Sydenham's chorea (SC) is the most common form of acquired chorea in childhood and a neurological manifestation of acute rheumatic fever (ARF). It is characterized by involuntary, rapid, and irregular movements. Although the diagnosis is clinical, neuroimaging can play a valuable complementary role. SC arises from an autoimmune response triggered by a streptococcal infection, targeting the basal ganglia. In some cases, it may be the sole manifestation of ARF. We present two pediatric cases with acute hemichorea. In one of them, chorea was the only clinical manifestation. Brain MRIs showed punctate ischemic lesions compatible with postinfectious vasculitis, allowing exclusion of other causes of vasculopathy and movement disorders. Both patients were treated with valproic acid and aspirin as an antiplatelet agent. Clinical and radiological improvement was observed in both cases, allowing treatment withdrawal after six months without symptom recurrence. La corea de Sydenham (CS) es la forma más frecuente de corea adquirida en la infancia y una manifestación neurológica de la fiebre reumática (FR) aguda. Se caracteriza por movimientos involuntarios, rápidos e irregulares. Aunque el diagnóstico es clínico, la neuroimagen puede cumplir un rol complementario valioso. La CS surge como resultado de una respuesta autoinmune desencadenada por una infección estreptocócica, dirigida contra los ganglios basales. En ciertos casos, puede ser la única manifestación de FR. Se presentan dos casos pediátricos con hemicorea aguda. En uno de ellos, la corea fue la única manifestación clínica. Las resonancias magnéticas (RM) mostraron lesiones isquémicas puntiformes compatibles con vasculitis postinfecciosa, permitiendo descartar otras causas de vasculopatía y de trastornos del movimiento. Ambos pacientes fueron tratados con ácido valproico y aspirina como tratamiento antiplaquetario. Se observó una buena evolución clínica e imagenológica, lo que permitió suspender el tratamiento a los seis meses sin recurrencia de los síntomas.
[This corrects the article DOI: 10.1016/j.ynirp.2026.100336.].
Maple Syrup Urine Disease (MSUD) is a rare autosomal recessive metabolic disorder characterised by defective branched-chain amino acid (BCAA) catabolism, leading to neurotoxicity, recurrent metabolic crises, and neurodevelopmental impairment. Evidence on long-term outcomes in paediatric cohorts, particularly with pharmacological adjuncts such as sodium phenylbutyrate (NaPBA) and radiological recovery, remains limited. We undertook a retrospective review of 13 paediatric patients with MSUD (69.2% classic phenotype, 30.8% intermittent) followed at a tertiary metabolic centre in Türkiye between 2003 and 2022. Demographic, biochemical, neurodevelopmental, neuroimaging, and genetic data were evaluated, with specific attention to dietary management, haemodialysis during acute decompensation, and NaPBA therapy. All patients exhibited neurodevelopmental delay, which was more pronounced in the classic phenotype. Milestone-level analysis demonstrated delays in walking (85%), sentence formation (92.3%), and toilet training (92.3%). One year after dietary intervention, mean plasma concentrations of leucine, isoleucine, and valine decreased by 60.9%, 55.9%, and 65.0%, respectively (p < 0.01). Haemodialysis during metabolic crises rapidly reduced leucine (- 73.8%) and ammonia (- 66%), though was more frequently required in patients with the classic phenotype. NaPBA treatment was associated with lower leucine levels during follow-up (p < 0.05). Baseline MRI abnormalities were identified in 87% of patients; 57% showed complete resolution post-treatment, with partial radiological improvement observed alongside clinical follow-up. A phenotype-specific approach combining early dietary intervention, timely haemodialysis in acute crises, and selective use of NaPBA may support metabolic stabilisation and radiological improvement in selected patients. Larger multicentre studies are warranted to validate these findings and refine management protocols.
