Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease

Mapping of catecholaminergic denervation, neurodegeneration, and inflammation in 6-OHDA-treated Parkinson's disease mice

Efforts to develop disease-modifying treatments for Parkinson's disease (PD) have been hindered by the lack of animal models replicating all hallmarks of PD and the insufficient attention to extra-nigrostriatal regions pathologically critical for the prodromal appearance of non-motor symptoms. Among PD models, 6-hydroxydopamine (6-OHDA) infusion in mice has gained prominence since 2012, primarily focusing on the nigrostriatal region. This study characterized tyrosine hydroxylase-positive neuron and fiber loss across the brain following a unilateral 6-OHDA (20 µg) infusion into the dorsal striatum. Our analysis integrates immunolabeling, brain clearing (iDISCO+), light sheet microscopy, and computational methods, including fMRI and machine learning tools. We also examined sex differences, disease progression, neuroinflammatory responses, and pro-apoptotic signaling in nigrostriatal regions of C57BL/6 mice exposed to varying 6-OHDA dosages (5, 10, or 20 µg) followed by 1, 7, and 14 days of recovery. This comprehensive, spatiotemporal analysis of 6-OHDA-induced pathology was used to map the time course of neuronal degeneration and the onset of neuroinflammation.

Mapping of catecholaminergic denervation, neurodegeneration, and inflammation in 6-OHDA-treated Parkinson's disease mice

Efforts to develop disease-modifying treatments for Parkinson's disease (PD) have been hindered by the lack of animal models replicating all hallmarks of PD and the insufficient attention to extra-nigrostriatal regions pathologically critical for the prodromal appearance of non-motor symptoms. Among PD models, 6-hydroxydopamine (6-OHDA) infusion in mice has gained prominence since 2012, primarily focusing on the nigrostriatal region. This study characterized widespread tyrosine hydroxylase-positive neuron and fiber loss across the brain following a unilateral 6-OHDA (20 μg) infusion into the dorsal striatum. Our analysis integrates immunolabeling, brain clearing (iDISCO+), light sheet microscopy, and computational methods, including fMRI and machine learning tools. We also examined sex differences, disease progression, neuroinflammatory responses, and pro-apoptotic signaling in nigrostriatal regions of C57BL/6 mice exposed to varying 6-OHDA dosages (5, 10, or 20 μg). This comprehensive, spatiotemporal analysis of 6-OHDA-induced pathology may guide the future design of experimental PD studies and neurotherapeutic development.

A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research

Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.

Directionally encoded color track density imaging in brain tumor patients: A potential application to neuro-oncology surgical planning

BACKGROUND

Diffusion magnetic resonance imaging white matter tractography, an increasingly popular preoperative planning modality used for pre-surgical planning in brain tumor patients, is employed with the goal of maximizing tumor resection while sparing postoperative neurological function. Clinical translation of white matter tractography has been limited by several shortcomings of standard diffusion tensor imaging (DTI), including poor modeling of fibers crossing through regions of peritumoral edema and low spatial resolution for typical clinical diffusion MRI (dMRI) sequences. Track density imaging (TDI) is a post-tractography technique that uses the number of tractography streamlines and their long-range continuity to map the white matter connections of the brain with enhanced image resolution relative to the acquired dMRI data, potentially offering improved white matter visualization in patients with brain tumors. The aim of this study was to assess the utility of TDI-based white matter maps in a neurosurgical planning context compared to the current clinical standard of DTI-based white matter maps.

METHODS

Fourteen consecutive brain tumor patients from a single institution were retrospectively selected for the study. Each patient underwent 3-Tesla dMRI scanning with 30 gradient directions and a b-value of 1000 s/mm2. For each patient, two directionally encoded color (DEC) maps were produced as follows. DTI-based DEC-fractional anisotropy maps (DEC-FA) were generated on the scanner, while DEC-track density images (DEC-TDI) were generated using constrained spherical deconvolution based tractography. The potential clinical utility of each map was assessed by five practicing neurosurgeons, who rated the maps according to four clinical utility statements regarding different clinical aspects of pre-surgical planning. The neurosurgeons rated each map according to their agreement with four clinical utility statements regarding if the map 1 identified clinically relevant tracts, (2) helped establish a goal resection margin, (3) influenced a planned surgical route, and (4) was useful overall. Cumulative link mixed effect modeling and analysis of variance were performed to test the primary effect of map type (DEC-TDI vs. DEC-FA) on rater score. Pairwise comparisons using estimated marginal means were then calculated to determine the magnitude and directionality of differences in rater scores by map type.

RESULTS

A majority of rater responses agreed with the four clinical utility statements, indicating that neurosurgeons found both DEC maps to be useful. Across all four investigated clinical utility statements, the DEC map type significantly influenced rater score. Rater scores were significantly higher for DEC-TDI maps compared to DEC-FA maps. The largest effect size in rater scores in favor of DEC-TDI maps was observed for clinical utility statement 2, which assessed establishing a goal resection margin.

CONCLUSION

We observed a significant neurosurgeon preference for DEC-TDI maps, indicating their potential utility for neurosurgical planning.

HYDI-DSI revisited: Constrained non-parametric EAP imaging without q-space re-gridding

