Free water imaging in Parkinson's disease and atypical parkinsonian disorders

BACKGROUND

Free water (FW)-corrected diffusion measures are more precise compared to standard diffusion measures. This study comprehensively evaluates FW and corrected diffusion metrics for whole brain white and deep gray matter (WM, GM) structures in patients with Parkinson's disease (PD), progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) and attempts to ascertain the probable patterns of WM abnormalities.

METHOD

Diffusion MRI was acquired for subjects with PD (n = 133), MSA (n = 25), PSP (n = 30) and matched healthy controls (HC) (n = 99, n = 24, n = 12). Diffusion metrics of FA, MD, AD, RD were generated and FW, corrected FA maps were calculated using a bi-tensor model. TBSS was carried out at 5000 permutations with significance at p < 0.05. For GM, diffusivity maps were extracted from the basal ganglia, and analyzed at an FDR with p < 0.05.

RESULTS

Compared to HC, PD showed focal changes in FW. MSA showed changes in the cerebellum and brainstem, and PSP showed increase in FW involving supratentorial WM and midbrain. All three showed increased substantia nigra FW. MSA, PSP demonstrated increased FW in bilateral putamen. PD showed increased FW in left GP externa, and bilateral thalamus. Compared to HC, MSA had increased FW in bilateral GP interna, and left thalamic. PSP had an additional increase in FW of the right GP externa, right GP interna, and bilateral thalamus.

CONCLUSION

The present study demonstrated definitive differences in the patterns of FW alterations between PD and atypical parkinsonian disorders suggesting the possibility of whole brain FW maps being used as markers for diagnosis of these disorders.

Structural and functional pathology in cocaine use disorder with polysubstance use: A multimodal fusion approach structural-functional pathology in cocaine use disorder

Cocaine use disorder (CUD) is described as a compulsive urge to seek and consume cocaine despite the inimical consequences. MRI studies from different modalities have shown that CUD patients exhibit structural and/or functional connectivity pathology among several brain regions. Nevertheless, both connectivities are commonly studied and analyzed separately, which may potentially obscure its relationship between them, and with the clinical pathology. Here, we compare structural and functional brain networks in CUD patients and healthy controls (HC) using multimodal fusion. The sample consisted of 63 (8 females) CUD patients and 42 (9 females) healthy controls (HC), recruited as part of the SUDMEX CONN database. For this, we computed a battery of graph-based measures from multi-shell diffusion-weighted imaging and resting state fc-fMRI to quantify local and global connectivity. Then we used multimodal canonical component analysis plus joint independent component analysis (mCCA+jICA) to compare between techniques and evaluate group differences and its association with clinical alteration. Unimodal results showed a striatal decrease in the participation coefficient but applied supervised data fusion revealed other regions with cocaine-related alterations in joint functional communication. When performing multimodal fusion analysis, we observed a higher centrality of the interrelationship and a lower participation coefficient in patients with CUD. In contrast to the unimodal approach, the multimodal fusion method was able to reveal latent information about brain regions involved in impairment due to cocaine abuse. The present results could help in understanding the pathology of CUD to develop better pre-treatment/post-treatment intervention designs.

Role of Arterial Spin Labeling (ASL) Images in Parkinson's Disease (PD): A Systematic Review

RATIONALE AND OBJECTIVES

Parkinson's disease is a chronic progressive neurodegenerative disorder with standard structural MRIs often showing no gross abnormalities. Quantitative perfusion MRI modality Arterial Spin Labeling (ASL) is helpful in identifying PD specific perfusion patterns. Absolute Cerebral blood flow (CBF) measurement using ASL provides insights into regional perfusion abnormalities. We reviewed the role of ASL to identify specific brain regions responsible for motor, non-motor symptoms and neurovascular changes observed in PD. Challenges in assessing the blood perfusion level are discussed with future development for improving the evaluation of ASL perfusion maps.

MATERIALS AND METHODS

We included CBF quantification studies using ASL for PD diagnosis. A systematic search was performed in Pubmed, Scopus and Web of Science. The perfusion parameters CBF and arterial arrival time (AAT) measured using ASL were considered for brain region assessment. Clinical aspects of PD have been analyzed using ASL perfusion maps.

RESULTS

The systematic search identified 153 unique records. Thirty articles were selected after verification of inclusion and exclusion criteria. Voxel and region-based analyses in white and gray matter tissues have been performed to identify PD-specific perfusion patterns by reported articles. Predominant brain regions such as basal ganglia sub-regions, frontoparietal network, precuneus, occipital lobe, sensory motor area regions, visual network, which are associated with motor and non-motor symptoms in PD, were identified with CBF hypoperfusion, indicating neuronal loss and cerebrovascular dysfunction.

