Search Results (1,466)

Background/Objectives: Recent studies reported that ¹⁸F-Fluorodeoxyglucose (FDG) positron –emission tomography/computed tomography (PET/CT), even in comparison with other traditional methods, can play a role in diagnosing AE and supporting early treatment. In the present study, we further investigated whether ¹⁸F-FDG PET/CT may be a complementary diagnostic tool to conventional procedures in patients with acute symptoms of suspected AE in the early phase. Methods: Eleven consecutive patients with recent acute symptoms suggestive of AE were retrospectively enrolled and underwent brain PET/CT after receiving an intravenous injection of 3.7 MBq/kg of ¹⁸F-FDG. Results: PET/CT showed abnormal FDG uptake in 9/11 patients classified as AE, while it was negative in the remaining 2/11 cases with vascular lesions. Magnetic resonance imaging (MRI), conducted in only 10/11 cases—one patient was a pacemaker wearer—identified suspected AE areas in 3/10 cases, ischemic lesions in another 3/10, and nonspecific data in the remaining 4/10 cases. Cerebrospinal fluid (CSF) tests revealed autoantibody delayed occurrence only in three patients (anti-GAD65, anti-Ma2, and anti-LGI1). After first-line treatment, 3/9 patients showed clinical improvement. Another 3/9 patients experienced partial improvement but with recurrence and new AE brain areas identified by PET/CT, which also detected favorable responses to second-line treatment in 2/3 cases. The remaining 3/9 patients, who were not responsive to treatment, ultimately died. Conclusions: In this study, PET/CT was effective in early identification of AE and enabling rapid therapy, even with inconclusive MRI and persistently negative or delayed positive CSF tests. PET/CT may aid in evaluating treatment response and detecting relapse. Notably, a negative PET/CT was associated with AE absence. Full article

Background and Objectives: Hemiplegic or unilateral cerebral palsy (UCP) is primarily characterized by motor impairment, mainly affecting the upper limb. Research has centered on factors influencing the varying degrees of motor deficit in UCP, using neuroscience advancements for in vivo exploration of brain structure (morphometry) and cortical reorganization (functional magnetic resonance imaging (fMRI)). This study aims to evaluate functional activation in the motor cortex in UCP and to explore how lesion characteristics and timing affect neuroplasticity and motor function. Materials and Methods: Between 2017 and 2021, structural and functional MRIs were performed on 44 UCP patients (mean age 15.5 years, 24 males, 20 females), all with Manual Ability Classification System (MACS) levels I-III and Intelligence Quotient (IQ) ≥ 50. The lesion characteristics of size, type, and time of occurrence (ante-, peri-, or early postnatal) were analyzed. An association was sought between the characteristics of the lesion and the degree of motor deficit of the upper limb, as determined by the MACS level. fMRI assessed cortical activation during a finger-tapping task for the paretic hand and compared activation patterns based on lesion characteristics. Results: Six lesion types were identified, with arterial ischemic stroke being the most common and largest in volume. Lesion size strongly correlated with patients’ MACS levels, while lesion type and timing showed no association with the severity of motor impairment classified by MACS. Motor reorganization varied, with activation occurring ipsi-, contra-, or bilaterally to the affected hand, depending on lesion size and type. Smaller, subcortical lesions primarily showed ipsilesional activation, while larger, cortical lesions did not exhibit a specific group activation, possibly due to varying individual reorganization. No association was found between the lesion timing and the reorganization model. Conclusions: Motor functional reorganization in UCP is closely linked to lesion characteristics, with smaller, subcortical lesions favoring typical organization in the contralateral motor cortex. The timing of the lesion does not significantly affect cortical reorganization. Lesion size was a key determinant of motor function, whereas lesion type (e.g., ischemic stroke) and timing (early vs. late occurrence) were less critical for predicting functional outcome. Full article

