Cognitive Assessment System.

Patients who have suffered hemiplegia from stroke may have some spontaneous recovery that is likely due to reorganization within the cerebral cortex. Understanding this physiology should be useful in devising ways to help patients have better improvement. Techniques, such as transcranial magnetic stimulation and neuroimaging with positron emission tomography, can be used to demonstrate the organization of the human motor system and do reveal plastic reorganization in a number of situations. In relation to stroke, the best recoveries are seen when there is preservation of the corticospinal tract from the hemisphere contralateral to the weakness, but it may well be that areas nearby to the normal motor output areas can partially substitute. There is some evidence for increased corticospinal tract activity originating ipsilateral to the hemiplegia in patients, but this may actually be associated with worse recovery. Patterns of use can influence cortical organization and should play a valuable role in rehabilitation. Certain drugs may affect this process either positively or negatively.

Cortical mechanisms of recovery of function after stroke

In the last three decades, functional magnetic resonance imaging (fMRI) has transformed the field of cognitive neuroscience. A standard analytic approach entails aligning a set of individual activation maps in a common brain space, performing a statistical test in each voxel, and interpreting significant activation clusters with respect to macroanatomic landmarks. In the last several years, however, this group-analytic approach is being increasingly replaced by analyses where neural responses are examined within each brain individually. In this opinion piece, I trace the origins of individual-subject analyses in human neuroscience and speculate on why group analyses had risen vastly in popularity during the 2000s. I then discuss a core problem with group analyses — their limited utility in informing the human cognitive architecture — and talk about how the individual-subject functional localization approach solves this problem. Finally, I discuss other reasons for why researchers have been turning to individual-subject analyses, and argue that such approaches are likely to be the future of human neuroscience.

The early origins and the growing popularity of the individual-subject analytic approach in human neuroscience

Spatial smoothing is a common pre-processing step in the analysis of functional brain imaging data. It can increase sensitivity to signals of specific shapes and sizes (Rosenfeld and Kak [1982]: Digital Picture Processing, vol. 2. Orlando, Fla.: Academic; Worsley et al. [1996]: Hum Brain Mapping 4:74-90). Also, some amount of spatial smoothness is required if methods from the theory of Gaussian random fields are to be used (Holmes [1994]: Statistical Issues in Functional Brain Mapping. PhD thesis, University of Glasgow). Smoothing is most often implemented as a convolution of the imaging data with a smoothing kernel, and convolution is most efficiently performed using the Convolution Theorem and the Fast Fourier Transform (Cooley and Tukey [1965]: Math Comput 19:297-301; Priestly [1981]: Spectral Analysis and Time Series. San Diego: Academic; Press et al. [1992]: Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. Cambridge: Cambridge University Press). An undesirable side effect of smoothing is an artifact along the edges of the brain, where brain voxels become smoothed with non-brain voxels. This results in a dark rim which might be mistaken for hypoactivity. In this short methodological paper, we present a method for correcting functional brain images for the edge artifact due to smoothing, while retaining the use of the Convolution Theorem and the Fast Fourier Transform for efficient calculation of convolutions.

An efficient method for correcting the edge artifact due to smoothing

P03.045. Porro, C. A., Cettolo, V., Francescato, M. P., & Baraldi, P. (1998). Temporal and intensity coding of pain in human cortex. J Neurophysiol, 80(6), 3312-3320. Porter, R. J., Lunn, B. S., Walker, L. L., Gray, J. M., Ballard, C. G., & O'Brien, J. T. (2000). Cognitive deficit induced by acute tryptophan depletion in patients with Alzheimer's disease. Am J Psychiatry, 157(4), 638-640.

Investigation of Cognitive Impairments using functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET) in patients with mild Alzheimer's disease

We present a procedure for the statistical analysis of single-subject PET data sets with a low number of scans (six total, two scans for each of three conditions) and describe the results of applying this method of analysis on the PET data of 10 normal subjects, performing a motor task, with six scans per subject. Development of this procedure, which combines well-established methods of statistical analysis in functional neuroimaging (a pooled estimate of variance and spatial-extent-based analysis), was motivated by the need for a statistical, individual analysis of PET data sets with a low number of scans from stroke patients in which the location and extent of an infarct varied from subject-to-subject. The results of this spatial-extent-based, single-subject analysis of PET data (including coregistration of significant PET activation to every individual's MRI image and transformation into Talairach space) showed activation in all major cortical motor areas, while no activation was detected in small structures such as the putamen and thalamus. Individual variability in the pattern of statistically significant regions of activation across 10 normal subjects, performing the same motor task, was observed. The results obtained in this study from individual analysis of a low number of PET scans of normal subjects are consistent with previous results of group PET analysis of normal subjects performing similar motor tasks, and these current results indicate that this procedure can help in the examination of PET data sets demanding a single-subject analysis (e.g., involving stroke).

Joe-Marie Maisog

Papers

Analysis of single‐subject data sets with a low number of PET scans