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Background: Neuroimaging studies suggest that appetitive drive is enhanced in obesity., Objective: To test if appetitive drive varies in direct proportion to the level of body adiposity after accounting for genetic factors that contribute to both brain response and obesity risk., Subjects/methods: Participants were adult monozygotic (n = 54) and dizygotic (n = 30) twins with at least one member of the pair with obesity. Body composition was assessed by dual-energy X-ray absorptiometry. Hormonal and appetite measures were obtained in response to a standardized meal that provided 20% of estimated daily caloric needs and to an ad libitum buffet meal. Pre- and post-meal functional magnetic resonance imaging (fMRI) assessed brain response to visual food cues in a set of a priori appetite-regulating regions. Exploratory voxelwise analyses outside a priori regions were performed with correction for multiple comparisons., Results: In a group of 84 adults, the majority with obesity (75%), body fat mass was not associated with hormonal responses to a meal (glucose, insulin, glucagon-like peptide-1 and ghrelin, all P>0.40), subjective feelings of hunger (β=-0.01 mm [95% CI -0.35, 0.34] P = 0.97) and fullness (β=0.15 mm [-0.15, 0.44] P = 0.33), or buffet meal intake in relation to estimated daily caloric needs (β=0.28% [-0.05, 0.60] P = 0.10). Body fat mass was also not associated with brain response to high-calorie food cues in appetite-regulating regions (Pre-meal β=-0.12 [-0.32, 0.09] P = 0.26; Post-meal β=0.18 [-0.02, 0.37] P = 0.09; Change by a meal β=0.29 [-0.02, 0.61] P = 0.07). Conversely, lower fat mass was associated with being weight reduced (β=-0.05% [-0.07, -0.03] P<0.001) and greater pre-meal activation to high-calorie food cues in the dorsolateral prefrontal cortex (Z = 3.63 P = 0.017)., Conclusions: In a large study of adult twins, the majority with overweight or obesity, the level of adiposity was not associated with excess appetitive drive as assessed by behavioral, hormonal, or fMRI measures., (Copyright © 2021. Published by Elsevier Inc.)

Here, we present unprocessed and preprocessed Attention Network Test data from 25 adults with Parkinson's disease and 21 healthy adults, along with the associated defaced structural scans. The preprocessed data has been processed with a provided Analysis of Functional NeuroImages afni&#95;proc.py script and includes structural scans that were skull-stripped before defacing. All acquired demographic and neuropsychological data are included., Competing Interests: No competing interests were disclosed., (Copyright: © 2019 Day TKM et al.)

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.)

We develop a two-stage spatial point process model that introduces new characterizations of activation patterns in multisubject functional Magnetic Resonance Imaging (fMRI) studies. Conventionally multisubject fMRI methods rely on combining information across subjects one voxel at a time in order to identify locations of peak activation in the brain. The two-stage model that we develop here addresses shortcomings of standard methods by explicitly modeling the spatial structure of functional signals and recognizing that corresponding cross-subject functional signals can be spatially misaligned. In our first stage analysis, we introduce a marked spatial point process model that captures the spatial features of the functional response and identifies a configuration of activation units for each subject. The locations of these activation units are used as input for the second stage model. The point process model of the second stage analysis is developed to characterize multisubject activation patterns by estimating the strength of cross-subject interactions at different spatial ranges. The model uses spatial neighborhoods to account for the cross-subject spatial misalignment in corresponding functional units. We applied our methods to an fMRI study of 21 individuals who performed an attention test. We identified four brain regions that are involved in the test and found that our model results agree well with our understanding of how these regions engage with the tasks performed during the attention test. Our results highlighted that cross-subject interactions are stronger in brain areas that have a more specific function in performing the experimental tasks than in other areas., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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.