Functional impairments associated with mental health conditions are on the rise. Predicting functional outcomes may improve the targeting of preventive interventions. While prognostic models have primarily focused on psychosis, early recognition services require a transdiagnostic approach. This study aimed to predict global functioning within a 2-year follow-up using baseline clinical and structural magnetic resonance imaging (MRI) data in a population-based sample of young, help-seeking individuals presenting with affective and anxiety symptoms as well as attention-deficit hyperactivity disorder. We classified 357 help-seeking individuals aged 18-35 years recruited from 9 sites as "impaired" (Global Assessment of Functioning [GAF] ≤60; n=228) or "nonimpaired" (GAF>60; n=129) at year 1 and/or year 2 follow-up. GAF classification group status at follow-up was predicted using linear support vector machine (SVM), decision tree, and large language model (LLM) Llama-3 using clinical assessments and/or structural MRI. Leave-one-site-out (SVM) or external sample (LLM) was used for validation. SVM achieved balanced accuracy of 69.2% using clinical features only. Items related to baseline occupational functioning, interpersonal relationships, cognitive functioning, psychotic and affective symptoms, as well as the presence of anxiety disorder, were most predictive. The decision tree further reduced the feature set to 5 predictive items, achieving balanced accuracy of 76.6%. Although amygdala and hippocampal subregions achieved balanced accuracy of 57.1%, structural MRI did not improve the overall prediction. Llama-3 performed comparably well to SVM (balanced accuracy of 72.6%). Machine learning demonstrated good performance in predicting global functioning. Interestingly, the out-of-the-box LLM performed comparably well without being trained or fine-tuned, highlighting the potential of leveraging free-text data for mental health prognosis.
Pituitary adenomas represent one of the most common intracranial tumors, and cavernous sinus invasion (CSI remains a major challenge for surgical management. Although the Knosp grading system provides a widely used radiological framework, its subjective nature and inter-observer variability limit diagnostic reliability. In recent years, advanced computational methods have been investigated to improve the preoperative prediction of invasion. This review synthesizes current evidence on the use of radiomics, machine learning (ML), and deep learning (DL) approaches in the detection and assessment of CSI in pituitary adenomas, with particular emphasis on their comparative performance against traditional imaging methods. Studies employing MRI-based radiomic feature extraction, ML classifiers, and convolutional neural networks were analyzed. Reported models commonly incorporated intensity, texture, and shape descriptors, or applied end-to-end DL architectures for automated prediction. Performance metrics such as accuracy, sensitivity, specificity, AUC, and Dice similarity coefficients were compared across studies, with Knosp grade serving as a frequent benchmark. Evidence suggests that ML and DL models consistently outperform conventional MRI interpretation in predicting CSI. Radiomics pipelines integrating quantitative imaging features with clinical variables achieved high diagnostic accuracy, while CNN-based models trained on contrast-enhanced MRI often exceeded AUC values of 0.85. Furthermore, automated segmentation frameworks demonstrated reliable delineation of tumor boundaries, facilitating improved assessment of invasive behavior. Despite promising outcomes, limitations such as small sample sizes, single-center designs, and lack of external validation restrict broad clinical adoption. Radiomics and AI-driven approaches show substantial potential for enhancing preoperative evaluation of pituitary adenomas with CSI. Standardized imaging protocols, multicenter collaborations, and transparent model validation are essential for future integration into neurosurgical decision-making.
Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a rare inflammatory demyelinating disorder that can affect children and remains challenging to diagnose. Neuroimaging plays a key role, as MRI patterns in the brain, optic nerves, and spinal cord differ from those of multiple sclerosis and Aquaporin-4 (AQP4)‑positive neuromyelitis optica spectrum disorder (NMOSD). The 2023 international consensus criteria require a compatible core clinical phenotype, positive serum MOG‑immunoglobulin G (IgG), and exclusion of a better alternative diagnosis, with supportive MRI features when needed. The authors recently managed a pediatric patient with an acute demyelinating syndrome, positive serum MOG‑IgG, and characteristic compatible lesions. This case illustrates key clinical and imaging features of pediatric MOGAD, shows how the 2023 criteria can be applied in practice, and highlights radiologic clues that help distinguish MOGAD from other demyelinating disorders.
Painful legs and moving toes syndrome (PLMT) is a neurological disorder characterized by leg pain accompanied by involuntary toe movements. It may be misdiagnosed as restless legs syndrome (RLS) due to overlapping clinical features. We report the case of a 69-year-old woman with a seven-year history of pain involving all toes of both feet that improved with standing or walking and worsened when lying down. She had no sleep disturbance and was unaware of involuntary movements. A trial of dopamine agonist therapy was ineffective. Careful observation revealed involuntary toe movements, supporting a clinical diagnosis of PLMT. Neuroimaging revealed lumbosacral lesions, but no findings clearly explained her symptoms, and nerve conduction studies showed no evidence of peripheral neuropathy. This case highlights the importance of observing the patient's feet over a prolonged period and considering PLMT in patients with chronic leg pain that is exacerbated at rest but lacks typical RLS features.
