The Network Structure of Cognitive Impairment: From Subjective Cognitive Decline to Alzheimer's Disease.

It was proposed that a reorganization of the relationships between cognitive functions occurs in dementia, a vision that surpasses the idea of a mere decline of specific domains. The complexity of cognitive structure, as assessed by neuropsychological tests, can be captured by exploratory graph analysis (EGA). EGA was applied to the neuropsychological assessment of people (humans) with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and Alzheimer's disease (AD; total N = 638). Both sexes were included. In AD, memory scores detach from the other cognitive functions, and memory subdomains reduce their reciprocal relation. SCD showed a pattern of segregated neuropsychological domains, and MCI showed a noisy and less stable pattern. Results suggest that AD drives a reorganization of cognitive functions toward a less-fractionated architecture compared with preclinical conditions. Cognitive functions show a reorganization that goes beyond the performance decline. Results also have clinical implications in test interpretations and usage.

Sarcopenia in subjects with Alzheimer's disease: prevalence and comparison of agreement between EGWSOP1, EGWSOP2, and FNIH criteria.

BACKGROUND: Sarcopenia is an age-related clinical syndrome characterized by the progressive loss of muscle mass and muscle strength. It appears to be closely linked to dementia, particularly Alzheimer's disease (AD); however, its prevalence among AD patients remains unclear. In this study, we assessed differences in sarcopenia prevalence between non-demented individuals and AD patients. Moreover, we assessed sex-specific differences in sarcopenia prevalence and explored the diagnostic value of the Muscle Quality Index (MQI) for diagnosing sarcopenia among AD patients. METHOD: Cross-sectional study including 145 patients with probable AD and 51 older adults with normal cognition. Sarcopenia was diagnosed according to the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP1 and EWGSOP2) and of the Foundation for the National Institutes of Health (FNIH). The MQI was computed as the ratio of handgrip strength to skeletal muscle mass. RESULTS: No significant difference in sarcopenia prevalence was observed between AD patients and controls. Prevalence ranged from 3.4 to 23.4% in AD patients and from 2 to 11.8% in controls, depending on diagnostic criteria. Prevalence was higher using EWGSOP1 and decreased using EWGSOP2 and FNIH. Prevalence was higher in males than in females with AD. The MQI was lower in AD patients than in controls (95%CI: - 0.23, - 0.05, p < 0.001), but displayed poor diagnostic accuracy in identifying sarcopenia cases. CONCLUSIONS: AD patients and controls show comparable sarcopenia prevalence. Sarcopenia prevalence is higher in males than females among AD patients and higher when using EWGSOP1 compared to FNIH and EWGSOP2 criteria.

Sleep and circadian rhythm disruptions in behavioral variant frontotemporal dementia.

INTRODUCTION: Sleep and rest-activity rhythm alterations are common in neurodegenerative diseases. However, their characterization in patients with behavioral variant frontotemporal dementia (bvFTD) has proven elusive. We investigated rest-activity rhythm alterations, sleep disturbances, and their neural correlates in bvFTD. METHODS: Twenty-seven bvFTD patients and 25 healthy controls completed sleep questionnaires and underwent 7 days of actigraphy while concurrently maintaining a sleep diary. Cortical complexity and thickness were calculated from T1-weighted magnetic resonance (MR) images. RESULTS: Compared to controls, bvFTD patients showed longer time in bed (95% confidence interval [CI]: 79.31, 321.83) and total sleep time (95% CI: 24.38, 321.88), lower sleep efficiency (95% CI: -12.58, -95.54), and rest-activity rhythm alterations in the morning and early afternoon. Increased sleep duration was associated with reduced cortical thickness in frontal regions. DISCUSSION: Patients with bvFTD showed longer sleep duration, lower sleep quality, and rest-activity rhythm alterations. Actigraphy could serve as a cost-effective and accessible tool for ecologically monitoring changes in sleep duration in bvFTD patients. HIGHLIGHTS: We assessed sleep and circadian rhythms in behavioral variant frontotemporal dementia (bvFTD) using actigraphy. Patients with bvFTD show increased sleep duration and reduced sleep quality. Patients with bvFTD show rest-activity alterations in the morning and early afternoon. Sleep duration is associated with reduced cortical thickness in frontal regions. These alterations may represent an early sign of neurodegeneration.

Development and Validation of Automated Magnetic Resonance Parkinsonism Index 2.0 to Distinguish Progressive Supranuclear Palsy-Parkinsonism From Parkinson's Disease.

