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Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
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http://dx.doi.org/10.1007/s12264-024-01214-1 | DOI Listing |
Geroscience
September 2025
Department of Biological Sciences, College of Natural Sciences, Kangwon National University, Kangwon, 24341, Republic of Korea.
Alzheimer's disease (AD) represents a growing global health burden, underscoring the urgent need for reliable diagnostic and prognostic biomarkers. Although several disease-modifying treatments have recently become available, their effects remain limited, as they primarily delay rather than halt disease progression. Thus, the early and accurate identification of individuals at elevated risk for conversion to AD dementia is crucial to maximize the effectiveness of these therapies and to facilitate timely intervention strategies.
View Article and Find Full Text PDFNeuroimage
September 2025
UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, INSERM, Sorbonne Université, Paris, France; Assistance Publique-Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Département R3S, Paris, France. Electronic address:
Background: Neural respiratory drive (NRD) is a clinically relevant biomarker in patients with chronic obstructive pulmonary disease (COPD). However, its analysis is challenging due to several technical considerations, including the need to obtain a stable recording over a short time period. However, a short recording duration may be inadequate to comprehensively record clinically relevant information, particularly during sleep, because NRD varies across sleep stages and over time.
View Article and Find Full Text PDFNeuroscience
September 2025
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.
Achieving a deep understanding of brain mechanisms requires multi-scale perspectives to capture the architecture of complex networks. In this study, we focused on patients with cognitive impairment and constructed individual brain networks from neuroimaging data. We introduced a Significant Edges Selection (SES) method, which effectively extracts the most informative connections while suppressing noise.
View Article and Find Full Text PDFJ Dev Behav Pediatr
September 2025
Division of Developmental Medicine, Boston Children's Hospital, Boston, MA.
Objective: Attention-deficit/hyperactivity disorder (ADHD) is one of the most common childhood psychiatric disorders and a common presenting concern in primary and developmental pediatric care. However, objective diagnostic tools are currently not available, leading to delayed and missed diagnoses. The current systematic review aimed to determine whether objective indices can serve as diagnostic markers for pediatric ADHD.
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