Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe

Early astrocytosis in autosomal dominant Alzheimer's disease measured in vivo by multi-tracer positron emission tomography

Studying autosomal dominant Alzheimer's disease (ADAD), caused by gene mutations yielding nearly complete penetrance and a distinct age of symptom onset, allows investigation of presymptomatic pathological processes that can identify a therapeutic window for disease-modifying therapies. Astrocyte activation may occur in presymptomatic Alzheimer's disease (AD) because reactive astrocytes surround β-amyloid (Aβ) plaques in autopsy brain tissue. Positron emission tomography was performed to investigate fibrillar Aβ, astrocytosis and cerebral glucose metabolism with the radiotracers (11)C-Pittsburgh compound-B (PIB), (11)C-deuterium-L-deprenyl (DED) and (18)F-fluorodeoxyglucose (FDG) respectively in presymptomatic and symptomatic ADAD participants (n = 21), patients with mild cognitive impairment (n = 11) and sporadic AD (n = 7). Multivariate analysis using the combined data from all radiotracers clearly separated the different groups along the first and second principal components according to increased PIB retention/decreased FDG uptake (component 1) and increased DED binding (component 2). Presymptomatic ADAD mutation carriers showed significantly higher PIB retention than non-carriers in all brain regions except the hippocampus. DED binding was highest in presymptomatic ADAD mutation carriers. This suggests that non-fibrillar Aβ or early stage plaque depostion might interact with inflammatory responses indicating astrocytosis as an early contributory driving force in AD pathology. The novelty of this finding will be investigated in longitudinal follow-up studies.

Increased central microglial activation associated with peripheral cytokine levels in premanifest Huntington's disease gene carriers

Previous studies have shown activation of the immune system and altered immune response in Huntington's disease (HD) gene carriers. Here, we hypothesized that peripheral and central immune responses could be concurrent pathophysiological events and represent a global innate immune response to the toxic effects of mutant huntingtin in HD gene carriers. We sought to investigate our hypothesis using [(11)C]PK11195 PET as a translocator protein (TSPO) marker of central microglial activation, together with assessment of peripheral plasma cytokine levels in a cohort of premanifest HD gene carriers who were more than a decade from predicted symptomatic conversion. Data were also compared to those from a group of healthy controls matched for age and gender. We found significantly increased peripheral plasma IL-1β levels in premanifest HD gene carriers compared to the group of normal controls (P=0.018). Premanifest HD gene carriers had increased TSPO levels in cortical, basal ganglia and thalamic brain regions (P<0.001). Increased microglial activation in somatosensory cortex correlated with higher plasma levels of IL-1β (rs=0.87, P=0.013), IL-6 (rs=0.85, P=0.013), IL-8 (rs=0.68, P=0.045) and TNF-α (rs=0.79; P=0.013). Our findings provide first in vivo evidence for an association between peripheral and central immune responses in premanifest HD gene carriers, and provide further supporting evidence for the role of immune dysfunction in the pathogenesis of HD.

Presynaptic Serotoninergic Regulation of Emotional Processing: A Multimodal Brain Imaging Study

BACKGROUND

The amygdala is a central node in the brain network that processes aversive emotions and is extensively innervated by dorsal raphe nucleus (DRN) serotonin (5-hydroxytryptamine [5-HT]) neurons. Alterations in DRN 5-HT1A receptor availability cause phenotypes characterized by fearful behavior in preclinical models. However, it is unknown whether 5-HT1A receptor availability is linked specifically to the processing of aversive emotions in humans or whether it modulates connectivity in brain networks involved in emotion processing. To answer this question, we investigated the relationship between DRN 5-HT1A receptor availability and amygdala reactivity to aversive emotion and functional connectivity within the amygdala-cortical network.

METHODS

We studied 15 healthy human participants who underwent positron emission tomography scanning with [(11)C]CUMI-101, a 5-HT1A partial agonist radioligand, and functional magnetic resonance imaging of brain responses during an incidental emotion processing task including happy, fearful, and neutral faces. Regional estimates of 5-HT1A receptor binding potential (nondisplaceable) were obtained by calculating total volumes of distribution for presynaptic DRN and amygdala. Connectivity between the amygdala and corticolimbic areas was assessed using psychophysiologic interaction analysis with the amygdala as the seed region.

RESULTS

Analysis of the fear versus neutral contrast revealed a significant negative correlation between amygdala response and DRN binding potential (nondisplaceable) (r = -.87, p < .001). Availability of DRN 5-HT1A receptors positively correlated with amygdala connectivity with middle frontal gyrus, anterior cingulate cortex, bilateral precuneus, and left supramarginal gyrus for fearful (relative to neutral) faces.

CONCLUSIONS

Our data show that DRN 5-HT1A receptor availability is linked specifically to the processing of aversive emotions in the amygdala and the modulation of amygdala-cortical connectivity.

Effect of emotional valence on retrieval-related recapitulation of encoding activity in the ventral visual stream

While prior work has shown greater retrieval-related reactivation in the ventral visual stream for emotional stimuli compared to neutral stimuli, the effects of valence on retrieval-related recapitulation of successful encoding processes (Dm effects) have yet to be investigated. Here, seventeen participants (aged 19-35) studied line drawings of negative, positive, or neutral images followed immediately by the complete photo. After a 20-min delay, participants performed a challenging recognition memory test, distinguishing the studied line drawing outlines from novel ones. First, results replicated earlier work by demonstrating that negative and positive hits elicited greater ventral occipito-temporal cortex (VOTC) activity than neutral hits during both encoding and retrieval. Moreover, the amount of activation in portions of the VOTC correlated with the magnitude of participants' emotional memory enhancement. Second, results revealed significant retrieval-related recapitulation of Dm effects (Hits>Misses) in VOTC (anterior inferior temporal gyri) only for negative stimuli. Third, connectivity between the amygdala and fusiform gyrus during the encoding of negative stimuli increased the likelihood of fusiform activation during successful retrieval. Together, these results suggest that recapitulation in posterior VOTC reflects memory for the affective dimension of the stimuli (Emotional Hits>Neutral Hits) and the magnitude of activation in some of these regions is related to superior emotional memory. Moreover, for negative stimuli, recapitulation in more anterior portions of the VOTC is greater for remembered than forgotten items. The current study offers new evidence for effects of emotion on recapitulation of activity and functional connectivity in support of memory.

Stereotaxic Magnetic Resonance Imaging Brain Atlases for Infants from 3 to 12 Months

BACKGROUND

Accurate labeling of brain structures within an individual or group is a key issue in neuroimaging. Methods for labeling infant brains have depended on the labels done on adult brains or average magnetic resonance imaging (MRI) templates based on adult brains. However, the features of adult brains differ in several ways from infant brains, so the creation of a labeled stereotaxic atlas based on infants would be helpful. The current work builds on the recent creation of age-appropriate average MRI templates during the first year (3, 4.5, 6, 7.5, 9, and 12 months) by creating anatomical label sets for each template.

