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At least 19 recordsLinked to original sources

PubMed2026

Bidirectional causal relationships between plasma proteins, neuroimaging metrics and risk of Alzheimer's disease.

BACKGROUND: Changes in neuroimaging metrics are among the first detectable pathophysiological alterations in Alzheimer's disease (AD). Proteins are closely linked to fluctuations in neuroimaging metrics. Therefore, the analysis of the proteomic signature associated with neuroimaging metrics holds significant promise for uncovering therapeutic targets that contribute to AD. METHODS: GWAS data concerning the Brain Imaging Data Structure (BIDs). The AD cohort comprised a total of 401,661 individuals diagnosed with AD, alongside 10,520 control participants. For a bidirectional MR analysis involving neuroimaging metrics, proteomics, and AD, the methods utilized included inverse variance weighted (IVW), MR Egger, weighted median, weighted mode, and the Wald ratio approaches. RESULTS: We identified 12 neuroimaging metrics that demonstrate significant relevance to AD (thickness of the left total hemisphere, volume of the right thalamus, and et al.). These metrics are structural magnetic resonance imaging (MRI) biomarkers that remain stable throughout the entire course of AD, from the preclinical stage through mild cognitive impairment (MCI) to dementia. Additionally, we found a substantial number of 1633 proteins that also show a noteworthy causal relationship with AD. Functional enrichment analysis indicated that these proteins were predominantly focused within various pathways linked to AD, encompassing those involved in the synaptic vesicle cycle, synaptic membranes, neurotransmitter release, and the activity of GABA receptors. In addition, our research indicates that the significant relationships observed between the identified proteins and AD are influenced by neuroimaging metrics. Notably, we found that these neuroimaging metrics play a crucial role in mediating a substantial 67% of the inverse relationship that exists between PTPRC and the phenotypic characteristics associated with AD. CONCLUSIONS: This study successfully establishes a connection between proteomic and neuroimaging metrics, as well as the AD that influence them. By creating this relationship, the research offers important information that aids in comprehending the intricate mechanisms involved in AD.

Alzheimer Disease↗

PubMed2025

Neuroimaging PheWAS and molecular phenotyping implicate PSMC3 in Alzheimer's Disease.

INTRODUCTION: Neuroimaging genetics have advanced Alzheimer's disease (AD) research, yet frameworks mechanistically connecting genes to neurological outcomes via functional genomics are needed to elucidate genetic associations. To address this challenge, we assessed relationships between AD-associated variants and disease via their impact on gene expression and neuroimaging phenotypes. METHODS: We mapped established AD genes to neuroimaging traits using NeuroimaGene atlas and predicted transcript-driven AD neurological features by comparing gene-derived neuroimaging features to clinical neuroimaging data. Genetic correlation and covariance analyses characterized shared genetic architecture between AD endophenotypes and neuroimaging features and identified neuroimaging features associated with dementia family history. RESULTS: Our analyses implicate PSMC3 expression as a strong contributor to AD pathophysiology and indicate AD endophenotypes, including dementia family history, linked to frontal cortex thickness, volume, and cerebrospinal fluid volume changes. DISCUSSION: Our findings prioritize AD genes whose regulation is associated with vulnerable brain regions, offering a potential mechanistic framework for downstream functional validation.

Alzheimer’s Disease↗

PubMed2026

Functional neuroimaging subtypes of obsessive-compulsive disorder: A systematic review and meta-analysis.

Obsessive-compulsive disorder (OCD) exhibits substantial clinical heterogeneity that may reflect underlying neurobiological diversity. Neuroimaging-based subtyping may advance precision psychiatry by identifying biologically distinct subgroups with differential treatment responses. This study systematically synthesized evidence from functional neuroimaging subtyping studies in OCD to identify reproducible neurobiological subtypes, characterize their clinical profiles, and establish a consensus-based classification framework. We reviewed 40 original studies employing machine learning, clustering, normative modeling, or classification approaches, encompassing approximately 8,150 patients. Consensus clustering identified three reproducible neurobiological subtypes. The Limbic-Hyperactive subtype, comprising approximately 40% of patients, exhibited amygdala and insula hyperconnectivity, elevated anxiety levels, predominant contamination and washing symptoms, and favorable response to cognitive-behavioral therapy. The Fronto-Striatal-Hypoconnected subtype, comprising approximately 35% of patients, demonstrated reduced orbitofrontal-striatal connectivity, cognitive inflexibility, predominant checking and ordering symptoms, and a favorable response to selective serotonin reuptake inhibitors. The Global-Disrupted subtype, comprising approximately 25% of patients, exhibited widespread connectivity disruption, greater symptom severity, and poor treatment response. Support vector machine classification achieved 81.5% accuracy for subtype assignment, though classification of OCD versus healthy controls showed limited generalizability in multisite settings (AUC 0.567-0.673). These findings support a neuroimaging-based framework for personalized treatment selection but require prospective validation.

