A multimodal encoding model applied to imaging decision-related neural cascades in the human brain.

Perception and cognition in the brain are naturally characterized as spatiotemporal processes. Decision-making, for example, depends on coordinated patterns of neural activity cascading across the brain, running in time from stimulus to response and in space from primary sensory regions to the frontal lobe. Measuring this cascade is key to developing an understanding of brain function. Here we report on a novel methodology that employs multi-modal imaging for inferring this cascade in humans at unprecedented spatiotemporal resolution. Specifically, we develop an encoding model to link simultaneously measured electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to infer high-resolution spatiotemporal brain dynamics during a perceptual decision. After demonstrating replication of results from the literature, we report previously unobserved sequential reactivation of a substantial fraction of the pre-response network whose magnitude correlates with a proxy for decision confidence. Our encoding model, which temporally tags BOLD activations using time localized EEG variability, identifies a coordinated and spatially distributed neural cascade that is associated with a perceptual decision. In general the methodology illuminates complex brain dynamics that would otherwise be unobservable using fMRI or EEG acquired separately.

Network Configurations in the Human Brain Reflect Choice Bias during Rapid Face Processing.

Network interactions are likely to be instrumental in processes underlying rapid perception and cognition. Specifically, high-level and perceptual regions must interact to balance pre-existing models of the environment with new incoming stimuli. Simultaneous electroencephalography (EEG) and fMRI (EEG/fMRI) enables temporal characterization of brain-network interactions combined with improved anatomical localization of regional activity. In this paper, we use simultaneous EEG/fMRI and multivariate dynamical systems (MDS) analysis to characterize network relationships between constitute brain areas that reflect a subject's choice for a face versus nonface categorization task. Our simultaneous EEG and fMRI analysis on 21 human subjects (12 males, 9 females) identifies early perceptual and late frontal subsystems that are selective to the categorical choice of faces versus nonfaces. We analyze the interactions between these subsystems using an MDS in the space of the BOLD signal. Our main findings show that differences between face-choice and house-choice networks are seen in the network interactions between the early and late subsystems, and that the magnitude of the difference in network interaction positively correlates with the behavioral false-positive rate of face choices. We interpret this to reflect the role of saliency and expectations likely encoded in frontal "late" regions on perceptual processes occurring in "early" perceptual regions.SIGNIFICANCE STATEMENT Our choices are affected by our biases. In visual perception and cognition such biases can be commonplace and quite curious-e.g., we see a human face when staring up at a cloud formation or down at a piece of toast at the breakfast table. Here we use multimodal neuroimaging and dynamical systems analysis to measure whole-brain spatiotemporal dynamics while subjects make decisions regarding the type of object they see in rapidly flashed images. We find that the degree of interaction in these networks accounts for a substantial fraction of our bias to see faces. In general, our findings illustrate how the properties of spatiotemporal networks yield insight into the mechanisms of how we form decisions.

Fusing multiple neuroimaging modalities to assess group differences in perception-action coupling.

In the last few decades, non-invasive neuroimaging has revealed macro-scale brain dynamics that underlie perception, cognition and action. Advances in non-invasive neuroimaging target two capabilities; 1) increased spatial and temporal resolution of measured neural activity, and 2) innovative methodologies to extract brain-behavior relationships from evolving neuroimaging technology. We target the second. Our novel methodology integrated three neuroimaging methodologies and elucidated expertise-dependent differences in functional (fused EEG-fMRI) and structural (dMRI) brain networks for a perception-action coupling task. A set of baseball players and controls performed a Go/No-Go task designed to mimic the situation of hitting a baseball. In the functional analysis, our novel fusion methodology identifies 50ms windows with predictive EEG neural correlates of expertise and fuses these temporal windows with fMRI activity in a whole-brain 2mm voxel analysis, revealing time-localized correlations of expertise at a spatial scale of millimeters. The spatiotemporal cascade of brain activity reflecting expertise differences begins as early as 200ms after the pitch starts and lasting up to 700ms afterwards. Network differences are spatially localized to include motor and visual processing areas, providing evidence for differences in perception-action coupling between the groups. Furthermore, an analysis of structural connectivity revealed that the players have significantly more connections between cerebellar and left frontal/motor regions, and many of the functional activation differences between the groups are located within structurally defined network modules that differentiate expertise. In short, our novel method illustrates how multimodal neuroimaging can provide specific macro-scale insights into the functional and structural correlates of expertise development.