Recovery from anterior cruciate ligament reconstruction (ACLR) induces bilateral functional and physiological adaptations. Neurophysiologic measures of motor control have focused on the involved knee joint, limiting understanding regarding the extent of bilateral neural adaptations. Therefore, the aim of this study was to investigate differences in neural activity during uninvolved-limb motor control after ACLR compared to healthy controls. Methods: Fifteen participants with left ACLR (8 female and 7 male, 21.53 ± 2.7 years, 173.22 ± 10.0 cm, 72.15 ± 16.1 kg, Tegner 7.40 ± 1.1, 43.33 ± 33.1 mo. post-surgery, 2 patellar tendon, and 13 hamstring) and 15 matched controls (8 female, 23.33 ± 2.7 years, 174.92 ± 9.7 cm, 72.14 ± 15.4 kg, Tegner 7.33 ± 1.0) participated. Neural activity was evaluated using functional magnetic resonance imaging on a 3T Siemens Magnetom scanner during four 30-s cycles of a right (uninvolved) knee flexion-extension task paced with a metronome (1.2 Hz) and was completed interspersed with 30 s of rest. A significance threshold of p < 0.05 was used for all analyses, cluster corrected for multiple comparisons, and z-thresholds of >3.1 (subject level), and >2.3 (group level). Results: The ACLR group had greater neural activity in one statistically significant cluster corresponding to the left middle frontal gyrus (MFG) (834 voxels, z = 3.81, p < 0.01 multiple comparisons corrected) compared to controls. Conclusions: These data indicate a potential contribution to uninvolved-knee neuromuscular deficits after injury and support the limitations of using the uninvolved side as a clinical reference. Uninvolved knee motor control after ACLR may require greater cognitive demand. Clinicians should be aware that the uninvolved limb might also demonstrate whole brain alterations limiting clinical inference from functional symmetry. Full article

Background and Objectives: Epithelial ovarian–tubal–peritoneal cancer (EOC) is the most common type of ovarian cancer. Optimal cytoreductive surgery is the most important prognostic factor in its management. When complete cytoreduction is anticipated to be challenging, neoadjuvant systemic chemotherapy (NACT) becomes an alternative. Imaging modalities are utilized in the decision-making process for primary treatment. The purpose of this study is to evaluate the diagnostic performance and accuracy of preoperative MRI, CT, and 18F-FDG PET/CT in detecting the extent of EOC. Materials and Methods: Between 2017 and 2018, 24 patients with primary (with or without neoadjuvant chemotherapy) or recurrent EOC diagnosed at the Department of Gynecologic Oncology, Istanbul University, Istanbul Faculty of Medicine, were enrolled in this study. These 24 women underwent preoperative imaging modalities within 7 days prior to surgery. The results were compared with histopathological findings, considered the gold standard. Results: We evaluated 24 anatomic regions most commonly involved in EOC. The sensitivity of MRI, CT, and PET/CT in detecting ≥ 0.5 cm implants was 95%, 84%, and 86%, respectively. However, when including implants < 0.5 cm, sensitivity decreased significantly to 40%, 38%, and 42%, respectively. The calculated area under the curve (AUC) for tumors, including those < 0.5 cm, was evaluated as weak for all three modalities (MRI: 0.689, CT: 0.678, PET/CT: 0.691), with PET/CT detecting the largest area. For detecting tumors ≥ 0.5 cm, the AUCs were 0.974, 0.921, and 0.923 for MRI, CT, and PET/CT, respectively. The largest AUC was calculated with MRI, and the AUCs for all three methods were evaluated as excellent. Accuracy was comparable among all three imaging modalities, and no statistically significant differences were found (p < 0.05). Conclusions: While imaging modalities are valuable tools for evaluating abdominal spread in epithelial ovarian cancer (EOC), they have demonstrated limited success in detecting miliary disease. The risk of false negatives for miliary tumors on PET/CT may be mitigated by combining it with other imaging modalities such as MRI or CT. Further investigations are necessary to identify more accurate imaging techniques for this challenging clinical scenario. Full article

