• Diffusion
• Longitudinal study
• Spinocerebellar ataxia type 1
• White matter integrity

• Alzheimer’s disease
• cerebellum
• diffusion tensor imaging
• probabilistic tractography
• white matter

The diagnosis of Parkinson's disease and atypical Parkinsonism remains clinically difficult, especially at the early stage of the disease, since there is a significant overlap of symptoms. Multimodal MRI has significantly improved diagnostic accuracy and understanding of the pathophysiology of Parkinsonian disorders. Structural and quantitative MRI sequences provide biomarkers sensitive to different tissue properties that detect abnormalities specific to each disease and contribute to the diagnosis. Machine learning techniques using these MRI biomarkers can effectively differentiate atypical Parkinsonian syndromes. Such approaches could be implemented in a clinical environment and improve the management of Parkinsonian patients. This review presents different structural and quantitative MRI techniques, their contribution to the differential diagnosis of atypical Parkinsonian disorders and their interest for individual-level diagnosis.

• diagnosis
• machine learning
• multimodal magnetic resonance imaging
• multiple system atrophy
• parkinson's disease
• progressive supranuclear palsy

• SCA1
• SCA2
• SCA3
• SCA6
• Spinocerebeflar ataxias
• diffusion MRI

• P41 EB027061 NIBIB NIH HHS
• S10 OD017974 NIH HHS
• P41 RR008079 NCRR NIH HHS
• UL1 TR000114 NCATS NIH HHS
• R01 NS070815 NINDS NIH HHS
• National Institute of Neurological Disorders and Stroke
• National Institutes of Health
• National Center for Advancing Translational Sciences

Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson’s disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3 T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.

Neuroanatomical differences in the cerebellum are among the most consistent findings in multiple system atrophy (MSA) patients. This study performed a detailed cerebellar morphology in MSA patients and its two subtypes: MSA-P (parkinson's symptoms predominate) and MSA-C (cerebellar symptoms predominant), and their relations to profiles of motor and cognitive deficits.

Structure MRI data were acquired from 63 healthy controls and 61 MSA patients; voxel-based morphometry and the Spatially Unbiased Infratentorial Toolbox cerebellar atlas were performed to identify the cerebellar gray volume changes in MSA and its subtypes. Further, the gray matter changes were correlated with the clinical motor/cognitive scores.

Patients with MSA exhibited widespread loss of cerebellar volume bilaterally, relative to healthy controls. In those with MSA-C, gray matter loss was detected from anterior (bilateral lobule IV-V) to posterior (bilateral crus I/II, bilateral lobule IX, left lobule VIII) cerebellar lobes. Lower anterior cerebellar volume negatively correlated with disease duration and motor performance, whereas posterior lobe integrity positively correlated with cognitive assessment. In patients with MSA-P, atrophy of anterior lobe (bilateral lobules IV-V) and posterior lobe in part (left lobule VI, bilateral IX) was evident; and in left cerebellar lobule IX, gray matter loss negatively correlated with motor scores. Direct comparison of MSA-P and MSA-C group outcomes showed divergence in right cerebellar crus II only.

Our data suggest that volumetric abnormalities of cerebellum contribute substantially to motor and cognitive performance in patients with MSA. In patients with MSA-P and MSA-C, affected regions of cerebellum differed.

•

Cerebellum atrophy contributed substantially to motor and cognitive behavior in MSA.

• Cerebellum
• Cognitive
• Motor
• Multiple system atrophy
• SUIT

• Ataxias
• Cerebellum
• MR
• PET
• Radiotracers
• SPECT

• Ataxia/diagnostic imaging
• Ataxia/pathology
• Ataxia/physiopathology
• Brain Diseases/diagnostic imaging
• Brain Diseases/pathology
• Brain Diseases/physiopathology
• Humans
• Magnetic Resonance Imaging/methods
• Neuroimaging/methods
• Positron-Emission Tomography/methods
• Spinal Cord Diseases/diagnostic imaging
• Spinal Cord Diseases/pathology
• Spinal Cord Diseases/physiopathology
• Tomography, Emission-Computed, Single-Photon/methods

As gene-based therapies may soon arise for patients with spinocerebellar ataxia (SCA), there is a critical need to identify biomarkers of disease progression with effect sizes greater than clinical scores, enabling trials with smaller sample sizes.

We enrolled a unique cohort of patients with SCA1 (n = 15), SCA2 (n = 12), SCA3 (n = 20) and SCA7 (n = 10) and 24 healthy controls of similar age, sex and body mass index. We collected longitudinal clinical and imaging data at baseline and follow-up (mean interval of 24 months). We performed both manual and automated volumetric analyses. Diffusion tensor imaging (DTI) and a novel tractography method, called fixel-based analysis (FBA), were assessed at follow-up. Effect sizes were calculated for clinical scores and imaging parameters.

