Search Results (1,903)

Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a common, disabling toxicity with no validated biomarkers. MRI-based functional neuroimaging could offer insight into central pain processing and may reveal reproducible brain signatures of CIPN. Methods: Following PRISMA 2020 (PROSPERO: CRD420251132102), we systematically reviewed whole-brain MRI studies in adult cancer patients with CIPN. Eligible MRI techniques included task-based fMRI, resting-state fMRI, perfusion MRI, and structural MRI. Data were synthesized through voxelwise activation likelihood estimation (ALE), systems-level region-of-interest (ROI) mapping, and proportion meta-analysis of regional involvement. Results: Of 2488 screened records, five observational studies were included. The voxelwise ALE analysis did not identify clusters surviving correction, but dispersed foci appeared within the default mode network (DMN), prefrontal executive cortex, and primary sensorimotor regions, suggesting the engagement of these pain-processing networks. ROI synthesis confirmed consistent alterations in the DMN and executive prefrontal and sensorimotor cortices in CIPN patients compared with controls, while the brainstem/periaqueductal gray and cerebellum were rarely implicated. Proportion meta-analysis further quantified these differences: CIPN patients showed altered involvement in 30% (95% CI 0.16–0.48) of contrasts, with the highest frequencies in the DMN (50%), sensorimotor (33%), and executive prefrontal regions (33%). By contrast, control-higher contrasts were less frequent (10%, 95% CI 0.03–0.27), highlighting CIPN-related increases particularly in self-referential and somatosensory networks. Conclusions: Across analytic approaches, CIPN is characterized by reproducible alterations in the DMN and executive prefrontal and sensorimotor networks. These central pain signatures represent promising MRI-based biomarkers for identifying and monitoring CIPN in oncology. Full article

Background: Neuroinflammation is a key contributor to the progression of several neurodegenerative disorders, including Alzheimer’s disease, stroke, and small vessel disease. Emerging evidence highlights the role of circulating microRNAs (miRNAs) as non-invasive biomarkers of neuroinflammation and neuronal injury. miR-106a-5p, a member of the miR-17~92 cluster, is known to regulate inflammation, apoptosis, and vascular function. While typically studied in plasma or cerebrospinal fluid, gastric juice miRNAs represent a novel and underexplored source for biomarker discovery within the gut–brain axis. This exploratory study aimed to investigate the association between gastric juice miR-106a-5p expression and markers of neuroinflammation, including C-reactive protein (CRP), lactate dehydrogenase (LDH), and imaging-based evidence of neurodegeneration. Methods: A prospective, observational study was conducted on 38 participants (22 with neurodegenerative pathology and 16 healthy controls). Gastric juice samples were analyzed for miR-106a-5p using RT-qPCR, normalized to U6 snRNA. ΔCt values were used to determine relative expression. Statistical analyses included t-tests/Wilcoxon tests, ROC curve analysis, and correlation testing, with significance set at p < 0.05. Results: Patients with neurodegenerative changes exhibited significantly lower gastric miR-106a-5p expression compared to controls (p = 0.044). Elevated CRP and LDH levels were associated with higher ΔCt values (indicating lower expression), with p-values of 0.019 and 0.023, respectively. ROC analysis showed moderate diagnostic accuracy (AUC = 0.701) for miR-106a in identifying neurodegenerative status. miR-106a levels also correlated inversely with carotid intima-media thickness and brain MRI abnormalities, also reduced gastric miR-106a-5p expression is associated with systemic inflammation and neuroimaging evidence of neurodegeneration. Conclusions: While causality cannot be inferred, these findings suggest that gastric miR-106a may serve as a promising non-invasive biomarker within the gut–brain axis framework. Further longitudinal and mechanistic studies are warranted to validate its clinical utility and explore its potential role in monitoring neuroinflammatory conditions. Full article

