Perceptual and Semantic Phases of Face Identification Processing: A Multivariate Electroencephalography Study.

Rapid identification of a familiar face requires an image-invariant representation of person identity. A varying sample of familiar faces is necessary to disentangle image-level from person-level processing. We investigated the time course of face identity processing using a multivariate electroencephalography analysis. Participants saw ambient exemplars of celebrity faces that differed in pose, lighting, hairstyle, and so forth. A name prime preceded a face on half of the trials to preactivate person-specific information, whereas a neutral prime was used on the remaining half. This manipulation helped dissociate perceptual- and semantic-based identification. Two time intervals within the post-face onset electroencephalography epoch were sensitive to person identity. The early perceptual phase spanned 110-228 msec and was not modulated by the name prime. The late semantic phase spanned 252-1000 msec and was sensitive to person knowledge activated by the name prime. Within this late phase, the identity response occurred earlier in time (300-600 msec) for the name prime with a scalp topography similar to the FN400 ERP. This may reflect a matching of the person primed in memory with the face on the screen. Following a neutral prime, the identity response occurred later in time (500-800 msec) with a scalp topography similar to the P600f ERP. This may reflect activation of semantic knowledge associated with the identity. Our results suggest that processing of identity begins early (110 msec), with some tolerance to image-level variations, and then progresses in stages sensitive to perceptual and then to semantic features.

Category representations in the brain are both discretely localized and widely distributed.

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

A multivariate distance-based analytic framework for connectome-wide association studies.

The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.

Reduced functional integration and segregation of distributed neural systems underlying social and emotional information processing in autism spectrum disorders.

A growing body of evidence suggests that autism spectrum disorders (ASDs) are related to altered communication between brain regions. Here, we present findings showing that ASD is characterized by a pattern of reduced functional integration as well as reduced segregation of large-scale brain networks. Twenty-three children with ASD and 25 typically developing matched controls underwent functional magnetic resonance imaging while passively viewing emotional face expressions. We examined whole-brain functional connectivity of two brain structures previously implicated in emotional face processing in autism: the amygdala bilaterally and the right pars opercularis of the inferior frontal gyrus (rIFGpo). In the ASD group, we observed reduced functional integration (i.e., less long-range connectivity) between amygdala and secondary visual areas, as well as reduced segregation between amygdala and dorsolateral prefrontal cortex. For the rIFGpo seed, we observed reduced functional integration with parietal cortex and increased integration with right frontal cortex as well as right nucleus accumbens. Finally, we observed reduced segregation between rIFGpo and the ventromedial prefrontal cortex. We propose that a systems-level approach-whereby the integration and segregation of large-scale brain networks in ASD is examined in relation to typical development-may provide a more detailed characterization of the neural basis of ASD.

Interaction of COMT val158met and externalizing behavior: relation to prefrontal brain activity and behavioral performance.

A promising approach in neuroimaging studies aimed at understanding effects of single genetic variants on behavior is the study of gene-trait interactions. Variation in the catechol-O-methyl-transferase gene (COMT) is associated with the regulation of dopamine levels in the prefrontal cortex and with cognitive functioning. Given the involvement of dopaminergic neurotransmission in externalizing behavior, a trait characterized by impulsivity and aggression, especially in men, externalizing (as a trait) may index a set of genetic, environmental, and neural characteristics pertinent to understanding phenotypic effects of genetic variation in the COMT gene. In the current study, we used a gene-trait approach to investigate effects of the COMT val(158)met polymorphism and externalizing on brain activity during moments involving low or high demands on cognitive control. In 104 male participants, interference-related activation depended conjointly on externalizing and val(158)met: stronger activation in the dorsal anterior cingulate and lateral prefrontal cortex was found for val/val individuals with high trait externalizing while stronger activation in cingulate motor areas and sensorimotor precuneus was found for met/met individuals with low externalizing. Our results suggest that the val/val genotype, coupled with high levels of trait externalizing, lowers the efficiency of stimulus conflict resolution, whereas the met/met genotype, coupled with low levels of externalizing, lowers the efficiency of response selection.

Low frequency fluctuations reveal integrated and segregated processing among the cerebral hemispheres.

