Decoding Continuous Variables from Neuroimaging Data: Basic and Clinical Applications
نویسندگان
چکیده
The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.
منابع مشابه
Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data
Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman’s general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffu...
متن کاملNeuroimaging in Iran: A Review
ABSTRACTNeuroimaging allows noninvasive evaluation of the anatomy, physiology, and function of the brain. It is widely used for diagnosis, treatment planning, and treatment evaluation of neurological disorders as well as understanding functions of the brain in health and disease. Neuroimaging modalities include X-ray computed tomography (CT), magnetic resonance imaging (MRI), single photon emis...
متن کاملDecoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities
Pain is a highly subjective experience. Self-report is the gold standard for pain assessment in clinical practice, but it may not be available or reliable in some populations. Neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have the potential to be used to provide physiology-based and quantitative nociceptive pain assessment tools that c...
متن کاملDecoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data
Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we des...
متن کامل