Decoding Patterns of Human Brain Activity

Abstract

Considerable information about mental states can be decoded from noninvasive measures of human brain activity. Analyses of brain activity patterns can reveal what a person is seeing, perceiving, attending to, or remembering. Moreover, multidimensional models can be used to investigate how the brain encodes complex visual scenes or abstract semantic information. Such feats of "brain reading" or "mind reading," though impressive, raise important conceptual, methodological, and ethical issues. What does successful decoding reveal about the cognitive functions performed by a brain region? How should brain signals be spatially selected and mathematically combined to ensure that decoding reflects inherent computations of the brain rather than those performed by the decoder? We highlight recent advances and describe how multivoxel pattern analysis can provide a window into mind-brain relationships with unprecedented specificity, when carefully applied. However, as brain-reading technology advances, issues of neuroethics and mental privacy will be important to consider.

Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations

“…Features can relate to the location, magnitude or spatial extent of activation, or functional connectivity in a network or between specific brain areas; these variables are correlated with a behavioural measure, such as a reported pain experience (BOX 3). Multivariate approaches integrate multiple features of brain imaging data into an integrated predictive model 3,4,59,60 . Machine learning and statistical techniques are often used to identify patterns in these data, and are optimized to jointly predict patient status, the experience of pain, analgesia, and other outcomes.…”

“…Karen D. Davis 1,2,3 , Herta Flor 4 , Henry T. Greely 5 , Gian Domenico Iannetti 6 , Sean Mackey 7 , Markus Ploner 8 , Amanda Pustilnik 9,10 , Irene Tracey 11 , Rolf-Detlef Treede 12 and Tor D. Wager 13,14 Abstract | Chronic pain is the greatest source of disability globally and claims related to chronic pain feature in many insurance and medico-legal cases. Brain imaging (for example, functional MRI, PET, EEG and magnetoencephalography) is widely considered to have potential for diagnosis, prognostication, and prediction of treatment outcome in patients with chronic pain.…”

“…When applied to functional brain images, machine learning can be used to detect response patterns (for example, intensity and spatial distribution of functional MRI (fMRI) signals or spatial-temporal patterns of EEG signals) associated with a given experimental variable (for example, the intensity of pain perception) 61,126 . Multivariate pattern analysis (MVPA) is a machine learning technique in which a pattern classifier identifies the fMRI responses elicited by different stimuli 3,4,59,60 . MVPA is different from conventional univariate fMRI analysis approaches, which use a general linear model to detect average regional activity and consider a single voxel at a time within a given brain region (see figure, top right).…”

“…Chronic pain is defined as pain that is present every day for >3 months (or is present on ≥50% of days for 6 months) or beyond the expected period of healing and does not have the warning function that acute pain does [1][2][3][4] . Chronic pain is associated with enormous personal and societal costs; individuals with chronic pain often have a reduced quality of life and unmet therapeutic needs, and society is struggling to cope with the large numbers of people with this condition.…”

From the Perspective of Artificial Intelligence: A New Approach to the Nature of Consciousness

“…The authors believe that it can, because it suggests a causal structure of consciousness and thus something that may help in singling out relevant architectures in an artificial agent. For instance, the approach suggests that being conscious is not a matter of either having the right internal code [34] [35], or having a central global dashboard [36] [37], or processing information in a certain way [38]. The advantage of this proposal is that it allows for rather precise indications as to why the causal coupling between the environment and the agent ought to be realized.…”