Search Results (354)

A wide range of strategies have been developed to modulate dysfunctional brain activities. This narrative review provides a comparative analysis of biophysical, genetic, and biological neuromodulation approaches with an emphasis on their known or unknown molecular targets and translational potential. The review incorporates data from both preclinical and clinical studies covering deep brain stimulation, transcranial electrical and magnetic stimulation, focused ultrasound, chemogenetics, optogenetics, magnetogenetics, and toxin-based neuromodulation. Each method was assessed based on specificity, safety, reversibility, and mechanistic clarity. Biophysical methods are widely used in clinical practice but often rely on empirical outcomes due to undefined molecular targets. Genetic tools offer cell-type precision in experimental systems but face translational barriers related to delivery and safety. Biological agents, such as botulinum neurotoxins, provide long-lasting yet reversible inhibition via well-characterized molecular pathways. However, they require stereotaxic injections and remain invasive. To overcome individual limitations and improve targeting, delivery, and efficacy, there is a growing interest in the synthesis of multiple approaches. This review highlights a critical gap in the mechanistic understanding of commonly used methods. Addressing this gap by identifying molecular targets may help to improve therapeutic precision. This concise review could be valuable for researchers looking to enter the evolving field of the neuromodulation of brain function. Full article

This review article aims to provide an overview of superconducting magnetic quantum sensors and their applications in the biomedical field, particularly in the neurological field. These quantum sensors are based on superconducting quantum interference devices (SQUIDs), the operating principles of which will be presented along with the most relevant characteristics. Emphasis will be placed on the magnetic flux and magnetic field noise, which are essential for applications, especially brain investigations requiring ultra-high magnetic field sensitivity. The main configurations of SQUID magnetometers used for highly sensitive applications will be shown, stressing their design aspects. In particular, the configurations based on the superconducting flux transformer and the multiloop will be explained. We will discuss the most critical application of SQUID magnetometers, magnetoencephalography, which measures the weak magnetic signals produced by neuronal currents. Starting from the realization of a multichannel system for magnetoencephalography, we will present an accurate comparison with recent systems using optically pumped magnetometers. Finally, we will discuss the main clinical applications of magnetoencephalography. Full article

Alzheimer’s disease (AD) is a leading cause of dementia, characterized by progressive cognitive and language impairments that significantly impact communication and quality of life. Neuromodulation techniques, including repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), and deep brain stimulation (DBS), have emerged as promising interventions. This study employs bibliometric analysis to evaluate global research trends in neuromodulation treatments for AD-related language impairments. A total of 88 publications from the Web of Science Core Collection (2006–2024) were analyzed using bibliometric methods. Key indicators such as publication trends, citation patterns, collaboration networks, and research themes were examined to map the intellectual landscape of this field. The analysis identified 580 authors across 65 journals, with an average of 34.82 citations per article. Nearly half of the publications were produced after 2021, indicating rapid recent growth. The findings highlight a predominant focus on non-invasive neuromodulation methods, particularly rTMS and tDCS, within neurosciences and neurology. While research activity is increasing, significant challenges persist, including ethical concerns, operational constraints, and the translational gap between research and clinical applications. This study provides insights into the current research landscape and future directions for neuromodulation in AD-related language impairments. The results emphasize the need for novel neuromodulation techniques and interdisciplinary collaboration to enhance therapeutic efficacy and clinical integration. Full article

For automatic tumor segmentation in magnetic resonance imaging (MRI), deep learning offers very powerful technical support with significant results. However, the success of supervised learning is strongly dependent on the quantity and accuracy of labeled training data, which is challenging to acquire in MRI. Semi-supervised learning approaches have arisen to tackle this difficulty, yielding comparable brain tumor segmentation outcomes with fewer labeled samples. This literature review explores key semi-supervised learning techniques for medical image segmentation, including pseudo-labeling, consistency regularization, generative adversarial networks, contrastive learning, and holistic methods. We specifically examine the application of these approaches in brain tumor MRI segmentation. Our findings suggest that semi-supervised learning can outperform traditional supervised methods by providing more effective guidance, thereby enhancing the potential for clinical computer-aided diagnosis. This literature review serves as a comprehensive introduction to semi-supervised learning in tumor MRI segmentation, including glioma segmentation, offering valuable insights and a comparative analysis of current methods for researchers in the field. Full article