Background: Mild traumatic brain injury (mTBI) is frequently followed by persistent cognitive, affective, and sensory complaints despite unremarkable conventional structural imaging. Resting-state functional MRI (rs-fMRI) has been increasingly employed to detect subtle alterations in large-scale brain networks. However, variability in analytical approaches and the potential influence of neurovascular factors complicate interpretation of BOLD-derived connectivity findings. Objective: This study provides a focused, evidence-informed synthesis integrating umbrella review principles with a targeted narrative analysis of recent high-quality rs-fMRI studies in mild traumatic brain injury (mTBI). Rather than a comprehensive systematic review, the aim was to identify convergent patterns of network dysfunction while critically examining methodological constraints, including neurovascular confounds and variability in analytical approaches. Conclusions: This synthesis supports a network-level model of mTBI characterized by distributed connectivity disturbances. However, given the limited number of eligible studies and substantial methodological heterogeneity, findings should be interpreted as qualitative convergence rather than quantitative generalization. Future longitudinal, multimodal, and standardized imaging approaches are required to clarify the translational relevance of rs-fMRI findings. Full article

Reactive astrocytes are a hallmark of several neurological diseases in multiple sclerosis and experimental demyelination models. Their morphological alterations are commonly assessed by qualitative histopathology, yet quantitative tools are required to better capture astrocytic heterogeneity and to allow correlations with imaging-derived biomarkers. Here, we present a workflow for the quantitative analysis of Glial Fibrillary Acidic Protein (GFAP) network remodeling in astrocytes in the cuprizone model of demyelination. C57BL/6 mice were intoxicated with cuprizone for 3 or 5 weeks to induce progressive demyelination, microglial activation, and reactive astrogliosis. Brain sections were processed for anti-GFAP immunohistochemistry, and individual astrocytes from the stratum oriens of the hippocampus were digitally reconstructed. Diverse parameters of GFAP topology, including soma size, process length, branching order, convex hull area, and ramification index, were extracted using either the commercial Neurolucida^® 360 software or the open-source Simple Neurite Tracer (SNT) plugin in ImageJ. Principal component analysis revealed clear differences between control astrocytes and astrocytes in cuprizone-intoxicated animals, with reactive astrocytes displaying increased numbers of primary processes, enhanced bifurcation, and process complexity. Comparative evaluation of Neurolucida^® 360 and SNT demonstrated that both tools are suitable for astrocyte reconstruction, although Neurolucida^® 360 enabled faster and more detailed tracing. This protocol provides a reproducible pipeline for the quantitative assessment of astrocyte morphology under control and pathological conditions, thereby supporting future efforts to link cellular remodeling to functional outcomes in neuroinflammatory disease models. Full article