Clinical scores worsened as atrophy increased over time (p < 0.05). However, atrophy of cerebellum and pons showed very large effect sizes (>1.2) compared to clinical scores (<0.8). FBA, applied for the first time to SCA, was sensitive to microstructural cross-sectional differences that were not captured by conventional DTI metrics, especially in the less studied SCA7 group. FBA also showed larger effect sizes than DTI metrics.

This study showed that volumetry outperformed clinical scores to measure disease progression in SCA1, SCA2, SCA3 and SCA7. Therefore, we advocate the use of volumetric biomarkers in therapeutic trials of autosomal dominant ataxias. In addition, FBA showed larger effect size than DTI to detect cross-sectional microstructural alterations in patients relative to controls.

•

Biomarkers are needed to test upcoming therapies for spinocerebellar ataxia.

• Apparent fiber density
• CCFS, composite cerebellar functional severity score
• CFE, connectivity-based fixel enhancement
• CSD, constrained spherical deconvolution
• CST, corticospinal tract
• DTI, diffusion tensor imaging
• Diffusion imaging.
• FA, fractional anisotropy
• FBA, fixel-based analysis
• FC, fiber cross-section
• FD, fiber density
• FDC, fiber density and cross-section
• FOD, fiber orientation distribution
• FOV, Field of view
• Fixel analysis
• GRAPPA, generalized autocalibrating partial parallel acquisition
• Imaging biomarkers
• MPRAGE, magnetization-prepared rapid gradient-echo
• MRI, magnetic resonance imaging
• RD, radial diffusivity
• SARA, scale for the assessment and rating of ataxia
• SCA, spinocerebellar ataxias
• SNR, signal-to-noise ratio
• Spinocerebellar ataxia
• TBSS, tract-based spatial statistics
• TE, echo time
• TR, repetition time

• afferents
• cerebellum
• diffusivity
• efferents
• fractional anisotropy
• neuroanatomy
• tractography

• asymptomatic
• ataxia
• spinocerebellar ataxia type 3
• tract-based spatial statistics
• white matter tracts

• Ataxia
• Atrophy
• Autonomic neuropathy
• Cerebellum
• Erectile dysfunction
• Idiopathic late-onset cerebellar ataxia
• Middle cerebellar peduncle
• Multiple system atrophy
• Parkinsonism
• Pathologic laughing and crying
• Pons
• REM sleep behavior disorder
• Sporadic adult-onset ataxia
• Urinary incontinence

• Ataxia/complications
• Atrophy
• Biomarkers
• Cerebellum/diagnostic imaging
• Cerebellum/pathology
• Cerebral Cortex/diagnostic imaging
• Cerebral Cortex/pathology
• Diffusion Magnetic Resonance Imaging/methods
• Female
• Gray Matter/diagnostic imaging
• Gray Matter/pathology
• Humans
• Image Interpretation, Computer-Assisted/methods
• Male
• Middle Aged
• Neural Pathways/diagnostic imaging
• Neural Pathways/pathology

• Diffusion tensor imaging
• Spinocerebellar ataxia

• Biomarkers/analysis
• Cerebellum/abnormalities
• Cerebellum/diagnostic imaging
• Cerebellum/pathology
• Child
• Developmental Disabilities/diagnostic imaging
• Developmental Disabilities/pathology
• Diffusion Tensor Imaging/methods
• Female
• Humans
• Image Interpretation, Computer-Assisted/methods
• Male
• Nervous System Malformations/diagnostic imaging
• Nervous System Malformations/pathology
• Pyramidal Tracts/diagnostic imaging
• Pyramidal Tracts/pathology
• White Matter/diagnostic imaging
• White Matter/pathology

• diagnostic imaging
• diffusion tensor imaging
• magnetic resonance imaging
• multiple system atrophy
• neuroimaging

We describe common and less common diseases that can cause magnetic resonance signal abnormalities of middle cerebellar peduncles (MCP), offering a systematic approach correlating imaging findings with clinical clues and pathologic mechanisms. Myelin abnormalities, different types of edema or neurodegenerative processes, can cause areas of abnormal T2 signal, variable enhancement, and patterns of diffusivity of MCP. Pathologies such as demyelinating disorders or certain neurodegenerative entities (e.g., multiple system atrophy or fragile X-associated tremor-ataxia syndrome) appear to have predilection for MCP. Careful evaluation of concomitant imaging findings in the brain or brainstem; and focused correlation with key clinical findings such as immunosuppression for progressive multifocal leukoencephalopahty; hypertension, post-transplant status or high dose chemotherapy for posterior reversible encephalopathy; electrolyte disorders for myelinolysis or suspected toxic-drug related encephalopathy; would yield an appropriate and accurate differential diagnosis in the majority of cases.

