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Progressive supranuclear palsy (PSP) and idiopathic normal pressure hydrocephalus (iNPH) share several clinical and radiological features, making the differential diagnosis challenging. In this study, we aimed to differentiate between these two diseases using a machine learning approach based on cortical thickness and volumetric data.Twenty-three iNPH patients, 50 PSP patients and 55 control subjects were enrolled. All participants underwent a brain 3T-MRI, and cortical thickness and volumes were extracted using Freesurfer 6 on T1-weighted images and compared among groups. Finally, the performance of a machine learning approach with random forest using the extracted cortical features was investigated to differentiate between iNPH and PSP patients.iNPH patients showed cortical thinning and volume loss in the frontal lobe, temporal lobe and cingulate cortex, and thickening in the superior parietal gyrus in comparison with controls and PSP patients. PSP patients only showed mild thickness and volume reduction in the frontal lobe, compared to control subjects. Random Forest algorithm distinguished iNPH patients from controls with AUC of 0.96 and from PSP patients with AUC of 0.95, while a lower performance (AUC 0.76) was reached in distinguishing PSP from controls.This study demonstrated a more severe and widespread cortical involvement in iNPH than in PSP, possibly due to the marked lateral ventricular enlargement which characterizes iNPH. A machine learning model using thickness and volumetric data led to accurate differentiation between iNPH and PSP patients, which may help clinicians in the differential diagnosis and in the selection of patients for shunt procedures.

Imaging studies investigating cerebellar gray matter (GM) in essential tremor (ET) showed conflicting results. Moreover, no large study explored the cerebellum in ET patients with resting tremor (rET), a syndrome showing enhanced blink reflex recovery cycle (BRrc).To investigate cerebellar GM in ET and rET patients using voxel-based morphometry (VBM) analysis.Seventy ET patients with or without resting tremor and 39 healthy controls were enrolled. All subjects underwent brain 3 T-MRI and BRrc recording. We compared the cerebellar GM volumes between ET (n = 40) and rET (n = 30) patients and controls through a VBM analysis. Moreover, we investigated possible correlations between cerebellar GM volume and R2 component of BRrc.rET and ET patients had similar disease duration. All rET patients and none of ET patients had enhanced BRrc. No differences in the cerebellar volume were found when ET and rET patients were compared to each other or with controls. By considering together the two tremor syndromes in a large patient group, the VBM analysis showed bilateral clusters of reduced GM volumes in Crus II in comparison with controls. The linear regression analysis in rET patients revealed a cluster in the left Crus II where the decrease in GM volume correlated with the R2BRrc increase.Our study suggests that ET and rET are different tremor syndromes with similar mild cerebellar gray matter involvement. In rET patients, the left Crus II may play a role in modulating the brainstem excitability, encouraging further studies on the role of cerebellum in these patients.

Introduction There is some debate on the relationship between essential tremor with rest tremor (rET) and the classic ET syndrome, and only few MRI studies compared ET and rET patients. This study aimed to explore structural cortical differences between ET and rET, to improve the knowledge of these tremor syndromes. Methods Thirty-three ET patients, 30 rET patients and 45 control subjects (HC) were enrolled. Several MR morphometric variables (thickness, surface area, volume, roughness, mean curvature) of brain cortical regions were extracted using Freesurfer on T1-weighted images and compared among groups. The performance of a machine learning approach (XGBoost) using the extracted morphometric features was tested in discriminating between ET and rET patients. Results rET patients showed increased roughness and mean curvature in some fronto-temporal areas compared with HC and ET, and these metrics significantly correlated with cognitive scores. Cortical volume in the left pars opercularis was also lower in rET than in ET patients. No differences were found between ET and HC. XGBoost discriminated between rET and ET with mean AUC of 0.86 ± 0.11 in cross-validation analysis, using a model based on cortical volume. Cortical volume in the left pars opercularis was the most informative feature for classification between the two ET groups. Conclusion Our study demonstrated higher cortical involvement in fronto-temporal areas in rET than in ET patients, which may be linked to the cognitive status. A machine learning approach based on MR volumetric data demonstrated that these two ET subtypes can be distinguished using structural cortical features.

