Continuous carry-over designs for fMRI

This paper describes continuous carry-over fMRI experiments. In these studies, stimuli are presented in an unbroken, sequential manner, and can be used to estimate simultaneously the mean difference in neural activity between stimuli as well as the effect of one stimulus upon another (carry-over effects). Neural adaptation, which has been the basis of many recent fMRI studies, is shown to be a specific form of carry-over effect. With this approach, the adapting effects of stimuli may be studied in a continuous sequence, as opposed to within isolated pairs or blocks. Additionally, the average, direct effect of a stimulus upon neural response can form the basis of a simultaneously obtained distributed pattern analysis, allowing comparison of neural population coding on focal (within voxel) and distributed (across voxel) spatial scales. These studies are ideally conducted with serially balanced sequences, in which every stimulus precedes and follows every other stimulus. While m-sequences can provide this stimulus order, the type 1 index 1 sequence of Finney and Outhwaite may be used in fMRI studies for those experimental designs for which an m-sequence solution does not exist. Continuous carry-over designs with serially balanced sequences are argued to be particularly well suited to the characterization of "similarity spaces," in which the perceptual similarity of stimuli is related to the structure of neural representation both within and across voxels. These concepts are illustrated with a worked example involving the neural representation of color. It is shown that data from a single scanning session are sufficient to detect direct and carry-over effects, as well as demonstrate the correspondence of the similarity structure of distributed patterns of neural firing and the perceptual similarity of a set of colors.

[1]  D. Brainard 5 – Color Appearance and Color Difference Specification , 2003 .

[2]  E. Galanter,et al.  Psychophysical Scaling ' " , 2006 .

[3]  D. Cross,et al.  Sequential Effects in Magnitude Scaling: Models and Theory , 1990 .

[4]  M. D’Esposito,et al.  Empirical analyses of BOLD fMRI statistics. I. Spatially unsmoothed data collected under null-hypothesis conditions. , 1997, NeuroImage.

[5]  Jacob Beck,et al.  Contrast and assimilation in lightness judgments , 1966 .

[6]  M. D’Esposito,et al.  Empirical Analyses of BOLD fMRI Statistics , 1997, NeuroImage.

[7]  D. Plaut,et al.  Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. , 2004, Psychological review.

[8]  C. Theobald,et al.  Design sequences for sensory studies: achieving balance for carry-over and position effects. , 2007, The British journal of mathematical and statistical psychology.

[9]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.

[10]  J. Gabrieli,et al.  Effects of Semantic and Associative Relatedness on Automatic Priming , 1998 .

[11]  Karl J. Friston,et al.  Characterizing Stimulus–Response Functions Using Nonlinear Regressors in Parametric fMRI Experiments , 1998, NeuroImage.

[12]  R. Dolan,et al.  fMRI-adaptation reveals dissociable neural representations of identity and expression in face perception. , 2004, Journal of neurophysiology.

[13]  Geoffrey M. Boynton,et al.  Efficient Design of Event-Related fMRI Experiments Using M-Sequences , 2002, NeuroImage.

[14]  S Edelman,et al.  Representation is representation of similarities , 1996, Behavioral and Brain Sciences.

[15]  Eero P. Simoncelli,et al.  Characterizing Neural Gain Control using Spike-triggered Covariance , 2001, NIPS.

[16]  R. Henson,et al.  Neural response suppression, haemodynamic repetition effects, and behavioural priming , 2003, Neuropsychologia.

[17]  Karl J. Friston,et al.  Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.

[18]  R. Schvaneveldt,et al.  The basis of transfer in artificial grammar learning , 2000, Memory & cognition.

[19]  Jacco A. de Zwart,et al.  Method for functional MRI mapping of nonlinear response , 2003, NeuroImage.

[20]  R. Desimone,et al.  Neural mechanisms for visual memory and their role in attention. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Vaidehi S. Natu,et al.  Category-Specific Cortical Activity Precedes Retrieval During Memory Search , 2005, Science.

[22]  M. D’Esposito,et al.  The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.

[23]  Karl J. Friston,et al.  Stochastic Designs in Event-Related fMRI , 1999, NeuroImage.

[24]  N. Kanwisher,et al.  Cortical Regions Involved in Perceiving Object Shape , 2000, The Journal of Neuroscience.

[25]  K. Grill-Spector,et al.  fMR-adaptation: a tool for studying the functional properties of human cortical neurons. , 2001, Acta psychologica.

[26]  D. J. Finney,et al.  Serially balanced sequences in bioassay , 1956, Proceedings of the Royal Society of London. Series B - Biological Sciences.

[27]  T. Shallice,et al.  Face repetition effects in implicit and explicit memory tests as measured by fMRI. , 2002, Cerebral cortex.

[28]  Tai Sing Lee,et al.  Adaptive contrast gain control and information maximization , 2005, Neurocomputing.

[29]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[30]  Stephen A Engel,et al.  Adaptation of Oriented and Unoriented Color-Selective Neurons in Human Visual Areas , 2005, Neuron.

[31]  F ATTNEAVE,et al.  Dimensions of similarity. , 1950, The American journal of psychology.

[32]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[33]  Mark Steyvers,et al.  Multidimensional Scaling , 2018, IBM SPSS Statistics 25 Step by Step.

[34]  Ian J Deary,et al.  Carryover Bias in Visual Assessment , 2001, Perception.

[35]  Brian A Wandell,et al.  Visual field map clusters in human cortex , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[36]  S. Shevell The Science of Color , 2003 .