• Middle cerebellar peduncle
• Brachium pontis
• Magnetic resonance imaging
• Multiple sclerosis
• Progressive multifocal leukoencephalopathy
• Posterior reversible encephalopathy
• Toxic encephalopathy

• Alzheimer’s disease
• Amyotrophic lateral sclerosis
• Degenerative ataxias
• Diffusion-MRI
• Huntington’s disease
• Parkinson’s disease

• Algorithms
• Brain/pathology
• Contrast Media
• Diffusion Magnetic Resonance Imaging/methods
• Humans
• Image Enhancement/methods
• Image Interpretation, Computer-Assisted/methods
• Neurodegenerative Diseases/pathology
• Reproducibility of Results
• Sensitivity and Specificity

• Diffusion tensor imaging
• Diffusion weighted imaging
• Multisystem atrophy
• Q-ball imaging
• Spinocerebellar ataxia

• Adult
• Aged
• Anisotropy
• Cerebellum/pathology
• Diagnosis, Differential
• Diffusion Magnetic Resonance Imaging/methods
• Diffusion Tensor Imaging
• Female
• Humans
• Image Processing, Computer-Assisted
• Male
• Middle Aged
• Models, Statistical
• Multiple System Atrophy/diagnosis
• Multiple System Atrophy/pathology
• Pons/pathology
• Pyramidal Tracts/pathology
• Spinocerebellar Ataxias/diagnosis
• Spinocerebellar Ataxias/pathology

• R01 NS062051 NINDS NIH HHS
• R01 NS082351 NINDS NIH HHS
• R01 N5062051 PHS HHS
• R01 N5082351 PHS HHS

Multiple system atrophy is a rare and fatal neurodegenerative disorder characterized by progressive autonomic failure, ataxia and parkinsonism in any combination. The clinical manifestations reflect central autonomic and striatonigral degeneration as well as olivopontocerebellar atrophy. Glial cytoplasmic inclusions, composed of α-synuclein and other proteins are considered the cellular hallmark lesion. The cerebellar variant of MSA (MSA-C) denotes a distinctive motor subtype characterized by progressive adult onset sporadic gait ataxia, scanning dysarthria, limb ataxia and cerebellar oculomotor dysfunction. In addition, there is autonomic failure and variable degrees of parkinsonism. A range of other disorders may present with MSA-C like features and therefore the differential diagnosis of MSA-C is not always straightforward. Here we review key aspects of MSA-C including pathology, pathogenesis, diagnosis, clinical features and treatment, paying special attention to differential diagnosis in late onset sporadic cerebellar ataxias.

• Cerebellar type
• Idiopathic late onset cerebellar ataxia
• Multiple system atrophy
• Sporadic adult onset ataxia

Spinocerebellar ataxias (SCAs) are autosomal-dominant neurodegenerative diseases that are clinically and genetically heterogeneous. SCAs are characterized by a range of neurological symptoms. SCA12 is an autosomal-dominant (AD) ataxia caused by a CAG repeat expansion mutation in a presumed promoter region of the gene PPP2R2B in a non-coding region on chromosome 5q32. This study sought to determine changes in different positions in a single Uyghur SCA12 pedigree by measuring the apparent diffusion coefficient (ADC) and fractional anisotropy (FA).

A single Uyghur pedigree was collected and was confirmed to possess SCA12 by genetic diagnosis, among which 13 cases were patients and 54 cases were “healthy” individuals. Five patients were presymptomatic and 15 individuals selected as a control group were examination in the same time. DTI was performed on a 1.5T scanner, with b=1000 s/mm² and 15 directions. ADC and FA were measured by regions of interest positioned in the corticospinal tract at the level of the pons (pons), superior peduncle (SCP), middle cerebellar peduncle (MCP), cerebellar cortex (CeC), cerebral cortex (CC), and cerebellar vermis (CV) white matter.

Compared with the controls, the ADC was significantly elevated in the CeC, SCP, CC, and CV regions in SCA12 patients. The FA significantly decreased in the CC region in SCA12 patients and the CC and CV regions in SCA12 presymptomatic patients. The course of the disease, SARA score, and ADC values in CV showed highly positive correlations.

SCA12 pedigree patients exhibited microstructural damage in the brain white matter. The damage in white matter fiber may first occur in the CC and CV regions in SCA12 presymptomatic patients. The ADC values in the CV region could reflect disease severity in SCA12 patients.