Obsessive–Compulsive Disorder (OCD) is a chronic psychiatric condition with a lifetime prevalence of 2–3%. It significantly burdens quality of life and is associated with substantial economic and disease burdens. Cognitive-behavioral therapy and high-dose selective serotonin reuptake inhibitors are considered the first-line treatments for OCD. Approximately two-thirds of patients with Obsessive–Compulsive Disorder (OCD) exhibit inadequate responses to current standard therapies, thus lacking adequate therapy, resulting in a loss of quality of life and huge economic burdens. Repetitive transcranial stimulation (rTMS) is a non-invasive, safe, and well-tolerated intervention that modulates prefrontal cortical circuits involved in OCD. A previous systematic review explored the therapeutic effects of rTMS applied to the dorsolateral prefrontal cortex (dlPFC) area in patients with treatment-resistant OCD. It showed that the application of high-frequency and low-frequency (LF) rTMS to the dlPFC region yielded controversial post-treatment Y-BOCS (Yale-Brown Obsessive–Compulsive Scale) findings due to factors such as small sample sizes, short-term study durations, and variations in rTMS protocols. Objectives: Thus, we propose a theoretical protocol based on previous findings to assess better the effect of LF rTMS for treatment-resistant OCD patients. Methods: We will recruit patients with moderate to severe OCD and limited response to previous treatments from in- and outpatient clinics. We will use fMRI for precious localization of the right dlPFC and application of 1 Hz stimulation of in total 2000 pulses with three times 40 s inter-train intervals 5 days a week, in 6 consecutive weeks. The primary outcome will be the mean reduction in Y-BOCS at the end of this study. Conclusions: This study highlights rTMS’s potential to reform OCD treatment, accentuate safety, accessibility, clinical integration, and future research foundations. Full article

In amyloid brain PET, after parcellation using the finite element method (FEM)-based algorithm FreeSurfer and voxel-based algorithm PMOD, SUVr examples can be extracted and compared. This study presents the classification SUVr threshold in PET images of F-18 florbetaben (FBB), F-18 flutemetamol (FMM), and F-18 florapronol (FPN) and compares and analyzes the classification performance according to computational algorithm in each brain region. PET images were co-registered after the generated MRI was registered with standard template information. Using MATLAB script, SUVr was calculated using the built-in parcellation number labeled in the brain region. PMOD and FreeSurfer with different algorithms were used to load the PET image, and after registration in MRI, it was normalized to the MRI template. The volume and SUVr of the individual gray matter space region were calculated using an automated anatomical labeling atlas. The SUVr values of eight regions of the frontal cortex (FC), lateral temporal cortex (LTC), mesial temporal cortex (MTC), parietal cortex (PC), occipital cortex (OC), anterior and posterior cingulate cortex (GCA, GCP), and composite were calculated. After calculating the correlation of SUVr using the FreeSurfer and PMOD algorithms and calculating the AUC for amyloid-positive/negative subjects, the classification ability was calculated, and the SVUr threshold was calculated using the Youden index. The correlation coefficients of FreeSurfer and PMOD SUVr calculations of the eight regions of the brain cortex were FBB (0.95), FMM (0.94), and FPN (0.91). The SUVr threshold was SUVr(LTC,min) = 1.264 and SUVr(THA,max) = 1.725 when calculated using FPN-FreeSurfer, and SUVr(MTC,min) = 1.093 and SUVr(MCT,max) = 1.564 when calculated using FPN-PMOD. The AUC comparison showed that there was no statistically significant difference (p > 0.05) in the SUVr classification results using the three radiopharmaceuticals, specifically for the LTC and OC regions in the PMOD analysis, and the LTC and PC regions in the FreeSurfer analysis. The SUVr calculation using PMOD (voxel-based algorithm) has a strong correlation with the calculation using FreeSurfer (FEM-based algorithm); therefore, they complement each other. Quantitative classification analysis with high accuracy is possible using the suggested SUVr threshold. The SUVr classification performance was good in the order of FMM, FBB, and FPN, and showed a good classification performance in the LTC region regardless of the type of radiotracer and analysis algorithm. Full article

