AIMC Topic: Brain

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Frequency-Specific Alternations in the Amplitude of Fluctuations in Tension-Type Headache: A Machine Learning Study.

Journal of neuroscience research
Brain neural signal at different frequency bands relates to different functions. However, the frequency-specific properties of spontaneous brain activity in tension-type headache (TTH)-the most rampant primary headache-remain largely unknown. We inve...

OpenMAP-T1: A Rapid Deep-Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain.

Human brain mapping
This study introduces OpenMAP-T1, a deep-learning-based method for rapid and accurate whole-brain parcellation in T1- weighted brain MRI, which aims to overcome the limitations of conventional normalization-to-atlas-based approaches and multi-atlas l...

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Journal of psychopathology and clinical science
Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors...

Leveraging normative personality data and machine learning to examine the brain structure correlates of obsessive-compulsive personality disorder traits.

Journal of psychopathology and clinical science
Brain structure correlates of obsessive-compulsive personality disorder (OCPD) remain poorly understood as limited OCPD assessment has precluded well-powered studies. Here, we tested whether machine learning (ML; elastic net regression, gradient boos...

Using machine learning to derive neurobiological subtypes of general psychopathology in late childhood.

Journal of psychopathology and clinical science
Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses often do not adequately map onto neurobiological features. Alternatively, our study used structural imag...

Deep coupled registration and segmentation of multimodal whole-brain images.

Bioinformatics (Oxford, England)
MOTIVATION: Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requiremen...

Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.

Radiology. Artificial intelligence
Purpose To guide the attention of a deep learning (DL) model toward MRI characteristics of brain lesions by incorporating radiology report-derived textual features to achieve interpretable lesion detection. Materials and Methods In this retrospective...

Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning.

Journal of integrative neuroscience
BACKGROUND: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very l...

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

Human brain mapping
There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently betwee...