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Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases.
Diagnostics (Basel)
December 2024
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
: The accurate and early distinction of glioblastomas (GBMs) from single brain metastases (BMs) provides a window of opportunity for reframing treatment strategies enabling optimal and timely therapeutic interventions. We sought to leverage physiologically sensitive parameters derived from diffusion tensor imaging (DTI) and dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI) along with machine learning-based methods to distinguish GBMs from single BMs. : Patients with histopathology-confirmed GBMs ( = 62) and BMs ( = 26) and exhibiting contrast-enhancing regions (CERs) underwent 3T anatomical imaging, DTI and DSC-PWI prior to treatment.
View Article and Find Full Text PDFContinuous theta-burst stimulation demonstrates language-network-specific causal effects on syntactic processing.
Neuroimage
January 2025
Max Planck Partner Group, School of International Chinese Language Education, Beijing Normal University, Beijing, China; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Electronic address:
Hierarchical syntactic structure processing is proposed to be at the core of the human language faculty. Syntactic processing is supported by the left fronto-temporal language network, including a core area in the inferior frontal gyrus as well as its interaction with the posterior temporal lobe (i.e.
View Article and Find Full Text PDFAbnormal dynamic functional networks during pain-free periods: resting-state co-activation pattern analysis in primary dysmenorrhea: Abnormal dynamic functional networks in primary dysmenorrhea.
Neuroimage
January 2025
Department of Nuclear Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China. Electronic address:
Chronic pain alters the configuration of brain functional networks. Primary dysmenorrhea (PDM) is a form of chronic visceral pain, which has been identified spatial alterations in brain functional networks using static functional connectivity analysis methods. However, the dynamics alterations of brain functional networks during pain-free periovulation phase remain unclear.
View Article and Find Full Text PDFPredicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.
J Alzheimers Dis
January 2025
Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA.
Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.
Objective: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults.
Methods: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.
Individualized Spectral Features in First-episode and Drug-naïve Major Depressive Disorder: Insights from Periodic and Aperiodic EEG Analysis.
Biol Psychiatry Cogn Neurosci Neuroimaging
January 2025
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China; Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou 510006, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan. Electronic address:
Background: The detection of abnormal brain activity plays an important role in the early diagnosis and treatment of major depressive disorder (MDD). Recent studies have shown that the decomposition of the electroencephalography (EEG) spectrum into periodic and aperiodic components is useful for identifying the drivers of electrophysiologic abnormalities and avoiding individual differences.
Methods: This study aimed to elucidate the pathologic changes in individualized periodic and aperiodic activities and their relationships with the symptoms of MDD.
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