Assessing of the Italian version of the Memory Strategy Test (TMS) in people with Parkinson disease: a preliminary descriptive psychometric study.

BACKGROUND: Previous literature has shown that executive functions (EF) are related to performance in memory (M) tasks. The Test of Memory strategies (TMS) is a psychometric test that examines EF and M simultaneously and it was recently validated on an Italian healthy cohort. The first aim of the study was to apply TMS, for the first time, on a sample of patients with Parkinson's disease (PD), who are characterized by mild cognitive impairment. The second aim is to investigate whether TMS scores can discriminate PD patients from healthy controls. METHOD: Ninety-eight subjects were enrolled, including 68 patients with PD, and 30 Italian healthy controls (HC), who also underwent a memory evaluation through well-known tests. RESULTS: Confirmatory factor analysis (CFA) demonstrated that TMS of PD patients had a bi-dimensional structure as previously found in healthy cohort. In detail, The TMS-1 and TMS-2 lists require greater involvement of the EF factor, while TMS-3, TMS-4 and TMS-5 the M factor. Receiver operating characteristic (ROC) curves and precision-recall (PR) curves showed that the M subscale can distinguish between HC and PD, while EF had poor discrimination power. CONCLUSION: The hypothesized prediction model of TMS test seems to have adequate ability to discriminate PD from HC especially for the M function.

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Progressive supranuclear palsy with marked ventricular dilatation mimicking normal pressure hydrocephalus.

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 Socio-Sexual Experiences in Southern Italians during the COVID-19 Pandemic: A Clustering Analysis.

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.

Magnetic Resonance Imaging Biomarkers Distinguish Normal Pressure Hydrocephalus From Progressive Supranuclear Palsy.

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.

Autism-associated 16p11.2 microdeletion impairs prefrontal functional connectivity in mouse and human.

Human genetic studies are rapidly identifying variants that increase risk for neurodevelopmental disorders. However, it remains unclear how specific mutations impact brain function and contribute to neuropsychiatric risk. Chromosome 16p11.2 deletion is one of the most common copy number variations in autism and related neurodevelopmental disorders. Using resting state functional MRI data from the Simons Variation in Individuals Project (VIP) database, we show that 16p11.2 deletion carriers exhibit impaired prefrontal connectivity, resulting in weaker long-range functional coupling with temporal-parietal regions. These functional changes are associated with socio-cognitive impairments. We also document that a mouse with the same genetic deficiency exhibits similarly diminished prefrontal connectivity, together with thalamo-prefrontal miswiring and reduced long-range functional synchronization. These results reveal a mechanistic link between specific genetic risk for neurodevelopmental disorders and long-range functional coupling, and suggest that deletion in 16p11.2 may lead to impaired socio-cognitive function via dysregulation of prefrontal connectivity.

Assessment of the Corticospinal Tract Profile in Pure Lower Motor Neuron Disease: A Diffusion Tensor Imaging Study.

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.

The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures.

Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, which perfectly mimic epileptic seizures. Several patterns of neural abnormalities have been described without identifying a definite neurobiological substrate. In this multicenter cross-sectional study, we applied a multivariate classification algorithm on morphological brain imaging metrics to extract reliable biomarkers useful to distinguish patients from controls at an individual level. Twenty-three patients with PNES and 21 demographically matched healthy controls (HC) underwent an extensive neuropsychiatric/neuropsychological and neuroimaging assessment. One hundred and fifty morphological brain metrics were used for training a random forest (RF) machine-learning (ML) algorithm. A typical complex psychopathological construct was observed in PNES. Similarly, univariate neuroimaging analysis revealed widespread neuroanatomical changes affecting patients with PNES. Machine-learning approach, after feature selection, was able to perform an individual classification of PNES from controls with a mean accuracy of 74.5%, revealing that brain regions influencing classification accuracy were mainly localized within the limbic (posterior cingulate and insula) and motor inhibition systems (the right inferior frontal cortex (IFC)). This study provides Class II evidence that the considerable clinical and neurobiological heterogeneity observed in individuals with PNES might be overcome by ML algorithms trained on surface-based magnetic resonance imaging (MRI) data.

The corticospinal tract profile in amyotrophic lateral sclerosis.

This work evaluates the potential in diagnostic application of a new advanced neuroimaging method, which delineates the profile of tissue properties along the corticospinal tract (CST) in amyotrophic lateral sclerosis (ALS), by means of diffusion tensor imaging (DTI). Twenty-four ALS patients and twenty-four demographically matched healthy subjects were enrolled in this study. The Automated Fiber Quantification (AFQ), a tool for the automatic reconstruction of white matter tract profiles, based on a deterministic tractography algorithm to automatically identify the CST and quantify its diffusion properties, was used. At a group level, the highest non-overlapping DTI-related differences were detected in the cerebral peduncle, posterior limb of the internal capsule, and primary motor cortex. Fractional anisotropy (FA) decrease and mean diffusivity (MD) and radial diffusivity (RD) increases were detected when comparing ALS patients to controls. The machine learning approach used to assess the clinical utility of this DTI tool revealed that, by combining all DTI metrics measured along tract between the cerebral peduncle and the corona radiata, a mean 5-fold cross validation accuracy of 80% was reached in discriminating ALS from controls. Our study provides a useful new neuroimaging tool to characterize ALS-related neurodegenerative processes by means of CST profile. We demonstrated that specific microstructural changes in the upper part of the brainstem might be considered as a valid biomarker. With further validations this method has the potential to be considered a promising step toward the diagnostic utility of DTI measures in ALS. Hum Brain Mapp 38:727-739, 2017. © 2016 Wiley Periodicals, Inc.

