Reduced Human Leukocyte Antigen (HLA) Protection in Gulf War Illness (GWI)

Background

Gulf War Illness (GWI) is a disease of unknown etiology with symptoms suggesting the involvement of an immune process. Here we tested the hypothesis that Human Leukocyte Antigen (HLA) composition might differ between veterans with and without GWI.

Methods

We identified 144 unique alleles of Class I and II HLA genes in 82 veterans (66 with and 16 without GWI). We tested the hypothesis that a subset of HLA alleles may classify veterans in their respective group using a stepwise linear discriminant analysis. In addition, each participant rated symptom severity in 6 domains according to established GWI criteria, and an overall symptom severity was calculated.

Findings

We found 6 Class II alleles that classified participants 84.1% correctly (13/16 control and 56/66 GWI). The number of copies of the 6 alleles was significantly higher in the control group, suggesting a protective role. This was supported by a significant negative dependence of overall symptom severity on the number of allele copies, such that symptom severity was lower in participants with larger numbers of allele copies.

Interpretation

These results indicate a reduced HLA protection (i.e. genetic susceptibility) in veterans with GWI.

Funding

University of Minnesota and U.S. Department of Veterans Affairs.

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Differences in Human Leukocyte Antigen distinguished veterans with Gulf War Illness (GWI) from healthy Gulf War era veterans.

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Veterans with GWI show genetic susceptibility which is associated with increased severity of hallmark symptoms of GWI.

A large number of Gulf War veterans suffer from diffuse and debilitating symptoms that resemble altered immune functioning. This study evaluated whether the Human Leukocyte Antigen (HLA) gene, which is central to immune functioning, differs between veterans with GWI and unaffected Gulf War veterans. The findings highlight robust differences in HLA composition between the two groups and suggest that veterans with GWI are characterized by genetic susceptibility that confers risk for typical GWI symptoms. These findings provide compelling genetic evidence of immune dysfunction in GWI.

Differential neural activity patterns for spatial relations in humans: a MEG study

Children learn the words for above-below relations earlier than for left-right relations, despite treating these equally well in a simple visual categorization task. Even as adults--conflicts in congruency, such as when a stimulus is depicted in a spatially incongruent manner with respect to salient global cues--can be challenging. Here we investigated the neural correlates of encoding and maintaining in working memory above-below and left-right relational planes in 12 adults using magnetoencephalography in order to discover whether above-below relations are represented by the brain differently than left-right relations. Adults performed perfectly on the task behaviorally, so any differences in neural activity were attributed to the stimuli's cognitive attributes. In comparing above-below to left-right relations during stimulus encoding, we found the greatest differences in neural activity in areas associated with space and movement. In comparing congruent to incongruent trials, we found the greatest differential activity in premotor areas. For both contrasts, brain areas involved in the encoding phase were also involved in the maintenance phase, which provides evidence that those brain areas are particularly important in representing the relational planes or congruency types throughout the trial. When comparing neural activity associated with the relational planes during working memory, additional right posterior areas were implicated, whereas the congruent-incongruent contrast implicated additional bilateral frontal and temporal areas. These findings are consistent with the hypothesis left-right relations are represented differently than above-below relations.

Neural mechanisms underlying the exploration of small city maps using magnetoencephalography

