Dataset: Machine Learning applied to Neuroimaging

Research Context
Machine learning and deep learning techniques have become increasingly important for analyzing complex neuroimaging and electrophysiological data in the study of Alzheimer’s disease and mild cognitive impairment. These approaches enable the identification of subtle structural, functional, and signal-based patterns that are difficult to detect using traditional analytical methods, supporting early diagnosis and biomarker discovery.

What the Dataset Contains
This dataset compiles structured information extracted from peer-reviewed studies that apply machine learning and deep learning methods to neuroimaging and electroencephalography data for Alzheimer’s disease, mild cognitive impairment, and cognitively normal populations. The dataset includes curated metadata describing imaging modalities, machine learning algorithms, sample characteristics, validation strategies, main findings, and reported biomarkers.
The included studies span multiple data modalities, including structural magnetic resonance imaging, resting-state functional magnetic resonance imaging, positron emission tomography, and electroencephalography, enabling cross-modal comparison of machine learning performance and biomarker relevance.

Notable Insights
Machine learning models applied to neuroimaging data can reliably differentiate Alzheimer’s disease from mild cognitive impairment and cognitively normal controls.
Functional connectivity alterations, particularly within default mode and frontoparietal networks, are recurrent biomarkers in functional magnetic resonance imaging–based studies.
Structural magnetic resonance imaging studies consistently identify hippocampal and temporal lobe atrophy as key discriminative features.
Electroencephalography-based deep learning approaches demonstrate promising performance for non-invasive classification across cognitive stages.
Model performance varies substantially depending on feature extraction methods and validation strategies, highlighting the importance of methodological transparency.

Data Interpretation and Intended Use
The dataset is designed for meta-analytical research, methodological comparison of machine learning approaches, and educational purposes. Researchers can use this dataset to:
Conduct systematic reviews and meta-analyses of machine learning performance in neuroimaging.
Compare biomarkers across imaging modalities.
Evaluate the impact of validation strategies on reported classification accuracy.
Support reproducibility and secondary analyses in computational neuroscience research.

Data Acquisition Summary
All data were manually extracted from peer-reviewed publications retrieved from major scientific databases and curated into a standardized structure compatible with Mendeley Data. Each study is documented through structured summary files and a consolidated dataset table, ensuring traceability, consistency, and reproducibility.