Anatomical ROIs Research Articles

Associations of hippocampal cholinergic receptor binding and subcortical cerebrovascular disease with whole brain volume in cognitive aging and AD

AbstractBackgroundAlzheimer’s Disease (AD) is associated with compromise to the cholinergic system. Using PET ligand imaging, our group has reported reduced nicotinic cholinergic receptors (nAchR) in the hippocampus in AD relative to cognitively unimpaired older adults. Studies have increasingly identified that chronic ischemic cerebrovascular disease (CVD) serves as a risk factor for neurodegeneration, and also directly correlates with B‐amyloid and tau accumulation (Flaherty et al., 2020; Wei et al., 2019). We sought to determine the unique contributions of cholinergic dysfunction and CVD to brain health in cognitive aging. We therefore tested differential contributions of T1 white matter hypointensities and hippocampal nAchR binding in predicting whole brain volume among individuals with varying levels of cognitive impairment.Method84 individuals (clinical Alzheimer’s Disease (AD), n=27; Mild Cognitive Impairment (MCI), n=25; and Cognitively Unimpaired (CU), n=32) completed MRI and 2‐18F‐fluoro‐3(2(S)azetidinylmethoxy) pyridine (2FA) ‐PET imaging sessions. Total Brain Volume, excluding cerebellum, brain stem, and ventricles, was calculated using FreeSurfer. SPM was used to generate nAchR binding estimates using anatomical ROI of bilateral hippocampus.ResultWhen covarying for diagnostic group, age, and use of cholinesterase inhibitors, hierarchical linear regressions identified a significant association between hippocampal nAchR binding and total brain volume (B(SE)=.013(.003); p<.001), while the association between white matter hypointensities and total brain volume was not significant (B(SE)=‐.99(.84); p=.24). Stratified by group, the same pattern emerged in a combined sample of clinical AD and MCI (nAchR binding B(SE)=.013(.005); p<.01, hypointensities B(SE)=‐.18(.91); p=.84). In CU, however, white matter hypointensities were negatively associated with total brain volume (B(SE)=‐10.60(3.53); p<.01), while the relationship between hippocampal nAchR binding and total brain volume was not significant (B(SE)=.006(.005); p=.26).ConclusionWhite matter hypointensities are associated with a measure of brain health among CU; such vascular risk factors may be important to track in preventative treatment. Hippocampal cholinergic function, relative to hypointensities, may be more closely aligned with brain health among those with cognitive decline. Longitudinal study of vascular and cholinergic systems and their interactions are warranted; corroboration of these findings in longitudinal studies may elucidate different treatment targets across stages of AD.

Dimensional Affective Processing in BD

Bipolar Disorder (BD) involves altered neural affective processing, but studies comparing BD patients to controls have yielded inconsistent results. This might relate to substantial variability in the nature and severity of mood symptoms among individuals with BD. Hence, we dimensionally examined the relationship between depressive and manic symptom severity and neural responses to positive and negative affective stimuli. 39 Participants with BD completed measures of depression and mania severity prior to completing a cognitive-affective processing task during fMRI. A multiple regression model was run in SPM to identify brain regions correlated with depressive and manic symptoms during positive-neutral and negative-neutral contrasts. A-priori anatomical ROIs were defined bilaterally in frontal, parietal and limbic regions. Results showed that depression severity was associated with increased activation in frontal, parietal, and limbic ROIs, regardless of valence. Mania severity was correlated with both increased and decreased activation, particularly within frontal subdivisions and during the processing of positively valenced images. In conclusion, dimensional modeling of symptom severity captures variance in neural responses to affect, which may have been previously undetected due to heterogeneity when examined at the group level. Future fMRI studies comparing BD patients and controls should account for symptom variability in BD.

