and to minimize confounding of lithium non-response with non-adherence. Conclusions The paper outlines the rationale, design and methodology of the first study being undertaken by the newly established R-LiNK collaboration and describes how the project may help to refine the clinical response phenotype and could translate into the personalization of lithium treatment.

BACKGROUND PINK1 mutations are likely to affect mitochondrial function. The objective of this study was to study brain mitochondrial function in patients with early-onset Parkinson's disease, with or without PINK1 mutations. METHODS We investigated brain intracellular pH, mitochondrial activity, and energetics with functional magnetic resonance spectroscopy in patients with early-onset Parkinson's disease with PINK1 mutations (n = 10), early-onset Parkinson's disease without PINK1 mutations (n = 10), and healthy sex- and age-matched subjects (n = 20). We measured peak areas of phosphocreatine and beta adenosine triphosphate. RESULTS The EOPD- group had normal PCr + βATP contents at rest (P = NS) and under activation (P = NS), but reduced contents during recovery (P < 0.001). The EOPD+ group had abnormal PCr + βATP contents at rest (P < 0.001) and during activation (P < 0.001); during recovery, the contents only partially recovered (P < 0.001). Brain intracellular pH alterations were more severe with EOPD+ than with EOPD-. CONCLUSIONS Brain mitochondrial impairments were similar in early-onset Parkinson's disease without PINK1 mutations and late-onset Parkinson's disease. However, mitochondrial impairments were more severe in early-onset Parkinson's disease with PINK1 mutations. © 2020 International Parkinson and Movement Disorder Society.

Functional magnetic resonance imaging (fMRI) is a popular approach for understanding the functional connectivity of human brain. Recently, dynamic functional connectivity has been used to analyze connectivity variations on resting state fMRI. Here, we use task based fMRI (using the Poffenberger Paradigm) data collected in mono- and dizygotic twin pairs. The task is to examine if the two groups of twins can be discriminated by using the dynamic connectivity, so to prove that genetic background has an effect on functional connectivity. To this aim, we have explored the dynamic connectivity patterns of task-relevant and task-orthogonal sub-networks using graph Laplacian representation in combination with a metric defined on the space of covariance matrices to compute the similarity between twins' dynamics in the mental state. Linear SVMs with an unsupervised feature selection (Laplacian Score) were then used to discriminate the two classes of twins.

Background Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness data, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between the predictions and clinical variables. Methods 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI (sMRI) session and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20484 vertices) were extracted. Supervised (linear Support Vector Machine) classification was used to differentiate FEP from HC, within a repeated nested Cross-Validation (CV) framework through the NeuroMiner software. In both inner and outer CVs, a 10-fold CV cycle was employed. We performed repeated nested CV at the outer cross-validation cycle by randomly permuting the participants within their groups (10 permutations) and repeating the CV cycle for each of these permutations. As feature preprocessing, regression of covariates (age, sex, and site), Principal Component Analysis and Scaling were applied. All preprocessing steps were implemented within the CV. Further analyses were conducted by stratifying the sample for MRI scanner, sex and by performing random resampling with increasingly reduced sample sizes. Results Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy (BAC) of 66.2% and an Area Under the Curve of 72%, compared to a BAC of 59% and an Area Under the Curve of 61% obtained with the ROI-based approach. The two BACs were significantly different based on the McNemar’s Test. By stratifying our sample for MRI scanner, we increased the overall BAC to more than 70% and we also increased generalizability across sites. Temporal areas resulted the most influential regions in the classification. The predictive decision scores presented significant correlations with age at onset, duration of treatment and the presence of affective vs non-affective psychosis. Discussion Cortical thickness could represent a valid measure to classify FEP subjects, showing temporal areas as potential markers in the early stages of psychosis. The assessment of site-dependent variables allowed us to increase the across-site generalizability of the model, thus attempting to address an important machine learning limitation, especially in the framework of large multi-site cohort and big data analysis.

Very preterm (VPT) infants admitted to Neonatal Intensive Care Unit (NICU) are at risk for altered brain growth and less-than-optimal socio-emotional development. Recent research suggests that early NICU-related stress contributes to socio-emotional impairments in VPT infants at 3 months through epigenetic regulation (i.e., DNA methylation) of the serotonin transporter gene (SLC6A4). In the present longitudinal study we assessed: (a) the effects of NICU-related stress and SLC6A4 methylation variations from birth to discharge on brain development at term equivalent age (TEA); (b) the association between brain volume at TEA and socio-emotional development (i.e., Personal-Social scale of Griffith Mental Development Scales, GMDS) at 12 months corrected age (CA). Twenty-four infants had complete data at 12-month-age. SLC6A4 methylation was measured at a specific CpG previously associated with NICU-related stress and socio-emotional stress. Findings confirmed that higher NICU-related stress associated with greater increase of SLC6A4 methylation at NICU discharge. Moreover, higher SLC6A4 discharge methylation was associated with reduced anterior temporal lobe (ATL) volume at TEA, which in turn was significantly associated with less-than-optimal GMDS Personal-Social scale score at 12 months CA. The reduced ATL volume at TEA mediated the pathway linking stress-related increase in SLC6A4 methylation at NICU discharge and socio-emotional development at 12 months CA. These findings suggest that early adversity-related epigenetic changes might contribute to the long-lasting programming of socio-emotional development in VPT infants through epigenetic regulation and structural modifications of the developing brain.

