A brief review of non-invasive brain imaging technologies and the near-infrared optical bioimaging

Is non-invasive neuromodulation a viable technique to improve neuroplasticity in individuals with acquired brain injury? A review

Objective

This study aimed to explore and evaluate the efficacy of non-invasive brain stimulation (NIBS) as a standalone or coupled intervention and understand its mechanisms to produce positive alterations in neuroplasticity and behavioral outcomes after acquired brain injury (ABI).

Data sources

Cochrane Library, Web of Science, PubMed, and Google Scholar databases were searched from January 2013 to January 2024.

Study selection

Using the PICO framework, transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) randomized controlled trials (RCTs), retrospective, pilot, open-label, and observational large group and single-participant case studies were included. Two authors reviewed articles according to pre-established inclusion criteria.

Data extraction

Data related to participant and intervention characteristics, mechanisms of change, methods, and outcomes were extracted by two authors. The two authors performed quality assessments using SORT.

Results

Twenty-two studies involving 657 participants diagnosed with ABIs were included. Two studies reported that NIBS was ineffective in producing positive alterations or behavioral outcomes. Twenty studies reported at least one, or a combination of, positively altered neuroplasticity and improved neuropsychological, neuropsychiatric, motor, or somatic symptoms. Twenty-eight current articles between 2020 and 2024 have been studied to elucidate potential mechanisms of change related to NIBS and other mediating or confounding variables.

Discussion

tDCS and TMS may be efficacious as standalone interventions or coupled with neurorehabilitation therapies to positively alter maladaptive brain physiology and improve behavioral symptomology resulting from ABI. Based on postintervention and follow-up results, evidence suggests NIBS may offer a direct or mediatory contribution to improving behavioral outcomes post-ABI.

Conclusion

More research is needed to better understand the extent of rTMS and tDCS application in affecting changes in symptoms after ABI.

Silicon-RosIndolizine fluorophores with shortwave infrared absorption and emission profiles enable in vivo fluorescence imaging

In vivo fluorescence imaging in the shortwave infrared (SWIR, 1,000-1,700 nm) and extended SWIR (ESWIR, 1,700-2,700 nm) regions has tremendous potential for diagnostic imaging. Although image contrast has been shown to improve as longer wavelengths are accessed, the design and synthesis of organic fluorophores that emit in these regions is challenging. Here we synthesize a series of silicon-RosIndolizine (SiRos) fluorophores that exhibit peak emission wavelengths from 1,300-1,700 nm and emission onsets of 1,800-2,200 nm. We characterize the fluorophores photophysically (both steady-state and time-resolved), electrochemically and computationally using time-dependent density functional theory. Using two of the fluorophores (SiRos1300 and SiRos1550), we formulate nanoemulsions and use them for general systemic circulatory SWIR fluorescence imaging of the cardiovascular system in mice. These studies resulted in high-resolution SWIR images with well-defined vasculature visible throughout the entire circulatory system. This SiRos scaffold establishes design principles for generating long-wavelength emitting SWIR and ESWIR fluorophores.

Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks

BACKGROUND AND OBJECTIVE

The central organ of the human nervous system is the brain, which receives and sends stimuli to the various parts of the body to engage in daily activities. Uncontrolled growth of brain cells can result in tumors which affect the normal functions of healthy brain cells. An automatic reliable technique for detecting tumors is imperative to assist medical practitioners in the timely diagnosis of patients. Although machine learning models are being used, with minimal data availability to train, development of low-order based models integrated with machine learning are a tool for reliable detection.

METHODS

In this study, we focus on comparing a low-order model such as proper orthogonal decomposition (POD) coupled with convolutional neural network (CNN) on 2D images from magnetic resonance imaging (MRI) scans to effectively identify brain tumors. The explainability of the coupled POD-CNN prediction output as well as the state-of-the-art pre-trained transfer learning models such as MobileNetV2, Inception-v3, ResNet101, and VGG-19 were explored.

RESULTS

The results showed that CNN predicted tumors with an accuracy of 99.21% whereas POD-CNN performed better with about 1/3rd of computational time at an accuracy of 95.88%. Explainable AI with SHAP showed MobileNetV2 has better prediction in identifying the tumor boundaries.

CONCLUSIONS

Integration of POD with CNN is carried for the first time to detect brain tumor detection with minimal MRI scan data. This study facilitates low-model approaches in machine learning to improve the accuracy and performance of tumor detection.