Hybrid Diffusion Imaging (HYDI) was one of the first attempts to use multi-shell samplings of the q-space to infer diffusion properties beyond Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI). HYDI was intended as a flexible protocol embedding both DTI (for lower b-values) and HARDI (for higher b-values) processing, as well as Diffusion Spectrum Imaging (DSI) when the entire data set was exploited. In the latter case, the spherical sampling of the q-space is re-gridded by interpolation to a Cartesian lattice whose extent covers the range of acquired b-values, hence being acquisition-dependent. The Discrete Fourier Transform (DFT) is afterwards used to compute the corresponding Cartesian sampling of the Ensemble Average Propagator (EAP) in an entirely non-parametric way. From this lattice, diffusion markers such as the Return To Origin Probability (RTOP) or the Mean Squared Displacement (MSD) can be numerically estimated. We aim at re-formulating this scheme by means of a Fourier Transform encoding matrix that eliminates the need for q-space re-gridding at the same time it preserves the non-parametric nature of HYDI-DSI. The encoding matrix is adaptively designed at each voxel according to the underlying DTI approximation, so that an optimal sampling of the EAP can be pursued without being conditioned by the particular acquisition protocol. The estimation of the EAP is afterwards carried out as a regularized Quadratic Programming (QP) problem, which allows to impose positivity constraints that cannot be trivially embedded within the conventional HYDI-DSI. We demonstrate that the definition of the encoding matrix in the adaptive space allows to analytically (as opposed to numerically) compute several popular descriptors of diffusion with the unique source of error being the cropping of high frequency harmonics in the Fourier analysis of the attenuation signal. They include not only RTOP and MSD, but also Return to Axis/Plane Probabilities (RTAP/RTPP), which are defined in terms of specific spatial directions and are not available with the former HYDI-DSI. We report extensive experiments that suggest the benefits of our proposal in terms of accuracy, robustness and computational efficiency, especially when only standard, non-dedicated q-space samplings are available.

TL-HARDI: Transform learning based accelerated reconstruction of HARDI data

Diffusion magnetic resonance imaging (dMRI) is being extensively used to study the neural architecture of the brain. High angular resolution diffusion imaging (HARDI), a variant of diffusion MRI, measures the diffusion of water molecules along the angular gradient directions in the q-space. It provides better estimates of fiber orientations compared to the traditionally used diffusion tensor imaging (DTI). However, HARDI requires acquisition of relatively large number of samples leading to longer scanning times. Several approaches based on compressive sensing (CS) have been proposed to accelerate HARDI acquisition, leveraging on the sparse representation of the HARDI signal in a pre-specified sparsifying basis. In this paper, we propose to carry out reconstruction of compressively sensed HARDI data using an adaptively learned transform. The transform is learned (i) from the compressive measurements on-the-fly, thereby, eliminating the overhead of choosing fixed sparsifying transforms, and (ii) on overlapping patches of the data, thereby, capturing local image structure effectively. Experiments are conducted on multiple real HARDI data for varying sampling ratios and sampling schemes. The performance of the proposed "TL-HARDI" method is compared with the state-of-the-art methods on various known image quality metrics as well as on dMRI feature maps derived from the reconstructed images. The proposed method is observed to yield better reconstruction than the existing state-of-the-art methods in both quantitative and qualitative comparisons.

Tractography methods and findings in brain tumors and traumatic brain injury

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

Cortical Activation During Finger Tapping Task Performance in Parkinson's Disease Is Influenced by Priming Conditions: An ALE Meta-Analysis

The finger tapping task (FTT) is commonly used in the evaluation of dyskinesia among patients with Parkinson's disease (PD). Past research has indicated that cortical activation during FTT is different between self-priming and cue-priming conditions. To evaluate how priming conditions affect the distribution of brain activation and the reorganization of brain function, and to investigate the differences in brain activation areas during FTT between PD patients and healthy control (HC) participants, we conducted an activation likelihood estimation (ALE) meta-analysis on the existing literature. Analyses were based on data from 15 independent samples that included 181 participants with PD and 164 HC participants. We found that there was significantly more activation in the middle frontal gyrus, precentral gyrus, post-central gyrus, superior parietal lobe, inferior parietal lobule, cerebellum, and basal ganglia during FTT in PD patients than in HCs. In self-priming conditions, PD patients had less activation in the parietal lobe and insular cortex but more activation in the cerebellum than the HCs. In cue-priming conditions, the PD patients showed less activation in the cerebellum and frontal-parietal areas and more activation in the superior frontal gyrus and superior temporal gyrus than the HCs. Our study illustrates that cue-priming manipulations affect the distribution of activity in brain regions involved in motor control and motor performance in PD patients. In cue-priming conditions, brain activity in regions associated with perceptual processing and inhibitory control was enhanced, while sensory motor areas associated with attention and motor control were impaired.

Nigral diffusivity, but not free water, correlates with iron content in Parkinson's disease

The loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinson's disease. Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and reductions in fractional anisotropy as well as increases in the free water compartment in metrics derived from bi-compartment models. However, studies examining nigral diffusivity changes from Parkinson's disease with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. We aimed to examine Parkinson's disease-related changes in nigral iron and diffusion measures from single- and bi-compartment models as well as assess the effect of iron on these diffusion measures in two separate Parkinson's cohorts. Iron-sensitive data and diffusion data were analysed in two cohorts: First, a discovery cohort consisting of 71 participants (32 control participants and 39 Parkinson's disease participants) was examined. Second, an external validation cohort, obtained from the Parkinson's Progression Marker's Initiative, consisting of 110 participants (58 control participants and 52 Parkinson's disease participants) was examined. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment (discovery cohort: P = 0.006; external cohort: P = 0.01) and iron content (discovery cohort: P < 0.001; validation cohort: P = 0.02) were found to increase in substantia nigra of the Parkinson's disease group in both cohorts. However, diffusion markers derived from the single-compartment model (i.e. mean diffusivity and fractional anisotropy) were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort (iron-mean diffusivity: r = -0.400, P = 0.006) and validation cohort (iron-mean diffusivity: r = -0.387, P = 0.003) but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. In conclusion, the variability of nigral diffusion metrics derived from the single-compartment model in Parkinson's disease may be attributed to competing influences of increased iron content, which tends to drive diffusivity down, and increases in the free water compartment, which tends to drive diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.

