Search Results (60)

The tip-of-the-tongue (TOT) phenomenon is a transient state in which speakers momentarily fail to retrieve a known word despite preserved semantic knowledge and a strong sense of imminent recall. This review integrates cognitive and neural evidence with emerging neurochemical perspectives to develop a comprehensive biomedical framework for word-finding failures. Cognitive models of semantic–phonological transmission and interloper interference have been refined through structural, functional, and metabolic imaging to elucidate the mechanisms underlying TOT states across the lifespan. Functional neuroimaging implicates a left-lateralized fronto-temporal network, particularly the inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), and temporal pole, in retrieval monitoring and conflict resolution. Structural MRI and diffusion imaging link increased TOT frequency to reduced integrity of the arcuate and uncinate fasciculi and diminished network efficiency. Proton magnetic resonance spectroscopy (¹H-MRS) introduces a neurochemical dimension, with studies of related language tasks implicating lower γ-aminobutyric acid (GABA) and altered glutamate concentrations in frontal and temporal cortices as potential contributors to slower naming and heightened retrieval interference. Together, these findings converge on a model in which transient lexical blocks arise from local disruptions in excitation–inhibition (E/I) balance that impair signal propagation within language circuits. By uniting behavioral, neuroimaging, and neurochemical perspectives, TOT research reveals how subtle perturbations in cortical homeostasis manifest as everyday cognitive lapses and highlights potential biomedical strategies to maintain communicative efficiency across the lifespan. Full article

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

Magnetosomes are organelle-like structures within magnetotactic bacteria that store iron biominerals in membrane-bound vesicles. In bacteria, formation of these structures is highly regulated by approximately 30 genes, which are conserved throughout different species. To compartmentalize iron in mammalian cells and provide gene-based contrast for magnetic resonance imaging, we introduced key magnetosome proteins. The expression of essential magnetosome genes mamI and mamL as fluorescent fusion proteins in a human melanoma cell line confirmed their co-localization and interaction. Here, we investigate the expression of two more essential magnetosome genes, mamB and mamE, using confocal microscopy to describe fluorescent fusion protein expression patterns and analyze the observed intracellular mobility. Custom software was developed to characterize fluorescent particle trajectories. In mammalian cells, essential magnetosome proteins display different diffusive behaviours. However, all magnetosome proteins travelled at similar velocities when interacting with mammalian mobile elements, suggesting that MamL, MamL + MamI, MamB, and MamE interact with similar molecular motor proteins. These results confirm that localization and interaction of essential magnetosome proteins are feasible within the mammalian intracellular compartment. Full article

Despite technological progress, glioblastoma (GBM) continues to confer dismal prognoses. Modern neuroimaging methods are assuming an ever greater role in diagnosing and monitoring brain tumors. This review shows current neuroimaging approaches and systemic therapeutic strategies for glioblastoma, with a focus on emerging and innovative treatments. Advances in multiparametric magnetic resonance imaging—MRI (diffusion, perfusion, and spectroscopy) and novel positron emission tomography (PET) tracers, complemented by radiomics and artificial intelligence (AI), now refine tumor delineation, differentiate progression from treatment effects, and may help predict treatment responses. Maximal safe resection followed by chemoradiotherapy with temozolomide remains the standard, with the greatest benefit seen in O⁶-methylguanine DNA methyltransferase (MGMT) promoter-methylated tumors. Bevacizumab and other targeted modalities offer mainly progression-free, not overall survival, gains. Immune checkpoint inhibitors (e.g., nivolumab) have not improved survival in unselected GBM, while early multi-antigen CAR-T (chimeric antigen receptor T-cell) strategies show preliminary bioactivity without established durability. While actionable alterations (NTRK fusions and BRAF V600E) justify selective targeted therapy trials, their definitive benefit in classical GBM is unproven. Future priorities include harmonized imaging molecular integration, AI-driven prognostic modeling, novel PET tracers, and strategies to breach or transiently open the blood–brain barrier to enhance drug delivery. Convergence of these domains may convert diagnostic precision into improved patient outcomes. Full article