The COVID-19 epidemic and its lockdown dramatically impacted the general well-being of the population and affected sociosexual experiences, thus modifying sexual behavior, desire, and well-being. Clustering analysis has not yet been applied to research and data investigating sociosexuality. The cluster analysis method could be a valid support for clinicians in investigating the condition of a population with respect to problems related to sociosexuality. The aim of the present study was to analyze the different perceptions of the sociosexual experiences in southern population during the COVID-19 pandemic.We enrolled 734 (450 female) participants with a carried out anonymous web-based survey from the 16th of April 2020 to the 3rd June of 2020. The revised Sociosexual Orientation Inventory (SOI-R) is a self-report test assessing three theoretically meaningful facets of sociosexual orientation (behavior, attitude, and desire).We found eleven clusters, and the findings showed, for the first time, an intra- and inter-diagnostic heterogeneity in the sexual profile of participants. Theoretically, we identified subtype clusters whose sexual attitude was to avoid sexual promiscuity with significant gender differences. Women show a greater propensity for attitude and desire facet than men.Our new method of unsupervised learning could represent a reliable tool to support socio-cultural analysis studies on issues influenced by cultural mechanisms in a quick and explanatory way, as in the case of sexual orientation and attitude differences between men and women.Understanding these gaps is fundamental for policy makers, managers of social networks, those who deal with engaged couples and families, and sexuality starting from the very youngest adolescents. We claim to devise a strategy to measure how much a sexist culture implicitly and explicitly limits the freedom of sexual expression and how this can affect psycho-sexual well-being in a society.The online version contains supplementary material available at 10.1007/s13178-022-00771-2.

Background Idiopathic normal pressure hydrocephalus and PSP share several clinical and radiological features, making differential diagnosis, at times, challenging. Objectives To differentiate idiopathic normal pressure hydrocephalus from PSP using MR volumetric and linear measurements. Methods Twenty-seven idiopathic normal pressure hydrocephalus patients, 103 probable PSP patients, and 43 control subjects were consecutively enrolled. Automated ventricular volumetry was performed using Freesurfer 6 on MR T1 -weighted images. Linear measurements, such as callosal angle and a new measure, termed MR Hydrocephalic Index, were calculated on MR T1 -weighted images. Receiver operating characteristic analyses were used for differentiating between patient groups. Generalizability and reproducibility of the results were validated, dividing each participant group in two cohorts used as training and testing subsets. Results Ventricular volumes and linear measurements (callosal angle and Magnetic Resonance Hydrocephalic Index) revealed greater ventricular enlargement in patients with idiopathic normal pressure hydrocephalus than in PSP patients and controls. PSP patients had ventricular volume larger than controls. Automated ventricular volumetry and Magnetic Resonance Hydrocephalic Index were the most accurate measures (98.5%) in differentiating patients with idiopathic normal pressure hydrocephalus from PSP patients, whereas callosal angle misclassified several PSP patients and showed low positive predictive value (70.0%) in differentiating between these two diseases. All measurements accurately differentiated idiopathic normal pressure hydrocephalus patients from controls. Accuracy values obtained in the training set (automated ventricular volumetry, 98.4%; Magnetic Resonance Hydrocephalic Index, 98.4%; callosal angle, 87.5%) were confirmed in the testing set. Conclusions Our study demonstrates that AVV and Magnetic Resonance Hydrocephalic Index were the most accurate measures for differentiation between idiopathic normal pressure hydrocephalus and PSP patients. Magnetic Resonance Hydrocephalic Index is easy to measure and can be used in clinical practice to prevent misdiagnosis and ineffective shunt procedures in idiopathic normal pressure hydrocephalus mimics. © 2020 International Parkinson and Movement Disorder Society.