METHODS

We created stereotaxic atlases for the age-specific average MRI templates. Manual delineation of cortical and subcortical areas was done on the average templates based on infants during the first year. We also applied a procedure for automatic computation of macroanatomical atlases for individual infant participants using two manually segmented adult atlases (Hammers, LONI Probabilistic Brain Atlas-LPBA40). To evaluate our methods, we did manual delineation of several cortical areas on selected individuals from each age. Linear and nonlinear registration of the individual and average template was used to transform the average atlas into the individual participant's space, and the average-transformed atlas was compared to the individual manually delineated brain areas. We also applied these methods to an external data set - not used in the atlas creation - to test generalizability of the atlases.

RESULTS

Age-appropriate manual atlases were the best fit to the individual manually delineated regions, with more error seen at greater age discrepancy. There was a close fit between the manually delineated and the automatically labeled regions for individual participants and for the age-appropriate template-based atlas transformed into participant space. There was close correspondence between automatic labeling of individual brain regions and those from the age-appropriate template. These relationships held even when tested on an external set of images.

CONCLUSION

We have created age-appropriate labeled templates for use in the study of infant development at 6 ages (3, 4.5, 6, 7.5, 9, and 12 months). Comparison with manual methods was quite good. We developed three stereotaxic atlases (one manual, two automatic) for each infant age, which should allow more fine-grained analysis of brain structure for these populations than was previously possible with existing tools. The template-based atlases constructed in the current study are available online (http://jerlab.psych.sc.edu/NeurodevelopmentalMRIDatabase).

Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis

Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant's structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant's non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.

Network structure of brain atrophy in de novo Parkinson's disease

We mapped the distribution of atrophy in Parkinson's disease (PD) using magnetic resonance imaging (MRI) and clinical data from 232 PD patients and 117 controls from the Parkinson's Progression Markers Initiative. Deformation-based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation.

Multimodal neuroimaging computing: the workflows, methods, and platforms

The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.

Multimodal Partial-Volume Correction: Application to 18F-Fluoride PET/CT Bone Metastases Studies

(18)F-fluoride PET/CT offers the opportunity for accurate skeletal metastasis staging, compared with conventional imaging methods. (18)F-fluoride is a bone-specific tracer whose uptake depends on osteoblastic activity. Because of the resulting increase in bone mineralization and sclerosis, the osteoblastic process can also be detected morphologically in CT images. Although CT is characterized by high resolution, the potential of PET is limited by its lower spatial resolution and the resulting partial-volume effect. In this context, the synergy between PET and CT presents an opportunity to resolve this limitation using a novel multimodal approach called synergistic functional-structural resolution recovery (SFS-RR). Its performance is benchmarked against current resolution recovery technology using the point-spread function (PSF) of the scanner in the reconstruction procedure.

METHODS

The SFS-RR technique takes advantage of the multiresolution property of the wavelet transform applied to both functional and structural images to create a high-resolution PET image that exploits the structural information of CT. Although the method was originally conceived for PET/MR imaging of brain data, an ad hoc version for whole-body PET/CT is proposed here. Three phantom experiments and 2 datasets of metastatic bone (18)F-fluoride PET/CT images from primary prostate and breast cancer were used to test the algorithm performances. The SFS-RR images were compared with the manufacturer's PSF-based reconstruction using the standardized uptake value (SUV) and the metabolic volume as metrics for quantification.

RESULTS

When compared with standard PET images, the phantom experiments showed a bias reduction of 14% in activity and 1.3 cm(3) in volume estimates for PSF images and up to 20% and 2.5 cm(3) for the SFS-RR images. The SFS-RR images were characterized by a higher recovery coefficient (up to 60%) whereas noise levels remained comparable to those of standard PET. The clinical data showed an increase in the SUV estimates for SFS-RR images up to 34% for peak SUV and 50% for maximum SUV and mean SUV. Images were also characterized by sharper lesion contours and better lesion detectability.

CONCLUSION

The proposed methodology generates PET images with improved quantitative and qualitative properties. Compared with standard methods, SFS-RR provides superior lesion segmentation and quantification, which may result in more accurate tumor characterization.

Creating 3D visualizations of MRI data: A brief guide

While magnetic resonance imaging (MRI) data is itself 3D, it is often difficult to adequately present the results papers and slides in 3D. As a result, findings of MRI studies are often presented in 2D instead. A solution is to create figures that include perspective and can convey 3D information; such figures can sometimes be produced by standard functional magnetic resonance imaging (fMRI) analysis packages and related specialty programs. However, many options cannot provide functionality such as visualizing activation clusters that are both cortical and subcortical (i.e., a 3D glass brain), the production of several statistical maps with an identical perspective in the 3D rendering, or animated renderings. Here I detail an approach for creating 3D visualizations of MRI data that satisfies all of these criteria. Though a 3D ‘glass brain’ rendering can sometimes be difficult to interpret, they are useful in showing a more overall representation of the results, whereas the traditional slices show a more local view. Combined, presenting both 2D and 3D representations of MR images can provide a more comprehensive view of the study’s findings.

Top-down modulation in the infant brain: Learning-induced expectations rapidly affect the sensory cortex at 6 months

Recent theoretical work emphasizes the role of expectation in neural processing, shifting the focus from feed-forward cortical hierarchies to models that include extensive feedback (e.g., predictive coding). Empirical support for expectation-related feedback is compelling but restricted to adult humans and nonhuman animals. Given the considerable differences in neural organization, connectivity, and efficiency between infant and adult brains, it is a crucial yet open question whether expectation-related feedback is an inherent property of the cortex (i.e., operational early in development) or whether expectation-related feedback develops with extensive experience and neural maturation. To determine whether infants' expectations about future sensory input modulate their sensory cortices without the confounds of stimulus novelty or repetition suppression, we used a cross-modal (audiovisual) omission paradigm and used functional near-infrared spectroscopy (fNIRS) to record hemodynamic responses in the infant cortex. We show that the occipital cortex of 6-month-old infants exhibits the signature of expectation-based feedback. Crucially, we found that this region does not respond to auditory stimuli if they are not predictive of a visual event. Overall, these findings suggest that the young infant's brain is already capable of some rudimentary form of expectation-based feedback.

Quantitative Functional Arterial Spin Labeling (fASL) MRI--Sensitivity and Reproducibility of Regional CBF Changes Using Pseudo-Continuous ASL Product Sequences

Arterial spin labeling (ASL) magnetic resonance imaging is increasingly used to quantify task-related brain activation. This study assessed functional ASL (fASL) using pseudo-continuous ASL (pCASL) product sequences from two vendors. By scanning healthy participants twice with each sequence while they performed a motor task, this study assessed functional ASL for 1) its sensitivity to detect task-related cerebral blood flow (CBF) changes, and 2) its reproducibility of resting CBF and absolute CBF changes (delta CBF) in the motor cortex. Whole-brain voxel-wise analyses showed that sensitivity for motor activation was sufficient with each sequence, and comparable between sequences. Reproducibility was assessed with within-subject coefficients of variation (wsCV) and intraclass correlation coefficients (ICC). Reproducibility of resting CBF was reasonably good within (wsCV: 14.1–15.7%; ICC: 0.69–0.77) and between sequences (wsCV: 15.1%; ICC: 0.69). Reproducibility of delta CBF was relatively low, both within (wsCV: 182–297%; ICC: 0.04–0.32) and between sequences (wsCV: 185%; ICC: 0.45), while inter-session variation was low. This may be due to delta CBF’s small mean effect (0.77–1.32 mL/100g gray matter/min). In conclusion, fASL seems sufficiently sensitive to detect task-related changes on a group level, with acceptable inter-sequence differences. Resting CBF may provide a consistent baseline to compare task-related activation to, but absolute regional CBF changes are more variable, and should be interpreted cautiously when acquired with two pCASL product sequences.

