Histogram analysis of DTI-derived indices reveals pontocerebellar degeneration and its progression in SCA2

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .

Nucleus accumbens degeneration in spinocerebellar ataxia type 2: a preliminary study

BACKGROUND

Spinocerebellar ataxia type 2 (SCA2) exhibits mainly cerebellar and oculomotor dysfunctions but also, frequently, cognitive impairment and neuropsychological symptoms. The mechanism of the progression of SCA2 remains unclear. This study aimed to evaluate longitudinal structural changes in the brains of SCA2 patients based on atrophy rate.

METHODS

The OpenNeuro Dataset ds001378 was used. It comprises the demographic data and two magnetic resonance images each of nine SCA2 patients and 16 healthy controls. All structural images were preprocessed using FreeSurfer software, and each region's bilateral volume was summed. Atrophy rates were calculated based on the concept of symmetrised percent change and compared between SCA2 patients and healthy controls using non-parametric statistics. As post hoc analysis, correlation analysis was performed between infratentorial volume ratio and the accumbens area atrophy rates in SCA2 patients.

RESULTS

There were no significant differences between groups for age, gender, and the time between scans. Statistical analysis indicated a significantly larger atrophy rate of the accumbens area in SCA2 patients than in controls. Additionally, the infratentorial volume ratio and accumbens area atrophy rates showed moderate negative correlation.

CONCLUSIONS

This study found that nucleus accumbens (NAc) atrophy was significantly accelerated in SCA2 patients. Anatomically, the NAc is densely connected with infratentorial brain regions, so it is reasonable to posit that degeneration propagates from the cerebellum and brainstem to the NAc and other supratentorial areas. Functionally, the NAc is essential for appropriate behaviour, so NAc degeneration might contribute to neuropsychological symptoms in SCA2 patients.

White matter integrity assessment in spinocerebellar ataxia type 2 (SCA2) patients

AIM

To assess the burden of white matter (WM) damage in the cerebrum and cerebellum of spinocerebellar ataxia type 2 (SCA2) patients in an attempt to identify key regions affected by the neurodegenerative processes using diffusion tensor imaging (DTI).

MATERIALS AND METHODS

Nine SCA2 patients and 16 age-matched healthy controls were examined twice (SCA2 patients 3.6 ± 0.7 years and controls 3.3 ± 1.0 years apart) on the same 1.5 T scanner by acquiring T1-weighted and diffusion-weighted (b-value = 1,000 s/mm2) images. Using tract-based spatial statistics, DTI analysis on fractional anisotropy (FA), mean diffusivity (MD), axial (AD)/radial (RD) diffusivity was performed.

RESULTS

At baseline magnetic resonance imaging (MRI), FA was significantly decreased in SCA2 patients in the corticospinal tracts, inferior and superior cerebellar peduncles, middle cerebellar peduncles, cerebral peduncles, right superior and posterior corona radiata. RD was only significantly increased in SCA2 patients in the middle cerebellar peduncles. No significant AD and MD changes were observed. Tract-based spatial statistics (TBSS) analysis between SCA2 patients at baseline and at follow-up showed no significant changes in any of the DTI metrics.

CONCLUSIONS

DTI is a sensitive tool for following the progression of WM neurodegeneration and severity assessment in patients with SCA2. These findings add to a better understanding of the neurological underpinnings of the symptoms experienced by SCA2 patients.

Progressive white matter degeneration in patients with spinocerebellar ataxia type 2

PURPOSE

Spinocerebellar ataxia type 2 (SCA2) is a progressive neurodegenerative disorder characterized by cerebellar atrophy. However, studies to elucidate the longitudinal progression of the neuropathology are limited. We sought to identify brain macrostructural and microstructural alterations in patients with SCA2 using fixel-based analysis (FBA) to better understand its distribution patterns and progression.

METHODS

We enrolled 9 patients with SCA2 and 16 age- and gender-matched controls. Longitudinal clinical and imaging data were collected at baseline, and 3.5 years later. Fiber density (FD), fiber-bundle cross-section (FC), and a combination of FD and FC (FDC) were calculated. The paired t-test was used to examine longitudinal differences. The associations between fixel-based metrics and clinical variables were explored in SCA2 patients.