Approximatively, 10% of patients initially diagnosed with Parkinson's disease (PD) show preserved presynaptic dopaminergic function in the nigrostriatal pathway on DAT-SPECT imaging. This syndrome is not compatible with PD diagnosis, and is known as scans without evidence of dopaminergic deficit (SWEDD).To investigate structural connectivity of cerebello-subcortico-cortical networks, including the nigrostriatal pathway, in an international cohort of subjects with SWEDD compared to normal controls using probabilistic tractography.Twenty-eight patients with SWEDD and 21 age- and sex-matched healthy controls (HC) were selected from the Parkinson's Progression Markers Initiative (PPMI) database. All participants underwent whole-brain 3D T1-weighted and diffusion-weighted MRI, as well as DAT-SPECT. Probabilistic tractography was performed in network-mode between regions of the cerebello-thalamo-basal ganglia-cortical circuits, to extract the connectivity strength between pairs of nodes of the circuit, as well as volumetric and diffusion measures of each reconstructed tract. Analysis of covariance with age and sex as covariates of non-interest was performed to assess group differences. Statistical significance was set at p 0.05 after false-discovery-rate correction for multiple comparisons.Compared to HC, patients with SWEDD showed increased fractional anisotropy in bilateral thalamo-putamen-precentral, left nigro-putaminal and left thalamo-pallidal pathways. Furthermore, we found decreased mean streamline length in bilateral thalamo-nigro-cerebellar pathways and in the left nigro-caudate connection.Clinical heterogeneity of SWEDD syndrome may account for involvement of different brain circuits, such as the cerebello-thalamo-cortical and the nigrostriatal pathways, characteristic of different tremulous disorders.

Scans without evidence of dopaminergic deficit (SWEDD) refers to patients who mimics motor and non-motor symptoms of Parkinson's disease (PD) but showing integrity of dopaminergic system. For this reason, the differential diagnosis between SWEDD and PD patients is often not possible in absence of dopamine imaging. Machine Learning (ML) showed optimal performance in automatically distinguishing these two diseases from clinical and imaging data. However, the most common applied ML algorithms provide high accuracy at expense of findings intelligibility. In this work, a novel ML glass-box model, the Explainable Boosting Machine (EBM), based on Generalized Additive Models plus interactions (GA2Ms), was employed to obtain interpretability in classifying PD and SWEDD while still providing optimal performance. Dataset (168 healthy controls, HC; 396 PD; 58 SWEDD) was obtained from PPMI database and consisted of 178 among clinical and imaging features. Six binary EBM classifiers were trained on feature space with (SBR) and without (noSBR) dopaminergic striatal specific binding ratio: HC-PDSBR, HC-SWEDDSBR, PD-SWEDDSBR and HC-PDnoSBR, HC-SWEDDnoSBR, PD-SWEDDnoSBR. Excellent AUC-ROC (1) was reached in classifying HC from PD and SWEDD, both with and without SBR, and by PD-SWEDDSBR (0.986), while PD-SWEDDnoSBR showed lower AUC-ROC (0.882). Apart from optimal accuracies, EBM algorithm was able to provide global and local explanations, revealing that the presence of pairwise interactions between UPSIT Booklet #1 and Epworth Sleepiness Scale item 3 (ESS3), MDS-UPDRS-III pronation-supination movements right hand (NP3PRSPR) and MDS-UPDRS-III rigidity left upper limb (NP3RIGLU) could provide good performance in predicting PD and SWEDD also without imaging features.

Background: Progressive supranuclear palsy (PSP) patients can show ventricular enlargement mimicking normal pressure hydrocephalus (NPH). The aim of this study was to distinguish PSP patients with marked ventricular dilatation (PSP-vd) from those with normal ventricular system and to evaluate the coexistence of NPH in PSP-vd patients. Methods: One hundred three probable PSP patients, 18 definite NPH patients, and 41 control subjects were enrolled in the study. Evans index (EI) > 0.32 associated with callosal angle (CA) < 100° was used to identify PSP-vd patients. Automated ventricular volumetry (AVV) and Magnetic Resonance Hydrocephalic Index (MRHI) were performed on T1-weighted MR images to evaluate the presence of NPH in PSP-vd patients. Results: Twelve (11.6%) out of 103 PSP patients had both abnormal EI and CA values (PSP-vd). In two of these 12 patients, AVV and MRHI values suggested PSP + NPH. In the remaining 10 PSP-vd patients, AVV and MRHI values were higher than PSP patients with normal ventricular system and controls, but lower than PSP + NPH and NPH patients, suggesting a non-hydrocephalic ventricular enlargement. Discussion: Our study provides evidence that the combination of EI and CA biomarkers allowed to identify PSP patients with marked ventricular dilatation mimicking NPH. Only a few of these patients had PSP + NPH. Recognition of these PSP patients with enlarged ventricles can positively impact the care of this disease, helping clinicians to identify patients with PSP + NPH who could benefit from shunt procedure and avoid surgery in those with enlarged ventricles without NPH.

