Search Results (1,629)

Early diagnosis is critical for the effective management of neurodegenerative disorders, and retinal alterations have emerged as promising early biomarkers due to the retina’s close developmental and functional link to the brain. The zebrafish (Danio rerio), with its rapid development, transparent embryos, and evolutionarily conserved visual system, represents a powerful and versatile model for studying retinal degeneration. This review discusses a range of behavioral assays—including visual adaptation, motion detection, and color discrimination—that are employed to evaluate retinal function in zebrafish. These methods enable the detection of subtle visual deficits that may precede overt anatomical damage, providing a non-invasive, efficient strategy for early diagnosis and high-throughput drug screening. Importantly, these behavioral tests also serve as sensitive functional readouts to evaluate the efficacy of pharmacological treatments over time. Compared to traditional murine models, zebrafish offer advantages such as lower maintenance costs, faster development, optical transparency for live imaging, and ethical benefits due to reduced use of higher vertebrates. However, variability in experimental protocols highlights the need for standardization to ensure reliability and reproducibility. Full article

Background: Chronic primary orofacial pain (COFP) affects approximately 7% of the population and often leads to reduced quality of life. Patients frequently undergo multiple assessments and treatments across healthcare disciplines, often without a definitive diagnosis. The 2019 ICD-11 classification of chronic primary pain clusters together COFP subtypes based on chronicity and associated functional and emotional impairment. Objective: This study aimed to evaluate whether these subtypes of COFP share common underlying mechanisms by comparing neuroimaging findings. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Searches were performed using Medline (OVID) and Scopus up to April 2025. Inclusion criteria focused on MRI-based neuroimaging studies of participants diagnosed with COFP subtypes. Data extraction included participant demographics, imaging modality, brain regions affected, and pain assessment tools. Quality assessment used a modified Coleman methodological score. Results: Fourteen studies met the inclusion criteria, all utilising MRI and including two COFP subtypes (temporomandibular disorder and burning mouth syndrome). Resting- and task-state imaging revealed overlapping alterations in several brain regions, including the thalamus, somatosensory cortices (S1, S2), cingulate cortex, insula, prefrontal cortex, basal ganglia, medial temporal lobe, and primary motor area. These changes were consistent across both TMD and BMS populations. Conclusions: The findings suggest that chronic primary orofacial pain conditions (TMD and BMS) may share common central neuroplastic changes, supporting the hypothesis of a unified pathophysiological mechanism. This has implications for improving diagnosis and treatment strategies, potentially leading to more targeted and effective care for these patients. Full article

The quality of life (QoL) perception has been studied in neurological diseases; however, there is limited information linking brain morphological characteristics, QoL, and cognition. Human behavior and perception are associated with specific brain areas that interact through diffuse electrochemical networking. We used magnetic resonance imaging (MRI) to analyze the brain region volume (BRV) correlation with the scores of Rand’s 36-item Short Form Survey (SF-36) and cognitive domains (memory and dementia status). We analyzed data from 420 adult participants in the Maracaibo Aging Study (MAS). Principal component analysis with oblimin axis rotation was used to gather redundant information from brain parcels and SF-36 domains. Canonical correlation was used to analyze the relationships between SF-36 domains and BRV (adjusted for intracranial cavity), as well as sex, age, education, obesity, and hypertension. The average age (±SD) of subjects was 56 ± 11.5 years; 71% were female; 39% were obese; 12% had diabetes, 52% hypertension, and 7% dementia. No sex-related differences were found in memory and orientation scores, but women had lower QoL scores. The 1st and 2nd canonical correlation roots support the association of SF-36 domains (except social functioning and role emotional) and total brain volume, frontal lobe volume, frontal pole, lateral orbital lobe, cerebellar, and entorhinal areas. Other variables, including age, dementia, memory score, and systolic blood pressure, had a significant influence. The results of this study demonstrate significant correlations between BRV and SF-36 components, adjusted for covariates. The frontal lobe and insula were associated with the mental health component; the lateral-orbital frontal lobe and entorhinal area were correlated with the physical component. Full article