Functional consequences of acute tryptophan depletion on raphe nuclei connectivity and network organization in healthy women

Previous research on central nervous serotonin (5-HT) function provided evidence for a substantial involvement of 5-HT in the regulation of brain circuitries associated with cognitive and affective processing. The underlying neural networks comprise core subcortical/cortical regions such as amygdala and medial prefrontal cortex, which are assumed to be modulated amongst others by 5-HT. Beside the use of antidepressants, acute tryptophan depletion (ATD) is a widely accepted technique to manipulate of 5-HT synthesis and its respective metabolites in humans by means of a dietary and non-pharmacological tool. We used a double-blind, randomized, cross-over design with two experimental challenge conditions, i.e. ATD and tryptophan (TRP) supplementation (TRYP+) serving as a control. The aim was to perturb 5-HT synthesis and to detect its impact on brain functional connectivity (FC) of the upper serotonergic raphe nuclei, the amygdala and the ventromedial prefrontal cortex as well as on network organization using resting state fMRI. 30 healthy adult female participants (age: M ​= ​24.5 ​± ​4.4 ​yrs) were included in the final analysis. ATD resulted in a 90% decrease of TRP in the serum relative to baseline. Compared to TRYP ​+ ​for the ATD condition a significantly lower FC of the raphe nucleus to the frontopolar cortex was detected, as well as greater functional coupling between the right amygdala and the ventromedial prefrontal cortex. FC of the raphe nucleus correlated significantly with the magnitude of TRP changes for both challenge conditions (ATD & TRYP+). Network-based statistical analysis using time series from 260 independent anatomical ROIs revealed significantly greater FC after ATD compared to TRYP+ in several brain regions being part of the default-mode (DMN) and the executive-control networks (ECN), but also of salience or visual networks. Finally, we observed an impact of ATD on the rich-club organization in terms of decreased rich-club coefficients compared to TRYP+.In summary we could confirm previous findings that the putative decrease in brain 5-HT synthesis via ATD significantly alters FC of the raphe nuclei as well as of specific subcortical/cortical regions involved in affective, but also in cognitive processes. Moreover, an ATD-effect on the so-called rich-club organization of some nodes with the high degree was demonstrated. This may indicate effects of brain 5-HT on the integration of information flow from several brain networks.

Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.

This study investigated the potential of deep convolutional neural networks (CNN) for automatic classification of FP-CIT SPECT in multi-site or multi-camera settings with variable image characteristics. The study included FP-CIT SPECT of 645 subjects from the Parkinson's Progression Marker Initiative (PPMI), 207 healthy controls, and 438 Parkinson's disease patients. SPECT images were smoothed with an isotropic 18-mm Gaussian kernel resulting in 3 different PPMI settings: (i) original (unsmoothed), (ii) smoothed, and (iii) mixed setting comprising all original and all smoothed images. A deep CNN with 2,872,642 parameters was trained, validated, and tested separately for each setting using 10 random splits with 60/20/20% allocation to training/validation/test sample. The putaminal specific binding ratio (SBR) was computed using a standard anatomical ROI predefined in MNI space (AAL atlas) or using the hottest voxels (HV) analysis. Both SBR measures were trained (ROC analysis, Youden criterion) using the same random splits as for the CNN. CNN and SBR trained in the mixed PPMI setting were also tested in an independent sample from clinical routine patient care (149 with non-neurodegenerative and 149 with neurodegenerative parkinsonian syndrome). Both SBR measures performed worse in the mixed PPMI setting compared to the pure PPMI settings (e.g., AAL-SBR accuracy = 0.900 ± 0.029 in the mixed setting versus 0.957 ± 0.017 and 0.952 ± 0.015 in original and smoothed setting, both p < 0.01). In contrast, the CNN showed similar accuracy in all PPMI settings (0.967 ± 0.018, 0.972 ± 0.014, and 0.955 ± 0.009 in mixed, original, and smoothed setting). Similar results were obtained in the clinical sample. After training in the mixed PPMI setting, only the CNN provided acceptable performance in the clinical sample. These findings provide proof of concept that a deep CNN can be trained to be robust with respect to variable site-, camera-, or scan-specific image characteristics without a large loss of diagnostic accuracy compared with mono-site/mono-camera settings. We hypothesize that a single CNN can be used to support the interpretation of FP-CIT SPECT at many different sites using different acquisition hardware and/or reconstruction software with only minor harmonization of acquisition and reconstruction protocols.