CONCLUSION

CBF and AAT values derived from ASL can potentially be used as biomarkers to discriminate PD from similar brain-related disorders.

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Neurite orientation dispersion and density imaging in cocaine use disorder

Cocaine use disorder (CUD) is characterized by a compulsive search for cocaine. Several studies have shown that cocaine users exhibit cognitive deficits, including lack of inhibition and decision-making as well as brain volume and diffusion-based white-matter alterations in a wide variety of brain regions. However, the non-specificity of standard volumetric and diffusion-tensor methods to detect structural micropathology may lead to wrong conclusions. To better understand microstructural pathology in CUD, we analyzed 60 CUD participants (3 female) and 43 non-CUD controls (HC; 2 female) retrospectively from our cross-sectional Mexican SUD neuroimaging dataset (SUDMEX-CONN), using multi-shell diffusion-weighted imaging and the neurite orientation dispersion and density imaging (NODDI) analysis, which aims to more accurately model microstructural pathology. We used Viso values of NODDI that employ a three-compartment model in white (WM) and gray-matter (GM). These values were also correlated with clinical measures, including psychiatric severity status, impulsive behavior and pattern of cocaine and tobacco use in the CUD group. We found higher whole-brain microstructural pathology in WM and GM in CUD patients than controls. ROI analysis revealed higher Viso-NODDI values in superior longitudinal fasciculus, cingulum, hippocampus cingulum, forceps minor and Uncinate fasciculus, as well as in frontal and parieto-temporal GM structures. We also found correlations between significant ROI and impulsivity, onset age of cocaine use and weekly dosage with Viso-NODDI. However, we did not find correlations with psychopathology measures. Overall, although their clinical relevance remains questionable, microstructural pathology seems to be present in CUD both in gray and white matter.

Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging

Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2-FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics-based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1-contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first-order and gray-level co-occurrence matrix features are crucial in detecting local recurrence, while multimodal gray-level difference matrix and first-order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics-based phenotypic changes that may potentially serve as MR-based biomarkers for customized therapeutic intervention.

Multimodal Brain Connectomics-Based Prediction of Parkinson’s Disease Using Graph Attention Networks

Background

A multimodal connectomic analysis using diffusion and functional MRI can provide complementary information on the structure–function network dynamics involved in complex neurodegenerative network disorders such as Parkinson’s disease (PD). Deep learning-based graph neural network models generate higher-level embeddings that could capture intricate structural and functional regional interactions related to PD.

Objective

This study aimed at investigating the role of structure–function connections in predicting PD, by employing an end-to-end graph attention network (GAT) on multimodal brain connectomes along with an interpretability framework.

Methods

The proposed GAT model was implemented to generate node embeddings from the structural connectivity matrix and multimodal feature set containing morphological features and structural and functional network features of PD patients and healthy controls. Graph classification was performed by extracting topmost node embeddings, and the interpretability framework was implemented using saliency analysis and attention maps. Moreover, we also compared our model with unimodal models as well as other state-of-the-art models.

Results

Our proposed GAT model with a multimodal feature set demonstrated superior classification performance over a unimodal feature set. Our model demonstrated superior classification performance over other comparative models, with 10-fold CV accuracy and an F1 score of 86% and a moderate test accuracy of 73%. The interpretability framework highlighted the structural and functional topological influence of motor network and cortico-subcortical brain regions, among which structural features were correlated with onset of PD. The attention maps showed dependency between large-scale brain regions based on their structural and functional characteristics.

Conclusion

Multimodal brain connectomic markers and GAT architecture can facilitate robust prediction of PD pathology and provide an attention mechanism-based interpretability framework that can highlight the pathology-specific relation between brain regions.

Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset

OBJECTIVE

The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized.

METHODS

We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data.

RESULTS

Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism.

CONCLUSION

Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders.

SIGNIFICANCE

ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.

A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization

Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.

Disrupted structural connectome and neurocognitive functions in Duchenne muscular dystrophy: classifying and subtyping based on Dp140 dystrophin isoform

OBJECTIVE

Neurocognitive disabilities in Duchenne muscular dystrophy (DMD) children beginning in early childhood and distal DMD gene deletions involving disruption of Dp140 isoform are more likely to manifest significant neurocognitive impairments. MRI data analysis techniques like brain-network metrics can provide information on microstructural integrity and underlying pathophysiology.