Brain Extraction Using Label Propagation and Group Agreement: Pincram

Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present pincram, an automatic, versatile method for accurately labelling the adult brain on T1-weighted 3D MR head images. The method uses an iterative refinement approach to propagate labels from multiple atlases to a given target image using image registration. At each refinement level, a consensus label is generated. At the subsequent level, the search for the brain boundary is constrained to the neighbourhood of the boundary of this consensus label. The method achieves high accuracy (Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of > 0.97) and performs better than many state-of-the-art methods as evidenced by independent evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the program generates the "success index," a scalar metadatum indicative of the accuracy of the output label. Pincram is available as open source software.

Early-stage differentiation between presenile Alzheimer's disease and frontotemporal dementia using arterial spin labeling MRI

Objective

To investigate arterial spin labeling (ASL)-MRI for the early diagnosis of and differentiation between the two most common types of presenile dementia: Alzheimer’s disease (AD) and frontotemporal dementia (FTD), and for distinguishing age-related from pathological perfusion changes.

Methods

Thirteen AD and 19 FTD patients, and 25 age-matched older and 22 younger controls underwent 3D pseudo-continuous ASL-MRI at 3 T. Gray matter (GM) volume and cerebral blood flow (CBF), corrected for partial volume effects, were quantified in the entire supratentorial cortex and in 10 GM regions. Sensitivity, specificity and diagnostic performance were evaluated in regions showing significant CBF differences between patient groups or between patients and older controls.

Results

AD compared with FTD patients had hypoperfusion in the posterior cingulate cortex, differentiating these with a diagnostic performance of 74 %. Compared to older controls, FTD patients showed hypoperfusion in the anterior cingulate cortex, whereas AD patients showed a more widespread regional hypoperfusion as well as atrophy. Regional atrophy was not different between AD and FTD. Diagnostic performance of ASL to differentiate AD or FTD from controls was good (78-85 %). Older controls showed global hypoperfusion compared to young controls.

Conclusion

ASL-MRI contributes to early diagnosis of and differentiation between presenile AD and FTD.

Key Points

• ASL-MRI facilitates differentiation of early Alzheimer’s disease and frontotemporal dementia.

• Posterior cingulate perfusion is lower in Alzheimer’s disease than frontotemporal dementia.

• Compared to controls, Alzheimer’s disease patients show hypoperfusion in multiple regions.

• Compared to controls, frontotemporal dementia patients show focal anterior cingulate hypoperfusion.

• Global decreased perfusion in older adults differs from hypoperfusion in dementia.

The relative importance of imaging markers for the prediction of Alzheimer's disease dementia in mild cognitive impairment - Beyond classical regression

Selecting a set of relevant markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has become a challenging task given the wealth of regional pathologic information that can be extracted from multimodal imaging data.

Here, we used regularized regression approaches with an elastic net penalty for best subset selection of multiregional information from AV45-PET, FDG-PET and volumetric MRI data to predict conversion from MCI to AD. The study sample consisted of 127 MCI subjects from ADNI-2 who had a clinical follow-up between 6 and 31 months. Additional analyses assessed the effect of partial volume correction on predictive performance of AV45- and FDG-PET data.

Predictor variables were highly collinear within and across imaging modalities. Penalized Cox regression yielded more parsimonious prediction models compared to unpenalized Cox regression. Within single modalities, time to conversion was best predicted by increased AV45-PET signal in posterior medial and lateral cortical regions, decreased FDG-PET signal in medial temporal and temporobasal regions, and reduced gray matter volume in medial, basal, and lateral temporal regions. Logistic regression models reached up to 72% cross-validated accuracy for prediction of conversion status, which was comparable to cross-validated accuracy of non-linear support vector machine classification. Regularized regression outperformed unpenalized stepwise regression when number of parameters approached or exceeded the number of training cases. Partial volume correction had a negative effect on the predictive performance of AV45-PET, but slightly improved the predictive value of FDG-PET data.

Penalized regression yielded more parsimonious models than unpenalized stepwise regression for the integration of multiregional and multimodal imaging information. The advantage of penalized regression was particularly strong with a high number of collinear predictors.

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Use of regularized Cox and logistic regression for dementia prediction

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Regularized regression deals with a high number of highly collinear predictors.

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Regularized regression yields a parsimonious and plausible prediction model.

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Prediction accuracy of regularized regression is superior to machine learning.

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Partial volume correction of PET data modulates prediction accuracy.

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.

Feature Selection Based on the SVM Weight Vector for Classification of Dementia

Computer-aided diagnosis of dementia using a support vector machine (SVM) can be improved with feature selection. The relevance of individual features can be quantified from the SVM weights as a significance map (p-map). Although these p-maps previously showed clusters of relevant voxels in dementia-related brain regions, they have not yet been used for feature selection. Therefore, we introduce two novel feature selection methods based on p-maps using a direct approach (filter) and an iterative approach (wrapper). To evaluate these p-map feature selection methods, we compared them with methods based on the SVM weight vector directly, t-statistics, and expert knowledge. We used MRI data from the Alzheimer's disease neuroimaging initiative classifying Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients who converted to AD (MCIc), MCI patients who did not convert to AD (MCInc), and cognitively normal controls (CN). Features for each voxel were derived from gray matter morphometry. Feature selection based on the SVM weights gave better results than t-statistics and expert knowledge. The p-map methods performed slightly better than those using the weight vector. The wrapper method scored better than the filter method. Recursive feature elimination based on the p-map improved most for AD-CN: the area under the receiver-operating-characteristic curve (AUC) significantly increased from 90.3% without feature selection to 92.0% when selecting 1.5%-3% of the features. This feature selection method also improved the other classifications: AD-MCI 0.1% improvement in AUC (not significant), MCI-CN 0.7%, and MCIc-MCInc 0.1% (not significant). Although the performance improvement due to feature selection was limited, the methods based on the p-map generally had the best performance, and were therefore better in estimating the relevance of individual features.

A database of age-appropriate average MRI templates

This article summarizes a life-span neurodevelopmental MRI database. The study of neurostructural development or neurofunctional development has been hampered by the lack of age-appropriate MRI reference volumes. This causes misspecification of segmented data, irregular registrations, and the absence of appropriate stereotaxic volumes. We have created the "Neurodevelopmental MRI Database" that provides age-specific reference data from 2 weeks through 89 years of age. The data are presented in fine-grained ages (e.g., 3 months intervals through 1 year; 6 months intervals through 19.5 years; 5 year intervals from 20 through 89 years). The base component of the database at each age is an age-specific average MRI template. The average MRI templates are accompanied by segmented partial volume estimates for segmenting priors, and a common stereotaxic atlas for infant, pediatric, and adult participants. The database is available online (http://jerlab.psych.sc.edu/NeurodevelopmentalMRIDatabase/).