Phase images from gradient echo MRI sequences reflect underlying magnetic field inhomogeneities but are inherently wrapped within the range of −π to π, requiring phase unwrapping to recover the true phase. In this study, we present DIP-UP (Deep Image Prior for Unwrapping Phase), a framework designed to refine two pre-trained deep learning models for phase unwrapping: PHUnet3D and PhaseNet3D. We compared the DIP-refined models to their original versions, as well as to the conventional PRELUDE algorithm from FSL, using both simulated and in vivo brain data. Results demonstrate that DIP refinement improves unwrapping accuracy (achieving ~99%) and robustness to noise, surpassing the original networks and offering comparable performance to PRELUDE while being over three times faster. This framework shows strong potential for enhancing downstream MRI phase-based analyses. Full article

Background: Invasive and non-invasive neuromodulation in psychiatry represents a burgeoning field that leverages advanced neuromodulation techniques to address substance use disorders (SUDs). Aims: This narrative review synthesizes findings from multiple reviews to evaluate the efficacy of neuromodulation in treating SUDs. Methods: A comprehensive literature search was conducted between December 2024 and April 2025, focusing on systematic reviews and meta-analyses that examined various neuromodulation modalities, including repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), and deep brain stimulation (DBS). The selected reviews were analyzed to identify common themes, outcomes, and gaps in the current understanding of these treatments for SUDs. Results: 11 reviews met the final inclusion criteria; 5 focused on non-invasive neuromodulation (rTMS, tDCS) and 6 on invasive neuromodulation (DBS). Non-invasive neurostimulation was associated with modest improvements in craving and cognitive dysfunction in individuals with SUDs. Similarly, invasive neuromodulation (DBS), through high-frequency stimulation of the bilateral nucleus accumbens, appeared to reduce cravings and improve comorbid psychiatric symptoms in both preclinical and human studies. Importantly, small sample sizes, heterogeneity in targets and stimulation protocols, and short follow-up periods significantly limit the generalizability of current findings from both non-invasive and invasive neuromodulation studies. Conclusions: As novel and more effective therapies for the treatment of SUD are desperately needed, procedural interventional psychiatry holds promise. However, despite encouraging results, existing evidence is still preliminary, and larger, rigorously designed studies are warranted to further establish the safety and efficacy of neuromodulatory interventions for SUD treatment. Full article

Magnetic resonance imaging (MRI) is a powerful medical imaging technique used for acquiring high-resolution anatomical and functional images of the human body. With the development of an 11.74 Tesla (T) human MRI system at our facility, we are designing novel radiofrequency (RF) coils optimized for brain imaging at ultra-high fields. To meet specific absorption rate (SAR) safety limits, this study focuses on localized imaging of individual brain lobes rather than whole-brain array designs. Conventional loop coils, while widely used, offer limited |B₁|-field uniformity at 500 MHz—the Larmor frequency at 11.74 T, which can reduce image quality. Therefore, it is important to develop antennas and coils for highly uniform fields. As an alternative, we propose an elliptical microstrip design, which combines the compact resonant properties of microstrips with the enhanced field coverage provided by loop geometry. We simulated a three-element elliptical microstrip array and compared its performance with a conventional loop coil. The proposed design demonstrated improved magnetic field uniformity and coverage across targeted brain regions. Preliminary bench-top validation confirmed the feasibility of resonance tuning at 500 MHz, supporting its potential for future high-field MRI applications. Full article