Brain morphological abnormalities are common in patients with neurodevelopmental disorders (NDDs) and other neuropsychiatric disorders, often reflecting abnormal brain development and function. Genetic studies have found common genetic factors in NDDs and other neuropsychiatric disorders, although the etiology of brain structural changes in these disorders remains poorly understood. In this study, we analyzed magnetic resonance imaging (MRI) and genetic data from more than 30K individuals from the UK Biobank to evaluate whether NDD-risk copy number variants (CNVs) are also associated with neuroanatomical changes in both patients and neurotypical individuals. We found that the size differences in brain regions such as corpus callosum and cerebellum were associated with the deletions of specific areas of the human genome, and that specific neuroanatomical changes confer a risk of neuropsychiatric disorders. Furthermore, we observed that gene sets located in these genomic regions were enriched for pathways crucial for brain development and for phenotypes commonly observed in patients with NDDs. These findings highlight the link between CNVs, brain structure abnormalities, and the shared pathophysiology of NDDs and other neuropsychiatric disorders, providing new insights into the underlying mechanisms of these disorders and the identification of potential biomarkers for better diagnosis. Full article

Background: The central variant of posterior reversible encephalopathy syndrome (cvPRES) is an atypical subtype of PRES. Although no unifying definitions exists, it is most often characterized by vasogenic edema involving “central” structures, such as the brainstem, subcortical nuclei, and spinal cord, with relative sparing of the parieto-occipital lobes. Methods: This systematic review and meta-analysis followed the PRISMA guidelines and was pre-registered on PROSPERO [CRD42023483806]. Both the Joanna Briggs Institute and New-Castle Ottawa scale were used for case reports and cohort studies, respectively. The meta-analysis was completed using R-Studio and its associated “metafor” package. Results: A comprehensive search in four databases yielded 70 case reports/series (n = 100) and 12 cohort studies. The meta-analysis revealed a pooled incidence rate of 13% (95% CI: 9–18%) for cvPRES amongst included cohort studies on PRES. Significant heterogeneity was observed (I² = 71% and a τ² = 0.2046). The average age of affected individuals was 40.9 years, with a slightly higher prevalence in males (54%). The most common etiological factor was hypertension (72%). Fifty percent had an SBP >200 mmHg at presentation and a mean arterial pressure (MAP) of 217.6 ± 40.82. Imaging revealed an increased T₂ signal involving the brain stem (88%), most often in the pons (62/88; 70.45%), and 18/100 (18%) cases of PRES with spinal cord involvement (PRES-SCI). Management primarily involved blood pressure reduction, with adjunctive therapies for underlying causes such as anti-seizure medications or hemodialysis. The MAP between isolated PRES-SCI and cvPRES without spinal cord involvement did not show significant differences (p = 0.5205). Favorable outcomes were observed in most cases, with a mortality rate of only 2%. Conclusions: cvPRES is most often associated with higher blood pressure compared to prior studies with typical PRES. The pons is most often involved. Despite the severity of blood pressure and critical brain stem involvement, those with cvPRES have favorable functional outcomes and a lower mortality rate than typical PRES, likely attributable to reversible vasogenic edema without significant neuronal dysfunction. Full article

Accumulating studies have shown that astrocytes are essential for regulating neurons at both synaptic and circuit levels. The main mechanism of brain astrocytic intracellular Ca²⁺ activity is through the release of Ca²⁺ via the inositol 1,4,5-trisphosphate receptor type 2 (IP3R2) from the endoplasmic reticulum (ER). Studies using IP3R2 knockout mouse models (Itpr2^−/−) have shown that eliminating IP3R2 leads to a significant reduction in astrocytic Ca²⁺ activity However, there is ongoing controversy regarding the effect of this IP3R2-dependent reduction in astrocytic Ca²⁺ transients on neuronal activity. In our study, we employed dual-color two-photon Ca²⁺ imaging to study astrocytes and neurons simultaneously in vibrissa somatosensory cortex (vS1) in awake-behaving wild-type and Itpr2^−/− mice. We systematically characterized and compared both recorded astrocytic and neuronal Ca²⁺ activities in wild-type and Itpr2^−/− mice during various animal behaviors, particularly during the transition period from stillness to locomotion. We report that vS1 astrocytic Ca²⁺ elevation in both wild-type and Itpr2^−/− mice was significantly modulated by free whisking and locomotion. However, vS1 neurons were only significantly modulated by locomotion in wild-type mice, but not in Itpr2^−/− mice. Our study suggests a non-synaptic modulatory mechanism on functions of astrocytic IP3R2-dependent Ca²⁺ transients to local neurons. Full article