BACKGROUND: Differentiating progressive supranuclear palsy-parkinsonism (PSP-P) from Parkinson's disease (PD) is clinically challenging. OBJECTIVE: This study aimed to develop an automated Magnetic Resonance Parkinsonism Index 2.0 (MRPI 2.0) algorithm to distinguish PSP-P from PD and to validate its diagnostic performance in two large independent cohorts. METHODS: We enrolled 676 participants: a training cohort (n = 346; 43 PSP-P, 194 PD, and 109 control subjects) from our center and an independent testing cohort (n = 330; 62 PSP-P, 171 PD, and 97 control subjects) from an international research group. We developed a new in-house algorithm for MRPI 2.0 calculation and assessed its performance in distinguishing PSP-P from PD and control subjects in both cohorts using receiver operating characteristic curves. RESULTS: The automated MRPI 2.0 showed excellent performance in differentiating patients with PSP-P from patients with PD and control subjects both in the training cohort (area under the receiver operating characteristic curve [AUC] = 0.93 [95% confidence interval, 0.89-0.98] and AUC = 0.97 [0.93-1.00], respectively) and in the international testing cohort (PSP-P versus PD, AUC = 0.92 [0.87-0.97]; PSP-P versus controls, AUC = 0.94 [0.90-0.98]), suggesting the generalizability of the results. The automated MRPI 2.0 also accurately distinguished between PSP-P and PD in the early stage of the diseases (AUC = 0.91 [0.84-0.97]). A strong correlation (r = 0.91, P < 0.001) was found between automated and manual MRPI 2.0 values. CONCLUSIONS: Our study provides an automated, validated, and generalizable magnetic resonance biomarker to distinguish PSP-P from PD. The use of the automated MRPI 2.0 algorithm rather than manual measurements could be important to standardize measures in patients with PSP-P across centers, with a positive impact on multicenter studies and clinical trials involving patients from different geographic regions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A New MRI Measure to Early Differentiate Progressive Supranuclear Palsy From De Novo Parkinson's Disease in Clinical Practice: An International Study.

BACKGROUND: Enlargement of the third ventricle has been reported in atypical parkinsonism. We investigated whether the measurement of third ventricle width could distinguish Parkinson's disease (PD) from progressive supranuclear palsy (PSP). METHODS: We assessed a new MR T1-weighted measurement (third ventricle width/internal skull diameter) in a training cohort of 268 participants (98 PD, 73 PSP, 98 controls from our center) and in a testing cohort of 291 participants (82 de novo PD patients and 133 controls from the Parkinson's Progression Markers Initiative, 76 early-stage PSP from an international research group). PD diagnosis was confirmed after a 4-year follow-up. Diagnostic performance of the third ventricle/internal skull diameter was assessed using receiver operating characteristic curve with bootstrapping; the area under the curve of the training cohort was compared with the area under the curve of the testing cohort using the De Long test. RESULTS: In both cohorts, third ventricle/internal skull diameter values did not differ between PD and controls but were significantly lower in PD than in PSP patients (P < 0.0001). In PD, third ventricle/internal skull diameter values did not change significantly between baseline and follow-up evaluation. Receiver operating characteristic analysis accurately differentiated PD from PSP in the training cohort (area under the curve, 0.94; 95% CI, 91.1-97.6; cutoff, 5.72) and in the testing cohort (area under the curve, 0.91; 95% CI, 87.0-97.0; cutoff,: 5.88), validating the generalizability of the results. CONCLUSION: Our study provides a new reliable and validated MRI measurement for the early differentiation of PD and PSP. The simplicity and generalizability of this biomarker make it suitable for routine clinical practice and for selection of patients in clinical trials worldwide. © 2020 International Parkinson and Movement Disorder Society.

Magnetic Resonance Imaging and Neurofilament Light in the Differentiation of Parkinsonism.

