A Comparative White Matter Study with Parkinson's disease, Parkinson's Disease with Dementia and Alzheimer's Disease

Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations

Tractography has created new horizons for researchers to study brain connectivity in vivo. However, tractography is an advanced and challenging method that has not been used so far for medical data analysis at a large scale in comparison to other traditional brain imaging methods. This work allows tractography to be used for large scale and high-quality medical analytics. BUndle ANalytics (BUAN) is a fast, robust, and flexible computational framework for real-world tractometric studies. BUAN combines tractography and anatomical information to analyze the challenging datasets and identifies significant group differences in specific locations of the white matter bundles. Additionally, BUAN takes the shape of the bundles into consideration for the analysis. BUAN compares the shapes of the bundles using a metric called bundle adjacency which calculates shape similarity between two given bundles. BUAN builds networks of bundle shape similarities that can be paramount for automating quality control. BUAN is freely available in DIPY. Results are presented using publicly available Parkinson's Progression Markers Initiative data.

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.

Fixel-based analysis reveals fiber-specific alterations during the progression of Parkinson's disease

Disruption of brain circuits is one of the core mechanisms of Parkinson's disease (PD). Understanding structural connection alterations in PD is important for effective treatment. However, due to methodological limitations, most studies were unable to account for confounding factors such as crossing fibers and were unable to identify damages to specific fiber tracts. In the present study, we aimed to demonstrate tract-specific white matter structural changes in PD patients and their relationship with clinical symptoms. Ninety-eight PD patients, divided into early (ES) and middle stage (MS) groups, and 76 healthy controls (HCs) underwent brain magnetic resonance imaging scans and clinical assessments. Fixel-based analysis was used to investigate fiber tract alterations in PD patients. Compared to HCs, the PD patients showed decreased fiber density (FD) in the corpus callosum (CC), increased FD in the cortical spinal tract (CST), and increased fiber-bundle cross-section (FC, log-transformed: log-FC) in the superior cerebellar peduncle (SCP). Analysis of variance (ANOVA) revealed significant differences in FD in the CST and log-FC in the SCP among the three groups. Post-hoc analysis revealed that the mean FD values of the CST were higher in ES and MS patient groups compared to HCs, and the mean log-FC values of the SCP were higher in ES and MS patient groups compared to HCs. Additionally, the FD values of the CC in PD patients were negatively correlated with the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) scores (r = -0.257, p = 0.032), Hamilton Depression Rating Scale 17 Items (HAMD-17) scores (r = -0.230, p = 0.033), and Hamilton Anxiety Scale (HAMA) scores (r = -0.248, p = 0.032). Moreover, log-FC values of the SCP (r = 0.274, p = 0.028) and FD values of the CST (r = 0.384, p < 0.001) were positively correlated with the UPDRS-III scores. We concluded that PD patients had both decreased and increased white matter integrity within specific fiber bundles. Additionally, these white matter alterations were different across disease stages, suggesting the occurrence of complex pathological and compensatory changes during the development of PD.

Phase Image Texture Analysis for Motion Detection in Diffusion MRI (PITA-MDD)

PURPOSE

Pronounced spin phase artifacts appear in diffusion-weighted imaging (DWI) with only minor subject motion. While DWI data corruption is often identified as signal drop out in diffusion-weighted (DW) magnitude images, DW phase images may have higher sensitivity for detecting subtle subject motion.

METHODS

This article describes a novel method to return a metric of subject motion, computed using an image texture analysis of the DW phase image. This Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) method is computationally fast and reliably detects subject motion from diffusion-weighted images. A threshold of the motion metric was identified to remove motion-corrupted slices, and the effect of removing corrupted slices was assessed on the reconstructed FA maps and fiber tracts.

RESULTS

Using a motion-metric threshold to remove the motion-corrupted slices results in superior fiber tracts and fractional anisotropy maps. When further compared to a state-of-the-art magnitude-based motion correction method, PITA-MDD was able to detect comparable corrupted slices in a more computationally efficient manner.

CONCLUSION

In this study, we evaluated the use of DW phase images to detect motion corruption. The proposed method can be a robust and fast alternative for automatic motion detection in the brain with multiple applications to inform prospective motion correction or as real-time feedback for data quality control during scanning, as well as after data is already acquired.