The COVID-19 outbreak has dramatically impacted on socioeconomic structure, individual freedom, general wellbeing, psychological health and sexuality. Indeed, social distancing, home confinement and the fear of contagion have reduced the possibility of romantic encounters thus influencing sexual activity, desire and behavior and, consequently, modifying socio-sexual experiences. The aim of this study is to examine sociosexuality and sociosexual experiences in southern Italians during the COVID-19 pandemic.

Progressive supranuclear palsy (PSP) patients can show ventricular enlargement mimicking normal pressure hydrocephalus (NPH). The aim of this study was to distinguish PSP patients with marked ventricular dilatation (PSP-vd) from those with normal ventricular system and to evaluate the coexistence of NPH in PSP-vd patients.One hundred three probable PSP patients, 18 definite NPH patients, and 41 control subjects were enrolled in the study. Evans index (EI) 0.32 associated with callosal angle (CA) 100° was used to identify PSP-vd patients. Automated ventricular volumetry (AVV) and Magnetic Resonance Hydrocephalic Index (MRHI) were performed on T1-weighted MR images to evaluate the presence of NPH in PSP-vd patients.Twelve (11.6%) out of 103 PSP patients had both abnormal EI and CA values (PSP-vd). In two of these 12 patients, AVV and MRHI values suggested PSP + NPH. In the remaining 10 PSP-vd patients, AVV and MRHI values were higher than PSP patients with normal ventricular system and controls, but lower than PSP + NPH and NPH patients, suggesting a non-hydrocephalic ventricular enlargement.Our study provides evidence that the combination of EI and CA biomarkers allowed to identify PSP patients with marked ventricular dilatation mimicking NPH. Only a few of these patients had PSP + NPH. Recognition of these PSP patients with enlarged ventricles can positively impact the care of this disease, helping clinicians to identify patients with PSP + NPH who could benefit from shunt procedure and avoid surgery in those with enlarged ventricles without NPH.

Introduction Several structural and functional neuroimaging studies have shown that the Supplementary Motor Area (SMA) is affected by tau pathology in patients with Progressive Supranuclear Palsy (PSP). The aim of the study was to investigate the biochemical profile of SMA in PSP patients, using proton magnetic resonance spectroscopy (1H-MRS). Methods Sixteen PSP patients and 18 healthy controls participated in this study. 1H-MRS was performed by using a Point RESolving Spectroscopy (PRESS) single-voxel sequence implemented on a 3-T scanner. A voxel of 25 × 25 × 15 mm involving the right and left SMA was acquired in all subjects. Peak areas of N-acetyl-aspartate + N-acetyl-aspartyl-glutamate (NAA), creatine with phosphocreatine (Cr), glycerophosphocholine + phosphocholine (Cho), glutamate + glutamine (Glx), glutathione (GSH), myo-Inositol (mI) and Scyllo-Inositol (Scyllo) were calculated using a version 6.3-1K of the fitting program LCModel. Comparative analysis was performed on both absolute concentrations and ratio values relative to Cr. Results PSP patients showed a significant decrease in Scyllo concentration and Scyllo/Cr ratio values in SMA, compared to controls, whereas no difference between groups was found for the other ratio values. Of note, the attention and working memory functions were positively related to Scyllo and Scyllo/Cr values in PSP patients. Conclusions Our study demonstrates that Scyllo and Scyllo/Cr were significantly reduced in the SMA of PSP patients. Because Scyllo seems to be able to protect against formation of toxic fibrils of amyloid-beta fragments and tau oligomers deposition, these preliminary findings may open new perspectives to investigate Scyllo as a new potential disease-modifying therapy for PSP.