Mitochondria support the bioenergetic processes that enable brain function and cognition, but we have lacked a label-free, non-invasive approach to explore how brain mitochondria are linked to ageing, disease, and cognition in humans. A recently introduced MitoBrainMap neuroimaging framework predicts mitochondrial features from magnetic resonance data alone, potentially bridging cellular biology with macroscale brain organization. Here, we tested whether this framework captures meaningful age- and pathology-related mitochondrial variation. Consistent with existing literature, we find that MR-predicted mitochondrial density and tissue respiratory capacity consistently declined with age, whereas mitochondrial respiratory capacity-an index of mitochondrial quality-was relatively preserved across the lifespan. Moreover, the relations among specific mitochondrial features predicted from our algorithm were consistent with their biological organization, supporting preliminary construct validity for MR-predicted mitochondrial features. In patients with rare mitochondrial diseases, predicted maps revealed region-specific alterations in mitochondrial density and respiratory chain components, particularly the expected compensatory upregulation of complex II, but not of other mitochondrial genome-encoded components. Finally, the MR-based mitochondrial features were associated with the energetic stress marker GDF15 measured in blood, as well as with cognitive performance measures, linking the novel predictions of brain mitochondria to systemic stress and behavior. These findings introduce a first-generation, label-free, neuroimaging-based mitochondrial mapping as a non-invasive window into living human brain mitochondria.
TMEM106B is a frontotemporal lobar degeneration (FTLD) genetic susceptibility factor, and TMEM106B protein aggregates are a feature of aging and neurodegeneration. Whether TMEM106B protein levels are associated with clinical features is unknown. To investigate the clinical associations of cerebrospinal fluid (CSF) TMEM106B in FTLD. This cross-sectional study was conducted in 2 independent frontotemporal dementia (FTD) cohorts (recruitment from April 2009 through July 2023, with analyses from January 2025 through April 2026), with a 2-year follow up. This multicenter clinical study integrated clinical, genetic, biomarker, and neuroimaging data. Individuals were recruited through the University of California, San Francisco (n = 3733), or ALLFTD (n = 2343). Participants with available CSF were included. A discovery cohort (n = 271) included participants with sporadic neuropathology-confirmed FTLD; presymptomatic or symptomatic carriers of pathogenic variants in C9orf72, GRN, or MAPT; or controls. An independent validation cohort (n = 383) included participants with clinically diagnosed sporadic FTD, Alzheimer disease (AD), and controls. CSF samples for TMEM106B quantification with aptamer proteomics (SomaScan version 3.0 [discovery cohort] and SomaScan version 4.1 [validation cohort]). Parametric tests compared the primary outcome, CSF TMEM106B, by disease severity, TMEM106B rs1990622 genotype, sex, clinical syndrome, pathological diagnosis, and pathogenic variant and determined associations with brain volume. In the discovery (n = 271; 136 women [51%]; median [IQR] age, 59 [38-80] years) and validation (n = 383; 183 women [48%]; median [IQR] age, 64 [50-78] years) cohorts, lower CSF TMEM106B was associated with more severe disease (β, -0.15; 95% CI, -0.24 to -0.04; P = .003), lower frontotemporal brain volumes (β, 0.42; 95% CI, 0.24-0.61; P < .001), and faster clinical progression (β, -2.21; 95% CI, -3.70 to -0.72; P = .001). Associations of TMEM106B with clinical disease severity were independent of those with neurofilament light chain. TMEM106B levels were influenced by TMEM106B rs1990622 genotype, where individuals with the protective G/G genotype had lower levels than the risk A/A genotype. CSF TMEM106B levels did not differentiate between FTLD subtypes or between FTLD and AD. Per the results of this cross-sectional study, TMEM106B is detectable in CSF and levels reflect disease severity in sporadic and genetic FTLD and AD, but levels are also influenced by the TMEM106B rs1990622 genotype. CSF TMEM106B could support further studies to understand the mechanisms of disease and develop clinical tools in FTLD and other neurodegenerative diseases.