Resting-state functional magnetic resonance imaging (fMRI) has provided a novel approach for examining interhemispheric interaction, demonstrating a high degree of functional connectivity between homotopic regions in opposite hemispheres. However, heterotopic resting-state functional connectivity (RSFC) remains relatively uncharacterized. In the present study, we examine non-homotopic regions, characterizing heterotopic RSFC and comparing it to intrahemispheric RSFC, to examine the impact of hemispheric separation on the integration and segregation of processing in the brain. Resting-state fMRI scans were acquired from 59 healthy participants to examine inter-regional correlations in spontaneous low frequency fluctuations in BOLD signal. Using a probabilistic atlas, we correlated probability-weighted time series from 112 regions (56 per hemisphere) distributed throughout the entire cerebrum. We compared RSFC for pairings of non-homologous regions located in different hemispheres (heterotopic connectivity) to RSFC for the same pairings when located within hemisphere (intrahemispheric connectivity). For positive connections, connectivity strength was greater within each hemisphere, consistent with integrated intrahemispheric processing. However, for negative connections, RSFC strength was greater between the hemispheres, consistent with segregated interhemispheric processing. These patterns were particularly notable for connections involving frontal and heteromodal regions. The distribution of positive and negative connectivity was nearly identical within and between the hemispheres, though we demonstrated detailed regional variation in distribution. We discuss implications for leading models of interhemispheric interaction. The future application of our analyses may provide important insight into impaired interhemispheric processing in clinical and aging populations.

Reading networks at rest.

Resting-state functional connectivity (RSFC) approaches offer a novel tool to delineate distinct functional networks in the brain. In the present functional magnetic resonance imaging (fMRI) study, we elucidated patterns of RSFC associated with 6 regions of interest selected primarily from a meta-analysis on word reading (Bolger DJ, Perfetti CA, Schneider W. 2005. Cross-cultural effect on the brain revisited: universal structures plus writing system variation. Hum Brain Mapp. 25: 92-104). In 25 native adult readers of English, patterns of positive RSFC were consistent with patterns of task-based activity and functional connectivity associated with word reading. Moreover, conjunction analyses highlighted the posterior left inferior frontal gyrus and the posterior left middle temporal gyrus (post-LMTG) as potentially important loci of functional interaction among 5 of the 6 reading networks. The significance of the post-LMTG has typically been unappreciated in task-based studies on unimpaired readers but is frequently reported to be a locus of hypoactivity in dyslexic readers and exhibits intervention-induced changes of activity in dyslexic children. Finally, patterns of negative RSFC included not only regions of the so-called default mode network but also regions involved in effortful controlled processes, which may not be required once reading becomes automatized. In conclusion, the current study supports the utility of resting-state fMRI for investigating reading networks and has direct relevance for the understanding of reading disorders such as dyslexia.

The oscillating brain: complex and reliable.

The human brain is a complex dynamic system capable of generating a multitude of oscillatory waves in support of brain function. Using fMRI, we examined the amplitude of spontaneous low-frequency oscillations (LFO) observed in the human resting brain and the test-retest reliability of relevant amplitude measures. We confirmed prior reports that gray matter exhibits higher LFO amplitude than white matter. Within gray matter, the largest amplitudes appeared along mid-brain structures associated with the "default-mode" network. Additionally, we found that high-amplitude LFO activity in specific brain regions was reliable across time. Furthermore, parcellation-based results revealed significant and highly reliable ranking orders of LFO amplitudes among anatomical parcellation units. Detailed examination of individual low frequency bands showed distinct spatial profiles. Intriguingly, LFO amplitudes in the slow-4 (0.027-0.073 Hz) band, as defined by Buzsáki et al., were most robust in the basal ganglia, as has been found in spontaneous electrophysiological recordings in the awake rat. These results suggest that amplitude measures of LFO can contribute to further between-group characterization of existing and future "resting-state" fMRI datasets.

Broca's region: linking human brain functional connectivity data and non-human primate tracing anatomy studies.