Objective To evaluate whether increased cerebrospinal fluid (CSF) pressure causes alteration of periventricular white matter (WM) microstructure in patients with idiopathic intracranial hypertension (IIH). Methods In a prospective study, patients with refractory chronic headache with and without IIH performed a neuroimaging study including 3T MRI, 3D Phase Contrast MR venography, and diffusion tensor imaging (DTI) of the brain. Whole‐brain voxel‐wise comparisons of DTI abnormalities of WM were performed using tract‐based spatial statistics. A correlation analysis between DTI indices and CSF opening pressure, highest peak, and mean pressure was also performed in patients with IIH. Results We enrolled 62 consecutive patients with refractory chronic headaches. Thirty‐five patients with IIH, and 27 patients without increased intracranial pressure. DTI analysis revealed no fractional anisotropy changes, but decreased mean, axial, and radial diffusivity in body (IIHMD = 0.80 ± 0.04, non‐IIHMD = 0.84 ± 0.4, IIHAD = 1.67 ± 0.07, non‐IIHAD = 1.74 ± 0.05, IIHRD = 0.38 ± 0.04, non‐IIHRD = 0.42 ± 0.05 [mm2/sec × 10−3]) of corpus callosum, and in right superior corona radiata (IIHMD = 0.75 ± 0.04, non‐IIHMD = 0.79 ± 0.05, IIHAD = 1.19 ± 0.07, non‐IIHAD = 1.28 ± 0.09, IIHRD = 0.59 ± 0.03, non‐IIHRD = 0.53 ± 0.03 [mm2/sec × 10−3]) of 35 patients with IIH compared with 27 patients without increased intracranial pressure. DTI indices were negatively correlated with high CSF pressures (P

Aim: The aim of this study was to evaluate the corticospinal tract (CST) diffusion profile in pure lower motor neuron disease (pLMND) patients who at baseline did not show any clinical or electrophysiological involvement of upper motor neurons (UMN), and in amyotrophic lateral sclerosis (ALS) patients. Materials and Methods: Fifteen ALS patients with delayed central motor conduction time (CMCT) and 14 pLMND patients with normal CMCT were enrolled together with 15 healthy controls. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) maps were obtained. The tract profile of CST was reconstructed with the automated fiber quantification tool and its diffusion properties were quantified voxel-by-voxel and then compared pairwise between groups. Moreover, a random forest (RF) classifier was trained to evaluate the ability of CST diffusion metrics in distinguishing pairwise the groups from the controls. Results: ALS patients presented wide microstructural abnormalities in the entire CST as assessed by FA decrease and RD increase while pLMND patients showed focal FA decrease and a larger AD increase in the cerebral peduncle and posterior limb of the internal capsule in comparison with controls. RF revealed that diffusion tensor imaging (DTI) metrics accurately distinguished ALS patients and pLMND patients from controls (96.67 and 95.71% accuracy, respectively). Conclusions: Our study demonstrates that the CST was impaired in both ALS and pLMND patients, thus suggesting that DTI metrics are a reliable tool in detecting subtle changes of UMN in pLMND patients, also in the absence of clinical and CMCT abnormalities.

Progressive supranuclear palsy (PSP) is a rare, rapidly progressive neurodegenerative disease. Richardson's syndrome (PSP-RS) and predominant parkinsonism (PSP-P) are characterized by wide range of cognitive and behavioural disturbances, but these variants show similar cognitive pattern of alterations, leading difficult differential diagnosis. For this reason, we explored with an Artificial Intelligence approach, whether cognitive impairment could differentiate the phenotypes. Forty Parkinson's disease (PD) patients, 25 PSP-P, 40 PSP-RS, and 34 controls were enrolled following the consensus criteria diagnosis. Participants were evaluated with neuropsychological battery for cognitive domains. Random Forest models were used for exploring the discriminant power of the cognitive tests in distinguishing among the four groups. The classifiers for distinguishing diseases from controls reached high accuracies (86% for PD, 95% for PSP-P, 99% for PSP-RS). Regarding the differential diagnosis, PD was discriminated from PSP-P with 91% (important variables: HAMA, MMSE, JLO, RAVLT_I, BDI-II) and from PSP-RS with 92% (important variables: COWAT, JLO, FAB). PSP-P was distinguished from PSP-RS with 84% (important variables: JLO, WCFST, RAVLT_I, Digit span_F). This study revealed that PSP-P, PSP-RS and PD had peculiar cognitive deficits compared with healthy subjects, from which they were discriminated with optimal accuracies. Moreover, high accuracies were reached also in differential diagnosis. Most importantly, Machine Learning resulted to be useful to the clinical neuropsychologist in choosing the most appropriate neuropsychological tests for the cognitive evaluation of PSP patients.