Background/Objectives: Assessment of the hemodynamic status of the brain in patients with cerebrovascular diseases is crucial for providing valuable clinical information. Various magnetic resonance perfusion sequences are used in studies, and one of the current challenges is comparing methods utilizing exogenous and endogenous contrast. This meta-analysis aimed to evaluate the correlation between arterial spin labeling (ASL)-derived perfusion parameters and those obtained by dynamic susceptibility contrast (DSC) perfusion in stroke and steno-occlusive diseases. Methods: A systematic review and meta-analysis were conducted, including 14 studies that reported correlation coefficients between perfusion MRI sequences in the assessment of stroke or steno-occlusive diseases. The correlation between ASL-derived cerebral blood flow (ASL-CBF) and DSC-derived cerebral blood flow (DSC-CBF) was analyzed, considering different magnetic field strengths (1.5 T and 3.0 T), sequence types, and brain regions. Additionally, real and normalized data were compared. Results: A moderate positive correlation was found between ASL-CBF and DSC-CBF (R = 0.464). Subgroup analysis demonstrated that ASL-CBF and DSC-CBF correlated at 3.0 T (R = 0.401) and 1.5 T (R = 0.700). No significant differences were observed in correlation coefficients based on sequence type or brain region. Normalized data demonstrated a higher correlation coefficient compared to real data (Rreal = 0.393, Rnorm = 0.496). Additionally, the correlation coefficient between ASL-CBF and DSC-derived mean transit time (DSC-MTT) for all included studies was R = −0.422. Conclusions: ASL-derived perfusion parameters demonstrate moderate-to-high agreement with DSC perfusion parameters in stroke and steno-occlusive patients. These findings support the potential utility of ASL as a non-invasive alternative to DSC perfusion imaging in clinical and research settings. Full article

Background/Objectives: The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these nerve cells grow rapidly and abnormally, it can lead to the development of a brain tumor. Brain tumors are severe conditions that can significantly reduce a person’s lifespan. Failure to detect or delayed diagnosis of brain tumors can have fatal consequences. Accurately identifying and classifying brain tumors poses a considerable challenge for medical professionals, especially in terms of diagnosing and treating them using medical imaging analysis. Errors in diagnosing brain tumors can significantly impact a person’s life expectancy. Magnetic Resonance Imaging (MRI) is highly effective in early detection, diagnosis, and classification of brain cancers due to its advanced imaging abilities for soft tissues. However, manual examination of brain MRI scans is prone to errors and heavily depends on radiologists’ experience and fatigue levels. Swift detection of brain tumors is crucial for ensuring patient safety. Methods: In recent years, computer-aided diagnosis (CAD) systems incorporating deep learning (DL) and machine learning (ML) technologies have gained popularity as they offer precise predictive outcomes based on MRI images using advanced computer vision techniques. This article introduces a novel hybrid CAD approach named ViT-PCA-RF, which integrates Vision Transformer (ViT) and Principal Component Analysis (PCA) with Random Forest (RF) for brain tumor classification, providing a new method in the field. ViT was employed for feature extraction, PCA for feature dimension reduction, and RF for brain tumor classification. The proposed ViT-PCA-RF model helps detect early brain tumors, enabling timely intervention, better patient outcomes, and streamlining the diagnostic process, reducing patient time and costs. Our research trained and tested on the Brain Tumor MRI (BTM) dataset for multi-classification of brain tumors. The BTM dataset was preprocessed using resizing and normalization methods to ensure consistent input. Subsequently, our innovative model was compared against traditional classifiers, showcasing impressive performance metrics. Results: It exhibited outstanding accuracy, specificity, precision, recall, and F1 score with rates of 99%, 99.4%, 98.1%, 98.1%, and 98.1%, respectively. Conclusions: Our innovative classifier’s evaluation underlined our model’s potential, which leverages ViT, PCA, and RF techniques, showing promise in the precise and effective detection of brain tumors. Full article

Background: Previous studies have suggested that electromagnetic pulse (EMP) can induce openings in the blood–brain barrier (BBB). However, the temporal variation and spatial distribution of BBB permeability after EMP radiation are difficult to assess using conventional histopathological approaches. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a valuable tool for the in vivo evaluation of BBB permeability. The main purpose of this study was to investigate the temporal variation and spatial distribution of BBB permeability after EMP radiation in rats using DCE-MRI. Methods: The dose of EMP was estimated through simulations utilizing a digital rat model comprising 16 distinct brain regions. Then, the changes in BBB permeability of the different rat brain regions at different time points (3 h and 24 h) after EMP radiation were evaluated using quantitative DCE-MRI. Furthermore, the spatial difference in BBB permeability was assessed 3 h after exposure. Finally, the dose–effect relationship between the electric field strength and the BBB permeability was also investigated. Results: The results demonstrated that the changes in the values of volume transfer constant (ΔK^trans) significantly increased in several rat brain regions at 3 h after 400 kV/m EMP radiation. These changes vanished 24 h after exposure. Meanwhile, no significant spatial differences in BBB permeability were observed after EMP radiation. Moreover, Pearson’s correlation analysis showed that there was a significant positive linear relationship between BBB permeability and the electric field strength within an external electric field strength range of 0 to 400 kV/m at 3 h after EMP radiation. Conclusions: EMP radiation can induce a reversible increase in BBB permeability in rats. Moreover, no significant differences in BBB permeability were found across different brain regions. Additionally, the degree of BBB permeability was positively correlated with the regional electric field strength of EMP radiation within an external electric field strength range of 0 to 400 kV/m at 3 h after EMP radiation. These results indicate the promising potential of employing EMP for transient openings in the BBB, which could facilitate clinical pharmacological interventions via drug delivery into the brain. Full article