Quantification of [18F]DPA-714 binding in the human brain: initial studies in healthy controls and Alzheimer's disease patients

Fluorine-18 labelled N,N-diethyl-2-(2-[4-(2-fluoroethoxy)phenyl]-5,7-dimethylpyrazolo[1,5-α]pyrimidine-3-yl)acetamide ([(18)F]DPA-714) binds to the 18-kDa translocator protein (TSPO) with high affinity. The aim of this initial methodological study was to develop a plasma input tracer kinetic model for quantification of [(18)F]DPA-714 binding in healthy subjects and Alzheimer's disease (AD) patients, and to provide a preliminary assessment whether there is a disease-related signal. Ten AD patients and six healthy subjects underwent a dynamic positron emission tomography (PET) study along with arterial sampling and a scan protocol of 150 minutes after administration of 250 ± 10 MBq [(18)F]DPA-714. The model that provided the best fits to tissue time activity curves (TACs) was selected based on Akaike Information Criterion and F-test. The reversible two tissue compartment plasma input model with blood volume parameter was the preferred model for quantification of [(18)F]DPA-714 kinetics, irrespective of scan duration, volume of interest, and underlying volume of distribution (VT). Simplified reference tissue model (SRTM)-derived binding potential (BPND) using cerebellar gray matter as reference tissue correlated well with plasma input-based distribution volume ratio (DVR). These data suggest that [(18)F]DPA-714 cannot be used for separating individual AD patients from healthy subjects, but further studies including TSPO binding status are needed to substantiate these findings.

Relationship of contextual cueing and hippocampal volume in amnestic mild cognitive impairment patients and cognitively normal older adults

There is currently some debate as to whether hippocampus mediates contextual cueing. In the present study, we examined contextual cueing in patients diagnosed with mild cognitive impairment (MCI) and healthy older adults, with the main goal of investigating the role of hippocampus in this form of learning. Amnestic MCI (aMCI) patients and healthy controls completed the contextual cueing task, in which they were asked to search for a target (a horizontal T) in an array of distractors (rotated L's). Unbeknownst to them, the spatial arrangement of elements on some displays was repeated thus making the configuration a contextual cue to the location of the target. In contrast, the configuration for novel displays was generated randomly on each trial. The difference in response times between repeated and novel configurations served as a measure of contextual learning. aMCI patients, as a group, were able to learn spatial contextual cues as well as healthy older adults. However, better learning on this task was associated with higher hippocampal volume, particularly in right hemisphere. Furthermore, contextual cueing performance was significantly associated with hippocampal volume, even after controlling for age and MCI status. These findings support the role of the hippocampus in learning of spatial contexts, and also suggest that the contextual cueing paradigm can be useful in detecting neuropathological changes associated with the hippocampus.

Depressive symptoms accelerate cognitive decline in amyloid-positive MCI patients

PURPOSE

Late-life depression even in subsyndromal stages is strongly associated with Alzheimer's disease (AD). Furthermore, brain amyloidosis is an early biomarker in subjects who subsequently suffer from AD and can be sensitively detected by amyloid PET. Therefore, we aimed to compare amyloid load and glucose metabolism in subsyndromally depressed subjects with mild cognitive impairment (MCI).

METHODS

[(18)F]AV45 PET, [(18)F]FDG PET and MRI were performed in 371 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative Subjects were judged β-amyloid-positive (Aβ+; 206 patients) or β-amyloid-negative (Aβ-; 165 patients) according to [(18)F]AV45 PET. Depressive symptoms were assessed by the Neuropsychiatric Inventory Questionnaire depression item 4. Subjects with depressive symptoms (65 Aβ+, 41 Aβ-) were compared with their nondepressed counterparts. Conversion rates to AD were analysed (mean follow-up time 21.5 ± 9.1 months) with regard to coexisting depressive symptoms and brain amyloid load.

RESULTS

Aβ+ depressed subjects showed large clusters with a higher amyloid load in the frontotemporal and insular cortices (p < 0.001) with coincident hypermetabolism (p < 0.001) in the frontal cortices than nondepressed subjects. Faster progression to AD was observed in subjects with depressive symptoms (p < 0.005) and in Aβ+ subjects (p < 0.001). Coincident depressive symptoms additionally shortened the conversion time in all Aβ+ subjects (p < 0.005) and to a greater extent in those with a high amyloid load (p < 0.001).

CONCLUSION

Our results clearly indicate that Aβ+ MCI subjects with depressive symptoms have an elevated amyloid load together with relative hypermetabolism of connected brain areas compared with cognitively matched nondepressed individuals. MCI subjects with high amyloid load and coexistent depressive symptoms are at high risk of faster conversion to AD.

Morphological alterations in the caudate, putamen, pallidum, and thalamus in Parkinson's disease

Like many neurodegenerative diseases, the clinical symptoms of Parkinsons disease (PD) do not manifest until significant progression of the disease has already taken place, motivating the need for sensitive biomarkers of the disease. While structural imaging is a potentially attractive method due to its widespread availability and non-invasive nature, global morphometric measures (e.g., volume) have proven insensitive to subtle disease change. Here we use individual surface displacements from deformations of an average surface model to capture disease related changes in shape of the subcortical structures in PD. Data were obtained from both the University of British Columbia (UBC) [n = 54 healthy controls (HC) and n = 55 Parkinsons disease (PD) patients] and the publicly available Parkinsons Progression Markers Initiative (PPMI) [n = 137 (HC) and n = 189 (PD)] database. A high dimensional non-rigid registration algorithm was used to register target segmentation labels (caudate, putamen, pallidum, and thalamus) to a set of segmentation labels defined on the average-template. The vertex-wise surface displacements were significantly different between PD and HC in thalamic and caudate structures. However, overall displacements did not correlate with disease severity, as assessed by the Unified Parkinson's Disease Rating Scale (UPDRS). The results from this study suggest disease-relevant shape abnormalities can be robustly detected in subcortical structures in PD. Future studies will be required to determine if shape changes in subcortical structures are seen in the prodromal phases of the disease.

Neural substrates of levodopa-responsive gait disorders and freezing in advanced Parkinson's disease: a kinesthetic imagery approach

Gait disturbances, including freezing of gait, are frequent and disabling symptoms of Parkinson's disease. They often respond poorly to dopaminergic treatments. Although recent studies have shed some light on their neural correlates, their modulation by dopaminergic treatment remains quite unknown. Specifically, the influence of levodopa on the networks involved in motor imagery (MI) of parkinsonian gait has not been directly studied, comparing the off and on medication states in the same patients. We therefore conducted an [H2 (15) 0] Positron emission tomography study in eight advanced parkinsonian patients (mean disease duration: 12.3 ± 3.8 years) presenting with levodopa-responsive gait disorders and FoG, and eight age-matched healthy subjects. All participants performed three tasks (MI of gait, visual imagery and a control task). Patients were tested off, after an overnight withdrawal of all antiparkinsonian treatment, and on medication, during consecutive mornings. The order of conditions was counterbalanced between subjects and sessions. Results showed that imagined gait elicited activations within motor and frontal associative areas, thalamus, basal ganglia and cerebellum in controls. Off medication, patients mainly activated premotor-parietal and pontomesencephalic regions. Levodopa increased activation in motor regions, putamen, thalamus, and cerebellum, and reduced premotor-parietal and brainstem involvement. Areas activated when patients are off medication may represent compensatory mechanisms. The recruitment of these accessory circuits has also been reported for upper-limb movements in Parkinson's disease, suggesting a partly overlapping pathophysiology between imagined levodopa-responsive gait disorders and appendicular signs. Our results also highlight a possible cerebellar contribution in the pathophysiology of parkinsonian gait disorders through kinesthetic imagery.

