Enlarging and shrinking focal perivascular spaces.

BACKGROUND AND PURPOSE: Perivascular spaces (PVS) are interstitial fluid-filled spaces surrounding blood vessels traversing the deep gray nuclei and white matter of the brain. These are commonly encountered on CT and MR imaging and are generally asymptomatic and of no clinical significance. However, occasional changes in the size of focal PVS, for example, when enlarging, may mimic pathologies including neoplasms and infections, hence potentially confounding radiological interpretation. Given these potential diagnostic issues, we sought to better characterize common clinical and imaging features of focal PVS demonstrating size fluctuations. MATERIALS AND METHODS: Upon institutional approval, we retrospectively identified 4 cases demonstrating PVS with size changes at our institution. To supplement our cases, we also performed a literature review, which identified an additional 14 cases. Their clinical and imaging data were analyzed to identify characteristic features. RESULTS: Of the 18 total cases (including the 4 institutional cases), 10 cases increased and 8 decreased in size. These focal PVS ranged from 0.4-4.5 cm in size. Whereas a decrease in size did not represent a diagnostic issue, focal increase in size of PVS led to concerning differential diagnoses in at least 30% of the radiology reports. These enlarging PVS were most found in the basal ganglia and temporal lobe, and in patients with previous brain radiation treatment. CONCLUSION: Focal size change of PVS can occur, especially years after brain radiation treatment. Being cognizant of this benign finding is important to consider in the differential diagnosis to avoid undue patient anxiety or unnecessary medical intervention.

Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology.

BACKGROUND AND PURPOSE: Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field. MATERIALS AND METHODS: We performed a bibliometric analysis of the American Journal of Neuroradiology; the journal was queried for original research articles published since inception (January 1, 1980) to December 3, 2022 that contained any of the following key terms: "machine learning," "artificial intelligence," "radiomics," "deep learning," "neural network," "generative adversarial network," "object detection," or "natural language processing." Articles were screened by 2 independent reviewers, and categorized into statistical modeling (type 1), AI/ML development (type 2), both representing developmental research work but without a direct clinical integration, or end-user application (type 3), which is the closest surrogate of potential AI/ML integration into day-to-day practice. To better understand the limiting factors to type 3 articles being published, we analyzed type 2 articles as they should represent the precursor work leading to type 3. RESULTS: A total of 182 articles were identified with 79% being nonintegration focused (type 1 n = 53, type 2 n = 90) and 21% (n = 39) being type 3. The total number of articles published grew roughly 5-fold in the last 5 years, with the nonintegration focused articles mainly driving this growth. Additionally, a minority of type 2 articles addressed bias (22%) and explainability (16%). These articles were primarily led by radiologists (63%), with most (60%) having additional postgraduate degrees. CONCLUSIONS: AI/ML publications have been rapidly increasing in neuroradiology with only a minority of this growth being attributable to end-user application. Areas identified for improvement include enhancing the quality of type 2 articles, namely external validation, and addressing both bias and explainability. These results ultimately provide authors, editors, clinicians, and policymakers important insights to promote a shift toward integrating practical AI/ML solutions in neuroradiology.

Successful magnetic resonance-guided focused ultrasound treatment of tremor in patients with a skull density ratio of 0.4 or less.

OBJECTIVE: The use of magnetic resonance-guided focused ultrasound (MRgFUS) for the treatment of tremor-related disorders and other novel indications has been limited by guidelines advocating treatment of patients with a skull density ratio (SDR) above 0.45 ± 0.05 despite reports of successful outcomes in patients with a low SDR (LSDR). The authors' goal was to retrospectively analyze the sonication strategies, adverse effects, and clinical and imaging outcomes in patients with SDR ≤ 0.4 treated for tremor using MRgFUS. METHODS: Clinical outcomes and adverse effects were assessed at 3 and 12 months after MRgFUS. Outcomes and lesion location, volume, and shape characteristics (elongation and eccentricity) were compared between the SDR groups. RESULTS: A total of 102 consecutive patients were included in the analysis, of whom 39 had SDRs ≤ 0.4. No patient was excluded from treatment because of an LSDR, with the lowest being 0.22. Lesioning temperatures (> 52°C) and therapeutic ablations were achieved in all patients. There were no significant differences in clinical outcome, adverse effects, lesion location, and volume between the high SDR group and the LSDR group. SDR was significantly associated with total energy (rho = -0.459, p < 0.001), heating efficiency (rho = 0.605, p < 0.001), and peak temperature (rho = 0.222, p = 0.025). CONCLUSIONS: The authors' results show that treatment of tremor in patients with an LSDR using MRgFUS is technically possible, leading to a safe and lasting therapeutic effect. Limiting the number of sonications and adjusting the energy and duration to achieve the required temperature early during the treatment are suitable strategies in LSDR patients.

Computational modeling of whole-brain dynamics: a review of neurosurgical applications.

A major goal of modern neurosurgery is the personalization of treatment to optimize or predict individual outcomes. One strategy in this regard has been to create whole-brain models of individual patients. Whole-brain modeling is a subfield of computational neuroscience that focuses on simulations of large-scale neural activity patterns across distributed brain networks. Recent advances allow for the personalization of these models by incorporating distinct connectivity architecture obtained from noninvasive neuroimaging of individual patients. Local dynamics of each brain region are simulated with neural mass models and subsequently coupled together, considering the subject's empirical structural connectome. The parameters of the model can be optimized by comparing model-generated and empirical data. The resulting personalized whole-brain models have translational potential in neurosurgery, allowing investigators to simulate the effects of virtual therapies (such as resections or brain stimulations), assess the effect of brain pathology on network dynamics, or discern epileptic networks and predict seizure propagation in silico. The information gained from these simulations can be used as clinical decision support, guiding patient-specific treatment plans. Here the authors provide an overview of the rapidly advancing field of whole-brain modeling and review the literature on neurosurgical applications of this technology.