METHODS

A prospective study on 95 participants [DMD = 57, and healthy controls (HC) = 38]. The muscular dystrophy functional rating scale (MDFRS) scores, neuropsychology batteries, and multiplex ligand-dependent probe amplification (MLPA) testing were used for clinical assessment, IQ estimation, and genotypic classification. Diffusion MRI and network-based statistics were used to analyze structural connectomes at various levels and correlate with clinical markers.

RESULTS

Motor and executive sub-networks were extracted and analyzed. Out of 57 DMD children, 23 belong to Dp140 + and 34 to Dp140- subgroup. Motor disabilities are pronounced in Dp140- subgroup as reflected by lower MDFRS scores. IQ parameters are significantly low in all-DMD cases; however, the Dp140- has specifically lowest scores. Significant differences were observed in global efficiency, transitivity, and characteristic path length between HC and DMD. Subgroup analysis demonstrates that the significance is mainly driven by participants with Dp140- than Dp140 + isoform. Finally, a random forest classifier model illustrated an accuracy of 79% between HC and DMD and 90% between DMD- subgroups.

CONCLUSIONS

Current findings demonstrate structural network-based characterization of abnormalities in DMD, especially prominent in Dp140-. Our observations suggest that participants with Dp140 + have relatively intact connectivity while Dp140- show widespread connectivity alterations at global, nodal, and edge levels. This study provides valuable insights supporting the genotype-phenotype correlation of brain-behavior involvement in DMD children.

Structural connectivity predicts sequential processing differences in music perception ability

To relate individual differences in music perception ability with whole brain white matter connectivity, we scanned a group of 27 individuals with varying degrees of musical training and assessed musical ability in sensory and sequential music perception domains using the Profile of Music Perception Skills-Short version (PROMS-S). Sequential processing ability was estimated by combining performance on tasks for Melody, Standard Rhythm, Embedded Rhythm, and Accent subscores while sensory processing ability was ascertained via tasks of Tempo, Pitch, Timbre, and Tuning. Controlling for musical training, gender, and years of training, network-based statistics revealed positive linear associations between total PROMS-S scores and increased interhemispheric fronto-temporal and parieto-frontal white matter connectivity, suggesting a distinct segregated structural network for music perception. Secondary analysis revealed two subnetworks for sequential processing ability, one comprising ventral fronto-temporal and subcortical regions and the other comprising dorsal fronto-temporo-parietal regions. A graph-theoretic analysis to characterize the structural network revealed a positive association of modularity of the whole brain structural connectome with the d' total score. In addition, the nodal degree of the right posterior cingulate cortex also showed a significant positive correlation with the total d' score. Our results suggest that a distinct structural network of connectivity across fronto-temporal, cerebellar, and cerebro-subcortical regions is associated with music processing abilities and the right posterior cingulate cortex mediates the connectivity of this network.

Developing a Radiomics Signature for Supratentorial Extra-Ventricular Ependymoma Using Multimodal MR Imaging

Rationale and Objectives: To build a machine learning-based diagnostic model that can accurately distinguish adult supratentorial extraventricular ependymoma (STEE) from similarly appearing high-grade gliomas (HGG) using quantitative radiomic signatures from a multi-parametric MRI framework.

Materials and Methods: We computed radiomic features on the preprocessed and segmented tumor masks from a pre-operative multimodal MRI dataset [contrast-enhanced T1 (T1ce), T2, fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC)] from STEE (n = 15), HGG-Grade IV (HGG-G4) (n = 24), and HGG-Grade III (HGG-G3) (n = 36) patients, followed by an optimum two-stage feature selection and multiclass classification. Performance of multiple classifiers were evaluated on both unimodal and multimodal feature sets and most discriminative radiomic features involved in classification of STEE from HGG subtypes were obtained.

Results: Multimodal features demonstrated higher classification performance over unimodal feature set in discriminating STEE and HGG subtypes with an accuracy of 68% on test data and above 80% on cross validation, along with an overall above 90% specificity. Among unimodal feature sets, those extracted from FLAIR demonstrated high classification performance in delineating all three tumor groups. Texture-based radiomic features particularly from FLAIR were most important in discriminating STEE from HGG-G4, whereas first-order features from T2 and ADC consistently ranked higher in differentiating multiple tumor groups.

Conclusions: This study illustrates the utility of radiomics-based multimodal MRI framework in accurately discriminating similarly appearing adult STEE from HGG subtypes. Radiomic features from multiple MRI modalities could capture intricate and complementary information for a robust and highly accurate multiclass tumor classification.