RESULTS

At baseline, patients with SCA2 displayed multiple white matter tracts with significantly decreased FD, FC, and FDC in the corticospinal tract, cerebellar peduncles, brainstem, corpus callosum, thalamus, striatum, and prefrontal cortex, compared to controls. Over time, many of these macrostructural and microstructural alterations progressed, manifesting lower FD, FC, and FDC in corticospinal tract, middle cerebellar peduncle, brainstem, striatum, fornix, and cingulum. No significant brain white matter alterations were found in the healthy controls over time. There was no association between the FBA-derived metrics and clinical variables in SCA2.

CONCLUSION

This study provides evidence of brain macrostructural and microstructural alterations and of progression over time in SCA2. The FBA-derived metrics may serve as potential biomarkers of SCA2 progression.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4.

Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers

Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.

Altered Regional Brain Homogeneity of BOLD Signal in CADASIL: A Resting State fMRI Study

BACKGROUND AND PURPOSE

The cognitive decline in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is assumed to be due to a cortical-subcortical disconnection secondary to damage to the cerebral white matter (WM). Using resting state functional MRI (rsfMRI) and analysis of the regional homogeneity (ReHo), we examined a group of CADASIL patients and a group of healthy subjects in order to: (1) explore possible differences between the two groups; and (2) to assess, in CADASIL patients, whether any ReHo abnormalities correlate with individual burdens of WM T₂ -weighted hyperintensity and diffusion tensor imaging (DTI)-derived index of mean diffusivity (MD) of the cerebral WM, an index reflecting microstructural damage in CADASIL.

METHODS

Twenty-three paucisymptomatic CADASIL patients (13 females; age mean ± standard deviation = 43.6 ± 11.1 years; three symptomatic and 20 with no or few symptoms) and 16 healthy controls (nine females; age 46.6 ± 11.0 years) were examined with T₁ -weighted, T₂ -weighted fluid attenuated inversion recovery images, DTI, and rsfMRI.

RESULTS

When compared to controls, CADASIL patients showed four clusters of significantly lower ReHo values in cortical areas belonging to networks involved in inhibition and attention, including the right insula, the left superior frontal gyrus, and the bilateral anterior cingulated cortex. ReHo changes did not correlate with an individual patient's lesion burden or MD.

CONCLUSIONS

This study reveals decreased ReHo of rsfMRI signals in cortical areas involved in inhibition and attention processes, suggesting a potential role for these functional cortical changes in CADASIL.

Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

Assessment of Cerebral and Cerebellar White Matter Microstructure in Spinocerebellar Ataxias 1, 2, 3, and 6 Using Diffusion MRI

Development of imaging biomarkers for rare neurodegenerative diseases such as spinocerebellar ataxia (SCA) is important to non-invasively track progression of disease pathology and monitor response to interventions. Diffusion MRI (dMRI) has been shown to identify cross-sectional degeneration of white matter (WM) microstructure and connectivity between healthy controls and patients with SCAs, using various analysis methods. In this paper, we present dMRI data in SCAs type 1, 2, 3, and 6 and matched controls, including longitudinal acquisitions at 12–24-month intervals in a subset of the cohort, with up to 5 visits. The SCA1 cohort also contained 3 premanifest patients at baseline, with 2 showing ataxia symptoms at the time of the follow-up scans. We focused on two aspects: first, multimodal evaluation of the dMRI data in a cross-sectional approach, and second, longitudinal trends in dMRI data in SCAs. Three different pipelines were used to perform cross-sectional analyses in WM: region of interest (ROI), tract-based spatial statistics (TBSS), and fixel-based analysis (FBA). We further analyzed longitudinal changes in dMRI metrics throughout the brain using ROI-based analysis. Both ROI and TBSS analyses identified higher mean (MD), axial (AD), and radial (RD) diffusivity and lower fractional anisotropy (FA) in the cerebellum for all SCAs compared to controls, as well as some cerebral alterations in SCA1, 2, and 3. FBA showed lower fiber density (FD) and fiber crossing (FC) regions similar to those identified by ROI and TBSS analyses. FBA also highlighted corticospinal tract (CST) abnormalities, which was not detected by the other two pipelines. Longitudinal ROI-based analysis showed significant increase in AD in the middle cerebellar peduncle (MCP) for patients with SCA1, suggesting that the MCP may be a good candidate region to monitor disease progression. The patient who remained symptom-free throughout the study displayed no microstructural abnormalities. On the other hand, the two patients who were at the premanifest stage at baseline, and showed ataxia symptoms in their follow-up visits, displayed AD values in the MCP that were already in the range of symptomatic patients with SCA1 at their baseline visit, demonstrating that microstructural abnormalities are detectable prior to the onset of ataxia.