The neural mechanisms underlying spatial cognition in the context of exploring realistic city maps are unknown. We conducted a novel brain imaging study to address the question of whether and how features of special importance for map exploration are encoded in the brain to make a spatial decision. Subjects explored by eyes small city maps exemplifying five different street network types in order to locate a hypothetical City Hall, while neural activity was recorded continuously by 248 magnetoencephalography (MEG) sensors at high temporal resolution. Monitoring subjects' eye positions, we locally characterized the maps by computing three spatial parameters of the areas that were explored. We computed the number of street intersections, the total street length, and the regularity index in the circular areas of 6 degrees of visual angle radius centered on instantaneous eye positions. We tested the hypothesis that neural activity during exploration is associated with the spatial parameters and modulated by street network type. All time series were rendered stationary and nonautocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We then assessed the associations between the prewhitened time-varying MEG time series from 248 sensors and the prewhitened spatial parameters time series, for each street network type, using multiple linear regression analyses. In accord with our hypothesis, ongoing neural activity was strongly associated with the spatial parameters through localized and distributed networks, and neural processing of these parameters depended on the type of street network. Overall, processing of the spatial parameters seems to predominantly involve right frontal and prefrontal areas, but not for all street network layouts. These results are in line with findings from a series of previous studies showing that frontal and prefrontal areas are involved in the processing of spatial information and decision making. Modulation of neural processing of the spatial parameters by street network type suggests that some street network layouts may contain other types of spatial information that subjects use to explore maps and make spatial decisions.

Diagnosis of posttraumatic stress disorder (PTSD) based on correlations of prewhitened fMRI data: outcomes and areas involved

Successful diagnosis of PTSD has been achieved using neural correlations from prewhitened magnetoencephalographic (MEG) time series (Georgopoulos et al. in J Neural Eng 7:16011, 2010. doi:10.1088/1741-2560/7/1/016011; James et al. 2015). Here, we show that highly successful classification of PTSD and control subjects can be obtained using neural correlations from prewhitened resting-state fMRI data. All but one PTSD (14/15; sensitivity = 93.3 %) and all but one control (20/21; specificity = 95.2 %) subjects were correctly classified using 15 out of 2701 possible correlations between 74 brain areas. In contrast, correlations of the same but non-prewhitened data yielded chance-level classifications. We conclude that, if properly processed, fMRI has the prospect of aiding significantly in PTSD diagnosis. Twenty-five brain areas were most prominently involved in correct subject classification, including areas from all cortical lobes and the left pallidum.

Neural communication in posttraumatic growth

Posttraumatic growth (PTG), or positive psychological changes following exposure to traumatic events, is commonly reported among trauma survivors. In the present study, we examined neural correlates of PTG in 106 veterans with PTSD and 193 veteran controls using task-free magnetoencephalography (MEG), diagnostic interviews and measures of PTG, and traumatic event exposure. Global synchronous neural interactions (SNIs) were significantly modulated downward with increasing PTG scores in controls (p = .005), but not in veterans with PTSD (p = .601). This effect was primarily characterized by negative slopes in local neural networks, was strongest in the medial prefrontal cortex, and was much stronger and more extensive in the control than the PTSD group. The present study complements previous research highlighting the role of neural adaptation in healthy functioning.

DSM-5 personality traits discriminate between posttraumatic stress disorder and control groups

The relevance of personality traits to the study of psychopathology has long been recognized, particularly in terms of understanding patterns of comorbidity. In fact, a multidimensional personality trait model reflecting five higher-order personality dimensions-negative affect, detachment, antagonism, disinhibition, and psychoticism-is included in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and represented in the Personality Inventory for DSM-5 (PID-5). However, evaluation of these dimensions and underlying personality facets within clinical samples has been limited. In the present study, we utilized the PID-5 to evaluate the personality profile elevation and composition of 150 control veterans and 35 veterans diagnosed with posttraumatic stress disorder (PTSD). Results indicated that veterans with PTSD endorsed significantly more personality pathology than control veterans, with scores on detachment and psychoticism domains most clearly discriminating between the two groups. When personality domain scores were considered as parts of each subject's personality profile, a slightly different picture emerged. Specifically, the PTSD composition was primarily characterized by detachment and negative affect, followed by disinhibition, psychoticism, and antagonism in that order of relative importance. The profile of the control group was significantly different, mostly accounted for differences in antagonism and psychoticism. Using these complementary analytic strategies, the findings demonstrate the relevance of personality pathology to PTSD, highlight internalizing features of PTSD, and pave the way for future research aimed at evaluating the role of shared maladaptive personality traits in underlying the comorbidity of PTSD and related disorders.

