A post-processing/region of interest (ROI) method for discriminating patterns of activity in statistical maps of fMRI data

Visual inspection of independent components: defining a procedure for artifact removal from fMRI data

Artifacts in functional magnetic resonance imaging (fMRI) data, primarily those related to motion and physiological sources, negatively impact the functional signal-to-noise ratio in fMRI studies, even after conventional fMRI preprocessing. Independent component analysis' demonstrated capacity to separate sources of neural signal, structured noise, and random noise into separate components might be utilized in improved procedures to remove artifacts from fMRI data. Such procedures require a method for labeling independent components (ICs) as representing artifacts to be removed or neural signals of interest to be spared. Visual inspection is often considered an accurate method for such labeling as well as a standard to which automated labeling methods are compared. However, detailed descriptions of methods for visual inspection of ICs are lacking in the literature. Here we describe the details of, and the rationale for, an operationalized fMRI data denoising procedure that involves visual inspection of ICs (96% inter-rater agreement). We estimate that dozens of subjects/sessions can be processed within a few hours using the described method of visual inspection. Our hope is that continued scientific discussion of and testing of visual inspection methods will lead to the development of improved, cost-effective fMRI denoising procedures.

Defining the face processing network: optimization of the functional localizer in fMRI

Functional localizers that contrast brain signal when viewing faces versus objects are commonly used in functional magnetic resonance imaging studies of face processing. However, current protocols do not reliably show all regions of the core system for face processing in all subjects when conservative statistical thresholds are used, which is problematic in the study of single subjects. Furthermore, arbitrary variations in the applied thresholds are associated with inconsistent estimates of the size of face-selective regions-of-interest (ROIs). We hypothesized that the use of more natural dynamic facial images in localizers might increase the likelihood of identifying face-selective ROIs in individual subjects, and we also investigated the use of a method to derive the statistically optimal ROI cluster size independent of thresholds. We found that dynamic facial stimuli were more effective than static stimuli, identifying 98% (versus 72% for static) of ROIs in the core face processing system and 69% (versus 39% for static) of ROIs in the extended face processing system. We then determined for each core face processing ROI, the cluster size associated with maximum statistical face-selectivity, which on average was approximately 50 mm(3) for the fusiform face area, the occipital face area, and the posterior superior temporal sulcus. We suggest that the combination of (a) more robust face-related activity induced by a dynamic face localizer and (b) a cluster-size determination based on maximum face-selectivity increases both the sensitivity and the specificity of the characterization of face-related ROIs in individual subjects.

Relevance to self: A brief review and framework of neural systems underlying appraisal

We argue that many similar findings observed in cognitive, affective, and social neuroimaging research may compose larger processes central to generating self-relevance. In support of this, recent findings from these research domains were reviewed to identify common systemic activation patterns. Superimposition of these patterns revealed evidence for large-scale supramodal processes, which are argued to mediate appraisal of self-relevant content irrespective of specific stimulus types (e.g. words, pictures) and task domains (e.g. induction of reward, fear, pain, etc.). Furthermore, we distinguish between two top-down sub-systems involved in appraisal of self-relevance, one that orients pre-attentive biasing information (e.g. anticipatory or mnemonic) to salient or explicitly self-relevant phenomena, and another that engages introspective processes (e.g. self-reflection, evaluation, recollection) either in conjunction with or independent of the former system. Based on aggregate patterns of activation derived from the reviewed studies, processes in a ventral medial prefrontal cortex (MPFC)-subcortical network appear to track with the former pathway, and processes in a dorsal MPFC-cortical-subcortical network with the latter. As a whole, the purpose of this framework is to re-conceive the functionality of these systems in terms of supramodal processes that more directly reflect the influences of relevance to the self.

Local linear discriminant analysis (LLDA) for group and region of interest (ROI)-based fMRI analysis

A post-processing method for group discriminant analysis of fMRI is proposed. It assumes that the fMRI data have been pre-processed and analyzed so that each voxel is given a statistic specifying task-related activation(s), and that individually specific regions of interest (ROIs) have been drawn for each subject. The method then utilizes Local Linear Discriminant Analysis (LLDA) to jointly optimize the individually-specific and group linear combinations of ROIs that maximally discriminates between groups (or between tasks, if using the same subjects). LLDA tries to linearly transform each subject's voxel-based activation statistics within ROIs to a common vector space of ROI combinations, enabling the relative similarity of different subjects' activation to be assessed. We applied the method to data recorded from 10 normal subjects during a motor task expected to activate both cortical and subcortical structures. The proposed method detected activation in multiple cortical and subcortical structures that were not present when the data were analyzed by warping the data to a common space. We suggest that the method be applied to group fMRI data when warping to a common space may be ill-advised, such as examining activation in small subcortical structures susceptible to mis-registration, or examining older or neurological patient populations.