Humans↗

PubMed1991

EEG and neuroimaging localization in partial epilepsy.

We have studied cortical localization provided by surface and sphenoidal electroencephalograms (EEGs) and that of computed tomography (CT), magnetic resonance imaging (MR) and single photon emission tomography (SPECT) in 58 patients with partial epilepsy. Each patient had EEG, MR and SPECT during a hospitalization period of 1-2 weeks. CT scans were obtained either during the same period or had been performed in the preceding year. EEG evaluation consisted of 3-5 days of continuous monitoring including video-telemetry and ambulatory recording as well as conventional EEGs with special electrode placements. Additionally 33 of 58 patients (55%) who were potential surgical candidates had sphenoidal recordings. All patients had an abnormal EEG which showed evidence of epileptic hyperexcitability. EEG abnormality was localized in 43 patients (74%). Neuroimaging studies were focally abnormal in 38 patients (66%); 12 CT (21%), 29 MR (50%) and 24 SPECT (41%). Thirty four of 43 patients with localized EEG had at least 1 focally abnormal neuroimaging study (79%), whereas 4 of 15 (27%) patients with non-localized EEG did so. Twenty-eight of 29 patients with focal MR (97%), 11 of 12 patients with focal CT (92%) and 20 of 24 patients with focal SPECT (83%) had a concordant focal EEG. EEG and neuroimaging localization agreed in all 15 patients in whom both MR and SPECT disclosed a concordant focal abnormality. This study demonstrates a significant (P less than 0.005) correlation between surface/sphenoid EEG and neuroimaging localization in partial epilepsy.

Adolescent↗

PubMed2026

Complexity in disguise: a systematic review of fractal analysis in psychiatric neuroimaging.

OBJECTIVES: Psychiatric diagnosis and fractal studies are complex processes that extend beyond clinical evaluation and require careful methodological considerations in neuroimaging. Over the years, fractals have helped reduce these complexities in research, but they still cannot grant clinical diagnoses. Thus, the main objective was a systematic review exploring the potential applications of fractal analysis in characterizing psychiatric conditions through neuroimaging techniques-including both functional and structural MRI. MATERIALS AND METHODS: A systematic literature review was conducted on PubMed, identifying thirty-nine original studies that met the inclusion criteria. Areas showing statistical significance (p < 0.05) were reported. These studies were categorized according to DSM-V classification and examined for the description of psychiatric conditions through the fractal analysis. RESULTS: The review primarily focuses on young adults with psychiatric conditions compared to control groups. Schizophrenia and Autism Spectrum Disorder are major areas of investigation, and fractal dimension (FD) is the primary analysis method used to reflect brain patterns. Studies that calculated whole-brain FD may have underestimated local abnormalities due to the inclusion of a high percentage of tissue, potentially resulting in overlooked findings. Notably, abnormalities in the frontal cortex represent a common neurobiological feature across several psychiatric conditions. CONCLUSIONS: The findings from this systematic review shed light on the use of fractal analysis to quantify complex brain patterns in both psychiatric patients and healthy individuals. However, it is essential to recognize the need for further research to elucidate a fractal analysis protocol that allows for optimal extraction of psychiatric insights. KEY POINTS: Question Fractal analysis applied to structural and functional MRI help characterize brain alterations across psychiatric conditions. Findings This review shows consistent fractal patterns across multiple psychiatric disorders, especially in frontal regions. Despite heterogeneous methodologies, results highlight shared structural and functional abnormalities. Clinical relevance Fractal analysis may offer complementary characterization of subtle brain organization across psychiatric disorders. Its potential clinical utility-such as improving diagnostic characterization, earlier detection, among others-remains limited by the current absence of a standardized protocol.