Highlights • PSP patients can show marked ventricular enlargement (PSP-vd). • PSP-vd patients had increased FA and AxD in CST regions close to lateral ventricles. • CST alterations in PSP-vd are due to CST compression by the enlarged ventricles. • CST alterations in PSP-vd are similar to those found in NPH but of lesser degree. • Higher ventriculomegaly is associated with higher severity of the CST alterations., Background Microstructural alterations of corticospinal tract (CST) have been found in idiopathic normal pressure hydrocephalus (iNPH). No study, however, investigated the effect of ventricular dilatation on CST in Progressive Supranuclear Palsy (PSP). Objective The aim of this study was to investigate CST diffusion profile in a large cohort of PSP patients with and without ventricular dilatation. Methods Twenty-three iNPH patients, 87 PSP patients and 26 controls were enrolled. Evans index (EI) and ventricular volume (VV) were measured in all patients. CST tractography was performed to calculate FA, MD, AxD and RD in six different anatomical regions: medulla oblungata (MO), pons (P), cerebral peduncle (CP), posterior limb of internal capsule (PLIC), corona radiata (CR), subcortical white matter (SWM). ANCOVA was used for comparing CST diffusion profiles between the groups and association between CST microstructural metrics and measures of ventricular dilatation (EI and VV) was assessed. Results Thirty-three PSP patients had ventricular dilatation (EI > 0.30, PSP-vd) while 54 PSP patients had normal ventricular system (EI ≤ 0.30, PSP-wvd). iNPH patients had the most marked FA and AxD increase in PLIC and CR of CST followed by PSP-vd, PSP-wvd and controls; RD was altered only in iNPH. A strong correlation was found between CST diffusion metrics and EI or VV. Conclusions Our findings confirm the microstructural changes of CST in iNPH patients and demonstrate for the first time similar alterations in PSP-vd patients, suggesting a crucial role of ventricular dilatation in the mechanical compression of CST.

The COVID-19 outbreak has dramatically impacted on socioeconomic structure, individual freedom, general wellbeing, psychological health and sexuality. Indeed, social distancing, home confinement and the fear of contagion have reduced the possibility of romantic encounters thus influencing sexual activity, desire and behavior and, consequently, modifying socio-sexual experiences. The aim of this study is to examine sociosexuality and sociosexual experiences in southern Italians during the COVID-19 pandemic.

Thousands of resting state functional magnetic resonance imaging (RS-fMRI) articles have been published on brain disorders. For precise localization of abnormal brain activity, a voxel-level comparison is needed. Because of the large number of voxels in the brain, multiple comparison correction (MCC) must be performed to reduce false positive rates, and a smaller P value (usually including either liberal or stringent MCC) is widely recommended. The study suggests that a stringent MCC may reduce the number of false positive results and that the brain regions surviving MCC are true positive results and thus can improve the reproducibility between studies. However, determining the clusters that represent true positives is difficult because ground truths have not been established for many brain disorders that do not present visible structural abnormalities, e.g., Parkinson's disease (PD) and many psychiatric disorders. Meta-analysis provides robust results that can be used to investigate reproducibility across studies. However, most existing neuroimaging meta-analytic articles are based on only a few peak coordinates reported in the original articles, namely coordinate-based meta-analysis (CB-meta). The current REST-meta-PD study included raw RS-fMRI data from 15 cohorts of PD patients and performed a meta-analysis of the amplitude of low frequency fluctuations (ALFF). We used the robustness results of the meta-analysis to test whether liberal or stringent MCC could increase reproducibility and reduce false positive rates.