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.

Brain dynamics of post-task resting state are influenced by expertise: Insights from baseball players.

Post-task resting state dynamics can be viewed as a task-driven state where behavioral performance is improved through endogenous, non-explicit learning. Tasks that have intrinsic value for individuals are hypothesized to produce post-task resting state dynamics that promote learning. We measured simultaneous fMRI/EEG and DTI in Division-1 collegiate baseball players and compared to a group of controls, examining differences in both functional and structural connectivity. Participants performed a surrogate baseball pitch Go/No-Go task before a resting state scan, and we compared post-task resting state connectivity using a seed-based analysis from the supplementary motor area (SMA), an area whose activity discriminated players and controls in our previous results using this task. Although both groups were equally trained on the task, the experts showed differential activity in their post-task resting state consistent with motor learning. Specifically, we found (1) differences in bilateral SMA-L Insula functional connectivity between experts and controls that may reflect group differences in motor learning, (2) differences in BOLD-alpha oscillation correlations between groups suggests variability in modulatory attention in the post-task state, and (3) group differences between BOLD-beta oscillations that may indicate cognitive processing of motor inhibition. Structural connectivity analysis identified group differences in portions of the functionally derived network, suggesting that functional differences may also partially arise from variability in the underlying white matter pathways. Generally, we find that brain dynamics in the post-task resting state differ as a function of subject expertise and potentially result from differences in both functional and structural connectivity. Hum Brain Mapp 37:4454-4471, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Knowing when not to swing: EEG evidence that enhanced perception-action coupling underlies baseball batter expertise.

Given a decision that requires less than half a second for evaluating the characteristics of the incoming pitch and generating a motor response, hitting a baseball potentially requires unique perception-action coupling to achieve high performance. We designed a rapid perceptual decision-making experiment modeled as a Go/No-Go task yet tailored to reflect a real scenario confronted by a baseball hitter. For groups of experts (Division I baseball players) and novices (non-players), we recorded electroencephalography (EEG) while they performed the task. We analyzed evoked EEG single-trial variability, contingent negative variation (CNV), and pre-stimulus alpha power with respect to the expert vs. novice groups. We found strong evidence for differences in inhibitory processes between the two groups, specifically differential activity in supplementary motor areas (SMA), indicative of enhanced inhibitory control in the expert (baseball player) group. We also found selective activity in the fusiform gyrus (FG) and orbital gyrus in the expert group, suggesting an enhanced perception-action coupling in baseball players that differentiates them from matched controls. In sum, our results show that EEG correlates of decision formation can be used to identify neural markers of high-performance athletes.

Prestimulus EEG alpha oscillations modulate task-related fMRI BOLD responses to auditory stimuli.

EEG alpha-band activity is generally thought to represent an inhibitory state related to decreased attention and play a role in suppression of task-irrelevant stimulus processing, but a competing hypothesis suggests an active role in processing task-relevant information - one in which phase dynamics are involved. Here we used simultaneous EEG-fMRI and a whole-brain analysis to investigate the effects of prestimulus alpha activity on the event-related BOLD response during an auditory oddball task. We separately investigated the effects of the posterior alpha rhythm's power and phase on activity related to task-relevant stimulus processing and also investigated higher-level decision-related processing. We found stronger decision-related BOLD activity in areas late in the processing stream when subjects were in the high alpha power state prior to stimulus onset, but did not detect any effect in primary sensory regions. Our phase analysis revealed correlates in the bilateral thalamus, providing support for a thalamo-cortical loop in attentional modulations and suggesting that the cortical alpha rhythm acts as a cyclic modulator of task-related responses very early in the processing stream. Our results help to reconcile the competing inhibition and active-processing hypotheses for ongoing alpha oscillations and begin to tease apart the distinct roles and mechanisms underlying their power and phase.

