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Context: Behavioral studies suggest that responses to food consumption are altered in children with obesity (OB)., Objective: To test central nervous system and peripheral hormone response by functional MRI and satiety-regulating hormone levels before and after a meal., Design and Setting: Cross-sectional study comparing children with OB and children of healthy weight (HW) recruited from across the Puget Sound region of Washington., Participants: Children (9 to 11 years old; OB, n = 54; HW, n = 22), matched for age and sex., Intervention and Outcome Measures: Neural activation to images of high- and low-calorie food and objects was evaluated across a set of a priori appetite-processing regions that included the ventral and dorsal striatum, amygdala, substantia nigra/ventral tegmental area, insula, and medial orbitofrontal cortex. Premeal and postmeal hormones (insulin, peptide YY, glucagon-like peptide-1, active ghrelin) were measured., Results: In response to a meal, average brain activation by high-calorie food cues vs objects in a priori regions was reduced after meals in children of HW (Z = -3.5, P < 0.0001), but not in children with OB (z = 0.28, P = 0.78) despite appropriate meal responses by gut hormones. Although premeal average brain activation by high-calorie food cues was lower in children with OB vs children of HW, postmeal activation was higher in children with OB (Z = -2.1, P = 0.04 and Z = 2.3, P = 0.02, respectively). An attenuated central response to a meal was associated with greater degree of insulin resistance., Conclusions: Our data suggest that children with OB exhibit an attenuated central, as opposed to gut hormone, response to a meal, which may predispose them to overconsumption of food or difficulty with weight loss., (Copyright © 2019 Endocrine Society.)

Background: Clinical stroke is prevalent in American Indians, but the lifestyle risk factors for vascular brain injury have not been well-studied in this population. The purpose of this study was to correlate brain magnetic resonance imaging (MRI) findings with obesity, alcohol use, and smoking behaviors in elderly American Indians from the Strong Heart Study., Methods: Cranial MRI scans (n = 789) were analyzed for dichotomous measures of infarcts, hemorrhages, white matter hyperintensities (WMH), and cerebral atrophy and continuous measures of total brain, WMH, and hippocampal volume. Poisson regression was used to estimate prevalence ratios, and linear regression was used to estimate measures of association for continuous outcomes. Models were adjusted for the risk factors of interest as well as age, sex, study site, income, education, hypertension, diabetes, and low-density lipoprotein cholesterol., Results: Smoking was associated with increased hippocampal atrophy (p = 0.002) and increased prevalence of sulcal widening (p < 0.001). Relative to nonsmokers, smokers with more than 25 pack-years of smoking had a 27% (95% CI 7-47%) increased prevalence of high-grade sulci, p = 0.005. Body mass index was inversely associated with prevalence of nonlacunar infarcts and sulcal widening (all p = 0.004). Alcohol use was not significantly associated with any of the measured MRI findings., Conclusions: This study found similar associations between smoking and vascular brain injury among American Indians, as seen in other populations. In particular, these findings support the role of smoking as a key correlate for cerebral atrophy., (© 2019 S. Karger AG, Basel.)

Background: Clinical stroke is prevalent in American Indians, but the risk factors for cerebrovascular pathology have not been well-studied in this population. The purpose of this study was to correlate abnormalities on brain magnetic resonance imaging (MRI) with clinical risk factors in a cohort of elderly American Indians., Methods: Brain MRI scans from 789 participants of the Strong Heart Study were analyzed for infarcts, hemorrhage, white matter disease, and measures of cerebral atrophy including ventricular and sulcal grade and total brain volume. Clinical risk factors included measures of hypertension, diabetes, and high levels of low-density lipoprotein (LDL) cholesterol. Regression models adjusted for potential confounders were used to estimate associations between risk factors and brain MRI outcomes., Results: -Hypertension was associated with the presence of infarcts (p = 0.001), ventricle enlargement (p = 0.01), and increased white matter hyperintensity volume (p = 0.01). Diabetes was associated with increased prevalence of cerebral atrophy (p < 0.001), ventricular enlargement (p = 0.001), and sulcal widening (p = 0.001). High LDL was not significantly associated with any of the measured cranial imaging outcomes., Conclusions: This study found risk factors for cerebrovascular disease in American Indians similar to those seen in other populations and provides additional evidence for the important roles of hypertension and diabetes in promoting cerebral infarcts and brain atrophy, respectively., (© 2019 S. Karger AG, Basel.)