The relationship between heart rate and functional connectivity of brain regions involved in autonomic control

The peripheral autonomic nervous system (ANS) adjusts the heart rate (HR) to intrinsic and extrinsic demands. It is controlled by a group of functionally connected brain regions assembling the so-called central autonomic network (CAN). More specifically, forebrain cortical regions, limbic and brainstem structures within the CAN have been identified as important components of circuits involved in HR regulation. The present study aimed to investigate whether functional connectivity (FC) between these regions varies in subjects with different heart rates. Thus, 84 healthy subjects were separated according to their HR in slow, medium and fast. We observed a direct association between HR and FC in CAN regions, where stronger FC was related to slower HR. This relationship, however, is non-linear, follows an exponential course and is not restricted to CAN areas only. The network-based analysis (NBS) using time series from 262 independent anatomical ROIs revealed significantly increased functional connectivity in subjects with slow HR compared to subjects with fast HR mainly in regions being part of the salience network, but also of the default-mode network. We additionally simulated the effect of aliasing on the functional connectivity using several TRs and heart rates to exclude the possibility that FC differences might be due to different aliasing effects in the data. The result of the simulation indicated that aliasing cannot explain our findings.Thus, present results imply a functionally meaningful coupling between FC and HR that need to be accounted for in future studies. Moreover, given the established link between HR and emotional, cognitive and social processes, present findings may also be considered to explain individual differences in brain activation or connectivity when using corresponding paradigms in the MR scanner to investigate such processes.

Increased brain entropy of resting-state fMRI mediates the relationship between depression severity and mental health-related quality of life in late-life depressed elderly

BackgroundEntropy analysis is a computational method used to quantify the complexity in a system, and loss of brain complexity is hypothesized to be related to mental disorders. Here, we applied entropy analysis to the resting-state functional magnetic resonance imaging (rs-fMRI) signal in subjects with late-life depression (LLD), an illness combined with emotion dysregulation and aging effect. MethodsA total of 35 unremitted depressed elderly and 22 control subjects were recruited. Multiscale entropy (MSE) analysis was performed in the entire brain, 90 automated anatomical labeling-parcellated ROIs, and five resting networks in each study participant. LimitationsDue to ethical concerns, all the participants were under medication during the study. ResultsRegionally, subjects with LLD showed decreased entropy only in the right posterior cingulate gyrus but had universally increased entropy in affective processing (putamen and thalamus), sensory, motor, and temporal nodes across different time scales. We also found higher entropy in the left frontoparietal network (FPN), which partially mediated the negative correlation between depression severity and mental components of the quality of life, reflecting the possible neural compensation during depression treatment. ConclusionMSE provides a novel and complementary approach in rs-fMRI analysis. The temporal-spatial complexity in the resting brain may provide the adaptive variability beneficial for the elderly with depression.

Numerical and Visual Evaluation of Compressed Sensing MRI Using 3D Cartesian Sampling

We performed numerical and visual evaluation of compressed sensing MRI (CS-MRI) using 3D Cartesian sampling by numerical simulation. Three brain anatomical ROIs (white matter, gray matter, cerebrospinal fluid) of a T1-weighted image (T1WI), a T2-weighted image (T2WI) and a proton density-weighted image (PDWI) were used for numerical evaluation. Sampling ratio of the Cartesian grid was 30%. Reconstruction was performed by the projection onto convex sets (POCS) method with soft thresholding, subject to data fidelity constraints. In the absence of noise, RMSE of 3D-T1WI was 1.50, ant that of the 2D-T1WI of the transverse plane was in the range of 1.06 to 1.54; anatomical ROIs was in the range of 0.75 to 2.80; those of T2WI were 3.20, 2.77 to 3.06, and 1.81 to 4.51; those of PDWI were 1.69, 1.33 to 1.49, and 1.08 to 1.86. Visual evaluation was performed by three radiologists on the basis of three categories: artifact, anatomical structure, and tissue contrast. Average score of the visual evaluation indicated that anatomical structure and tissue contrast of CS images were equal to those of the original image, although a few artifacts were visible. If noise level was assumed to be 20 dB or less, anatomical structure and tissue contrast were not significantly degraded compared to noise-free CS images.

Evidencing a place for the hippocampus within the core scene processing network.