Background/Objectives: We provide a framework for determining how far into the future the spatiotemporal dynamics of tumor growth can be accurately predicted using routinely available magnetic resonance imaging (MRI) data. Our analysis is applied to a coupled set of reaction-diffusion equations describing the spatiotemporal development of tumor cellularity and vascularity, initialized and constrained with diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data, respectively. Methods: Motivated by experimentally acquired murine glioma data, the rat brain serves as the computational domain within which we seed an in silico tumor. We generate a set of 13 virtual tumors defined by different combinations of model parameters. The first parameter combination was selected as it generated a tumor with a necrotic core during our simulated ten-day experiment. We then tested 12 additional parameter combinations to study a range of high and low tumor cell proliferation and diffusion values. Each tumor is grown for ten days via our model system to establish “ground truth” spatiotemporal tumor dynamics with an infinite signal-to-noise ratio (SNR). We then systematically reduce the quality of the imaging data by decreasing the SNR, downsampling the spatial resolution (SR), and decreasing the sampling frequency, our proxy for reduced temporal resolution (TR). With each decrement in image quality, we assess the accuracy of the calibration and subsequent prediction by comparing it to the corresponding ground truth data using the concordance correlation coefficient (CCC) for both tumor and vasculature volume fractions, as well as the Dice similarity coefficient for tumor volume fraction. Results: All tumor CCC and Dice scores for each of the 13 virtual tumors are >0.9 regardless of the SNR/SR/TR combination. Vasculature CCC scores with any SR/TR combination are >0.9 provided the SNR ≥ 80 for all virtual tumors; for the special case of high-proliferating tumors (i.e., proliferation > 0.0263 day⁻¹), any SR/TR combination yields CCC and Dice scores > 0.9 provided the SNR ≥ 40. Conclusions: Our systematic evaluation demonstrates that reaction-diffusion models can maintain acceptable longitudinal prediction accuracy—especially for tumor predictions—despite limitations in the quality and quantity of experimental data. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

Background/Objectives: Cerebral small vessel disease (CSVD) often manifests as enlarged perivascular spaces (ePVS), which are linked to reduced processing speed even in asymptomatic individuals. Glymphatic dysfunction (or glymphopathy) has been proposed as a mechanism underlying ePVS, with the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index serving as a potential non-invasive surrogate marker. This study aimed to examine the association between DTI-ALPS index, ePVS burden, and processing speed in community-dwelling adults without overt neurological symptoms, stratified by QRISK3 cardio-cerebrovascular risk prediction score. Methods: Sixty young-to-middle-aged adults (aged 25–65 years), classified as low-to-moderate QRISK3 scores, underwent brain 3T diffusion magnetic resonance imaging (MRI) to evaluate ePVS burden and calculate DTI-ALPS indices. Processing speed index (PSI) was assessed using the Wechsler Adult Intelligence Scale—Version IV (WAIS-IV). Results: Approximately 43% of subjects reported having ePVS with significantly lower DTI-ALPS indices and PSI compared to those without ePVS. The DTI-ALPS index was inversely correlated with ePVS burden and positively correlated with PSI. Mediation analysis showed that the lower DTI-ALPS partially mediated the association between ePVS burden and slower processing speed. Conclusions: Visible ePVS in our cohort may reflect early glymphopathy and subtle cognitive slowing, while the DTI-ALPS index may serve as an early biomarker for preclinical CSVD-related cognitive vulnerability, supporting targeted prevention strategies. Full article

Sport injury recognition is rapidly evolving with the integration of new emerging technologies. This systematic review aims to identify and evaluate technologies capable of detecting injuries during sports participation. A comprehensive search of PUBMED, Sport Discus, Web of Science, and ScienceDirect was conducted following the PRISMA 2020 guidelines. The review was registered on PROSPERO (CRD42024608964). Inclusion criteria focused on prospective studies involving athletes of all ages, evaluating tools which are utilised to identify injuries in sports settings. The review included research between 2014 and 2024; retrospective, conceptual, and fatigue-focused studies were excluded. Risk of bias was assessed using the Critical Appraisal Skills Program (CASP) tool. Of 4283 records screened, 70 full-text articles were assessed, with 21 studies meeting the final inclusion criteria. The technologies were grouped into advanced imaging (Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DFI), and Quantitative Susceptibility Mapping (QSM), with biomarkers (i.e., Neurofilament Light (NfL), Tau protein, Glial Fibrillary Acidic Protein (GFAP), Salivary MicroRNAs, and Immunoglobulin A (IgA), and sideline assessments (i.e., the King–Devick test, KD-Eye Tracking, modified Balance Error Scoring System (mBESS), DETECT, ImPACT structured video analysis, and Instrumented Mouth Guards (iMGs)), which demonstrated feasibility for immediate sideline identification of injury. Future research should improve methodological rigour through larger, diverse samples and controlled designs, with real-world testing environments. Following this guidance, the application of emerging technologies may assist medical staff, coaches, and national governing bodies in identifying injuries in a sports setting, providing real-time assessment. Full article