OBJECTIVE The acquisition and refinement of cognitive and behavioral skills during development is associated with the maturation of various brain oscillatory activities. Most developmental investigations have identified distinct patterns of low-frequency electrophysiological activity that are characteristic of various behavioral milestones. In this investigation, the authors focused on the cross-sectional developmental properties of high-frequency spectral power from the brain's default mode network (DMN) during goal-directed behavior. METHODS The authors contrasted regionally specific, time-evolving high gamma power (HGP) in the lateral DMN cortex between 3 young children (age range 3-6 years) and 3 adults by use of electrocorticography (ECoG) recordings over the left perisylvian cortex during a picture-naming task. RESULTS Across all participants, a nearly identical and consistent response suppression of HGP, which is a functional signature of the DMN, was observed during task performance recordings acquired from ECoG electrodes placed over the lateral DMN cortex. This finding provides evidence of relatively early maturation of the DMN. Furthermore, only HGP relative to evoked alpha and beta band power showed this level of consistency across all participants. CONCLUSIONS Regionally specific, task-evoked suppression of the high-frequency components of the cortical power spectrum is established early in brain development, and this response may reflect the early maturation of specific cognitive and/or computational mechanisms.

Objective: To test the relationship of anxiety to caloric intake and food cue perception in women and men., Methods: Fifty-five twins (26 complete, 3 incomplete pairs; 51% women) underwent 2 functional magnetic resonance imaging (fMRI) scans (before and after a standardized meal) and then ate at an ad libitum buffet to objectively assess food intake. State and trait anxiety were assessed using the State-Trait Anxiety Inventory. During the fMRI scans, participants viewed blocks of fattening and nonfattening food images, and nonfood objects., Results: In women, higher trait anxiety was associated with a higher body mass index (BMI) (r = 0.40, p = .010). Trait anxiety was positively associated with kilocalories consumed at the buffet (r = 0.53, p = .005) and percent kilocalories consumed from fat (r = 0.30, p = .006), adjusted for BMI. In within-pair models, which control for shared familial and genetic factors, higher trait anxiety remained associated with kilocalories consumed at the buffet (p = .66, p = .014), but not with BMI. In men, higher state anxiety was related to macronutrient choices, but not to total caloric intake or BMI. FMRI results revealed that women with high trait anxiety did not suppress activation by fattening food cues across brain regions associated with satiety perception after eating a standardized meal (low anxiety, mean difference = -15.4, p < .001; high anxiety, mean difference = -1.53, p = .82, adjusted for BMI)., Conclusions: In women, trait anxiety may promote excess caloric consumption through altered perception of high-calorie environmental food cues, placing women with genetic predispositions toward weight gain at risk of obesity., Trial Registration: Clinicaltrials.govidentifier:NCT02483663.

Functional imaging investigations into the brain's resting state interactions have yielded a wealth of insight into the intrinsic and dynamic neural architecture supporting cognition and behavior. Electrophysiological studies however have highlighted the fact that synchrony across large-scale cortical systems is composed of spontaneous interactions occurring at timescales beyond the traditional resolution of fMRI, a feature that limits the capacity of fMRI to draw inference on the true directional relationship between network nodes. To approach the question of directionality in resting state signals, we recorded resting state functional MRI (rsfMRI) and electrocorticography (ECoG) from four human subjects undergoing invasive epilepsy monitoring. Using a seed-point based approach, we employed phase-amplitude coupling (PAC) and biPhase Locking Values (bPLV), two measures of cross-frequency coupling (CFC) to explore both outgoing and incoming connections between the seed and all non-seed, site electrodes. We observed robust PAC between a wide range of low-frequency phase and high frequency amplitude estimates. However, significant bPLV, a CFC measure of phase-phase synchrony, was only observed at specific narrow low and high frequency bandwidths. Furthermore, the spatial patterns of outgoing PAC connectivity were most closely associated with the rsfMRI connectivity maps. Our results support the hypothesis that PAC is relatively ubiquitous phenomenon serving as a mechanism for coordinating high-frequency amplitudes across distant neuronal assemblies even in absence of overt task structure. Additionally, we demonstrate that the spatial distribution of a seed-point rsfMRI sensorimotor network is strikingly similar to specific patterns of directional PAC. Specifically, the high frequency activities of distal patches of cortex owning membership in a rsfMRI sensorimotor network were most likely to be entrained to the phase of a low frequency rhythm engendered from the neural populations at the seed-point, suggestive of greater directional coupling from the seed out to the site electrodes., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Neural responses to highly energetic food cues are robust and are suppressed by eating. It is not known if neural responsiveness to food cues is an inherited trait and possibly even one that mediates the genetic influences on body weight that have been previously observed., Objective: We investigated the inherited influence on brain responses to high-calorie visual food cues before and after a meal., Design: With the use of a monozygotic twin study design, 21 healthy monozygotic twin pairs consumed a standardized breakfast and, 3.5 h later, underwent the first of 2 functional MRI (fMRI) scans with the use of visual food cues. After the first fMRI session, twins consumed a standardized meal, which was followed by the second fMRI. Serial ratings of appetite and food appeal were obtained. An ad libitum buffet was used to measure total caloric and macronutrient intakes. Intraclass correlations (ICCs) were used to test for inherited influences by comparing whether intrapair similarity was greater than interpair similarity., Results: Body mass index was highly correlated within twin pairs (ICC: 0.96; P < 0.0001). ICCs also showed a strong intrapair similarity for the meal-induced change in hunger (ICC: 0.41; P = 0.03), fullness (ICC: 0.39; P = 0.04), and the appeal of fattening food (ICC: 0.57; P < 0.001). Twins ate a similar number of kilocalories at the buffet (ICC: 0.43; P = 0.02). Before the meal, the global brain activation across regions involved in satiety processing was not more similar in twins than in unrelated individuals. However, significant ICCs were present after the meal (ICC: 0.39; P = 0.04) and for the meal-induced change in activation by high-calorie visual food cues (ICC: 0.52; P < 0.01)., Conclusion: Inherited factors influence both satiety perception and the effect of a meal to alter regional brain responses to images of highly energetic food. This trial was registered at clinicaltrials.gov as NCT02483663., (© 2016 American Society for Nutrition.)

