Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

Area of Science:

• Neuroscience
• Genetics
• Computational Biology

Background:

• Brain-imaging-genetic analysis combines neuroimaging and genomic data to elucidate brain function and disorders.
• Understanding the genetic basis of brain disorders is crucial for developing effective treatments.
• High dimensionality and noise in multimodal datasets present significant analytical challenges.

Purpose of the Study:

• To review the evolution of brain-imaging-genetic analysis methodologies.
• To highlight the strengths and weaknesses of various analytical approaches.
• To discuss current challenges and future prospects in the field.

Main Methods:

• Review of methodologies from massive univariate analyses to deep learning.
• Analysis of data fusion techniques for high-dimensional, multimodal datasets.
• Evaluation of statistical signal-to-noise ratio challenges in combined analyses.

Main Results:

• Progress from early univariate methods to advanced deep learning approaches has been documented.
• Key strengths and limitations of each methodological stage are identified.
• The review synthesizes the development trajectory of brain-imaging-genetic analysis.

Conclusions:

• Brain-imaging-genetic analysis holds significant potential for understanding brain disorders.
• Overcoming data dimensionality and noise remains a critical challenge.
• Future research should focus on advanced computational methods and integrated analysis strategies.