Background Atrial fibrillation (AF) is a common cause of stroke. Silent cerebral infarctions (SCIs) are known to occur in the presence and absence of AF, but the association between these disorders has not been well-defined. Purpose To estimate the association between AF and SCIs and the prevalence of SCIs in stroke-free patients with AF. Data Sources Searches of MEDLINE, PsycINFO, Cochrane Library, CINAHL, and EMBASE from inception to 8 May 2014 without language restrictions and manual screening of article references. Study Selection Observational studies involving adults with AF and no clinical history of stroke or prosthetic valves who reported SCIs. Data Extraction Study characteristics and study quality were assessed in duplicate. Data Synthesis Eleven studies including 5317 patients with mean ages from 50.0 to 83.6 years reported on the association between AF and SCIs. Autopsy studies were heterogeneous and low-quality; therefore, they were excluded from the meta-analysis of the risk estimates. When computed tomography (CT) and magnetic resonance imaging (MRI) studies were combined, AF was associated with SCIs in patients with no history of symptomatic stroke (odds ratio, 2.62 [95% CI, 1.81 to 3.80]; I2 = 32.12%; P for heterogeneity = 0.118). This association was independent of AF type (paroxysmal vs. persistent). The results were not altered significantly when the analysis was restricted to studies that met at least 70% of the maximum possible quality score (odds ratio, 3.06 [CI, 2.24 to 4.19]). Seventeen studies reported the prevalence of SCIs. The overall prevalence of SCI lesions on MRI and CT among patients with AF was 40% and 22%, respectively. Limitation Most studies were cross-sectional, and autopsy studies were heterogeneous and not sufficiently sensitive to detect small lesions. Conclusion Atrial fibrillation is associated with more than a 2-fold increase in the odds for SCI. Primary Funding Source Deane Institute for Integrative Research in Atrial Fibrillation and Stroke, Massachusetts General Hospital.

In this study, we investigated cortical thickness and functional connectivity across longitudinal acupuncture treatments in patients with knee osteoarthritis (OA). Over a period of four weeks (six treatments), we collected resting state functional magnetic resonance imaging (fMRI) scans from 30 patients before their first, third and sixth treatments. Clinical outcome showed a significantly greater Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score (improvement) with verum acupuncture compared to the sham acupuncture. Longitudinal cortical thickness analysis showed that the cortical thickness at left posterior medial prefrontal cortex (pMPFC) decreased significantly in the sham group across treatment sessions as compared with verum group. Resting state functional connectivity (rsFC) analysis using the left pMPFC as a seed showed that after longitudinal treatments, the rsFC between the left pMPFC and the rostral anterior cingulate cortex (rACC), medial frontal pole (mFP) and periaquiduct grey (PAG) are significantly greater in the verum acupuncture group as compared with the sham group. Our results suggest that acupuncture may achieve its therapeutic effect on knee OA pain by preventing cortical thinning and decreases in functional connectivity in major pain related areas, therefore modulating pain in the descending pain modulatory pathway.

Diffusion imaging is critical for detecting acute brain injury. However, normal apparent diffusion coefficient (ADC) maps change rapidly in early childhood, making abnormality detection difficult. In this paper, we explored clinical PACS and electronic healthcare records (EHR) to create age-specific ADC atlases for clinical radiology reference. Using the EHR and two rounds of multi-expert reviews, we found ADC maps from 201 children 0–6 years of age scanned between 2006–2013 who had brain MRIs with no reported abnormalities and normal clinical evaluations 2+ years later. These images were grouped in 10 age bins, densely sampling the first 1 year of life (5 bins, including neonates and 4 quarters) and representing the 1–6 year age range (an age bin per year). Unbiased group-wise registration was used to construct ADC atlases for 10 age bins. We used the atlases to quantify (a) cross-sectional normative ADC variations; (b) spatiotemporal heterogeneous ADC changes; and (c) spatiotemporal heterogeneous volumetric changes. The quantified age-specific whole-brain and region-wise ADC values were compared to those from age-matched individual subjects in our study and in multiple existing independent studies. The significance of this study is that we have shown that clinically-acquired images can be used to construct normative age-specific atlases. These first of their kind age-specific normative ADC atlases quantitatively characterize changes of myelination-related water diffusion in the first 6 years of life. The quantified voxel-wise spatiotemporal ADC variations provide standard references to assist radiologists toward more objective interpretation of abnormalities in clinical images. Our atlases are available at https://www.nitrc.org/projects/mgh_adcatlases.

Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0-90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL's BET, AFNI's 3dSkullStrip, FreeSurfer's HWA, BrainSuite's BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov .