56 research outputs found

    An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment

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    One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and personalized therapeutics. In this work, we present an explainable, geometric, weighted-graph attention neural network (xGW-GAT) to identify functional networks predictive of the progression of gait difficulties in individuals with PD. xGW-GAT predicts the multi-class gait impairment on the MDS Unified PD Rating Scale (MDS-UPDRS). Our computational- and data-efficient model represents functional connectomes as symmetric positive definite (SPD) matrices on a Riemannian manifold to explicitly encode pairwise interactions of entire connectomes, based on which we learn an attention mask yielding individual- and group-level explainability. Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals with PD, xGW-GAT identifies functional connectivity patterns associated with gait impairment in PD and offers interpretable explanations of functional subnetworks associated with motor impairment. Our model successfully outperforms several existing methods while simultaneously revealing clinically-relevant connectivity patterns. The source code is available at https://github.com/favour-nerrise/xGW-GAT .Comment: Accepted by the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). MICCAI Student-Author Registration (STAR) Award. 11 pages, 2 figures, 1 table, appendix. Source Code: https://github.com/favour-nerrise/xGW-GA

    Substantia Nigra Volume Dissociates Bradykinesia and Rigidity from Tremor in Parkinsonā€™s Disease: A 7 Tesla Imaging Study

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    Background: In postmortem analysis of late stage Parkinsonā€™s disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of bradykinesia and rigidity, but not tremor. Objective: To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI. Methods: We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for bradykinesia/rigidity, tremor, and postural instability/gait difficulty). Results: We found that smaller SN correlated with longer disease duration (rā€Š=ā€Šā€“0.49, pā€Š=ā€Š0.004), more severe MDS-UPDRS motor score (rā€Š=ā€Šā€“0.42, pā€Š=ā€Š0.016), and more severe bradykinesia-rigidity subscore (rā€Š=ā€Šā€“0.47, pā€Š=ā€Š0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume

    Substantia Nigra Volume Dissociates Bradykinesia and Rigidity from Tremor in Parkinsonā€™s Disease: A 7 Tesla Imaging Study

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    Background: In postmortem analysis of late stage Parkinsonā€™s disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of bradykinesia and rigidity, but not tremor. Objective: To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI. Methods: We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for bradykinesia/rigidity, tremor, and postural instability/gait difficulty). Results: We found that smaller SN correlated with longer disease duration (rā€Š=ā€Šā€“0.49, pā€Š=ā€Š0.004), more severe MDS-UPDRS motor score (rā€Š=ā€Šā€“0.42, pā€Š=ā€Š0.016), and more severe bradykinesia-rigidity subscore (rā€Š=ā€Šā€“0.47, pā€Š=ā€Š0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume

    Lunar Volatiles and Solar System Science

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    Understanding the origin and evolution of the lunar volatile system is not only compelling lunar science, but also fundamental Solar System science. This white paper (submitted to the US National Academies' Decadal Survey in Planetary Science and Astrobiology 2023-2032) summarizes recent advances in our understanding of lunar volatiles, identifies outstanding questions for the next decade, and discusses key steps required to address these questions

    Reliability of remote National Alzheimer's Coordinating Center Uniform Data Set data

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    Abstract INTRODUCTION The National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) neuropsychological battery is being used to track cognition in participants across the country, but it is unknown if scores obtained through remote administration can be combined with data obtained in person. METHODS The remote UDS battery includes the blind version of the Montreal Cognitive Assessment (MoCA), Number Span, Semantic and Phonemic Fluency, and Craft Story. For these tests, we assessed intraclass correlation coefficients (ICCs) between inā€person and remote scores in 3838 participants with both inā€person and remote UDS assessments, and we compared annual score changes between modalities in a subset that had two remote assessments. RESULTS All tests exhibited moderate to good reliability between modalities (ICCsĀ =Ā 0.590ā€“0.787). Annual score changes were also comparable between modalities except for Craft Story Immediate Recall, Semantic Fluency, and Phonemic Fluency. DISCUSSION Our findings generally support combining remote and inā€person scores for the majority of UDS tests

    Distinct alterations in Parkinson's medication-state and disease-state connectivity

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    Altered brain connectivity has been described in people with Parkinson's disease and in response to dopaminergic medications. However, it is unclear whether dopaminergic medications primarily ā€˜normalizeā€™ disease related connectivity changes or if they induce unique alterations in brain connectivity. Further, it is unclear how these disease- and medication-associated changes in brain connectivity relate differently to specific motor manifestations of disease, such as bradykinesia/rigidity and tremor. In this study, we applied a novel covariance projection approach in combination with a bootstrapped permutation test to resting state functional MRI data from 57 Parkinson's disease and 20 healthy control participants to determine the Parkinson's medication-state and disease-state connectivity changes associated with different motor manifestations of disease. First, we identified brain connections that best classified Parkinson's disease ON versus OFF dopamine and Parkinson's disease versus healthy controls, achieving 96.9Ā±5.9% and 72.7Ā±12.4% classification accuracy, respectively. Second, we investigated the connections that significantly contribute to the classifications. We found that the connections greater in Parkinson's disease OFF compared to ON dopamine are primarily between motor (cerebellum and putamen) and posterior cortical regions, such as the posterior cingulate cortex. By contrast, connections that are greater in ON compared to OFF dopamine are between the right and left medial prefrontal cortex. We also identified the connections that are greater in healthy control compared to Parkinson's disease and found the most significant connections are associated with primary motor regions, such as the striatum and the supplementary motor area. Notably, these are different connections than those identified in Parkinson's disease OFF compared to ON. Third, we determined which of the Parkinson's medication-state and disease-state connections are associated with the severity of different motor symptoms. We found two connections correlate with both bradykinesia/rigidity severity and tremor severity, whereas four connections correlate with only bradykinesia/rigidity severity, and five connections correlate with only tremor severity. Connections that correlate with only tremor severity are anchored by the cerebellum and the supplemental motor area, but only those connections that include the supplemental motor area predict dopaminergic improvement in tremor. Our results suggest that dopaminergic medications do not simply ā€˜normalizeā€™ abnormal brain connectivity associated with Parkinson's disease, but rather dopamine drives distinct connectivity changes, only some of which are associated with improved motor symptoms. In addition, the dissociation between of connections related to severity of bradykinesia/rigidity versus tremor highlights the distinct abnormalities in brain circuitry underlying these specific motor symptoms. Keywords: Parkinson's disease, Classification, Functional magnetic resonance imaging, Dopamine, Riemannian geometr
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