The compatibility and safety of contrast media injectors (CMIs) at ultra-high magnetic field strengths remains a critical challenge. This study aimed to investigate a custom-designed CMI powered by a ceramic motor in a newly developed 5T MRI environment, comparing it with a commercial CMI commonly used in a clinic. Three key performance aspects of the CMI were assessed in the 5T environment: translational attraction force, injection flow rates, and total injected volume. Potential imaging artifacts were checked. The custom-designed CMI demonstrated robust performance in the 5T environment, maintaining injection accuracy across all test locations and ensuring translational attraction forces remained within safe thresholds, even in the most challenging positions. Importantly, the custom-designed CMI exhibited no significant radiofrequency (RF) interference, and no imaging artifacts were observed across routine clinical sequences. In contrast, the commercial 3T CMI showed RF interference in several sensitive tests, such as the gradient echo (GRE) sequence with a 0° flip angle and frequency-based detection methods, underscoring the need for field-specific CMI designs tailored to ultra-high field environments. Further tests were performed in monkey livers and a human brain in vivo. The custom-designed CMI proved to be safe, accurate, and fully compatible with the 5T environment. Full article

Background: Multiple sclerosis (MS) is a chronic and heterogeneous disease characterized by demyelination and axonal loss and damage. Magnetic resonance imaging (MRI) has been employed to distinguish these changes in various types of MS lesions. Objectives: We aimed to evaluate intravoxel incoherent motion (IVIM) diffusion and perfusion MRI metrics across different brain regions in healthy individuals and various types of MS lesions, including enhanced, non-enhanced, and black hole lesions. Methods: A prospective study included 237 patients with MS (65 males and 172 females) and 29 healthy control participants (25 males and 4 females). The field strength was 1.5 Tesla. The imaging sequences included three-dimensional (3D) T₁, 3D fluid-attenuated inversion recovery, two-dimensional (2D) T₁, T₂-weighted imaging, and 2D diffusion-weighted imaging (DWI) sequences. IVIM-derived parameters—apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f)—were quantified for commonly observed lesion types (2506 lesions from 224 patients with MS, excluding 13 patients due to MRI artifacts or not meeting the diagnostic criteria for RR-MS) and for corresponding brain regions in 29 healthy control participants. A one-way analysis of variance, followed by post-hoc analysis (Tukey’s test), was performed to compare mean values between the healthy and MS groups. Receiver operating characteristic curve analyses, including area under the curve, sensitivity, and specificity, were conducted to determine the cutoff values of IVIM parameters for distinguishing between the groups. A p-value of ≤0.05 and 95% confidence intervals were used to report statistical significance and precision, respectively. Results: All IVIM parametric maps in this study discriminated among most MS lesion types. ADC, D, and D* values for MS black hole lesions were significantly higher (p < 0.0001) than those for other MS lesions and healthy controls. ADC, D, and D* maps demonstrated high sensitivity and specificity, whereas f maps exhibited low sensitivity but high specificity. Conclusions: IVIM parameters provide valuable diagnostic and clinical insights by demonstrating high sensitivity and specificity in evaluating different categories of MS lesions. Full article