OBJECTIVE: Accurate diagnosis is particularly challenging in Parkinson's disease (PD), multiple system atrophy (MSAp), and progressive supranuclear palsy (PSP). We compare the utility of 3 promising biomarkers to differentiate disease state and explain disease severity in parkinsonism: the Automated Imaging Differentiation in Parkinsonism (AID-P), the Magnetic Resonance Parkinsonism Index (MRPI), and plasma-based neurofilament light chain protein (NfL). METHODS: For each biomarker, the area under the curve (AUC) of receiver operating characteristic curves were quantified for PD versus MSAp/PSP and MSAp versus PSP and statistically compared. Unique combinations of variables were also assessed. Furthermore, each measures association with disease severity was determined using stepwise multiple regression. RESULTS: For PD versus MSAp/PSP, AID-P (AUC, 0.900) measures had higher AUC compared with NfL (AUC, 0.747) and MRPI (AUC, 0.669), P < 0.05. For MSAp versus PSP, AID-P (AUC, 0.889), and MRPI (AUC, 0.824) measures were greater than NfL (AUC, 0.537), P < 0.05. We then combined measures to determine if any unique combination provided enhanced accuracy and found that no combination performed better than the AID-P alone in differentiating parkinsonisms. Furthermore, we found that the AID-P demonstrated the highest association with the MDS-UPDRS (Radj2 -AID-P, 26.58%; NfL,15.12%; MRPI, 12.90%). CONCLUSIONS: Compared with MRPI and NfL, AID-P provides the best overall differentiation of PD versus MSAp/PSP. Both AID-P and MRPI are effective in differentiating MSAp versus PSP. Furthermore, combining biomarkers did not improve classification of disease state compared with using AID-P alone. The findings demonstrate in the current sample that the AID-P and MRPI are robust biomarkers for PD, MSAp, and PSP. © 2020 International Parkinson and Movement Disorder Society.

Automated MRI Classification in Progressive Supranuclear Palsy: A Large International Cohort Study.

BACKGROUND: The Magnetic Resonance Parkinsonism Index is listed as one of the most reliable imaging morphometric markers for diagnosis of progressive supranuclear palsy (PSP). However, the use of this index in diagnostic workup has been limited until now by the low generalizability of published results because of small monocentric patient cohorts, the lack of data validation in independent patient series, and manual measurements used for index calculation. The objectives of this study were to investigate the generalizability of Magnetic Resonance Parkinsonism Index performance validating previously established cutoff values in a large international cohort of PSP patients subclassified into PSP-Richardson's syndrome and PSP-parkinsonism and to standardize the use of the automated Magnetic Resonance Parkinsonism Index by providing a web-based platform to obtain homogenous measures around the world. METHODS: In a retrospective international multicenter study, a total of 173 PSP patients and 483 non-PSP participants were enrolled. A web-based platform (https://mrpi.unicz.it) was used to calculate automated Magnetic Resonance Parkinsonism Index values. RESULTS: Magnetic Resonance Parkinsonism Index values showed optimal performance in differentiating PSP-Richardson's syndrome and PSP-parkinsonism patients from non-PSP participants (93.6% and 86.5% of accuracy, respectively). The Magnetic Resonance Parkinsonism Index was also able to differentiate PSP-Richardson's syndrome and PSP-parkinsonism patients in an early stage of the disease from non-PSP participants (90.1% and 85.9%, respectively). The web-based platform provided the automated Magnetic Resonance Parkinsonism Index calculation in 94% of cases. CONCLUSIONS: Our study provides the first evidence on the generalizability of automated Magnetic Resonance Parkinsonism Index measures in a large international cohort of PSP-Richardson's syndrome and PSP-parkinsonism patients. The web-based platform enables widespread applicability of the automated Magnetic Resonance Parkinsonism Index to different clinical and research settings. © 2020 International Parkinson and Movement Disorder Society.

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.

Track density imaging in progressive supranuclear palsy: A pilot study.

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder characterized by white matter (WM) changes in different supra- and infratentorial brain structures. We used track density imaging (TDI) to characterize WM microstructural alterations in patients with PSP-Richardson's Syndrome (PSP-RS). Moreover, we investigated the diagnostic utility of TDI in distinguishing patients with PSP-RS from those with Parkinson's disease and healthy controls (HC). Twenty PSP-RS patients, 21 PD patients, and 23 HC underwent a 3 T MRI diffusion-weighted (DW) imaging. Then, we combined constrained spherical deconvolution and WM probabilistic tractography to reconstruct track density maps by calculating the number of WM streamlines traversing each voxel. Voxel-wise analysis was performed to assess group differences in track density maps. A support vector machine (SVM) approach was also used to evaluate the performance of TDI for discriminating between groups. Relative to PD patients, decreases in track density in PSP-RS patients were found in brainstem, cerebellum, thalamus, corpus callosum, and corticospinal tract. Similar findings were obtained between PSP-RS patients and HC. No differences in TDI were observed between PD and HC. SVM approach based on whole-brain analysis differentiated PD patients from PSP-RS with an area under the curve (AUC) of 0.82. The AUC reached a value of 0.98 considering only the voxels belonging to the superior cerebellar peduncle. This study shows that TDI may represent a useful approach for characterizing WM alterations in PSP-RS patients. Moreover, track density decrease in PSP could be considered a new feature for the differentiation of patients with PSP-RS from those with PD.