Pre-stimulus functional networks modulate task performance in time-pressured evidence gathering and decision-making.

Rapid perceptual decision-making is believed to depend upon efficient allocation of neural resources to the processing of transient stimuli within task-relevant contexts. Given decision-making under severe time pressure, it is reasonable to posit that the brain configures itself, prior to processing stimulus information, in a way that depends upon prior beliefs and/or anticipation. However, relatively little is known about such configuration processes, how they might be manifested in the human brain, or ultimately how they mediate task performance. Here we show that network configuration, defined via pre-stimulus functional connectivity measures estimated from functional magnetic resonance imaging (fMRI) data, is predictive of performance in a time-pressured Go/No-Go task. Specifically, using connectivity measures to summarize network properties, we show that pre-stimulus brain state can be used to discriminate behaviorally correct and incorrect trials, as well as behaviorally correct commission and omission trial categories. More broadly, our results show that pre-stimulus functional configurations of cortical and sub-cortical networks can be a major determiner of task performance.

Simultaneous EEG-fMRI reveals a temporal cascade of task-related and default-mode activations during a simple target detection task.

Focused attention continuously and inevitably fluctuates, and to completely understand the mechanisms responsible for these modulations it is necessary to localize the brain regions involved. During a simple visual oddball task, neural responses measured by electroencephalography (EEG) modulate primarily with attention, but source localization of the correlates is a challenge. In this study we use single-trial analysis of simultaneously-acquired scalp EEG and functional magnetic resonance image (fMRI) data to investigate the blood oxygen level dependent (BOLD) correlates of modulations in task-related attention, and we unravel the temporal cascade of these transient activations. We hypothesize that activity in brain regions associated with various task-related cognitive processes modulates with attention, and that their involvements occur transiently in a specific order. We analyze the fMRI BOLD signal by first regressing out the variance linked to observed stimulus and behavioral events. We then correlate the residual variance with the trial-to-trial variation of EEG discriminating components for identical stimuli, estimated at a sequence of times during a trial. Post-stimulus and early in the trial, we find activations in right-lateralized frontal regions and lateral occipital cortex, areas that are often linked to task-dependent processes, such as attentional orienting, and decision certainty. After the behavioral response we see correlates in areas often associated with the default-mode network and introspective processing, including precuneus, angular gyri, and posterior cingulate cortex. Our results demonstrate that during simple tasks both task-dependent and default-mode networks are transiently engaged, with a distinct temporal ordering and millisecond timescale.

Education modulates the impact of white matter lesions on the risk of mild cognitive impairment and dementia.

OBJECTIVES: Conflicting results have been reported regarding the association between white matter lesions (WML) and cognitive impairment. We hypothesized that education, a marker of cognitive reserve (CR), could modulate the effects of WML on the risk of mild cognitive impairment (MCI) or dementia. METHODS: We followed 500 healthy subjects from a cohort of community-dwelling persons aged 65 years and over (ESPRIT Project). At baseline, WML volume was measured using a semi-automatic method on T2-weighted MRI. Standardized cognitive and neurological evaluations were repeated after 2, 4, and 7 years. The sample was dichotomized according to education level into low (≤8 years) and high (>8 years) education groups. Cox proportional hazard models were constructed to study the association between WML and risk of MCI/dementia. RESULTS: The interaction between education level and WML volume reached significance (p = 0.017). After adjustment for potential confounders, the association between severe WML and increased MCI/dementia risk was significant in the low education group (≤8 years) (p = 0.02, hazard ratio [HR]: 3.77 [1.29-10.99]), but not in the high education group (>8 years) (p = 0.82, HR: 1.07 [0.61-1.87]). CONCLUSIONS: Severe WML significantly increases the risk of developing MCI/dementia over a 7-year period in low educated participants. Subjects with higher education levels were seen to be more likely to be resilient to the deleterious effects of severe WML. The CR hypothesis suggests several avenues for dementia prevention.