Exposure to blast waves without kinetic, penetrating, thermal, or toxic components causes a distinct form of traumatic brain injury, termed primary blast-induced TBI (pbTBI). Clinical manifestations of pbTBI span a wide spectrum, ranging from life-threatening intracranial hemorrhage, hyperemia, and delayed cerebral edema to mild and transient neurological symptoms without detectable structural abnormalities on routine imaging. At the mild end of the spectrum, symptoms after a single exposure may resolve quickly, yet repeated exposures—even at very low levels, termed “subconcussive”—can develop into post-concussive syndrome (PCS) or persistent post-concussive symptoms (PPCS) in a subset of individuals. Despite extensive studies, the molecular pathobiology linking primary blast exposure to delayed and sometimes chronic neurobehavioral deficits remains incompletely understood. A mechanistic framework connecting blast-wave physics to biomechanics to biological vulnerability may therefore help define exposure hazards, interpret clinical symptomatology, and guide diagnostic and therapeutic development. This review summarizes the physics of primary blast waves, the resulting biomechanical responses, and candidate biological substrates, emphasizing structures and interfaces with distinct acoustic impedances across anatomical, tissue, cellular, and molecular scales. We synthesize evidence supporting the hypothesis that the cerebral vasculature and endothelial cells represent critically vulnerable substrates of primary blast-wave injury, in part because the vascular tree constitutes the brain’s largest and most widely distributed interface between compartments with different acoustic impedances. Across experimental and human studies, endothelial stress, vascular injury, and downstream neuroinflammation emerge as convergent molecular responses to primary blast exposure. Temporal dynamics are central to understanding pbTBI because many blast-induced processes unfold in sequential phases. These observations support conceptualizing pbTBI as a condition characterized by prominent cerebrovascular injury of varying severity with secondary consequences for neuronal signaling, network function, and behavior. Within this framework, cerebrovascular and neurovascular unit (NVU) dysfunction provides a parsimonious bridge between primary blast-wave exposure and chronic symptom trajectories, where vascular pathology may offer more accessible therapeutic targets than neuronal injury. Key knowledge gaps include identifying which physical component(s) of the blast are most injurious, establishing biologically meaningful dose–response relationships at molecular and physiological levels, and defining windows of vulnerability during recovery that are relevant to repeated exposures. Addressing these gaps is essential for refining safety protocols, improving diagnostic specificity through mechanism-informed biomarkers, and developing evidence-based molecular and vascular therapeutic targets for pbTBI-associated conditions. Progress will require integrating waveform-aware dosimetry with longitudinal physiological and molecular monitoring across both preclinical and human cohorts. Such integration offers a practical path toward translating blast physics into actionable medical guidance for prevention, triage, and recovery management. Full article

Attention Deficit Hyperactivity Disorder (ADHD) is a heterogeneous neurodevelopmental disorder whose diagnosis is mainly based on behavioral assessment and is often delayed due to clinical complexity and limited availability of specialists. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable source of information for supporting automated and objective diagnosis. However, existing studies often do not fully capture the complex interactions of functional connectivity between different brain regions. To address this limitation, this work proposes a graph-based deep learning framework for ADHD classification from rs-fMRI that combines functional connectivity modeling with graph representation learning. The approach used Phase-Locking Value (PLV)-based connectivity estimation and Graph Sample and Aggregate (GraphSAGE) to jointly capture regional brain activity and inter-regional interactions in a scalable and efficient manner. GraphSAGE improves robustness to noise and inter-subject variability by aggregating information from stable local graph neighborhoods. This integration allows the model to learn discriminative connectivity-aware representations while remaining robust to signal variability and adaptable to multi-site data. The proposed framework was evaluated on the publicly available ADHD-200 dataset across multiple acquisition sites as well as on a combined multi-site dataset. The results indicate consistent performance across individual sites and on the combined dataset. The model achieved an Accuracy of 0.89, an AUC of 0.96, and a Specificity of 0.96 on the combined dataset, outperforming several existing methods in this setting. By integrating PLV-based connectivity with GraphSAGE learning, the approach provides an effective and scalable solution for automated ADHD classification from rs-fMRI data, contributing to data-driven approaches for the analysis of neurodevelopmental disorders. Full article

Tomatoes are the major dietary source of lycopene, a carotenoid that crosses the blood–brain barrier and exerts antioxidant and anti-inflammatory effects. However, the impact of tomato consumption on cognitive function in healthy adults remains unclear. This study assessed the effects of concentrated tomato paste on cognitive performance and explored potential mechanisms, including brain-derived neurotrophic factor (BDNF) and functional brain connectivity. A randomized, two-period crossover trial (ClinicalTrials.gov: NCT05891977) was conducted in 47 healthy adults aged 40–55 years assigned to two 3-month interventions separated by a 1-month washout: (a) daily consumption of concentrated tomato paste (0.5 g/kg body weight) and (b) a lycopene-restricted control diet. Cognitive performance was evaluated using validated neuropsychological tests (d2-R, Face-Name Associative Memory Exam, Modified Wisconsin Card Sorting Test), alongside plasma lycopene and BDNF, and resting-state functional magnetic resonance imaging (fMRI). Forty-two participants completed the study. Tomato intake improved selective attention (concentration performance: +7.2 points; processing speed: +8.3 points) and associative memory (face-name matching: +0.8 points). Plasma BDNF showed a borderline increase with tomato intake (mean difference 15.2 ng/mL). Resting-state fMRI revealed changes in brain networks, including reduced connectivity in frontoparietal and auditory networks, contrasting with reductions in the dorsal attention network during the control period. These findings provide evidence that tomato consumption may support cognitive function and modulate brain connectivity in healthy middle-aged adults. Full article

