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.

Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy.

Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP). Materials and Methods In this retrospective study, T1-weighted MR images in healthy controls (n = 84) were used to train DL models for segmenting the midbrain, pons, middle cerebellar peduncle (MCP), superior cerebellar peduncle (SCP), third ventricle, and frontal horns (FHs). Internal, external, and clinical test datasets (n = 305) were used to assess segmentation model reliability. DL masks from test datasets were used to automatically extract midbrain and pons areas and the width of MCP, SCP, third ventricle, and FHs. Automated measurements were compared with those manually performed by an expert radiologist. Finally, these measures were combined to calculate the midbrain to pons area ratio, MR parkinsonism index (MRPI), and MRPI 2.0, which were used to differentiate patients with PSP (n = 71) from those with Parkinson disease (PD) (n = 129). Results Dice coefficients above 0.85 were found for all brain regions when comparing manual and DL-based segmentations. A strong correlation was observed between automated and manual measurements (Spearman ρ > 0.80, P < .001). DL-based measurements showed excellent performance in differentiating patients with PSP from those with PD, with an area under the receiver operating characteristic curve above 0.92. Conclusion The automated approach successfully segmented and measured the brainstem and ventricular structures. DL-based models may represent a useful approach to support the diagnosis of PSP and potentially other conditions associated with brainstem and ventricular alterations. Keywords: MR Imaging, Brain/Brain Stem, Segmentation, Quantification, Diagnosis, Convolutional Neural Network Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Mohajer in this issue.

Narcissistic Personality Disorder as Prodromal Feature of Early-Onset, GRN-Positive bvFTD: A Case Report.

Background: Behavioral variant frontotemporal dementia (bvFTD) typically involves subtle changes in personality that can delay a timely diagnosis. Objective: Here, we report the case of a patient diagnosed of GRN-positive bvFTD at the age of 52 presenting with a 7-year history of narcissistic personality disorder, accordingly to DSM-5 criteria. Methods: The patient was referred to neurological and neuropsychological examination. She underwent 3 Tesla magnetic resonance imaging (MRI) and genetic studies. Results: The neuropsychological examination revealed profound deficits in all cognitive domains and 3T brain MRI showed marked fronto-temporal atrophy. A mutation in the GRN gene further confirmed the diagnosis. Conclusions: The present case documents an unusual onset of bvFTD and highlights the problematic nature of the differential diagnosis between prodromal psychiatric features of the disease and primary psychiatric disorders. Early recognition and diagnosis of bvFTD can lead to appropriate management and support for patients and their families. This case highlights the importance of considering neurodegenerative diseases, such as bvFTD, in the differential diagnosis of psychiatric disorders, especially when exacerbations of behavioral traits manifest in adults.

Nucleus Basalis of Meynert Degeneration Predicts Cognitive Decline in Corticobasal Syndrome.

BACKGROUND: Cognitive changes are common in corticobasal syndrome (CBS) and significantly impact quality of life and caregiver burden. However, relatively few studies have investigated the neural substrates of cognitive changes in CBS, and reliable predictors of cognitive impairment are currently lacking. The nucleus basalis of Meynert (NbM), which serves as the primary source of cortical cholinergic innervation, has been functionally associated with cognition. This study aimed to explore whether patients with CBS exhibit reduced NbM volumes compared with healthy control participants and whether NbM degeneration can serve as a predictor of cognitive impairment in patients with CBS. METHODS: In this study, we investigated in vivo volumetric changes of the NbM in 38 patients with CBS and 84 healthy control participants. Next, we assessed whether gray matter degeneration of the NbM evaluated at baseline could predict cognitive impairment during a 12-month follow-up period in patients with CBS. All volumetric analyses were performed using 3T T1-weighted images obtained from the 4-Repeat Tauopathy Neuroimaging Initiative. RESULTS: Patients with CBS displayed significantly lower NbM volumes than control participants (p < .001). Structural damage of the NbM also predicted the development of cognitive impairment in patients with CBS as assessed by longitudinal measurements of the Clinical Dementia Rating Sum of Boxes (p < .001) and Mini-Mental State Examination (p = .035). CONCLUSIONS: Our findings suggest that NbM atrophy may represent a promising noninvasive in vivo marker of cognitive decline in CBS and provide new insights into the neural mechanisms that underlie cognitive impairment in CBS.

