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Deep
learning–based cognitive impairment brain imaging analysis: New methods, new
technologies, and new paradigms
Qingqin Xu, Jianwei Lu, Zhongfu Zhang, Dongsheng Xu, Chengxiang Guo
2026, 21 (9):
4135-4147.
doi: 10.4103/NRR.NRR-D-25-00332
Cognitive impairment
arising from ischemic stroke, Alzheimer’s disease, and Parkinson’s disease
presents distinct structural and network-level alterations. Brain magnetic
resonance imaging offers a non-invasive and high-resolution approach to assess
these changes, while deep learning provides powerful tools for automated
analysis. Given that accurate lesion delineation, precise localization of
abnormal regions, and reliable disease classification are fundamental to
clinical decision-making. This review aims to explore the application of deep
learning techniques to brain magnetic resonance imaging analysis of cognitive
impairments caused by these disorders, with a focus on three core tasks: lesion
segmentation, object detection, and image classification. Recent widely
accepted findings indicate that ischemic stroke studies have achieved
state-of-the-art lesion segmentation performance, with optimized U-shaped
convolutional network (U-Net) and hybrid convolutional neural
network-transformer models reaching Dice scores up to 0.911 in delineating
focal damage. Alzheimer’s disease research has advanced classification and
staging accuracy by more than 10% compared with unimodal baselines through
three-dimensional convolutional neural network, Transformers, and multimodal
fusion, enabling more precise detection of diffuse cortical atrophy.
Parkinson’s disease imaging, despite lacking overt structural lesions, has
leveraged ResNet and Vision Transformer backbones to identify subtle and
spatially distributed abnormalities, improving early-stage differentiation.
Persistent challenges include the scarcity of large, high-quality annotated
datasets, substantial inter-site variability, high annotation costs, and
limited interpretability, hindering clinical integration. Addressing these
barriers will require advances in federated learning to mitigate data scarcity
while preserving privacy, domain adaptation techniques to reduce inter-site
variability, automated annotation, and low-resource training strategies to
lower labeling costs, and explainable artificial intelligence to improve
interpretability, thereby ensuring model robustness, privacy, and transparency.
This review highlights emerging methods, innovative technologies, and novel
paradigms that are redefining brain imaging analysis in cognitive impairment.
Mechanistically, deep learning improves cognitive impairment analysis by
integrating hierarchical and multiscale spatial features, modeling long-range
functional connectivity disruptions, and fusing structural with functional
imaging to better represent network-level pathology. In conclusion, aligning
network architectures with disease-specific imaging characteristics and task
requirements can greatly enhance the accuracy, robustness, and generalizability
of magnetic resonance imaging analyses for cognitive impairment. Future work
should focus on multimodal fusion, structure-function coupling, cross-disease
evaluations, and embedding artificial intelligence tools into clinical
workflows to support early detection, individualized treatment planning, and
large-scale clinical adoption.
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