Cognitive mechanisms underlying instructed choice exploration of small city maps

We investigated the cognitive mechanisms underlying the exploration and decision-making in realistic and novel environments. Twelve human subjects were shown small circular U.S. city maps with two locations highlighted on the circumference, as possible choices for a post office (“targets”). At the beginning of a trial, subjects fixated a spot at the center of the map and ultimately chose one of the two locations. A space syntax analysis of the map paths (from the center to each target) revealed that the chosen location was associated with the less convoluted path, as if subjects navigated mentally the paths in an “ant's way,” i.e., by staying within street boundaries, and ultimately choosing the target that could be reached from the center in the shortest way, and the fewest turns and intersections. The subjects' strategy for map exploration and decision making was investigated by monitoring eye position during the task. This revealed a restricted exploration of the map delimited by the location of the two alternative options and the center of the map. Specifically, subjects explored the areas around the two target options by repeatedly looking at them before deciding which one to choose, presumably implementing an evaluation and decision-making process. The ultimate selection of a specific target was significantly associated with the time spent exploring the area around that target. Finally, an analysis of the sequence of eye fixations revealed that subjects tended to look systematically toward the target ultimately chosen even from the beginning of the trial. This finding indicates an early cognitive selection bias for the ensuing decision process.

An information theory analysis of spatial decisions in cognitive development

Performance in a cognitive task can be considered as the outcome of a decision-making process operating across various knowledge domains or aspects of a single domain. Therefore, an analysis of these decisions in various tasks can shed light on the interplay and integration of these domains (or elements within a single domain) as they are associated with specific task characteristics. In this study, we applied an information theoretic approach to assess quantitatively the gain of knowledge across various elements of the cognitive domain of spatial, relational knowledge, as a function of development. Specifically, we examined changing spatial relational knowledge from ages 5 to 10 years. Our analyses consisted of a two-step process. First, we performed a hierarchical clustering analysis on the decisions made in 16 different tasks of spatial relational knowledge to determine which tasks were performed similarly at each age group as well as to discover how the tasks clustered together. We next used two measures of entropy to capture the gradual emergence of order in the development of relational knowledge. These measures of "cognitive entropy" were defined based on two independent aspects of chunking, namely (1) the number of clusters formed at each age group, and (2) the distribution of tasks across the clusters. We found that both measures of entropy decreased with age in a quadratic fashion and were positively and linearly correlated. The decrease in entropy and, therefore, gain of information during development was accompanied by improved performance. These results document, for the first time, the orderly and progressively structured "chunking" of decisions across the development of spatial relational reasoning and quantify this gain within a formal information-theoretic framework.

Development and application of a diagnostic algorithm for posttraumatic stress disorder

Intact cognitive functions rely on synchronous neural activity; conversely, alterations in synchrony are thought to underlie psychopathology. We recently demonstrated that anomalies in synchronous neural interactions (SNI) determined by magnetoencephalography represent a putative PTSD biomarker. Here we develop and apply a regression-based diagnostic algorithm to further validate SNI as a PTSD biomarker in 432 veterans (235 controls; 138 pure PTSD; 59 PTSD plus comorbid disorders). Correlation coefficients served as proximities in multidimensional scaling (MDS) to obtain a two-dimensional representation of the data. In addition, least absolute shrinkage and selection operator (LASSO) regression was used to derive a diagnostic algorithm for PTSD. Performance of this algorithm was assessed by the area under the receiver operating characteristic (ROC) curves, sensitivity, and specificity in 1000 randomly divided testing and validation datasets and in independent samples. MDS revealed that individuals with PTSD, regardless of comorbid psychiatric conditions, are highly distinct from controls. Similarly, application of the LASSO regression-derived prediction model demonstrated remarkable classification accuracy (AUCs≥0.93 for men, AUC=0.82 for women). Neural functioning in individuals with PTSD, regardless of comorbid psychiatric diagnoses, can be used as a diagnostic test to determine patient disease status, further validating SNI as a PTSD biomarker.