Neurobiological Effects of Transcranial Direct Current Stimulation over the Inferior Frontal Gyrus: A Systematic Review on Cognitive Enhancement in Healthy and Neurological Adults

The neurobiological effects of transcranial direct current stimulation (tDCS) have still not been unequivocally clarified. Some studies have suggested that the application of tDCS over the inferior frontal gyrus (IFG) enhances different aspects of cognition in healthy and neurological individuals, exerting neural changes over the target area and its neural surroundings. In this systematic review, randomized sham-controlled trials in healthy and neurological adults were selected through a database search to explore whether tDCS over the IFG combined with cognitive training modulates functional connectivity or neural changes. Twenty studies were finally included, among which twelve measured tDCS effects through functional magnetic resonance (fMRI), two through functional near-infrared spectroscopy (fNIRS), and six through electroencephalography (EEG). Due to the high heterogeneity observed across studies, data were qualitatively described and compared to assess reliability. Overall, studies that combined fMRI and tDCS showed widespread changes in functional connectivity at both local and distant brain regions. The findings also suggested that tDCS may also modulate electrophysiological changes underlying the targeted area. However, these outcomes were not always accompanied by corresponding significant behavioral results. This work raises the question concerning the general efficacy of tDCS, the implications of which extend to the steadily increasing tDCS literature.

Exploring Peptido-Nanocomposites in the Context of Amyloid Diseases

Therapeutic peptides are a major class of pharmaceutical drugs owing to their target-binding specificity as well as their versatility in inhibiting aberrant protein-protein interactions associated with human pathologies. Within the realm of amyloid diseases, the use of peptides and peptidomimetics tailor-designed to overcome amyloidogenesis has been an active research endeavor since the late 90s. In more recent years, incorporating nanoparticles for enhancing the biocirculation and delivery of peptide drugs has emerged as a frontier in nanomedicine, and nanoparticles have further demonstrated a potency against amyloid aggregation and cellular inflammation to rival strategies employing small molecules, peptides, and antibodies. Despite these efforts, however, a fundamental understanding of the chemistry, characteristics and function of peptido-nanocomposites is lacking, and a systematic analysis of such strategy for combating a range of amyloid pathogeneses is missing. Here we review the history, principles and evolving chemistry of constructing peptido-nanocomposites from bottom up and discuss their future application against amyloid diseases that debilitate a significant portion of the global population.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

Technical note: High-efficient and wireless transcranial ultrasound excitation based on electromagnetic acoustic transducer

BACKGROUND

The generation of transcranial ultrasound is usually based on the piezoelectric effect, so it is necessary to attach transducers around the skull. However, the skull will cause serious attenuation and scattering of ultrasound, which makes it particularly difficult for transcranial ultrasound imaging and modulation.

PURPOSE

In transcranial ultrasound imaging, there is significant attenuation and scattering of ultrasound waves by the skull bone. To mitigate this influence and enable precise imaging and high-efficient transcranial ultrasound for specific patients (such as stroke patients who already require craniotomy as part of their surgical care), this paper proposes to use EMAT to excite metal plates placed inside the skull based on the excellent penetration characteristics of EM waves into the skull, generating ultrasound signals, which can completely avoid the influence of skull on ultrasound transmission.

METHODS

Based on an efficient wireless transcranial ultrasound experimental platform, we first verified that the skull would not affect the propagation of electromagnetic waves generated by EMAT. In addition, the distribution of the transcranial sound field generated by EMAT was measured.

RESULTS

EMAT can generate 1.0 MHz ultrasound by wireless excitation of a 0.1 mm thick copper plate through an adult skull with a thickness of ∼1 cm, and the frequency and amplitude of the generated ultrasound are not affected by the skull. The results indicated that the electromagnetic waves successfully penetrated the skull, with a recorded strength of approximately 2 mV. We also found that the ultrasound signals generated by the EMAT probe through the skull remained unaffected, measuring around 2 mV. In addition, the measurement of the transcranial sound field distribution (80*50 mm2 ) generated by EMAT shows that compared with the traditional extracranial ultrasound generation method, the sound field distribution generated by the wireless excitation of the intracranial copper plate based on EAMT is no longer affected by the uneven and irregular skull.

CONCLUSION

Our experiments involved validating the penetration capabilities of electromagnetic waves utilizing the EMAT probe through a 7 (5+2) mm thick organic glass plate and a real human skull ranging from 8 to 15 mm in thickness. The efficient and wireless transcranial ultrasound excitation proposed in this paper may be possible for transcranial ultrasound imaging and therapy.