Functional neuroimaging studies have identified several “core” brain regions that are preferentially activated by scene stimuli, namely posterior parahippocampal gyrus (PHG), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS). The hippocampus (HC), too, is thought to play a key role in scene processing, although no study has yet investigated scene‐sensitivity in the HC relative to these other “core” regions. Here, we characterised the frequency and consistency of individual scene‐preferential responses within these regions by analysing a large dataset (n = 51) in which participants performed a one‐back working memory task for scenes, objects, and scrambled objects. An unbiased approach was adopted by applying independently‐defined anatomical ROIs to individual‐level functional data across different voxel‐wise thresholds and spatial filters. It was found that the majority of subjects had preferential scene clusters in PHG (max = 100% of participants), RSC (max = 76%), and TOS (max = 94%). A comparable number of individuals also possessed significant scene‐related clusters within their individually defined HC ROIs (max = 88%), evidencing a HC contribution to scene processing. While probabilistic overlap maps of individual clusters showed that overlap “peaks” were close to those identified in group‐level analyses (particularly for TOS and HC), inter‐individual consistency varied across regions and statistical thresholds. The inter‐regional and inter‐individual variability revealed by these analyses has implications for how scene‐sensitive cortex is localised and interrogated in functional neuroimaging studies, particularly in medial temporal lobe regions, such as the HC. Hum Brain Mapp 37:3779–3794, 2016. © 2016 Wiley Periodicals, Inc.

Concept of an upright wearable positron emission tomography imager in humans.

BackgroundPositron Emission Tomography (PET) is traditionally used to image patients in restrictive positions, with few devices allowing for upright, brain‐dedicated imaging. Our team has explored the concept of wearable PET imagers which could provide functional brain imaging of freely moving subjects. To test feasibility and determine future considerations for development, we built a rudimentary proof‐of‐concept prototype (Helmet_PET) and conducted tests in phantoms and four human volunteers.MethodsTwelve Silicon Photomultiplier‐based detectors were assembled in a ring with exterior weight support and an interior mechanism that could be adjustably fitted to the head. We conducted brain phantom tests as well as scanned four patients scheduled for diagnostic F18‐ FDG PET/CT imaging. For human subjects the imager was angled such that field of view included basal ganglia and visual cortex to test for typical resting‐state pattern. Imaging in two subjects was performed ~4 hr after PET/CT imaging to simulate lower injected F18‐ FDG dose by taking advantage of the natural radioactive decay of the tracer (F18 half‐life of 110 min), with an estimated imaging dosage of 25% of the standard.ResultsWe found that imaging with a simple lightweight ring of detectors was feasible using a fraction of the standard radioligand dose. Activity levels in the human participants were quantitatively similar to standard PET in a set of anatomical ROIs. Typical resting‐state brain pattern activation was demonstrated even in a 1 min scan of active head rotation.ConclusionTo our knowledge, this is the first demonstration of imaging a human subject with a novel wearable PET imager that moves with robust head movements. We discuss potential research and clinical applications that will drive the design of a fully functional device. Designs will need to consider trade‐offs between a low weight device with high mobility and a heavier device with greater sensitivity and larger field of view.

AICHA: An atlas of intrinsic connectivity of homotopic areas

BackgroundAtlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has a homologue in the other hemisphere. New methodWe present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1—homogeneity of its constituting voxels intrinsic activity, and 2—a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling. ResultsAICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 gray nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities. Comparison with existing methodROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy. ConclusionAICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization.

Automatic detection system for multiple region of interest registration to account for posture changes in head and neck radiotherapy

Despite immobilization of head and neck (H and N) cancer patients, considerable posture changes occur over the course of radiotherapy (RT). To account for the posture changes, we previously implemented a multiple regions of interest (mROIs) registration system tailored to the H and N region for image-guided RT correction strategies. This paper is focused on the automatic segmentation of the ROIs in the H and N region. We developed a fast and robust automatic detection system suitable for an online image-guided application and quantified its performance. The system was developed to segment nine high contrast structures from the planning CT including cervical vertebrae, mandible, hyoid, manubrium of sternum, larynx and occipital bone. It generates nine 3D rectangular-shaped ROIs and informs the user in case of ambiguities. Two observers evaluated the robustness of the segmentation on 188 H and N cancer patients. Bland–Altman analysis was applied to a sub-group of 50 patients to compare the registration results using only the automatically generated ROIs and those manually set by two independent experts. Finally the time performance and workload were evaluated. Automatic detection of individual anatomical ROIs had a success rate of 97%/53% with/without user notifications respectively. Following the notifications, for 38% of the patients one or more structures were manually adjusted. The processing time was on average 5 s. The limits of agreement between the local registrations of manually and automatically set ROIs was comprised between ±1.4 mm, except for the manubrium of sternum (−1.71 mm and 1.67 mm), and were similar to the limits agreement between the two experts. The workload to place the nine ROIs was reduced from 141 s (±20 s) by the manual procedure to 59 s (±17 s) using the automatic method. An efficient detection system to segment multiple ROIs was developed for Cone-Beam CT image-guided applications in the H and N region and is clinically implemented in our department.