Cognitive modulation of local and callosal neural interactions in decision making

Traditionally, the neurophysiological mechanisms of cognitive processing have been investigated at the single cell level. Here we show that the dynamic, millisecond-by-millisecond, interactions between neuronal events measured by local field potentials are modulated in an orderly fashion by key task variables of a space categorization task performed by monkeys. These interactions were stronger during periods of higher cognitive load and varied in sign (positive, negative). They were observed both within area 7a of the posterior parietal cortex and between symmetric 7a areas of the two hemispheres. Time lags for maximum interactions were longer for opposite- vs. same-hemisphere recordings, and lags for negative interactions were longer than for positive interactions in both recording sites. These findings underscore the involvement of dynamic neuronal interactions in cognitive processing within and across hemispheres. They also provide accurate estimates of lags in callosal interactions, very comparable to similar estimates of callosal conduction delays derived from neuroanatomical measurements (Caminiti et al., 2013).

Departure from Network Equilibrium (DNE): an efficient and scalable measure of instantaneous network dynamics, with an application to magnetoencephalography

The assessment of the dynamic status of a network is currently unavailable. It is important to know how far a network is away from its equilibrium (as an indicator of instability) at a moment, and over periods of time. Here, we introduce the Departure from Network Equilibrium (DNE), a new measure of instantaneous network dynamics. DNE is simple, fast to compute, and scalable with network size. We present the results of its application on white noise networks (as a basis) and on networks derived from magnetoencephalographic recordings from the human brain.

Motor directional tuning across brain areas: directional resonance and the role of inhibition for directional accuracy

Motor directional tuning (Georgopoulos et al., 1982) has been found in every brain area in which it has been sought for during the past 30-odd years. It is typically broad, with widely distributed preferred directions and a population signal that predicts accurately the direction of an upcoming reaching movement or isometric force pulse (Georgopoulos et al., 1992). What is the basis for such ubiquitous directional tuning? How does the tuning come about? What are the implications of directional tuning for understanding the brain mechanisms of movement in space? This review addresses these questions in the light of accumulated knowledge in various sub-fields of neuroscience and motor behavior. It is argued (a) that direction in space encompasses many aspects, from vision to muscles, (b) that there is a directional congruence among the central representations of these distributed “directions” arising from rough but orderly topographic connectivities among brain areas, (c) that broad directional tuning is the result of broad excitation limited by recurrent and non-recurrent (i.e., direct) inhibition within the preferred direction loci in brain areas, and (d) that the width of the directional tuning curve, modulated by local inhibitory mechanisms, is a parameter that determines the accuracy of the directional command.

Neural network modulation by trauma as a marker of resilience: differences between veterans with posttraumatic stress disorder and resilient controls

IMPORTANCE

Posttraumatic stress disorder (PTSD) and resilience reflect 2 distinct outcomes after exposure to potentially traumatic events. The neural mechanisms underlying these different outcomes are not well understood.

OBJECTIVE

To examine the effect of trauma on synchronous neural interactions for veterans with PTSD and resilient controls using magnetoencephalography.

DESIGN

Participants underwent diagnostic interviews, a measure of exposure to potentially traumatic events, and magnetoencephalography.

SETTING

U.S. Department of Veterans Affairs medical center.

PARTICIPANTS

Eighty-six veterans with PTSD and 113 resilient control veterans recruited from a large Midwestern Medical Center.

MAIN OUTCOME MEASURES

Multiple regression analyses were performed to examine the effect of lifetime trauma on global and local synchronous neural interactions. In analyses examining the local synchronous neural interactions, the partial regression coefficient indicates the strength and direction of the effect of trauma on the synchronous interactions between the 2 neural signals recorded by a pair of sensors. The partial regression coefficient, or slope, is the primary outcome measure for these analyses.