Introduction: Navigated transcranial magnetic stimulation (nTMS) is an advanced, noninvasive method for stimulation-based functional brain mapping. Its main clinical value in neurosurgery lies in preoperative identification of eloquent cortical areas and the integration of functional information into neuronavigation-based surgical planning. State of the Art: This narrative review with a structured literature search summarizes the historical and technical foundations of TMS/nTMS, but primarily focuses on neurosurgical applications, including motor and language mapping, comparison with functional MRI and direct cortical stimulation, safety considerations, and practical limitations. Broader neurological and therapeutic applications are discussed as contextual extensions rather than as a comprehensive disease-specific review. Clinical Implications: Current evidence is strongest for preoperative motor mapping in patients with tumors located in or near the motor–eloquent cortex. Language mapping, neurological diagnostics, and therapeutic repetitive TMS (rTMS) applications remain more heterogeneous and require careful interpretation according to the level of evidence, protocol standardization, and patient selection. Future Directions: Further multicenter studies, standardized mapping protocols, integration with advanced imaging and tractography, and health-system implementation strategies are needed to define the optimal role of nTMS in personalized neurosurgical and neurological care. Full article

Background: Reaction time (RT) is a fundamental measure of information processing speed in cognitive neuroscience and is influenced by both structural and functional brain properties. While prior studies have independently linked white matter microstructure and EEG alpha oscillations to cognitive performance, their joint contribution to distinct aspects of RT remains unclear. This study aims to investigate whether multimodal data can dissociate neural systems underlying cognitive and motor components of processing speed. Methods: We analyzed diffusion tensor imaging, resting-state individual EEG alpha peak frequency (IAF), demographic variables, and behavioral RT measures from a GO/NO-GO paradigm in 24 healthy adults from the Cuban Human Brain Mapping Project. Behavioral metrics included the mean, standard deviation and skewness of reaction times for simple and complex tasks. Sparse multiple canonical correlation analysis was applied to identify multivariate associations across modalities. Results: Two significant latent dimensions were identified. The first dimension linked bilateral fronto-temporal association tracts (SLF, IFOF, UNC) with complex RT performance, reflecting higher-order cognitive processing. The second dimension associated motor and interhemispheric tracts (CGC, CST, ILF, forceps major and minor) with intra-individual asymmetric variability (skewness) across tasks, indicating a motor-execution consistency system. IAF did not significantly contribute to either dimension. Sex showed strong associations with both components. Conclusions: Distinct white matter networks were associated with separable cognitive and motor aspects of processing speed, while resting-state alpha frequency did not show stable contributions with behavioral variability in this sample. IAF showed minimal contribution within the identified sparse multivariate dimensions. These findings highlight the importance of multimodal and multivariate approaches for understanding and potentially disentangling complex brain–behavior relationships. Full article

