The Cougar Point tuff: Implications for thermochemical zonation and longevity of high-temperature, large-volume silicic magmas of the Miocene Yellowstone hotspot

The 12.7-10.5 Ma Cougar Point Tuff in southern Idaho, USA, consists of 10 large-volume (>10²-10³ km³ each), high-temperature (800-1000 °C), rhyolitic ash-flow tuffs erupted from the Bruneau-Jarbidge volcanic center of the Yellowstone hotspot. These tuffs provide evidence for compositional and thermal zonation in pre-eruptive rhyolite magma, and suggest the presence of a long-lived reservoir that was tapped by numerous large explosive eruptions. Pyroxene compositions exhibit discrete compositional modes with respect to Fe and Mg that define a linear spectrum punctuated by conspicuous gaps. Airfall glass compositions also cluster into modes, and the presence of multiple modes indicates tapping of different magma volumes during early phases of eruption. Equilibrium assemblages of pigeonite and augite are used to reconstruct compositional and thermal gradients in the pre-eruptive reservoir. The recurrence of identical compositional modes and of mineral pairs equilibrated at high temperatures in successive eruptive units is consistent with the persistence of their respective liquids in the magma reservoir. Recurrence intervals of identical modes range from 0.3 to 0.9 Myr and suggest possible magma residence times of similar duration. Eruption ages, magma temperatures, Nd isotopes, and pyroxene and glass compositions are consistent with a long-lived, dynamically evolving magma reservoir that was chemically and thermally zoned and composed of multiple discrete magma volumes.

High-temperature, low-H₂O silicic magmas of the Yellowstone Hotspot: an experimental study of Rhyolite from the Bruneau-Jarbidge eruptive center, Central Snake River Plain, USA

The phase relations have been investigated experimentally at 200 and 500 MPa as a function of water activity for one of the least evolved (Indian Batt Rhyolite) and of a more evolved rhyolite composition (Cougar Point Tuff XV) from the 12·8-8·1 Ma Bruneau-Jarbidge eruptive center of the Yellowstone hotspot. Particular priority was given to accurate determination of the water content of the quenched glasses using infrared spectroscopic techniques. Comparison of the composition of natural and experimentally synthesized phases confirms that high temperatures (>900°C) and extremely low melt water contents (<1·5 wt % H₂O) are required to reproduce the natural mineral assemblages. In melts containing 0·5-1·5 wt % H₂O, the liquidus phase is clinopyroxene (excluding Fe-Ti oxides, which are strongly dependent on fO₂), and the liquidus temperature of the more evolved Cougar Point Tuff sample (BJR; 940-1000°C) is at least 30°C lower than that of the Indian Batt Rhyolite lava sample (IBR2; 970-1030°C). For the composition BJR, the comparison of the compositions of the natural and experimental glasses indicates a pre-eruptive temperature of at least 900°C. The composition of clinopyroxene and pigeonite pairs can be reproduced only for water contents below 1·5 wt % H₂O at 900°C, or lower water contents if the temperature is higher. For the composition IBR2, a minimum temperature of 920°C is necessary to reproduce the main phases at 200 and 500 MPa. At 200 MPa, the pre-eruptive water content of the melt is constrained in the range 0·7-1·3 wt % at 950°C and 0·3-1·0 wt % at 1000°C. At 500 MPa, the pre-eruptive temperatures are slightly higher (by 30-50°C) for the same ranges of water concentration. The experimental results are used to explore possible proxies to constrain the depth of magma storage. The crystallization sequence of tectosilicates is strongly dependent on pressure between 200 and 500 MPa. In addition, the normative Qtz-Ab-Or contents of glasses quenched from melts coexisting with quartz, sanidine and plagioclase depend on pressure and melt water content, assuming that the normative Qtz and Ab/Or content of such melts is mainly dependent on pressure and water activity, respectively. The combination of results from the phase equilibria and from the composition of glasses indicates that the depth of magma storage for the IBR2 and BJR compositions may be in the range 300-400 MPa (13 km) and 200-300 MPa (10 km), respectively.

Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all metaanalyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Quantifying the heritability of task-related brain activation and performance during the N-back working memory task: A twin fMRI study

Working memory-related brain activation has been widely studied, and impaired activation patterns have been reported for several psychiatric disorders. We investigated whether variation in N-back working memory brain activation is genetically influenced in 60 pairs of twins, (29 monozygotic (MZ), 31 dizygotic (DZ); mean age 24.4 ± 1.7S.D.). Task-related brain response (BOLD percent signal difference of 2 minus 0-back) was measured in three regions of interest. Although statistical power was low due to the small sample size, for middle frontal gyrus, angular gyrus, and supramarginal gyrus, the MZ correlations were, in general, approximately twice those of the DZ pairs, with non-significant heritability estimates (14-30%) in the low-moderate range. Task performance was strongly influenced by genes (57-73%) and highly correlated with cognitive ability (0.44-0.55). This study, which will be expanded over the next 3 years, provides the first support that individual variation in working memory-related brain activation is to some extent influenced by genes.