Refining initial diagnosis of Parkinson's disease after follow-up: A 4-year prospective clinical and magnetic resonance imaging study.

BACKGROUND: No prospective study of patients with Parkinson's disease (PD) has investigated the appearance of vertical gaze abnormalities, a feature suggestive of progressive supranuclear palsy (PSP). OBJECTIVE: To identify, within a cohort of patients with an initial diagnosis of PD, those who developed vertical gaze abnormalities during a 4-year follow-up, and to investigate the performance of new imaging biomarkers in predicting vertical gaze abnormalities. METHODS: A total of 110 patients initially classified as PD and 74 controls were enrolled. All patients underwent clinical assessment at baseline and every year up to the end of the follow-up. The pons/midbrain area ratio 2.0 and the Magnetic Resonance Parkinsonism Index 2.0 were calculated. RESULTS: After 4-year follow-up, 100 of 110 patients maintained the diagnosis of PD, whereas 10 PD patients (9.1%) developed vertical gaze abnormalities, suggesting an alternative diagnosis of PSP-parkinsonism. At baseline, the Magnetic Resonance Parkinsonism Index 2.0 was the most accurate biomarker in differentiating PD patients who developed vertical gaze abnormalities from those who maintained an initial diagnosis of PD. At the end of follow-up, both of these biomarkers accurately distinguished PSP-parkinsonism from PD. CONCLUSIONS: Our results demonstrate that a number of patients with an initial diagnosis of PD developed vertical gaze abnormalities during a 4-year follow-up, and the diagnosis was changed from PD to PSP-parkinsonism. In PD patients, baseline Magnetic Resonance Parkinsonism Index 2.0 showed the best performance in predicting the clinical evolution toward a PSP-parkinsonism phenotype, enabling PSP-parkinsonism patients to be identified at the earliest stage of the disease for promising disease-modifying therapies. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Functional activity changes in memory and emotional systems of healthy subjects with déjà vu.

Déjà vu (DV) is a fascinating and mysterious human experience that has attracted interest from psychologists and neuroscientists for over a century. In recent years, several studies have been conducted to unravel the psychological and neurological correlates of this phenomenon. However, the neural mechanisms underlying the DV experience in benign manifestations are still poorly understood. Thirty-three healthy volunteers completed an extensive neuropsychiatric and neuropsychological battery including personality evaluation. The presence of DV was assessed with the Inventory for Deja vu Experiences Assessment. Participants underwent episodic memory learning test, and 2 days later during event-related functional magnetic resonance imaging (fMRI), they are asked to rate old and new pictures as a novel, moderately/very familiar, or recollected. We identified 18 subjects with DV (DV+) and 15 without DV (DV-) matched for demographical, neuropsychological, and personality characteristics. At a behavioral level, no significant difference was detected in the episodic memory tasks between DV+ and DV-. Functional magnetic resonance imaging analysis revealed that DV+, independently from task conditions, were characterized by increased activity of the bilateral insula coupled with reduced activation in the right parahippocampal, both hippocampi, superior/middle temporal gyri, thalami, caudate nuclei, and superior frontal gyri with respect to DV-. Our study demonstrates that individuals who experienced DV are not characterized by different performance underlying familiarity/recollection memory processes. However, fMRI results provide evidence that the physiological DV experience is associated with the employment of different neural responses of brain regions involved in memory and emotional processes.

A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations.

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener-Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.

Neuroticism modulates brain visuo-vestibular and anxiety systems during a virtual rollercoaster task.

