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Lactate is now recognized as an important intermediate in brain metabolism, but its role is still under investigation. In this work we mapped the distribution of lactate and bicarbonate produced from intravenously injected 13C-pyruvate over the whole brain using a new imaging method, hyperpolarized 13C MRI (N = 14, ages 23 to 77). Segmenting the 13C-lactate images into brain atlas regions revealed a pattern of lactate that was preserved across individuals. Higher lactate signal was observed in cortical grey matter compared to white matter and was highest in the precuneus, cuneus and lingual gyrus. Bicarbonate signal, indicating flux of [1–13C]pyruvate into the TCA cycle, also displayed consistent spatial distribution. One-way ANOVA to test for significant differences in lactate among atlas regions gave F = 87.6 and p

Lactate is now recognized as an important intermediate in brain metabolism, but its role is still under investigation. In this work we mapped the distribution of lactate and bicarbonate produced from intravenously injected

MR-guided focused ultrasound (MRgFUS) can temporarily permeabilize the blood-brain barrier (BBB), noninvasively, to allow therapeutics access to the central nervous system. However, its secondary and potential neuromodulation effects are not well understood. We aimed to characterize the functional impact of MRgFUS BBB opening in human subjects, based on the phase I trial in patients with Alzheimer's disease. We analyzed for changes in bilateral frontoparietal networks in resting state functional MRI from five subjects after BBB opening in the right frontal lobe. We found a transient functional connectivity decrease within only the ipsilateral frontoparietal network that was recovered by the next day. Additionally, baseline to month three comparisons did not reveal any significant differences from matched-controls from the Alzheimer's Disease Neuroimaging Initiative. Overall, MRgFUS may transiently affect neurologic function, but the functional organization is restored at one day and remains unchanged at three months. This first in human data has implications for the development of MRgFUS as a drug delivery platform to pathologic brain tissue and potential use for non-invasive neuromodulation.

An efficient approach to certain types of biomedical research requires a scale that precludes involvement of all critical contributors in all aspects of experimental design, execution, and as well as writing of most, if not all, derived works. Guarantors of both the integrity of the data and of its subsequent analyses are required. When separate groups are responsible for each of these activities, each should be readily identifiable both in the primary publication and in all subsequent citations. We describe the publication policy of the Alzheimer Disease Neuroimaging Initiative (ADNI), its origins and its acceptance by the editorial and scientific communities.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder, rapidly increasing in prevalence as the population ages. As the potential for disease-modifying therapy grows, a large body of literature has aimed at finding reliable, noninvasive biomarkers of AD, to allow for early intervention and sensitive tracking of therapeutic response. Task-related functional brain imaging techniques have been increasingly used to examine episodic memory function in AD. In the present study we completed a quantitative meta-analysis of this growing literature, to establish consensus and elucidate consistent patterns across this important research area. Results from encoding and retrieval paradigms were analyzed using the activation likelihood estimation (ALE) technique for patient and control groups. Second-level ALE analyses directly compared activation between these two groups. Results indicated a number of consistent findings across the included studies. Controls showed consistently greater activity than patients in a number of regions including the MTL and frontal pole across encoding and retrieval paradigms. Patients demonstrated increased activation likelihood in areas of the ventral lateral prefrontal cortex and other regions. Our findings quantitatively confirm the widely-cited deficits in MTL activity among AD patients, and also bring to light a pattern of differential prefrontal involvement, which may be implicated in compensatory changes occurring in AD. On the whole, this study quantitatively demonstrates that functional imaging studies show consistent, if complex, patterns of brain activation differences between patients and controls. These findings support the continued evaluation of functional neuroimaging for clinical use.

In functional magnetic resonance imaging (fMRI) studies, long experiment times and small intensity changes associated with brain activation frequently lead to image artifacts due to head motion. Methods to minimize and correct for head motion by restraint, fast imaging, and retrospective image registration are typically combined but do not completely solve the problem, particularly for specific patient populations. As an initial step toward optimizing future designs of head restraints and improving motion correction techniques, the head motion characteristics of groups of stroke subjects, age-matched controls, and young adults were investigated with the aid of an MR simulator and a highly accurate position tracking system. Position measurements were recorded during motor tasks involving either the hand or the foot. Head motion was strongly dependent on the subject group and less upon the task conditions based on ANOVA calculations (P < 0.05). The stroke subjects exhibited approximately twice the head motion compared to that of age-matched controls, and the latter's head motion was about twice that of young adults. Moreover, the range of head motion in stroke subjects over all tasks was approximately 2 ± 1 mm, with the motion occurring predominantly as translation in the superior–inferior direction and pitch rotation (nodding). These results lead to several recommendations on the design of fMRI motor experiments and suggest that improved motion correction strategies are required to examine such patient populations comprehensively.

Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer's disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. Unfortunately, morphological heterogeneity in AD can prevent accurate demarcation of the hippocampus. Recent developments in automated volumetry commonly use multi-template fusion driven by expert manual labels, enabling highly accurate and reproducible segmentation in disease and healthy subjects. However, there are several protocols to define the hippocampus anatomically in vivo , and the method used to generate atlases may impact automatic accuracy and sensitivity — particularly in pathologically heterogeneous samples. Here we report a fully automated segmentation technique that provides a robust platform to directly evaluate both technical and biomarker performance in AD among anatomically unique labeling protocols. For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) baseline and 24-month dataset. We based these atlas libraries on the protocols of (Haller et al., 1997; Killiany et al., 1993; Malykhin et al., 2007; Pantel et al., 2000; Pruessner et al., 2000), and a single operator performed all manual tracings to generate de facto “ground truth” labels. All methods distinguished between normal elders, mild cognitive impairment (MCI), and AD in the expected directions, and showed comparable correlations with measures of episodic memory performance. Only more inclusive protocols distinguished between stable MCI and MCI-to-AD converters, and had slightly better associations with episodic memory. Moreover, we demonstrate that protocols including more posterior anatomy and dorsal white matter compartments furnish the best voxel-overlap accuracies (Dice Similarity Coefficient = 0.87–0.89), compared to expert manual tracings, and achieve the smallest sample sizes required to power clinical trials in MCI and AD. The greatest distribution of errors was localized to the caudal hippocampus and the alveus-fimbria compartment when these regions were excluded. The definition of the medial body did not significantly alter accuracy among more comprehensive protocols. Voxel-overlap accuracies between automatic and manual labels were lower for the more pathologically heterogeneous Sunnybrook study in comparison to the ADNI-1 sample. Finally, accuracy among protocols appears to significantly differ the most in AD subjects compared to MCI and normal elders. Together, these results suggest that selection of a candidate protocol for fully automatic multi-template based segmentation in AD can influence both segmentation accuracy when compared to expert manual labels and performance as a biomarker in MCI and AD.

Subcortical hyperintensities (SH) are a commonly observed phenomenon on MRI of the aging brain (Kertesz et al., 1988). Conflicting behavioral, cognitive and pathological associations reported in the literature underline the need to develop an intracranial volumetric analysis technique to elucidate pathophysiological origins of SH in Alzheimer's disease (AD), vascular cognitive impairment (VCI) and normal aging (De Leeuw et al., 2001; Mayer and Kier, 1991; Pantoni and Garcia, 1997; Sachdev et al., 2008). The challenge is to develop processing tools that effectively and reliably quantify subcortical small vessel disease in the context of brain tissue compartments. Segmentation and brain region parcellation should account for SH subtypes which are often classified as: periventricular (pvSH) and deep white (dwSH), incidental white matter disease or lacunar infarcts and Virchow-Robin spaces. Lesion Explorer (LE) was developed as the final component of a comprehensive volumetric segmentation and parcellation image processing stream built upon previously published methods (Dade et al., 2004; Kovacevic et al., 2002). Inter-rater and inter-method reliability was accomplished both globally and regionally. Volumetric analysis showed high inter-rater reliability both globally (ICC=.99) and regionally (ICC=.98). Pixel-wise spatial congruence was also high (SI=.97). Whole brain pvSH volumes yielded high inter-rater reliability (ICC=.99). Volumetric analysis against an alternative kNN segmentation revealed high inter-method reliability (ICC=.97). Comparison with visual rating scales showed high significant correlations (ARWMC: r=.86; CHIPS: r=.87). The pipeline yields a comprehensive and reliable individualized volumetric profile for subcortical vasculopathy that includes regionalized (26 brain regions) measures for: GM, WM, sCSF, vCSF, lacunar and non-lacunar pvSH and dwSH.

Structural MR imaging has become essential to the evaluation of regional brain changes in both healthy aging and disease-related processes. Several methods have been developed to measure structure size and regional brain volumes, but many of these methods involve substantial manual tracing and/or landmark identification. We present a new technique, semiautomatic brain region extraction (SABRE), for the rapid and reliable parcellation of cortical and subcortical brain regions. We combine the SABRE parcellation with tissue compartment segmentation [NeuroImage 17 (2002) 1087] to produce measures of gray matter (GM), white matter (WM), ventricular CSF, and sulcal CSF for 26 brain regions. Because SABRE restricts user input to a few easily identified landmarks, inter-rater reliability is high for all volumes, with all coefficients between 0.91 and 0.99. To assess construct validity, we contrasted SABRE-derived volumetric data from healthy young and older adults. Results from the SABRE parcellation and tissue segmentation showed significant differences in multiple brain regions in keeping with regional atrophy described in the literature by researchers using lengthy manual tracing methods. Our findings show that SABRE is a reliable semiautomatic method for assessing regional tissue volumes that provides significant timesavings over purely manual methods, yet maintains information about individual cortical landmarks.

A new protocol is introduced for brain extraction and automatic tissue segmentation of MR images. For the brain extraction algorithm, proton density and T2-weighted images are used to generate a brain mask encompassing the full intracranial cavity. Segmentation of brain tissues into gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) is accomplished on a T1-weighted image after applying the brain mask. The fully automatic segmentation algorithm is histogram-based and uses the Expectation Maximization algorithm to model a four-Gaussian mixture for both global and local histograms. The means of the local Gaussians for GM, WM, and CSF are used to set local thresholds for tissue classification. Reproducibility of the extraction procedure was excellent, with average variation in intracranial capacity (TIC) of 0.13 and 0.66% TIC in 12 healthy normal and 33 Alzheimer brains, respectively. Repeatability of the segmentation algorithm, tested on healthy normal images, indicated scan–rescan differences in global tissue volumes of less than 0.30% TIC. Reproducibility at the regional level was established by comparing segmentation results within the 12 major Talairach subdivisions. Accuracy of the algorithm was tested on a digital brain phantom, and errors were less than 1% of the phantom volume. Maximal Type I and Type II classification errors were low, ranging between 2.2 and 4.3% of phantom volume. The algorithm was also insensitive to variation in parameter initialization values. The protocol is robust, fast, and its success in segmenting normal as well as diseased brains makes it an attractive clinical application.