Search Results (286)

This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R²_CV = 0.980; FSS: R²_CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article

Background and Aims: Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease frequently associated with fatigue and mild cognitive impairment. Brain-derived neurotrophic factor (BDNF) plays key roles in neuroplasticity, immune regulation, and metabolism. This study aimed to evaluate plasma BDNF levels in early-stage PBC and examine their clinical and biochemical associations. Methods: In this observational study, plasma BDNF, IL-6, and IL-18 concentrations were measured by ELISA in 45 patients with early-stage PBC and 31 age- and sex-matched healthy controls (mean age 60.5 years; 96% women). All participants underwent liver elastography using point shear wave elastography (ElastPQ), Doppler ultrasound, laboratory testing, and assessment of cognitive function (PHES) and fatigue severity (MFIS). Non-invasive fibrosis scores (APRI, FIB-4) were calculated. Results: Median plasma BDNF concentrations were significantly higher in PBC patients than in controls [median: 21.04 ng/mL (IQR: 10.68–38.07) vs. 5.80 ng/mL (IQR: 4.58–7.54); p < 0.0001]. In PBC patients, higher BDNF levels correlated inversely with liver stiffness measured by ElastPQ (R = −0.39, p = 0.0258), spleen dimensions, splenic vein flow volume (R = −0.49, p = 0.0018), suggesting an association with milder liver fibrosis and early hemodynamic alterations. A trend toward association between BDNF and IL-6 levels was observed in multivariate analysis. No significant associations were found between BDNF concentrations and markers of hepatocellular injury, cognitive performance, or fatigue severity. Conclusions: Plasma BDNF concentrations are elevated in early-stage PBC and inversely correlate with liver fibrosis severity. No significant associations were found with hepatocellular injury, cognitive function, or fatigue. These findings suggest that BDNF may play a protective role against hepatic fibrogenesis, or alternatively, that BDNF concentrations may decline with advancing liver disease. Further studies are needed to clarify its significance in PBC. Full article

Background/Objectives: Sotatercept has demonstrated efficacy in pulmonary arterial hypertension (PAH), but its use has not been studied in patients with Group 3 pulmonary hypertension (PH). Additionally, patients with connective tissue disease-associated PAH (CTD-PAH) were underrepresented in the STELLAR trial. Given the limited treatment options for pulmonary hypertension in patients with interstitial lung disease (PH-ILD), this study aimed to evaluate the use of sotatercept in CTD-PAH patients with concomitant ILD. Methods: Eligible patients (n = 7) had a confirmed diagnosis of CTD-PAH with concomitant ILD. The patients were already receiving background PAH therapy. Baseline hemodynamic and clinical measurements were reassessed after 24 weeks of sotatercept therapy. The variables assessed included six-minute walk distance (6MWD), pulmonary vascular resistance (PVR), echocardiographic right ventricular systolic pressure (eRVSP), N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, World Health Organization (WHO) functional class, and supplemental oxygen requirements. Results: The study included seven patients with a mean age of 57 years (range: 39–73 years). After 24 weeks, the mean 6MWT distance increased from 211 m to 348 m (p < 0.01). Mean PVR decreased from 7.77 WU at baseline to 4.53 WU (p < 0.01). Mean eRVSP decreased from 79.43 mmHg to 54.14 mmHg (p < 0.01). NT-proBNP decreased from 3056.86 pg/mL to 1404.29 pg/mL (p < 0.01). The WHO functional class and supplemental oxygen requirements improved in all patients. Conclusions: Sotatercept was tolerated in patients with CTD-PAH and ILD, with no evidence of adverse respiratory effects. When added to foundational PAH therapy, sotatercept resulted in significant improvements across multiple parameters. These findings suggest that sotatercept may be a promising therapeutic option as an adjunctive treatment in this patient population. Full article

Over the past decade, substantial progress has been made in understanding the pathophysiology of dystonia. The number of identified genes has surged—exceeding 400 by 2024—with approximately 76.6% linked to neurodevelopmental disorders. Despite this, the genetic diagnostic yield remains modest (12–36%), and many newly discovered genes have yet to reveal novel mechanistic insights. The limited number of studies exploring dystonia-related pathways in animal models restricts the generalizability of findings to human disease, raising concerns about their external validity. Developing experimental models remains a challenge, particularly given the importance of critical developmental windows—periods during central nervous system maturation when disruptions can have lasting effects. Some models also exhibit delayed symptom onset, prompting a shift toward faster-developing organisms such as Drosophila. There is a pressing need for standardized, scalable protocols that enable precise evaluation of specific neural tissues. Advances in neuroimaging have improved our understanding of dystonia-related brain networks at both regional and whole-brain levels. The emerging concept of “network kernels” has provided new perspectives on brain connectivity. However, future imaging studies should incorporate effective connectivity analyses to distinguish between hemodynamic and neuronal contributions and to clarify neurobiological pathways. This review synthesizes current knowledge from genetics, animal models, and neuroimaging to present an integrated view of dystonia’s neurobiological underpinnings. Full article

MRI perfusion is used to diagnose and monitor neurological conditions such as brain tumors, stroke, dementia, and traumatic brain injury. Dynamic Susceptibility Contrast (DSC) is the most widely available quantitative MRI technique for perfusion imaging. Even in its most basic implementation, DSC MRI provides critical hemodynamic metrics like cerebral blood flow (CBF), blood volume (CBV), mean transit time (MTT), and time between the peak of arterial input and residue function (Tmax), through the dynamic tracking of a gadolinium-based contrast agent. Notwithstanding its high clinical importance and widespread use, the reproducibility and diagnostic reliability are impeded by a lack of standardized pre-processing protocols and quality controls. A comprehensive literature review and the authors’ aggregated experience identified common DSC MRI artefacts and corresponding pre-processing methods. Pre-processing methods to correct for artefacts were evaluated for their practical applicability and validation status. A consensus on the pre-processing was established by a multidisciplinary team of experts. Acquisition-related artefacts include geometric distortions, slice timing misalignment, and physiological noise. Intrinsic artefacts include motion, B₁ inhomogeneities, Gibbs ringing, and noise. Motion can be mitigated using rigid-body alignment, but methods for addressing B₁ inhomogeneities, Gibbs ringing, and noise remain underexplored for DSC MRI. Pre-processing of DSC MRI is critical for reliable diagnostics and research. While robust methods exist for correcting geometric distortions, motion, and slice timing issues, further validation is needed for methods addressing B₁ inhomogeneities, Gibbs ringing, and noise. Implementing adequate mitigation methods for these artefacts could enhance reproducibility and diagnostic accuracy, supporting the growing reliance on DSC MRI in neurological imaging. Finally, we emphasize the crucial importance of pre-scan quality assurance with phantom scans. Full article

Background: Due to neurohormonal disturbances that occur following brain death, thyroid hormone therapy has been proposed as a means to enhance cardiac function in brain-dead organ donors. However, it remains unclear whether thyroid hormone administration improves clinical outcomes in potential heart donors. Methods: A comprehensive review of clinical trials was conducted to evaluate the impact of thyroid hormone therapy on heart viability and transplantation outcomes. A total of nine randomized controlled trials (RCTs) involving 1189 potential heart donors were included. Results: Thyroid hormone supplementation effectively restored circulating thyroid hormone levels in brain-dead donors. However, findings regarding improvements in cardiac function and transplantation outcomes were inconsistent across studies. While some RCTs reported marginal improvements in hemodynamic parameters and heart transplant viability, these results were not consistently replicated. Furthermore, most studies did not demonstrate a significant enhancement in recipient survival or graft function associated with thyroid hormone therapy. Conclusion: Although thyroid hormone therapy restores thyroid hormone levels in brain-dead donors, current evidence does not consistently support its effectiveness in improving donor heart viability or recipient outcomes. Further research is necessary to clarify the role of thyroid hormone therapy in donor management and its impact on long-term transplant success. Full article