A meta-analysis of diffusion tensor imaging of substantia nigra in patients with Parkinson's disease

Parkinson’s disease (PD) is a common neurodegenerative disease characterized by severe, selective loss of pigmented neurons in the substantial nigra (SN). Previous studies have indicated that such loss could be detected by diffusion tensor imaging (DTI). Here, we try to consolidate current DTI data to both quantitatively determine the imaging changes in SN, as well as explore the potential use of DTI for PD diagnosis. Fourteen research articles are included in this meta-analysis, each obtained by searching PubMed, EMBASE, or Cochrane library database dated until July 2017. The articles contain 14 trials with 298 total PD patients and 283 healthy controls (HCs). The results show not only significantly lower FA values of SN in PD compared to that of HCs (WMD = −0.02, 95% CI = [−0.03, −0.02], p < 0.00001), but also a significantly higher MD in PD compared to HCs (WMD = 0.05, 95% CI = [0.03, 0.07], P < 0.0001). This indicates that the sharp difference detected between PD patients and HCs can be detected by DTI. By further analyzing the heterogeneity, we found that FA measurement of SN could be potentially used as a surrogate, noninvasive diagnostic marker toward PD diagnosis.

Serum Insulin-Like Growth Factor-1 in Parkinson's Disease; Study of Cerebrospinal Fluid Biomarkers and White Matter Microstructure

Background: Growing evidence shows that impaired signaling of Insulin-like Growth Factor-1 (IGF-1) is associated with neurodegenerative disorders, such as Parkinson's disease (PD). However, there is still controversy regarding its proinflammatory or neuroprotective function. In an attempt to elucidate the contribution of IGF-1 in PD, we aimed to discover the relation between serum IGF-1 levels in drug-naïve early PD patients and cerebrospinal fluid (CSF) biomarkers as well as microstructural changes in brain white matter. Methods: The association between quartiles of serum IGF-1 levels and CSF biomarkers (α-synuclein, dopamine, amyloid-β_1-42, total tau, and phosphorylated tau) was investigated using adjusted regression models in 404 drug-naïve early PD patients with only mild motor manifestations and 188 age- and sex-matched healthy controls (HC) enrolled in the Parkinson's Progression Markers Initiative (PPMI). By using region of interest analysis and connectometry approach, we tracked the white matter microstructural integrity and diffusivity patterns in a subgroup of study participants with available diffusion MRI data to investigate the association between subcomponents of neural pathways with serum IGF-1 levels. Results: PD patients had higher levels of IGF-1 compared to HC, although not statistically significant (mean difference: 3.60, P = 0.44). However, after adjustment for possible confounders and correction for False Discovery Rate (FDR), IGF-1 was negatively correlated with CSF α-synuclein, total and phosphorylated tau levels only in PD subjects. The imaging analysis proved a significant negative correlation (FDR corrected P-value = 0.013) between continuous levels of serum IGF-1 in patients with PD and the connectivity, but not integrity, in following fibers while controlling for age, sex, body mass index, depressive symptoms, education years, cognitive status and disease duration: middle cerebellar peduncle, cingulum, genu and splenium of the corpus callosum. No significant association was found between brain white matter microstructral measures or CSF markers of healthy controls and levels of IGF-1. Conclusion: Altered connectivity in specific white matter structures, mainly involved in cognitive and motor deterioration, in association with higher serum IGF-1 levels might propose IGF-1 as a potential associate of worse outcome in response to higher burden of α-synucleinopathy and tauopathy in PD.

Neuroimaging in Dementia

Although the diagnosis of dementia still is primarily based on clinical criteria, neuroimaging is playing an increasingly important role. This is in large part due to advances in techniques that can assist with discriminating between different syndromes. Magnetic resonance imaging remains at the core of differential diagnosis, with specific patterns of cortical and subcortical changes having diagnostic significance. Recent developments in molecular PET imaging techniques have opened the door for not only antemortem but early, even preclinical, diagnosis of underlying pathology. This is vital, as treatment trials are underway for pharmacological agents with specific molecular targets, and numerous failed trials suggest that earlier treatment is needed. This article provides an overview of classic neuroimaging findings as well as new and cutting-edge research techniques that assist with clinical diagnosis of a range of dementia syndromes, with an emphasis on studies using pathologically proven cases.