Artificial Intelligence (AI) and computer-aided diagnosis (CAD) have revolutionised various aspects of modern life, particularly in the medical domain. These technologies enable efficient solutions for complex challenges, such as accurately segmenting brain tumour regions, which significantly aid medical professionals in monitoring and treating patients. This research focuses on segmenting glioma brain tumour lesions in MRI images by analysing them at the pixel level. The aim is to develop a deep learning-based approach that enables ensemble learning to achieve precise and consistent segmentation of brain tumours. While many studies have explored ensemble learning techniques in this area, most rely on aggregation functions like the Weighted Arithmetic Mean (WAM) without accounting for the interdependencies between classifier subsets. To address this limitation, the Choquet integral is employed for ensemble learning, along with a novel evaluation framework for fuzzy measures. This framework integrates coalition game theory, information theory, and Lambda fuzzy approximation. Three distinct fuzzy measure sets are computed using different weighting strategies informed by these theories. Based on these measures, three Choquet integrals are calculated for segmenting different components of brain lesions, and their outputs are subsequently combined. The BraTS-2020 online validation dataset is used to validate the proposed approach. Results demonstrate superior performance compared with several recent methods, achieving Dice Similarity Coefficients of 0.896, 0.851, and 0.792 and 95% Hausdorff distances of 5.96 mm, 6.65 mm, and 20.74 mm for the whole tumour, tumour core, and enhancing tumour core, respectively. Full article

Background/Objectives: The glymphatic system plays a crucial role in clearing brain metabolic waste, and its dysfunction has been correlated to various neurological disorders. The Diffusion Tensor Imaging Analysis Along the Perivascular Space (DTI-ALPS) index has been proposed as a non-invasive marker of glymphatic function by measuring diffusivity along perivascular spaces; however, its sensitivity to sleep-related changes in glymphatic activity has not yet been validated. This study aimed to evaluate the feasibility of using the DTI-ALPS index as a quantitative marker of dynamic glymphatic activity during sleep. Methods: Diffusion tensor imaging (DTI) data were obtained from 12 healthy male participants (age = 24.44 ± 2.5 years; Pittsburgh Sleep Quality Index (PSQI) < 5), once while awake and 16 times during sleep, following 24 h sleep deprivation and administration of 10 mg zolpidem. Simultaneous MR-compatible electroencephalography was used to determine whether the subject was asleep or awake. DTI preprocessing included eddy current correction and tensor fitting. The DTI-ALPS index was calculated from nine regions of interest in projection and association areas aligned to standard space. The final analysis included nine participants (age = 24.56 ± 2.74 years; PSQI < 5) who maintained a continuous sleep state for 1 h without awakening. Results: Among nine ROI pairs, three showed significant increases in the DTI-ALPS index during sleep compared to wakefulness (Friedman test; p = 0.027, 0.029, 0.034). These ROIs showed changes at 14, 19, and 25 min after sleep induction, with FDR-corrected p-values of 0.024, 0.018, and 0.018, respectively. Conclusions: This study demonstrated a statistically significant increase in the DTI-ALPS index within 30 min after sleep induction through time-series DTI analysis during wakefulness and sleep, supporting its potential as a biomarker reflecting glymphatic activity. Full article