Mitochondrial dysfunction represents a central factor within the pathogenesis of the Alzheimer's disease (AD) spectrum. We hypothesized that in vivo measurements of lactate (lac), a by-product of glycolysis, would correlate with functional impairment and measures of brain health in a cohort of 15 amnestic mild cognitive impairment (aMCI) individuals. Lac was quantified from the precuneus/posterior cingulate (PPC) using 2-dimensional J-resolved magnetic resonance spectroscopy (MRS). Additionally, standard behavioral and imaging markers of aMCI disease progression were acquired. PPC lac was negatively correlated with performance on the Wechsler logical memory tests and on the minimental state examination even after accounting for gray matter, cerebral spinal fluid volume, and age. No such relationships were observed between lac and performance on nonmemory tests. Significant negative relationships were also noted between PPC lac and hippocampal volume and PPC functional connectivity. Together, these results reveal that aMCI individuals with a greater disease progression have increased concentrations of PPC lac. Because lac is upregulated as a compensatory response to mitochondrial impairment, we propose that J-resolved MRS of lac is a noninvasive, surrogate biomarker of impaired metabolic function and would provide a useful means of tracking mitochondrial function during therapeutic trials targeting brain metabolism.

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.

We report the development of an interactive approach to single-word language production studies in fMRI. The approach, adaptive pacing, involves real-time adjustment of stimulus presentation times based on individual subject performance timing and content. At the same time, it maintains a stochastic distribution of interstimulus intervals to avoid confounding task covariates with speech-related signal variance. Adaptive pacing of overt speech production is an example of a new class of paradigms that require an observational approach to data acquisition and benefit from a "time-aware" acquisition and processing environment. The advantages of adaptive pacing in fMRI of impaired subjects are expected to be the acquisition of more informative data per unit time, less contamination of data by correlates of non-language processes such as emotion, and facilitation of experiments that combine normal and impaired subjects.

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.)

Voxel-based morphometry (VBM) is an automated statistical technique used to detect regional differences in tissue density and tissue amount based on spatially standardized structural magnetic resonance (MR) images. Developed initially to discern differences between groups of subjects, VBM is now being used to characterize structural abnormalities in individual brains. While VBM performance has been qualitatively assessed for this purpose, to date no quantitative validation study has been performed. This study evaluated several commonly used variants of VBM for detecting structural differences at the individual level by assessing their performance in MR images of 10 subjects with stable focal brain lesions. Results were quantitatively compared to expert tracings of the lesions, the current gold standard for lesion detection and delineation. Additionally, analyses using two sets of simulated lesion data were performed to examine the relative impact of the underlying processing steps on VBM results. Performance metrics revealed that (1) for this application, VBM had low sensitivity; (2) detection sensitivity was altered by model parameterization; (3) underperformance was due to the adverse influence of lesions on the preprocessing steps and to insufficient statistical power; and (4) VBM could not satisfactorily delineate the spatial extent of lesions, even in simulations that avoided preprocessing artifacts. In its current form, VBM is not a suitable stand-alone technique for detecting or spatially characterizing focal lesions.

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.

Alterations to interactions between networked brain regions underlie cognitive impairment in many neurodegenerative diseases, providing an important physiological link between brain structure and cognitive function. Previous attempts to characterize the effects of Parkinson's disease (PD) on network functioning using resting-state functional magnetic resonance imaging (rs-fMRI), however, have yielded inconsistent and contradictory results. Potential problems with prior work arise in the specifics of how the area targeted by the diseases (the basal ganglia) interacts with other brain regions. Specifically, current computational models point to the fact that the basal ganglia contributions should be captured with modulatory (i.e., second-order) rather than direct (i.e., first-order) functional connectivity measures. Following this hypothesis, a principled but manageable large-scale brain architecture, the Common Model of Cognition, was used to identify differences in basal ganglia connectivity in PD by analyzing resting-state fMRI data from 111 participants (70 patients with PD; 41 healthy controls) using Dynamic Causal Modeling (DCM). Specifically, the functional connectivity of the basal ganglia was modeled as two second-level, modulatory connections that control projections from sensory cortices to the prefrontal cortex, and from the hippocampus and medial temporal lobe to the prefrontal cortex. We then examined group differences between patients with PD and healthy controls in estimated modulatory effective connectivity in these connections. The Modulatory variant of the Common Model of Cognition outperformed the Direct model across all subjects. It was also found that these second-level modulatory connections had higher estimates of effective connectivity in the PD group compared to the control group, and that differences in effective connectivity were observed for all direct connections between the PD and control groups.We make the case that accounting for modulatory effective connectivity better captures the effects of PD on network functioning and influences the interpretation of the directionality of the between-group results. Limitations include that the PD group was scanned on dopaminergic medication, results were derived from a reasonable but small number of individuals and the ratio of PD to healthy control participants was relatively unbalanced. Future research will examine if the observed effect holds for individuals with PD scanned off their typical dopaminergic medications. [ABSTRACT FROM AUTHOR]