Predictive and discriminative localization of pathology using high resolution class activation maps with CNNs

PURPOSE

Existing class activation mapping (CAM) techniques extract the feature maps only from a single layer of the convolutional neural net (CNN), generally from the final layer and then interpolate to upsample to the original image resolution to locate the discriminative regions. Consequently these provide a coarse localization that may not be able to capture subtle abnormalities in medical images. To alleviate this, our work proposes a technique called high resolution class activation mapping (HR-CAMs) that can provide enhanced visual explainability to the CNN models.

METHODS

HR-CAMs fuse feature maps by training a network using the input from multiple layers of a trained CNN, thus gaining information from every layer that can localize abnormalities with greater details in original image resolution. The technique is validated qualitatively and quantitatively on a simulated dataset of 8,000 images followed by applications on multiple image analysis tasks that include (1) skin lesion classification (ISIC open dataset-25,331 cases) and (2) predicting bone fractures (MURA open dataset-40,561 images) (3) predicting Parkinson's disease (PD) from neuromelanin sensitive MRI (small cohort-80 subjects).

RESULTS

We demonstrate that our model creates clinically interpretable subject specific high resolution discriminative localizations when compared to widely used CAMs and Gradient-CAMs.

CONCLUSION

HR-CAMs provide finer delineation of abnormalities thus facilitating superior explainability to CNNs as has been demonstrated from its rigorous validation.

Radiomics on routine T1-weighted MRI can delineate Parkinson's disease from multiple system atrophy and progressive supranuclear palsy

OBJECTIVES

This study aimed to explore the feasibility of radiomics features extracted from T1-weighted MRI images to differentiate Parkinson's disease (PD) from atypical parkinsonian syndromes (APS).

METHODS

Radiomics features were computed from T1 images of 65 patients with PD, 61 patients with APS (31: progressive supranuclear palsy and 30: multiple system atrophy), and 75 healthy controls (HC). These features were extracted from 19 regions of interest primarily from subcortical structures, cerebellum, and brainstem. Separate random forest classifiers were applied to classify different groups based on a reduced set of most important radiomics features for each classification as determined by the random forest-based recursive feature elimination by cross-validation method.

RESULTS

The PD vs HC classifier illustrated an accuracy of 70%, while the PD vs APS classifier demonstrated a superior test accuracy of 92%. Moreover, a 3-way PD/MSA/PSP classifier performed with 96% accuracy. While first-order and texture-based differences like Gray Level Co-occurrence Matrix (GLCM) and Gray Level Difference Matrix for the substantia nigra pars compacta and thalamus were highly discriminative for PD vs HC, textural features mainly GLCM of the ventral diencephalon were highlighted for APS vs HC, and features extracted from the ventral diencephalon and nucleus accumbens were highlighted for the classification of PD and APS.

CONCLUSIONS

This study establishes the utility of radiomics to differentiate PD from APS using routine T1-weighted images. This may aid in the clinical diagnosis of PD and APS which may often be indistinguishable in early stages of disease.

KEY POINTS

• Radiomics features were extracted from T1-weighted MRI images. • Parkinson's disease and atypical parkinsonian syndromes were classified at an accuracy of 92%. • This study establishes the utility of radiomics to differentiate Parkinson's disease and atypical parkinsonian syndromes using routine T1-weighted images.

Abnormal structural connectivity in progressive supranuclear palsy-Richardson syndrome

OBJECTIVES

Progressive supranuclear palsy-Richardson syndrome (PSP-RS) is characterized by symmetrical parkinsonism with postural instability and frontal dysfunction. This study aims to use the whole brain structural connectome (SC) to gain insights into the underlying disconnectivity which may be implicated in the clinical features of PSP-RS.

METHODS

Sixteen patients of PSP-RS and 12 healthy controls were recruited. Disease severity was quantified using PSP rating scale (PSPRS), and mini-mental scale was applied to evaluate cognition. Thirty-two direction diffusion MRIs were acquired and used to compute the structural connectome of the whole brain using deterministic fiber tracking. Group analyses were performed at the edge-wise, nodal, and global levels. Age and gender were used as nuisance covariates for all the subsequent analyses, and FDR correction was applied.

RESULTS

Network-based statistics revealed a 34-edge network with significantly abnormal edge-wise connectivity in the patient group. Of these, 25 edges were cortical connections, of which 68% were frontal connections. Abnormal deep gray matter connections were predominantly comprised of connections between structures of the basal ganglia. The characteristic path length of the SC was lower in PSP-RS, and nodal analysis revealed abnormal degree, strength, local efficiency, betweenness centrality, and participation coefficient in several nodes.