Intracerebral hemorrhage (ICH) is a serious complication of oral warfarin in patients with mechanical heart valves, leading to high mortality and disability. This study aimed to evaluate the clinical and radiological features and the variables associated with patient outcomes in such cases, particularly in resource-limited settings. This retrospective observational study included 125 patients on warfarin with mechanical heart valves who developed spontaneous ICH and presented within 24 hours of symptom onset. Hematoma volume was calculated using the ABC/2 method. Outcomes were assessed at 90 days across the entire cohort using mortality data and the modified Rankin Scale (mRS), with mRS ≥3 indicating poor functional recovery. Among the 125 patients, 42 (33.6%) died within 90 days, and 38 (30.4%) experienced poor functional recovery (mRS ≥3). Unadjusted analyses identified several shared clinical and radiological variables significantly associated with both mortality and poor functional recovery, respectively: advanced age >60 years (p=0.024; p=0.005), hypertension (p=0.007; p=0.045), smoking (p=0.002; p=0.040), atrial fibrillation (p=0.012; p=0.011), anemia (p=0.024; p<0.001), hematoma volume >30 cc (p=0.033; p=0.004), midline shift (p<0.001; p=0.010), and higher composite ICH scores (p=0.035; p=0.014). Furthermore, intraventricular extension (p=0.005) and severe admission blood pressure >230/140 mmHg (p=0.019) were specifically linked to increased mortality. A low admission Glasgow Coma Scale (GCS) score <8 (p<0.001) was associated with poor functional recovery among survivors. While an association was observed between neurosurgical intervention and improved functional outcomes in unadjusted analyses, this finding should be interpreted with caution, given the potential selection bias inherent in the retrospective design. Warfarin-associated ICH in patients with mechanical heart valves remains a devastating complication characterized by high rates of mortality and long-term functional disability. This study identified several clinical and radiological variables associated with adverse outcomes, including advanced age, large hematoma volume, midline shift, intraventricular extension, low admission GCS, and concurrent comorbidities such as anemia and severe hypertension. Optimizing outcomes in this vulnerable population requires early risk stratification using comprehensive clinical grading systems such as the ICH score, rapid neuroimaging, rigorous blood pressure control, international normalized ratio (INR) reversal, and individualized, selective neurosurgical interventions.
While genetic factors strongly influence brain aging trajectories, variants conferring cognitive resilience remain poorly characterized. The neurokinin-3 receptor (NK3-R), encoded by Tachykinin Receptor 3 ( TACR3) , modulates cholinergic signaling in memory circuits vulnerable to aging. Previous studies linked the non-WT expression of the TACR3 variant rs2765 with cognitive decline and reduced volume of the hippocampus and basal forebrain, but systematic replication and mechanistic validation were lacking. We investigated rs2765 in the preregistered AgeGain cohort of cognitively healthy older adults (n=188) with independent validation in the ADNI cohort (n=809) which includes persons with and without Alzheimer's Disease (AD) that show healthy cognition, mild cognitive impairment or dementia. Analyses integrated structural neuroimaging, longitudinal cognitive assessments, epigenetic aging (PhenoAge), genome-wide methylation profiling, and mechanistic validation through luciferase assays and cross-species protein expression studies. The infrequent protective rs2765 WT variant, found in 12.8% of Europeans, conferred 49% slower cognitive decline ( p = 0.002) for amyloid-positive individuals of the ADNI cohort and 3.7 years younger epigenetic age ( p = 0.013, 95% CI: 0.79-6.67 years) in the cognitively healthy AgeGain cohort. WT carriers showed larger hippocampal and basal forebrain volumes across cohorts, with Allen Brain Atlas integration revealing these outcomes to occur exclusively in regions where TACR3 expression positively correlated with gray matter volume. Mechanistically, the non-WT variant ameliorated RBMX-mediated post-transcriptional regulation, reducing NK3-R protein expression by 25-40% in vitro and ex vivo murine brain slice models. Senescence-accelerated mice exhibited reduced endogenous NK3-R expression, phenocopying the predicted functional consequences of the variant. In AgeGain participants, genome-wide methylation profiling identified 2,313 differentially methylated CpGs affecting 228 pathways spanning glutamatergic signaling, acetylcholine receptor pathways, chromatin remodeling, and angiogenesis, suggesting coordinated molecular reprogramming from synaptic function to systemic aging. rs2765 WT confers resilience to age- and AD-related cognitive decline through RBMX-dependent regulation of NK3-R expression, with effects of remarkable size cascading from memory to systemic aging. rs2765 genotyping could stratify individuals for NK3-R modulator therapy (e.g., fezolinetant or senktides) and identify those maintaining function despite pathological burden, complementing APOE-based risk assessment in precision geromedicine.
