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The sphingosine-1-phosphate receptor 1 (S1PR1) radiotracer [ 11 C] CS1P1 has shown promise in proof-of-concept PET imaging of neuroinflammation in multiple sclerosis (MS). Our HPLC radiometabolite analysis of human plasma samples collected during PET scans with [ 11 C] CS1P1 detected a radiometabolite peak that is more lipophilic than [ 11 C] CS1P1 . Radiolabeled metabolites that cross the blood-brain barrier complicate quantitative modeling of neuroimaging tracers; thus, characterizing such radiometabolites is important. Here, we report our detailed investigation of the metabolite profile of [ 11 C] CS1P1 in rats, nonhuman primates, and humans. CS1P1 is a fluorine-containing ligand that we labeled with C-11 or F-18 for preclinical studies; the brain uptake was similar for both radiotracers. The same lipophilic radiometabolite found in human studies also was observed in plasma samples of rats and NHPs for CS1P1 labeled with either C-11 or F-18. We characterized the metabolite in detail using rats after injection of the nonradioactive CS1P1 . To authenticate the molecular structure of this radiometabolite, we injected rats with 8 mg/kg of CS1P1 to collect plasma for solvent extraction and HPLC injection, followed by LC/MS analysis of the same metabolite. The LC/MS data indicated in vivo mono-oxidation of CS1P1 produces the metabolite. Subsequently, we synthesized three different mono-oxidized derivatives of CS1P1 for further investigation. Comparing the retention times of the mono-oxidized derivatives with the metabolite observed in rats injected with CS1P1 identified the metabolite as N -oxide 1 , also named TZ82121 . The MS fragmentation pattern of N -oxide 1 also matched that of the major metabolite in rat plasma. To confirm that metabolite TZ82121 does not enter the brain, we radiosynthesized [ 18 F] TZ82121 by the oxidation of [ 18 F] FS1P1 . Radio-HPLC analysis confirmed that [ 18 F] TZ82121 matched the radiometabolite observed in rat plasma post injection of [ 18 F] FS1P1 . Furthermore, the acute biodistribution study in SD rats and PET brain imaging in a nonhuman primate showed that [ 18 F] TZ82121 does not enter the rat or nonhuman primate brain. Consequently, we concluded that the major lipophilic radiometabolite N -oxide [ 11 C] TZ82121 , detected in human plasma post injection of [ 11 C] CS1P1 , does not enter the brain to confound quantitative PET data analysis. [ 11 C] CS1P1 is a promising S1PR1 radiotracer for detecting S1PR1 expression in the CNS.

Recent work using novel neuroimaging methods has revealed shorter white matter fiber bundle length (FBL) in older compared to younger adults. Shorter FBL also corresponds to poorer performance on cognitive measures sensitive to advanced age. However, it is unclear if individual factors such as cognitive reserve (CR) effectively moderate the relationship between FBL and cognitive performance. This study examined CR as a potential moderator of cognitive performance and brain integrity as defined by FBL. Sixty-three healthy adults underwent neuropsychological evaluation and 3T brain magnetic resonance imaging. Cognitive performance was measured using the Repeatable Battery of Assessment of Neuropsychological Status (RBANS). FBL was quantified from tractography tracings of white matter fiber bundles, derived from the diffusion tensor imaging. CR was determined by estimated premorbid IQ. Analyses revealed that lower scores on the RBANS were associated with shorter whole brain FBL (p = 0.04) and lower CR (p = 0.01) CR moderated the relationship between whole brain FBL and RBANS score (p < 0.01). Tract-specific analyses revealed that CR also moderated the association between FBL in the hippocampal segment of the cingulum and RBANS performance (p = 0.03). These results demonstrate that lower cognitive performance on the RBANS is more common with low CR and short FBL. On the contrary, when individuals have high CR, the relationship between FBL and cognitive performance is attenuated. Overall, CR protects older adults against lower cognitive performance despite age-associated reductions in FBL.