Characterization in humans of 18F-MNI-444, a PET radiotracer for brain adenosine 2A receptors

PET with selective adenosine 2A receptor (A2A) radiotracers can be used to study a variety of neurodegenerative and neuropsychiatric disorders in vivo and to support drug-discovery studies targeting A2A. The aim of this study was to describe the first in vivo evaluation of (18)F-MNI-444, a novel PET radiotracer for imaging A2A, in healthy human subjects.

METHODS

Ten healthy human volunteers were enrolled in this study; 6 completed the brain PET studies and 4 participated in the whole-body PET studies. Arterial blood was collected for invasive kinetic modeling of the brain PET data. Noninvasive methods of data quantification were also explored. Test-retest reproducibility was evaluated in 5 subjects. Radiotracer distribution and dosimetry was determined using serial whole-body PET images acquired over 6 h post-radiotracer injection. Urine samples were collected to calculate urinary excretion.

RESULTS

After intravenous bolus injection, (18)F-MNI-444 rapidly entered the brain and displayed a distribution consistent with known A2A densities in the brain. Binding potentials ranging from 2.6 to 4.9 were measured in A2A-rich regions, with an average test-retest variability of less than 10%. The estimated whole-body radiation effective dose was approximately 0.023 mSv/MBq.

CONCLUSION

(18)F-MNI-444 is a useful PET radiotracer for imaging A2A in the human brain. The superior in vivo brain kinetic properties of (18)F-MNI-444, compared with previously developed A2A radiotracers, provide the opportunity to foster global use of in vivo A2A PET imaging in neuroscience research.

The effects of age on resting state functional connectivity of the basal ganglia from young to middle adulthood

The basal ganglia nuclei are critical for a variety of cognitive and motor functions. Much work has shown age-related structural changes of the basal ganglia. Yet less is known about how the functional interactions of these regions with the cerebral cortex and the cerebellum change throughout the lifespan. Here, we took advantage of a convenient sample and examined resting state functional magnetic resonance imaging data from 250 adults 18 to 49 years of age, focusing specifically on the caudate nucleus, pallidum, putamen, and ventral tegmental area/substantia nigra (VTA/SN). There are a few main findings to report. First, with age, caudate head connectivity increased with a large region of ventromedial prefrontal/medial orbitofrontal cortex. Second, across all subjects, pallidum and putamen showed negative connectivity with default mode network (DMN) regions such as the ventromedial prefrontal cortex and posterior cingulate cortex, in support of anti-correlation of the "task-positive" network (TPN) and DMN. This negative connectivity was reduced with age. Furthermore, pallidum, posterior putamen and VTA/SN connectivity to other TPN regions, such as somatomotor cortex, decreased with age. These results highlight a distinct effect of age on cerebral functional connectivity of the dorsal striatum and VTA/SN from young to middle adulthood and may help research investigating the etiologies or monitoring outcomes of neuropsychiatric conditions that implicate dopaminergic dysfunction.

18F-florbetapir PET in patients with frontotemporal dementia and Alzheimer disease

Pathologic deposition of amyloid β (Aβ) protein is a key component in the pathogenesis of Alzheimer disease (AD) but not a feature of frontotemporal dementia (FTD). PET ligands for Aβ protein are increasingly used in diagnosis and research of dementia syndromes. Here, we report a PET study using (18)F-florbetapir in healthy controls and patients with AD and FTD.

METHODS

Ten healthy controls (mean age ± SD, 62.5 ± 5.2 y), 10 AD patients (mean age ± SD, 62.6 ± 4.5), and 8 FTD patients (mean age ± SD, 62.5 ± 9.6) were recruited to the study. All patients underwent detailed clinical and neuropsychologic assessment and T1-weighted MR imaging and were genotyped for apolipoprotein E status. All participants underwent dynamic (18)F-florbetapir PET on a high-resolution research tomograph, and FTD patients also underwent (18)F-FDG PET scans. Standardized uptake value ratios (SUVRs) were extracted for predefined gray and white matter regions of interest using cerebellar gray matter as a reference region. Static PET images were evaluated by trained raters masked to clinical status and regional analysis.

RESULTS

Total cortical gray matter (18)F-florbetapir uptake values were significantly higher in AD patients (median SUVR, 1.73) than FTD patients (SUVR, 1.13, P = 0.002) and controls (SUVR, 1.26, P = 0.04). (18)F-Florbetapir uptake was also higher in AD patients than FTD patients and controls in the frontal, parietal, occipital, and cingulate cortices and in the central subcortical regions. Only 1 FTD patient (homozygous for apolipoprotein E ε4) displayed high cortical (18)F-florbetapir retention, whereas (18)F-FDG PET demonstrated mesiofrontal hypometabolism consistent with the clinical diagnosis of FTD. Most visual raters classified 1 control (10%) and 8 AD (80%) and 2 FTD (25%) patients as amyloid-positive, whereas ratings were tied in another 2 FTD patients and 1 healthy control.

CONCLUSION

Cortical (18)F-florbetapir uptake is low in most FTD patients, providing good discrimination from AD. However, visual rating of FTD scans was challenging, with a higher rate of discordance between interpreters than in AD and control subjects.

An algorithm for optimal fusion of atlases with different labeling protocols

In this paper we present a novel label fusion algorithm suited for scenarios in which different manual delineation protocols with potentially disparate structures have been used to annotate the training scans (hereafter referred to as "atlases"). Such scenarios arise when atlases have missing structures, when they have been labeled with different levels of detail, or when they have been taken from different heterogeneous databases. The proposed algorithm can be used to automatically label a novel scan with any of the protocols from the training data. Further, it enables us to generate new labels that are not present in any delineation protocol by defining intersections on the underling labels. We first use probabilistic models of label fusion to generalize three popular label fusion techniques to the multi-protocol setting: majority voting, semi-locally weighted voting and STAPLE. Then, we identify some shortcomings of the generalized methods, namely the inability to produce meaningful posterior probabilities for the different labels (majority voting, semi-locally weighted voting) and to exploit the similarities between the atlases (all three methods). Finally, we propose a novel generative label fusion model that can overcome these drawbacks. We use the proposed method to combine four brain MRI datasets labeled with different protocols (with a total of 102 unique labeled structures) to produce segmentations of 148 brain regions. Using cross-validation, we show that the proposed algorithm outperforms the generalizations of majority voting, semi-locally weighted voting and STAPLE (mean Dice score 83%, vs. 77%, 80% and 79%, respectively). We also evaluated the proposed algorithm in an aging study, successfully reproducing some well-known results in cortical and subcortical structures.