Consistent spatial patterns of coherent activity, representing large-scale networks, have been reliably identified in multiple populations. Most often, these studies have examined "stationary" connectivity. However, there is a growing recognition that there is a wealth of information in the time-varying dynamics of networks which has neural underpinnings, which changes with age and disease and that supports behavior. Using factor analysis of overlapping sliding windows across 25 participants with Parkinson disease (PD) and 21 controls (ages 41-86), we identify factors describing the covarying correlations of regions (dynamic connectivity) within attention networks and the default mode network, during two baseline resting-state and task runs. Cortical regions that support attention networks are affected early in PD, motivating the potential utility of dynamic connectivity as a sensitive way to characterize physiological disruption to these networks. We show that measures of dynamic connectivity are more reliable than comparable measures of stationary connectivity. Factors in the dorsal attention network (DAN) and fronto-parietal task control network, obtained at rest, are consistently related to the alerting and orienting reaction time effects in the subsequent Attention Network Task. In addition, the same relationship between the same DAN factor and the alerting effect was present during tasks. Although reliable, dynamic connectivity was not invariant, and changes between factor scores across sessions were related to changes in accuracy. In summary, patterns of time-varying correlations among nodes in an intrinsic network have a stability that has functional relevance.

Correlations among low-frequency spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal reflect the connectivity of intrinsic large-scale networks in the brain. These correlations have typically been characterized over the entire timecourse (mean connectivity), but the mean correlations between regions vary dynamically. By focusing on the linear relationship between activity in network nodes within the default mode network (DMN), dorsal attention network (DAN), and fronto-parietal task control network (FPTC) captured by their inter-correlations, we demonstrate that this dynamic pattern of fluctuations reveals a detailed substructure, that this substructure is robust across individuals, and that the expression of specific factors is correlated with age. To do this, we conducted a chained P-technique factor analysis of the correlations in nonoverlapping temporal windows across N=145 normal aging subjects (age 56-89). The expression of factors within the DMN, FPTC, and DAN was highly correlated with age: Decreased intercorrelations within nodes in each factor were correlated with advanced age. Although these findings converge with those from stationary analysis, the ability to quantify age-related changes in the coherence of fluctuating connectivity may yield more insights into age-related cognitive decline.

Multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data is an emerging approach for probing the neural correlates of cognition. MVPA allows cognitive states to be modeled as distributed patterns of neural activity and classified according to stimulus conditions. In practice, building a robust, generalizable classification model can be challenging because the number of voxels (features) far exceeds the number of stimulus instances/data observations. To avoid model overfitting, there is a need to select informative voxels before building a classification model. In this paper, we propose a robust feature (voxel) selection framework using mutual information (MI) and partial least square regression (PLS) to establish an informativeness index for prioritizing selection of voxels based on the degree of their association to the experimental conditions. We evaluated the robustness of our proposed framework by assessing performance of standard classification algorithms, when combined with our feature selection approach, in a publicly-available fMRI dataset of object-level representation widely used to benchmark MVPA performance (Haxby, 2001). The computational results suggest that our feature selection framework based on MI and PLS drastically improves the classification accuracy relative to those previously reported in the literature. Our results also suggest that highly informative voxels may provide meaningful insight into the functional-anatomic relationship of brain activity and stimulus conditions.