Brodmann areas 6, 44 and 45 in the ventrolateral frontal cortex of the left hemisphere of the human brain constitute the anterior language production zone. The anatomical connectivity of these areas with parietal and temporal cortical regions was recently examined in an autoradiographic tract-tracing study in the macaque monkey. Studies suggest strong correspondence between human resting state functional connectivity (RSFC) based on functional magnetic resonance imaging data and experimentally demonstrated anatomical connections in non-human primates. Accordingly, we hypothesized that areas 6, 44 and 45 of the human brain would exhibit patterns of RSFC consistent with patterns of anatomical connectivity observed in the macaque. In a primary analysis, we examined the RSFC associated with regions-of-interest placed in ventrolateral frontal areas 6, 44 and 45, on the basis of local sulcal and gyral anatomy. We validated the results of the primary hypothesis-driven analysis with a data-driven partitioning of ventrolateral frontal cortex into regions exhibiting distinct RSFC patterns, using a spectral clustering algorithm. The RSFC of ventrolateral frontal areas 6, 44 and 45 was consistent with patterns of anatomical connectivity shown in the macaque. We observed a striking dissociation between RSFC for the ventral part of area 6 that is involved in orofacial motor control and RSFC associated with Broca's region (areas 44 and 45). These findings indicate rich and differential RSFC patterns for the ventrolateral frontal areas controlling language production, consistent with known anatomical connectivity in the macaque brain, and suggest conservation of connectivity during the evolution of the primate brain.

On distance-based permutation tests for between-group comparisons.

Permutation tests based on distances among multivariate observations have found many applications in the biological sciences. Two major testing frameworks of this kind are multiresponse permutation procedures and pseudo-F tests arising from a distance-based extension of multivariate analysis of variance. In this article, we derive conditions under which these two frameworks are equivalent. The methods and equivalence results are illustrated by reanalyzing an ecological data set and by a novel application to functional magnetic resonance imaging data.

Development of anterior cingulate functional connectivity from late childhood to early adulthood.

Human cerebral development is remarkably protracted. Although microstructural processes of neuronal maturation remain accessible only to morphometric post-mortem studies, neuroimaging tools permit the examination of macrostructural aspects of brain development. The analysis of resting-state functional connectivity (FC) offers novel possibilities for the investigation of cerebral development. Using seed-based FC methods, we examined the development of 5 functionally distinct cingulate-based intrinsic connectivity networks (ICNs) in children (n = 14, 10.6 +/- 1.5 years), adolescents (n = 12, 15.4 +/- 1.2) and young adults (n=14, 22.4 +/- 1.2). Children demonstrated a more diffuse pattern of correlation with voxels proximal to the seed region of interest (ROI) ("local FC"), whereas adults exhibited more focal patterns of FC, as well as a greater number of significantly correlated voxels at long distances from the seed ROI. Adolescents exhibited intermediate patterns of FC. Consistent with evidence for different maturational time courses, ICNs associated with social and emotional functions exhibited the greatest developmental effects. Our findings demonstrate the utility of FC for the study of developing functional organization. Moreover, given that ICNs are thought to have an anatomical basis in neuronal connectivity, measures of FC may provide a quantitative index of brain maturation in healthy subjects and those with neurodevelopmental disorders.

The resting brain: unconstrained yet reliable.

Recent years have witnessed an upsurge in the usage of resting-state functional magnetic resonance imaging (fMRI) to examine functional connectivity (fcMRI), both in normal and pathological populations. Despite this increasing popularity, concerns about the psychologically unconstrained nature of the "resting-state" remain. Across studies, the patterns of functional connectivity detected are remarkably consistent. However, the test-retest reliability for measures of resting state fcMRI measures has not been determined. Here, we quantify the test-retest reliability, using resting scans from 26 participants at 3 different time points. Specifically, we assessed intersession (>5 months apart), intrasession (<1 h apart), and multiscan (across all 3 scans) reliability and consistency for both region-of-interest and voxel-wise analyses. For both approaches, we observed modest to high reliability across connections, dependent upon 3 predictive factors: 1) correlation significance (significantly nonzero > nonsignificant), 2) correlation valence (positive > negative), and 3) network membership (default mode > task positive network). Short- and long-term measures of the consistency of global connectivity patterns were highly robust. Finally, hierarchical clustering solutions were highly reproducible, both across participants and sessions. Our findings provide a solid foundation for continued examination of resting state fcMRI in typical and atypical populations.