TractLearn: A geodesic learning framework for quantitative analysis of brain bundles

Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies on metrics either coming from the tractography process itself or from each voxel along the bundle. Statistical detection of abnormal voxels in the context of disease usually relies on univariate and multivariate statistics models, such as the General Linear Model (GLM). Yet in the case of high-dimensional low sample size data, the GLM often implies high standard deviation range in controls due to anatomical variability, despite the commonly used smoothing process. This can lead to difficulties to detect subtle quantitative alterations from a brain bundle at the voxel scale. Here we introduce TractLearn, a unified framework for brain fascicles quantitative analyses by using geodesic learning as a data-driven learning task. TractLearn allows a mapping between the image high-dimensional domain and the reduced latent space of brain fascicles using a Riemannian approach. We illustrate the robustness of this method on a healthy population with test-retest acquisition of multi-shell diffusion MRI data, demonstrating that it is possible to separately study the global effect due to different MRI sessions from the effect of local bundle alterations. We have then tested the efficiency of our algorithm on a sample of 5 age-matched subjects referred with mild traumatic brain injury. Our contributions are to propose: 1/ A manifold approach to capture controls variability as standard reference instead of an atlas approach based on a Euclidean mean. 2/ A tool to detect global variation of voxels' quantitative values, which accounts for voxels' interactions in a structure rather than analyzing voxels independently. 3/ A ready-to-plug algorithm to highlight nonlinear variation of diffusion MRI metrics. With this regard, TractLearn is a ready-to-use algorithm for precision medicine.

Apparent propagator anisotropy from single-shell diffusion MRI acquisitions

PURPOSE

The apparent propagator anisotropy (APA) is a new diffusion MRI metric that, while drawing on the benefits of the ensemble averaged propagator anisotropy (PA) compared to the fractional anisotropy (FA), can be estimated from single-shell data.

THEORY AND METHODS

Computation of the full PA requires acquisition of large datasets with many diffusion directions and different b-values, and results in extremely long processing times. This has hindered adoption of the PA by the community, despite evidence that it provides meaningful information beyond the FA. Calculation of the complete propagator can be avoided under the hypothesis that a similar sensitivity/specificity may be achieved from apparent measurements at a given shell. Assuming that diffusion anisotropy (DiA) is nondependent on the b-value, a closed-form expression using information from one single shell (ie, b-value) is reported.

RESULTS

Publicly available databases with healthy and diseased subjects are used to compare the APA against other anisotropy measures. The structural information provided by the APA correlates with that provided by the PA for healthy subjects, while it also reveals statistically relevant differences in white matter regions for two pathologies, with a higher reliability than the FA. Additionally, APA has a computational complexity similar to the FA, with processing-times several orders of magnitude below the PA.

CONCLUSIONS

The APA can extract more relevant white matter information than the FA, without any additional demands on data acquisition. This makes APA an attractive option for adoption into existing diffusion MRI analysis pipelines.

Efficient and accurate EAP imaging from multi-shell dMRI with micro-structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT)

A number of computational techniques have been lately devised to image the Ensemble Average Propagator (EAP) within the white matter of the brain, propelled by the deployment of multi-shell acquisition protocols and databases: approaches like Mean Apparent Propagator Imaging (MAP-MRI) and its Laplacian-regularized version (MAPL) aim at describing the low frequency spectrum of the EAP (limited by the maximum b-value acquired) and afterwards computing scalar indices that embed useful descriptions of the white matter, e. g. the Return-to-Origin, Plane, or Axis Probabilities (RTOP, RTPP, RTAP). These methods resort to a non-parametric, bandwidth limited representation of the EAP that implies fitting a set of 3-D basis functions in a large-scale optimization problem. We propose a semi-parametric approach inspired by signal theory: the EAP is approximated as the spherical convolution of a Micro-Structure adaptive Gaussian kernel with a non-parametric orientation histogram, which aims at representing the low-frequency response of an ensemble of coherent sets of fiber bundles at the white matter. This way, the optimization involves just the 2 to 3 parameters that describe the kernel, making our approach far more efficient than the related state of the art. We devise dual Fourier domains Integral Transforms to analytically compute RTxP-like scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The so-called MiSFIT is both time efficient (a typical multi-shell data set can be processed in roughly one minute) and accurate: it provides estimates of widely validated indices like RTOP, RTPP, and RTAP comparable to MAPL for a wide variety of white matter configurations.

Altered white matter microarchitecture in Parkinson's disease: a voxel-based meta-analysis of diffusion tensor imaging studies

This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson's disease (PD) reflected by fractional anisotropy (FA), addressing clinical profiles and methodology-related heterogeneity. Web-based publication databases were searched to conduct a meta-analysis of whole-brain diffusion tensor imaging studies comparing PD with healthy controls (HC) using the anisotropic effect size-signed differential mapping. A total of 808 patients with PD and 760 HC coming from 27 databases were finally included. Subgroup analyses were conducted considering heterogeneity with respect to medication status, disease stage, analysis methods, and the number of diffusion directions in acquisition. Compared with HC, patients with PD had decreased FA in the left middle cerebellar peduncle, corpus callosum (CC), left inferior fronto-occipital fasciculus, and right inferior longitudinal fasciculus. Most of the main results remained unchanged in subgroup meta-analyses of medicated patients, early stage patients, voxel-based analysis, and acquisition with 30 diffusion directions. The subgroup meta-analysis of medication-free patients showed FA decrease in the right olfactory cortex. The cerebellum and CC, associated with typical motor impairment, showed the most consistent FA decreases in PD. Medication status, analysis approaches, and the number of diffusion directions have an important impact on the findings, needing careful evaluation in future meta-analyses.

123I-FP-CIT SPECT validation of nigro-putaminal MRI tractography in dementia with Lewy bodies

Background

Assessment of nigrostriatal degeneration is a key element to discriminate between dementia with Lewy bodies (DLB) and Alzheimer disease (AD), and it is often evaluated using ioflupane (¹²³I-FP-CIT) single-photon emission computed tomography (SPECT). Given the limited availability of ¹²³I-FP-CIT SPECT, we evaluated if a mask-based approach to nigroputaminal magnetic resonance imaging (MRI) diffusion-weighted tractography could be able to capture microstructural changes reflecting nigroputaminal degeneration in DLB.

Methods

A nigroputaminal bundle mask was delineated on 12 healthy volunteers (HV) and applied to MRI diffusion-weighted data of 18 subjects with DLB, 21 subjects with AD and another group of 12 HV. The correlation between nigroputaminal fractional anisotropy (FA) values and ¹²³I-FP-CIT SPECT findings was investigated. Shapiro-Wilk, ANOVA, ANCOVA, and parametric correlation statistics as well as receiver operating characteristic (ROC) analysis were used.