• DTI
• frontotemporal dementia
• tract
• tractography
• white matter

• Alzheimer Disease/diagnosis
• Alzheimer Disease/pathology
• Anisotropy
• Atrophy
• Brain/pathology
• C9orf72 Protein
• Cohort Studies
• Diffusion Tensor Imaging
• Female
• Frontotemporal Dementia/diagnosis
• Frontotemporal Dementia/genetics
• Frontotemporal Dementia/pathology
• Genotyping Techniques
• Gray Matter/pathology
• Humans
• Image Processing, Computer-Assisted
• Magnetic Resonance Imaging
• Male
• Middle Aged
• Mutation
• Nerve Fibers, Myelinated/pathology
• Proteins/genetics
• Sensitivity and Specificity
• White Matter/pathology
• tau Proteins/genetics

• Wellcome Trust
• 091673 Wellcome Trust
• MR/J014257/1 Medical Research Council
• 091673/Z/10/Z Wellcome Trust

• diffusion tensor imaging (DTI)
• fractional anisotropy (FA)
• magnetic resonance imaging (MRI)
• mean diffusivity (MD)
• multiple system atrophy (MSA)

• Connectivity
• Connectivité
• Diffusion tensor imaging
• Imagerie du tenseur de diffusion
• Parkinson-plus syndrome
• Parkinsonian syndromes
• Syndromes parkinsoniens plus
• Tractographie
• Tractography

• ataxia
• cerebellum
• idiopathic late-onset cerebellar ataxia
• multiple system atrophy
• sporadic adult-onset ataxia of unknown etiology

• Dystonia
• Extrapyramidal signs
• Movement disorders
• Parkinsonism
• Spinocerebellar ataxia
• Tremor

• Movement disorders
• frontostriatal
• neuroimaging
• psychopathology

• DTI
• MRI
• Machado-Joseph disease
• VBM

Clinical differentiation of parkinsonian syndromes such as the Parkinson variant of multiple system atrophy (MSA-P) and cerebellar subtype (MSA-C) from Parkinson's disease is difficult in the early stage of the disease. To identify the correlative pattern of brain changes for differentiating parkinsonian syndromes, we applied discriminant analysis techniques by magnetic resonance imaging (MRI). T1-weighted volume data and diffusion tensor images were obtained by MRI in eighteen patients with MSA-C, 12 patients with MSA-P, 21 patients with Parkinson's disease, and 21 healthy controls. They were evaluated using voxel-based morphometry and tract-based spatial statistics, respectively. Discriminant functions derived by step wise methods resulted in correct classification rates of 0.89. When differentiating these diseases with the use of three independent variables together, the correct classification rate was the same as that obtained with step wise methods. These findings support the view that each parkinsonian syndrome has structural deviations in multiple brain areas and that a combination of structural brain measures can help to distinguish parkinsonian syndromes.

• networks
• mri
• biomarkers
• ataxia

• Adult
• Age of Onset
• Anisotropy
• Cerebellum/pathology
• Cerebellum/physiopathology
• Diffusion Tensor Imaging
• Disease Progression
• Humans
• Image Interpretation, Computer-Assisted
• Middle Aged
• Nerve Net/pathology
• Nerve Net/physiopathology
• Spinocerebellar Ataxias/pathology
• Spinocerebellar Ataxias/physiopathology

• R01 NS054942 NINDS NIH HHS
• R01-NS-54942 NINDS NIH HHS

• neurochemical profile
• magnetic resonance spectroscopy
• sca
• multiple system atrophy
• ataxia

• Biomarkers/analysis
• Female
• Humans
• Magnetic Resonance Spectroscopy
• Male
• Middle Aged
• Multiple System Atrophy/diagnosis
• Spinocerebellar Ataxias/diagnosis

• M01 RR000400 NCRR NIH HHS
• P30 NS057091 NINDS NIH HHS
• P41 RR008079 NCRR NIH HHS
• P41RR008079 NCRR NIH HHS
• R21 NS056172 NINDS NIH HHS
• M01 RR00400 NCRR NIH HHS
• UL1 TR000114 NCATS NIH HHS

• msa
• msa-c
• msa-p
• vbm
• corpus callosum
• follow-up

• Aged
• Atrophy/pathology
• Brain Stem/pathology
• Cerebellum/pathology
• Corpus Callosum/pathology
• Female
• Follow-Up Studies
• Humans
• Image Processing, Computer-Assisted
• Magnetic Resonance Imaging
• Male
• Middle Aged
• Multiple System Atrophy/pathology

• R01 EB007813 NIBIB NIH HHS
• M01 RR000865 NCRR NIH HHS
• P41 RR013642 NCRR NIH HHS
• U54 RR021813 NCRR NIH HHS
• R01 AG020098 NIA NIH HHS
• R01 EB008281 NIBIB NIH HHS
• R01 HD050735 NICHD NIH HHS
• R01 EB008432 NIBIB NIH HHS