A tensor-based morphometry study of genetic influences on brain structure using a new fluid registration method

We incorporated a new Riemannian fluid registration algorithm into a general MRI analysis method called tensor-based morphometry to map the heritability of brain morphology in MR images from 23 monozygotic and 23 dizygotic twin pairs. All 92 3D scans were fluidly registered to a common template. Voxelwise Jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. Heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. Lobar volume heritability was also studied using the ACE genetic model. The performance of this Riemannian algorithm was compared to a more standard fluid registration algorithm: 3D maps from both registration techniques displayed similar heritability patterns throughout the brain. Power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. The Riemannian algorithm outperformed the standard fluid registration.

Mapping the regional influence of genetics on brain structure variability - A Tensor-Based Morphometry study

Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8 ± 1.8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course.

Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study

Genetic correlation (rg) analysis determines how much of the correlation between two measures is due to common genetic influences. In an analysis of 4 Tesla diffusion tensor images (DTI) from 531 healthy young adult twins and their siblings, we generalized the concept of genetic correlation to determine common genetic influences on white matter integrity, measured by fractional anisotropy (FA), at all points of the brain, yielding an NxN genetic correlation matrix rg(x,y) between FA values at all pairs of voxels in the brain. With hierarchical clustering, we identified brain regions with relatively homogeneous genetic determinants, to boost the power to identify causal single nucleotide polymorphisms (SNP). We applied genome-wide association (GWA) to assess associations between 529,497 SNPs and FA in clusters defined by hubs of the clustered genetic correlation matrix. We identified a network of genes, with a scale-free topology, that influences white matter integrity over multiple brain regions.

Gene network effects on brain microstructure and intellectual performance identified in 472 twins

A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess approximately one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here, we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their nontwin siblings (mean age: 23.7 2.1 SD years; 193 male/279 female).Wecombined clustering with genome-wide scanning to find brain systems withcommongenetic determination.Wethen filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it computationally more tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus.

Mapping genetic influences on ventricular structure in twins

Despite substantial progress in measuring the anatomical and functional variability of the human brain, little is known about the genetic and environmental causes of these variations. Here we developed an automated system to visualize genetic and environmental effects on brain structure in large brain MRI databases. We applied our multi-template segmentation approach termed "Multi-Atlas Fluid Image Alignment" to fluidly propagate hand-labeled parameterized surface meshes, labeling the lateral ventricles, in 3D volumetric MRI scans of 76 identical (monozygotic, MZ) twins (38 pairs; mean age = 24.6 (SD = 1.7)); and 56 same-sex fraternal (dizygotic, DZ) twins (28 pairs; mean age = 23.0 (SD = 1.8)), scanned as part of a 5-year research study that will eventually study over 1000 subjects. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps, derived from path analysis, revealed patterns of heritability, and their significance, in 3D. Path coefficients for the 'ACE' model that best fitted the data indicated significant contributions from genetic factors (A = 7.3%), common environment (C = 38.9%) and unique environment (E = 53.8%) to lateral ventricular volume. Earlier-maturing occipital horn regions may also be more genetically influenced than later-maturing frontal regions. Maps visualized spatially-varying profiles of environmental versus genetic influences. The approach shows promise for automatically measuring gene-environment effects in large image databases.

Neural correlates of semantic priming for ambiguous words: An event-related fMRI study

We investigated the neural correlates of semantic priming by using event-related fMRI to record blood oxygen level dependent (BOLD) responses while participants performed speeded lexical decisions (word/nonword) on visually presented related versus unrelated prime-target pairs. A long stimulus onset asynchrony of 1000 ms was employed, which allowed for increased controlled processing and selective frequency-based ambiguity priming. Conditions included an ambiguous word prime (e.g. bank) and a target related to its dominant (e.g. money) or subordinate meaning (e.g. river). Compared to an unrelated condition, primed dominant targets were associated with increased activity in the LIFG, the right anterior cingulate and superior temporal gyrus, suggesting postlexical semantic integrative mechanisms, while increased right supramarginal activity for the unrelated condition was consistent with expectancy based priming. Subordinate targets were not primed and were associated with reduced activity primarily in occipitotemporal regions associated with word recognition, which may be consistent with frequency-based meaning suppression. These findings provide new insights into the neural substrates of semantic priming and the functional-anatomic correlates of lexical ambiguity suppression mechanisms.