Spontaneous brain activity is required for the development and maintenance of normal brain function. Many disease processes disrupt the organization of intrinsic brain activity, but few pervasively reduce the amplitude of resting state blood oxygen level dependent (BOLD) fMRI fluctuations. We report the case of a female with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, longitudinally studied during the course of her illness to determine the contribution of NMDAR signaling to spontaneous brain activity. Resting state BOLD fMRI was measured at the height of her illness and 18 weeks following discharge from hospital. Conventional resting state networks were defined using established methods. Correlation and covariance matrices were calculated by extracting the BOLD time series from regions of interest and calculating either the correlation or covariance quantity. The intrinsic activity was compared between visits, and to expected activity from 45 similarly aged healthy individuals. Near the height of the illness, the patient exhibited profound loss of consciousness, high-amplitude slowing of the electroencephalogram, and a severe reduction in the amplitude of spontaneous BOLD fMRI fluctuations. The patient's neurological status and measures of intrinsic activity improved following treatment. We conclude that NMDAR-mediated signaling plays a critical role in the mechanisms that give rise to organized spontaneous brain activity. Loss of intrinsic activity is associated with profound disruptions of consciousness and cognition.

Amyloid-beta plaques are a hallmark of Alzheimer's disease (AD) that can be assessed by amyloid imaging (e.g., Pittsburgh B compound [PiB]) and summarized as a scalar value. Summary values may have clinical utility but are an average over many regions of interest, potentially obscuring important topography. This study investigates the longitudinal evolution of amyloid topographies in cognitively normal older adults who had normal (N = 131) or abnormal (N = 26) PiB scans at baseline. At 3 years follow-up, 16 participants with a previously normal PiB scan had conversion to PiB scans consistent with preclinical AD. We investigated the multivariate relationship (canonical correlation) between baseline and follow-up PiB topographies. Furthermore, we used penalized regression to investigate the added information derived from PiB topography compared to summary measures. PiB accumulation can be local, that is, a topography predicting the same topography in the future, and/or distributed, that is, one topography predicting another. Both local and distributed PiB accumulation was associated with conversion of PiB status. Additionally, elements of the multivariate topography, and not the commonly used summary scalar, correlated with future PiB changes. Consideration of the entire multivariate PiB topography provides additional information regarding the development of amyloid-beta pathology in very early preclinical AD., Competing Interests: statement The authors report no conflict of interest. This study was funded by the NIH as specified in the acknowledgements., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: Currently, the ability for imaging to capture brain adaptations to injury that occurs in multiple sclerosis (MS) is limited. In particular, how the brain initially contends with the earliest clinical manifestations of white matter injury has yet to be defined. The purpose of this study was to determine the impact of acute optic neuritis (ON) on resting state functional connectivity magnetic resonance imaging (rs-fcMRI)., Methods: Fifteen patients with a clinically isolated syndrome of acute ON were evaluated at an academic center in a prospective study. Subjects were assessed with structural and functional vision measures, including optical coherence tomography (OCT), high- and low-contrast letter acuity testing, and visual fields and quality-of-life measures (VFQ-25). The rs-fcMRI was compared with age- and sex-matched healthy controls., Results: We observed reduced functional connectivity within the visual system and a loss of anticorrelations between the visual system and nonvisual networks. Stronger functional connectivity between visual regions correlated with better quality of life, as measured by the VFQ-25, and better acuity scores for both high- and low-contrast testing in the affected eye., Conclusions: The rs-fcMRI functional connectivity changes within (intranetwork) and between (internetwork) resting state networks occur after acute ON, indicating immediate cortical responses to focal inflammatory demyelination. Thus, focal white matter injury in the central nervous system acutely results in widespread network alterations that may lead to functional neurologic changes seen in MS.