CONCLUSIONS

Significant alterations in the structural connectivity of the whole brain connectome were observed in PSP-RS. The higher degree of abnormality observed in nodes belonging to the frontal lobe and basal ganglia substantiates the predominant frontal dysfunction and parkinsonism observed in PSP-RS. The findings of this study support the concept that PSP-RS may be a network-based disorder.

Normative Baseline for Radiomics in Brain MRI: Evaluating the Robustness, Regional Variations, and Reproducibility on FLAIR Images

BACKGROUND

Radiomics in neuroimaging has gained momentum as a noninvasive prediction tool not only to differentiate between types of brain tumors, but also to create phenotypic signatures in neurological and neuropsychiatric disorders. However, there is currently little understating about the robustness and reproducibility of radiomic features in a baseline normative population.

PURPOSE

To investigate the intra- and interscanner reproducibility, spatial robustness, and sensitivity of radiomics on fluid attenuation inversion recovery (FLAIR) images, which are widely used in neuro-oncology investigations.

STUDY TYPE

Retrospective.

POPULATION

Three separate datasets of healthy controls: 1) 87 subjects (age range 12-64 years), 2) intrascanner three timepoints, four subjects, and 3) interscanner, eight subjects at three different sites.

FIELD STRENGTH/SEQUENCE

T₂ -weighted FLAIR at 1.5T and 3.0T.

ASSESSMENT

Spatial variance across lobes, and their relation with age/gender, intra- and inter-scanner reproducibility (with and without site harmonization) of radiomics.

STATISTICAL TESTS

Analysis of variance (ANOVA), interclass correlation (ICC), coefficient of variation (CoV), Bland-Altman analysis.

RESULTS

Analysis of data revealed no differences between genders; however, multiple radiomic features were highly associated with age (P < 0.05). Spatial variability was also evaluated where only 29.04% gray matter and 38.7% white matter features demonstrated an ICC >0.5. Furthermore, the results demonstrated intra-scanner reliability (ICC >0.5); however, inter-scanner reproducibility was poor, with ICC < 0.5 for 82% gray matter and 78.5% white matter features. The inter-scanner reliability improved (ICC < 0.5 for 39.67% gray matter and 38% white matter features) using site-harmonization techniques.

DATA CONCLUSION

These findings suggest that, accounting for age, spatial locations in radiomics-based analysis and use of intersite radiomics harmonization is crucial before interpreting these features for pathological inference. Level of Evidence 3. Technical Efficacy Stage 1. J. MAGN. RESON. IMAGING 2021;53:394-407.

In Vivo Evaluation of White Matter Abnormalities in Children with Duchenne Muscular Dystrophy Using DTI

BACKGROUND AND PURPOSE

Duchenne muscular dystrophy is an X-linked disorder characterized by progressive muscle weakness and prominent nonmotor manifestations, such as a low intelligence quotient and neuropsychiatric disturbance. We investigated WM integrity in patients with Duchenne muscular dystrophy using DTI.

MATERIALS AND METHODS

Fractional anisotropy and mean, axial, and radial diffusivity (DTI measures) were used to assess WM microstructural integrity along with neuropsychological evaluation in patients with Duchenne muscular dystrophy (n = 60) and controls (n = 40). Exon deletions in the DMD gene were confirmed using multiplex ligation-dependent probe amplification. Patients were classified into proximal (DMD Dp140+) and distal (DMD Dp140-) subgroups based on the location of the exon deletion and expression of short dystrophin Dp140 isoform. WM integrity was examined using whole-brain Tract-Based Spatial Statistics and atlas-based analysis of DTI data. The Pearson correlation was performed to investigate the possible relationship between neuropsychological scores and DTI metrics.

RESULTS

The mean ages of Duchenne muscular dystrophy and control participants were 8.0 ± 1.2 years and 8.2 ± 1.4 years, respectively. The mean age at disease onset was 4.1 ± 1.8 years, and mean illness duration was 40.8 ± 25.2 months. Significant differences in neuropsychological scores were observed between the proximal and distal gene-deletion subgroups, with more severe impairment in the distal-deletion subgroup (P < .05). Localized fractional anisotropy changes were seen in the corpus callosum, parietal WM, and fornices in the patient subgroup with Dp140+, while widespread changes were noted in the Dp140- subgroup. The Dp140+ subgroup showed increased axial diffusivity in multiple WM regions relative to the Dp140- subgroup. No significant correlation was observed between clinical and neuropsychological scores and diffusion metrics.