Reproducibility of 3D delayed gadolinium enhanced MRI of cartilage (dGEMRIC) of the knee at 3.0 T in patients with early stage osteoarthritis

To assess the reproducibility of 3D delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) at 3 T in early stage knee osteoarthritis (OA) patients. In 20 patients, 3D dGEMRIC at 3 T was acquired twice within 7 days. To correct for patient motion during acquisition, all images were rigidly registered in 3D. Eight anatomical cartilage ROIs were analysed on both images of each patient. Capability of dGEMRIC to yield T1 maps that reproducibly distinguish spatial differences in cartilage quality was assessed in two ROIs within a single slice in each patient. Reproducibility was assessed using ICCs and Bland-Altman plots. ICCs ranged from 0.87 to 0.95, indicating good reproducibility. T1 maps revealed reproducible spatial differences in cartilage quality (ICC 0.79). Based on the Bland-Altman plots, we defined a threshold of 95 ms to determine if a change in dGEMRIC outcome in longitudinal research was statistically significant. 3D knee dGEMRIC at 3 T combined with 3D image registration is a highly reproducible measure of cartilage quality in early stage OA. Therefore, dGEMRIC may be a valuable tool in the non-invasive evaluation of cartilage quality changes in longitudinal research in patients with early stage OA and focal cartilage defects.

Effects of training intervention on quadriceps heads in untrained middle-aged women

Effects of training intervention on quadriceps heads in untrained middle-aged women

Application of neuroanatomical features to tractography clustering

Diffusion tensor imaging allows unprecedented insight into brain neural connectivity in vivo by allowing reconstruction of neuronal tracts via captured patterns of water diffusion in white matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering subsequent data analysis intractable. As a remedy, fiber clustering techniques are able to group fibers into dozens of bundles and thus facilitate analyses. Most existing fiber clustering methods rely on geometrical information of fibers, by viewing them as curves in 3D Euclidean space. The important neuroanatomical aspect of fibers, however, is ignored. In this article, the neuroanatomical information of each fiber is encapsulated in the associativity vector, which functions as the unique "fingerprint" of the fiber. Specifically, each entry in the associativity vector describes the relationship between the fiber and a certain anatomical ROI in a fuzzy manner. The value of the entry approaches 1 if the fiber is spatially related to the ROI at high confidence; on the contrary, the value drops closer to 0. The confidence of the ROI is calculated by diffusing the ROI according to the underlying fibers from tractography. In particular, we have adopted the fast marching method for simulation of ROI diffusion. Using the associativity vectors of fibers, we further model fibers as observations sampled from multivariate Gaussian mixtures in the feature space. To group all fibers into relevant major bundles, an expectation-maximization clustering approach is employed. Experimental results indicate that our method results in anatomically meaningful bundles that are highly consistent across subjects.

Adjusting for global effects in voxel-based morphometry: Gray matter decline in normal aging