Bickerstaff brainstem encephalitis (BBE) is a rare immune-mediated neurological disorder characterized by a wide spectrum of clinical presentations, often following a preceding infection. Although supportive findings on neuroimaging, cerebrospinal fluid (CSF) analysis, and antiganglioside antibodies may aid diagnosis, the condition remains largely clinical, particularly in atypical cases. We report the case of a 60-year-old man who presented with acute onset of fever and altered sensorium, rapidly progressing to severe quadriparesis and respiratory failure requiring mechanical ventilation. Neuroimaging and CSF analysis were unremarkable, with no evidence of albuminocytological dissociation. Nerve conduction studies revealed severe sensorimotor neuropathy involving all four limbs. In view of poor response to empirical antimicrobial therapy and after exclusion of infectious etiologies, we suspected an autoimmune process and initiated the patient on intravenous immunoglobulin (IVIG). We observed significant clinical improvement from the second dose onward, with progressive recovery of sensorium, motor power, and respiratory function. We successfully weaned the patient off ventilatory support, decannulated, and discharged him without residual neurological deficits. This case highlights the diagnostic challenge that atypical presentations of Bickerstaff encephalitis pose in the absence of classical radiological and CSF findings. It underscores the importance of maintaining a high index of clinical suspicion and initiating timely immunotherapy, even when conventional diagnostic markers are lacking.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, often characterized by challenges in social interaction, repetitive behaviors, and restricted interests in work. Early diagnosis is critical for effective intervention, but current methods often rely on subjective behavioral assessments that can delay identification. This underscores the need for reliable biomarkers that can facilitate earlier detection of ASD. Biomarkers are primarily proteins in nature, found in blood, saliva, or other tissues, and have the potential to enhance diagnostic accuracy and speed. They can reveal underlying neurobiological changes associated with ASD, providing objective data to support clinical findings. The search for altered levels of certain amino acids and neurotransmitters and specific genetic biomarkers such as single-nucleotide polymorphisms (SNPs) and copy number variants (CNVs) has gained momentum, driven by the need for more definitive early diagnostic tools. Neuroinflammatory and neuroimaging biomarkers through MRI and fMRI have also shown promise for early detection of ASD. Recent advancements in biosensor technology have significantly improved the prospects for biomarker discovery in ASD. Innovations in nanotechnology and microfluidics have enabled the development of highly sensitive and specific biosensors that can trace minute quantities of biomarkers. These devices allow for non-invasive sampling and real-time monitoring, making early screening more feasible and accessible for young children. This review article mainly focuses on how the integration of these biosensor and biomarker technologies could transform the early detection of ASD, ultimately facilitating timely interventions and improving patients' outcomes.
Pseudoaneurysm of the mitral-aortic intervalvular fibrosa (MAIVF) represents a rare but severe complication of infective endocarditis, classically associated with highly virulent organisms such as Staphylococcus aureus. Staphylococcus epidermidis, a coagulase-negative staphylococcus traditionally considered a low-virulence commensal, is increasingly recognized as a cause of clinically significant native-valve endocarditis. We report a 72-year-old man with native bicuspid aortic valve who presented with syncope and was found to have S. epidermidis bacteraemia. Transthoracic echocardiography demonstrated an aortic valve mass, but transoesophageal echocardiography (TEE) revealed extensive periannular destruction including MAIVF pseudoaneurysm, posterior aortic annular abscess, and a large mitral valve vegetation with associated thrombus. Neuroimaging confirmed multiple embolic cerebral infarcts. Despite aggressive medical therapy, the patient's clinical trajectory precluded surgical intervention, and he ultimately transitioned to comfort-focused care. To our knowledge, this represents one of the first reported cases of native-valve S. epidermidis endocarditis complicated by destructive MAIVF involvement in the absence of prosthetic material. This case underscores the underappreciated pathogenic potential of coagulase-negative staphylococci and highlights the critical role of TEE in detecting periannular complications that may be missed on transthoracic imaging.
