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.

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.

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.

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.

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.

Aberrant global and local efficiency of the executive subnetwork in essential tremor.

This study aims to use probabilistic tractography to ascertain the global (GE) and local efficiency (LE) of the executive subnetwork in essential tremor (ET). Significantly lower GE of the whole executive subnetwork and lower LE of the left rostral middle frontal gyrus, frontal pole, inferior frontal gyrus, bilateral anterior cingulate cortex, and medial orbitofrontal cortex. These results imply ineffective and inadequate communication of the executive subnetwork, and may be causally associated with the executive dysfunction observed in ET.

Clinical correlates of abnormal subcortical volumes in Essential Tremor.

Essential tremor (ET) is considered to be a neurodegenerative disorder and it is plausible that the observed motor and non-motor symptoms may be attributable to functional alterations secondary to abnormalities of subcortical nuclei. This study aims to compare the volumes of subcortical nuclei in patients with ET to ascertain neuroimaging correlates of motor and non-motor features of ET. Forty patients of ET and 40 age- and gender-matched healthy controls (HC) were enrolled in this study. Tremor severity was quantified with the Fahn-Tolosa-Marin tremor rating scale. Patients of ET with and without a rest tremor were also compared. Structural imaging was performed on a 3T scanner, and volumes of subcortical structures were obtained using Freesurfer. There was no difference in total brain volume between ET and HC. However, compared to HC, significantly lower volumes of bilateral thalamus, hippocampus, and ventral diencephalon were observed in patients with ET. A significantly higher volume was observed in the right caudate nucleus, pallidum, amygdala, and bilateral putamen, and nucleus accumbens. No difference was observed between patients of ET with and without a rest tremor. Patients with ET have significant alterations in volumes of subcortical nuclei, which are not limited to the motor domain and include structures involved in cognitive and behavioral functions. These results add to the growing concept of a neurodegenerative pathophysiology of ET with abnormalities extending beyond the cerebellum.

Abnormal hippocampal subfields are associated with cognitive impairment in Essential Tremor.

Multi-domain cognitive impairment (CI) has been frequently described in patients with essential tremor (ET). However, the exact neuroanatomical basis for this impairment is uncertain. This study aims to ascertain the role of the hippocampal formation in cognitive impairment in ET. Forty patients with ET and 40 age, gender and education matched healthy controls (HC) were enrolled. Cognition was assessed using a structured neuropsychological battery and patients were categorized as ET with CI (ETCI) and ET without CI (ETNCI). Automatic segmentation of hippocampal subfields was performed using FreeSurfer 6.0. The obtained volumes were correlated with scores of neuropsychological tests. Significant atrophy of the left subiculum, CA4, granule-cell layer of dentate gyrus, right molecular layer, and hypertrophy of bilateral parasubiculum, right hippocampus-amygdala-transition-area, bilateral hippocampal tail (HT) and widening of right hippocampal fissure was observed in ET. Trends toward atrophy of right subiculum, and widening of left HF was also observed. Comparison of HC and ETCI revealed atrophy of right subiculum, hypertrophy of bilateral parasubiculum, HT, and widening of left HF. ETCI showed a trend toward widening of right HF. ETNCI had isolated left parasubicular hypertrophy and in comparison, to ETNCI the ETCI subgroup had atrophy of bilateral fimbria. Significant correlations were observed between the volumes of HT, HF, fimbria and scores of tests for executive function, working and verbal memory. Patients with ET have significant volumetric abnormalities of several hippocampal subfields and these abnormalities may be important contributors for some forms of cognitive impairment observed in ET.

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.

Abnormalities of white and grey matter in early multiple system atrophy: comparison of parkinsonian and cerebellar variants.

OBJECTIVE: Multiple system atrophy (MSA) is a neurodegenerative disorder with progressive motor and autonomic dysfunction. There is a paucity of information on the early neurostructural changes in MSA, especially its subtypes, MSA-P (patients with predominant parkinsonism) and MSA-C (patients with predominant cerebellar signs). This study investigates the abnormalities of grey matter (GM) and white matter (WM) in early MSA and its subtypes using multi-modal voxel-based analysis. MATERIALS AND METHODS: Twenty-six patients with MSA with duration of symptoms ≤ 2.5 years (mean duration: 1.6 ±0.9 years) were assessed clinically and with 3T MRI. Voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) were performed to identify the structural changes in MSA and its subtypes. The GM changes and diffusion parameters of WM tracts were correlated with the clinical scores. The results were compared with MRI of 25 age- and gender-matched healthy controls. RESULTS: The early structural changes in MSA included GM loss of the cerebellum and subcallosal gyrus with widespread involvement of supratentorial and infratentorial WM fibres. In MSA-C, GM loss was limited to the cerebellum with WM changes predominantly affecting the infratentorial WM and association tracts. In contrast, MSA-P did not demonstrate any GM loss and the WM involvement was mainly supratentorial. There was no significant correlation between structural changes and clinical severity score. CONCLUSION: In early MSA, WM microstructure was more affected than GM. These changes were greater in MSA-C than in MSA-P, suggesting variable deterioration in the subtypes of MSA. KEY POINTS: • Structural changes in early multiple system atrophy were evaluated using multi-modal neuroimaging. • White matter was more affected than grey matter in early MSA. • Clinical variables did not correlate with early structural changes.

Hippocampal subfield atrophy in patients with Parkinson's disease and psychosis.