Neuroscientific research has consistently found that the perception of an affective state in another activates the observer's own neural substrates for the corresponding state, which is likely the neural mechanism for "true empathy." However, to date there has not been a brain-imaging investigation of so-called "cognitive empathy", whereby one "actively projects oneself into the shoes of another person," imagining someone's personal, emotional experience as if it were one's own. In order to investigate this process, we conducted a combined psychophysiology and PET and study in which participants imagined: (1) a personal experience of fear or anger from their own past; (2) an equivalent experience from another person as if it were happening to them; and (3) a nonemotional experience from their own past. When participants could relate to the scenario of the other, they produced patterns of psychophysiological and neuroimaging activation equivalent to those of personal emotional imagery, but when they could not relate to the other's story, differences emerged on all measures, e.g., decreased psychophysiological responses and recruitment of a region between the inferior temporal and fusiform gyri. The substrates of cognitive empathy overlap with those of personal feeling states to the extent that one can relate to the state and situation of the other.

Model quality is rarely assessed in fMRI data analyses and less often reported. This may have contributed to several shortcomings in the current fMRI data analyses, including: (1) Model mis-specification, leading to incorrect inference about the activation-maps, SPM[t] and SPM[F]; (2) Improper model selection based on the number of activated voxels, rather than on model quality; (3) Under-utilization of systematic model building, resulting in the common but suboptimal practice of using only a single, pre-specified, usually over-simplified model; (4) Spatially homogenous modeling, neglecting the spatial heterogeneity of fMRI signal fluctuations; and (5) Lack of standards for formal model comparison, contributing to the high variability of fMRI results across studies and centers. To overcome these shortcomings, it is essential to assess and report the quality of the models used in the analysis. In this study, we applied images of the Durbin-Watson statistic (DW-map) and the coefficient of multiple determination (R(2)-map) as complementary tools to assess the validity as well as goodness of fit, i.e., quality, of models in fMRI data analysis. Higher quality models were built upon reduced models using classic model building. While inclusion of an appropriate variable in the model improved the quality of the model, inclusion of an inappropriate variable, i.e., model mis-specification, adversely affected it. Higher quality models, however, occasionally decreased the number of activated voxels, whereas lower quality or inappropriate models occasionally increased the number of activated voxels, indicating that the conventional approach to fMRI data analysis may yield sub-optimal or incorrect results. We propose that model quality maps become part of a broader package of maps for quality assessment in fMRI, facilitating validation, optimization, and standardization of fMRI result across studies and centers. Hum. Brain Mapping 20:227-238, 2003., (Copyright 2003 Wiley-Liss, Inc.)

A cross-cohort PET analysis was performed in 62 normal subjects (31 men and 31 women) to address the issue of whether men and women have different physiologic correlates of naming visually presented concrete entities. The subjects named nonunique concrete entities in one or more conceptual categories and also performed a face orientation decision task. A second analysis was performed in 24 additional subjects to assess whether there were gender effects related to the face orientation decision task and to constrain the interpretation of the first analysis. Male subjects engaged the left inferotemporal region and several other left hemisphere regions more than female subjects did during visual naming. Areas showing more activity in female subjects included the right inferior frontal gyrus and right precentral cortex, regions that were less active in visual naming than in the face orientation decision task. In other words, the male subjects engaged the latter regions less or deactivated them more than female subjects. The results can be interpreted as showing a greater modulation of activity in both hemispheres for men compared to women. Although the gender effects we found are smaller than the task effects, they are not negligible for the purposes of performing and interpreting functional imaging studies.

Normative data on the in vivo size of the human brain and its major anatomically defined subdivisions are not readily available. In this study, high-resolution magnetic resonance imaging was used to measure regional brain volumes in 46 normal, right-handed adults (23 men, 23 women) between the ages of 22-49 years. Parcellation of the brain was based on neuroanatomical landmarks. The following brain regions were measured: the cerebral hemispheres, frontal lobe, temporal lobe, parietal lobe, occipital lobe, cingulate gyrus, insula, cerebellum, corpus callosum, and lateral ventricles. Males tend to be significantly larger than females, for the whole brain and for nearly all of its major subdivisions, including the corpus callosum. However, the proportional sizes of regions relative to total volume of the hemisphere are remarkably similar in males and females. Variation in size of region is always greater than variation in proportional representation. Asymmetries in brain regions are not profound, with the exception of the cingulate gyrus, which is larger in the left hemisphere. Brain regions are highly correlated in size, with the exception of the lateral ventricles. After controlling for hemisphere size, the volumes of the frontal and parietal lobes are significantly negatively correlated. The occipital lobe tends to be less sexually dimorphic than other major lobes, and less correlated with other brain regions for volume. These results have implications for understanding whether or not certain sectors of the brain have shown relative expansion over the course of hominid and hominoid evolution., (Copyright 2002 Wiley-Liss, Inc.)

Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance. [ABSTRACT FROM AUTHOR]

We investigated the functional organisation of neural systems supporting language production when the primary language articulators are also used for meaningful, but nonlinguistic, expression such as pantomime. Fourteen hearing nonsigners and 10 deaf native users of American Sign Language (ASL) participated in an H2 15O-positron emission tomography study in which they generated action pantomimes or ASL verbs in response to pictures of tools and manipulable objects. For pantomime generation, participants were instructed to 'show how you would use the object'. For verb generation, signers were asked to 'generate a verb related to the object'. The objects for this condition were selected to elicit handling verbs that resemble pantomime (e.g., TO-HAMMER, hand configuration and movement mimic the act of hammering) and nonhandling verbs that do not (e.g., POUR-SYRUP, produced with a 'Y' handshape). For the baseline task, participants viewed pictures of manipulable objects and an occasional nonmanipulable object and decided whether the objects could be handled, gesturing 'yes' (thumbs up) or 'no' (hand wave). Relative to baseline, generation of ASL verbs engaged left inferior frontal cortex, but when nonsigners produced pantomimes for the same objects, no frontal activation was observed. Both groups recruited left parietal cortex during pantomime production. However, for deaf signers the activation was more extensive and bilateral, which may reflect a more complex and integrated neural representation of hand actions. We conclude that the production of pantomime vs. ASL verbs (even those that resemble pantomime) engages partially segregated neural systems that support praxic vs. linguistic functions. [ABSTRACT FROM AUTHOR]

Using high resolution MRI scans and automated tissue segmentation, gray and white matter (GM, WM) volumes of the frontal, temporal, parietal, and occipital lobes, cingulate gyrus, and insula were calculated. Subjects included 23 male and 23 female healthy, right-handed subjects. For all structures, male volumes were greater than female, but the gray/white (G/W) ratio was consistently higher across structures in women than men. Sexual dimorphism was greater for WM than GM: most of the G/W ratio sex differences can be attributed to variation in WM volume. The corpus callosum, although larger in men, is less sexually dimorphic than the WM as a whole. Several regions demonstrate pair-wise asymmetries in G/W ratio and WM volume. Both the cingulate gyrus and insula exhibit strong asymmetries. The left cingulate gyrus is significantly larger than the right, and the G/W ratio of the left insula is significantly greater than that of the right. Although statistically significant sex differences and asymmetries are present at this level of analysis, we argue that researchers should be wary of ascribing cognitive functional significance to these patterns at this time. This is not to say, however, that these patterns are not important for understanding the natural history of the human brain, and its evolution and development. [Copyright &y& Elsevier]

The neural correlates of naming concrete entities such as tools (with nouns) and naming actions (with verbs) are partially distinct: the former are linked to the left inferotemporal (IT) region, whereas the latter are linked to the left frontal opercular (FO) and left posterior middle temporal (MT) regions. This raises an intriguing question: How would such neural patterns be influenced by noun-verb homonymy, specifically, naming tasks in which the target words denote objects or actions (e.g., "comb")? To explore this, we conducted a PET study in which 10 normal participants named visually presented tools or actions. The factor of homonymy yielded interesting effects: For tools, non-homonymous nouns (e.g., "camera") activated left IT, whereas homonymous nouns (e.g., "comb") activated both left IT and left FO. For actions, non-homonymous (e.g., "juggle") and homonymous (e.g., "comb") verbs activated left FO, MT, and IT, but there was evidence that the FO and MT activations were less widespread for the homonymous verbs. We also found that retrieval of the same exact words (e.g., "comb" and "comb") produced differential activation in left MT-there was greater MT activation when the words were being used to name actions, than when they were being used to name tools. Our results suggest that noun-verb homonymy has an important influence on the patterns of neural activation associated with words denoting objects and actions, and that even when the phonological forms are identical, the patterns of neural activation are different according to the demands of the task. [ABSTRACT FROM AUTHOR]