Humans↗

PubMed2026

NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which involves structural brain degeneration, electrophysiological dysfunction, and molecular dysregulation. Most existing deep learning approaches rely on a single modality or limited multimodal combinations, thereby failing to capture the complex cross-domain interactions underlying AD progression. Furthermore, the scarcity of large-scale datasets containing synchronized neuroimaging, electrophysiological, and genomic measurements restricts the development of comprehensive multimodal diagnostic systems. To address these challenges, this study proposes NeuroOmics-Net, a multimodal deep learning framework for Alzheimer's disease analysis that integrates structural magnetic resonance imaging (sMRI), electroencephalography (EEG), and gene expression data. The proposed framework combines a Hierarchical Multi-View Encoder (HME) for modality-specific feature extraction, a Cross-Omics Attention Fusion (CAF) module for adaptive integration of complementary biomarkers, and a Disease Progression Graph Learning (DPGL) module for modeling progression-related relationships across biological domains. To facilitate cross-modal integration from independent cohorts, Regularized Canonical Correlation Analysis (RCCA) is employed to align heterogeneous feature representations within a shared latent space. Experiments were conducted using publicly available datasets from ADNI, PhysioNet, and GEO repositories comprising 1120 diagnosis-aligned samples. The proposed framework achieved 94.3% classification accuracy and an AUC of 0.975 for distinguishing normal controls (NC), mild cognitive impairment (MCI), and Alzheimer's disease subjects, while attaining 93.7% accuracy for predicting conversion from stable mild cognitive impairment (sMCI) to progressive mild cognitive impairment (pMCI). However, a fairness sensitivity analysis using stratified demographic reweighting revealed accuracy ranging from 90.8% (low-education, high-comorbidity proxy subgroup) to 96.1% (low-risk, high-reserve proxy subgroup), a demographic parity gap of 5.3 percentage points, indicating that overall accuracy reflects a performance ceiling in a relatively homogeneous research cohort rather than a realistic estimate for demographically diverse clinical populations. Comparative evaluations demonstrated consistent improvements over state-of-the-art unimodal and multimodal deep learning models. Interpretability analysis further identified clinically relevant biomarkers, including hippocampal and entorhinal atrophy, theta-alpha EEG alterations, and APOE-associated molecular pathways. Because sMRI, EEG, and gene expression data were sourced from separate, unpaired cohorts with no subjects possessing all three synchronized measurements, all reported cross-modal associations reflect population-level statistical correspondence across diagnosis-matched groups rather than within-subject physiological coupling; no claim of intra-individual causal cross-modal interaction is made. These findings demonstrate that NeuroOmics-Net provides an effective computer-aided framework for multimodal biomedical data processing and Alzheimer's disease analysis. By integrating neuroimaging, electrophysiological, and genomic information, the proposed approach enables accurate diagnosis, progression prediction, and biologically interpretable decision support for clinical and translational applications.

Humans↗

PubMed2026

Systematic proteomic analysis of neuroimaging metrics identifies therapeutic targets for pituitary neuroendocrine.

The relationship between proteomics and neuroimaging metrics (NIMs) is still not fully understood. By examining the specific proteins expressed in different NIMs, researchers can gain insights into how these NIMs contribute to pituitary neuroendocrine tumors (PitNETs), ultimately enabling the development of targeted interventions and treatments. We identified 15 significantly NIMs and 18 proteins that exhibit a noteworthy causal relationship with the risk of developing PitNETs by forward MR analysis. Additionally, 10 proteins and one distinct neuroimaging metric with PitNETs. Then, the MR results indicated the identification of 33 significant relationships, which connect proteins with NIMs across five distinct categories. Then, we discovered that NIMs are capable of mediating 63% of the inverse relationship observed between WNT3 and the phenotypic characteristics of PitNETs. The results also suggested that WNT3 was associated with hypothalamic function, pituitary function, thyroid function, adrenal function, and gonadal function. Additionally, WNT3 expression was higher in PitNETs and was verified in multiple datasets. This research effectively connects the roles of protein markers with the structures of the brain and the PitNETs that can affect it. By establishing this link, the study provides valuable insights that can help in understanding the complex mechanisms that contribute to PitNETs.

Proteomics↗

PubMed1992

Neurobehavioral probes for physiologic neuroimaging studies.

The potential of physiologic neuroimaging for contributing to the understanding of behavior and the psychopathologic condition is being enhanced by increased application of "neurobehavioral probes," tasks performed during measurement. Thus far, little attention has been paid to the psychometric properties of such tasks as reliability, difficulty, and construct validity. We propose steps for applying such probes, considering issues in defining the behavior and task selection. Few available neuropsychometric tasks meet criteria for optimal use in neuroimaging studies, and a procedure is outlined for developing new probes. Highlighted are issues encountered during the phases of conceptualization, assembly and screening of items, task construction, and the psychometric validation. A set of language tasks illustrates the process. The procedure may enhance efficiency of acquiring knowledge in this area where the magnitude of potential may be matched by the costs and complexity of research.

Behavior↗

PubMed1991

Primary extracranial neuroblastoma with central nervous system metastases characterization by clinicopathologic findings and neuroimaging.