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.

Grey-matter correlates of empathy in 4-Repeat Tauopathies.

Loss of empathy is an early and central symptom of frontotemporal lobar degeneration spectrum diseases. We aimed to investigate the topographical distribution of morphometric brain changes associated with empathy in Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS) patients. Twenty-seven participants with CBS and 31 with PSP were evaluated using Interpersonal Reactivity Index scales in correlation with gray matter atrophy using a voxel-based morphometry approach. Lower levels of empathy were associated with an increased atrophy in fronto-temporal cortical structures. At subcortical level, empathy scores were positively correlated with gray matter volume in the amygdala, hippocampus and the cerebellum. These findings allow to extend the traditional cortico-centric view of cognitive empathy to the cerebellar regions in patients with neurodegenerative disorders and suggest that the cerebellum may play a more prominent role in social cognition than previously appreciated.

Cortical signature of depressive symptoms in frontotemporal dementia: A surface-based analysis.

BACKGROUND AND OBJECTIVES: Depressive symptoms are frequently reported in patients affected by frontotemporal dementia (FTD). At structural MRI, cortical features of depressed FTD patients have been poorly described. Our objective was to investigate correlations between cortical measures and depression severity in FTD patients. METHODS: Data were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) database. We included 98 controls and 92 FTD patients, n = 38 behavioral variant FTD (bvFTD), n = 26 non-fluent variant Primary Progressive Aphasia (nfvPPA), and n = 28 semantic variant Primary Progressive Aphasia (svPPA). Patients underwent clinical and cognitive evaluations, as well as a 3D T1-weighted MRI on a 3 Tesla scanner (Siemens, Trio Tim system). Depression was evaluated by means of Geriatric Depression Scale (GDS). Surface-based analysis was performed on T1-weighted images to evaluate cortical thickness, a measure of gray matter integrity, and local gyrification index (lGI), a quantitative metric of cortical folding. RESULTS: Patients affected by svPPA were more depressed than controls at NPI and depression severity at GDS was higher in svPPA and bvFTD. Severity of depression correlated with a decrease in lGI in left precentral and superior frontal gyrus, supramarginal and postcentral gyrus and right precentral, supramarginal, superior parietal and superior frontal gyri. Furthermore, depression severity correlated positively with cortical thickness in the left medial orbitofrontal cortex. DISCUSSION: We found that lGI was associated with depressive symptoms over brain regions involved in the pathophysiology of major depressive disorder. This finding provides novel insights into the mechanisms underlying psychiatric symptoms in FTD.

Asymmetry of radiomics features in the white matter of patients with primary progressive aphasia.

Introduction: Primary Progressive Aphasia (PPA) is a neurological disease characterized by linguistic deficits. Semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants are the two main clinical subtypes. We applied a novel analytical framework, based on radiomic analysis, to investigate White Matter (WM) asymmetry and to examine whether asymmetry is associated with verbal fluency performance. Methods: Analyses were performed on T1-weighted images including 56 patients with PPA (31 svPPA and 25 nfvPPA) and 53 age- and sex-matched controls. Asymmetry Index (AI) was computed for 86 radiomics features in 34 white matter regions. The relationships between AI, verbal fluency performance (semantic and phonemic) and Boston Naming Test score (BNT) were explored through Spearman correlation analysis. Results: Relative to controls, WM asymmetry in svPPA patients involved regions adjacent to middle temporal cortex as part of the inferior longitudinal (ILF), fronto-occipital (IFOF) and superior longitudinal fasciculi. Conversely, nfvPPA patients showed an asymmetry of WM in lateral occipital regions (ILF/IFOF). A higher lateralization involving IFOF, cingulum and forceps minor was found in nfvPPA compared to svPPA patients. In nfvPPA patients, semantic fluency was positively correlated to asymmetry in ILF/IFOF tracts. Performances at BNT were associated with AI values of the middle temporal (ILF/SLF) and parahippocampal (ILF/IFOF) gyri in svPPA patients. Discussion: Radiomics features depicted distinct pathways of asymmetry in svPPA and nfvPPA involving damage of principal fiber tracts associated with speech and language. Assessing asymmetry of radiomics in PPA allows achieving a deeper insight into the neuroanatomical damage and may represent a candidate severity marker for language impairments in PPA patients.