Heavy water coupling gel for short-wave infrared photoacoustic imaging

Significance

Changes in lipid, water, and collagen (LWC) content in tissue are associated with numerous medical abnormalities (cancer, atherosclerosis, and Alzheimer's disease). Standard imaging modalities are limited in resolution, specificity, and/or penetration for quantifying these changes. Short-wave infrared (SWIR) photoacoustic imaging (PAI) has the potential to overcome these challenges by exploiting the unique optical absorption properties of LWC > 1000    nm .

Aim

This study's aim is to harness SWIR PAI for mapping LWC changes in tissue. The focus lies in devising a reflection-mode PAI technique that surmounts current limitations related to SWIR light delivery.

Approach

To enhance light delivery for reflection-mode SWIR PAI, we designed a deuterium oxide (D 2 O , "heavy water") gelatin (HWG) interface for opto-acoustic coupling, intended to significantly improve light transmission above 1200 nm.

Results

HWG permits light delivery > 1    mJ up to 1850 nm, which was not possible with water-based coupling (> 1    mJ light delivery up to 1350 nm). PAI using the HWG interface and the Visualsonics Vevo LAZR-X reveals a signal increase up to 24 dB at 1720 nm in lipid-rich regions.

Conclusions

By overcoming barriers related to light penetration, the HWG coupling interface enables accurate quantification/monitoring of biomarkers like LWC using reflection-mode PAI. This technological stride offers potential for tracking changes in chronic diseases (in vivo) and evaluating their responses to therapeutic interventions.

Measurement of brainstem diameter in small-breed dogs using magnetic resonance imaging

Measurement of brainstem diameters (midbrain, pons, and medulla oblongata)is of potential clinical significance, as changes in brainstem size may decrease or increase due to age, neurodegenerative disorders, or neoplasms. In human medicine, numerous studies have reported the normal reference range of brainstem size, which is hitherto unexplored in veterinary medicine, particularly for small-breed dogs. Therefore, this study aims to investigate the reference range of brainstem diameters in small-breed dogs and to correlate the measurements with age, body weight (BW), and body condition score (BCS). Herein, magnetic resonance (MR) images of 544 small-breed dogs were evaluated. Based on the exclusion criteria, 193 dogs were included in the midbrain and pons evaluation, and of these, 119 dogs were included in the medulla oblongata evaluation. Using MR images, the height and width of the midbrain, pons, and medulla oblongata were measured on the median and transverse plane on the T1-weighted image. For the medulla oblongata, two points were measured for each height and width. The mean values of midbrain height (MH), midbrain width (MW), pons height (PH), pons width (PW), medulla oblongata height at the fourth ventricle level (MOHV), medulla oblongata height at the cervicomedullary (CM) junction level (MOHC), rostral medulla oblongata width (RMOW), and caudal medulla oblongata width (CMOW) were 7.18 ± 0.56 mm, 17.42 ± 1.21 mm, 9.73 ± 0.64 mm, 17.23 ± 1.21 mm, 6.06 ± 0.53 mm, 5.77 ± 0.40 mm, 18.93 ± 1.25 mm, and 10.12 ± 1.08 mm, respectively. No significant differences were found between male and female dogs for all the measurements. A negative correlation was found between age and midbrain diameter, including MH (p < 0.001) and MW (p  = 0.002). All brainstem diameters were correlated positively with BW (p  < 0.05). No significant correlation was found between BCS and all brainstem diameters. Brainstem diameters differed significantly between breeds (p  < 0.05), except for MW (p  = 0.137). This study assessed linear measurements of the brainstem diameter in small-breed dogs. We suggest that these results could be useful in assessing abnormal conditions of the brainstem in small-breed dogs.

fMRI-Based Alzheimer's Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

In the present scenario, Alzheimer's Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.

Cardiovascular Brain Circuits

The cardiovascular system is hardwired to the brain via multilayered afferent and efferent polysynaptic axonal connections. Two major anatomically and functionally distinct though closely interacting subcircuits within the cardiovascular system have recently been defined: The artery-brain circuit and the heart-brain circuit. However, how the nervous system impacts cardiovascular disease progression remains poorly understood. Here, we review recent findings on the anatomy, structures, and inner workings of the lesser-known artery-brain circuit and the better-established heart-brain circuit. We explore the evidence that signals from arteries or the heart form a systemic and finely tuned cardiovascular brain circuit: afferent inputs originating in the arterial tree or the heart are conveyed to distinct sensory neurons in the brain. There, primary integration centers act as hubs that receive and integrate artery-brain circuit-derived and heart-brain circuit-derived signals and process them together with axonal connections and humoral cues from distant brain regions. To conclude the cardiovascular brain circuit, integration centers transmit the constantly modified signals to efferent neurons which transfer them back to the cardiovascular system. Importantly, primary integration centers are wired to and receive information from secondary brain centers that control a wide variety of brain traits encoded in engrams including immune memory, stress-regulating hormone release, pain, reward, emotions, and even motivated types of behavior. Finally, we explore the important possibility that brain effector neurons in the cardiovascular brain circuit network connect efferent signals to other peripheral organs including the immune system, the gut, the liver, and adipose tissue. The enormous recent progress vis-à-vis the cardiovascular brain circuit allows us to propose a novel neurobiology-centered cardiovascular disease hypothesis that we term the neuroimmune cardiovascular circuit hypothesis.