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that measures brain hemodynamic activity by detecting changes in oxyhemoglobin and deoxyhemoglobin concentrations using light in the near-infrared spectrum. This study aims to provide a comprehensive characterization of fNIRS signals acquired with a prototypal continuous-wave fNIRS device during a breath-holding task, to evaluate the impact of respiratory activity on scalp hemodynamics within the framework of Network Physiology. To this end, information-theoretic and spectral analysis methods were applied to characterize the dynamics of fNIRS signals. In the time domain, time-resolved information-theoretic measures, including entropy, conditional entropy and, information storage, were employed to assess the complexity and predictability of the fNIRS signals. These measures highlighted distinct informational dynamics across the breathing and apnea phases, with conditional entropy showing a significant modulation driven by respiratory activity. In the frequency domain, power spectral density was estimated using a parametric method, allowing the identification of distinct frequency bands related to vascular and respiratory components. The analysis revealed significant modulations in both the amplitude and frequency of oscillations during the task, particularly in the high-frequency band associated with respiratory activity. Our observations demonstrate that the proposed analysis provides novel insights into the characterization of fNIRS signals, enhancing the understanding of the impact of task-induced peripheral cardiovascular responses on NIRS hemodynamics. Full article

Artificial intelligence (AI) is revolutionizing the field of medical imaging, offering unprecedented capabilities in data analysis, image interpretation, and decision support. Transcranial Doppler (TCD) and Transcranial Color-Coded Doppler (TCCD) are widely used, non-invasive modalities for evaluating cerebral hemodynamics in acute and chronic conditions. Yet, their reliance on operator expertise and subjective interpretation limits their full potential. AI, particularly machine learning and deep learning algorithms, has emerged as a transformative tool to address these challenges by automating image acquisition, optimizing signal quality, and enhancing diagnostic accuracy. Key applications reviewed include the automated identification of cerebrovascular abnormalities such as vasospasm and embolus detection in TCD, AI-guided workflow optimization, and real-time feedback in general ultrasound imaging. Despite promising advances, significant challenges remain, including data standardization, algorithm interpretability, and the integration of these tools into clinical practice. Developing robust, generalizable AI models and integrating multimodal imaging data promise to enhance diagnostic and prognostic capabilities in TCD and ultrasound. By bridging the gap between technological innovation and clinical utility, AI has the potential to reshape the landscape of neurovascular and diagnostic imaging, driving advancements in personalized medicine and improving patient outcomes. This review highlights the critical role of interdisciplinary collaboration in achieving these goals, exploring the current applications and future directions of AI in TCD and TCCD imaging. This review included 41 studies on the application of artificial intelligence (AI) in neurosonology in the diagnosis and monitoring of vascular and parenchymal brain pathologies. Machine learning, deep learning, and convolutional neural network algorithms have been effectively utilized in the analysis of TCD and TCCD data for several conditions. Conversely, the application of artificial intelligence techniques in transcranial sonography for the assessment of parenchymal brain disorders, such as dementia and space-occupying lesions, remains largely unexplored. Nonetheless, this area holds significant potential for future research and clinical innovation. 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