Microstructural white matter alterations in patients with drug induced parkinsonism

Drug-induced parkinsonism (DIP) is the second most common etiology of parkinsonism. And yet, there is little information on structural imaging in DIP to elucidate the accurate underlying pathomechanisms. To investigate microstructural white matter (WM) in patients with DIP using diffusion tensor image and to determine its relationship to severity of parkinsonian motor symptoms and cognitive function. A total of 42 patients with DIP, 65 with Parkinson's disease, and 33 control subjects were recruited from a movement disorders outpatient clinic. We performed comparative analysis of fractional anisotropy (FA) and mean diffusivity (MD) values among groups using tract-based spatial statistics. Correlation analysis between WM integrity and parkinsonian motor symptoms and cognitive performance was also performed in DIP patients using voxel-wise statistical analysis. DIP patients had significantly lower FA and higher MD values over widespread WM areas than control subjects. The patients with DIP had poor cognitive performance relative to control subjects, which correlated well with WM properties. Additionally, the parkinsonian motor symptoms were negatively correlated with FA values. In contrast, exposure time to the offending drugs prior to the development of parkinsonism or duration of parkinsonism showed no significant association with FA or MD values. The present study demonstrates that disruption of the WM microstructure is extensive in patients with DIP, and it is correlated with clinical parameters of parkinsonism and cognitive performance. This suggests that DIP may be reflective of underlying abnormality of microstructural WM. Hum Brain Mapp 38:6043-6052, 2017. © 2017 Wiley Periodicals, Inc.

Substantia nigra fractional anisotropy is not a diagnostic biomarker of Parkinson's disease: A diagnostic performance study and meta-analysis

OBJECTIVES

Our goal was to estimate the diagnostic accuracy of substantia nigra fractional anisotropy (SN-FA) for Parkinson's disease (PD) diagnosis in a sample similar to the clinical setting, including patients with essential tremor (ET) and healthy controls (HC). We also performed a systematic review and meta-analysis to estimate mean change in SN-FA induced by PD and its diagnostic accuracy.

METHODS

Our sample consisted of 135 subjects: 72 PD, 21 ET and 42 HC. To address inter-scanner variability, two 3.0-T MRI scans were performed. MRI results of this sample were pooled into a meta-analysis that included 1,432 subjects (806 PD and 626 HC). A bivariate model was used to evaluate diagnostic accuracy measures.

RESULTS

In our sample, we did not observe a significant effect of disease on SN-FA and it was uninformative for diagnosis. The results of the meta-analysis estimated a 0.03 decrease in mean SN-FA in PD relative to HC (CI: 0.01-0.05). However, the discriminatory capability of SN-FA to diagnose PD was low: pooled sensitivity and specificity were 72 % (CI: 68-75) and 63 % (CI: 58-70), respectively. There was high heterogeneity between studies (I² = 91.9 %).

CONCLUSIONS

SN-FA cannot be used as an isolated measure to diagnose PD.

KEY POINTS

• SN-FA appears insufficiently sensitive and specific to diagnose PD. • Radiologists must be careful when translating mean group results to clinical practice. • Imaging protocol and analysis standardization is necessary for developing reproducible quantitative biomarkers.

A novel method for evaluating brain function and microstructural changes in Parkinson's disease

In this study, microstructural brain damage in Parkinson's disease patients was examined using diffusion tensor imaging and tract-based spatial statistics. The analyses revealed the presence of neuronal damage in the substantia nigra and putamen in the Parkinson's disease patients. Moreover, disease symptoms worsened with increasing damage to the substantia nigra, confirming that the substantia nigra and basal ganglia are the main structures affected in Parkinson's disease. We also found that microstructural damage to the putamen, caudate nucleus and frontal lobe positively correlated with depression. Based on the tract-based spatial statistics, various white matter tracts appeared to have microstructural damage, and this correlated with cognitive disorder and depression. Taken together, our results suggest that diffusion tensor imaging and tract-based spatial statistics can be used to effectively study brain function and microstructural changes in patients with Parkinson's disease. Our novel findings should contribute to our understanding of the histopathological basis of cognitive dysfunction and depression in Parkinson's disease.