Graph theory models can produce simple, biologically informative metrics of the topology of resting-state functional connectivity (FC) networks. However, typical graph theory approaches model FC relationships between regions (nodes) as unweighted edges, complicating their interpretability in studies of disease or aging. We extended existing techniques and constructed fully connected weighted graphs for groups of age-matched human immunodeficiency virus (HIV) positive (n = 67) and HIV negative (n = 77) individuals. We compared test-retest reliability of weighted versus unweighted metrics in an independent study of healthy individuals (n = 22) and found weighted measures to be more stable. We quantified 2 measures of node centrality (closeness centrality and eigenvector centrality) to capture the relative importance of individual nodes. We also quantified 1 measure of graph entropy (diversity) to measure the variability in connection strength (edge weights) at each node. HIV was primarily associated with differences in measures of centrality, and age was primarily associated with differences in diversity. HIV and age were associated with divergent measures when evaluated at the whole graph level, within individual functional networks, and at the level of individual nodes. Graph models may allow us to distinguish previously indistinguishable effects related to HIV and age on FC., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Much effort in recent years has focused on understanding the effects of Alzheimer's disease (AD) on neural function. This effort has resulted in an enormous number of papers describing different facets of the functional derangement seen in AD. A particularly important tool for these investigations has been resting-state functional connectivity. Attempts to comprehensively synthesize resting-state functional connectivity results have focused on the potential utility of functional connectivity as a biomarker for disease risk, disease staging, or prognosis. While these are all appropriate uses of this technique, the purpose of this review is to examine how functional connectivity disruptions inform our understanding of AD pathophysiology. Here, we examine the rationale and methodological considerations behind functional connectivity studies and then provide a critical review of the existing literature. In conclusion, we propose a hypothesis regarding the development and spread of functional connectivity deficits seen in AD.

Alzheimer's disease (AD) has a long preclinical phase in which amyloid and tau cerebral pathology accumulate without producing cognitive symptoms. Resting state functional connectivity magnetic resonance imaging has demonstrated that brain networks degrade during symptomatic AD. It is unclear to what extent these degradations exist before symptomatic onset. In this study, we investigated graph theory metrics of functional integration (path length), functional segregation (clustering coefficient), and functional distinctness (modularity) as a function of disease severity. Further, we assessed whether these graph metrics were affected in cognitively normal participants with cerebrospinal fluid evidence of preclinical AD. Clustering coefficient and modularity, but not path length, were reduced in AD. Cognitively normal participants who harbored AD biomarker pathology also showed reduced values in these graph measures, demonstrating brain changes similar to, but smaller than, symptomatic AD. Only modularity was significantly affected by age. We also demonstrate that AD has a particular effect on hub-like regions in the brain. We conclude that AD causes large-scale disconnection that is present before onset of symptoms., (Copyright © 2014 Elsevier Inc. All rights reserved.)

This work is to determine whether apolipoprotein E (APOE) genotype modulates the effect of cholinesterase inhibitor (ChEI) treatment on resting state functional connectivity magnetic resonance imaging (rs-fcMRI) in patients with Alzheimer disease (AD). We retrospectively studied very mild and mild AD participants who were treated (N=25) or untreated (N=19) with ChEIs with respect to rs-fcMRI measure of 5 resting state networks (RSNs): default mode, dorsal attention (DAN), control (CON), salience (SAL), and sensory motor. For each network, a composite score was computed as the mean of Pearson correlations between pairwise time courses extracted from areas comprising this network. The composite scores were analyzed as a function of ChEI treatment and APOE ε4 allele. Across all participants, significant interactions between ChEI treatment and APOE ε4 allele were observed for all 5 RSNs. Within APOE ε4 carriers, significantly greater composite scores were observed in the DAN, CON, and SAL for treated compared with untreated participants. Within APOE ε4 noncarriers, treated and untreated participants did not have significantly different composite scores for all RSNs. These data suggest that APOE genotype affects the response to ChEI using rs-fcMRI. Rs-fcMRI may be useful for assessing the therapeutic effect of medications in AD clinical trials.