Results from studies that have examined age-related changes in gray matter based on structural MRI scans have not always been consistent. Reasons for this variability likely include small or unevenly-distributed samples, different methods for tissue class segmentation and spatial normalization, and the use of different statistical models. Particularly relevant to the latter is the method of adjusting for global (total) gray matter when making inferences about regionally-specific changes. In the current study, we use voxel-based morphometry (VBM) to explore the impact of these methodological choices in assessing age-related changes in gray matter volume in a sample of 420 adults evenly distributed between the ages of 18–77 years. At a broad level, we replicate previous findings, showing age-related gray matter decline in nearly all parts of the brain, with particularly rapid decline in inferior regions of frontal cortex (e.g., insula and left inferior frontal gyrus) and the central sulcus. Segmentation was improved by increasing the number of tissue classes and using less age-biased templates, and registration was improved by using a diffeomorphic flow-based algorithm (DARTEL) rather than a “constrained warp” approach. Importantly, different approaches to adjusting for global effects – not adjusting, Local Covariation, Global Scaling, and Local Scaling – significantly affected regionally-specific estimates of age-related decline, as demonstrated by ranking age effects across anatomical ROIs. Split-half cross-validation showed that, on average, Local Covariation explained a greater proportion of age-related variance across these ROIs than did Global Scaling. Nonetheless, the appropriate choice for global adjustment depends on one's assumptions and specific research questions. More generally, these results emphasize the importance of being explicit about the assumptions underlying key methodological choices made in VBM analyses and the inferences that follow.

Brain structural trajectories over the adult lifespan

The aim of this large-sample cross-sectional voxel-based morphometry (VBM) study of anatomical brain data was to investigate linear and nonlinear age-related trajectories of grey matter volume in the human brain during the adult lifespan. To date, there are only a few structural brain studies investigating local nonlinear aspects at the voxel level, i.e., without using anatomical ROIs as a priori hypothesis. Therefore, we analyzed 547 T1-weighted MR images of healthy adult brains with an age range of 19 to 86 years, including 161 scans of subjects with ages 60 and older. We found that the gray matter volume in some regions did not linearly decrease over time, but rather exhibited a delayed decline. Nonlinear age trajectories were observed in the medial temporal lobe regions, the basal ganglia, and parts of the cerebellum. Their trajectories indicated a preservation of grey matter volume during the early adult lifespan. Interestingly, we found nonlinear grey matter structural dynamics specifically in parts of the brain that have been extensively discussed in the context of learning and memory. We propose a hypothesis in relation to the functional role of these brain regions that may explain these results.

Mapping selective neuronal loss and microglial activation in the salvaged neocortical penumbra in the rat

Rescuing the ischemic penumbra from infarction is the mainstay of acute stroke therapy. However, the rescued penumbra may be affected by selective neuronal loss (SNL) and microglial activation (MA), which may hinder functional recovery and hence represent potential new therapeutic targets. Imaging them in vivo is currently attracting considerable interest, but relevant rat models are needed to underpin methods development and validation. Although striatal SNL/MA is well described following proximal MCA occlusion (MCAo), neocortical SNL/MA is still poorly characterized, yet has greater clinical relevance. This study aimed to assess the distribution and intensity of neocortical SNL and MA in a distal clip MCAo model known to cause severe neocortical ischemia. Spontaneously hypertensive rats were subjected to 45 min distal MCAo with ipsilateral common carotid artery occlusion. At day 14, post mortem SNL and MA were mapped using NeuN and OX42 immunohistochemistry, respectively. In a separate group, cerebral blood flow (CBF) was mapped during MCAo using 14C-iodoantipyrine autoradiography. Values for SNL, MA, and CBF were obtained in the same set of anatomical ROIs covering the cortical MCA territory. Extensive SNL and MA affected the non-infarcted MCA cortex, adopting a well-defined regional distribution and a striking patchy/pseudo-columnar pattern. Regional intensities of SNL and MA were strongly inter-correlated, and also strikingly related to occlusion CBF, showing sharp rises for CBF <40%, i.e. the penumbra threshold. This rat model may be useful in providing in vitro reference for studies aiming to validate novel imaging tracers of SNL and MA in vivo.

Category-specificity in the human medial temporal lobe cortex.

The medial temporal lobe cortex (MTLC) occupies a pivotal position at the interface between neocortical association areas and the hippocampus. It has been suggested that the MTLC contains functionally distinct regions, with perirhinal cortex (PRc) preferentially supporting object processing and posterior parahippocampal cortex (PHc) preferentially supporting encoding of spatial information. Measuring differential BOLD responsiveness to objects, scenes, and other stimulus categories, we find a double dissociation between an anterior PRc response to objects and a posterior PHc response to scene stimuli. Furthermore, an anatomical ROI based approach was undertaken in an effort to understand the response profile underlying this double dissociation. We did not see any evidence for a sharp border between putatively distinct scene-preferential and object-preferential MTLC regions. Instead, scene-preferential responsiveness was noted to drop off in a graded, linear fashion in successively anterior MTLC regions until object-preferential responsiveness emerged in anterior PRc, although objects produced above baseline responses across the anterior-posterior extent of the parahippocampal gyrus. Other stimulus categories, such as faces and words, led to above baseline activation in either a few confined regions (faces) or none at all (words). Thus, what differentiated regions along the parahippocampal gryus was the relative response to objects and scenes, not simply above baseline responses to either category. This pattern raises the possibility that posterior PHc, and anterior PRc are situated at the ends of a single organizational continuum supported by the entire length of MTLC.

