Low-Intensity Ultrasound Ameliorates Brain Organoid Integration and Rescues Microcephaly Deficits

Low-intensity Ultrasound Promotes Brain Organoid Integration and Improves Microcephaly Defects Background of the Study Brain organoids are generated through the differentiation of pluripotent stem cells (PSCs) and exhibit impressive cellular diversity, capable of mimicking functional networks similar to the human brain. These organoid methods hold ...

Mapping Interictal Discharges Using Intracranial EEG-fMRI to Predict Postsurgical Outcomes

Mapping Interictal Discharges Using Intracranial EEG-fMRI to Predict Postsurgical Outcomes

Using Intracranial EEG-fMRI Mapping of Intermittent Discharges to Predict Epilepsy Surgery Outcomes Background and Objective Epilepsy is a common neurological disorder, and many patients are unresponsive to pharmacological treatments, making surgery one of the primary therapeutic options. However, accurately localizing the seizure onset zone (SOZ) ...

Distinct Virtual Histology of Grey Matter Atrophy in Four Neuroinflammatory Diseases

Research Background The core focus of this study is the manifestation of gray matter atrophy in neuroinflammatory diseases. Gray matter atrophy typically appears in four types of neuroinflammatory demyelinating diseases: Multiple Sclerosis (MS), Neuromyelitis Optica Spectrum Disorders (NMOSD) positive (AQP4+) and negative (AQP4-) for aquaporin-4 an...

Diffusion Model Optimization with Deep Learning

Diffusion Model Optimization with Deep Learning

Dimond: A Study on Optimizing Diffusion Models through Deep Learning Academic Background In brain science and clinical applications, Diffusion Magnetic Resonance Imaging (dMRI) is an essential tool for non-invasively mapping the microstructure and neural connectivity of brain tissue. However, accurately estimating parameters of the diffusion signal...

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning Research Background and Motivation Diffusion Tensor Imaging (DTI) boasts unparalleled advantages in mapping the microstructure and structural connectivity of live human brain tissue. However, traditional DTI techniques require extensive angular sampling, leading to pr...

Scalp nerve block alleviates headaches associated with sonication during transcranial magnetic resonance–guided focused ultrasound

In this academic paper, the authors attempt to address the common headache complication during magnetic resonance-guided focused ultrasound (mrgFUS) treatment. Headache is a common complication, and in severe cases, it may even cause patients to be unable to tolerate ultrasound radiation and terminate the treatment. There is currently no establishe...

Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation

This paper reports on research investigating how zebra finches recover their complex learned behaviors following large-scale neuronal perturbations. The researchers selectively disrupted the activity of the projection neurons in the HVC (hyperpallium ventralis) region critical for song sequence generation in zebra finches using genetic tools, leadi...

Closed-loop recruitment of striatal interneurons prevents compulsive-like grooming behaviors

Closed-loop recruitment of striatal interneurons prevents compulsive-like grooming behaviors

Obsessive-compulsive behaviors have long been associated with overactivation of the striatum. The GABA-ergic parvalbumin-positive interneurons (PVIs) in the striatum play a crucial role in regulating striatal activity and inhibiting inappropriate motivated behaviors. To investigate the potential role of striatal PVIs in regulating obsessive-compuls...

Dimensionality reduction beyond neural subspaces with slice tensor component analysis

Background Introduction: Large-scale neural recording data can typically be described by patterns of co-activated neurons. However, the view of constraining neural activity variability to a fixed low-dimensional subspace may overlook higher-dimensional structures, such as fixed neural sequences or slowly evolving latent spaces. This study argues th...