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]

Introduction: The study of Alzheimer's disease investigates topographic patterns of degeneration in the context of connected networks comprised of functionally distinct domains using increasingly sophisticated molecular techniques. Therefore, obtaining high precision and accuracy of neuropathologic tissue sampling will enhance the reliability of molecular studies and contribute to the understanding of Alzheimer's disease pathology. Neuroimaging tools can help assess these aspects of current sampling protocols as well as contribute directly to their improvement. Methods: Using a virtual sampling method on magnetic resonance images (MRIs) from 35 participants (21 women), we compared the precision and accuracy of traditional neuropathologic vs. neuroimaging-guided sampling. The impact of the resulting differences was assessed by evaluating the functional connectivity pattern of regions selected by each approach. Results: Virtual sampling using the traditional neuropathologic approach had low neuroanatomical precision and accuracy for all cortical regions tested. Neuroimaging-guided strategies narrowed these gaps. Discrepancies in the location of traditional and neuroimaging-guided samples corresponded to differences in fMRI measures of functional connectivity. Discussion: Integrating neuroimaging tools with the neuropathologic assessment will improve neuropathologic-neuroimaging correlations by helping to ensure specific functional domains are accurately sampled for quantitative molecular neuropathologic applications. Our neuroimaging-based simulation of current sampling practices provides a benchmark of precision and accuracy against which to measure improvements when using novel tissue sampling approaches. Our results suggest that relying on gross landmarks alone to select samples at autopsy leads to significant variability, even when sampled by the same neuropathologist. Further, this exercise highlights how sampling precision could be enhanced if neuroimaging were integrated with the standard neuropathologic assessment. More accurate targeting and improved biological homogeneity of sampled brain tissue will facilitate the interpretation of neuropathological analyses in AD and the downstream research applications of brain tissue from biorepositories. [ABSTRACT FROM AUTHOR]

Objective: Hippocampal pathology is a consistent feature in persons with temporal lobe epilepsy (TLE) and a strong biomarker of memory impairment. Histopathological studies have identified selective patterns of cell loss across hippocampal subfields in TLE, the most common being cellular loss in the cornu ammonis 1 (CA1) and dentage gyrus (DG). Structural neuroimaging provides a non-invasive method to understand hippocampal pathology, but traditionally only at a whole-hippocampal level. However, recent methodological advances have enabled the non-invasive quantification of subfield pathology in patients, enabling potential integration into clinical workflow. In this study, we characterize patterns of hippocampal subfield atrophy in patients with TLE and examine the associations between subfield atrophy and clinical characteristics. Participants and Methods: High-resolution T2 and T1-weighted MRI were collected from 31 participants (14 left TLE; 6 right TLE; 11 healthy controls [HC], aged 18-61 years). Reconstructions of hippocampal subfields and estimates of their volumes were derived using the Automated Segmentation of Hippocampal Subfields (ASHS) pipeline. Total hippocampal volume was calculated by combining estimates of the subfields CA1-3, DG, and subiculum. To control for variations in head size, all volume estimates were divided by estimates of total brain volume. To assess disease effects on hippocampal atrophy, hippocampi were recoded as either ipsilateral or contralateral to the side of seizure focus. Two sample t-tests at a whole-hippocampus level were used to test for ipsilateral and contralateral volume loss in patients relative to HC. To assess whether we replicated the selective histopathological patterns of subfield atrophy, we carried out mixed-effects ANOVA, coding for an interaction between diagnostic group and hippocampal subfield. Finally, to assess effects of disease load, non-parametric correlations were performed between subfield volume and age of first seizure and duration of illness. Results: Patients had significantly smaller total ipsilateral hippocampal volume compared with HC (d=1.23, p<.005). Contralateral hippocampus did not significantly differ between TLE and HC. Examining individual subfields for the ipsilateral hemisphere revealed significant main-effects for group (F(1, 29)=8.2, p<0.01), subfields (F(4, 115)=550.5, p<0.005), and their interaction (F(4, 115)=8.1, p<0.001). Post-hoc tests revealed that TLE had significantly smaller volume in the ipsilateral CA1 (d=-2.0, p<0.001) and DG (d = -1.4, p<0.005). Longer duration of illness was associated with smaller volume of ipsilateral CA2 (p=-0.492, p<0.05) and larger volume of contralateral whole-hippocampus (p=0.689, p<0.001), CA1 (p=0.614, p < 0.005), and DG (p=0.450, p<0.05). Conclusions: Histopathological characterization after surgery has revealed important associations between hippocampal subfield cell loss and memory impairments in patients with TLE. Here we demonstrate that non-invasive neuroimaging can detect a pattern of subfield atrophy in TLE (i.e., CA1/DG) that matches the most common form of histopathologically-observed hippocampal sclerosis in TLE (HS Type 1) and has been linked directly to both verbal and visuospatial memory impairment. Finally, we found evidence that longer disease duration is associated with larger contralateral hippocampal volume, driven by increases in CA1 and DG. This may reflect subfield-specific functional reorganization to the unaffected brain tissue, a compensatory effect which may have important implications for patient function and successful treatment outcomes. [ABSTRACT FROM AUTHOR]