Simultaneous EEG-fMRI reveals temporal evolution of coupling between supramodal cortical attention networks and the brainstem.

Cortical and subcortical networks have been identified that are commonly associated with attention and task engagement, along with theories regarding their functional interaction. However, a link between these systems has not yet been demonstrated in healthy humans, primarily because of data acquisition and analysis limitations. We recorded simultaneous EEG-fMRI while subjects performed auditory and visual oddball tasks and used these data to investigate the BOLD correlates of single-trial EEG variability at latencies spanning the trial. We focused on variability along task-relevant dimensions in the EEG for identical stimuli and then combined auditory and visual data at the subject level to spatially and temporally localize brain regions involved in endogenous attentional modulations. Specifically, we found that anterior cingulate cortex (ACC) correlates strongly with both early and late EEG components, whereas brainstem, right middle frontal gyrus (rMFG), and right orbitofrontal cortex (rOFC) correlate significantly only with late components. By orthogonalizing with respect to event-related activity, we found that variability in insula and temporoparietal junction is reflected in reaction time variability, rOFC and brainstem correlate with residual EEG variability, and ACC and rMFG are significantly correlated with both. To investigate interactions between these correlates of temporally specific EEG variability, we performed dynamic causal modeling (DCM) on the fMRI data. We found strong evidence for reciprocal effective connections between the brainstem and cortical regions. Our results support the adaptive gain theory of locus ceruleus-norepinephrine (LC-NE) function and the proposed functional relationship between the LC-NE system, right-hemisphere ventral attention network, and P300 EEG response.

Spatial distribution of cerebral white matter lesions predicts progression to mild cognitive impairment and dementia.

CONTEXT: White matter lesions (WML) increase the risk of dementia. The relevance of WML location is less clear. We sought to determine whether a particular WML profile, based on the density and location of lesions, could be associated with an increased risk of mild cognitive impairment (MCI) or dementia over the following 7 years. METHODS: In 426 healthy subjects from a cohort of community-dwelling people aged 65 years and over (ESPRIT Project), standardized cognitive and neurological evaluations were repeated after 2, 4 and 7 years. Patterns of WML were computed with a supervised data mining approach (decision trees) using the regional WML volumes (frontal, parietal, temporal, and occipital regions) and the total WML volume estimated at baseline. Cox proportional hazard models were then constructed to study the association between WML patterns and risk of MCI/dementia. RESULTS: Total WML volume and percentage of WML in the temporal region proved to be the best predictors of progression to MCI and dementia. Specifically, severe total WML load with a high proportion of lesions in the temporal region was significantly associated with the risk of developing MCI or dementia. CONCLUSIONS: Above a certain threshold of damage, a pattern of WML clustering in the temporal region identifies individuals at increased risk of MCI or dementia. As this WML pattern is observed before the onset of clinical symptoms, it may facilitate the detection of patients at risk of MCI/dementia.

White matter hyperintensities and cerebral amyloidosis: necessary and sufficient for clinical expression of Alzheimer disease?