CONCLUSIONS

Widespread WM differences are evident in patients with Duchenne muscular dystrophy relative to healthy controls. Distal mutations in particular are associated with extensive WM abnormalities and poor neuropsychological profiles.

Abnormalities in the white matter tracts in patients with Parkinson disease and psychosis

OBJECTIVE

The objective of the current study was to compare the microstructural integrity of the white matter (WM) tracts in patients having Parkinson disease (PD) with and without psychosis (PD-P and PD-NP) through diffusion tensor imaging (DTI).

METHODS

This cross-sectional study involved 48 PD-NP and 42 PD-P who were matched for age, sex, and education. Tract-based spatial statistics (TBSS) was used to compare several DTI metrics from the diffusion-weighted MRIs obtained through a 3-Tesla scanner. A set of neuropsychological tests was used for the cognitive evaluation of all patients.

RESULTS

The severity and stage of PD were not statistically different between the groups. The PD-P group performed poorly in all the neuropsychological domains compared with the PD-NP group. TBSS analysis revealed widespread patterns of abnormality in the fractional anisotropy (FA) in the PD-P group, which also correlated with some of the cognitive scores. These tracts include inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, right parieto-occipital WM, body of the corpus callosum, and corticospinal tract.

CONCLUSION

This study provides novel insights into the putative role of WM tract abnormalities in the pathogenesis of PD-P by demonstrating significant alterations in several WM tracts. Additional longitudinal studies are warranted to confirm the findings of our research.

Microstructural abnormalities of substantia nigra in Parkinson's disease: A neuromelanin sensitive MRI atlas based study

Microstructural changes associated with degeneration of dopaminergic neurons of the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) have been studied using Diffusion Tensor Imaging (DTI). However, these studies show inconsistent results, mainly due to methodological variations in delineation of SNc. To mitigate this, our work aims to construct a probabilistic atlas of SNc based on a 3D Neuromelanin Sensitive MRI (NMS‐MRI) sequence and demonstrate its applicability to investigate microstructural changes on a large dataset of PD. Using manual segmentation and deformable registration we created a novel SNc atlas in the MNI space using NMS‐MRI sequences of 27 healthy controls (HC). We first quantitatively evaluated this atlas and then employed it to investigate the micro‐structural abnormalities in SNc using diffusion MRI from 133 patients with PD and 99 HCs. Our results demonstrated significant increase in diffusivity with no changes in anisotropy. In addition, we also observed an asymmetry of the diffusion metrics with a higher diffusivity and lower anisotropy in the left SNc than the right. Finally, a multivariate classifier based on SNc diffusion features could delineate patients with PD with an average accuracy of 71.7%. Overall, from this work we establish a normative baseline for the SNc region of interest using NMS‐MRI while the application on PD data emphasizes on the contribution of diffusivity measures rather than anisotropy of white matter in PD.

Atrophy of cerebellar peduncles in essential tremor: a machine learning-based volumetric analysis

BACKGROUND

Subtle cerebellar signs are frequently observed in essential tremor (ET) and may be associated with cerebellar dysfunction. This study aims to evaluate the macrostructural integrity of the superior, middle, and inferior cerebellar peduncles (SCP, MCP, ICP) and cerebellar gray and white matter (GM, WM) volumes in patients with ET, and compare these volumes between patients with and without cerebellar signs (ETc and ETnc).

METHODS

Forty patients with ET and 37 age- and gender-matched healthy controls were recruited. Atlas-based region-of-interest analysis of the SCP, MCP, and ICP and automated analysis of cerebellar GM and WM volumes were performed. Peduncular volumes were employed in a multi-variate classification framework to attempt discrimination of ET from controls.

RESULTS

Significant atrophy of bilateral MCP and ICP and bilateral cerebellar GM was observed in ET. Cerebellar signs were present in 20% of subjects with ET. Comparison of peduncular and cerebellar volumes between ETnc and ETc revealed atrophy of right SCP, bilateral MCP and ICP, and left cerebellar WM in ETc. The multi-variate classifier could discriminate between ET and controls with a test accuracy of 86.66%.

CONCLUSIONS

Patients with ET have significant atrophy of cerebellar peduncles, particularly the MCP and ICP. Additional atrophy of the SCP is observed in the ETc group. These abnormalities may contribute to the pathogenesis of cerebellar signs in ET.

KEY POINTS

• Patients with ET have significant atrophy of bilateral middle and inferior cerebellar peduncles and cerebellar gray matter in comparison with healthy controls. • Patients of ET with cerebellar signs have significant atrophy of right superior cerebellar peduncle, bilateral middle and inferior cerebellar peduncle, and left cerebellar white matter in comparison with ET without cerebellar signs. • A multi-variate classifier employing peduncular volumes could discriminate between ET and controls with a test accuracy of 86.66%.