Cerebrovascular disease is a common comorbidity in older adults, typically assessed in terms of white matter hyperintensities (WMHs) on MRI. While it is well known that WMHs exacerbate cognitive symptoms, the exact relation of WMHs with cognitive performance and other degenerative diseases is unknown. Furthermore, based on location, WMHs are often classified into periventricular and deep WMHs and are believed to have different pathological origins. Whether the two types of WMHs influence cognition differently is unclear. Using regression models, we assessed the independent association of these two types of WMHs with cognitive performance in two separate studies focused on distinct degenerative diseases, early Alzheimer's (mild cognitive impairment), and Parkinson's disease. We further tested if the two types of WMHs were differentially associated with reduced cortical cerebral blood flow (CBF) as measured by arterial spin labeling and increased mean diffusivity (MD, a marker of tissue injury) as measured by diffusion imaging. Our approach revealed that both deep and periventricular WMHs were associated with poor performance on tests of global cognition (Montreal cognitive Assessment, MoCA), task processing (Trail making test), and category fluency in the study of mild cognitive impairment. They were associated with poor performance in global cognition (MoCA) and category fluency in the Parkinson's disease study. Of note, more associations were detected between cognitive performance and deep WMHs than between cognitive performance and periventricular WMHs. Mechanistically, both deep and periventricular WMHs were associated with increased MD. Both deep and periventricular WMHs were also associated with reduced CBF in the gray matter. [ABSTRACT FROM AUTHOR]

Background: Variants in the first intron of the fat mass and obesityassociated (FTO) gene increase obesity risk. People with "high-risk" FTO genotypes exhibit preference for high-fat foods, reduced satiety responsiveness, and greater food intake consistent with impaired satiety. Objective:We sought central nervous system mechanisms that might underlie impaired satiety perception in people with a higher risk of obesity based on their FTO genotype. Design: We performed a cross-sectional study in a sample that was enriched for obesity and included 20 higher-risk participants with the AA (risk) genotype at the rs9939609 locus of FTO and 94 lowerrisk participants with either the AT or TT genotype. We compared subjective appetite, appetite-regulating hormones, caloric intake at a buffet meal, and brain response to visual food cues in an extended satiety network using functionalMRI scans acquired before and after a standardized meal. Results: Higher-risk participants reported less subjective fullness (Χ² = 7.48, P < 0.01), rated calorie-dense food as more appealing (Χ² = 3.92, P < 0.05), and consumed ~350 more kilocalories than lower-risk participants (β = 348 kcal, P = 0.03), even after adjusting for fat or lean mass. Premeal, the higher-risk group had greater activation by "fattening" food images (compared with objects) in the medial orbital frontal cortex (β = 11.6; 95% CI: 1.5, 21.7; P < 0.05). Postmeal, the higher-risk subjects had greater activation by fattening (compared with nonfattening) food cues in the ventral tegmental area/substantia nigra (β = 12.8; 95% CI: 2.7, 23.0; P < 0.05), amygdala (β = 10.6; 95% CI: 0.7, 20.5; P < 0.05), and ventral striatum (β = 6.9; 95% CI: 0.2, 13.7; P < 0.05). Moreover, postmeal activation by fattening food cues within the preselected extended satiety network was positively associated with energy intake at the buffet meal (R² = 0.29, P = 0.04) and this relation was particularly strong in the dorsal striatum (R² = 0.28, P = 0.01), amygdala (R² = 0.28, P = 0.03), and ventral tegmental area/substantia nigra (R² = 0.27, P = 0.01). Conclusion: The findings are consistent with a model inwhich allelic variants in FTO raise obesity risk through impaired central nervous system satiety processing, thereby increasing food intake. This study is registered at clinicaltrials.gov as NCT02483663. [ABSTRACT FROM AUTHOR]