Regional cerebral blood flow estimated by early PiB uptake is reduced in mild cognitive impairment and associated with age in an amyloid-dependent manner

Early uptake of [(11)C]-Pittsburgh Compound B (ePiB, 0-6 minutes) estimates cerebral blood flow. We studied ePiB in 13 PiB-negative and 10 PiB-positive subjects with mild cognitive impairment (MCI, n = 23) and 11 PiB-positive and 74 PiB-negative cognitively healthy elderly control subjects (HCS, n = 85) in 6 bilateral volumes of interest: posterior cingulate cortex (PCC), hippocampus (hipp), temporoparietal region, superior parietal gyrus, parahippocampal gyrus (parahipp), and inferior frontal gyrus (IFG) for the associations with cognitive status, age, amyloid deposition, and apolipoprotein E ε4-allele. We observed no difference in ePiB between PiB-positive and -negative subjects and carriers and noncarriers. EPiB decreased with age in PiB-positive subjects in bilateral superior parietal gyrus, bilateral temporoparietal region, right IFG, right PCC, and left parahippocampal gyrus but not in PiB-negative subjects. MCI had lower ePiB than HCS (left PCC, left IFG, and left and right hipp). Lowest ePiB values were found in MCI of 70 years and older, who also displayed high cortical PiB binding. This suggests that lowered regional cerebral blood flow indicated by ePiB is associated with age in the presence but not in the absence of amyloid pathology.

Structural and functional connectivity mapping of the vestibular circuitry from human brainstem to cortex

Structural and functional interconnections of the bilateral central vestibular network have not yet been completely delineated. This includes both ipsilateral and contralateral pathways and crossing sites on the way from the vestibular nuclei via the thalamic relay stations to multiple "vestibular cortex" areas. This study investigated "vestibular" connectivity in the living human brain in between the vestibular nuclei and the parieto-insular vestibular cortex (PIVC) by combined structural and functional connectivity mapping using diffusion tensor imaging and functional connectivity magnetic resonance imaging in 24 healthy right-handed volunteers. We observed a congruent functional and structural link between the vestibular nuclei and the ipsilateral and contralateral PIVC. Five separate and distinct vestibular pathways were identified: three run ipsilaterally, while the two others cross either in the pons or the midbrain. Two of the ipsilateral projections run through the posterolateral or paramedian thalamic subnuclei, while the third bypasses the thalamus to reach the inferior part of the insular cortex directly. Both contralateral pathways travel through the posterolateral thalamus. At the cortical level, the PIVC regions of both hemispheres with a right hemispherical dominance are interconnected transcallosally through the antero-caudal splenium. The above-described bilateral vestibular circuitry in its entirety takes the form of a structure of a rope ladder extending from the brainstem to the cortex with three crossings in the brainstem (vestibular nuclei, pons, midbrain), none at thalamic level and a fourth cortical crossing through the splenium of the corpus callosum.

Brains for all the ages: structural neurodevelopment in infants and children from a life-span perspective

Magnetic resonance imaging (MRI) is a noninvasive method to measure brain structure and function that may be applied to human participants of all ages. This chapter reviews our recent work creating a life-span Neurodevelopmental MRI Database. It provides age-specific reference data in fine-grained age intervals from 2 weeks through 89 years. The reference data include average MRI templates, segmented tissue priors, and a common stereotaxic atlas for pediatric and adult participants. The database will be useful for neuroimaging research over a wide range of ages and may be used to make life-span comparisons. The chapter reviews the application of this database to the study of neurostructural development, including a new volumetric study of segmented brain tissue over the life span. We also show how this database could be used to create "study-specific" MRI templates for special groups and apply this to the MRIs of Chinese children. Finally, we review recent use of the database in the study of brain activity in pediatric populations.

Robust whole-brain segmentation: application to traumatic brain injury

We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.

The interaction of process and domain in prefrontal cortex during inductive reasoning

Inductive reasoning is an everyday process that allows us to make sense of the world by creating rules from a series of instances. Consistent with accounts of process-based fractionations of the prefrontal cortex (PFC) along the left-right axis, inductive reasoning has been reliably localized to left PFC. However, these results may be confounded by the task domain, which is typically verbal. Indeed, some studies show that right PFC activation is seen with spatial tasks. This study used fMRI to examine the effects of process and domain on the brain regions recruited during a novel pattern discovery task. Twenty healthy young adult participants were asked to discover the rule underlying the presentation of a series of letters in varied spatial locations. The rules were either verbal (pertaining to a single semantic category) or spatial (geometric figures). Bilateral ventrolateral PFC activations were seen for the spatial domain, while the verbal domain showed only left ventrolateral PFC. A conjunction analysis revealed that the two domains recruited a common region of left ventrolateral PFC. The data support a central role of left PFC in inductive reasoning. Importantly, they also suggest that both process and domain shape the localization of reasoning in the brain.

Improved longitudinal [(18)F]-AV45 amyloid PET by white matter reference and VOI-based partial volume effect correction

Amyloid positron-emission-tomography (PET) offers an important research and diagnostic tool for investigating Alzheimer's disease (AD). The majority of amyloid PET studies have used the cerebellum as a reference region, and clinical studies have not accounted for atrophy-based partial volume effects (PVE). Longitudinal studies using cerebellum as reference tissue have revealed only small mean increases and high inter-subject variability in amyloid binding. We aimed to test the effects of different reference regions and PVE-correction (PVEC) on the discriminatory power and longitudinal performance of amyloid PET. We analyzed [(18)F]-AV45 PET and T1-weighted MRI data of 962 subjects at baseline and two-year follow-up data of 258 subjects. Cortical composite volume-of-interest (VOI) values (COMP) for tracer uptake were generated using either full brain atlas VOIs, gray matter segmented VOIs or gray matter segmented VOIs after VOI-based PVEC. Standard-uptake-value ratios (SUVR) were calculated by scaling the COMP values to uptake in cerebellum (SUVRCBL), brainstem (SUVRBST) or white matter (SUVRWM). Mean SUV, SUVR, and changes after PVEC were compared at baseline between diagnostic groups of healthy controls (HC; N=316), mild cognitive impairment (MCI; N=483) and AD (N=163). Receiver operating characteristics (ROC) were calculated for the discriminations between HC, MCI and AD, and expressed as area under the curve (AUC). Finally, the longitudinal [(18)F]-AV45-PET data were used to analyze the impact of quantitation procedures on apparent changes in amyloid load over time. Reference region SUV was most constant between diagnosis groups for the white matter. PVEC led to decreases of COMP-SUV in HC (-18%) and MCI (-10%), but increases in AD (+7%). Highest AUCs were found when using PVEC with white matter scaling for the contrast between HC/AD (0.907) or with brainstem scaling for the contrast between HC/MCI (0.658). Longitudinal increases were greatest in all diagnosis groups with application of PVEC, and inter-subject variability was lowest for the white matter reference. Thus, discriminatory power of [(18)F]-AV45-PET was improved by use of a VOI-based PVEC and white matter or brainstem rather than cerebellum reference region. Detection of longitudinal amyloid increases was optimized with PVEC and white matter reference tissue.