RESULTS

Global synchronous neural interactions were significantly modulated downward with increasing lifetime trauma scores in resilient control veterans (P = .003) but not in veterans with PTSD (P = .91). This effect, which was primarily characterized by negative slopes (i.e., decorrelations) in small neural networks, was strongest in the right superior temporal gyrus. Significant negative slopes were more common, stronger, and observed between sensors at shorter distances than positive slopes in both hemispheres (P < .001 for all) for controls but not for veterans with PTSD. CONCLUSIONS. Neural modulation involving decorrelation of neural networks in the right superior temporal gyrus and, to a lesser extent, other areas distinguishes resilient veterans from those with PTSD and is postulated to have an important role in healthy response to trauma.

Exploring small city maps

The exploration of city maps has exploded recently due to the wide availability, increasing use of, and reliance on small positioning and navigational devices for personal use. In this study, subjects explored small, 3-mile diameter circular maps exemplifying five different types of street networks common in the United States, in order to locate a hypothetical city hall. Chosen locations indicated that subjects are able to identify more accessible sites. Monitoring eye position revealed that women explored maps faster, using more widely dispersed but more narrowly focused gaze clusters than men. The type of street network influenced the time spent by the eyes in a locale and differentially affected the size of gaze clusters between women and men, underscoring the complex interactions of gender-specific strategies with street network types.

A compact and realistic cerebral cortical layout derived from prewhitened resting-state fMRI time series: Cherniak's adjacency rule, size law, and metamodule grouping upheld

We used hierarchical tree clustering to derive a functional organizational chart of 52 human cortical areas (26 per hemisphere) from zero-lag correlations calculated between single-voxel, prewhitened, resting-state BOLD fMRI time series in 18 subjects. No special “resting-state networks” were identified. There were four major features in the resulting tree (dendrogram). First, there was a strong clustering of homotopic, left-right hemispheric areas. Second, cortical areas were concatenated in multiple, partially overlapping clusters. Third, the arrangement of the areas revealed a layout that closely resembled the actual layout of the cerebral cortex, namely an orderly progression from anterior to posterior. And fourth, the layout of the cortical areas in the tree conformed to principles of efficient, compact layout of components proposed by Cherniak. Since the tree was derived on the basis of the strength of neural correlations, these results document an orderly relation between functional interactions and layout, i.e., between structure and function.

Spatiotemporal neural interactions underlying continuous drawing movements as revealed by magnetoencephalography

Continuous and sequential movements are controlled by widely distributed brain regions. A series of studies have contributed to understanding the functional role of these regions in a variety of visuomotor tasks. However, little is known about the neural interactions underpinning continuous movements. In the current study, we examine the spatiotemporal neural interactions underlying continuous drawing movements and the association of them with behavioral components. We conducted an experiment in which subjects copied a pentagon continuously for ~45 s using an XY joystick, while neuromagnetic fluxes were recorded from their head using a 248-sensor whole-head magnetoencephalography (MEG) device. Each sensor time series was rendered stationary and non-autocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We used the directional variability of the movement as a behavioral measure of the controls generated. The main objective of this study was to assess the relation between neural interactions and the variability of movement direction. That is, we divided the continuous recordings into consecutive periods (i.e., time-bins) of 51 steps duration and computed the pairwise cross-correlations between the prewhitened time series in each time-bin. The circular standard deviation of the movement direction within each time-bin provides an estimate of the directional variability of the 51-ms trajectory segment. We looked at the association between neural interactions and variability of movement direction, separately for each pair of sensors, by running a cross-correlation analysis between the strength of the MEG pairwise cross-correlations and the circular standard deviations. We identified two types of neuronal networks: in one, the neural interactions are correlated with the directional variability of the movement at negative time-lags (feedforward), and in the other, the neural interactions are correlated with the directional variability of the movement at positive time-lags (feedback). Sensors associated mostly with feedforward processes are distributed in the left hemisphere and the right occipital-temporal junction, whereas sensors related to feedback processes are distributed in the right hemisphere and the left cerebellar hemisphere. These results are in line with findings from a series of previous studies showing that specific brain regions are involved in feedforward and feedback control processes to plan, perform, and correct movements. Additionally, we looked at whether changes in movement direction modulate the neural interactions. Interestingly, we found a preponderance of sensors associated with changes in movement direction over the right hemisphere-ipsilateral to the moving hand. These sensors exhibit stronger coupling with the rest of the sensors for trajectory segments with high rather than low directional movement variability. We interpret these results as evidence that ipsilateral cortical regions are recruited for continuous movements when the curvature of the trajectory increases. To the best of our knowledge, this is the first study that shows how neural interactions are associated with a behavioral control parameter in continuous and sequential movements.