Brain activity during automatic semantic priming revealed by event-related functional magnetic resonance imaging

Semantic priming occurs when a subject is faster in recognising a target word when it is preceded by a related word compared to an unrelated word. The effect is attributed to automatic or controlled processing mechanisms elicited by short or long interstimulus intervals (ISIs) between primes and targets. We employed event-related functional magnetic resonance imaging (fMRI) to investigate blood oxygen level dependent (BOLD) responses associated with automatic semantic priming using an experimental design identical to that used in standard behavioural priming tasks. Prime-target semantic strength was manipulated by using lexical ambiguity primes (e.g., bank) and target words related to dominant or subordinate meaning of the ambiguity. Subjects made speeded lexical decisions (word/nonword) on dominant related, subordinate related, and unrelated word pairs presented randomly with a short ISI. The major finding was a pattern of reduced activity in middle temporal and inferior prefrontal regions for dominant versus unrelated and subordinate versus unrelated comparisons, respectively. These findings are consistent with both a dual process model of semantic priming and recent repetition priming data that suggest that reductions in BOLD responses represent neural priming associated with automatic semantic activation and implicate the left middle temporal cortex and inferior prefrontal cortex in more automatic aspects of semantic processing.

Dopaminergic neuromodulation of semantic processing: A 4-T fMRI study with levodopa

There is emerging evidence that alterations in dopaminergic transmission can influence semantic processing, yet the neural mechanisms involved are unknown. The influence of levodopa (L-DOPA) on semantic priming was investigated in healthy individuals (n=20) using event-related functional magnetic resonance imaging with a randomized, double-blind crossover design. Critical prime-target pairs consisted of a lexical ambiguity prime and 1) a target related to the dominant meaning of the prime (e.g., bank-money), 2) a target related to the subordinate meaning (e.g., fence-sword), or 3) an unrelated target (e.g., ball-desk). Behavioral data showed that both dominant and subordinate meanings were primed on placebo. In contrast, there was preserved priming of dominant meanings and no significant priming of subordinate meanings on L-DOPA, the latter associated with decreased anterior cingulate and dorsal prefrontal cortex activity. Dominant meaning activation on L-DOPA was associated with increased activity in the left rolandic operculum and left middle temporal gyrus. These findings suggest that L-DOPA enhances frequency-based semantic focus via prefrontal and temporal modulation of automatic semantic priming and through engagement of anterior cingulate mechanisms supporting attentional/controlled priming.

Heritability of head motion during resting state functional MRI in 462 healthy twins

Head motion (HM) is a critical confounding factor in functional MRI. Here we investigate whether HM during resting state functional MRI (RS-fMRI) is influenced by genetic factors in a sample of 462 twins (65% fema≤ 101 MZ (monozygotic) and 130 DZ (dizygotic) twin pairs; mean age: 21 (SD=3.16), range 16-29). Heritability estimates for three HM components-mean translation (MT), maximum translation (MAXT) and mean rotation (MR)-ranged from 37 to 51%. We detected a significant common genetic influence on HM variability, with about two-thirds (genetic correlations range 0.76-1.00) of the variance shared between MR, MT and MAXT. A composite metric (HM-PC1), which aggregated these three, was also moderately heritable (h2=42%). Using a sub-sample (N=35) of the twins we confirmed that mean and maximum translational and rotational motions were consistent "traits" over repeated scans (r=0.53-0.59); reliability was even higher for the composite metric (r=0.66). In addition, phenotypic and cross-trait cross-twin correlations between HM and resting state functional connectivities (RS-FCs) with Brodmann areas (BA) 44 and 45, in which RS-FCs were found to be moderately heritable (BA44: h2-=0.23 (sd=0.041), BA45: h2-=0.26 (sd=0.061)), indicated that HM might not represent a major bias in genetic studies using FCs. Even so, the HM effect on FC was not completely eliminated after regression. HM may be a valuable endophenotype whose relationship with brain disorders remains to be elucidated.

Support for an auto-associative model of spoken cued recall: Evidence from fMRI

Cued recall and item recognition are considered the standard episodic memory retrieval tasks. However, only the neural correlates of the latter have been studied in detail with fMRI. Using an event-related fMRI experimental design that permits spoken responses, we tested hypotheses from an auto-associative model of cued recall and item recognition [Chappell, M., & Humphreys, M. S. (1994). An auto-associative neural network for sparse representations: Analysis and application to models of recognition and cued recall. Psychological Review, 101, 103-128]. In brief, the model assumes that cues elicit a network of phonological short term memory (STM) and semantic long term memory (LTM) representations distributed throughout the neocortex as patterns of sparse activations. This information is transferred to the hippocampus which converges upon the item closest to a stored pattern and outputs a response. Word pairs were learned from a study list, with one member of the pair serving as the cue at test. Unstudied words were also intermingled at test in order to provide an analogue of yes/no recognition tasks. Compared to incorrectly rejected studied items (misses) and correctly rejected (CR) unstudied items, correctly recalled items (hits) elicited increased responses in the left hippocampus and neocortical regions including the left inferior prefrontal cortex (LIPC), left mid lateral temporal cortex and inferior parietal cortex, consistent with predictions from the model. This network was very similar to that observed in yes/no recognition studies, supporting proposals that cued recall and item recognition involve common rather than separate mechanisms.

Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults

Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain's anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 T. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant's co-registered anatomical scans. The proportion of fiber connections between all pairs of cortical regions, or nodes, was found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70 × 70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.