Background: Estimates of hippocampal volume by magnetic resonance imaging have clinical and cognitive correlations and can assist in early Alzheimer disease diagnosis. However, little is known about the relationship between global or regional brain volumes and cognitive test performance in American Indians.Materials and Methods: American Indian participants (N=698; median age, 72 y) recruited for the Cerebrovascular Disease and its Consequences in American Indians study, an ancillary study of the Strong Heart Study cohort, were enrolled. Linear regression models assessed the relationship between magnetic resonance imaging brain volumes (total brain and hippocampi) and cognitive measures of verbal learning and recall, processing speed, verbal fluency, and global cognition.Results: After controlling for demographic and clinical factors, all volumetric measurements were positively associated with processing speed. Total brain volume was also positively associated with verbal learning, but not with verbal recall. Conversely, left hippocampal volume was associated with both verbal learning and recall. The relationship between hippocampal volume and recall performance was more pronounced among those with lower scores on a global cognitive measure. Controlling for APOE ε4 did not substantively affect the associations.Conclusions: These results support further investigation into the relationship between structural Alzheimer disease biomarkers, cognition, genetics, and vascular risk factors in aging American Indians. [ABSTRACT FROM AUTHOR]

Correction of echo planar imaging (EPI)-induced distortions (called "unwarping") improves anatomical fidelity for diffusion magnetic resonance imaging (MRI) and functional imaging investigations. Commonly used unwarping methods require the acquisition of supplementary images during the scanning session. Alternatively, distortions can be corrected by nonlinear registration to a non-EPI acquired structural image. In this study, we compared reliability using two methods of unwarping: (1) nonlinear registration to a structural image using symmetric normalization (SyN) implemented in Advanced Normalization Tools (ANTs); and (2) unwarping using an acquired field map. We performed this comparison in two different test-retest data sets acquired at differing sites (N = 39 and N = 32). In both data sets, nonlinear registration provided higher test-retest reliability of the output fractional anisotropy (FA) maps than field map-based unwarping, even when accounting for the effect of interpolation on the smoothness of the images. In general, field map-based unwarping was preferable if and only if the field maps were acquired optimally. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Connectivity information derived from diffusion MRI can be used to parcellate the cerebral cortex into anatomically and functionally meaningful subdivisions. Acquisition and processing parameters can significantly affect parcellation results, and there is no consensus on best practice protocols. We propose a novel approach for evaluating parcellation based on measuring the degree to which parcellation conforms to known principles of brain organization, specifically cortical field homogeneity and interhemispheric homology. The proposed metrics are well behaved on morphologically generated whole-brain parcels, where they correctly identify contralateral homologies and give higher scores to anatomically versus arbitrarily generated parcellations. The measures show that individual cortical fields have characteristic connectivity profiles that are compact and separable, and that the topological arrangement of such fields is strongly conserved between hemispheres and individuals. The proposed metrics can be used to evaluate the quality of parcellations at the subject and group levels and to improve acquisition and data processing for connectivity-based cortical parcellation. [ABSTRACT FROM PUBLISHER]

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]

Impairments in phonological processing have been associated with damage to the region of the left posterior superior temporal gyrus (pSTG), but the extent to which this area supports phonological processing, independent of semantic processing, is less clear. We used repetition priming and neural repetition suppression during functional magnetic resonance imaging (fMRT) in an auditory pseudoword repetition task as a semantics-free model of lexical (whole-word) phonological access. Across six repetitions, we observed repetition priming in terms of decreased reaction time and repetition suppression in terms of reduced neural activity. An additional analysis aimed at sublexical phonology did not show significant effects in the areas where repetition suppression was observed. To test if these areas were relevant to real word production, we performed a conjunction analysis with data from a separate fMRI experiment which manipulated word frequency (a putative index of lexical phonological access) in picture naming. The left pSTG demonstrated significant effects independently in both experiments, suggesting that this area participates specifically in accessing lexical phonology. [ABSTRACT FROM AUTHOR]