Brain histamine H1 receptor occupancy measured by PET after oral administration of levocetirizine, a non-sedating antihistamine

OBJECTIVE

Histamine H1 receptor (H1R) antagonists often have sedative side effects, which are caused by the blockade of the neural transmission of the histaminergic neurons. We examined the brain H1R occupancy (H1RO) and the subjective sleepiness of levocetirizine, a new second-generation antihistamine, comparing fexofenadine, another non-sedating antihistamine, as a negative active control.

METHODS

Eight healthy volunteers underwent positron emission tomography (PET) imaging with [(11)C]doxepin, a PET tracer that specifically binds to H1Rs, after a single oral administration of levocetirizine (5 mg), fexofenadine (60 mg) or placebo in a double-blind crossover study. Binding potential ratios and H1ROs in the cerebral cortices regions were calculated using placebo. Subjective sleepiness was assessed with the Line Analogue Rating Scale and the Stanford Sleepiness Scale.

RESULTS

There was no significant difference between the mean brain H1RO after levocetirizine administration (8.1%; 95% CI: -9.8 to 26.0%) and fexofenadine administration (-8.0%; 95% CI: -26.7 to 10.6%). Similarly, subjective sleepiness was not significantly different between the two antihistamines and placebo. Neither subjective sleepiness nor plasma concentrations was significantly correlated with the brain H1RO of the two antihistamines.

CONCLUSION

At therapeutic dose, levocetirizine does not bind significantly to the brain H1Rs and does not induce significant sedation.

Motor features in posterior cortical atrophy and their imaging correlates

Posterior cortical atrophy (PCA) is a neurodegenerative syndrome characterized by impaired higher visual processing skills; however, motor features more commonly associated with corticobasal syndrome may also occur. We investigated the frequency and clinical characteristics of motor features in 44 PCA patients and, with 30 controls, conducted voxel-based morphometry, cortical thickness, and subcortical volumetric analyses of their magnetic resonance imaging. Prominent limb rigidity was used to define a PCA-motor subgroup. A total of 30% (13) had PCA-motor; all demonstrating asymmetrical left upper limb rigidity. Limb apraxia was more frequent and asymmetrical in PCA-motor, as was myoclonus. Tremor and alien limb phenomena only occurred in this subgroup. The subgroups did not differ in neuropsychological test performance or apolipoprotein E4 allele frequency. Greater asymmetry of atrophy occurred in PCA-motor, particularly involving right frontoparietal and peri-rolandic cortices, putamen, and thalamus. The 9 patients (including 4 PCA-motor) with pathology or cerebrospinal fluid all showed evidence of Alzheimer's disease. Our data suggest that PCA patients with motor features have greater atrophy of contralateral sensorimotor areas but are still likely to have underlying Alzheimer's disease.

The norepinephrine transporter in attention-deficit/hyperactivity disorder investigated with positron emission tomography

IMPORTANCE

Attention-deficit/hyperactivity disorder (ADHD) research has long focused on the dopaminergic system's contribution to pathogenesis, although the results have been inconclusive. However, a case has been made for the involvement of the noradrenergic system, which modulates cognitive processes, such as arousal, working memory, and response inhibition, all of which are typically affected in ADHD. Furthermore, the norepinephrine transporter (NET) is an important target for frequently prescribed medication in ADHD. Therefore, the NET is suggested to play a critical role in ADHD.

OBJECTIVE

To explore the differences in NET nondisplaceable binding potential (NET BPND) using positron emission tomography and the highly selective radioligand (S,S)-[18F]FMeNER-D2 [(S,S)-2-(α-(2-[18F]fluoro[2H2]methoxyphenoxy)benzyl)morpholine] between adults with ADHD and healthy volunteers serving as controls.

DESIGN, SETTING, AND PARTICIPANTS

Twenty-two medication-free patients with ADHD (mean [SD] age, 30.7 [10.4] years; 15 [68%] men) without psychiatric comorbidities and 22 age- and sex-matched healthy controls (30.9 [10.6] years; 15 [68%] men) underwent positron emission tomography once. A linear mixed model was used to compare NET BPND between groups.

MAIN OUTCOMES AND MEASURES

The NET BPND in selected regions of interest relevant for ADHD, including the hippocampus, putamen, pallidum, thalamus, midbrain with pons (comprising a region of interest that includes the locus coeruleus), and cerebellum. In addition, the NET BPND was evaluated in thalamic subnuclei (13 atlas-based regions of interest).

RESULTS

We found no significant differences in NET availability or regional distribution between patients with ADHD and healthy controls in all investigated brain regions (F1,41<0.01; P=.96). Furthermore, we identified no significant association between ADHD symptom severity and regional NET availability. Neither sex nor smoking status influenced NET availability. We determined a significant negative correlation between age and NET availability in the thalamus (R2=0.29; P<.01 corrected) and midbrain with pons, including the locus coeruleus (R2=0.18; P<.01 corrected), which corroborates prior findings of a decrease in NET availability with aging in the human brain.

CONCLUSIONS AND RELEVANCE

Our results do not indicate involvement of changes in brain NET availability or distribution in the pathogenesis of ADHD. However, the noradrenergic transmitter system may be affected on a different level, such as in cortical regions, which cannot be reliably quantified with this positron emission tomography ligand. Alternatively, different key proteins of noradrenergic neurotransmission might be affected.

Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition

Multi-atlas patch-based label fusion methods have been successfully used to improve segmentation accuracy in many important medical image analysis applications. In general, to achieve label fusion a single target image is first registered to several atlas images. After registration a label is assigned to each target point in the target image by determining the similarity between the underlying target image patch (centered at the target point) and the aligned image patch in each atlas image. To achieve the highest level of accuracy during the label fusion process it's critical for the chosen patch similarity measurement to accurately capture the tissue/shape appearance of the anatomical structure. One major limitation of existing state-of-the-art label fusion methods is that they often apply a fixed size image patch throughout the entire label fusion procedure. Doing so may severely affect the fidelity of the patch similarity measurement, which in turn may not adequately capture complex tissue appearance patterns expressed by the anatomical structure. To address this limitation, we advance state-of-the-art by adding three new label fusion contributions: First, each image patch is now characterized by a multi-scale feature representation that encodes both local and semi-local image information. Doing so will increase the accuracy of the patch-based similarity measurement. Second, to limit the possibility of the patch-based similarity measurement being wrongly guided by the presence of multiple anatomical structures in the same image patch, each atlas image patch is further partitioned into a set of label-specific partial image patches according to the existing labels. Since image information has now been semantically divided into different patterns, these new label-specific atlas patches make the label fusion process more specific and flexible. Lastly, in order to correct target points that are mislabeled during label fusion, a hierarchical approach is used to improve the label fusion results. In particular, a coarse-to-fine iterative label fusion approach is used that gradually reduces the patch size. To evaluate the accuracy of our label fusion approach, the proposed method was used to segment the hippocampus in the ADNI dataset and 7.0 T MR images, sub-cortical regions in LONI LBPA40 dataset, mid-brain regions in SATA dataset from MICCAI 2013 segmentation challenge, and a set of key internal gray matter structures in IXI dataset. In all experiments, the segmentation results of the proposed hierarchical label fusion method with multi-scale feature representations and label-specific atlas patches are more accurate than several well-known state-of-the-art label fusion methods.