Results

DLB patients showed a higher nigroputaminal FA values compared with both AD and HV-controls groups (p = 0.001 for both comparisons), while no difference was observed between HV-controls and AD groups (p = 0.450); at ROC analysis, the area under the curve for the discriminating DLB and AD subjects was 0.820; FA values correlated with ¹²³I-FP-CIT values (on the left, r = -0.670; on the right, r = -720). No significant differences were observed for the FA of the corticospinal tract across the three groups (p = 0.740).

Conclusions

In DLB, nigroputaminal degeneration could be reliably assessed on MRI diffusion scans using a mask of nigroputaminal bundle trajectory. Nigroputaminal FA in DLB patients correlated with ¹²³I-FP-CIT values data may allow to differentiate these patients from AD patients and HV-controls.

Discriminating VCID subgroups: A diffusion MRI multi-model fusion approach

BACKGROUND

Vascular cognitive impairment and dementia (VCID) and Alzheimer's disease are predominant diseases among the aging population resulting in decline of various cognitive domains. Diffusion weighted MRI (DW-MRI) has been shown to be a promising aid in the diagnosis of such diseases. However, there are various models of DW-MRI and the interpretation of diffusion metrics depends on the model used in fitting data. Most previous studies are entirely based on parameters calculated from a single diffusion model.

NEW METHOD

We employ a data fusion framework wherein diffusion metrics from different models such as diffusion tensor imaging, diffusion kurtosis imaging and constrained spherical deconvolution model are fused using well known blind source separation approach to investigate white matter microstructural changes in population comprising of controls and VCID subgroups. Multiple comparisons between subject groups and prediction analysis using features from individual models and proposed fusion model are carried out to evaluate performance of proposed method.

RESULTS

Diffusion features from individual models successfully distinguished between controls and disease groups, but failed to differentiate between disease groups, whereas fusion approach showed group differences between disease groups too. WM tracts showing significant differences are superior longitudinal fasciculus, anterior thalamic radiation, arcuate fasciculus, optic radiation and corticospinal tract.

COMPARISON WITH EXISTING METHOD

ROC analysis showed increased AUC for fusion (AUC = 0.913, averaged across groups and tracts) compared to that of uni-model features (AUC = 0.77) demonstrating increased sensitivity of proposed method.

CONCLUSION

Overall our results highlight the benefits of multi-model fusion approach, providing improved sensitivity in discriminating VCID subgroups.

Micro-structure diffusion scalar measures from reduced MRI acquisitions

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant micro-structural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like Diffusion Tensor Imaging (DTI). The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space involving a huge amount of samples (diffusion gradients) for proper reconstruction. A collection of more efficient techniques have been proposed in the last decade based on parametric representations of the EAP, but they still imply acquiring a large number of diffusion gradients with different b-values (shells). Paradoxically, this has come together with an effort to find scalar measures gathering all the q-space micro-structural information probed in one single index or set of indices. Among them, the return-to-origin (RTOP), return-to-plane (RTPP), and return-to-axis (RTAP) probabilities have rapidly gained popularity. In this work, we propose the so-called "Apparent Measures Using Reduced Acquisitions" (AMURA) aimed at computing scalar indices that can mimic the sensitivity of state of the art EAP-based measures to micro-structural changes. AMURA drastically reduces both the number of samples needed and the computational complexity of the estimation of diffusion properties by assuming the diffusion anisotropy is roughly independent from the radial direction. This simplification allows us to compute closed-form expressions from single-shell information, so that AMURA remains compatible with standard acquisition protocols commonly used even in clinical practice. Additionally, the analytical form of AMURA-based measures, as opposed to the iterative, non-linear reconstruction ubiquitous to full EAP techniques, turns the newly introduced apparent RTOP, RTPP, and RTAP both robust and efficient to compute.

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography

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.

Cortico-basal white matter alterations occurring in Parkinson's disease

Magnetic resonance imaging studies typically use standard anatomical atlases for identification and analyses of (patho-)physiological effects on specific brain areas; these atlases often fail to incorporate neuroanatomical alterations that may occur with both age and disease. The present study utilizes Parkinson's disease and age-specific anatomical atlases of the subthalamic nucleus for diffusion tractography, assessing tracts that run between the subthalamic nucleus and a-priori defined cortical areas known to be affected by Parkinson's disease. The results show that the strength of white matter fiber tracts appear to remain structurally unaffected by disease. Contrary to that, Fractional Anisotropy values were shown to decrease in Parkinson's disease patients for connections between the subthalamic nucleus and the pars opercularis of the inferior frontal gyrus, anterior cingulate cortex, the dorsolateral prefrontal cortex and the pre-supplementary motor, collectively involved in preparatory motor control, decision making and task monitoring. While the biological underpinnings of fractional anisotropy alterations remain elusive, they may nonetheless be used as an index of Parkinson's disease. Moreover, we find that failing to account for structural changes occurring in the subthalamic nucleus with age and disease reduce the accuracy and influence the results of tractography, highlighting the importance of using appropriate atlases for tractography.

Predicting the Post-therapy Severity Level (UPDRS-III) of Patients With Parkinson's Disease After Drug Therapy by Using the Dynamic Connectivity Efficiency of fMRI

Parkinson's disease (PD) is a multi-systemic disease in the brain arising from the dysfunction of several neural networks. The diagnosis and treatment of PD have gained more attention for clinical researchers. While there have been many fMRI studies about functional topological changes of PD patients, whether the dynamic changes of functional connectivity can predict the drug therapy effect is still unclear. The primary objective of this study was to assess whether large-scale functional efficiency changes of topological network are detectable in PD patients, and to explore whether the severity level (UPDRS-III) after drug treatment can be predicted by the pre-treatment resting-state fMRI (rs-fMRI). Here, we recruited 62 Parkinson's disease patients and calculated the dynamic nodal efficiency networks based on rs-fMRI. With connectome-based predictive models using the least absolute shrinkage and selection operator, we demonstrated that the dynamic nodal efficiency properties predict drug therapy effect well. The contributed regions for the prediction include hippocampus, post-central gyrus, cingulate gyrus, and orbital gyrus. Specifically, the connections between hippocampus and cingulate gyrus, hippocampus and insular gyrus, insular gyrus, and orbital gyrus are positively related to the recovery (post-therapy severity level) after drug therapy. The analysis of these connection features may provide important information for clinical treatment of PD patients.

