IssuesinFMRI.ca

Russell Poldrack (UCLA)

Title:  Reading mental states from neuroimaging data: From reverse inference to pattern classification

Podcast: MP3 (40 MB)

Abstract: It is often commonly assumed that activation in particular regions is a signal for the engagement of specific mental processes.  For example, neuromarketers have claimed that activation in the amygdala signals threat, and many neuroimaging researchers use this kind of informal “reverse inference” when interpreting their results.  I will discuss the logical status of this form of inference and show how its utility is limited by the selectivity of activation in specific regions.  I then show how the question of reverse inference can be addressed more directly using statistical learning methods, and discuss how these methods provide a means to more directly relate cognitive processes to their neural substrates.

Dan Meegan (Guelph)

Title: How poor experimental design can affect fMRI interpretation.

Podcast: MP3 (31 MB)

Abstract: The accurate mapping of functional neuroanatomy using fMRI requires the linkage of co-occurring states of mind and brain. This presentation emphasizes the ease with which mistakes can be made when inferring mind states during fMRI scanning, and the interpretational consequences of such mistakes. Specific examples will be offered from studies investigating the neuroanatomical basis of working memory, in which faulty assumptions about the processes involved while people perform specific working memory tasks have lead to erroneous conclusions about the role of frontal cortex in working memory.

Ed Vul (MIT)

Title: Varieties of “Voodoo”

Podcast: MP3 (31 MB)

Abstract: fMRI experiments provide an abundance of data, of which only a small, a priori unknown, subset is relevant. As such, experimenters are faced with a challenging statistical problem: How to estimate both the location of the signal as well as the strength of the signal? (e.g., Which bit of the brain is correlated with neuroticism, and what is the strength of the correlation?) I will argue that current analysis techniques lead to overestimates of signal strength (e.g., correlation magnitude), that go largely unnoticed by researchers. I will advocate several analysis strategies that simply avoid this overestimation, and produce unbiased estimates of signal (correlation or otherwise).

Georg Northoff (University of Magdeburg)

Title: How can we investigate higher phenomena like the self in functional imaging? Empirical methodology for a First-Person Neuroscience

Podcast: MP3 (35 MB)

Abstract: Functional imaging investigates more and more originally philosophical concepts like the self, free will, consciousness, etc. in empirical ways. This raises many conceptual and methodological issues. The focus here is on the methodological issues of how we can investigate a concept like the self in functional imaging.

One of the main methodological challenges in investigating the neuronal processes underlying philosophical concepts like the self is to link first- and third-person data. Being based upon subjective experience, the self and its investigation relies on first-person data. This contrasts with neuroscience which requires third-person observation of neuronal states. Due to the neglect of first-person subjective experience, neuronal states as third-person data can be quantified and objectified. This, in contrast, remains impossible in the case of first-person data (see below for discussion of potential criticisms of the concept of first-person data) which are rather qualitative and subjective. If, however, the neuronal processes of subjective processes like the self are to  be investigated, subjective experience and neuronal states (i.e., first- and third-person data) have to be linked to each other in a systematic way. For this purpose, what is called First-Person Neuroscience may be an appropriate methodological strategy that aims at systematically linking first- and third-person data. I define “First-Person Neuroscience” as a methodological strategy  to systematically link first-person subjective experience  to third-person observation of neuronal states. The development of such methods distinguishes First-Person Neuroscience from neuroscience as it is commonly practiced which most often relies on third-person observation of neuronal states more or less  independently of subjective experience. I will present several examples from our own empirical investigations of the concept of self where we investigate self-relatedness in relation to other functions like reward and emotions.

Randy McIntosh (Rotman, University of Toronto)

Title: Rethinking Signal and Noise in Brain Imaging Data

Podcast: MP3 (33 MB)

Abstract: In relating brain signals measured with fMRI to mental processes, the assumption is that engaging such processes will activate key regions of the brain. Much like a computer, the region is ‘on’ when the process it subserves is required and ‘off’ when it is not. Some critical features of brain organization suggest we need to rethink this mapping. First, the network architecture enables the pattern of information flow to change without appreciable activity changes. Second, as a nonlinear system, the brain relies on both signal and noise to ensure optimal function. Indeed, the noise may be vital for enabling a full exploration of the cognitive landscape. Considering these two features defines new principles of brain-behaviour linkages, which may also impact our conceptualization of the cognitive constructs.