Migraine with aura (MwA) is a common and severely disabling neurological disorder, characterised by transient yet recurrent visual disturbances, including scintillating scotomas, flickering photopsias, and complex geometric patterns. These episodic visual phenomena significantly compromise daily functioning, productivity, and overall quality of life. Despite extensive research, the underlying pathophysiological mechanisms remain only partially understood. Cortical spreading depression (CSD), a propagating wave of neuronal and glial depolarisation, has been identified as a central process in MwA. This phenomenon is triggered by ion channel dysfunction, leading to elevated intracellular calcium levels and excessive glutamate release, which contribute to widespread cortical hyperexcitability. Genetic studies, particularly involving the CACNA gene family, further implicate dysregulation of calcium channels in the pathogenesis of MwA. Recent advances in neuroimaging, particularly functional magnetic resonance imaging (fMRI), have provided critical insights into the neurophysiology of MwA. These results support the central role of CSD as a basic mechanism behind MwA and imply that cortical dysfunction endures beyond brief episodes, possibly due to chronic neuronal dysregulation or hyperexcitability. The visual cortex of MwA patients exhibits activation patterns in comparison to other neuroimaging studies, supporting the possibility that it is a disease-specific biomarker. Its distinctive sensory and cognitive characteristics are influenced by a complex interplay of cortical, vascular, and genetic factors, demonstrating the multifactorial nature of MwA. We now know much more about the pathophysiology of MwA thanks to the combination of molecular and genetic research with sophisticated neuroimaging techniques like arterial spin labelling (ASL) and fMRI. This review aims to synthesize current knowledge and analyse molecular and neurophysiological targets, providing a foundation for developing targeted therapies to modulate cortical excitability, restore neural network stability, and alleviate the burden of migraine with aura. The most important and impactful research in our field has been the focus of this review, which highlights important developments and their contributions to the knowledge and treatment of migraine with aura. Full article

Background/Objectives: Brain tumors are among the severe and life-threatening conditions, with timely and accurate detection being crucial for determining the appropriate course of treatment. These tumors can vary widely in their aggressiveness, location, and type, making early diagnosis and precise classification essential for improving patient outcomes and survival rates. The complexity of brain tumors, combined with the detailed nature of MRI scans, presents significant challenges in the diagnostic process, underscoring the need for advanced tools to support clinicians in making informed decisions. New Method: This study focuses on developing and evaluating a proposed model for brain tumor classification using MRI images. The model employs a transfer learning approach fine-tuned to the brain tumor dataset, explicitly utilizing the EfficientNetB0 architecture. Results: The proposed model achieved an outstanding overall accuracy of 0.99, with precision, recall, and F1 scores exceeding 0.98 across all tumor classes. Comparison with Existing Methods: These results demonstrate the model’s potential as a reliable tool for assisting clinicians in diagnosing and classifying brain tumors. Conclusions: This is particularly valuable in the early stages of tumor development, where detection can be challenging, and early intervention is critical for successful treatment. Full article

Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), the asymptomatic precursors to multiple myeloma, affect up to 5% of the population over the age of 40. Bone involvement, a myeloma-defining event, represents a major source of morbidity for patients. Key goals for the management of myeloma precursor conditions include (1) identifying patients at the highest risk for progression to MM with bone involvement and (2) differentiating precursor states from active myeloma requiring treatment. Computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT with [¹⁸F]fluorodeoxyglucose (FDG) have improved sensitivity for the detection of myeloma bone disease compared to traditional skeletal surveys, and such advanced imaging also provides this field with better tools for detecting early signs of progression. Herein, we review the data supporting the use of advanced imaging for both diagnostics and prognostication in myeloma precursor conditions. Full article

Background/Objectives: To develop a computer-aided diagnosis (CAD) method for the classification of late gadolinium enhancement (LGE) cardiac MRI images into myocardial infarction (MI), myocarditis, and healthy classes using a fine-tuned VGG16 model hybridized with multi-layer perceptron (MLP) (VGG16-MLP) and assess our model’s performance in comparison to various pre-trained base models and MRI readers. Methods: This study included 361 LGE images for MI, 222 for myocarditis, and 254 for the healthy class. The left ventricle was extracted automatically using a U-net segmentation model on LGE images. Fine-tuned VGG16 was performed for feature extraction. A spatial attention mechanism was implemented as a part of the neural network architecture. The MLP architecture was used for the classification. The evaluation metrics were calculated using a separate test set. To compare the VGG16 model’s performance in feature extraction, various pre-trained base models were evaluated: VGG19, DenseNet121, DenseNet201, MobileNet, InceptionV3, and InceptionResNetV2. The Support Vector Machine (SVM) classifier was evaluated and compared to MLP for the classification task. The performance of the VGG16-MLP model was compared with a subjective visual analysis conducted by two blinded independent readers. Results: The VGG16-MLP model allowed high-performance differentiation between MI, myocarditis, and healthy LGE cardiac MRI images. It outperformed the other tested models with 96% accuracy, 97% precision, 96% sensitivity, and 96% F1-score. Our model surpassed the accuracy of Reader 1 by 27% and Reader 2 by 17%. Conclusions: Our study demonstrated that the VGG16-MLP model permits accurate classification of MI, myocarditis, and healthy LGE cardiac MRI images and could be considered a reliable computer-aided diagnosis approach specifically for radiologists with limited experience in cardiovascular imaging. Full article