Applications of generative adversarial networks in neuroimaging and clinical neuroscience

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.

Cannot Target What Cannot Be Seen: Molecular Imaging of Cancer Stem Cells

Cancer stem cells are known to play a key role in tumour development, proliferation, and metastases. Their unique properties confer resistance to therapy, often leading to treatment failure. It is believed that research into the identification, targeting, and eradication of these cells can revolutionise oncological treatment. Based on the principle that what cannot be seen, cannot be targeted, a primary step in cancer management is the identification of these cells. The current review aims to encompass the state-of-the-art functional imaging techniques that enable the identification of cancer stem cells via various pathways and mechanisms. The paper presents in vivo molecular techniques that are currently available or await clinical implementation. Challenges and future prospects are highlighted to open new research avenues in cancer stem cell imaging.

Nanotechnology-Inspired Extracellular Vesicles Theranostics for Diagnosis and Therapy of Central Nervous System Diseases

Shuttling various bioactive substances across the blood-brain barrier (BBB) bidirectionally, extracellular vesicles (EVs) have been opening new frontiers for the diagnosis and therapy of central nervous system (CNS) diseases. However, clinical translation of EV-based theranostics remains challenging due to difficulties in effective EV engineering for superior imaging/therapeutic potential, ultrasensitive EV detection for small sample volume, as well as scale-up and standardized EV production. In the past decade, continuous advancement in nanotechnology provided extensive concepts and strategies for EV engineering and analysis, which inspired the application of EVs for CNS diseases. Here we will review the existing types of EV-nanomaterial hybrid systems with improved diagnostic and therapeutic efficacy for CNS diseases. A summary of recent progress in the incorporation of nanomaterials and nanostructures in EV production, separation, and analysis will also be provided. Moreover, the convergence between nanotechnology and microfluidics for integrated EV engineering and liquid biopsy of CNS diseases will be discussed.

Near-Infrared Fluorescent Probes as Imaging and Theranostic Modalities for Amyloid-Beta and Tau Aggregates in Alzheimer's Disease

A person suspected of having Alzheimer's disease (AD) is clinically diagnosed for the presence of principal biomarkers, especially misfolded amyloid-beta (Aβ) and tau proteins in the brain regions. Existing radiotracer diagnostic tools, such as PET imaging, are expensive and have limited availability for primary patient screening and pre-clinical animal studies. To change the status quo, small-molecular near-infrared (NIR) probes have been rapidly developed, which may serve as an inexpensive, handy imaging tool to comprehend the dynamics of pathogenic progression in AD and assess therapeutic efficacy in vivo. This Perspective summarizes the biochemistry of Aβ and tau proteins and then focuses on structurally diverse NIR probes with coverages of their spectroscopic properties, binding affinity toward Aβ and tau species, and theranostic effectiveness. With the summarized information and perspective discussions, we hope that this paper may serve as a guiding tool for designing novel in vivo imaging fluoroprobes with theranostic capabilities in the future.

Pushing the frontiers: tools for monitoring neurotransmitters and neuromodulators

Neurotransmitters and neuromodulators have a wide range of key roles throughout the nervous system. However, their dynamics in both health and disease have been challenging to assess, owing to the lack of in vivo tools to track them with high spatiotemporal resolution. Thus, developing a platform that enables minimally invasive, large-scale and long-term monitoring of neurotransmitters and neuromodulators with high sensitivity, high molecular specificity and high spatiotemporal resolution has been essential. Here, we review the methods available for monitoring the dynamics of neurotransmitters and neuromodulators. Following a brief summary of non-genetically encoded methods, we focus on recent developments in genetically encoded fluorescent indicators, highlighting how these novel indicators have facilitated advances in our understanding of the functional roles of neurotransmitters and neuromodulators in the nervous system. These studies present a promising outlook for the future development and use of tools to monitor neurotransmitters and neuromodulators.