Psychosis, manifested through formed visual hallucinations or minor hallucinations, is a common non-motor symptom of Parkinson's disease (PD). The pathogenesis of psychosis in PD remains unclear; however, is possibly linked to structural and functional alterations in the hippocampus. To explore the role of hippocampus in psychosis, a detailed hippocampal subfield analysis was performed on PD patients with (PD-P) and without psychosis (PD-NP), and healthy controls (HC). An automated subfield parcellation was performed on T1 MRI images of 141 subjects (PD-P:42, PD-NP:51, and HC:48). The volumes of 12 subfields on each side were estimated and analyzed between the three groups and were corrected for multiple comparisons using false discovery rates. The volumes were also correlated to psychosis severity and specific neuropsychological tests and finally were employed to predict the psychosis severity in PD-P using a support vector regression (SVR) model. Compared to controls, PD-NP group did not demonstrate any significant differences; however, the PD-P group had significantly lower total hippocampal volume. Bilateral molecular layer, granule cell-dentate gyrus, left subiculum, and hippocampal tail and right CA3, CA4, and HATA illustrated significantly lower volumes, while bilateral hippocampal fissure demonstrated a significant widening. Compared to PD-NP, the PD-P group had higher volume of the bilateral hippocampal fissures. Finally, SVR could significantly predict the psychosis severity from all the subfield volumes. Our findings indicate a higher degeneration of specific hippocampal subfields in PD-P compared to controls and a trend of higher volume of hippocampal fissures in PD-P group than in PD-NP.

Sex differences in the structural connectome of the human brain.

Sex differences in human behavior show adaptive complementarity: Males have better motor and spatial abilities, whereas females have superior memory and social cognition skills. Studies also show sex differences in human brains but do not explain this complementarity. In this work, we modeled the structural connectome using diffusion tensor imaging in a sample of 949 youths (aged 8-22 y, 428 males and 521 females) and discovered unique sex differences in brain connectivity during the course of development. Connection-wise statistical analysis, as well as analysis of regional and global network measures, presented a comprehensive description of network characteristics. In all supratentorial regions, males had greater within-hemispheric connectivity, as well as enhanced modularity and transitivity, whereas between-hemispheric connectivity and cross-module participation predominated in females. However, this effect was reversed in the cerebellar connections. Analysis of these changes developmentally demonstrated differences in trajectory between males and females mainly in adolescence and in adulthood. Overall, the results suggest that male brains are structured to facilitate connectivity between perception and coordinated action, whereas female brains are designed to facilitate communication between analytical and intuitive processing modes.

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.

Disrupted structural connectome is associated with both psychometric and real-world neuropsychological impairment in diffuse traumatic brain injury.

Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse white matter pathology. The present study aimed to detect alterations in structural network topology in TBI and relate them to cognitive and real-world behavioral impairment. Twenty-two people with moderate to severe TBI with mostly diffuse pathology and 18 demographically matched healthy controls were included in the final analysis. Graph theoretical network analysis was applied to diffusion tensor imaging (DTI) data to characterize structural connectivity in both groups. Neuropsychological functions were assessed by a battery of psychometric tests and the Frontal Systems Behavior Scale (FrSBe). Local connection-wise analysis demonstrated reduced structural connectivity in TBI arising from subcortical areas including thalamus, caudate, and hippocampus. Global network metrics revealed that shortest path length in participants with TBI was longer compared to controls, and that this reduced network efficiency was associated with worse performance in executive function and verbal learning. The shortest path length measure was also correlated with family-reported FrSBe scores. These findings support the notion that the diffuse form of neuropathology caused by TBI results in alterations in structural connectivity that contribute to cognitive and real-world behavioral impairment.

Probabilistic Tractography-Based Tremor Network Connectivity in Tremor Dominant Parkinson's Disease and Essential Tremor plus.

Background: The basal ganglia-thalamocortical (BGTC) and cerebello-thalamocortical (CTC) networks are implicated in tremor genesis; however, exact contributions across disorders have not been studied. Objective: Evaluate the structural connectivity of BGTC and CTC in tremor-dominant Parkinson's disease (TDPD) and essential tremor plus (ETP) with the aid of probabilistic tractography and graph theory analysis. Methods: Structural connectomes of the BGTC and CTC were generated by probabilistic tractography for TDPD (n = 25), ETP (ET with rest tremor, n = 25), and healthy control (HC, n = 22). The Brain Connectivity Toolbox was used for computing standard topological graph measures of segregation, integration, and centrality. Tremor severity was ascertained using the Fahn-Tolosa-Marin tremor rating scale (FTMRS). Results: There was no difference in total FTMRS scores. Compared with HC, TDPD had a lower global efficiency and characteristic path length. Abnormality in segregation, integration, and centrality of bilateral putamen, globus pallidus externa (GPe), and GP interna (GPi), with reduction of centrality of right caudate and cerebellar lobule 8, was observed. ETP showed reduction in segregation and integration of right GPe and GPi, ventrolateral posterior nucleus, and centrality of right putamen, compared with HC. Differences between TDPD and ETP were a reduction of strength of the right putamen, and lower clustering coefficient, local efficiency, and strength of the left GPi in TDPD. Conclusions: Contrary to expectations, TDPD and ETP may not be significantly different with regard to tremor pathogenesis, with definite overlaps. There may be fundamental similarities in network disruption across different tremor disorders with the same tremor activation patterns, along with disease-specific changes.

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.