Background: The role of molecular imaging in urothelial cancer is less defined than other cancers, and its utility remains controversial due to limitations such as high urinary tracer excretion, complicating primary tumour assessment in the bladder and upper urinary tract. This review explores the current landscape of PET imaging in the clinical management of urothelial cancer, with a special emphasis on potential future advancements including emerging novel non-¹⁸F FDG PET agents, PET radiopharmaceuticals, and PET-MRI applications. Methods: We conducted a comprehensive literature search in the PubMed database, using keywords such as “PET”, “PET-CT”, “PET-MRI”, “FDG PET”, “Urothelial Cancer”, and “Theranostics”. Studies were screened for relevance, focusing on imaging modalities and advances in PET tracers for urothelial carcinoma. Non-English language, off-topic papers, and case reports were excluded, resulting in 80 articles being selected for discussion. Results: ¹⁸F FDG PET-CT has demonstrated superior sensitivity over conventional imaging, such as contrast-enhanced CT and MRI, for detecting lymph node metastasis and distant disease. Despite these advantages, FDG PET-CT is limited for T-staging of primary urothelial tumours due to high urinary excretion of the tracer. Emerging evidence supports the role of PETC-CT in assessing response to neoadjuvant chemotherapy and in identifying recurrence, with a high diagnostic accuracy reported in several studies. Novel PET tracers, such as ⁶⁸Ga-labelled FAPI, have shown promising results in targeting cancer-associated fibroblasts, providing higher tumour-to-background ratios and detecting lesions missed by traditional imaging. Antibody-based PET tracers, like those targeting Nectin-4, CAIX, and uPAR, are under investigation for their diagnostic and theranostic potential, and initial studies indicate that these agents may offer advantages over conventional imaging and FDG PET. Conclusions: Molecular imaging is a rapidly evolving field in urothelial cancer, offering improved diagnostic and prognostic capabilities. While ¹⁸F FDG PET-CT has shown utility in staging, further prospective research is needed to establish and refine standardised protocols and validate new tracers. Advances in theranostics and precision imaging may revolutionise urothelial cancer management, enhancing the ability to tailor treatments and improve patient outcomes. Full article

Objective: Functional magnetic resonance imaging data pose significant challenges due to their inherently noisy and complex nature, making traditional statistical models less effective in capturing predictive features. While deep learning models offer superior performance through their non-linear capabilities, they often lack transparency, reducing trust in their predictions. This study introduces the Time Reversal (TR) pretraining method to address these challenges. TR aims to learn temporal dependencies in data, leveraging large datasets for pretraining and applying this knowledge to improve schizophrenia classification on smaller datasets. Methods: We pretrained an LSTM-based model with attention using the TR approach, focusing on learning the direction of time in fMRI data, achieving over 98 % accuracy on HCP and UK Biobank datasets. For downstream schizophrenia classification, TR-pretrained weights were transferred to models evaluated on FBIRN, COBRE, and B-SNIP datasets. Saliency maps were generated using Integrated Gradients (IG) to provide post hoc explanations for pretraining, while Earth Mover’s Distance (EMD) quantified the temporal dynamics of salient features in the downstream tasks. Results: TR pretraining significantly improved schizophrenia classification performance across all datasets: median AUC scores increased from 0.7958 to 0.8359 (FBIRN), 0.6825 to 0.7778 (COBRE), and 0.6341 to 0.7224 (B-SNIP). The saliency maps revealed more concentrated and biologically meaningful salient features along the time axis, aligning with the episodic nature of schizophrenia. TR consistently outperformed baseline pretraining methods, including OCP and PCL, in terms of AUC, balanced accuracy, and robustness. Conclusions: This study demonstrates the dual benefits of the TR method: enhanced predictive performance and improved interpretability. By aligning model predictions with meaningful temporal patterns in brain activity, TR bridges the gap between deep learning and clinical relevance. These findings emphasize the potential of explainable AI tools for aiding clinicians in diagnostics and treatment planning, especially in conditions characterized by disrupted temporal dynamics. Full article