The present study examined the transfer of higher-order cognitive strategy training to inhibitory control. Middle school students enrolled in a comprehension- and reasoning-focused cognitive strategy training program and passive controls participated. The training program taught students a set of steps for inferring essential gist or themes from materials. Both before and after training or a comparable duration in the case of the passive controls, participants completed a semantically cued Go/No-Go task that was designed to assess the effects of depth of semantic processing on response inhibition and components of event-related potentials (ERP) related to response inhibition. Depth of semantic processing was manipulated by varying the level of semantic categorization required for response selection and inhibition. The SMART-trained group showed inhibitory control gains and changes in fronto-central P3 ERP amplitudes on inhibition trials; whereas, the control group did not. The results provide evidence of the transfer of higher-order cognitive strategy training to inhibitory control and modulation of ERPs associated with semantically cued inhibitory control. The findings are discussed in terms of implications for cognitive strategy training, models of cognitive abilities, and education., (Published by Elsevier Inc.)

The present study examined functional MRI (fMRI) BOLD signal changes in response to object categorization during response selection and inhibition. Young adults (N=16) completed a Go/NoGo task with varying object categorization requirements while fMRI data were recorded. Response inhibition elicited increased signal change in various brain regions, including medial frontal areas, compared with response selection. BOLD signal in an area within the right angular gyrus was increased when higher-order categorization was mandated. In addition, signal change during response inhibition varied with categorization requirements in the left inferior temporal gyrus (lIT). lIT-mediated response inhibition when inhibiting the response only required lower-order categorization, but lIT mediated both response selection and inhibition when selecting and inhibiting the response required higher-order categorization. The findings characterized mechanisms mediating response inhibition associated with semantic object categorization in the 'what' visual object memory system.

Importance: Resting-state functional connectivity magnetic resonance imaging has great potential for characterizing pathophysiological changes during the preclinical phase of Alzheimer disease., Objective: To assess the relationship between default mode network integrity and cerebrospinal fluid biomarkers of Alzheimer disease pathology in cognitively normal older individuals., Design, Setting, and Participants: Cross-sectional cohort study at The Charles F. and Joanne Knight Alzheimer's Disease Research Center at Washington University in St Louis, St Louis, Missouri, among 207 older adults with normal cognition (Clinical Dementia Rating, 0)., Main Outcomes and Measures: Resting-state functional connectivity magnetic resonance imaging measures of default mode network integrity., Results: Decreased cerebrospinal fluid Aβ42 and increased cerebrospinal fluid phosphorylated tau181 were independently associated with reduced default mode network integrity, with the most prominent decreases in functional connectivity observed between the posterior cingulate and medial temporal regions. Observed reductions in functional connectivity were unattributable to age or structural atrophy in the posterior cingulate and medial temporal areas. Similar resting-state functional connectivity magnetic resonance imaging findings in relation to cerebrospinal fluid biomarkers were obtained using region-of-interest analyses and voxelwise correlation mapping., Conclusions and Relevance: Both Aβ and tau pathology affect default mode network integrity before clinical onset of Alzheimer disease.

Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task., (Copyright © 2011 Wiley Periodicals, Inc.)

Throughout middle-childhood, inhibitory processes, which underlie many higher order cognitive tasks, are developing. Little is known about how inhibitory processes change as a task becomes conceptually more difficult during these important years. In adults, as Go/NoGo tasks become more difficult there is a systematic decrease in the P3NoGo response, indicating the use of effective inhibitory strategies (Maguire et al., 2009). This paper investigates the age at which children employ similar inhibitory strategies by studying behavioral and Event Related Potential (ERP) measures of response inhibition for three Go/NoGo tasks. Seventeen 7-8 year-olds and twenty 10-11-year-olds completed three Go/NoGo tasks that differed in the level of categorization necessary to respond. Both age groups displayed slower reaction times as the tasks became more difficult. Further, both groups displayed the predicted Go vs. NoGo P3 amplitude differences in the two simplest tasks, but no significant P3 differences for the most complex task. The reason for this pattern of responses was different in the different age groups. Similar to adults in previous work, the oldest children showed an attenuation of the P3 NoGo response with task difficulty, and no corresponding changes in the Go amplitude. The younger children displayed the opposite pattern, a significant increase in the Go amplitude with task difficulty, and no changes in the NoGo response. These response patterns indicate that efficient inhibitory strategies are developing throughout middle-childhood., (Copyright © 2011 Elsevier Inc. All rights reserved.)