Posterior limb of the internal capsule predicts poor quality of life in patients with Parkinson's disease: connectometry approach

Psychiatric symptoms and motor impairment are major contributions to the poor quality of life in patients with Parkinson's disease (PD). Here, we applied a novel diffusion-weighted imaging approach, diffusion MRI connectometry, to investigate the correlation of quality of life, evaluated by Parkinson's Disease Questionnaire (PDQ39) with the white matter structural connectivity in 27 non-demented PD patients (disease duration of 5.3 ± 2.9 years, H and Y stage = 1.5 ± 0.6, UPDRS-III = 13.7 ± 6.5, indicating unilateral and mild motor involvement). The connectometry analysis demonstrated bilateral posterior limbs of the internal capsule (PLIC) with increased connectivity related to the higher quality of life (FDR = 0.027) in a multiple regression model. The present study suggests for the first time a neural basis of the quality of life in PD in the light of major determinants of poor quality of life in these patients: anxiety, depression, apathy and motor impairment. Results in our sample of non-demented PD patients with relatively mild motor impairment and no apparent sign of depression/anxiety also identify a unique and inexplicable association of the PLIC to the quality of life in PD patients.

High angular resolution diffusion imaging correlates of depression in Parkinson's disease: a connectometry study

Depression is a significant disabling feature in Parkinson's disease (PD). However, the neuropathology of this comorbidity is still unclear. In fact, few studies have tried to elucidate the neural correlates of depression in PD and have mostly examined specific regions of interest. In this study, we applied diffusion MRI connectometry, a powerful complementary approach to investigate alterations in whole white matter pathways regarding the severity of depressive symptoms. Using a multiple regression model, the correlation of severity of depressive symptoms assessed by the Hospital Anxiety and Depression Scale (HADS) with white matter connectivity was surveyed in 27 non-demented PD patients related to 26 age, sex, and educational level-matched healthy subjects. Results revealed areas, where white matter quantitative anisotropy (QA) was correlated with depression score in PD patients, without any significant association in healthy controls. The analysis showed a significant negative association (false discovery rate < 0.05) between scores on depression subscale of HADS in PD patients and QA of left Cingulum, Genu, and Splenium of the Corpus Callosum, and anterior and posterior limbs of the right internal capsule. This finding might improve our understanding of the neural basis of depression and its severity in PD.

Track density imaging in progressive supranuclear palsy: A pilot study

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

Super-Resolution Track-Density Imaging Reveals Fine Anatomical Features in Tree Shrew Primary Visual Cortex and Hippocampus

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to study white and gray matter (GM) micro-organization and structural connectivity in the brain. Super-resolution track-density imaging (TDI) is an image reconstruction method for dMRI data, which is capable of providing spatial resolution beyond the acquired data, as well as novel and meaningful anatomical contrast that cannot be obtained with conventional reconstruction methods. TDI has been used to reveal anatomical features in human and animal brains. In this study, we used short track TDI (stTDI), a variation of TDI with enhanced contrast for GM structures, to reconstruct direction-encoded color maps of fixed tree shrew brain. The results were compared with those obtained with the traditional diffusion tensor imaging (DTI) method. We demonstrated that fine microstructures in the tree shrew brain, such as Baillarger bands in the primary visual cortex and the longitudinal component of the mossy fibers within the hippocampal CA3 subfield, were observable with stTDI, but not with DTI reconstructions from the same dMRI data. The possible mechanisms underlying the enhanced GM contrast are discussed.

Magnetic resonance imaging for the diagnosis of Parkinson's disease

The differential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology and error rates in the clinical diagnosis can be high even at specialized centres. Despite several limitations, magnetic resonance imaging (MRI) has undoubtedly enhanced the diagnostic accuracy in the differential diagnosis of neurodegenerative parkinsonism over the last three decades. This review aims to summarize research findings regarding the value of the different MRI techniques, including advanced sequences at high- and ultra-high-field MRI and modern image analysis algorithms, in the diagnostic work-up of Parkinson's disease. This includes not only the exclusion of alternative diagnoses for Parkinson's disease such as symptomatic parkinsonism and atypical parkinsonism, but also the diagnosis of early, new onset, and even prodromal Parkinson's disease.

A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles

In this work, we propose a diffusion MRI protocol for mining Parkinson's disease diffusion MRI datasets and recover robust disease-specific biomarkers. Using advanced high angular resolution diffusion imaging (HARDI) crossing fiber modeling and tractography robust to partial volume effects, we automatically dissected 50 white matter (WM) fascicles. These fascicles connect deep nuclei (thalamus, putamen, pallidum) to different cortical functional areas (associative, motor, sensorimotor, limbic), basal forebrain and substantia nigra. Then, among these 50 candidate WM fascicles, only the ones that passed a test-retest reproducibility procedure qualified for further tractometry analysis. Leveraging the unique 2-timepoints test-retest Parkinson's Progression Markers Initiative (PPMI) dataset of over 600 subjects, we found statistically significant differences in tract profiles along the subcortico-cortical pathways between Parkinson's disease patients and healthy controls. In particular, significant increases in FA, apparent fiber density, tract-density and generalized FA were detected in some locations of the nigro-subthalamo-putaminal-thalamo-cortical pathway. This connection is one of the major motor circuits balancing the coordination of motor output. Detailed and quantifiable knowledge on WM fascicles in these areas is thus essential to improve the quality and outcome of Deep Brain Stimulation, and to target new WM locations for investigation.