Altered structural connectivity of the motor subnetwork in multiple system atrophy with cerebellar features

OBJECTIVES

To investigate the structural connectivity of the motor subnetwork in multiple system atrophy with cerebellar features (MSA-C), a distinct subtype of MSA, characterized by predominant cerebellar symptoms.

METHODS

Twenty-three patients with MSA-C and 25 age- and gender-matched healthy controls were recruited for the study. Disease severity was quantified using the Unified Multiple System Atrophy Rating Scale (UMSARS). Diffusion MRI images were acquired and used to compute the structural connectomes (SCs) using probabilistic fiber tracking. The motor network with 12 brain regions and 26 cerebellar regions was extracted and was compared between the groups using analysis of variance at a global (network-wide), nodal (at each node), and edge (at each connection) levels, and was corrected for multiple comparisons. In addition, the acquired connectivity measures were correlated with duration of illness, total Unified MSA Rating Scale (UMSARS), and the motor component score.

RESULTS

Significantly lower global network metrics-global density, transitivity, clustering coefficient, and characteristic path length-were observed in MSA-C (corrected p < 0.05). Reduced nodal strength was observed in the bilateral ventral diencephalon, the left thalamus, and several cerebellar regions. Network-based statistics revealed significant abnormal edge-wise connectivity in 40 connections (corrected p < 0.01), with majority of deficits observed in the cerebellum. Finally, significant negative correlations were observed between UMSARS scores and thalamic and cerebellar connectivity (p < 0.05) as well as between duration of illness and cerebellar connectivity.

CONCLUSIONS

Abnormal connectivity of the basal ganglia and cerebellar network may be causally implicated for the motor features observed in MSA-C.

KEY POINTS

• Structural connectivity of the motor subnetwork was explored in patients with multiple system atrophy with cerebellar features (MSA-C) using probabilistic tractography. • The motor subnetwork in MSA-C has significant alterations in both basal ganglia and cerebellar connectivity, with a higher extent of abnormality in the cerebellum. • These findings may be causally implicated for the motor features of cerebellar dysfunction and parkinsonism observed in MSA-C.

Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning

BACKGROUND

Advances in white matter tractography enhance neurosurgical planning and glioma resection, but white matter tractography is limited by biological variables such as edema, mass effect, and tract infiltration or selection biases related to regions of interest or fractional anisotropy values.

OBJECTIVE

To provide an automated tract identification paradigm that corrects for artifacts created by tumor edema and infiltration and provides a consistent, accurate method of fiber bundle identification.

METHODS

An automated tract identification paradigm was developed and evaluated for glioma surgery. A fiber bundle atlas was generated from 6 healthy participants. Fibers of a test set (including 3 healthy participants and 10 patients with brain tumors) were clustered adaptively with this atlas. Reliability of the identified tracts in both groups was assessed by comparison with 2 experts with the Cohen κ used to quantify concurrence. We evaluated 6 major fiber bundles: cingulum bundle, fornix, uncinate fasciculus, arcuate fasciculus, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus, the last 3 tracts mediating language function.

RESULTS

The automated paradigm demonstrated a reliable and practical method to identify white mater tracts, despite mass effect, edema, and tract infiltration. When the tumor demonstrated significant mass effect or shift, the automated approach was useful for providing an initialization to guide the expert with identification of the specific tract of interest.

CONCLUSION

We report a reliable paradigm for the automated identification of white matter pathways in patients with gliomas. This approach should enhance the neurosurgical objective of maximal safe resections.

ABBREVIATIONS

AF, arcuate fasciculusDTI, diffusion tensor imagingIFOF, inferior fronto-occipital fasciculusILF, inferior longitudinal fasciculusROI, region of interestWM, white matter.

Connectome and Maturation Profiles of the Developing Mouse Brain Using Diffusion Tensor Imaging

This paper presents a comprehensive effort to establish a structural mouse connectome using diffusion tensor magnetic resonance imaging coupled with connectivity analysis tools. This work lays the foundation for imaging-based structural connectomics of the mouse brain, potentially facilitating a whole-brain network analysis to quantify brain changes in connectivity during development, as well as deviations from it related to genetic effects. A connectomic trajectory of maturation during postnatal ages 2-80 days is presented in the C57BL/6J mouse strain, using a whole-brain connectivity analysis, followed by investigations based on local and global network features. The global network measures of density, global efficiency, and modularity demonstrated a nonlinear relationship with age. The regional network metrics, namely degree and local efficiency, displayed a differential change in the major subcortical structures such as the thalamus and hippocampus, and cortical regions such as visual and motor cortex. Finally, the connectomes were used to derive an index of "brain connectivity index," which demonstrated a high correlation (r = 0.95) with the chronological age, indicating that brain connectivity is a good marker of normal age progression, hence valuable in detecting subtle deviations from normality caused by genetic, environmental, or pharmacological manipulations.