Background and Objectives: Non-invasive imaging biomarkers for the early detection of chronic kidney allograft injury are needed to improve long-term transplant outcomes. T1 mapping by magnetic resonance imaging (MRI) has emerged as a promising method to assess renal structure and function. This study aimed to determine the potential of MRI as a diagnostic tool for evaluating graft function and structural changes in kidney grafts 1 year after transplantation. Materials and Methods: Thirty-four kidney transplant recipients were prospectively recruited, with 27 completing the follow-up at one year. Renal MRI at 3T was performed to acquire T1, T2, and apparent diffusion coefficient (ADC) maps. Clinical parameters, including estimated glomerular filtration rate (eGFR), albumin-to-creatinine ratio (ACR), protein-to-creatinine ratio (PCR), and histological IF/TA scores, were collected. MRI parameters were compared across the groups stratified by clinical and histological markers. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) analysis. Results: At 1 year, T1 corticomedullary differentiation (CMD) values were significantly higher in patients with elevated ACR (≥3 mg/mmol), PCR (≥15 mg/mmol), and mild to moderate or severe IF/TA, reflecting a reduction in the corticomedullary gradient. T1 CMD demonstrated moderate-to-good diagnostic performance in detecting ACR (AUC 0.791), PCR (AUC 0.730), and IF/TA (AUC 0.839). No significant differences were observed in T2 or ADC values across these groups. T1 CMD also showed a significant positive correlation with ACR but not with eGFR, suggesting a closer association with structural rather than functional deterioration. Conclusions: T1 mapping, particularly T1 CMD, shows promise as a non-invasive imaging biomarker for detecting chronic allograft injury and monitoring renal function 1 year after kidney transplantation. Full article

Background and Objectives: Nonischemic cardiomyopathies comprise a wide spectrum of heart muscle disorders characterized by different morphological, functional, and tissue abnormalities. Cardiovascular magnetic resonance (CMR) represents the gold standard imaging modality for assessing cardiac morphology, systolic function, and tissue characterization, thereby aiding in early diagnosis, precise phenotyping, and tailored treatment. The aim of this review is to provide an up-to-date overview of CMR techniques for studying myocardial perfusion and their applications to nonischemic cardiomyopathy, not only to rule out an underlying ischemic aetiology but also to investigate the pathophysiological characteristics of microcirculatory dysfunction in these patients. Materials and Methods: We performed a structured review of the literature focusing on first-pass gadolinium perfusion sequences, stress protocols, and emerging pixel-wise perfusion mapping approaches. Studies were selected to illustrate the methods for image acquisition, post-processing, and quantification of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR), as well as to highlight associations with clinical endpoints. Results: First-pass CMR perfusion imaging reliably detects diffuse and regional microvascular dysfunction across cardiomyopathies. Semi-quantitative parameters (e.g., upslope, MPRI) and quantitative MBF mapping (mL/g/min) have demonstrated that impaired perfusion correlates with disease severity, extent of fibrosis, and adverse outcomes, including heart failure hospitalization, arrhythmias, and mortality. Novel automated pixel-wise mapping enhances reproducibility and diagnostic accuracy, distinguishing coronary microvascular dysfunction from balanced three-vessel disease. Microvascular dysfunction—present in approximately 50–60% of dilated cardiomyopathy (DCM), 40–80% of hypertrophic cardiomyopathy (HCM), and >95% of cardiac amyloidosis (CA) patients—has emerged as a key driver of adverse outcomes. Perfusion defects appear early, often preceding overt hypertrophy or fibrosis, and provide incremental prognostic value beyond conventional CMR metrics. Conclusions: CMR represents a powerful tool for detecting myocardial perfusion abnormalities in nonischemic cardiomyopathies, improving phenotyping, risk stratification, and personalized management. Further standardization of quantitative perfusion techniques will facilitate broader clinical adoption. Full article