IC-P2-114: Meta-analysis ROIs and minimal deformation templates improve PET-FDG as a candidate biomarker in Alzheimer's disease

Background: Research consistently finds hypometabolism of [18F]Fluorodeoxyglucose (FDG-PET) in patients diagnosed with Alzheimer’s Disease (AD). Identifying abnormalities in metabolism requires precise delineation of affected areas in order to maximize differences between groups, detect change over time and minimize variance. We generated meta-analysis ROIs (MetaROIs) from frequently cited coordinates in existing studies, and compared these regions with standard anatomical ROIs across three image preprocessing streams in order to identify potential bio-marker regions using data from the Alzheimer’s Disease Neuroimaging Initiative (AD 13), (Normal 12). Methods: Six terms relating to FDG-PET, AD, and Mild Cognitive Impairment (MCI) were used to search PubMed, resulting in 18 cross-sectional studies. Study coordinates were used to generate MetaROIs. A minimal deformation template (MDT) was generated for each group (AD, MCI, Normal), using high-resolution MR images, based on the method by Kochonuv et al. (2001). Preprocessing was done with three methods: (1) individual FDG-PET images were spatially normalized to the MNI FDG template, (2) individual MR images were spatially normalized to a group-specific template, then this transformation was applied to individual coregistered FDG-PET images, (3) same as (2) but additionally the FDG-PET image was masked with an individual gray-matter mask. Effect size and percent annual change delineated the most effective processing stream. Results: MetaROIs included frequently cited regions in bilateral posterior cingulate, bilateral angular gyri, and primarily left-sided frontal and temporal regions. MetaROIs exhibited larger mean group difference between AD and Normals compared to corresponding AAL MNI regions (effect size in posterior cingulate increased from 0.48 to 1.82). Normalization to MDT template further improved effect size by 7-15%. MetaROIs also increased twelve month percent change in AD patients (posterior cingulate increased from 8% to 11%). The same region in normals showed little change (MNI ROI 0.4% vs MetaROI 0.3%). Conclusions: The use of research-defined MetaROIs, along with a processing stream that reduced deformation and used individual gray matter masks, greatly improved the viability of FDG-PET as a candidate biomarker in AD.

Mapping of the norepinephrine transporter in the human brain using PET with (S,S)-[18F]FMeNER-D2

We have evaluated the detailed mapping of the norepinephrine transporter (NET) in the human brain with (S,S)-[18F]FMeNER-D2 using a template method and the generation of functional ROIs based on the PET information. Brain PET measurements were performed from 90 to 210 min after the injection of (S,S)-[18F]FMeNER-D2 in 20 healthy male subjects. Binding potential (BPND) was calculated as late time ratio of the target region to the reference region (caudate) minus one. BPND template images were generated from mean parametric images obtained in a group of 10 subjects using SPM2. On the BPND/MRI template images, functional ROIs based on several different BPND thresholds for the thalamus and brainstem were generated automatically using PMOD 2.8 software in addition to anatomical ROIs. PET/MRI data of another group of 10 subjects were used to evaluate the validity of the template method and the functional ROIs. NET BPND template images demonstrated higher binding in the medial thalamus whereas the anterior and the pulvinar divisions had lower binding. In the brainstem, high binding was detected around the cerebral aqueduct of the midbrain and within the dorsal pons, in a volume comprising locus coeruleus. Functional ROIs with higher BPND thresholds naturally yielded higher BPND and lower coefficients of variance than did anatomical ROIs. This study indicated that (S,S)-[18F]FMeNER-D2 combined with a template method provides detailed information on the distribution of NET in vivo and that functional ROIs on the template would be useful in further clinical studies.