Advanced morphological neuroimaging study in lateral temporal lobe epilepsy: A multicentric study.

Lateral temporal lobe epilepsy (lTLE) is a rare condition characterized by auditory auras or receptive aphasia, negative MRI, and relatively benign evolution. With the low number of cases in the world, our objective was to analyze a cohort of sporadic cases with lTLE (slTLE), in order to investigate possible cerebral morphological alterations. Forty patients with lTLE (34.93±12.08years of age) and 38 healthy controls (CTRL, 34.55±9.08years of age) were enrolled from four tertiary Italian epilepsy centers, which provided brain MRI T1-weighted images following a standard protocol for patients with epilepsy. We performed group comparison by following different approaches: voxel-based morphometry (VBM, SPM8), cortical thickness (CT), and local gyrification index (lGI) (FreeSurfer 5.3). At a more conservative threshold (p<0.05, FWE correction), no significant differences between groups survived, neither in VBM nor CT/lGI. Multicenter studies have more power than smaller studies in conducting sophisticated evaluations of rare diseases, and further investigations are required to develop a full picture of this rare phenotype.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge.

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

The neurocognitive profile of the cerebellum in multiple sclerosis.

In recent years, a high number of studies have demonstrated that neuropsychological functions are altered in multiple sclerosis (MS) patients with cerebellar lesions, mainly including attention, working memory and verbal fluency. Since the present literature is often elusive on this topic, we aim to provide a comprehensive report about the real impact of cerebellar damages (evaluated as volume, lesions or connectivity measures) on cognitive functions. In particular in this review, we report and discuss recent works from 2009 to 2015, which have demonstrated the key role of the cerebellum in cognitive impairment of MS patients.

Sex Differences in Conversion Risk from Mild Cognitive Impairment to Alzheimer's Disease: An Explainable Machine Learning Study with Random Survival Forests and SHAP.

Alzheimer's disease (AD) exhibits sex-linked variations, with women having a higher prevalence, and little is known about the sexual dimorphism in progressing from Mild Cognitive Impairment (MCI) to AD. The main aim of our study was to shed light on the sex-specific conversion-to-AD risk factors using Random Survival Forests (RSF), a Machine Learning survival approach, and Shapley Additive Explanations (SHAP) on dementia biomarkers in stable (sMCI) and progressive (pMCI) patients. With this purpose, we built two separate models for male (M-RSF) and female (F-RSF) cohorts to assess whether global explanations differ between the sexes. Similarly, SHAP local explanations were obtained to investigate changes across sexes in feature contributions to individual risk predictions. The M-RSF achieved higher performance on the test set (0.87) than the F-RSF (0.79), and global explanations of male and female models had limited similarity (<71.1%). Common influential variables across the sexes included brain glucose metabolism and CSF biomarkers. Conversely, the M-RSF had a notable contribution from hippocampus, which had a lower impact on the F-RSF, while verbal memory and executive function were key contributors only in F-RSF. Our findings confirmed that females had a higher risk of progressing to dementia; moreover, we highlighted distinct sex-driven patterns of variable importance, uncovering different feature contribution risks across sexes that decrease/increase the conversion-to-AD risk.

Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor.

Objective: To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET. Methods: We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients. Results: ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms. Conclusion: This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study.

INTRODUCTION: Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases. METHODS: Twenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3rdV/ID and serum Nf-L levels were tested in distinguishing among PSP, PD and HC. RESULTS: PSP patients showed higher serum Nf-L levels and larger 3rdV/ID ratio in comparison with both PD and HC groups (p < 0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85-0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC of 0.94 ± 0.04. CONCLUSION: Our findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.

Neuroimaging correlates of postural instability in Parkinson's disease.

BACKGROUND: Postural instability (PI) is a common disabling symptom in Parkinson's disease (PD), but little is known on its pathophysiological basis. OBJECTIVE: In this study, we aimed to identify the brain structures associated with PI in PD patients, using different MRI approaches. METHODS: We consecutively enrolled 142 PD patients and 45 control subjects. PI was assessed using the MDS-UPDRS-III pull-test item (PT). A whole-brain regression analysis identified brain areas where grey matter (GM) volume correlated with the PT score in PD patients. Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) were also used to compare unsteady (PT ≥ 1) and steady (PT = 0) PD patients. Associations between GM volume in regions of interest (ROI) and several clinical features were then investigated using LASSO regression analysis. RESULTS: PI was present in 44.4% of PD patients. The whole-brain approach identified the bilateral inferior frontal gyrus (IFG) and superior temporal gyrus (STG) as the only regions associated with the presence of postural instability. VBM analysis showed reduced GM volume in fronto-temporal areas (superior, middle, medial and inferior frontal gyrus, and STG) in unsteady compared with steady PD patients, and the GM volume of these regions was selectively associated with the PT score and not with any other motor or non-motor symptom. CONCLUSIONS: This study demonstrates a significant atrophy of fronto-temporal regions in unsteady PD patients, suggesting that these brain areas may play a role in the pathophysiological mechanisms underlying postural instability in PD. This result paves the way for further studies on postural instability in Parkinsonism.

Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor.

Introduction: Distinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data. Methods: We enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients. Results: Both structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase. Conclusion: Machine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.