Automated tract extraction via atlas based Adaptive Clustering

Advancements in imaging protocols such as the high angular resolution diffusion-weighted imaging (HARDI) and in tractography techniques are expected to cause an increase in the tract-based analyses. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest. In this work, we design a framework for automated extraction of white matter tracts. The framework introduces three main components, namely a connectivity based fiber representation, a fiber bundle atlas, and a clustering approach called Adaptive Clustering. The fiber representation relies on the connectivity signatures of fibers to establish an easy correspondence between different subjects. A group-wise clustering of these fibers that are represented by the connectivity signatures is then used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. Experiments on the HARDI scans of healthy individuals acquired repeatedly, demonstrate the applicability, reliability and the repeatability of our approach in extracting white matter tracts. By alleviating the seed region selection and the inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, the proposed framework expands the range of possible clinical applications and establishes the ability to perform tract-based analyses with large samples.

An integrated framework for high angular resolution diffusion imaging-based investigation of structural connectivity

Structural connectivity models hold great promise for expanding what is known about the ways information travels throughout the brain. The physiologic interpretability of structural connectivity models depends heavily on how the connections between regions are quantified. This article presents an integrated structural connectivity framework designed around such an interpretation. The framework provides three measures to characterize the structural connectivity of a subject: (1) the structural connectivity matrix describing the proportion of connections between pairs of nodes, (2) the nodal connection distribution (nCD) characterizing the proportion of connections that terminate in each node, and (3) the connection density image, which presents the density of connections as they traverse through white matter (WM). Individually, each possesses different information concerning the structural connectivity of the individual and could potentially be useful for a variety of tasks, ranging from characterizing and localizing group differences to identifying novel parcellations of the cortex. The efficiency of the proposed framework allows the determination of large structural connectivity networks, consisting of many small nodal regions, providing a more detailed description of a subject's connectivity. The nCD provides a gray matter contrast that can potentially aid in investigating local cytoarchitecture and connectivity. Similarly, the connection density images offer insight into the WM pathways, potentially identifying focal differences that affect a number of pathways. The reliability of these measures was established through a test/retest paradigm performed on nine subjects, while the utility of the method was evaluated through its applications to 20 diffusion datasets acquired from typically developing adolescents.

White matter atlas generation using HARDI based automated parcellation

Most diffusion imaging studies have used subject registration to an atlas space for enhanced quantification of anatomy. However, standard diffusion tensor atlases lack information in regions of fiber crossing and are based on adult anatomy. The degree of error associated with applying these atlases to studies of children for example has not yet been estimated but may lead to suboptimal results. This paper describes a novel technique for generating population-specific high angular resolution diffusion imaging (HARDI)-based atlases consisting of labeled regions of homogenous white matter. Our approach uses a fiber orientation distribution (FOD) diffusion model and a data driven clustering algorithm. White matter regional labeling is achieved by our automated data driven clustering algorithm that has the potential to delineate white matter regions based on fiber complexity and orientation. The advantage of such an atlas is that it is study specific and more comprehensive in describing regions of white matter homogeneity as compared to standard anatomical atlases. We have applied this state of the art technique to a dataset consisting of adolescent and preadolescent children, creating one of the first examples of a HARDI-based atlas, thereby establishing the feasibility of the atlas creation framework. The white matter regions generated by our automated clustering algorithm have lower FOD variance than when compared to the regions created from a standard anatomical atlas.

NEURONAL WHITE MATTER PARCELLATION USING SPATIALLY COHERENT NORMALIZED CUTS

This work presents an automated method for partitioning neuronal white matter (WM) into regions of interest with uniform WM architecture. These regions can then be used to replace atlas-derived regions for any subsequent statistical analysis. The fiber orientation distribution function is used as a model of WM architecture resulting in a voxel similarity function sensitive to both fiber orientations and configurations. The method utilizes the normalized cuts algorithm to partition WM voxels based on this similarity function along with a connected component labeling algorithm to ensure spatial compactness. We illustrate the algorithms ability to discern regions based on both orientation and complexity through its application to a simulated fiber crossing and an in-vivo dataset.