Imaging genetics approach to Parkinson's disease and its correlation with clinical score

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with both underlying genetic factors and neuroimaging findings. Existing neuroimaging studies related to the genome in PD have mostly focused on certain candidate genes. The aim of our study was to construct a linear regression model using both genetic and neuroimaging features to better predict clinical scores compared to conventional approaches. We obtained neuroimaging and DNA genotyping data from a research database. Connectivity analysis was applied to identify neuroimaging features that could differentiate between healthy control (HC) and PD groups. A joint analysis of genetic and imaging information known as imaging genetics was applied to investigate genetic variants. We then compared the utility of combining different genetic variants and neuroimaging features for predicting the Movement Disorder Society-sponsored unified Parkinson's disease rating scale (MDS-UPDRS) in a regression framework. The associative cortex, motor cortex, thalamus, and pallidum showed significantly different connectivity between the HC and PD groups. Imaging genetics analysis identified PARK2, PARK7, HtrA2, GIGYRF2, and SNCA as genetic variants that are significantly associated with imaging phenotypes. A linear regression model combining genetic and neuroimaging features predicted the MDS-UPDRS with lower error and higher correlation with the actual MDS-UPDRS compared to other models using only genetic or neuroimaging information alone.

Structural Brain Connectome and Cognitive Impairment in Parkinson Disease

Purpose To investigate the structural brain connectome in patients with Parkinson disease (PD) and mild cognitive impairment (MCI) and in patients with PD without MCI. Materials and Methods This prospective study was approved by the local ethics committees, and written informed consent was obtained from all subjects prior to enrollment. The individual structural brain connectome of 170 patients with PD (54 with MCI, 116 without MCI) and 41 healthy control subjects was obtained by using deterministic diffusion-tensor tractography. A network-based statistic was used to assess structural connectivity differences among groups. Results Patients with PD and MCI had global network alterations when compared with both control subjects and patients with PD without MCI (range, P = .004 to P = .048). Relative to control subjects, patients with PD and MCI had a large basal ganglia and frontoparietal network with decreased fractional anisotropy (FA) in the right hemisphere and a subnetwork with increased mean diffusivity (MD) involving similar regions bilaterally (P < .01). When compared with patients with PD without MCI, those with PD and MCI had a network with decreased FA, including basal ganglia and frontotemporoparietal regions bilaterally (P < .05). Similar findings were obtained by adjusting for motor disability (P < .05, permutation-corrected P = .06). At P < .01, patients with PD and MCI did not show network alterations relative to patients with PD without MCI. Network FA and MD values were used to differentiate patients with PD and MCI from healthy control subjects and patients with PD without MCI with fair to good accuracy (cross-validated area under the receiver operating characteristic curve [principal + secondary connected components] range, 0.75-0.85). Conclusion A disruption of structural connections between brain areas forming a network contributes to determine an altered information integration and organization and thus cognitive deficits in patients with PD. These results provide novel information concerning the structural substrates of MCI in patients with PD and may offer markers that can be used to differentiate between patients with PD and MCI and patients with PD without MCI. ^© RSNA, 2016 Online supplemental material is available for this article.

White matter microstructural characteristics in newly diagnosed Parkinson's disease: An unbiased whole-brain study

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder. Findings on specific white matter (WM) alterations in PD have been inconsistent. We hypothesized that WM changes occur in early PD patients and unbiased whole-brain analysis may provide additional evidence of pathological WM changes in PD. In this study, we examined various indexes of WM microstructure in newly diagnosed PD patients at the whole-brain level. 64 PDs with Hoehn & Yahr stage 1 (HY1PDs), 87 PDs with Hoehn & Yahr stage 2 (HYPD2s), and 60 controls (HCs) were recruited. Tract-based spatial statistics (TBSS) and diffusion connectometry were used to identify changes of WM pathways associated with PD. There were no significant differences in axial diffusivity, but HY1PDs exhibited greater fractional anisotropy (FA) and decreased mean and radial diffusivities (MD and RD) in callosal, projection, and association fibres than HCs and HY2PDs. Motor severity was inversely correlated with FA, but positively correlated with MD and RD in PD patients. Connectometry analysis also revealed increased WM density in the aforementioned tracts in PD patients, compared with HCs. Our study reveals WM enhancement, suggesting neural compensations in early PD. Longitudinal follow-up studies are warranted to identify the trajectory of WM changes alongside the progression of PD.

Magnetic resonance imaging as a potential biomarker for Parkinson's disease

Although a magnetic resonance imaging (MRI) biomarker for Parkinson's disease (PD) remains an unfulfilled objective, there have been numerous developments in MRI methodology and some of these have shown promise for PD. With funding from the National Institutes of Health and the Michael J Fox Foundation there will be further validation of structural, diffusion-based, and iron-focused MRI methods as possible biomarkers for PD. In this review, these methods and other strategies such as neurochemical and metabolic MRI have been covered. One of the challenges in establishing a biomarker is in the selection of individuals as PD is a heterogeneous disease with varying clinical features, different etiologies, and a range of pathologic changes. Additionally, longitudinal studies are needed of individuals with clinically diagnosed PD and cohorts of individuals who are at great risk for developing PD to validate methods. Ultimately an MRI biomarker will be useful in the diagnosis of PD, predicting the course of PD, providing a means to track its course, and provide an approach to select and monitor treatments.

Structural connectivity profile of scans without evidence of dopaminergic deficit (SWEDD) patients compared to normal controls and Parkinson's disease patients

BACKGROUND

In this study, we investigated the structural connectivity profile of patients with scans without evidence of dopaminergic deficit (SWEDD) compared with normal controls (NC) and patients with Parkinson's disease (PD). An accurate understanding of SWEDD is important so that appropriate therapeutic options can be presented to patients.

METHODS

Diffusion magnetic resonance imaging of NC (n = 40), SWEDD (n = 40) and PD patients (n = 40) was obtained from a research database. Tractography, the process of obtaining fiber information was performed. Connectivity analysis was performed on 16 connections in the cortico-basal ganglia-thalamo-cortical circuit. Group-wise differences among NC, PD and SWEDD patients were quantified in terms of structural connectivity based on fiber density. Then, we investigated correlations with the clinical score using the Movement Disorder Society-Sponsored Unified Parkinson's Disease Rating Scale (MDS-UPDRS). A support vector machine classifier and leave-one-out cross-validation were applied to separate the NC, SWEDD and PD groups.