Food Folio by Columbia Center for Eating Disorders: A Freely Available Food Image Database.

Food images are useful stimuli for the study of cognitive processes as well as eating behavior. To enhance rigor and reproducibility in task-based research, it is advantageous to have stimulus sets that are publicly available and well characterized. Food Folio by Columbia Center for Eating Disorders is a publicly available set of 138 images of Western food items. The set was developed for the study of eating disorders, particularly for use in tasks that capture eating behavior characteristic of these illnesses. It contains foods that are typically eaten, as well as those typically avoided, by individuals with eating disorders. Each image has now been rated across 17 different attributes by a large general United States population sample via Amazon's Mechanical Turk (n = 1054). Ratings included subjective attributes (e.g., tastiness, healthiness, and favorable texture) as well as estimates of nutrient content (e.g., fat and carbohydrate). Each participant rated a subset of stimulus set food items (46 foods) on all 17 dimensions. Additional description of the image set is provided in terms of physical image information and accurate nutritional information. Correlations between subjective ratings were calculated and an exploratory factor analysis and exploratory cluster analysis completed. Outcomes of the factor analysis suggested foods may be described along three latent factors of healthiness, tastiness, and umami taste; the cluster analysis highlighted five distinct clusters of foods varying on these same dimensions. Descriptive outcomes indicated that the stimulus set includes a range of foods that vary along multiple dimensions and thus is likely to be useful in addressing various research questions surrounding eating behavior and cognition in healthy populations, as well as in those with eating disorders. The provision of comprehensive descriptive information allows for stimulus selection that is optimized for a given research question and promotes strong inference.

Regional variation in interhemispheric coordination of intrinsic hemodynamic fluctuations.

Electrophysiological studies have long demonstrated a high degree of correlated activity between the left and right hemispheres, however little is known about regional variation in this interhemispheric coordination. Whereas cognitive models and neuroanatomical evidence suggest differences in coordination across primary sensory-motor cortices versus higher-order association areas, these have not been characterized. Here, we used resting-state functional magnetic resonance imaging data acquired from 62 healthy volunteers to examine interregional correlation in spontaneous low-frequency hemodynamic fluctuations. Using a probabilistic atlas, we correlated probability-weighted time series from 112 regions comprising the entire cerebrum. We then examined regional variation in correlated activity between homotopic regions, contrasting primary sensory-motor cortices, unimodal association areas, and heteromodal association areas. Consistent with previous studies, robustly correlated spontaneous activity was noted between all homotopic regions, which was significantly higher than that between nonhomotopic (heterotopic and intrahemispheric) regions. We further demonstrated substantial regional variation in homotopic interhemispheric correlations that was highly consistent across subjects. Specifically, there was a gradient of interhemispheric correlation, with highest correlations across primary sensory-motor cortices (0.758, SD=0.152), significantly lower correlations across unimodal association areas (0.597, SD=0.230) and still lower correlations across heteromodal association areas (0.517, SD=0.226). These results demonstrate functional differences in interhemispheric coordination related to the brain's hierarchical subdivisions. Synchrony across primary cortices may reflect networks engaged in bilateral sensory integration and motor coordination, whereas lower coordination across heteromodal association areas is consistent with functional lateralization of these regions. This novel method of examining interhemispheric coordination may yield insights regarding diverse disease processes as well as healthy development.

Functional connectivity of the human amygdala using resting state fMRI.

The amygdala is composed of structurally and functionally distinct nuclei that contribute to the processing of emotion through interactions with other subcortical and cortical structures. While these circuits have been studied extensively in animals, human neuroimaging investigations of amygdala-based networks have typically considered the amygdala as a single structure, which likely masks contributions of individual amygdala subdivisions. The present study uses resting state functional magnetic resonance imaging (fMRI) to test whether distinct functional connectivity patterns, like those observed in animal studies, can be detected across three amygdala subdivisions: laterobasal, centromedial, and superficial. In a sample of 65 healthy adults, voxelwise regression analyses demonstrated positively-predicted ventral and negatively-predicted dorsal networks associated with the total amygdala, consistent with previous animal and human studies. Investigation of individual amygdala subdivisions revealed distinct differences in connectivity patterns within the amygdala and throughout the brain. Spontaneous activity in the laterobasal subdivision predicted activity in temporal and frontal regions, while activity in the centromedial nuclei predicted activity primarily in striatum. Activity in the superficial subdivision positively predicted activity throughout the limbic lobe. These findings suggest that resting state fMRI can be used to investigate human amygdala networks at a greater level of detail than previously appreciated, allowing for the further advancement of translational models.