Consensus between pipelines in structural brain networks

Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study.

Manually segmented template library for 8-year-old pediatric brain MRI data with 16 subcortical structures

Manual segmentation of anatomy in brain MRI data taken to be the closest to the "gold standard" in quality is often used in automated registration-based segmentation paradigms for transfer of template labels onto the unlabeled MRI images. This study presents a library of template data with 16 subcortical structures in the central brain area which were manually labeled for MRI data from 22 children (8 male, [Formula: see text]). The lateral ventricle, thalamus, caudate, putamen, hippocampus, cerebellum, third vevntricle, fourth ventricle, brainstem, and corpuscallosum were segmented by two expert raters. Cross-validation experiments with randomized template subset selection were conducted to test for their ability to accurately segment MRI data under an automated segmentation pipeline. A high value of the dice similarity coefficient ([Formula: see text], [Formula: see text], [Formula: see text]) and small Hausdorff distance ([Formula: see text], [Formula: see text], [Formula: see text]) of the automated segmentation against the manual labels was obtained on this template library data. Additionally, comparison with segmentation obtained from adult templates showed significant improvement in accuracy with the use of an age-matched library in this cohort. A manually delineated pediatric template library such as the one described here could provide a useful benchmark for testing segmentation algorithms.

A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity

Introduction

Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies.

Methods

High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [¹⁸F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures).

Results

Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method.

Conclusions

Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure’s extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses.

An fMRI study of behavioral response inhibition in adolescents with and without histories of heavy prenatal alcohol exposure

Heavy prenatal alcohol exposure results in a range of deficits, including both volumetric and functional changes in brain regions involved in response inhibition such as the prefrontal cortex and striatum. The current study examined blood oxygen level-dependent (BOLD) response during a stop signal task in adolescents (ages 13-16 y) with histories of heavy prenatal alcohol exposure (AE, n=21) and controls (CON, n=21). Task performance was measured using percent correct inhibits during three difficulty conditions: easy, medium, and hard. Group differences in BOLD response relative to baseline motor responding were examined across all inhibition trials and for each difficulty condition separately. The contrast between hard and easy trials was analyzed to determine whether increasing task difficulty affected BOLD response. Groups had similar task performance and demographic characteristics, except for full scale IQ scores (AE<CON). The AE group demonstrated greater BOLD response in frontal, sensorimotor, striatal, and cingulate regions relative to controls, especially as task difficulty increased. When contrasting hard vs. easy inhibition trials, the AE group showed greater medial/superior frontal and cuneus BOLD response than controls. Results were unchanged after demographics and FAS diagnosis were statistically controlled. This was the first fMRI study to utilize a stop signal task, isolating fronto-striatal functioning, to assess response inhibition and the effects task difficulty in adolescents with prenatal alcohol exposure. Results suggest that heavy prenatal alcohol exposure disrupts neural function of this circuitry, resulting in immature cognitive processing and motor-association learning and neural compensation during response inhibition.

Common variants at 1p36 are associated with superior frontal gyrus volume

The superior frontal gyrus (SFG), an area of the brain frequently found to have reduced gray matter in patients with schizophrenia, is involved in self-awareness and emotion, which are impaired in schizophrenia. However, no genome-wide association studies of SFG volume have investigated in patients with schizophrenia. To identify single-nucleotide polymorphisms (SNPs) associated with SFG volumes, we demonstrated a genome-wide association study (GWAS) of gray matter volumes in the right or left SFG of 158 patients with schizophrenia and 378 healthy subjects. We attempted to bioinformatically ascertain the potential effects of the top hit polymorphism on the expression levels of genes at the genome-wide region. We found associations between five variants on 1p36.12 and the right SFG volume at a widely used benchmark for genome-wide significance (P<5.0 × 10(-8)). The strongest association was observed at rs4654899, an intronic SNP in the eukaryotic translation initiation factor 4 gamma, 3 (EIF4G3) gene on 1p36.12 (P=7.5 × 10(-9)). No SNP with genome-wide significance was found in the volume of the left SFG (P>5.0 × 10(-8)); however, the rs4654899 polymorphism was identified as the locus with the second strongest association with the volume of the left SFG (P=1.5 × 10(-6)). In silico analyses revealed a proxy SNP of rs4654899 had effect on gene expression of two genes, HP1BP3 lying 3' to EIF4G3 (P=7.8 × 10(-6)) and CAPN14 at 2p (P=6.3 × 10(-6)), which are expressed in moderate-to-high levels throughout the adult human SFG. These results contribute to understand genetic architecture of a brain structure possibly linked to the pathophysiology of schizophrenia.

Tauopathy PET and amyloid PET in the diagnosis of chronic traumatic encephalopathies: studies of a retired NFL player and of a man with FTD and a severe head injury

Single, severe traumatic brain injury (TBI) which elevates CNS amyloid, increases the risk of Alzheimer's disease (AD); while repetitive concussive and subconcussive events as observed in athletes and military personnel, may increase the risk of chronic traumatic encephalopathy (CTE). We describe two clinical cases, one with a history of multiple concussions during a career in the National Football League (NFL) and the second with frontotemporal dementia and a single, severe TBI. Both patients presented with cognitive decline and underwent [(18)F]-Florbetapir positron emission tomography (PET) imaging for amyloid plaques; the retired NFL player also underwent [(18)F]-T807 PET imaging, a new ligand binding to tau, the main constituent of neurofibrillary tangles (NFT). Case 1, the former NFL player, was 71 years old when he presented with memory impairment and a clinical profile highly similar to AD. [(18)F]-Florbetapir PET imaging was negative, essentially excluding AD as a diagnosis. CTE was suspected clinically, and [(18)F]-T807 PET imaging revealed striatal and nigral [(18)F]-T807 retention consistent with the presence of tauopathy. Case 2 was a 56-year-old man with personality changes and cognitive decline who had sustained a fall complicated by a subdural hematoma. At 1 year post injury, [(18)F]-Florbetapir PET imaging was negative for an AD pattern of amyloid accumulation in this subject. Focal [(18)F]-Florbetapir retention was noted at the site of impact. In case 1, amyloid imaging provided improved diagnostic accuracy where standard clinical and laboratory criteria were inadequate. In that same case, tau imaging with [(18)F]-T807 revealed a subcortical tauopathy that we interpret as a novel form of CTE with a distribution of tauopathy that mimics, to some extent, that of progressive supranuclear palsy (PSP), despite a clinical presentation of amnesia without any movement disorder complaints or signs. A key distinguishing feature is that our patient presented with hippocampal involvement, which is more frequently seen in CTE than in PSP. In case 2, focal [(18)F]-Florbetapir retention at the site of injury in an otherwise negative scan suggests focal amyloid aggregation. In each of these complex cases, a combination of [(18)F]-fluorodeoxyglucose, [(18)F]-Florbetapir and/or [(18)F]-T807 PET molecular imaging improved the accuracy of diagnosis and prevented inappropriate interventions.

Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia

Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range = 86 - 91%) than all other approaches (AUC = 57 - 84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself.

Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization

Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, as disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could effectively distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to global methods and other pattern analysis methods based on clinical expertise or statistics tests. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights into the underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.