Association of MRI Measurements with Cognitive Outcomes After STN-DBS in Parkinson's Disease

Developments in the prediction of cognitive changes following deep brain stimulation in persons with Parkinson's disease

INTRODUCTION

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) motor symptoms that improves function and quality of life in appropriately selected patients. Because mild to moderate cognitive declines can follow DBS and impact quality of life in a minority of patients, an important consideration involves the cognitive deficit and its prediction.

AREAS COVERED

The author briefly summarizes cognitive outcomes from DBS and reviews in more detail the risks/predictors of post-DBS cognitive dysfunction by mainly focusing on work published between 2018 and 2024 and using comprehensive neuropsychological (NP) evaluations. Most publications concern bilateral subthalamic nucleus (STN) DBS. Comment is offered on challenges and potential avenues forward.

EXPERT OPINION

STN DBS is relatively safe cognitively but declines occur especially in verbal fluency and executive function/working memory. Numerous predictors and risk factors for cognitive outcomes have been identified (age and pre-operative neuropsychological status appear the most robust) but precise risk estimates cannot yet be confidently offered. Future studies should employ study center consortia, follow uniform reporting criteria (to be developed), capitalize on advances in stimulation, biomarkers, and artificial intelligence, and address DBS in diverse groups. Advances offer an avenue to investigate the amelioration of cognitive deficits in PD using neuromodulation.

No evidence for an association of voxel-based morphometry with short-term non-motor outcomes in deep brain stimulation for Parkinson's disease

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy in advanced Parkinson's disease (PD). Motor and non-motor outcomes, however, show considerable inter-individual variability. Preoperative morphometry-based metrics have recently received increasing attention to explain treatment effects. As evidence for the prediction of non-motor outcomes is limited, we sought to investigate the association between metrics of voxel-based morphometry and short-term non-motor outcomes following STN-DBS in this prospective open-label study. Forty-nine PD patients underwent structural MRI and a comprehensive clinical assessment at preoperative baseline and 6-month follow-up. Voxel-based morphometry was used to assess associations between cerebral volume and non-motor outcomes corrected for multiple comparisons using a permutation-based approach. We replicated existing results associating volume loss of the superior frontal cortex with subpar motor outcomes. Overall non-motor burden, however, was not significantly associated with morphometric features, limiting its use as a marker to inform patient selection and holistic preoperative counselling.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

The altered spontaneous neural activity in patients with Parkinson's disease and its predictive value for the motor improvement of deep brain stimulation

BACKGROUND

This study aims to investigate the altered spontaneous neural activity in patients with Parkinson's disease (PD) revealed by amplitudes of low-frequency fluctuations (ALFF) of resting-state fMRI, and the feasibility of using ALFF as neuroimaging predictors for motor improvement after bilateral subthalamic nucleus (STN) deep brain stimulation (DBS).

METHODS

Fourty-four patients and 44 healthy controls were included in this study. First, the ALFF of patients with PD was compared with that of controls; then significant clusters were correlated with motor improvement after DBS (unified Parkinson's disease rating scale (UPDRS-III)) and other clinical variables. Second, regression and classification of the machine learning models were conducted to predict motor improvement after DBS. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the classification model.

RESULTS

Compared with healthy controls, patients with PD showed increased ALFF in the bilateral motor area and decreased ALFF in the bilateral temporal cortex and cerebellum. The Hoehn-Yahr stages correlated with ALFF within the bilateral cerebellum (p = 0.021), and UPDRS-III improvement correlated with ALFF in the left (p < 0.001) and right (p = 0.005) motor areas. The regression model showed a significant correlation between the predicted and observed UPDRS-III changes (R = 0.65, p < 0.001). The ROC analysis revealed an area under the curve (AUC) of 0.94 which differentiated moderate and superior DBS responders.

CONCLUSION

The results revealed altered ALFF patterns in patients with PD and their correlations with clinical variables. Both binary and continuous ALFF can potentially serve as predictive biomarkers for DBS response.

A systematic review of brain morphometry related to deep brain stimulation outcome in Parkinson's disease

While the efficacy of deep brain stimulation (DBS) is well-established in Parkinson’s Disease (PD), the benefit of DBS varies across patients. Using imaging features for outcome prediction offers potential in improving effectiveness, whereas the value of presurgical brain morphometry, derived from the routinely used imaging modality in surgical planning, remains under-explored. This review provides a comprehensive investigation of links between DBS outcomes and brain morphometry features in PD. We systematically searched PubMed and Embase databases and retrieved 793 articles, of which 25 met inclusion criteria and were reviewed in detail. A majority of studies (24/25), including 1253 of 1316 patients, focused on the outcome of DBS targeting the subthalamic nucleus (STN), while five studies included 57 patients receiving globus pallidus internus (GPi) DBS. Accumulated evidence showed that the atrophy of motor cortex and thalamus were associated with poor motor improvement, other structures such as the lateral-occipital cortex and anterior cingulate were also reported to correlated with motor outcome. Regarding non-motor outcomes, decreased volume of the hippocampus was reported to correlate with poor cognitive outcomes. Structures such as the thalamus, nucleus accumbens, and nucleus of basalis of Meynert were also reported to correlate with cognitive functions. Caudal middle frontal cortex was reported to have an impact on postsurgical psychiatric changes. Collectively, the findings of this review emphasize the utility of brain morphometry in outcome prediction of DBS for PD. Future efforts are needed to validate the findings and demonstrate the feasibility of brain morphometry in larger cohorts.

Magnetic resonance-guided focused ultrasound for Parkinson's disease since ExAblate, 2016-2019: a systematic review

INTRODUCTION

ExAblate received FDA approval for treatment of a range of movement disorders in 2016, including tremor-dominant Parkinson's disease (TDPD), dyskinetic PD, and essential tremor. This incisionless device allows for magnetic resonance-guided focused ultrasound (MRgFUS) for ablation of several regions of interest. Current studies should aim to measure pre- and post-operative neurocognitive functioning to better understand MRgFUS in PD and how it compares to deep brain stimulation, which has known cognitive risks among certain populations.

METHODS

PubMed, CINAHL, PsycINFO, and Cochrane Library databases were searched from January 2016 to January 2020. Guidelines for Preferred Reporting Items for Systematic Review and Meta-Analyses were used to review clinical trials comprehensively assessing pre- and post-operative neurocognitive functioning in PD patients undergoing MRgFUS. Due to limited extant literature in this area, TDPD was expanded to PD with severe dyskinesia.

RESULTS

Twenty-two abstracts were reviewed following removal of duplicates. After full-text review of eight articles, only two studies included comprehensive neuropsychological evaluations of PD patients undergoing MRgFUS thalamotomy or pallidotomy. Most excluded studies used only brief cognitive screeners to assess functioning. Cognitive declines appear to be minimal following MRgFUS in PD, with exceptions in verbal fluency and inhibition. These results are limited by sample size and sample diversity.

CONCLUSIONS

Significant methodological gaps were inadvertently discovered. Few studies to-date have administered comprehensive neuropsychological batteries to ascertain MRgFUS risks to neurocognitive functioning in PD. Studies must extend beyond brief screeners when assessing PD populations vulnerable to decline. Furthermore, consensus on a comprehensive battery would better serve replicability and the ability to engage in useful meta-analyses.