The substantia nigra (SN) has historically been regarded as a pivotal element of the brain’s motor circuits, notably within the context of the nigrostriatal pathway and Parkinson’s disease. However, recent advancements in neuroimaging techniques, particularly tractography, have facilitated the delineation of its anatomical projections. These techniques have revealed the involvement of the SN in a more extensive array of functional networks encompassing cognitive, emotional, and motivational domains. This paper reviews the current knowledge on the structural connectivity of the SN in humans based on diffusion tensor imaging and tractography. It summarizes the main projection pathways, including classical and newly described connections, such as the direct SN pars compacta connections to the thalamus, cortico–neural inputs, and connections to limbic regions and the hippocampus. Furthermore, the text delves into the distinctions between the SN pars compacta and SN pars reticulata subregions, exploring their parcellation based on connectivity. The paper demonstrates that the SN is a functionally diversified nucleus, the implications of which are significant for the understanding of both motor and neuropsychiatric disorders. The present study addresses the paucity of comprehensive treatment in this area and provides a framework for further research on dopaminergic circuits. Full article

Major depressive disorder is a mental illness characterized by persistent sadness or loss of interest that affects a person’s daily life. Early detection of this disorder is crucial for providing timely and effective treatment. Neuroimaging modalities, namely, functional magnetic resonance imaging, can be used to identify changes in brain regions related to major depressive disorder. In this study, regional homogeneity images, one of the derivative of functional magnetic resonance imaging is employed to detect major depressive disorder using the proposed feature/sample evolving voting ensemble approach. A total of 2380 subjects consisting of 1104 healthy controls and 1276 patients with major depressive disorder from Rest-meta-MDD consortium are studied. Regional homogeneity features from 90 regions are extracted using automated anatomical labeling template. These regional homogeneity features are then fed as an input to the proposed feature/sample selective evolving voting ensemble for classification. The proposed approach achieves an accuracy of 91.93%, and discriminative features obtained from the classifier are used to identify brain regions which may be responsible for major depressive disorder. A total of nine brain regions, namely, left superior temporal gyrus, left postcentral gyrus, left anterior cingulate gyrus, right inferior parietal lobule, right superior medial frontal gyrus, left lingual gyrus, right putamen, left fusiform gyrus, and left middle temporal gyrus, are identified. This study clearly indicates that these brain regions play a critical role in detecting major depressive disorder. Full article

Backgrounds/Objective: The impact of metabolic disturbances induced by an unbalanced diet on cognitive decline in mid-life is now widely observed, although the mechanisms are not well identified. Here we report that glutamatergic vasoactive pathways are a key feature of high-fat-diet (HFD)-induced neurogliovascular uncoupling in mice. Methods: C57Bl6/J mice are fed either with normal diet (ND) or high-fat diet (HFD) during 6 or 12 months and characterized for metabolic status. Cerebral vascular tree from pial to intraparenchymal arteries, is investigated with Halpern’s arteriography and with differential interference contrast infrared imaging of brain slices. Results: A 70% alteration in the myogenic tone of the basilar artery is observed as early as 6 months (M6) after the HFD. Infrared imaging revealed a 77% reduction in the glutamate-induced vasodilation of intraparenchymal arterioles appearing after 12 months (M12) of the HFD. The respective contributions of enzymes involved in glutamatergic pathways were altered as a function of HFD and time. The decrease in astrocytic COX I observed at M6 was followed by a loss of neuronal COX II and a compensatory action of NOS at M12. Conclusions: This HFD-induced neurogliovascular uncoupling pathway offers therapeutic targets to consider for improving cerebral vasoactive functions while preventing peripheral metabolic disturbances. Full article