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.

Introduction Schizophrenia is a severe disabling disorder with heterogeneous illness courses. In this longitudinal study we characterized schizophrenia patients with poor and good outcome (POS, GOS), using functional and imaging metrics. Patients were defined in accordance to Keefe's criteria (i.e. Kraepelinian and non-Kraepelinian patients ). Methods 35 POS patients, 35 GOS patients and 76 healthy controls (H) underwent clinical, functioning and magnetic resonance imaging (MRI) assessments twice over three years of follow-up. Information on psychopathology, treatment, disability (using the World Health Organization Disability Assessment Scale II, WHO-DAS-2) and prefrontal morphology was collected. Dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC) were manually traced. Results At baseline, subjects with POS showed significantly decreased right dorsolateral prefrontal cortex (DLPFC) white matter volumes (WM) compared to healthy controls and GOS patients (POS VS HC, p p = 0.03), with shrinkage of left DLPFC WM volumes at follow up ( t = 2.66, p = 0.01). Also, POS patients had higher disability in respect to GOS subjects both at baseline and after 3 years at the WHO-DAS-2 ( p Discussion Our study supports the hypothesis that POS is characterized by progressive deficits in brain structure and in “real-life” functioning. These are particularly notable in the DLPFC.

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.

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.

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).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.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.Compared with control subjects, patients with RRMS had an increase in FA of NAGM that strongly correlated with cortical lesion volume and clinical disability.

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.

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.

Damage to the structural connections of the thalamus is a frequent feature of traumatic brain injury (TBI) and can be a key factor in determining clinical outcome. Until recently it has been difficult to quantify the extent of this damage in vivo. Diffusion tensor imaging (DTI) provides a validated method to investigate traumatic axonal injury, and can be applied to quantify damage to thalamic connections. DTI can also be used to assess white matter tract structure using tractography, and this technique has been used to study thalamo-cortical connections in the healthy brain. However, the presence of white matter injury can cause failure of tractography algorithms. Here, we report a method for investigating thalamo-cortical connectivity that bypasses the need for individual tractography. We first created a template for a number of thalamo-cortical connections using probabilistic tractography performed in ten healthy subjects. This template for investigating white matter structure was validated by comparison with individual tractography in the same group, as well as in an independent control group (N = 11). We also evaluated two methods of masking tract location using the tract skeleton generated by tract based spatial statistics, and a cerebrospinal fluid mask. Voxel-wise estimates of fractional anisotropy derived from the template were more strongly correlated with individual tractography when both types of masking were used. The tract templates were then used to sample DTI measures from a group of TBI patients (N = 22), with direct comparison performed against probabilistic tractography in individual patients. Probabilistic tractography often failed to produce anatomically plausible tracts in TBI patients. Importantly, we show that this problem increases as tracts become more damaged, and leads to underestimation of the amount of traumatic axonal injury. In contrast, the tract template can be used in these cases, allowing a more accurate assessment of white matter damage. In summary, we propose a method suitable for assessing specific thalamo-cortical white matter connections after TBI that is robust to the presence of varying amounts of traumatic axonal injury, as well as highlighting the potential problems of applying tractography algorithms in patient populations., Highlights ► TBI produces significant damage to thalamo-cortical white matter connections. ► This damage disrupts probabilistic tractography in patients. ► The error associated with patient tractography increases with tract damage. ► A template approach allows more accurate estimation of tract damage after TBI.

Introduction Vascular changes in the brain are relevant in schizophrenia [e.g. 1] and in bipolar disorder [2]. The study of first episode psychosis (FEP) allows the analysis of brain morphology and function without confounds due to chronicity. Objectives To characterize brain perfusion in FEP. Aims To see if FEP exhibit modified perfusion in respect to healthy controls (HC), and identify the most affected brain areas. Methods We acquired T1 and DSC images of 35 FEP patients (45 +/- 10 years old) and 35 HC (42 +/- 8), using Gadolinium (0.1 mmol/Kg). We computed cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) [3] in the whole brain and in left and right frontal, parietal, temporal and occipital lobes, insula, caudate and cerebellum Results Mean values of all quantities resulted lower in patients, up to 12% for CBV in right frontal lobe, 11% for CBF in left cerebellum and 16% for MTT in right frontal lobe. We used a support vector machine (SVM) to classify subjects on the basis of the histogram of perfusion values. We found that the classification reached accuracies over 80%, especially in the frontal brain areas. Conclusions FEP show altered perfusion parameters, which allow automatic classification with good accuracy, showing that brain vascular characteristics can be considered as marker of psychosis. [1] Peruzzo et al (2011). J Neural Transm, 118, 4:563-70. [2] Agarwal et al (2008). J Affect Disord, 110, 1-2:106-14. [3] Ostergaard et al (1996). Magn Reson Med, 36, 5:715-25.