AIMC Topic: Neuroimaging

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Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
The application of machine learning (ML) and artificial intelligence (AI) algorithms in medical imaging is an emerging area of interest, particularly in the context of clinical decision-making. Here, we report on the overall performance (i.e., sensit...

Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models.

eLife
Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational ...

NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.

Scientific reports
Brain age prediction based on anatomical MRI scans, as an essentially new measure in neuroimaging and aging research, provides a crucial marker for the early diagnosis of neurodegenerative diseases, cognitive health appraisal, and biological age pred...

Association Between Choroid Plexus Morphological Alterations, Alzheimer Pathologies, and Cognitive Impairment: A Longitudinal Study.

Neurology
BACKGROUND AND OBJECTIVES: The choroid plexus (ChP) plays a crucial role in maintaining brain health. Alzheimer disease (AD) pathologies may damage the ChP and accelerate neurodegeneration. Previous imaging studies have found overall increased ChP vo...

Cross-modal fusion of brain imaging and clinical data for Parkinson's disease progression prediction.

PloS one
BACKGROUND: Machine learning shows great potential in science but struggles with complex, high-dimensional multi-omics data. PD progression is long, diagnosed mainly by clinical signs. This paper proposes a novel decision fusion method to improve the...

Universal black-box attacks against a third-party Alzheimer's diagnostic system.

Biomedical physics & engineering express
Artificial intelligence (AI) systems are increasingly used in medical imaging for disease diagnosis, yet their vulnerability to adversarial attacks poses significant risks for clinical deployment. In this work, we systematically evaluate the suscepti...

Using machine learning for detection of Parkinson's disease and mild cognitive impairment.

PloS one
BACKGROUND: Parkinson's disease is a movement disorder featuring motor symptoms and cognitive decline, which can manifest as mild cognitive impairment. The incidence of mild cognitive impairment increases with disease progression, and Parkinson's dis...

Subtyping schizophrenia via machine learning by using structural neuroimaging.

Translational psychiatry
Schizophrenia is a heterogeneous disorder with diverse clinical presentations and neuroanatomical alterations. Despite recent advances, we still lack a working hypothesis for the pathophysiology of schizophrenia. One reason might be the heterogeneous...

Distinct neuroimaging subtypes of ADHD among adolescents based on semi-supervised learning.

Translational psychiatry
Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder diagnosed and subtyped solely based on clinical traits, which are prone to subjective judgment and lack of reliability. Also, the clinical subtyping does...

Diffusion Models for Neuroimaging Data Augmentation: Assessing Realism and Clinical Relevance.

Journal of medical systems
Data scarcity remains a major obstacle to the application of deep learning techniques in medical imaging, particularly for rare neurodegenerative diseases. This study investigates the use of denoising diffusion probabilistic models (DDPMs) to generat...