Dan Lloyd (Trinity College)

Title: So many voxels, so little time.

Podcast: MP3 (31 MB)

Abstract: The colorful blobs that emblematize fMRI research are the endpoints of an elaborate analysis that begins with a multi-gigabyte flood of data pouring from the scanner through ten thousand or more channels.  To understand these data, they must be filtered and funneled repeatedly.  At every stage, information is lost.  In the late stages, assumptions about the relations of brain activity to cognition, behavior, and conscious experience enable hypothesis confirmation, but at a price:  the assumptions themselves escape scrutiny.  Some of these filters include:
1.  Localization (Specific cognitive functions (or mental states) are supported by specific regions of the brain.)
2. “Task clamping” (A subject’s brain activity exactly conforms to the experimenter’s instructions throughout the experiment.)
3.  Stationarity (Similar causes have similar effects over time; a repeated task generates similar brain activity with each repetition.)
4.  Uniformity (Patterns of brain activity shared by all observed subjects are more important than individual variations in explaining cognition and consciousness.) The quest for confirmation and some basic assumptions about nature motivate these assumptions, but do not necessitate them.  Indeed, phenomenology contradicts 2, 3, and 4.  In this talk, we’ll reexamine fMRI data without these assumptions, to see what (else) is there.

Kalina Christoff (UBC)

Title: Finding alternatives to task-based paradigms for studying human cognition

Podcast: MP3 (33 MB)

Abstract: The vast majority of neuroscientific investigations observe human cognition through the prism of task-based paradigms.  For example, human thought is often seen in terms of reasoning and problems solving – the goal-directed mental processes that occur in the course of trying to solve a particular task.  At the same time, task-unrelated thought processes, such as mind wandering and spontaneously occurring thoughts and memories, have remained largely hidden from direct scientific investigation.  I will argue that in order to achieve a more complete view of higher cognition, cognitive neuroscience will need to develop viable alternatives to its dominant task-based approach.  Our recent findings using experience sampling during fMRI demonstrate that one such possible alternative may be the use of self-reports about spontaneously occurring mental states.  By combining such self-reports with real-time fMRI information, future studies may be able to examine the relationship between brain activation and subjective mental states in a more direct fashion than possible so far, as well as to correct the systematic distortions in our view of human thought that task-based paradigms have led to.

Evan Thompson (University of Toronto)

Title: Subjective experience and spontaneous brain activity: a neurophenomenological approach.

Podcast: MP3 (32 MB)

Abstract: Neuroimaging studies that examine spontaneous fluctuations of activity in task-free conditions (the so-called resting state) indicate that in these conditions the brain is active in self-generated and organized ways. But what is the meaning of this so-called intrinsic activity? This lecture presents the following neurophenomenological proposal: Phenomenological information about first-person experience can be used to illuminate the meaning of spontaneous brain activity. In particular, individuals who can stabilize and flexibly switch between various modes of attention and awareness, including nonintrusively monitoring their own mental activities from moment to moment, can internally modulate spontaneous activity and provide detailed phenomenological information about its various components. This lecture will present evidence to support this proposal.

Eric Racine (Institut de recherches cliniques de Montréal)

Podcast: MP3 (33 MB)

Title: How public perceptions and expectations shape the ethical landscape of fMRI

Abstract: The interpretation of fMRI research involves interdisciplinary scholarship ranging from physics to cognitive science. This context of fundamental importance is often under appreciated from both an ethics and public perspective. In this talk, I will first review the general ethical landscape of fMRI. Second, I will present some data illustrating how public perceptions and expectations have important implications for future uses of fMRI. Finally, I will flesh out some points to consider in the ethical use and interpretation of fMRI research.