RESULTS

Pallidum-putamen and sensorimotor cortex-putamen connections showed significant group-wise differences among NC, PD and SWEDD patients and correlated with the MDS-UPDRS score.

CONCLUSIONS

Pallidum-putamen and sensorimotor cortex-putamen connections might form a structural connectivity profile unique to SWEDD and could be a potential imaging biomarker for future movement disorder research.

Characterizing structural neural networks in de novo Parkinson disease patients using diffusion tensor imaging

Parkinson disease (PD) can be considered as a brain multisystemic disease arising from dysfunction in several neural networks. The principal aim of this study was to assess whether large-scale structural topological network changes are detectable in PD patients who have not been exposed yet to dopaminergic therapy (de novo patients). Twenty-one drug-naïve PD patients and thirty healthy controls underwent a 3T structural MRI. Next, Diffusion Tensor Imaging (DTI) and graph theoretic analyses to compute individual structural white-matter (WM) networks were combined. Centrality (degree, eigenvector centrality), segregation (clustering coefficient), and integration measures (efficiency, path length) were assessed in subject-specific structural networks. Moreover, Network-based statistic (NBS) was used to identify whether and which subnetworks were significantly different between PD and control participants. De novo PD patients showed decreased clustering coefficient and strength in specific brain regions such as putamen, pallidum, amygdala, and olfactory cortex compared with healthy controls. Moreover, NBS analyses demonstrated that two specific subnetworks of reduced connectivity characterized the WM structural organization of PD patients. In particular, several key pathways in the limbic system, basal ganglia, and sensorimotor circuits showed reduced patterns of communications when comparing PD patients to controls. This study shows that PD is characterized by a disruption in the structural connectivity of several motor and non-motor regions. These findings provide support to the presence of disconnectivity mechanisms in motor (basal ganglia) as well as in non-motor (e.g., limbic, olfactory) circuits at an early disease stage of PD. Hum Brain Mapp 37:4500-4510, 2016. © 2016 Wiley Periodicals, Inc.

New Clinically Feasible 3T MRI Protocol to Discriminate Internal Brain Stem Anatomy

Two new 3T MR imaging contrast methods, track density imaging and echo modulation curve T2 mapping, were combined with simultaneous multisection acquisition to reveal exquisite anatomic detail at 7 canonical levels of the brain stem. Compared with conventional MR imaging contrasts, many individual brain stem tracts and nuclear groups were directly visualized for the first time at 3T. This new approach is clinically practical and feasible (total scan time = 20 minutes), allowing better brain stem anatomic localization and characterization.

COPPADIS-2015 (COhort of Patients with PArkinson's DIsease in Spain, 2015), a global--clinical evaluations, serum biomarkers, genetic studies and neuroimaging--prospective, multicenter, non-interventional, long-term study on Parkinson's disease progression

Background

Parkinson’s disease (PD) is a progressive neurodegenerative disorder causing motor and non-motor symptoms that can affect independence, social adjustment and the quality of life (QoL) of both patients and caregivers. Studies designed to find diagnostic and/or progression biomarkers of PD are needed. We describe here the study protocol of COPPADIS-2015 (COhort of Patients with PArkinson’s DIsease in Spain, 2015), an integral PD project based on four aspects/concepts: 1) PD as a global disease (motor and non-motor symptoms); 2) QoL and caregiver issues; 3) Biomarkers; 4) Disease progression.

Methods/design

Observational, descriptive, non-interventional, 5-year follow-up, national (Spain), multicenter (45 centers from 15 autonomous communities), evaluation study. Specific goals: (1) detailed study (clinical evaluations, serum biomarkers, genetic studies and neuroimaging) of a population of PD patients from different areas of Spain, (2) comparison with a control group and (3) follow-up for 5 years. COPPADIS-2015 has been specifically designed to assess 17 proposed objectives. Study population: approximately 800 non-dementia PD patients, 600 principal caregivers and 400 control subjects. Study evaluations: (1) baseline includes motor assessment (e.g., Unified Parkinson’s Disease Rating Scale part III), non-motor symptoms (e.g., Non-Motor Symptoms Scale), cognition (e.g., Parkinson’s Disease Cognitive Rating Scale), mood and neuropsychiatric symptoms (e.g., Neuropsychiatric Inventory), disability, QoL (e.g., 39-item Parkinson’s disease Quality of Life Questionnaire Summary-Index) and caregiver status (e.g., Zarit Caregiver Burden Inventory); (2) follow-up includes annual (patients) or biannual (caregivers and controls) evaluations. Serum biomarkers (S-100b protein, TNF-α, IL-1, IL-2, IL-6, vitamin B12, methylmalonic acid, homocysteine, uric acid, C-reactive protein, ferritin, iron) and brain MRI (volumetry, tractography and MTAi [Medial Temporal Atrophy Index]), at baseline and at the end of follow-up, and genetic studies (DNA and RNA) at baseline will be performed in a subgroup of subjects (300 PD patients and 100 control subjects). Study periods: (1) recruitment period, from November, 2015 to February, 2017 (basal assessment); (2) follow-up period, 5 years; (3) closing date of clinical follow-up, May, 2022. Funding: Public/Private.

Discussion

COPPADIS-2015 is a challenging initiative. This project will provide important information on the natural history of PD and the value of various biomarkers.

Longitudinal changes in free-water within the substantia nigra of Parkinson's disease

There is a clear need to develop non-invasive markers of substantia nigra progression in Parkinson's disease. We previously found elevated free-water levels in the substantia nigra for patients with Parkinson's disease compared with controls in single-site and multi-site cohorts. Here, we test the hypotheses that free-water levels in the substantia nigra of Parkinson's disease increase following 1 year of progression, and that baseline free-water levels in the substantia nigra predict the change in bradykinesia following 1 year. We conducted a longitudinal study in controls (n = 19) and patients with Parkinson's disease (n = 25). Diffusion imaging and clinical data were collected at baseline and after 1 year. Free-water analyses were performed on diffusion imaging data using blinded, hand-drawn regions of interest in the posterior substantia nigra. A group effect indicated free-water values were increased in the posterior substantia nigra of patients with Parkinson's disease compared with controls (P = 0.003) and we observed a significant group × time interaction (P < 0.05). Free-water values increased for the Parkinson's disease group after 1 year (P = 0.006), whereas control free-water values did not change. Baseline free-water values predicted the 1 year change in bradykinesia scores (r = 0.74, P < 0.001) and 1 year change in Montreal Cognitive Assessment scores (r = -0.44, P = 0.03). Free-water in the posterior substantia nigra is elevated in Parkinson's disease, increases with progression of Parkinson's disease, and predicts subsequent changes in bradykinesia and cognitive status over 1 year. These findings demonstrate that free-water provides a potential non-invasive progression marker of the substantia nigra.