Exercise-induced hypertension (EIH) has increasingly been observed among middle-aged long-distance runners, raising concerns about cardiovascular risk. This study aimed to investigate acute changes in cardiovascular biomarkers associated with vascular inflammation, oxidative stress, antioxidant defense, endothelial function, and myocardial burden in runners with EIH. Thirty-seven middle-aged male runners (aged 40–65 years) were categorized into a normal blood pressure group (NBPG; systolic blood pressure <210 mmHg, n = 23) and an EIH group (EIHG; ≥210 mmHg, n = 14) based on maximal systolic blood pressure during a graded exercise test (GXT). Participants performed a 30 min treadmill run at 80% heart rate reserve, and blood samples were collected before and after exercise. The biomarkers analyzed included high-sensitivity C-reactive protein (hs-CRP), derivatives of reactive oxygen metabolites (d-ROMs), biological antioxidant potential (BAP), nitric oxide (NO), superoxide dismutase (SOD), and N-terminal pro-brain natriuretic peptide (NT-proBNP). The results show that the EIHG exhibited increased NT-proBNP and SOD levels, along with a reduced NO response, indicating elevated myocardial stress and impaired vasodilation. hs-CRP was positively correlated with multiple hemodynamic indices, and SOD levels were associated with maximal systolic pressure and myocardial burden. These findings highlight the need for individualized monitoring and cardiovascular risk management in runners with EIH. Full article

Functional near-infrared spectroscopy (fNIRS) is a non-invasive method for monitoring brain activity by detecting hemodynamic changes. Studies have shown that it can identify ictal and pre-ictal hemodynamic variations, supporting its potential use as a complement to electroencephalography (EEG) in epilepsy monitoring. This study explores an expanded illumination and detection approach utilizing wide-based optodes and increased emitter–detector separation (EDS) to enhance fNIRS sensitivity to cortical hemodynamic changes while minimizing scalp contamination. A Monte Carlo simulation was designed to assess signal amplitude and sensitivity of fNIRS with varying emitter and detector diameters (1–15 mm) and EDS (30–50 mm). Signal strength, grey matter to scalp path ratio (GSPR), and percentage signal change per absorption coefficients (AC) variation were analyzed. Sensitivity to changes in AC of superficial and deep grey matter (SGM, DGM) and scalp was assessed. Increasing emitter and detector diameters substantially increased total detected photon packet weights, enabling practical use at larger EDS. Sensitivity to SGM AC changes tripled at 50 mm EDS, while GSPR increased by 80%, indicating reduced signal contamination from the scalp. Sensitivity to deep cortical hemodynamic changes also improved. Therefore, wide-based fNIRS optodes with increased EDS can enhance seizure-related hemodynamic detection, potentially improving epilepsy diagnostics. Full article

Background: Cranial Doppler ultrasonography (DS) is a non-invasive method for evaluating cerebral hemodynamics in neonates with perinatal asphyxia (PA). This study aimed to assess whether cerebral vascular resistance indices (RIs) measured within the first 24 h of life can predict the severity of brain injury. Methods: DS was performed on the anterior cerebral artery (ACA) and middle cerebral artery (MCA) between 6 and 24 h after birth in newborns diagnosed with PA. Prognostic value was evaluated by comparing RI values with cranial magnetic resonance imaging (MRI) results. Results: Of the 107 infants included in the study, 11 (10.3%) had severe brain damage, 27 (25.2%) had mild and 20 (18.7%) had moderate changes. The mean ACA RI was 0.61 ± 0.15 in the severe group and 0.70 ± 0.12 in the mild–moderate group (p = 0.023). MCA RI was 0.63 ± 0.20 and 0.71 ± 0.13, respectively. ROC analysis showed an area under the curve (AUC) of 0.901 for ACA RI with a cut-off of 0.58 (84% sensitivity and 84% specificity), and 0.874 for MCA RI with a cut-off of 0.59 (83% sensitivity and 84% specificity). Conclusions: Early ACA and MCA RI measurements via Doppler ultrasonography may serve as valuable predictors of brain injury severity in neonates with PA and should be considered alongside other clinical and imaging findings. Full article