IMPORTANCE: Current hypothetical models emphasize the importance of ß-amyloid in Alzheimer disease (AD) pathogenesis, although amyloid alone is not sufficient to account for the dementia syndrome. The impact of small-vessel cerebrovascular disease, visualized as white matter hyperintensities (WMHs) on magnetic resonance imaging scans, may be a key factor that contributes independently to AD presentation. OBJECTIVE: To determine the impact of WMHs and Pittsburgh Compound B (PIB) positron-emission tomography-derived amyloid positivity on the clinical expression of AD. DESIGN: Baseline PIB-positron-emission tomography values were downloaded from the Alzheimer's Disease Neuroimaging Initiative database. Total WMH volume was derived on accompanying structural magnetic resonance imaging data. We examined whether PIB positivity and total WMHs predicted diagnostic classification of patients with AD (n = 20) and control subjects (n = 21). A second analysis determined whether WMHs discriminated between those with and without the clinical diagnosis of AD among those who were classified as PIB positive (n = 28). A third analysis examined whether WMHs, in addition to PIB status, could be used to predict future risk for AD among subjects with mild cognitive impairment (n = 59). SETTING: The Alzheimer's Disease Neuroimaging Initiative public database. PARTICIPANTS: The study involved data from 21 normal control subjects, 59 subjects with mild cognitive impairment, and 20 participants with clinically defined AD from the Alzheimer Disease's Neuroimaging Initiative database. MAIN OUTCOME MEASURES: Clinical AD diagnosis and WMH volume. RESULTS: Pittsburgh Compound B positivity and increased total WMH volume independently predicted AD diagnosis. Among PIB-positive subjects, those diagnosed as having AD had greater WMH volume than normal control subjects. Among subjects with mild cognitive impairment, both WMH and PIB status at baseline conferred risk for future diagnosis of AD. CONCLUSIONS AND RELEVANCE: White matter hyperintensities contribute to the presentation of AD and, in the context of significant amyloid deposition, may provide a second hit necessary for the clinical manifestation of the disease. As risk factors for the development of WMHs are modifiable, these findings suggest intervention and prevention strategies for the clinical syndrome of AD.

Combined prospective and retrospective correction to reduce motion-induced image misalignment and geometric distortions in EPI.

Despite rigid-body realignment to compensate for head motion during an echo-planar imaging time-series scan, nonrigid image deformations remain due to changes in the effective shim within the brain as the head moves through the B(0) field. The current work presents a combined prospective/retrospective solution to reduce both rigid and nonrigid components of this motion-related image misalignment. Prospective rigid-body correction, where the scan-plane orientation is dynamically updated to track with the subject's head, is performed using an active marker setup. Retrospective distortion correction is then applied to unwarp the remaining nonrigid image deformations caused by motion-induced field changes. Distortion correction relative to a reference time-frame does not require any additional field mapping scans or models, but rather uses the phase information from the echo-planar imaging time-series itself. This combined method is applied to compensate echo-planar imaging scans of volunteers performing in-plane and through-plane head motions, resulting in increased image stability beyond what either prospective or retrospective rigid-body correction alone can achieve. The combined method is also assessed in a blood oxygen level dependent functional MRI task, resulting in improved Z-score statistics.

Regional vulnerability in Huntington's disease: fMRI-guided molecular analysis in patients and a mouse model of disease.

Although the huntingtin gene is expressed in brain throughout life, phenotypically Huntington's disease (HD) begins only in midlife and affects specific brain regions. Here, to investigate regional vulnerability in the disease, we used functional magnetic resonance imaging (fMRI) to translationally link studies in patients with a mouse model of disease. Using fMRI, we mapped cerebral blood volume (CBV) in three groups: HD patients, symptom-free carriers of the huntingtin genetic mutation, and age-matched controls. In contrast to a region in the anterior caudate, in which dysfunction was linked to genotype independent of phenotype, a region in the posterior body of the caudate was differentially associated with disease phenotype. Guided by these observations, we harvested regions from the anterior and posterior body of the caudate in postmortem control and HD human brain tissue. Gene-expression profiling identified two molecules whose expression levels were most strongly correlated with regional vulnerability - protein phosphatase 1 regulatory subunit 7 (PPP1R7) and Wnt inhibitory factor-1 (WIF-1). To verify and potentially extend these findings, we turned to the YAC128 (C57BL/6J) HD transgenic mice. By fMRI we longitudinally mapped CBV in transgenic and wildtype (WT) mice, and over time, abnormally low fMRI signal emerged selectively in the dorsal striatum. A relatively unaffected brain region, primary somatosensory cortex (S1), was used as a control. Both dorsal striatum and S1 were harvested from transgenic and WT mice and molecular analysis confirmed that PPP1R7 deficiency was strongly correlated with the phenotype. Together, converging findings in human HD patients and this HD mouse model suggest a functional pattern of caudate vulnerability and that variation in expression levels of herein identified molecules correlate with this pattern of vulnerability.