In Vivo 7T MRI of the Non-Human Primate Brainstem

Structural brain imaging provides a critical framework for performing stereotactic and intraoperative MRI-guided surgical procedures, with procedural efficacy often dependent upon visualization of the target with which to operate. Here, we describe tools for in vivo, subject-specific visualization and demarcation of regions within the brainstem. High-field 7T susceptibility-weighted imaging and diffusion-weighted imaging of the brain were collected using a customized head coil from eight rhesus macaques. Fiber tracts including the superior cerebellar peduncle, medial lemniscus, and lateral lemniscus were identified using high-resolution probabilistic diffusion tractography, which resulted in three-dimensional fiber tract reconstructions that were comparable to those extracted from sequential application of a two-dimensional nonlinear brain atlas warping algorithm. In the susceptibility-weighted imaging, white matter tracts within the brainstem were also identified as hypointense regions, and the degree of hypointensity was age-dependent. This combination of imaging modalities also enabled identifying the location and extent of several brainstem nuclei, including the periaqueductal gray, pedunculopontine nucleus, and inferior colliculus. These clinically-relevant high-field imaging approaches have potential to enable more accurate and comprehensive subject-specific visualization of the brainstem and to ultimately improve patient-specific neurosurgical targeting procedures, including deep brain stimulation lead implantation.

Recent imaging advances in neurology

Over the recent years, the application of neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) has considerably advanced the understanding of complex neurological disorders. PET is a powerful molecular imaging tool, which investigates the distribution and binding of radiochemicals attached to biologically relevant molecules; as such, this technique is able to give information on biochemistry and metabolism of the brain in health and disease. MRI uses high intensity magnetic fields and radiofrequency pulses to provide structural and functional information on tissues and organs in intact or diseased individuals, including the evaluation of white matter integrity, grey matter thickness and brain perfusion. The aim of this article is to review the most recent advances in neuroimaging research in common neurological disorders such as movement disorders, dementia, epilepsy, traumatic brain injury and multiple sclerosis, and to evaluate their contribution in the diagnosis and management of patients.

Parkinson Disease: Diffusion MR Imaging to Detect Nigrostriatal Pathway Loss in a Marmoset Model Treated with 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine

PURPOSE

To investigate the use of diffusion-tensor imaging (DTI) to detect denervation of the nigrostriatal pathway in a nonhuman primate model of Parkinson disease (PD) after treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP).

MATERIALS AND METHODS

This study was approved by the institutional committee for animal experiments. DTI was performed in marmosets (n = 6) by using a 7-T magnetic resonance (MR) imager before and 10 weeks after administration of MPTP. Fixed brains of a normal marmoset and a marmoset model of PD (n = 1) were analyzed by using microscopic tractography. Tyrosine-hydroxylase staining of dopaminergic neurons and three-dimensional histologic analysis also were performed in normal marmosets (n = 2) and a PD marmoset model (n = 2) to validate the course of the nigrostriatal pathway revealed at tractography. Statistical analysis of voxel-based and post hoc region-of-interest analyses of DTI maps was performed by using a paired t test.

RESULTS

At voxel-based analysis of DTI before and after treatment, MPTP-treated marmoset brains showed significantly increased axial and radial diffusivity in the bilateral nigrostriatal pathway (P < .05, false discovery rate corrected). The largest area of significantly increased diffusivity was an area of axial diffusivity in the right hemispere (177 mm(3)) that corresponded to the location of dopaminergic neurodegeneration at histologic evaluation. Region-of-interest analysis revealed a 27% increase in axial diffusivity in the right hemisphere (1.198 mm(2)/sec ± 0.111 to 1.522 mm(2)/sec ± 0.118; P = .002). Three-dimensional histologic analysis with tyrosine-hydroxylase staining showed the course of the nigrostriatal pathway and degeneration in the PD marmoset model as the absence of a tyrosine-hydroxylase stained region. Microscopic tractography showed that the connection of the substantia nigra to the striatum followed the same course as the nigrostriatal pathway and fewer fiber tracts in the PD marmoset model.

CONCLUSION

DTI and microscopic tractography showed the loss of fiber structures of the nigrostriatal pathway in the marmoset model of PD. The results of this study provide a potential basis for the use of DTI in the clinical diagnosis of PD.

Abnormal functional connectivity density in Parkinson's disease

The pathology of Parkinson's disease (PD) is not confined to the nigrostriatal pathway, but also involves widespread cerebral cortical areas. Using seed-based resting state functional connectivity, many previous studies have demonstrated that PD patients have abnormal functional integration. However, this technique strongly relies on a priori selection of the seed regions and may miss important unpredictable findings. Using an ultrafast voxel-wise functional connectivity density approach, this study performed a whole brain functional connectivity analysis to investigate the abnormal resting-state functional activities in PD patients. Compared with healthy controls, PD patients exhibited decreased short-range functional connectivity densities in regions that were mainly located in the ventral visual pathway and decreased long-range functional connectivity densities in the right middle and superior frontal gyrus, which have been speculated to be associated with visual hallucinations and cognitive dysfunction, respectively. PD patients also exhibited increased short- and long-range functional connectivity densities in the bilateral precuneus and posterior cingulate cortex, which may represent a compensatory process for maintaining normal brain function. The observed functional connectivity density alterations might be related to the disturbed structural connectivity of PD patients, leading to abnormal functional integration. Our results suggest that functional connectivity density mapping may provide a useful means to assess PD-related neurodegeneration and to study the pathophysiology of PD.