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) sequence sensitive to substances that alter the local magnetic field, such as calcium and iron, allowing phase information to distinguish between them. SWI is a 3D gradient–echo sequence with high spatial resolution that leverages both phase and magnitude effects. The interaction of paramagnetic (such as hemosiderin and deoxyhemoglobin), diamagnetic (including calcifications and minerals), and ferromagnetic substances with the local magnetic field distorts it, leading to signal changes. Neurodegenerative diseases are typically characterized by the progressive loss of neurons and their supporting cells within the neurovascular unit. This cellular decline is associated with a corresponding deterioration of both cognitive and motor abilities. Many neurodegenerative disorders are associated with increased iron accumulation or microhemorrhages in various brain regions, making SWI a valuable diagnostic tool in clinical practice. Suggestive SWI findings are known in Parkinson’s disease, Lewy body dementia, atypical parkinsonian syndromes, multiple sclerosis, cerebral amyloid angiopathy, amyotrophic lateral sclerosis, hereditary ataxias, Huntington’s disease, neurodegeneration with brain iron accumulation, and chronic traumatic encephalopathy. This review will assist radiologists in understanding the technical framework of SWI sequences for a correct interpretation of currently established MRI findings and for its potential future clinical applications. Full article

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture magnetic field maps (MFMs) around the head. Recent advancements have introduced optically pumped magnetometers (OPMs) as a promising alternative. Unlike SQUIDs, OPMs do not require cooling and can be placed closer to regions of interest (ROIs). This study aims to optimize the layout of OPM-MEG sensors, maximizing information capture with a limited number of sensors. We applied a sequential selection algorithm (SSA), originally developed for body surface potential mapping in electrocardiography, which requires a large database of full-head MFMs. While modern OPM-MEG systems offer full-head coverage, expected future clinical use will benefit from simplified procedures, where handling a lower number of sensors is easier and more efficient. To explore this, we converted full-head SQUID-MEG measurements of auditory-evoked fields (AEFs) into OPM-MEG layouts with 80 sensor sites. System conversion was done by calculating a current distribution on the brain surface using minimum norm estimation (MNE). We evaluated the SSA’s performance under different protocols, for example, using measurements of single or combined OPM components. We assessed the quality of estimated MFMs using metrics, such as the correlation coefficient (CC), root-mean-square error, and relative error. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95, localization error < 1 mm) capture most of the information contained in full-head MFMs. Our main finding is that for event-related fields, such as AEFs, which primarily originate from focal sources, a significantly smaller number of sensors than currently used in conventional MEG systems is sufficient to extract relevant information. Full article

Background/Objectives: Developing a treatment strategy that effectively prolongs the lives of people with brain tumors requires an accurate diagnosis of the condition. Therefore, improving the preoperative classification of meningiomas is a priority. Machine learning (ML) has made great strides thanks to the development of convolutional neural networks (CNNs) and computer-aided tumor detection systems. The deep convolutional layers automatically extract important and dependable information from the input space, in contrast to more traditional neural network layers. One recent and promising advancement in this field is ML. Still, there is a dearth of studies being carried out in this area. Methods: Therefore, starting with the analysis of magnetic resonance images, we have suggested in this research work a tried-and-tested and methodical strategy for real-time meningioma diagnosis by image segmentation using a very deep transfer learning CNN model or DNN model (VGG-16) with CUDA. Since the VGGNet CNN model has a greater level of accuracy than other deep CNN models like AlexNet, GoogleNet, etc., we have chosen to employ it. The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. Our VGGNet model takes in an sMRI FLAIR image input. The VGG’s convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1 × 1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution. All the hidden layers in our VGG network also use ReLU. A dataset consisting of 264 3D FLAIR sMRI image segments from three different classes (meningioma, tuberculoma, and normal) was employed. The number of epochs in the Sequential Model was set to 10. The Keras layers that we used were Dense, Dropout, Flatten, Batch Normalization, and ReLU. Results: According to the simulation findings, our suggested model successfully classified all of the data in the dataset used, with a 99.0% overall accuracy. The performance metrics of the implemented model and confusion matrix for tumor classification indicate the model’s high accuracy in brain tumor classification. Conclusions: The good outcomes demonstrate the possibility of our suggested method as a useful diagnostic tool, promoting better understanding, a prognostic tool for clinical outcomes, and an efficient brain tumor treatment planning tool. It was demonstrated that several performance metrics we computed using the confusion matrix of the previously used model were very good. Consequently, we think that the approach we have suggested is an important way to identify brain tumors. Full article