Behavioral variant frontotemporal dementia in patients with primary psychiatric disorder: A magnetic resonance imaging study.

BACKGROUND: The clinical diagnosis of behavioral variant frontotemporal dementia (bvFTD) in patients with a history of primary psychiatric disorder (PPD) is challenging. PPD shows the typical cognitive impairments observed in patients with bvFTD. Therefore, the correct identification of bvFTD onset in patients with a lifetime history of PPD is pivotal for an optimal management. METHODS: Twenty-nine patients with PPD were included in this study. After clinical and neuropsychological evaluations, 16 patients with PPD were clinically classified as bvFTD (PPD-bvFTD+), while in 13 cases clinical symptoms were associated with the typical course of the psychiatric disorder itself (PPD-bvFTD-). Voxel- and surface-based investigations were used to characterize gray matter changes. Volumetric and cortical thickness measures were used to predict the clinical diagnosis at a single-subject level using a support vector machine (SVM) classification framework. Finally, we compared classification performances of magnetic resonance imaging (MRI) data with automatic visual rating scale of frontal and temporal atrophy. RESULTS: PPD-bvFTD+ showed a gray matter decrease in thalamus, hippocampus, temporal pole, lingual, occipital, and superior frontal gyri compared to PPD-bvFTD- (p < .05, family-wise error-corrected). SVM classifier showed a discrimination accuracy of 86.2% in differentiating PPD patients with bvFTD from those without bvFTD. CONCLUSIONS: Our study highlights the utility of machine learning applied to structural MRI data to support the clinician in the diagnosis of bvFTD in patients with a history of PPD. Gray matter atrophy in temporal, frontal, and occipital brain regions may represent a useful hallmark for a correct identification of dementia in PPD at a single-subject level.

The impact of harmonization on radiomic features in Parkinson's disease and healthy controls: A multicenter study.

Radiomics is a challenging development area in imaging field that is greatly capturing interest of radiologists and neuroscientists. However, radiomics features show a strong non-biological variability determined by different facilities and imaging protocols, limiting the reproducibility and generalizability of analysis frameworks. Our study aimed to investigate the usefulness of harmonization to reduce site-effects on radiomics features over specific brain regions. We selected T1-weighted magnetic resonance imaging (MRI) by using the MRI dataset Parkinson's Progression Markers Initiative (PPMI) from different sites with healthy controls (HC) and Parkinson's disease (PD) patients. First, the investigation of radiomics measure discrepancies were assessed on healthy brain regions-of-interest (ROIs) via a classification pipeline based on LASSO feature selection and support vector machine (SVM) model. Then, a ComBat-based harmonization approach was applied to correct site-effects. Finally, a validation step on PD subjects evaluated diagnostic accuracy before and after harmonization of radiomics data. Results on healthy subjects demonstrated a dependence from site-effects that could be corrected with ComBat harmonization. LASSO regressor after harmonization was unable to select any feature to distinguish controls by site. Moreover, harmonized radiomics features achieved an area under the receiving operating characteristic curve (AUC) of 0.77 (compared to AUC of 0.71 for raw radiomics measures) in distinguish Parkinson's patients from HC. We found a not-negligible site-effect studying radiomics of HC pre- and post-harmonization of features. Our validation study on PD patients demonstrated a significant influence of non-biological noise source in diagnostic performances. Finally, harmonization of multicenter radiomic data represent a necessary step to make analysis pipelines reliable and replicable for multisite neuroimaging studies.

Bilateral Ultrasound-Guided Erector Spinae Plane Block for Pectus Excavatum Surgery: A Retrospective Propensity-Score Study.