Prospective active marker motion correction improves statistical power in BOLD fMRI.

Group level statistical maps of blood oxygenation level dependent (BOLD) signals acquired using functional magnetic resonance imaging (fMRI) have become a basic measurement for much of systems, cognitive and social neuroscience. A challenge in making inferences from these statistical maps is the noise and potential confounds that arise from the head motion that occurs within and between acquisition volumes. This motion results in the scan plane being misaligned during acquisition, ultimately leading to reduced statistical power when maps are constructed at the group level. In most cases, an attempt is made to correct for this motion through the use of retrospective analysis methods. In this paper, we use a prospective active marker motion correction (PRAMMO) system that uses radio frequency markers for real-time tracking of motion, enabling on-line slice plane correction. We show that the statistical power of the activation maps is substantially increased using PRAMMO compared to conventional retrospective correction. Analysis of our results indicates that the PRAMMO acquisition reduces the variance without decreasing the signal component of the BOLD (beta). Using PRAMMO could thus improve the overall statistical power of fMRI based BOLD measurements, leading to stronger inferences of the nature of processing in the human brain.

You Can't Think and Hit at the Same Time: Neural Correlates of Baseball Pitch Classification.

Hitting a baseball is often described as the most difficult thing to do in sports. A key aptitude of a good hitter is the ability to determine which pitch is coming. This rapid decision requires the batter to make a judgment in a fraction of a second based largely on the trajectory and spin of the ball. When does this decision occur relative to the ball's trajectory and is it possible to identify neural correlates that represent how the decision evolves over a split second? Using single-trial analysis of electroencephalography (EEG) we address this question within the context of subjects discriminating three types of pitches (fastball, curveball, slider) based on pitch trajectories. We find clear neural signatures of pitch classification and, using signal detection theory, we identify the times of discrimination on a trial-to-trial basis. Based on these neural signatures we estimate neural discrimination distributions as a function of the distance the ball is from the plate. We find all three pitches yield unique distributions, namely the timing of the discriminating neural signatures relative to the position of the ball in its trajectory. For instance, fastballs are discriminated at the earliest points in their trajectory, relative to the two other pitches, which is consistent with the need for some constant time to generate and execute the motor plan for the swing (or inhibition of the swing). We also find incorrect discrimination of a pitch (errors) yields neural sources in Brodmann Area 10, which has been implicated in prospective memory, recall, and task difficulty. In summary, we show that single-trial analysis of EEG yields informative distributions of the relative point in a baseball's trajectory when the batter makes a decision on which pitch is coming.

Regional white matter hyperintensity volume, not hippocampal atrophy, predicts incident Alzheimer disease in the community.

BACKGROUND New-onset Alzheimer disease (AD) is often attributed to degenerative changes in the hippocampus. However, the contribution of regionally distributed small vessel cerebrovascular disease, visualized as white matter hyperintensities (WMHs) on magnetic resonance imaging, remains unclear. OBJECTIVE To determine whether regional WMHs and hippocampal volume predict incident AD in an epidemiological study. DESIGN A longitudinal community-based epidemiological study of older adults from northern Manhattan, New York. SETTING The Washington Heights/Inwood Columbia Aging Project. PARTICIPANTS Between 2005 and 2007, 717 participants without dementia received magnetic resonance imaging scans. A mean (SD) of 40.28 (9.77) months later, 503 returned for follow-up clinical examination and 46 met criteria for incident dementia (45 with AD). Regional WMHs and relative hippocampal volumes were derived. Three Cox proportional hazards models were run to predict incident dementia, controlling for relevant variables. The first included all WMH measurements; the second included relative hippocampal volume; and the third combined the 2 measurements. MAIN OUTCOME MEASURE Incident AD. RESULTS White matter hyperintensity volume in the parietal lobe predicted time to incident dementia (hazard ratio [HR] = 1.194; P = .03). Relative hippocampal volume did not predict incident dementia when considered alone (HR = 0.419; P = .77) or with the WMH measures included in the model (HR = 0.302; P = .70). Including hippocampal volume in the model did not notably alter the predictive utility of parietal lobe WMHs (HR = 1.197; P = .049). CONCLUSIONS The findings highlight the regional specificity of the association of WMHs with AD. It is not clear whether parietal WMHs solely represent a marker for cerebrovascular burden or point to distinct injury compared with other regions. Future work should elucidate pathogenic mechanisms linking WMHs and AD pathology.