Background: Brain tumors represent a major cause of neurological morbidity and mortality, often requiring radiotherapy as a central component of treatment. While advances in radiation techniques have improved tumor control, increasing attention has been directed toward radiation-induced effects on healthy brain tissue, particularly regarding neuronal function and cognitive outcomes. Objective: This review aims to provide a structured synthesis of current evidence on radiation-induced neuronal dysfunction, integrating dose–volume parameters, neuroimaging biomarkers, and clinical neurological manifestations. Methods: A structured literature review was conducted using electronic databases including PubMed, Scopus, and Web of Science. Relevant studies evaluating dose–volume effects, neuroimaging findings, and clinical outcomes following cranial radiotherapy were included. Results: Dose–volume histogram (DVH) parameters, including mean brain dose and intermediate-dose volumes (V10–V30), as well as hippocampal dose, were identified as key factors associated with cognitive decline and neuronal dysfunction. Conventional MRI detects structural changes such as white matter injury and radionecrosis, while advanced techniques including diffusion tensor imaging (DTI) and functional MRI (fMRI) reveal microstructural damage and network disruption. These imaging findings correlate with a spectrum of clinical manifestations ranging from subtle cognitive impairment to significant neurological deficits. Conclusions: Radiation-induced neuronal dysfunction represents a complex and multifactorial process that extends beyond localized tissue injury. Integrating dose–volume considerations with advanced imaging biomarkers may improve risk stratification and support the development of neuroprotective strategies in patients undergoing cranial radiotherapy. Full article

Objective: This study aimed to evaluate the prognostic significance of positron emission tomography/computed tomography (PET/CT)-derived sarcopenia in patients with hormone receptor-positive, HER2-negative metastatic breast cancer treated with cyclin-dependent kinase 4/6 (CDK4/6) inhibitors. Methods: This retrospective single-center study included 77 patients treated between January 2018 and March 2025. Sarcopenia was assessed using skeletal muscle index (SMI) at the L3 level on fluorodeoxyglucose (FDG) PET/CT. Patients were classified as sarcopenic or non-sarcopenic. Clinical, nutritional parameters including body mass index (BMI) and prognostic nutritional index (PNI), and inflammatory parameters including pan-immune inflammation value (PIV) were analyzed. The primary endpoint was progression-free survival (PFS). Results: Sarcopenia was present in 35.1% of patients. After a median follow-up of 38 months, sarcopenic patients had significantly shorter PFS compared with non-sarcopenic patients (18 vs. 38 months; HR: 2.37, 95% CI 1.12–4.99, p = 0.02, multivariable analysis). In multivariable analysis, sarcopenia, recurrent disease, brain metastasis, and liver metastasis were independent predictors of PFS. No significant association was observed between sarcopenia and overall survival. BMI, PNI, and PIV were not associated with survival outcomes. Toxicity profiles were comparable between groups. Conclusions: PET/CT-derived sarcopenia may be a prognostic factor for PFS in patients receiving CDK4/6 inhibitors, whereas conventional nutritional and inflammatory markers are not. These findings support the clinical utility of imaging-based body composition assessment. Prospective studies incorporating functional measures of sarcopenia are warranted. Full article