Different lines of research suggest that anxiety-related personality traits may influence the visual and vestibular control of balance, although the brain mechanisms underlying this effect remain unclear. To our knowledge, this is the first functional magnetic resonance imaging (fMRI) study that investigates how individual differences in neuroticism and introversion, two key personality traits linked to anxiety, modulate brain regional responses and functional connectivity patterns during a fMRI task simulating self-motion. Twenty-four healthy individuals with variable levels of neuroticism and introversion underwent fMRI while performing a virtual reality rollercoaster task that included two main types of trials: (1) trials simulating downward or upward self-motion (vertical motion), and (2) trials simulating self-motion in horizontal planes (horizontal motion). Regional brain activity and functional connectivity patterns when comparing vertical versus horizontal motion trials were correlated with personality traits of the Five Factor Model (i.e., neuroticism, extraversion-introversion, openness, agreeableness, and conscientiousness). When comparing vertical to horizontal motion trials, we found a positive correlation between neuroticism scores and regional activity in the left parieto-insular vestibular cortex (PIVC). For the same contrast, increased functional connectivity between the left PIVC and right amygdala was also detected as a function of higher neuroticism scores. Together, these findings provide new evidence that individual differences in personality traits linked to anxiety are significantly associated with changes in the activity and functional connectivity patterns within visuo-vestibular and anxiety-related systems during simulated vertical self-motion. Hum Brain Mapp 38:715-726, 2017. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Magnetic Resonance Parkinsonism Index: diagnostic accuracy of a fully automated algorithm in comparison with the manual measurement in a large Italian multicentre study in patients with progressive supranuclear palsy.

OBJECTIVES: To investigate the reliability of a new in-house automatic algorithm for calculating the Magnetic Resonance Parkinsonism Index (MRPI), in a large multicentre study population of patients affected by progressive supranuclear palsy (PSP) or Parkinson's disease (PD), and healthy controls (HC), and to compare the diagnostic accuracy of the automatic and manual MRPI values. METHODS: The study included 88 PSP patients, 234 PD patients and 117 controls. MRI was performed using both 3T and 1.5T scanners. Automatic and manual MRPI values were evaluated, and accuracy of both methods in distinguishing PSP from PD and controls was calculated. RESULTS: No statistical differences were found between automated and manual MRPI values in all groups. The automatic MRPI values differentiated PSP from PD with an accuracy of 95 % (manual MRPI accuracy 96 %) and 97 % (manual MRPI accuracy 100 %) for 1.5T and 3T scanners, respectively. CONCLUSION: Our study showed that the new in-house automated method for MRPI calculation was highly accurate in distinguishing PSP from PD. Our automatic approach allows a widespread use of MRPI in clinical practice and in longitudinal research studies. KEY POINTS: • A new automatic method for calculating the MRPI is presented. • Automatic MRPI values are in good agreement with manual values. • Automatic MRPI can distinguish patients with PSP from patients with PD. • The automatic method overcomes MRPI application limitations in routine practice. • The automatic method may allow a more widespread use of MRPI.

Individual differences in depression are associated with abnormal function of the limbic system in multiple sclerosis patients.

BACKGROUND: Depression is common in patients with multiple sclerosis (MS), although the brain mechanisms of this psychiatric condition in MS are poorly understood. Specifically, it remains to be determined whether depression in MS is related to altered activity and functional connectivity patterns within limbic circuits. METHODS: Seventy-seven MS patients with variable levels of depression (as assessed via the Beck Depression Inventory) underwent functional magnetic resonance imaging while performing an emotional processing task. To conduct the functional connectivity analyses, the bilateral amygdala and hippocampus, two areas critically involved in the pathophysiology of depression, were chosen as 'seed' regions. Multiple regression models were used to assess how depression in MS patients was correlated with the activity and functional connectivity patterns within the limbic system. RESULTS: Depression scores in MS patients were negatively correlated: (1) with the activity in the subgenual cingulate cortex; (2) with the functional connectivity between the hippocampus and orbitofrontal cortex as well as the dorsolateral prefrontal cortex, and (3) with the functional connectivity between the amygdala and dorsolateral prefrontal cortex. CONCLUSIONS: Our study showed that individual differences in depression in MS patients were significantly associated with altered regional activity and functional connectivity patterns within the limbic system.

Structural 'connectomic' alterations in the limbic system of multiple sclerosis patients with major depression.

BACKGROUND: Major depression (MD) is a common psychiatric disorder in multiple sclerosis (MS). Despite the negative impact of MD on the quality of life of MS patients, little is known about its underlying brain mechanisms. OBJECTIVE: We studied the whole-brain connectivity patterns that were associated with MD in MS. Alterations were mainly expected within limbic circuits. METHODS: Diffusion tensor imaging data were collected in 20 MS patients with MD, 22 non-depressed MS patients and 16 healthy controls. We used deterministic tractography and graph analysis to study the white-matter connectivity patterns that characterized MS patients with MD. RESULTS: We found that MD in MS was associated with increased local path length in the right hippocampus and right amygdala. Further analyses revealed that these effects were driven by an increased shortest distance between both the right hippocampus and right amygdala and a series of regions including the dorsolateral and ventrolateral prefrontal cortex, orbitofrontal cortex, sensory-motor cortices and supplementary motor area. CONCLUSION: Our data provide strong support for neurobiological accounts positing that MD in MS is mediated by abnormal 'communications' within limbic circuits. We also found evidence that MD in MS may be linked with connectivity alterations at the limbic-motor interface, a group of regions that translates emotions into survival-oriented behaviors.

Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease.

BACKGROUND: The aim of the current study was to distinguish patients who had tremor-dominant Parkinson's disease (tPD) from those who had essential tremor with rest tremor (rET). METHODS: We combined voxel-based morphometry-derived gray matter and white matter volumes and diffusion tensor imaging-derived mean diffusivity and fractional anisotropy in a support vector machine (SVM) to evaluate 15 patients with rET and 15 patients with tPD. Dopamine transporter single-photon emission computed tomography imaging was used as ground truth. RESULTS: SVM classification of individual patients showed that no single predictor was able to fully discriminate patients with tPD from those with rET. By contrast, when all predictors were combined in a multi-modal algorithm, SVM distinguished patients with rET from those with tPD with an accuracy of 100%. CONCLUSIONS: SVM is an operator-independent and automatic technique that may help distinguish patients with tPD from those with rET at the individual level.

Effect of aging on magnetic resonance measures differentiating progressive supranuclear palsy from Parkinson's disease.

Imaging measurements, such as the ratio of the midsagittal areas of the midbrain and pons (midbrain/pons) and the Magnetic Resonance Parkinsonism Index (MRPI), have been proposed to differentiate progressive supranuclear palsy (PSP) from Parkinson's disease (PD). However, abnormal midbrain/pons values suggestive of PSP have also been reported in elderly individuals and in patients with PD. We investigated the effect of aging on single or combined imaging measurements of the brainstem. We calculated the midbrain/pons and the MRPI (the ratio of the midsagittal areas of the pons and the midbrain multiplied by the ratio of the middle cerebellar peduncle and superior cerebellar peduncle widths) in 152 patients affected by PD, 25 patients with PSP, and a group of 81 age-matched and sex-matched healthy controls using a 3-Tesla magnetic resonance imaging scanner. In healthy controls, aging was negatively correlated with midsagittal area of the midbrain and midbrain/pons values. In patients with PD, in addition to the effect of aging, the disease status further influenced the midbrain/pons values (R(2) = 0.23; P < 0.001). In both groups, MRPI values were not influenced either by aging or by disease status. No effect of aging on either midbrain/pons or MRPI values was shown in the patients with PSP. Our findings indicated that the MRPI was not significantly influenced by aging or disease-related changes occurring in PD; whereas, in contrast, the midbrain/pons was influenced. Therefore, the MRPI appears to be a more reliable imaging measurement compared with midbrain/pons values for differentiating PSP from PD and controls in an elderly population.

Explainable machine learning radiomics model for Primary Progressive Aphasia classification.

Introduction: Primary Progressive Aphasia (PPA) is a neurodegenerative disease characterized by linguistic impairment. The two main clinical subtypes are semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants. Diagnosing and classifying PPA patients represents a complex challenge that requires the integration of multimodal information, including clinical, biological, and radiological features. Structural neuroimaging can play a crucial role in aiding the differential diagnosis of PPA and constructing diagnostic support systems. Methods: In this study, we conducted a white matter texture analysis on T1-weighted images, including 56 patients with PPA (31 svPPA and 25 nfvPPA), and 53 age- and sex-matched controls. We trained a tree-based algorithm over combined clinical/radiomics measures and used Shapley Additive Explanations (SHAP) model to extract the greater impactful measures in distinguishing svPPA and nfvPPA patients from controls and each other. Results: Radiomics-integrated classification models demonstrated an accuracy of 95% in distinguishing svPPA patients from controls and of 93.7% in distinguishing svPPA from nfvPPA. An accuracy of 93.7% was observed in differentiating nfvPPA patients from controls. Moreover, Shapley values showed the strong involvement of the white matter near left entorhinal cortex in patients classification models. Discussion: Our study provides new evidence for the usefulness of radiomics features in classifying patients with svPPA and nfvPPA, demonstrating the effectiveness of an explainable machine learning approach in extracting the most impactful features for assessing PPA.