Residual functional connectivity in the split-brain revealed with resting-state functional MRI.

Split-brain patients present a unique opportunity to address controversies regarding subcortical contributions to interhemispheric coordination. We characterized residual functional connectivity in a complete commissurotomy patient by examining patterns of low-frequency BOLD functional MRI signal. Using independent components analysis and region-of-interest-based functional connectivity analyses, we demonstrate bilateral resting state networks in a patient lacking all major cerebral commissures. Compared with a control group, the patient's interhemispheric correlation scores fell within the normal range for two out of three regions examined. Thus, we provide evidence for bilateral resting state networks in a patient with complete commissurotomy. Such continued interhemispheric interaction suggests that, at least in part, cortical networks in the brain can be coordinated by subcortical mechanisms.

Network homogeneity reveals decreased integrity of default-mode network in ADHD.

Examination of spontaneous intrinsic brain activity is drawing increasing interest, thus methods for such analyses are rapidly evolving. Here we describe a novel measure, "network homogeneity", that allows for assessment of cohesiveness within a specified functional network, and apply it to resting-state fMRI data from adult ADHD and control participants. We examined the default mode network, a medial-wall based network characterized by high baseline activity that decreases during attention-demanding cognitive tasks. We found reduced network homogeneity within the default mode network in ADHD subjects compared to age-matched controls, particularly between the precuneus and other default mode network regions. This confirms previously published results using seed-based functional connectivity measures, and provides further evidence that altered precuneus connectivity is involved in the neuropathology of ADHD. Network homogeneity provides a potential alternative method for assessing functional connectivity of specific large-scale networks in clinical populations.

Differential contributions of the middle frontal gyrus functional connectivity to literacy and numeracy.

Literacy and numeracy equally affect an individual's success in and beyond schools, but these two competencies tend to be separately examined, particularly in neuroimaging studies. The current resting-state fMRI study examined the neural correlates of literacy and numeracy in the same sample of healthy adults. We first used an exploratory "Multivariate Distance Matrix Regression" (MDMR) approach to examine intrinsic functional connectivity (iFC), highlighting the middle frontal gyrus (MFG) for both competencies. Notably, there was a hemispheric asymmetry in the MDMR-based MFG findings, with literacy associated with the left MFG, whereas numeracy associated with the right MFG (R.MFG). Results of post-hoc seed-based correlation analyses further strengthened differential contributions of MFG connections to each competency. One of the most striking and novel findings from the present work was that numeracy was negatively related to R.MFG connections with the default network, which has been largely overlooked in the literature. Our results are largely consistent with prior neuroimaging work showing distinct neural mechanisms underlying literacy and numeracy, and also indicate potentially common iFC profiles to both competencies (e.g., R.MFG with cerebellum). Taken together, our iFC findings have a potential to provide novel insights into neural bases of literacy, numeracy, and impairments in these competencies.

Electrophysiological correlates of face-evoked person knowledge.

Face recognition includes identifying a face as perceptually familiar and recollecting biographical information, or person-knowledge, associated with the face. The majority of studies examining the neural basis of face recognition have confounded these stages by comparing brain responses evoked by novel and perceptually familiar famous faces. Here, we recorded EEG in two tasks in which subjects viewed two sets of faces that were equally perceptually familiar, but which had differing levels of associated person-knowledge. Our results dissociated the effects of person-knowledge from perceptual familiarity. Faces with associated biographical information elicited a larger ∼600ms centroparietal positivity in both a passive viewing task in which subjects viewed faces without explicitly responding, and an active question-answering task in which subjects indicated whether or not they knew particular facts about the faces. In the question task only, person-knowledge was associated with a negative ERP difference over right posterior scalp over the 170-450ms interval which appeared again at long latency (>900ms).