Background: Alzheimer’s disease is a progressive neurological condition marked by a decline in cognitive abilities. Early diagnosis is crucial but challenging due to overlapping symptoms among impairment stages, necessitating non-invasive, reliable diagnostic tools. Methods: We applied information geometry and manifold learning to analyze grayscale MRI scans classified into No Impairment, Very Mild, Mild, and Moderate Impairment. Preprocessed images were reduced via Principal Component Analysis (retaining 95% variance) and converted into statistical manifolds using estimated mean vectors and covariance matrices. Geodesic distances, computed with the Fisher Information metric, quantified class differences. Graph Neural Networks, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE, were utilized to categorize impairment levels using graph-based representations of the MRI data. Results: Significant differences in covariance structures were observed, with increased variability and stronger feature correlations at higher impairment levels. Geodesic distances between No Impairment and Mild Impairment (58.68, $p<0.001$) and between Mild and Moderate Impairment (58.28, $p<0.001$) are statistically significant. GCN and GraphSAGE achieve perfect classification accuracy (precision, recall, F1-Score: 1.0), correctly identifying all instances across classes. GAT attains an overall accuracy of 59.61%, with variable performance across classes. Conclusions: Integrating information geometry, manifold learning, and GNNs effectively differentiates AD impairment stages from MRI data. The strong performance of GCN and GraphSAGE indicates their potential to assist clinicians in the early identification and tracking of Alzheimer’s disease progression. Full article

The neuroscience of language processing in the human brain has a long history. Strings of letters that form meaningful words trigger lexical and semantic processing, which in turn lead to conscious awareness of what the words mean. However, it is still unclear how the brain processes normal words differently from number words and, more interestingly, how the brain processes number words differently from digits, both of which are meant to trigger quantity processing. While much of the literature deals with this topic, the time course of the respective differences in brain activity has been largely ignored. This may be because most studies have used functional magnetic resonance imaging (fMRI), which is known to have limited temporal resolution. This study used electroencephalography (EEG), more specifically event-related potentials (ERPs), to investigate brain potential differences between visual presentations of words, non-words, number words and digits. This approach made it possible to describe the time course of brain activity evoked by these four stimulus categories. Starting at about 200 ms post-stimulus, digits elicited the strongest negative ERP in the right occipito-parietal cortical region. Peaking at around 300 ms after stimulus onset, number words elicited the most negative going ERP in the left occipito-parietal area. Finally, starting at about 400 ms after stimulus onset, digits elicited by far the most negative ERP in the left inferior fronto-temporal area. All of these findings are supported by analytical statistics across all study participants. It is noteworthy that the last effect in the left inferior fronto-temporal area can also be seen for number words, but it is much smaller and not statistically significant. In summary, we found clear differences between brain activity related to the processing of words, non-words, number words, and digits, providing evidence that the left inferior fronto-temporal cortical area is specialised for the processing of quantities. Furthermore, it can be concluded that digits are better symbols for mediating quantity processing in the human brain than number words. Full article

Background: Problematic social media (SM) use is a growing concern, particularly among adolescents who are drawn to these platforms for social interactions important to their age group. SM dependence is characterized by excessive, uncontrolled usage that impairs personal, social, and professional aspects. Despite the ongoing debate over recognizing SM addiction as a distinct diagnostic category, the impact of social feedback, particularly through the “like” button, on brain activity remains under scrutiny. Objective: This systematic review aims to study the neural correlates of online social feedback, focusing on the effects of the “like” feedback on brain activity using fMRI and EEG. Methods: The review followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA). Results: The review included 11 studies with 504 participants, identifying key brain structures such as the amygdala, ventromedial prefrontal cortex (vmPFC), and ventral striatum involved in reward processing. Positive feedback (“likes”) activates areas like the nucleus accumbens (NACC), vmPFC, and amygdala, with NACC correlating with increased SM use intensity. Negative feedback activates the ventrolateral prefrontal cortex (vlPFC) and left medial prefrontal cortex (mPFC). Behavioral data indicates that positive feedback influences subsequent social interactions. Conclusions: The review highlights disparities in the literature regarding the neural response to social feedback, emphasizing the need for further research to clarify the roles of sex, personality traits, and the person giving feedback. Overall, understanding the neurobiological underpinnings of SM engagement is essential for developing effective interventions to prevent or address the negative effects of excessive SM use. Full article