Purpose: Obesity’s metabolic consequences are well documented; however, its neurobiological underpinnings remain elusive. This systematic review addresses a critical gap by synthesizing evidence on obesity-induced neuroplasticity across structural, functional, and molecular domains through advanced neuroimaging. Methods: According to PRISMA guidelines, we systematically searched (2015–2024) across PubMed/Web of Science, employing MeSH terms: (“Obesity” [Majr]) AND (“Neuroimaging” [Mesh] OR “Magnetic Resonance Imaging” [Mesh]). A total of 104 studies met the inclusion criteria. The inclusion criteria required the following: (1) multimodal imaging protocols (structural MRI/diffusion tensor imaging/resting-state functional magnetic resonance imaging (fMRI)/positron emission tomography (PET)); (2) pre-/post-intervention longitudinal design. Risk of bias was assessed via the Newcastle-Ottawa Scale. Key Findings: 1. Structural alterations: 7.2% mean gray matter reduction in prefrontal cortex (Cohen’s d = 0.81). White matter integrity decline (FA reduction β = −0.33, p < 0.001) across 12 major tracts. 2. Functional connectivity: Resting-state hyperactivity in mesolimbic pathways (fALFF + 23%, p-FDR < 0.05). Impaired fronto–striatal connectivity (r = −0.58 with BMI, 95% CI [−0.67, −0.49]). 3. Interventional reversibility: Bariatric surgery restored prefrontal activation (Δ = +18% vs. controls, p = 0.002). Neurostimulation (transcranial direct current stimulation (tDCS) enhanced cognitive control (post-treatment β = 0.42, p = 0.009). Conclusion: 1. Obesity induces multidomain neural reorganization beyond traditional reward circuits. 2. Neuroimaging biomarkers (e.g., striatal PET-dopamine binding potential) predict intervention outcomes (AUC = 0.79). 3. Precision neuromodulation requires tripartite integration of structural guidance, functional monitoring, and molecular profiling. Findings highlight neuroimaging’s pivotal role in developing stage-specific therapeutic strategies. Full article

Background/Objectives: Kidney transplantation (kTx) is the preferred treatment for end-stage kidney disease. Limited evaluation of structural changes in transplanted kidneys hinders the timely prediction of disease progression and the implementation of treatment modifications. Protocol biopsies provide valuable insights but are invasive and carry risks of biopsy-related complications. This study investigates whether multiparametric magnetic resonance imaging (MRI), including T1 and T2 mapping and diffusion-weighted imaging (DWI), can predict kidney function and the progression of interstitial fibrosis and tubular atrophy (IF/TA) in the early post-transplant period. Methods: A prospective study was conducted at The Hospital of Lithuanian University of Health Sciences Kauno Klinikos from May 2022 to March 2024. Thirty-four patients receiving kidney transplants from deceased donors underwent baseline biopsies and post-transplant MRI scans. Follow-up assessments included kidney function evaluation, biopsies, and MRI scans at three months post-transplant. Results: Significant correlations were observed between MRI parameters and kidney function: T1 and apparent diffusion coefficient (ADC) corticomedullary differentiation (CMD) correlated with eGFR at discharge (r = −0.338, p = 0.05; r = 0.392, p = 0.022, respectively). Linear and logistic regression models demonstrated that post-transplant T1 and ADC CMD values significantly predicted kidney function at discharge. Furthermore, T1 CMD values measured 10–15 days post-transplant predicted IF/TA progression at three months post-kTx, with an area under the curve of 0.802 (95% CI: 0.616–0.987, p = 0.001) and an optimal cut-off value of −149.71 ms. The sensitivity and specificity were 0.818 and 0.273, respectively (Youden’s index = 0.545). T2 mapping was not predictive. Conclusions: This study highlights the potential immediate clinical utility of MRI-derived biomarkers, particularly ADC and T1 CMD, in centers equipped with advanced imaging capabilities as tools for assessing kidney function in the early post-transplant period. With an AUROC of 0.802, T1 CMD demonstrates strong discriminatory power for predicting IF/TA progression early in the post-transplant period. Full article

