Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and physiological noise correction methods.

Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747-771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89-95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or "pipeline") in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was demonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods.

PHYCAA: data-driven measurement and removal of physiological noise in BOLD fMRI.

The effects of physiological noise may significantly limit the reproducibility and accuracy of BOLD fMRI. However, physiological noise evidences a complex, undersampled temporal structure and is often non-orthogonal relative to the neuronally-linked BOLD response, which presents a significant challenge for identifying and removing such artifact. This paper presents a multivariate, data-driven method for the characterization and removal of physiological noise in fMRI data, termed PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis). The method identifies high frequency, autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality that is task- and subject-dependent. We also demonstrate that increasing dimensionality of such physiological noise is correlated with increasing variability in externally-measured respiratory and cardiac processes. Using PHYCAA as a denoising technique significantly improves simulated signal detection with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al., 2000) physiological denoising algorithm, which uses externally measured cardiac and respiration signals.

A citywide prehospital protocol increases access to stroke thrombolysis in Toronto.

BACKGROUND AND PURPOSE: Intravenous tissue plasminogen activator for ischemic stroke is approved for eligible patients who can be treated within a 3-hour window, but treatment rates remain disappointingly low, often <5%. To improve rapid access to stroke thrombolysis in Toronto, Canada, a citywide prehospital acute stroke activation protocol was implemented by the provincial government to transport acute stroke patients directly to one of 3 regional stroke centers, bypassing local hospitals. This comprised a paramedic screening tool, ambulance destination decision rule, and formal memorandum of understanding of system stakeholders. This report describes the initial impact of the activation protocol at our regional stroke center. METHODS: We compared consecutive patients with stroke arriving to our stroke center during the first 4 months of this new triage protocol (February 14 to June 14, 2005) versus the same 4-month period in 2004. RESULTS: The protocol resulted in an immediate doubling in the number of patients with acute stroke arriving to our regional stroke center within 2.5 hours of symptom onset. We observed a 4-fold increase in patients who were eligible for and treated with tissue plasminogen activator. The tissue plasminogen activator treatment rate for ischemic stroke patients increased from 9.5% to 23.4% (P=0.01), and one in 2 patients with ischemic stroke arriving within 2.5 hours received thrombolysis during this period (one in 5 of patients with ischemic stroke overall). The median onset-to-needle time for tissue plasminogen activator-treated patients was significantly reduced. Many implementation challenges were identified and addressed. CONCLUSIONS: This prehospital triage was immediately successful in improving tissue plasminogen activator access for patients with ischemic stroke, enabling our center to achieve one of the highest tissue plasminogen activator treatment rates in North America and underscoring the need for coordinated systems of acute stroke care. Sustainability of such an initiative will be dependent on interdisciplinary teamwork, ongoing paramedic training, adequate hospital staffing, bed availability, and repatriation agreements with community hospitals.