The authors report the clinicopathologic and neuroimaging findings in ten children with primary abdominal or thoracic neuroblastoma who relapsed in the central nervous system (CNS) without evidence of concurrent intracranial extension from adjacent bone, dura, or dural sinus metastases. At diagnosis, the patients ranged in age from 0.3 to 4.5 years (median, 2 years). Their times to CNS relapse ranged from 2 to 34 months from diagnosis. In seven patients the relapse occurred from 1 to 14 months after elective discontinuation of therapy. In four patients, the CNS relapse was the primary (isolated) adverse event. Four patients could not be treated at the time of relapse, and they died within 7 days of progressive CNS disease. In the remaining group, craniospinal irradiation with or without administration of a platinum compound and an epipodophyllotoxin caused complete CNS remissions lasting 4, 5, 16, and 62+ months. Neuroimaging and autopsy findings indicated that cerebrospinal fluid is the major pathway for neuraxis dissemination by neuroblastoma cells. There was no evidence of dural penetration in any patient. The possibility of relapse in the neuraxis should be considered for any patient with neuroblastoma who had neurologic deterioration. A combination of craniospinal radiation and administration of a platinum compound and an epipodophyllotoxin will induce complete responses in some patients with neuraxis involvement by neuroblastoma, but the risk of subsequent failure outside the CNS remains high.

Abdominal Neoplasms↗

PubMed1992

Neuroimaging.

Recent advances in neuroimaging have led to an increase in the types of studies possible in the field of cognitive neuroscience. Researchers are now using neuroimaging to enhance classic approaches, such as lesion-behavior studies, as well as provide information about normal functions at levels that were previously difficult to assess.

Animals↗

PubMed1992

Emergence of optic pathway gliomas in children with neurofibromatosis type 1 after normal neuroimaging results.

We report the appearance of gliomas of the optic nerve or chiasm in four young children with neurofibromatosis type 1 whose previous neuroimaging studies showed no abnormalities; the age range of the children was 1 year 8 months to 5 years 9 months at the time the tumors were detected. Normal neuroimaging findings in an infant or young child with neurofibromatosis type 1 does not provide assurance that the optic nerves and chiasm will remain healthy.

Child, Preschool↗

PubMed1992

The evolution of neuroimaging of spinal cord injury patients over the last decade.

A review is presented concerning the development of new neuroimaging techniques in the last decade which have improved the diagnostic exploration of patients with spinal cord injuries, including studies of possible sequelae. A number of technical developments occurred in the 1980s, which have broadened the diagnostic capabilities of neuroimaging and made its investigative techniques more precise and less invasive. In a summarized way the new or upgraded modalities such as magnetic resonance imaging and angiography, computed tomography and ultrasound, are considered here with regard to the effect that they have had on the clinical management of patients with a traumatized spinal cord.

Diagnostic Imaging↗

PubMed1986

Diagnostic neuroimaging in stroke. The complementary roles of referring physician and neuroradiologist.

The number and sophistication of neuroimaging methods for identifying location, size, type, and causes of stroke or stroke-like syndromes have expanded rapidly. In acute stroke, computed tomography is used to distinguish between nonhemorrhagic and hemorrhagic infarction and to eliminate several potential alternative diagnoses. Cerebral angiography can identify surgically treatable lesions in patients experiencing transient ischemic attacks and thus aid in preventing stroke. The superb sensitivity of magnetic resonance imaging has opened new vistas in the ongoing investigation of multiinfarct dementia and small infarcts in neurologically "noneloquent" regions of the brain. The more complete the referring physician's clinical and neurologic workup, the more able is the neuroradiologist to structure and coordinate the safest, fastest, and most cost-effective plan of neuroimaging to diagnose a patient's neurologic problem.

Cerebral Angiography↗

PubMed2026

BriGHT: transcriptome-regularized multimodal neuroimaging for brain disorder prediction.