Severe traumatic brain injury (TBI) is a leading cause of mortality and long-term disability in polytrauma (PT) patients, and its clinical outcome remains difficult to predict due to clinical heterogeneity and secondary injury mechanisms. Current diagnostic and prognostic approaches based on clinical assessment and imaging are limited, particularly in PT where neurological evaluation is often impaired. This study aimed to compare plasma- and extracellular vesicle (EV)-associated microRNA (miRNA) signatures in patients with severe TBI and healthy controls to identify their potential as minimally invasive biomarkers and to improve understanding of molecular responses. For profiling circulating miRNAs, blood samples were collected at ≤3 h and at 48 h after admission. In the screening phase, plasma samples of n = 15 patients with severe isolated TBI (Abbreviated Injury Scale [AIS]Head ≥ 4, all other AIS ≤ 1) and n = 15 age- and sex-matched healthy controls were pooled (n = 5/pool) and subjected to next-generation sequencing (NGS). In the following validation phase, n = 25 severely injured trauma patients (Injury Severity Score [ISS] ≥ 16) were enrolled and stratified into PT without TBI (PT; AISHead = 0; n = 13) and isolated TBI (n = 12). Differentially expressed candidate miRNAs identified in the screening phase were validated in individual plasma and EV samples using reverse transcription droplet digital polymerase chain reaction (RT-ddPCR). Functional enrichment and pathway analyses were performed using miRNet. NGS identified more differentially expressed miRNAs in plasma (ER: 103; 48 h: 65) than in EVs (Emergency Room [ER]: 14; 48 h: 32). Functional enrichment analysis indicated associations with pathways related to cellular stress, senescence, growth factor signaling, transcriptional regulation, and apoptosis. In validation, 12 of 16 plasma and 10 of 15 EV-miRNAs were confirmed as differentially expressed in TBI patients; among these, three plasma and four EV miRNAs differed between TBI and PT. After adjustment, most plasma miRNAs were associated with injury severity rather than group status. EV miRNA profiles showed heterogeneous patterns, with miR-1469 associated with TBI group status in adjusted analysis, while miR-1237-5p was linked to injury severity and other EV miRNAs showed no consistent group-specific effects. Plasma miRNAs mainly correlated with systemic injury markers, whereas EV miR-1469 showed a moderate association with the Glasgow Coma Scale (GCS). Overall, circulating miRNA profiles after injury appear to be predominantly influenced by systemic trauma severity rather than TBI-specific effects. Plasma miRNAs mainly reflected general injury burden, whereas EV-associated miRNAs showed more heterogeneous patterns, with miR-1469 emerging as a candidate associated with TBI after adjustment for clinical covariates. These findings suggest that EV-derived miRNAs, particularly miR-1469, may provide more targeted signals related to brain injury and warrant further investigation. Full article

Introduction: Electronic nicotine delivery systems (ENDSs), also known as e-cigarettes or vapes, have shown popularity among the adolescent population. Compared to adults, less is known regarding the impacts of ENDS and nicotine on the adolescent brain. Adolescent research related to nicotine and other illicit substances can be difficult due to the requirement of parent/guardian consent, adolescent hesitancy for disclosure of product use, and the continually evolving vaping and nicotine products on the market. Despite these challenges, further research is needed to explore the impact of ENDS on the developing adolescent brain. The objective of the study was to evaluate reward sensitivity and cognitive flexibility in the adolescent population using functional magnetic resonance imaging (fMRI) through a probabilistic reversal learning task. Methods: This pilot study recruited participants aged 13–19 years old to complete fMRI testing. We specifically adapted a probabilistic reversal learning task that was previously used to measure reward sensitivity and cognitive flexibility in adults (including nicotine users). We were unable to recruit enough ENDS users to complete the planned analysis; therefore, we evaluated non-users as proof of concept for the use of the probabilistic reversal learning task in adolescents to support future research. Participants completed four blocks of a probabilistic reversal learning task, each lasting 6 min. During each block of the task, blood-oxygenation-level-dependent (BOLD) fMRI images were collected. The reward sensitivity and cognitive flexibility contrasts of parameter estimates were entered into a group analysis model. Due to the small sample size and exploratory nature of the study, we were interested in computing population-level estimates of brain activation that could be attributed to reward sensitivity (win-stay minus lose-stay trials) and cognitive flexibility (lose-shift trials minus lose-stay trials). Results: A total of twelve participants completed fMRI testing—ten non-users, one intermittent user, one regular user. Four of these participants (three non-users and one intermittent user) were excluded from the fMRI analysis due to excessive head movement and/or poor task performance. With the seven remaining non-users, we found no evidence of significant BOLD activation when strictly controlling the Type I error rate. Using a more liberal statistical threshold that did not control the Type I error rate, both contrasts resulted in suprathreshold clusters in occipital and posterior parietal regions, and the reward sensitivity contrast also resulted in suprathreshold clusters in the prefrontal cortex (bilateral middle occipital gyrus). Discussion/Conclusions: We did not find statistically significant BOLD activation, which is likely due to the small sample size. Suprathreshold clusters using the liberal statistical threshold may be feasible for use as regions of interest in future studies using this task. Notably, the prefrontal regions where the reward sensitivity contrast exceeded the liberal statistical threshold in our study were similar to those observed in previous studies of reward sensitivity in adults (including nicotine users) and adolescents. This pilot study explores the use of an fMRI reward-learning paradigm in the adolescent population, which can serve as a catalyst for future research related to nicotine use. Full article