Metabolic syndrome and localization of white matter hyperintensities in the elderly population.

BACKGROUND: Metabolic syndrome (MetS) is defined as a clustering of metabolic disorders: abdominal obesity, dyslipidemia, hypertension, and hyperglycemia. Although specific components of MetS have been associated with white matter hyperintensities (WMH), less is known about the association between MetS as a whole and WMH, especially in normal aging. We aimed to: (1) investigate this association in a cohort of healthy elderly individuals, and (2) examine the relationship between MetS and the regional distribution of WMH, to further understanding of the relationship between MetS and structural brain changes. METHODS: Analyses were carried out on 308 participants (48.1% men, age: 71.0 ± 3.9 years) from the French longitudinal ESPRIT (Enquête de Santé Psychologique--Risques, Incidence et Traitement) study, who were free of cerebrovascular disease cognitive and functional impairment. Logistic regression models were used to examine the cross-sectional association between MetS (defined using the National Cholesterol Education Program-Adult Treatment Panel III criteria) and (1) WMH volumes, and (2) WMH volumes according to their localization in insulofrontal and temporoparietal regions. RESULTS: After adjusting for potential confounders, participants with MetS had a twofold increased chance of presenting with high levels of WMH volume compared with those without (odds ratio [OR] = 2.74, 95% confidence interval [CI]: 1.25-6.03). MetS was specifically associated with an increase of temporoparietal WMH volumes, but no association was found between MetS and WMH localized in the insulofrontal region. CONCLUSION: Our findings suggest that effective management of MetS may reduce WMH accumulation in brain areas already vulnerable to the aging process.

White matter hyperintensity burden on magnetic resonance imaging in essential tremor.

BACKGROUND: Whereas structural abnormalities in the cerebellum have been associated with essential tremor (ET), the contribution of vascular disease via white matter hyperintensities (WMHs) and strokes has not been examined. In this study, we have explored these potential associations and hypothesized that ET would be associated with greater overall WMH volume, greater cerebellar WMH volume, and greater infarct presence. METHODS: In a cross-sectional magnetic resonance imaging (MRI) study of 540 community-dwelling elderly persons in northern Manhattan, New York, brain measures of total WMH volume and regional WMH volume were derived from T(2)-weighted fluid attenuated inverse recovery-weighted MR images. The presence of cerebral infarcts on MRI was determined as well. RESULTS: Total WMH volume was greater among 33 ET cases than 507 controls in both univariate (OR = 1.41, p = 0.038) and fully adjusted analyses (OR = 1.44, p = 0.03). Cerebellar WMH volume was associated with marginally increased odds of ET in a univariate model (OR = 1.52, p = 0.11) and significantly increased odds in a fully adjusted multivariate model (OR = 1.74, p = 0.049). Temporal lobe WMH volume was associated with significantly increased odds of ET in both univariate (OR = 3.36, p<0.001) and fully adjusted models (OR = 3.73, p<0.001). Large strokes were significantly more common in cases than in controls in unadjusted analyses (OR = 3.04, p = 0.02) and marginally in adjusted analyses (OR = 2.56-2.57, p = 0.045-0.056). The distribution of strokes did not differ by diagnosis. DISCUSSION: MRI data in this study indicated that ET was associated with greater total WMH volume, greater cerebellar WMH volume and possibly more strokes. Cerebrovascular disease could play a role in the development of ET.