A better understanding of carbon dioxide (CO(2)) effect on brain activity may have a profound impact on clinical studies using CO(2) manipulation to assess cerebrovascular reserve and on the use of hypercapnia as a means to calibrate functional magnetic resonance imaging (fMRI) signal. This study investigates how an increase in blood CO(2), via inhalation of 5% CO(2), may alter brain activity in humans. Dynamic measurement of brain metabolism revealed that mild hypercapnia resulted in a suppression of cerebral metabolic rate of oxygen (CMRO(2)) by 13.4% ± 2.3% (N=14) and, furthermore, the CMRO(2) change was proportional to the subject's end-tidal CO(2) (Et-CO(2)) change. When using functional connectivity MRI (fcMRI) to assess the changes in resting-state neural activity, it was found that hypercapnia resulted in a reduction in all fcMRI indices assessed including cluster volume, cross-correlation coefficient, and amplitude of the fcMRI signal in the default-mode network (DMN). The extent of the reduction was more pronounced than similar indices obtained in visual-evoked fMRI, suggesting a selective suppression effect on resting-state neural activity. Scalp electroencephalogram (EEG) studies comparing hypercapnia with normocapnia conditions showed a relative increase in low frequency power in the EEG spectra, suggesting that the brain is entering a low arousal state on CO(2) inhalation.

Despite the importance of semantic relationships to our understanding of semantic knowledge, the nature of the neural processes underlying these abilities are not well understood. In order to investigate these processes, 20 healthy adults listened to thematically related (e.g., leash-dog), taxonomically related (e.g., horse-dog), or unrelated (e.g., desk-dog) noun pairs as their EEG was recorded. The data were analyzed using both event-related potentials (ERP) and event-related spectral perturbation (ERSP) analyses. The spatiotemporal ERP and ERSP results were analyzed further with principal component analysis (PCA). When comparing unrelated to related word pairs, the expected N400 effect was confirmed, as well as differences in theta and alpha oscillations. When comparing thematically and taxonomically related word pairs, the ERP revealed no significant differences, but the ERSP did. Specifically, theta power increased over right frontal areas for thematic versus taxonomic relationships and alpha power increased over parietal areas for taxonomic versus thematic relationships. The different oscillatory patterns over different brain regions suggest that thematic and taxonomic relationships engage distinct neural processes. Specifically, thematic relationships engage memory processes, while taxonomic relationships may require additional inhibitory or attention processes., (Copyright 2010 Elsevier Inc. All rights reserved.)

A new method is developed for analyzing the time-varying spectral content of EEG data collected in cognitive tasks. The goal is to extract and summarize the most salient features of numerical results, which span space, time, frequency, task conditions, and multiple subjects. Direct generalization of an established approach for analyzing event-related potentials, which uses sequential PCA followed by ANOVA to test for differences between conditions across subjects, gave unacceptable results. The new method, termed STAT-PCA, advocates statistical testing for differences between conditions within single subjects, followed by sequential PCA across subjects. In contrast to PCA-ANOVA, it is demonstrated that STAT-PCA gives results which: 1) isolate task-related spectral changes, 2) are insensitive to the precise definition of baseline power, 3) are stable under deletion of a random subject, and 4) are interpretable in terms of the group-averaged power. Furthermore, STAT-PCA permits the detection of activity that is not only different between conditions, but also common to both conditions, providing a complete yet parsimonious view of the data. It is concluded that STAT-PCA is well suited for analyzing the time-varying spectral content of EEG during cognitive tasks.