Neuroimaging Biomarkers in SCA2 Gene Carriers

A variety of Magnetic Resonance (MR) and nuclear medicine (NM) techniques have been used in symptomatic and presymptomatic SCA2 gene carriers to explore, in vivo, the physiopathological biomarkers of the neurological dysfunctions characterizing the associated progressive disease that presents with a cerebellar syndrome, or less frequently, with a levodopa-responsive parkinsonian syndrome. Morphometry performed on T1-weighted images and diffusion MR imaging enable structural and microstructural evaluation of the brain in presymptomatic and symptomatic SCA2 gene carriers, in whom they show the typical pattern of olivopontocerebellar atrophy observed at neuropathological examination. Proton MR spectroscopy reveals, in the pons and cerebellum of SCA2 gene carriers, a more pronounced degree of abnormal neurochemical profile compared to other spinocerebellar ataxias with decreased NAA/Cr and Cho/Cr, increased mi/Cr ratios, and decreased NAA and increased mI concentrations. These neurochemical abnormalities are detectable also in presymtomatic gene carriers. Resting state functional MRI (rsfMRI) demonstrates decreased functional connectivity within the cerebellum and of the cerebellum with fronto-parietal cortices and basal ganglia in symptomatic SCA2 subjects. ¹⁸F-fluorodeoxyglucose Positron Emission Tomography (PET) shows a symmetric decrease of the glucose uptake in the cerebellar cortex, the dentate nucleus, the brainstem and the parahippocampal cortex. Single photon emission tomography and PET using several radiotracers have revealed almost symmetric nigrostriatal dopaminergic dysfunction irrespective of clinical signs of parkinsonism which are already present in presymtomatic gene carriers. Longitudinal small size studies have proven that morphometry and diffusion MR imaging can track neurodegeneration in SCA2, and hence serve as progression biomarkers. So far, such a capability has not been reported for proton MR spectroscopy, rsfMRI and NM techniques. A search for the best surrogate marker for future clinical trials represents the current challenge for the neuroimaging community.

A local group differences test for subject-level multivariate density neuroimaging outcomes

A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes can be quantified using summary statistics of voxel intensities. However, the manifestation of a disease process in imaging data is often unknown, or appears as a complex and nonlinear relationship between the voxel intensities on various modalities. When the relevant pattern is unknown, summary statistics are often unable to capture differences between disease groups, and their use may encourage post hoc searches for the optimal summary measure. In this study, we introduce the multi-modal density testing (MMDT) framework for the naive discovery of group differences in voxel intensity profiles. MMDT operationalizes multi-modal magnetic resonance imaging (MRI) data as multivariate subject-level densities of voxel intensities and utilizes kernel density estimation to develop a local two-sample test for individual points within the density space. Through simulations, we show that this method controls type I error and recovers relevant differences when applied to a specified point. Additionally, we demonstrate the ability to maintain power while controlling the family-wise error rate and false discovery rate when applying the test over a grid of points within the density space. Finally, we apply this method to a study of subjects with either multiple sclerosis (MS) or conditions that tend to mimic MS on MRI, and find significant differences between the two groups in their voxel intensity profiles within the thalamus.