OBJECTIVE: Pectus excavatum (PE) repair is burdened by severe postoperative pain. This retrospective study aimed to determine whether the analgesic effect of ultrasound-guided erector spinae plane block (ESPB) plus standard intravenous analgesia (SIVA) might be superior to SIVA alone in pain control after PE surgical repair via Ravitch or Nuss technique. DESIGN: A retrospective cohort study. SETTING: At a university hospital. PARTICIPANTS: All participants were scheduled for surgical repair of PE. INTERVENTIONS: From January 2017 to December 2019, all patients who received ESPB plus SIVA or SIVA alone were investigated retrospectively. A 2:1 propensity-score matching analysis considering preoperative variables was used to compare analgesia efficacy in 2 groups. All patients received a 24-hour continuous infusion of tramadol, 0.1 mg/kg/h, and ketorolac, 0.05 mg/kg/h, via elastomeric pump, and morphine, 2 mg, intravenously as a rescue drug. The ESPB group received preoperative bilateral ESPB block. Postoperative pain, reported using a numerical rating scale at 1, 12, 24, and 48 hours after surgery; the number of required rescue doses; total postoperative morphine milligram equivalents consumption; and the incidence of postoperative nausea and vomit were analyzed. MEASUREMENT AND MAIN RESULTS: A total of 105 patients were identified for analysis. Propensity-score matching resulted in 38 patients in the SIVA group and 19 patients in the ESPB group. Postoperative pain, the number of rescue doses, and postoperative nausea and vomit incidences were lower in the ESPB group (p < 0.005). CONCLUSIONS: Erector spinae plane block may be an effective option for pain management after surgical repair of PE as part of a multimodal approach. This study showed good perioperative analgesia, opioid sparing, and reduced opioid-related adverse effects.

Deep networks for behavioral variant frontotemporal dementia identification from multiple acquisition sources.

Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative syndrome whose clinical diagnosis remains a challenging task especially in the early stage of the disease. Currently, the presence of frontal and anterior temporal lobe atrophies on magnetic resonance imaging (MRI) is part of the diagnostic criteria for bvFTD. However, MRI data processing is usually dependent on the acquisition device and mostly require human-assisted crafting of feature extraction. Following the impressive improvements of deep architectures, in this study we report on bvFTD identification using various classes of artificial neural networks, and present the results we achieved on classification accuracy and obliviousness on acquisition devices using extensive hyperparameter search. In particular, we will demonstrate the stability and generalization of different deep networks based on the attention mechanism, where data intra-mixing confers models the ability to identify the disorder even on MRI data in inter-device settings, i.e., on data produced by different acquisition devices and without model fine tuning, as shown from the very encouraging performance evaluations that dramatically reach and overcome the 90% value on the AuROC and balanced accuracy metrics.

Neuroimaging Biomarkers in a Patient with Probable Psychiatric-Onset Prodromal Dementia with Lewy Bodies.

OBJECTIVES: Psychiatric-onset prodromal Dementia with Lewy Bodies (DLB) is a recently proposed clinical entity characterized by psychiatric presentation that may predate clinical dementia by many years. It is not yet clear how to identify patients with prominent late-onset psychiatric symptoms who may have underlying Lewy Bodies disease. Here, we describe how neuroimaging can assist the identification of this condition. METHODS: A 77-year-old man presented with late-onset psychosis. He underwent an extensive clinical and neuropsychological evaluation. These included Brain MRI with Arterial Spin labeling (ASL) which quantifies perfusion. [123I] FP-CIT SPECT and I-Metaiodobenzylguanidine (mIBG) scintigraphy assessed striatal dopaminergic and cardiac adrenergic integrity, respectively. RESULTS: Clinical evaluation revealed a history of REM sleep behavior disorder and parkinsonism induced by antipsychotics. The patient's cognitive function was normal. Conventional MRI showed parieto-occipital atrophy, and posterior hypoperfusion was revealed by ASL-MRI. Of note, the "cingulate island sign" was present. FP-CIT SPECT and I-Metaiodobenzylguanidine endorsed the suspicion of α-synucleinopathy. The patient fulfils the recently proposed key features of psychiatric-onset Prodromal DLB. DISCUSSION: Prodromal DLB is an emerging concept. Biomarkers have not been yet established. We propose that nuclear imaging and advanced MRI technics showing posterior hypoperfusion and the presence of the "cingulate island sign" could be promising biomarkers candidates.

A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease.

In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer's disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient's cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer's disease progression.

Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI.