Neuropsychological testing and structural magnetic resonance imaging as diagnostic biomarkers early in the course of schizophrenia and related psychoses

Making an accurate diagnosis of schizophrenia and related psychoses early in the course of the disease is important for initiating treatment and counseling patients and families. In this study, we developed classification models for early disease diagnosis using structural MRI (sMRI) and neuropsychological (NP) testing. We used sMRI measurements and NP test results from 28 patients with recent-onset schizophrenia and 47 healthy subjects, drawn from the larger sample of the Mind Clinical Imaging Consortium. We developed diagnostic models based on Linear Discriminant Analysis (LDA) following two approaches; namely, (a) stepwise (STP) LDA on the original measurements, and (b) LDA on variables created through Principal Component Analysis (PCA) and selected using the Humphrey-Ilgen parallel analysis. Error estimation of the modeling algorithms was evaluated by leave-one-out external cross-validation. These analyses were performed on sMRI and NP variables separately and in combination. The following classification accuracy was obtained for different variables and modeling algorithms. sMRI only: (a) STP-LDA: 64.3% sensitivity and 76.6% specificity, (b) PCA-LDA: 67.9% sensitivity and 72.3% specificity. NP only: (a) STP-LDA: 71.4% sensitivity and 80.9% specificity, (b) PCA-LDA: 78.5% sensitivity and 91.5% specificity. Combined sMRI-NP: (a) STP-LDA: 64.3% sensitivity and 83.0% specificity, (b) PCA-LDA: 89.3% sensitivity and 93.6% specificity. (i) Maximal diagnostic accuracy was achieved by combining sMRI and NP variables. (ii) NP variables were more informative than sMRI, indicating that cognitive deficits can be detected earlier than volumetric structural abnormalities. (iii) PCA-LDA yielded more accurate classification than STP-LDA. As these sMRI and NP tests are widely available, they can increase accuracy of early intervention strategies and possibly be used in evaluating treatment response.

A network analysis of developing brain cultures

We recorded electrical activity from four developing embryonic brain cultures (4-40 days in vitro) using multielectrode arrays (MEAs) with 60 embedded electrodes. Data were filtered for local field potentials (LFPs) and downsampled to 1 ms to yield a matrix of time series consisting of 60 electrode × 60 000 time samples per electrode per day per MEA. Each electrode time series was rendered stationary and nonautocorrelated by applying an ARIMA (25, 1, 1) model and taking the residuals (i.e. innovations). Two kinds of analyses were then performed. First, a pairwise crosscorrelation (CC) analysis (±25 1 ms lags) revealed systematic changes in CC with lag, day in vitro (DIV), and inter-electrode distance. Specifically, (i) positive CCs were 1.76× more prevalent and 1.44× stronger (absolute value) than negative ones, and (ii) the strength of CC increased with DIV and decreased with lag and inter-electrode distance. Second, a network equilibrium analysis was based on the instantaneous (1 ms resolution) logratio of the number of electrodes that were above or below their mean, called simultaneous departure from equilibrium, SDE. This measure possesses a major computational advantage over the pairwise crosscorrelation approach because it is very simple and fast to calculate, an important factor for the analysis of large networks. The results obtained with SDE covaried highly with CC over DIV, which further validates the usefulness of this measure as a computationally effective tool for large scale network analysis.