Spinal cord infarction (SCI) of arterial origin is a rare vascular event, and its incidence is probably underestimated. There are no strong epidemiological data, and the diagnostic pathway is complex and sometimes incomplete. Furthermore, many cases may be misdiagnosed as other forms of acute and subacute myelopathies. The focus of this review is the clinical and neuroradiological issues in diagnosing SCI and their respective reliability in a clinical setting. The new proposed diagnostic criteria of SCI, although not covering all aspects, highlight the need for a comprehensive approach, including even atypical cases, as the lack of cord compression on Magnetic Resonance Imaging (MRI) is the only mandatory feature for diagnosis. Some MRI features are supportive of the diagnosis, particularly when the anterior spinal artery territory is involved and diffusion-weighted imaging (DWI) is used. Several etiologies can be considered, considering traditional vascular risk factors and diseases affecting the aorta and its main branches, yet a significant proportion of cases remain without a definite etiology. The strongest predictor of SCI diagnosis is a clinical variable, i.e., a time to nadir of severe deficits < 12 h. Full article

Background/Objectives: White matter (WM) structural changes have been found in patients with chronic spinal pain (CSP). In these patients, pain neuroscience education followed by cognition-targeted exercise therapy (i.e., the Modern Pain Neuroscience Approach (MPNA)) was shown to be more effective than biomedically-focused education followed by symptom-contingent exercise therapy for improving clinical outcomes. The present study examined whether an MPNA, compared to biomedically-focused treatment, can change WM structure in regions of interest and whether potential WM structural changes are associated with clinical improvements in patients with CSP. Methods: Patients with CSP were randomized into an experimental (MPNA) or control (biomedically-focused) treatment group. Diffusion-weighted Magnetic Resonance Images were acquired pre-treatment, post-treatment, and at 1-year follow-up. WM structure was assessed using diffusion tensor imaging in 8 WM regions of interest, and linear mixed models assessed differences between groups in response to treatment. Results: No significant treatment x time interaction effects were found; however, significant main effects of time were found in 7 WM tracts. Significant main effects of time revealed increased fractional anisotropy (FA), decreased mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in the cingulum hippocampus, and decreased RD and MD in the superior cerebellar peduncle at 1-year follow-up compared to baseline. In contrast, decreased FA and/or increased MD, AD, or RD values were found in other WM tracts (e.g., anterior corona radiata) from pre-treatment to 1-year follow-up. Greater reduction in kinesiophobia was moderately correlated with a smaller decrease in RD in the superior cerebellar peduncle at 1-year follow-up compared to baseline. No other significant associations were found between WM structural changes and clinical improvements. Conclusions: In conclusion, in patients with CSP, regional WM structure changed over time irrespective of prescribed treatment (timespan of 12 months). Further research, including Neurite Orientation Dispersion and Density Imaging and a healthy control group, allowing for a more specific examination of WM microstructural changes in response to multimodal treatment in patients with CSP, is warranted. Full article

Acute vertigo or dizziness is a frequent presentation to the emergency department (ED), making up between 2.1% and 4.4% of all consultations. Given the nature of the ED where the priority is triage, diagnostic delays and misdiagnoses are common, with as many as a third of vertebrobasilar strokes presenting with acute vertigo or dizziness being missed. Here, we review diagnostic errors identified in the evaluation and treatment of the acutely dizzy patient and discuss strategies to overcome them. Lessons learned include focusing on structured history taking, asking about timing and triggers to inform a targeted examination, assessing subtle ocular motor findings (e.g., by use of HINTS(+)), and avoiding overreliance on brain imaging (including early magnetic resonance imaging including diffusion-weighted sequences [DWI-MRI]). Importantly, up to 20% of DWI-MRI may be false negatives if obtained within the first 24–48 h after symptom onset. Likewise, overreliance on focal neurologic findings to confirm a stroke diagnosis should be avoided because isolated dizziness, vertigo, or even unsteadiness may be the only symptoms in some patients with vertebrobasilar stroke. Furthermore, in patients with triggered episodic vestibular symptoms provocation maneuvers should be preferred over HINTS(+), and a potential diagnosis of stroke should not be immediately dismissed in younger patients presenting with a headache (where migraine may be more common), but the possibility of a vertebral artery dissection should be further evaluated. Importantly, moderate training of non-experts allows for significant improvement in diagnostic accuracy in the acutely dizzy patient and thus should be prioritized. Full article