Radiomics has been proposed as a useful approach to extrapolate novel morphological and textural information from brain Magnetic resonance images (MRI). Radiomics analysis has shown unique potential in the diagnostic work-up and in the follow-up of patients suffering from neurodegenerative diseases. However, the potentiality of this technique in distinguishing frontotemporal dementia (FTD) subtypes has so far not been investigated. In this study, we explored the usefulness of radiomic features in differentiating FTD subtypes, namely, the behavioral variant of FTD (bvFTD), the non-fluent and/or agrammatic (PNFA) and semantic (svPPA) variants of a primary progressive aphasia (PPA). Classification analyses were performed on 3 Tesla T1-weighted images obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative. We included 49 patients with bvFTD, 25 patients with PNFA, 34 patients with svPPA, and 60 healthy controls. Texture analyses were conducted to define the first-order statistic and textural features in cortical and subcortical brain regions. Recursive feature elimination was used to select the radiomics signature for each pairwise comparison followed by a classification framework based on a support vector machine. Finally, 10-fold cross-validation was used to assess classification performances. The radiomics-based approach successfully identified the brain regions typically involved in each FTD subtype, achieving a mean accuracy of more than 80% in distinguishing between patient groups. Note mentioning is that radiomics features extracted in the left temporal regions allowed achieving an accuracy of 91 and 94% in distinguishing patients with svPPA from those with PNFA and bvFTD, respectively. Radiomics features show excellent classification performances in distinguishing FTD subtypes, supporting the clinical usefulness of this approach in the diagnostic work-up of FTD.

The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia.

Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.

Cerebral Venous Thrombosis after SARS-CoV-2 Infection and Pfizer-BioNTech Vaccination against COVID-19.

In the last 3 years, COVID-19 pandemic has produced great impacts on global population in terms of health and social costs. Pneumonia represents only one of several pathologies associated to COVID-19 disease. Among these, the cerebral venous thrombosis (CVT), constitutes an important cause of stroke. Here, we report a case of CVT diagnosed approximately 2 weeks after first dose of Pfizer-BioNTech vaccination, in a patient affected by COVID-19 few months earlier. He presented with headache and severe asthenia. The laboratory tests put in evidence thrombocytopenia and D-dimer elevation. A brain magnetic resonance imaging (MRI) and a computed tomography (CT) demonstrated hemorrhagic and ischemic phenomena on the right ventral thalamic nuclei, left thalamus, hippocampal and parahippocampal regions and the splenium of the corpus callosum. The study revealed a poorly opacified vein of Galeno and straight sinus. Heparin administration improved his clinical status; platelets values also arose over time.

Neuroimaging Findings in a Patient with Anti-IgLON5 Disease: Cerebrospinal Fluid Dynamics Abnormalities.

Anti-IgLON5 disease is a recently described autoimmune neurodegenerative disorder characterized by insidious onset, slow progression and a variety of neurological features. Neuroimaging in most patients with anti-IgLON5 disease is normal or shows nonspecific findings. Here, we report a case of anti-IgLON5 disease presenting with parkinsonism, falls, sleep problems with severe nocturnal dyspnea attacks, dysphagia, and dysautonomia. Imaging findings were initially suggestive of progressive supranuclear palsy. An altered cerebrospinal fluid dynamic was found on an MRI as well as high-convexity hyperperfusion on a brain SPECT. Further case descriptions with neuroimaging are required to characterize cerebral and cerebrospinal fluid dynamics abnormalities in this rare condition.

Magnetic Resonance Parkinsonism Index Is Associated with REM Sleep Behavior Disorder in Parkinson's Disease.

We investigated the association between the Magnetic Resonance Parkinsonism Index (MRPI) and REM sleep behavior disorder (RBD). We included 226 de novo PD patients (82 PD-RBD and 144 PD-noRBD) and 19 idiopathic RBD patients. Furthermore, 3T T1-weighted MR images were used for automated brainstem calculations. MRPI values were higher in the PD-RBD (p = 0.004) compared to PD-noRBD patients. Moreover, MRPI proved to be a significant predictor of REM Behavior Disorder Screening Questionnaire scores in PD (ß = 0.195, p = 0.007) and iRBD patients (ß = 0.582, p = 0.003). MRPI can be used as an imaging marker of RBD in patients with de novo PD and iRBD.