Preterm birth is a major risk factor for disrupted brain development and subsequent neurodevelopmental disorders, yet the underlying mechanisms remain poorly understood. Further, typical neuroimaging analyses are particularly challenging in the neonatal brain: data is frequently low quality, and a lack of cellular development violates the assumptions relied on by many commonly-used techniques. In this study, we develop and present an advanced diffusion magnetic resonance imaging method to examine the microstructural organization of white matter in a clinically-acquired cohort of premature neonates. Using a novel approach that resolves multiple tissue compartments within the brain, we provide highly detailed orientation and quantification of white matter fibers and tissue signal fraction. We also utilize a series of automated segmentation algorithms to identify and measure these metrics across key tracts and subcortical regions. We investigate how these measures relate to postmenstrual age, as well as to clinical factors reflecting neonatal illness severity. We report successful segmentation and reconstruction of numerous white matter tracts throughout the neonatal brain. We further demonstrate the utility and functionality of microstructural analysis in a variety of pathologies commonly encountered in the neonatal clinical environment. Our results demonstrate tract-specific developmental trajectories, with early-maturing pathways showing higher microstructural organization. Exploratory analyses suggest that neonatal illness severity has modest, tissue-specific associations with microstructural properties. This work demonstrates that advanced microstructural imaging methods can extract meaningful white matter measurements from clinically-acquired scans, providing a practical framework for studying neonatal brain development in real-world hospital settings. These metrics are able to be calculated at extremely young ages, potentially allowing non-invasive study of vulnerable populations before detailed behavioral or neurological assessments are feasible.
Familial focal epilepsy with variable foci (FFEVF) is a genetic epilepsy disorder in which affected family members experience different focal seizures. Focal cortical dysplasia (FCD) is a frequent cause of drug-resistant epilepsy in the pediatric population. Patients with focal epilepsy are less frequently referred for presurgical evaluation when FCD is not identified on MRI or when they have genetic epilepsy. The authors describe the successful surgical management of 3 drug-resistant FFEVF patients who initially had either negative or inconclusive MRI studies. All 3 patients carried the same germline pathogenic variant in NPRL3, a GATOR1 complex gene. In patient 1, an ill-defined signal abnormality on MRI raised the suspicion for FCD and suggested that similar occult lesions might underlie the epilepsy of her relatives, whose scan results were initially reported as negative. Each patient was thus referred for comprehensive presurgical evaluation. Through the use of advanced neuroimaging, epileptogenic zones were identified in all 3 patients, allowing for targeted resection. At the last follow-up, all had remained seizure free for more than 5 years. When a genetic alteration is associated with FCD, such patients warrant imaging reexamination and exhaustive presurgical evaluation with advanced neuroimaging, as eventual lesion detection and subsequent resection can lead to long-term seizure freedom. https://thejns.org/doi/10.3171/CASE26127.
Inflammatory bowel disease (IBD) is recognized as a prototypical disorder of brain-gut interaction. Although neuroimaging research in this field has advanced rapidly in recent years, the findings remain fragmented across multiple disciplines, and a systematic integration of the literature is lacking. This study presents the first integrated bibliometric analysis and literature review to map the landscape and evolving trends of neuroimaging research in IBD over the past two decades and to identify the knowledge base and research frontiers. We conducted a systematic search of the Web of Science Core Collection and Scopus databases for IBD-related neuroimaging literature published between January 2000 and January 2026. Following the PRISMA guidelines, two independent reviewers screened titles, abstracts, and full texts. A total of 175 articles met the inclusion criteria. Data were extracted on study characteristics, neuroimaging modalities, and clinical findings. For the synthesis, we employed a dual approach: (1) a bibliometric analysis using VOSviewer, Biblioshiny, and CiteSpace to map publication trends, collaboration networks, and research hotspots; and (2) a structured literature review across five predefined dimensions: technical modalities, brain region-symptom associations, subtype differences, mechanistic pathways, and clinical translation. The systematic search and selection process identified 175 articles for final synthesis. The field has entered a phase of rapid expansion since 2021, with China and the United States as core contributing countries. Emerging frontiers include the "brain-gut axis" and the "default mode network." The literature synthesis indicates that: (1) brain alterations are predominantly localized within an emotional and interoceptive network (anterior cingulate cortex, insula, and amygdala), with abnormalities generally associated with abdominal pain, anxiety, and depression; and (2) Crohn's disease and ulcerative colitis appear to exhibit distinguishable neuroimaging phenotypes, though direct comparative studies remain limited. This study systematically clarifies the knowledge structure of the IBD neuroimaging field, demonstrates that the available neuroimaging evidence is consistent with the brain-gut axis as a central theoretical framework, and identifies subtype-specific neural characteristics. Future efforts should prioritize large-sample multicenter validation, longitudinal designs capable of testing mechanistic hypotheses, and multimodal data integration to transition the field from descriptive observations toward clinically meaningful applications,though substantial barriers-including small sample sizes, methodological heterogeneity, and lack of standardization-must first be overcome.