Cognitive models of word production correlate the word frequency effect (i.e., the fact that words which appear with less frequency take longer to produce) with an increased processing cost to activate the whole-word (lexical) phonological representation. We performed functional magnetic resonance imaging (fMRI) while subjects produced overt naming responses to photographs of animals and manipulable objects that had high name agreement but were of varying frequency, with the purpose of identifying neural structures participating specifically in activating whole-word phonological representations, as opposed to activating lexical semantic representations or articulatorymotor routines. Blood oxygen level-dependent responses were analyzed using a parametric approach based on the frequency with which each word produced appears in the language. Parallel analyses were performed for concept familiarity and word length, which provided indices of semantic and articulatory loads. These analyses permitted us to identify regions related to word frequency alone, and therefore, likely to be related specifically to activation of phonological word forms. We hypothesized that the increased processing cost of producing lower-frequency words would correlate with activation of the left posterior inferotemporal (IT) cortex, the left posterior superior temporal gyrus (pSTG), and the left inferior frontal gyrus (IFG). Scan-time response latencies demonstrated the expected word frequency effect. Analysis of the fMRI data revealed that activity in the pSTG was modulated by frequency but not word length or concept familiarity. In contrast, parts of IT and IFG demonstrated conjoint frequency and familiarity effects, and parts of both primarymotor regions demonstrated conjoint effects of frequency and word length. The results are consistent with a model of word production in which lexical-semantic and lexical-phonological information are accessed by overlapping neural systems within posterior and anterior language-related cortices, with pSTG specifically involved in accessing lexical phonology. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to examine differences in spatiotemporal gait parameters between older adults with amnestic mild cognitive impairment and normal cognition and to examine limbic and basal ganglia neural correlates of gait and executive function in older adults without dementia. Design: This was a cross-sectional study of 46 community-dwelling older adults, ages 70--95 yrs, with amnestic mild cognitive impairment (n = 23) and normal cognition (n = 23). Structural magnetic resonance imaging was used to attain volumetric measures of limbic and basal ganglia structures. Quantitative motion analysis was used to measure spatiotemporal parameters of gait. The Trail Making Test was used to assess executive function. Results: During fast-paced walking, older adults with amnestic mild cognitive impairment demonstrated significantly slower gait speed and shorter stride length compared with older adults with normal cognition. Stride length was positively correlated with hippocampal, anterior cingulate, and nucleus accumbens volumes (P < 0.05). Executive function was positively correlated with hippocampal, anterior cingulate, and posterior cingulate volumes (P <0.05). Conclusions: Compared with older adults with normal cognition, those with amnestic mild cognitive impairment demonstrated slower gait speed and shorter stride length, during fast-paced walking, and lower executive function. Hippocampal and anterior cingulate volumes demonstrated moderate positive correlation with both gait and executive function, after adjusting for age. [ABSTRACT FROM AUTHOR]

Abstract: Removal of the anterior temporal lobe (ATL) is an effective surgical treatment for intractable temporal lobe epilepsy but carries a risk of language and verbal memory deficits. Preoperative localization of functional zones in the ATL might help reduce these risks, yet fMRI protocols in current widespread use produce very little activation in this region. Based on recent evidence suggesting a role for the ATL in semantic integration, we designed an fMRI protocol comparing comprehension of brief narratives (Story task) with a semantically shallow control task involving serial arithmetic (Math task). The Story > Math contrast elicited strong activation throughout the ATL, lateral temporal lobe, and medial temporal lobe bilaterally in an initial cohort of 18 healthy participants. The task protocol was then implemented at 6 other imaging centers using identical methods. Data from a second cohort of participants scanned at these centers closely replicated the results from the initial cohort. The Story–Math protocol provides a reliable method for activation of surgical regions of interest in the ATL. The bilateral activation supports previous claims that conceptual processing involves both temporal lobes. Used in combination with language lateralization measures, reliable ATL activation maps may be useful for predicting cognitive outcome in ATL surgery, though the validity of this approach needs to be established in a prospective surgical series. [ABSTRACT FROM AUTHOR]