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The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

In this study, the authors use aerosol optical depth data to determine if aerosol levels were lower in major metropolitan areas around the world during the COVID-19 pandemic.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.

The authors looked at different models of semantic segmentation to determine which may be best used in the future for segmentation of CT scans to help diagnose certain conditions.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.

The authors looked at the effects of commonly used anesthetics in rodents on brain activity (specifically quasi-periodic patterns). Understanding effects on brain activity is important for researchers to understand when choosing rodent models for disease.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

The authors trained a machine learning model to detect kidney stones based on characteristics of urine. This method would allow for detection of kidney stones prior to the onset of noticeable symptoms by the patient.

Control of voluntary and involuntary movements is one of the most important aspects of human neurological function, but the mechanisms of motor control are not completely understood. In this study, the authors use transcranial magnetic stimulation (TMS) to stimulate a portion of the motor cortex while subjects performed either discrete (e.g. throwing) or rhythmic (e.g. walking) movements. By recording electrical activity in the muscles during this process, the authors showed that motor evoked potentials (MEPs) measured in the muscles during TMS stimulation are larger in amplitude for discrete movements than for rhythmic movements. Interestingly, they also found that MEPs during transitions between rhythmic and discrete movements were nearly identical and larger in amplitude than those recorded during either rhythmic or discrete movements. This research provides important insights into the mechanisms of neurological control of movement and will serve as the foundation for future studies to learn more about temporal variability in neural activity during different movement types.