MOTIVATION: Hypergraph-based models for brain disorder prediction mainly adopt imaging-derived hypergraphs as propagation backbones. However, the entanglement of topology construction and feature propagation leaves regional representations weakly constrained by underlying biological organization, making them vulnerable to subject-specific variation and noise, particularly in heterogeneous multimodal settings. RESULTS: We present BriGHT, a Brain transcriptome-reGularized Hypergraph framework for mulTimodal disorder prediction. BriGHT employs a transcriptome-derived structural reference as a soft anchoring prior to regularize neuroimaging ROI embeddings, stabilizing representation geometry while preserving disease-relevant subject-specific variation. BriGHT further incorporates a reliability-aware fusion module to estimate subject-specific modality reliability from prediction confidence, cross-modal consistency, and decision certainty, enabling adaptive integration under heterogeneous modality quality. Experiments on three neuroimaging cohorts (ADNI, ADHD-200, REST-meta-MDD) and four modalities (VBM, fMRI, FDG, AV45) demonstrate that BriGHT consistently outperforms competing graph/hypergraph learning methods across six brain disorder prediction tasks. Perturbation analyses show that BriGHT benefits from the spatial correspondence between transcriptomic modules and imaging ROIs, rather than from arbitrary hypergraph regularization alone. Ablation and meta-analytic interpretability analyses support the contribution of transcriptomic anchoring and adaptive fusion to robust and biologically meaningful brain disorder prediction. AVAILABILITY: The software is publicly available at: https://github.com/Yaolab-fantastic/BriGHT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗

PubMed1990

Neuroimaging of disseminated germ cell neoplasms.

The purpose of this study was to determine the role of neuroimaging in the management of patients with metastatic germ cell tumors. Retrospective evaluation of 299 patients treated in 1986 and 1987 for initial presentation or recurrence of testicular, retroperitoneal, and mediastinal germ cell tumors was performed to determine indications for neuroimaging, frequency and site of CNS metastases, and occurrence of other CNS abnormalities. Sixty-six patients required CNS imaging with myelography, CT, or MR. Studies were normal in 24 patients. Twenty patients had CNS metastases including 11 with intracranial metastases, eight with spine lesions, and one with both brain and spine involvement. Sixteen had cerebral or cerebellar atrophy of unclear origin and functional significance. Two patients had ventriculomegaly without symptoms of hydrocephalus. Four patients had questionable lesions that were never confirmed. None of the 25 asymptomatic patients with elevated serum tumor markers had brain metastases. Fifteen of 17 patients with focal neurologic deficits and three of six patients with seizures had CNS metastases. CNS imaging to detect germ cell tumor metastases is most useful in the presence of neurologic deficits or seizures but is not useful in patients with unexplained elevation of serum tumor markers in the absence of neurologic deficits.

Adolescent↗

PubMed1992

Rasmussen's encephalitis: neuroimaging findings in four patients.

Rasmussen's encephalitis is a devastating disease of childhood causing progressive neurologic deficits and intractable seizure activity. Patients frequently have episodes of epilepsia partialis continua and, much less frequently, generalized status epilepticus. The seizures are intractable despite aggressive medical management. In advanced cases, hemispherectomy appears to be the only option to control the seizures. Permanent physical and mental impairments are inevitable. The cause of this disease is unknown, although pathologic specimens demonstrate nonspecific changes that are compatible with viral encephalitis. The progressive brain damage is typically so insidious in onset and gradual in course that it is difficult to make an accurate diagnosis on the basis of clinical evidence. We retrospectively evaluated the CT, xenon CT, positron emission tomographic, and MR neuroimaging findings in four young patients with pathologically suspected Rasmussen's encephalitis (three patients had CT scans, two had xenon CT scans, two had MR scans, and one had a positron emission tomogram). All studies showed abnormalities of the involved cerebral hemisphere: CT and MR revealed nonspecific atrophy, xenon CT showed decreased cerebral blood flow, and positron emission tomography revealed a hypometabolic state. Rasmussen's encephalitis is a diagnosis of exclusion; however, the information obtained from neuroimaging studies in combination with the clinical course should suggest this disorder.

Adolescent↗

PubMedDate not supplied

Sedation in pediatric neuroimaging: the science and the art.

Sedation of infants and children for neuroimaging is a "necessary evil" that will be with us for the foreseeable future. Sedation must be accompanied safely in all patients with a high frequency of success on the first attempt. A carefully-considered written sedation plan that addresses the issues raised in this review will assure maximum safety and success in sedating infants and children at each facility involved in pediatric neuroimaging. An affirmative approach and careful attention to both the science and the art of pediatric sedation by all concerned professionals will produce excellent results and facilitate application of current imaging technology in the diagnosis of pediatric neurologic disease.

Child↗

PubMed1991

Neuroimaging of the spinal cord.

Neuroimaging of the spinal cord has taken on new dimensions in the past few years. With improvement in surface coils, elimination of artifacts, and fast scan imaging, myelography with all of its complications is on the wane. Computerized tomography is excellent for bony abnormalities, but most patients with spinal disease can be diagnosed with magnetic resonance imaging due to its excellent contrast, spatial resolution, and ability to actually see the spinal cord. However, at times CT is extremely helpful. This article reviews neuroimaging of the major diseases affecting the spine.

Humans↗