Brain metastases (BMs) are the most common intracranial tumors in adults. Their heterogeneity, potential multifocality, and complex biomolecular behavior pose significant diagnostic and therapeutic challenges. Artificial intelligence (AI) has the potential to revolutionize BM diagnosis by facilitating early lesion detection, precise imaging segmentation, and non-invasive molecular characterization. Machine learning (ML) and deep learning (DL) models have shown promising results in differentiating BMs from other intracranial tumors with similar imaging characteristics—such as gliomas and primary central nervous system lymphomas (PCNSLs)—and predicting tumor features (e.g., genetic mutations) that can guide individualized and targeted therapies. Intraoperatively, AI-driven systems can enable optimal tumor resection by integrating functional brain maps into preoperative imaging, thus facilitating the identification and safeguarding of eloquent brain regions through augmented reality (AR)-assisted neuronavigation. Even postoperatively, AI can be instrumental for radiotherapy planning personalization through the optimization of dose distribution, maximizing disease control while minimizing adjacent healthy tissue damage. Applications in systemic chemo- and immunotherapy include predictive insights into treatment responses; AI can analyze genomic and radiomic features to facilitate the selection of the most suitable, patient-specific treatment regimen, especially for those whose disease demonstrates specific genetic profiles such as epidermal growth factor receptor mutations (e.g., EGFR, HER2). Moreover, AI-based prognostic models can significantly ameliorate survival and recurrence risk prediction, further contributing to follow-up strategy personalization. Despite these advancements and the promising landscape, multiple challenges—including data availability and variability, decision-making interpretability, and ethical, legal, and regulatory concerns—limit the broader implementation of AI into the everyday clinical management of BMs. Future endeavors should thus prioritize the development of generalized AI models, the combination of large and diverse datasets, and the integration of clinical and molecular data into imaging, in an effort to maximally enhance the clinical application of AI in BM care and optimize patient outcomes. Full article

Background: Intraoperative ultrasound (IOUS) provides real-time imaging during brain tumor surgery but remains underused in brain metastasis resection. This study evaluates the effectiveness of 2D IOUS in improving the extent of resection compared to standard neuronavigation. Methods: We retrospectively analyzed 55 adult patients with brain metastases treated surgically at a single center. Patients were divided into two groups: IOUS-guided surgery (n = 20) and standard neuronavigation (n = 35). Gross total resection (GTR) was defined as the extent of resection > 96%, assessed volumetrically. Statistical analyses included chi-square tests, logistic regression, and ROC curve analysis. Results: GTR > 96% was achieved in 80% of IOUS-guided cases compared to 42.86% in the control group (p = 0.008). IOUS significantly increased the odds of achieving GTR (OR = 5.33, p = 0.011). Larger tumor volume reduced the likelihood of GTR (OR = 0.469, p = 0.025), but this effect was mitigated by IOUS use (interaction OR = 1.986, p = 0.044). The regression model showed excellent discrimination (AUC = 0.930, p < 0.001). Functional outcomes improved postoperatively in both groups. Conclusions: 2D IOUS significantly enhances the extent of resection in brain metastasis surgery, including that for larger tumors. Its accessibility, real-time feedback, and low cost support its wider adoption in neurosurgical practice, especially in settings with limited resources. Full article

Reports of individual differences in vividness of visual mental imagery (VMI) scores raise complex questions: Are Vividness of Visual Imagery Questionnaire (VVIQ) score differences actually measuring anything? What functions do these differences serve? What is their neurological foundation? A new analysis examined visual short-term memory (VSTM) and volumes of the hippocampi, primary visual cortices, and other cortical regions among vivid and non-vivid visual imagers. In a sample of 53 volunteers aged 54 to 80 with MRI scans, the performance of ten Low VVIQ scorers was compared to that of ten High VVIQ scorers. The groups included an aphantasic with a minimum VVIQ score and a hyperphantasic with a maximum VVIQ score. The study examined volumes for 12 hippocampal subfields, 11 fields implicated in visual mental imagery including area V1 and the fusiform gyrus, and 7 motor regions. In comparison to the Low VVIQ group, High VVIQ group yielded: (i) significantly more accurate VSTM performance; and (ii) significantly larger volumes of the hippocampi and primary visual cortex. Across 47 brain regions, the average volume for the High VVIQ group exceeded that of the Low VVIQ group by 11 percent. For 47 subfields, the volumes of the hphantasic exceeded those of the aphantasic person by an average of 57 percent. Females had more accurate visual short-term memory than males and younger people were more accurate than older people. The larger visual memory capacity of females was unmatched by larger regional volume differences, which suggests that the sex difference in visual memory is caused by factors other than cortical regional size. The study confirms the existence of robust empirical associations between VMI vividness, short-term memory, regional volume of hippocampal subfields and area V1. 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