Micro- and nanoplastics (MNPs) are increasingly recognized as frequent environmental pollutants with growing evidence of tissue-specific lipid disruption in exposed organisms. MNP exposure is unavoidable and has attracted global attention due to its potential public health and ecological security risks. Unlike earlier studies that emphasize oxidative stress and inflammation, recent findings show that lipids are among the earliest and most sensitive molecular targets of MNP exposure. Lipidomics investigations across animal models reveal consistent patterns of lipidome remodeling, including altered phospholipid composition, disrupted sphingolipid balance, increased neutral-lipid storage, and mitochondrial lipid damage in metabolically active tissues such as the liver, kidney, lung, adipose tissue, and brain. Mechanistically, MNPs perturb membrane bilayer organization, induce MUFA and PUFA peroxidation, and destabilize lysosomal and mitochondrial function. These alterations trigger cardiolipin oxidation, ceramide accumulation, lipid droplet biogenesis, and impaired lipophagy, which collectively promote metabolic stress, energy imbalance, and neurotoxic or hepatotoxic phenotypes. Despite the growing number of tissue-specific studies, a major gap remains in understanding systemic MNP toxicity. The present review uniquely emphasizes tissue-resolved lipidomic signatures to identify convergent pathways of lipid disruption and proposes a conceptual framework, the “Lipid–Stress Axis”, to explain how localized lipidome perturbations may propagate into broader physiological dysfunction. By integrating lipidomics with metabolomics, imaging, and systems-biology approaches, we highlight key lipid-based biomarkers, mechanistic insights, and research needs essential for improving risk assessment and developing mitigation strategies against MNP-induced lipid dysregulation. Full article

Objectives: Autism spectrum disorder (ASD) is increasingly conceptualized as a neurodevelopmental condition with prenatal origins. Advances in fetal magnetic resonance imaging (MRI), including high-resolution structural imaging and resting-state functional connectivity analysis, now enable in vivo characterization of the developing human brain before birth. This review examines whether fetal MRI biomarkers are associated with later ASD diagnosis or autistic traits. Methods: We conducted a PRISMA-informed narrative review of human studies identified through MEDLINE, EMBASE, SCOPUS, and Web of Science. Eligible studies included original human investigations using fetal MRI to assess brain structure and/or function, with postnatal ASD diagnosis or standardized autistic-trait outcomes. Results: Eight eligible studies provide converging evidence that neurodevelopmental divergence associated with ASD may be detectable in utero. Structural analyses consistently report prenatal volumetric alterations, particularly enlargement of the insular cortex between the second and third trimesters. Additional findings of regional overgrowth and hemispheric asymmetries suggest distributed deviations in cortical maturation. Functional fetal MRI studies further demonstrate atypical large-scale network organization prior to birth. Altered connectivity within cingulate, prefrontal, temporal, and cerebellar circuits has been prospectively associated with later autistic traits, indicating that network-level integration may diverge before behavioral symptoms emerge. Evidence from high-risk conditions, including isolated ventriculomegaly and tuberous sclerosis complex, reinforces the association between prenatal structural abnormalities and increased ASD risk. Conclusions: Current evidence suggests that structural and functional brain alterations identifiable by fetal MRI may precede the clinical manifestation of ASD. These findings support a model of ASD as a condition potentially rooted in prenatal neurodevelopmental divergence. However, larger, standardized, multicenter studies are required before fetal MRI biomarkers can be translated into predictive or clinical applications. Full article