Older adults with epilepsy are at increased risk for Alzheimer's disease (AD), yet the mechanisms underlying this association remain poorly understood. We applied a validated AD neuroimaging signature to older adults with epilepsy to examine 1) whether older adults with epilepsy mirror AD-related changes, 2) associations with clinical, cognitive, and plasma biomarker outcomes, and 3) utility for identifying subgroups at heightened risk for cognitive decline. Our multicenter, prospectively enrolled cohort allowed for direct examination of differences in AD signatures between those with early-onset and late-onset unexplained epilepsy. Participants included 449 older adults: 87 with focal epilepsy from the multicenter Brain Aging and Cognition in Epilepsy (BrACE) cohort (age=66.10 [SD=6.86], including early-onset (<55 years at seizure onset) and late-onset (≥55 years at seizure onset) epilepsy); 362 from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU) healthy controls and individuals with mild cognitive impairment (MCI) or AD dementia. An AD signature was derived from regional cortical thickness and hippocampal volume weighted by their sensitivity to AD-related neurodegeneration in prior work. Associations between the AD signature, epilepsy characteristics, plasma biomarkers (β-amyloid 42/40, phosphorylated tau [pTau217, pTau181], neurofilament light chain [NfL]), and cognition were evaluated in BrACE. Participants with epilepsy demonstrated more AD-like signatures compared to ADNI CU controls (β= -0.43, p adj <0.001), reflecting reduced thickness/volume in AD-vulnerable regions. This effect was stronger among early-onset (β= -0.57) versus late-onset (β= -0.26) epilepsy. In BrACE, the AD signature correlated with NfL (β= -0.30, p adj =0.050), memory performance (β= 0.30, p adj =0.006), and predicted greater odds of cognitive impairment specifically among those with early-onset, but not late-onset, epilepsy (interaction p adj =0.043). Further, among those with early-onset epilepsy, the AD signature significantly improved identification of cognitive impairment over and beyond the effects of plasma AD biomarkers ( p =0.041). Findings were similar when examining the effects of epilepsy duration rather than epilepsy onset age. AD neuroimaging signatures may help identify clinically meaningful subgroups among older adults with epilepsy, particularly when integrated with AD biomarkers. Findings support a multimodal framework for assessing AD-related risk in epilepsy and highlight interactive effects of epilepsy chronicity and AD-related processes that can influence cognitive outcomes.
A key challenge in neuroscience is inferring relationships between brain structure and function from high-dimensional, multimodal neuroimaging data. While conventional multivariate approaches often simplify statistical assumptions and estimate one-dimensional independent sources shared across modalities, the true relationships between latent sources are likely more complex-statistical dependence may exist both within and between modalities and span more than one dimension per modality. Here, we introduce Multimodal Subspace Independent Vector Analysis (MSIVA), a method for capturing both joint and unique vector sources from multiple data modalities by defining cross-modal and unimodal subspaces with variable dimensions. MSIVA enables flexible estimation of varying-size independent subspaces within modalities and their one-to-one linkage to corresponding subspaces across modalities. Crucially, it captures subject-level variability at the voxel level within independent subspaces, in contrast to traditional methods that share identical independent components across subjects. We evaluated three initialization workflows with five candidate subspace structures in multiple synthetic datasets and two large multimodal neuroimaging datasets, including structural MRI (sMRI) and functional MRI (fMRI). After confirming that MSIVA successfully recovered ground-truth subspace structures in synthetic data, we applied MSIVA to identify latent subspace structures in neuroimaging data. Subsequent subspace-specific canonical correlation analysis, brain-phenotype prediction, and voxelwise brain-age delta analysis revealed that MSIVA sources were strongly associated with multiple phenotype variables, including age, sex, schizophrenia, lifestyle factors, and cognitive functions. Further, we identified modality- and group-specific brain regions related to age (for example, cerebellum, precentral gyrus, and cingulate gyrus in sMRI; occipital lobe and superior frontal gyrus in fMRI), sex (for example, cerebellum in sMRI, frontal lobe in fMRI, and precuneus in both sMRI and fMRI), and schizophrenia (for example, cerebellar, frontal, and insular cortices in sMRI; occipital pole, lingual gyrus, and precuneus in fMRI), shedding light on linked phenotypic and neuropsychiatric biomarkers of brain structure and function.
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