The Minnesota Women Healthy Aging Project

The Minnesota Women Healthy Aging Project is an effort to understand how the brain changes with age and why some brains are more resilient than others. Using a unique methodology, researchers are evaluating the brain status of a number of women by taking multiple, multimodal measurements and relating those measures to cognitive abilities, language skills, and genetic information. The goal is to create a comprehensive databank that will provide information by which to characterize brain status, assess changes over time, and associate them with genomic makeup, cognitive function, and language ability. The project was initiated in 2010 and is being conducted through the University of Minnesota and the Minneapolis Veterans Affairs Health Care System. This article describes the project, which is the first of its kind, and its progress thus far.

Top-down spatial categorization signal from prefrontal to posterior parietal cortex in the primate

In the present study we characterized the strength and time course of category-selective responses in prefrontal cortex and area 7a of the posterior parietal cortex during a match-to-sample spatial categorization task. A monkey was trained to categorize whether the height of a horizontal sample bar, presented in rectangular frame at one of three vertical locations, was "high" or "low," depending on whether its position was above or below the frame's midline. After the display of this sample bar, and after a delay, choice bars were sequentially flashed in two locations: at the top and at the bottom of the frame ("choice" epoch). If the monkey timed its response to the display of the choice bar that matched the sample bar, he was rewarded. We found that cells in prefrontal cortex discriminated category early after the initial sample bar was shown, and continued to differentiate "up" from "down" trials throughout the delay and choice periods. In contrast, parietal cells did not differentiate category until the choice period. Therefore, our results support the notion of a top-down categorical signal that originates in prefrontal cortex and that is only represented in parietal cortex when it is necessary to express the categorical decision through a movement.

True associations between resting fMRI time series based on innovations

We calculated voxel-by-voxel pairwise crosscorrelations between prewhitened resting-state BOLD fMRI time series recorded from 60 cortical areas (30 per hemisphere) in 18 human subjects (nine women and nine men). Altogether, more than a billion-and-a-quarter pairs of BOLD time series were analyzed. For each pair, a crosscorrelogram was computed by calculating 21 crosscorrelations, namely at zero lag ± 10 lags of 2 s duration each. For each crosscorrelogram, in turn, the crosscorrelation with the highest absolute value was found and its sign, value, and lag were retained for further analysis. In addition, the crosscorrelations at zero lag (irrespective of the location of the peak) were also analyzed as a special case. Based on known varying density of anatomical connectivity, we distinguished four general brain groups for which we derived summary statistics of crosscorrelations between voxels within an area (group I), between voxels of paired homotopic areas across the two hemispheres (group II), between voxels of an area and all other voxels in the same (ipsilateral) hemisphere (group III), and voxels of an area and all voxels in the opposite (contralateral) hemisphere (except those in the homotopic area) (group IV). We found the following. (a) Most of the crosscorrelogram peaks occurred at zero lag, followed by ± 1 lag; (b) over all groups, positive crosscorrelations were much more frequent than negative ones; (c) average crosscorrelation was highest for group I, and decreased progressively for groups II-IV; (d) the ratio of positive over negative crosscorrelations was highest for group I and progressively smaller for groups II-IV; (e) the highest proportion of positive crosscorrelations (with respect to all positive ones) was observed at zero lag; and (f) the highest proportion of negative crosscorrelations (with respect to all negative ones) was observed at lag = 2. These findings reveal a systematic pattern of crosscorrelations with respect to their sign, magnitude, lag and brain group, as defined above. Given that these groups were defined along a qualitative gradient of known overall anatomical connectivity, our results suggest that functional interactions between two voxels may simply reflect the density of such anatomical connectivity between the areas to which the voxels belong.