Functional near-infrared spectroscopy (fNIRS) is a non-intrusive way to measure cortical hemodynamic activity. Predicting cognitive workload from fNIRS data has taken on a diffuse set of methods. To be applicable in real-world settings, models are needed, which can perform well across different sessions as well as different subjects. However, most existing works assume that training and testing data come from the same subjects and/or cannot generalize well across never-before-seen subjects. Additional challenges imposed by fNIRS data include not only the high variations in inter-subject fNIRS data but also the variations in intra-subject data collected across different blocks of sessions. To address these challenges, we propose an effective method, referred to as the block-wise domain adaptation (BWise-DA), which explicitly minimizes intra-session variance as well by viewing different blocks from the same subject and same session as different domains. We minimize the intra-class domain discrepancy and maximize the inter-class domain discrepancy accordingly. In addition, we propose an MLPMixer-based model for workload prediction. Experimental results demonstrate that the proposed model provides better performance compared to three different baseline models on three publicly-available workload datasets. Two of the datasets are collected from n-back tasks and one of them is from finger-tapping. Moreover, the experimental results show that our proposed contrastive learning method can also be leveraged to improve the performance of the baseline models. We also present a visualization study showing that the models are paying attention to the right regions in the brain, which are known to be involved in the respective tasks. Full article

Background/Objectives: Jaw deformities affect not only facial aesthetics but also various oral functions. While previous studies have demonstrated that mandibular prognathism (MP) alters masticatory-induced brain blood flow (BBF), the temporal characteristics of these hemodynamic changes have remained unclear. In this cross-sectional observational study, we investigated the following two specific objectives: (1) whether food hardness affects not only the magnitude but also the temporal patterns of BBF changes during mastication and (2) how malocclusion is associated with these temporal hemodynamic responses. Methods: Twenty-six participants with normal occlusion (NORM) and twenty patients with MP participated in this study. BBF was measured using functional near-infrared spectroscopy, while participants chewed soft paraffin or hard gummy candy. Maximum oxygenated hemoglobin (oxy-Hb) values and time-to-peak BBF were analyzed. Results: While food hardness did not significantly affect maximum oxy-Hb within groups, the MP group showed significantly lower responses during hard gummy candy mastication compared to the NORM group. The occlusal contact area exhibited significant positive correlation with maximum oxy-Hb values, while the ANB angle, an indicator of intermaxillary skeletal relationship, showed no significant correlation with BBF parameters. The hard gummy candy/paraffin ratio of maximum oxy-Hb was significantly higher in the NORM group compared to the MP group. Time-to-peak BBF was approximately twice as long for hard gummy candy compared to paraffin in both groups, with no significant differences between groups. Conclusions: These findings reveal that while MP attenuates the magnitude of masticatory-induced BBF, particularly during hard food mastication, the temporal adaptation to increased food hardness is preserved. This dissociation between magnitude and timing effects suggests that intact basic neurovascular coupling mechanisms would be maintained even in the condition of altered masticatory function in a MP subject, which is providing new insights for rehabilitation strategies in orthognathic surgery cases. Full article

A brain aneurysm is a structural deterioration of the arterial wall in the brain, resulting in the formation of a bulge in or ballooning of a blood vessel. Around 3–5% of the global population is affected by brain aneurysms, wherein only a small fraction results in rupture. Although an unruptured aneurysm is typically asymptomatic and not immediately life threatening, it poses a potential risk of rupture, which can lead to severe health complications or mortality. Therefore, it is crucial to detect and treat aneurysms during the unruptured phase. Moreover, a comprehensive understanding of the flow dynamics within the aneurysm and its parent artery is essential for accurate diagnosis and the prevention of aneurysm recurrence. While prior reviews have focused on computational fluid dynamics (CFD) studies on brain aneurysms, particularly patient-specific models from studies conducted over a decade ago, a more recent review is necessary. Additionally, reviewing various studies on the fluid dynamic behavior of treated aneurysms is crucial. Thus, the advancements in both experimental and computational studies on brain aneurysms must be explored to better understand their underlying fluid flow mechanisms and to develop robust treatment strategies. This review aims to summarize the different types of brain aneurysms, the screening and treatment processes, the key hemodynamic factors, and the fluid dynamic characteristics observed in aneurysms before and after treatment. Full article