Molecular Mechanisms and Therapeutics for Spinocerebellar Ataxia Type 2

The effective therapeutic treatment and the disease-modifying therapy for spinocerebellar ataxia type 2 (SCA2) (a progressive hereditary disease caused by an expansion of polyglutamine in the ataxin-2 protein) is not available yet. At present, only symptomatic treatment and methods of palliative care are prescribed to the patients. Many attempts were made to study the physiological, molecular, and biochemical changes in SCA2 patients and in a variety of the model systems to find new therapeutic targets for SCA2 treatment. A better understanding of the uncovered molecular mechanisms of the disease allowed the scientific community to develop strategies of potential therapy and helped to create some promising therapeutic approaches for SCA2 treatment. Recent progress in this field will be discussed in this review article.

Spinocerebellar ataxia

The spinocerebellar ataxias (SCAs) are a genetically heterogeneous group of autosomal dominantly inherited progressive disorders, the clinical hallmark of which is loss of balance and coordination accompanied by slurred speech; onset is most often in adult life. Genetically, SCAs are grouped as repeat expansion SCAs, such as SCA3/Machado-Joseph disease (MJD), and rare SCAs that are caused by non-repeat mutations, such as SCA5. Most SCA mutations cause prominent damage to cerebellar Purkinje neurons with consecutive cerebellar atrophy, although Purkinje neurons are only mildly affected in some SCAs. Furthermore, other parts of the nervous system, such as the spinal cord, basal ganglia and pontine nuclei in the brainstem, can be involved. As there is currently no treatment to slow or halt SCAs (many SCAs lead to premature death), the clinical care of patients with SCA focuses on managing the symptoms through physiotherapy, occupational therapy and speech therapy. Intense research has greatly expanded our understanding of the pathobiology of many SCAs, revealing that they occur via interrelated mechanisms (including proteotoxicity, RNA toxicity and ion channel dysfunction), and has led to the identification of new targets for treatment development. However, the development of effective therapies is hampered by the heterogeneity of the SCAs; specific therapeutic approaches may be required for each disease.

Neuroimaging Applications in Chronic Ataxias

Magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT) and positron emission tomography (PET) are the main instruments for neuroimaging investigation of patients with chronic ataxia. MRI has a predominant diagnostic role in the single patient, based on the visual detection of three patterns of atrophy, namely, spinal atrophy, cortical cerebellar atrophy and olivopontocerebellar atrophy, which correlate with the aetiologies of inherited or sporadic ataxia. In fact spinal atrophy is observed in Friedreich ataxia, cortical cerebellar atrophy in Ataxia Telangectasia, gluten ataxia and Sporadic Adult Onset Ataxia and olivopontocerebellar atrophy in Multiple System Atrophy cerebellar type. The 39 types of dominantly inherited spinocerebellar ataxias show either cortical cerebellar atrophy or olivopontocerebellar atrophy. T2 or T2* weighted MR images can contribute to the diagnosis by revealing abnormally increased or decreased signal with a characteristic distribution. These include symmetric T2 hyperintensity of the posterior and lateral columns of the cervical spinal cord in Friedreich ataxia, diffuse and symmetric hyperintensity of the cerebellar cortex in Infantile Neuro-Axonal Dystrophy, symmetric hyperintensity of the peridentate white matter in Cerebrotendineous Xanthomatosis, and symmetric hyperintensity of the middle cerebellar peduncles and peridentate white matter, cerebral white matter and corpus callosum in Fragile X Tremor Ataxia Syndrome. Abnormally decreased T2 or T2* signal can be observed with a multifocal distribution in Ataxia Telangectasia and with a symmetric distribution in the basal ganglia in Multiple System Atrophy. T2 signal hypointensity lining diffusely the outer surfaces of the brainstem, cerebellum and cerebrum enables diagnosis of superficial siderosis of the central nervous system. The diagnostic role of nuclear medicine techniques is smaller. SPECT and PET show decreased uptake of radiotracers investigating the nigrostriatal system in Multiple System Atrophy and in patients with Fragile X Tremor Ataxia Syndrome. Semiquantitative or quantitative MRI, SPECT and PET data describing structural, microstructural and functional changes of the cerebellum, brainstem, and spinal cord have been widely applied to investigate physiopathological changes in patients with chronic ataxias. Moreover they can track diseases progression with a greater sensitivity than clinical scales. So far, a few small-size and single center studies employed neuroimaging techniques as surrogate markers of treatment effects in chronic ataxias.