Citations

Abstract White matter hyperintensities (WMH) and infarcts found on magnetic resonance imaging (MR infarcts) are common biomarkers of cerebrovascular disease. In this review, we summarize the methods, publications, and conclusions stemming from the Alzheimer's Disease Neuroimaging Initiative (ADNI) related to these measures. We combine analysis of WMH and MR infarct data from across the three main ADNI cohorts with a review of existing literature discussing new methodologies and scientific findings derived from these data. Although ADNI inclusion criteria were designed to minimize vascular risk factors and disease, data across all the ADNI cohorts found consistent trends of increasing WMH volumes associated with advancing age, female sex, and cognitive impairment. ADNI, initially proposed as a study to investigate biomarkers of AD pathology, has also helped elucidate the impact of asymptomatic cerebrovascular brain injury on cognition within a cohort relatively free of vascular disease. Future ADNI work will emphasize additional vascular biomarkers. Highlights White matter hyperintensities (WMHs) are common to advancing age and likely reflect brain vascular injury among older individuals. WMH and to a lesser extent, magnetic resonance (MR) infarcts, affect risk for transition to cognitive impairment. WMHs and MR infarcts are present, even among Alzheimer's Disease Neuroimaging Initiative (ADNI) participants highly selected to have Alzheimer's disease (AD) as the primary pathology. WMH burden in ADNI is greater among individuals with cognitive impairment and has been associated with AD neurodegenerative markers and cerebral amyloidosis. The negative additive effects of cerebrovascular disease appear present, even in select populations, and future biomarker work needs to further explore this relationship.

Abstract Glioblastoma (GBM) is an aggressive form of cancer, comprising ≈80% of malignant brain tumors. However, there are no effective treatments for GBM due to its heterogeneity and the presence of the blood‐brain barrier (BBB), which restricts the delivery of therapeutics to the brain. Despite in vitro models contributing to the understanding of GBM, conventional 2D models oversimplify the complex tumor microenvironment. Organ‐on‐a‐chip (OoC) models have emerged as promising platforms that recapitulate human tissue physiology, enabling disease modeling, drug screening, and personalized medicine. There is a sudden increase in GBM‐on‐a‐chip models that can significantly advance the knowledge of GBM etiology and revolutionize drug development by reducing animal testing and enhancing translation to the clinic. In this review, an overview of GBM‐on‐a‐chip models and their applications is reported for drug screening and discussed current challenges and potential future directions for GBM‐on‐a‐chip models.

Automated detection of tonic seizures using wearable movement sensor and artificial neural network Larsen SA, Johansen DH, Beniczky S. Epilepsia . 2024 Jul 30. doi: 10.1111/epi.18077 . Epub ahead of print. PMID: 39076045. Although several validated wearable devices are available for the detection of generalized tonic-clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report the development and validation of an artificial neural network (ANN) model for automated detection of tonic seizures with visible clinical manifestation using a wearable wristband movement sensor (accelerometer and gyroscope). The dataset prospectively recorded for this study included 70 tonic seizures from 15 patients (7 males, age 3-46 years, median = 19 years). We trained an ANN model to detect tonic seizures. The independent test dataset comprised nocturnal recordings, including 10 tonic seizures from 3 patients and additional (distractor) data from 3 subjects without seizures. The ANN model detected nocturnal tonic seizures with visible clinical manifestation with a sensitivity of 100% (95% confidence interval = 69%-100%) and with an average false alarm rate of 0.16/night. The mean detection latency was 14.1 s (median = 10 s), with a maximum of 47 s. These data suggest that nocturnal tonic seizures can be reliably detected with movement sensors using ANN. Large-scale, multicenter prospective (phase 3) trials are needed to provide compelling evidence for the clinical utility of this device and detection algorithm.

Objective To describe the immediate clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) in treating long COVID symptoms. Long COVID currently impacts approximately 5.3% of US adults, presenting with persistent fatigue, depression, anxiety, cognitive impairments, and social function decline. Repetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex has improved these symptoms in some studies. Methods Five patients (ages 52–63 years, 3 female patients) underwent accelerated rTMS using intermittent theta burst stimulation (600 pulses over 3 minutes 20 seconds, 80%–120% of resting motor threshold) to the left dorsolateral prefrontal cortex. The treatment consisted of twice-daily sessions over 20–30 treatments. Outcomes were measured using the Patient-Reported Outcomes Measurement and Information System (PROMIS) questionnaires, assessing fatigue, depression, anxiety, cognitive function, and social function at baseline and immediately posttreatment. Results One patient discontinued immediately due to intolerable scalp pain. Posttreatment PROMIS scores showed the following: fatigue decreased from 74.5 to 61.8, depression from 60.3 to 51.5, and anxiety from 62.0 to 54.0. Scores increased for cognitive (26.8 to 32.3) and social (31.0 to 32.5) function. These changes, although not statistically significant, indicate a trend toward symptom reduction. Subjectively, 3 of 4 patients who completed the rTMS course reported improvements. Conclusion This case series suggests that rTMS may offer symptomatic relief in long COVID patients. Although the observed improvements in PROMIS were not statistically significant, even minimal improvements are clinically significant for patients enduring prolonged suffering. These preliminary findings are encouraging and underscore the need for further research with larger cohorts to validate the efficacy of rTMS in long COVID symptom relief.