Background Resting-state-fMRI is a technique used to explore the functional brain architecture in term of brain networks and their interactions. However, the robustness of Resting-state-fMRI analysis is negatively affected by physiological noise caused by subject head motion. The aim of our study was to provide new knowledge about the effect of normal aging on the head motion signals. New Method For the first time, we proposed a method for evaluating the most sensitive head motion parameters linked to subjects’aging. We enrolled 14-young(9females; mean-age = 28 ± 4.07) and 14-elderly(9females; mean-age = 66 ± 5.19) subjects. Along three axes(X,Y,Z), we extracted six motions parameters which reflected the head’s movements to characterize translations(x,y,z) and rotations(angles phi,theta,psi). We performed:1)univariate analysis for comparing the groups and correlation to investigate the relationship between age and movement parameters; 2)Support-Vector-Machine, using bootstrap and calculating the feature importance. Results Statistical analyses showed significant association between the aging and some motion’s parameters(rotation psi; translations y and z). These results were also confirmed by multivariate analysis with Support-Vector-Machine that presented an AUC of 90 %. Comparison to existing methods The proposed method shows that normal aging produces significant increase in head motion parameters, highlighting the critical effect of motion on resting data analyses in particular considering psi, y and z movements. To our knowledge and at the present, this represents the first study investigating the accurate characterization of motion parameters in aging. Conclusions Our results have a high impact to improve healthy control recruitment and appropriately decreasing the risk of signal distortion, according to the age of enrolled subjects.

The idea that individuals tend to choose a romantic partner following similarities on personality traits has always attracted much attention in the psychological literature, although results were controversial. We conducted a new data analysis approach to personality traits of 235 newlywed couples. Univariate analysis revealed that a neurotic husband is usually paired with a lesser extrovert and open wife. To figure out if this mating selection pattern may be translated in a mathematical predictive model a twofold approach was employed by using Partial Least Squares regression and machine learning algorithm. The experimental results demonstrate that marital assortment for personality is a multi-trait complementarity process but these data are unable to predict human mating.

Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kinds of Machine Learning technique could be effective to support early diagnosis of Multiple Sclerosis and which of them presented best performance in distinguishing Multiple Sclerosis patients from control subjects. We selected following algorithms: Random Forest, Support Vector Machine, Naïve-Bayes, K-nearest-neighbor and Artificial Neural Network. We applied the Independent Component Analysis to resting-state functional-MRI sequence to identify brain networks. We found 15 networks, from which we extracted the mean signals used into classification. We performed feature selection tasks in all algorithms to obtain the most important variables. We showed that best discriminant network between controls and early Multiple Sclerosis, was the sensori-motor I, according to early manifestation of motor/sensorial deficits in Multiple Sclerosis. Moreover, in classification performance, Random Forest and Support Vector Machine showed same 5-fold cross-validation accuracies (85.7%) using only this network, resulting to be best approaches. We believe that these findings could represent encouraging step toward the translation to clinical diagnosis and prognosis.

Background: The application of artificial intelligence to extract predictors of Gambling disorder (GD) is a new field of study. A plethora of studies have suggested that maladaptive personality dispositions may serve as risk factors for GD. New method: Here, we used Classification and Regression Trees algorithm to identify multivariate predictive patterns of personality profiles that could identify GD patients from healthy controls at an individual level. Forty psychiatric patients, recruited from specialized gambling clinics, without any additional comorbidity and 160 matched healthy controls completed the Five-Factor model of personality as measured by the NEO-PI-R, which were used to build the classification model. Results: Classification algorithm was able to discriminate individuals with GD from controls with an AUC of 77.3% (95% CI 0.65-0.88, p < 0.0001). A multidimensional construct of traits including sub-facets of openness, neuroticism and conscientiousness was employed by algorithm for classification detection. Comparison with existing method(s): To the best of our knowledge, this is the first study that combines behavioral data with machine learning approach useful to extract multidimensional features characterizing GD realm. Conclusion: Our study provides a proof-of-concept demonstrating the potential of the proposed approach for GD diagnosis. The multivariate combination of personality facets characterizing individuals with GD can potentially be used to assess subjects' vulnerability in clinical setting. (C) 2017 Elsevier B.V. All rights reserved.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.