Correction for Eddy Current-Induced Echo-Shifting Effect in Partial-Fourier Diffusion Tensor Imaging

In most diffusion tensor imaging (DTI) studies, images are acquired with either a partial-Fourier or a parallel partial-Fourier echo-planar imaging (EPI) sequence, in order to shorten the echo time and increase the signal-to-noise ratio (SNR). However, eddy currents induced by the diffusion-sensitizing gradients can often lead to a shift of the echo in k-space, resulting in three distinct types of artifacts in partial-Fourier DTI. Here, we present an improved DTI acquisition and reconstruction scheme, capable of generating high-quality and high-SNR DTI data without eddy current-induced artifacts. This new scheme consists of three components, respectively, addressing the three distinct types of artifacts. First, a k-space energy-anchored DTI sequence is designed to recover eddy current-induced signal loss (i.e., Type 1 artifact). Second, a multischeme partial-Fourier reconstruction is used to eliminate artificial signal elevation (i.e., Type 2 artifact) associated with the conventional partial-Fourier reconstruction. Third, a signal intensity correction is applied to remove artificial signal modulations due to eddy current-induced erroneous T₂^∗-weighting (i.e., Type 3 artifact). These systematic improvements will greatly increase the consistency and accuracy of DTI measurements, expanding the utility of DTI in translational applications where quantitative robustness is much needed.

White matter hypoperfusion and damage in dementia: post-mortem assessment

Neuroimaging has revealed a range of white matter abnormalities that are common in dementia, some that predict cognitive decline. The abnormalities may result from structural diseases of the cerebral vasculature, such as arteriolosclerosis and amyloid angiopathy, but can also be caused by nonstructural vascular abnormalities (eg, of vascular contractility or permeability), neurovascular instability or extracranial cardiac or vascular disease. Conventional histopathological assessment of the white matter has tended to conflate morphological vascular abnormalities with changes that reflect altered interstitial fluid dynamics or white matter ischemic damage, even though the latter may be of extracranial or nonstructural etiology. However, histopathology is being supplemented by biochemical approaches, including the measurement of proteins involved in the molecular responses to brain ischemia, myelin proteins differentially susceptible to ischemic damage, vessel-associated proteins that allow rapid measurement of microvessel density, markers of blood-brain barrier dysfunction and axonal injury, and mediators of white matter damage. By combining neuroimaging with histopathology and biochemical analysis, we can provide reproducible, quantitative data on the severity of white matter damage, and information on its etiology and pathogenesis. Together these have the potential to inform and improve treatment, particularly in forms of dementia to which white matter hypoperfusion makes a significant contribution.

Complex network-driven view of genomic mechanisms underlying Parkinson's disease: analyses in dorsal motor vagal nucleus, locus coeruleus, and substantia nigra

Parkinson's disease (PD)—classically characterized by severe loss of dopaminergic neurons in the substantia nigra pars compacta—has a caudal-rostral progression, beginning in the dorsal motor vagal nucleus and, in a less extent, in the olfactory system, progressing to the midbrain and eventually to the basal forebrain and the neocortex. About 90% of the cases are idiopathic. To study the molecular mechanisms involved in idiopathic PD we conducted a comparative study of transcriptional interaction networks in the dorsal motor vagal nucleus (VA), locus coeruleus (LC), and substantia nigra (SN) of idiopathic PD in Braak stages 4-5 (PD) and disease-free controls (CT) using postmortem samples. Gene coexpression networks (GCNs) for each brain region (patients and controls) were obtained to identify highly connected relevant genes (hubs) and densely interconnected gene sets (modules). GCN analyses showed differences in topology and module composition between CT and PD networks for each anatomic region. In CT networks, VA, LC, and SN hub modules are predominantly associated with neuroprotection and homeostasis in the ageing brain, whereas in the patient's group, for the three brain regions, hub modules are mostly related to stress response and neuron survival/degeneration mechanisms.