Functional brain maps of Tower of London performance : a positron emission tomography and functional magnetic resonance imaging study

Regional cerebral blood flow (rCBF) and blood oxygenation level-dependent (BOLD) contrasts represent different physiological measures of brain activation. The present study aimed to compare two functional brain imaging techniques (functional magnetic resonance imaging versus [15O] positron emission tomography) when using Tower of London (TOL) problems as the activation task. A categorical analysis (task versus baseline) revealed a significant BOLD increase bilaterally for the dorsolateral prefrontal and inferior parietal cortex and for the cerebellum. A parametric haemodynamic response model (or regression analysis) confirmed a task-difficulty-dependent increase of BOLD and rCBF for the cerebellum and the left dorsolateral prefrontal cortex. In line with previous studies, a task-difficulty-dependent increase of left-hemispheric rCBF was also detected for the premotor cortex, cingulate, precuneus, and globus pallidus. These results imply consistency across the two neuroimaging modalities, particularly for the assessment of prefrontal brain function when using a parametric TOL adaptation.

Single voxel Magnetic Resonance Spectroscopic (1H-MRS) analysis of deep brain structures after traumatic prefrontal brain injury (TBI) in rat

Chronic difficulties arising from mild brain injury (TBI) are difficult to predict because the processes underlying changes after TBI are poorly understood. In mild brain injury the extent of neuropsychiatric and cognitive symptoms correspond poorly to overt tissue loss (Barth 1983; Liu 2010). Cellular, immune and hormonal cascades occurring after injury and continuing during the healing process may impact uninjured brain regions sensitive to the effects of physiological and emotional stress, which receive projections from the injury site. Changes in these most basic properties due to injury or disease have profound implications for virtually every aspect of brain function through disruption of neurotransmitter, neuroendocrine and metabolic systems. In order to screen for changes in transmitter and metabolic activity, in this study we developed Single voxel proton Magnetic Resonance Spectroscopy (1H-MRS) for use in both injured and control animals. We first evaluated if 1H-MRS could be used to evaluate in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus in both control and injured animals after controlled cortical impact injury to the rat prefrontal cortex. We found that metabolite measurements for Myo-Inositol, Choline, creatine, Glutamate+Glutamine, and N-acetyl-acetate are attainable in deep brain structures in vivo in injured and controls rats. We next seek to evaluate longitudinally, in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus during the first month after controlled cortical impact injury to the rat prefrontal cortex. These ongoing studies will provide data on the changes in transmitters and metabolites over time in injured and non-injured subjects. These studies address some of the fundamental questions about how mild brain injury has such diverse effects on overall brain health and function.

A magnetic climbing robot for marine inspection services

Currently, the inspection of sea-going vessels is performed manually. Ship surveyors do a visual inspection; in some cases they also use cameras and non-destructive testing methods. Prior to a ship surveying process a lot of scaffolding has to be provided in order to make every spot accessible for the surveyor. In this work a robotic system is presented, which is able to access many areas of a cargo hold of a ship and perform visual inspection without any scaffolding. The paper also describes how the position of the acquired data is estimated with an optical 3D tracking unit and how critical points on the hull can be marked via a remote controlled marker device. Furthermore first results of onboard tests with the system are provided.

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.

Common Alzheimer's disease risk variant within the CLU gene affects white matter microstructure in young adults

There is a strong genetic risk for late-onset Alzheimer's disease (AD), but so far few gene variants have been identified that reliably contribute to that risk. A newly confirmed genetic risk allele C of the clusterin (CLU) gene variant rs11136000 is carried by ~88% of Caucasians. The C allele confers a 1.16 greater odds of developing late-onset AD than the T allele. AD patients have reductions in regional white matter integrity. We evaluated whether the CLU risk variant was similarly associated with lower white matter integrity in healthy young humans. Evidence of early brain differences would offer a target for intervention decades before symptom onset. We scanned 398 healthy young adults (mean age, 23.6 ± 2.2 years) with diffusion tensor imaging, a variation of magnetic resonance imaging sensitive to white matter integrity in the living brain. We assessed genetic associations using mixed-model regression at each point in the brain to map the profile of these associations with white matter integrity. Each C allele copy of the CLUvariant was associated with lower fractional anisotropy-a widely accepted measure of white matter integrity-in multiple brain regions, including several known to degenerate in AD. These regions included the splenium of the corpus callosum, the fornix, cingulum, and superior and inferior longitudinal fasciculi in both brain hemispheres. Young healthy carriers of the CLU gene risk variant showed a distinct profile of lower white matter integrity that may increase vulnerability to developing AD later in life.

Genetics of brain fiber architecture and intellectual performance

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a 2 = 0.55, p = 0.04, left; a 2 = 0.74, p = 0.006, right), bilateral parietal (a 2 = 0.85, p < 0.001, left; a 2 = 0.84, p < 0.001, right), and left occipital (a 2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto- occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata (p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition.

Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29

White matter microstructure is under strong genetic control, yet it is largely unknown how genetic influences change from childhood into adulthood. In one of the largest brain mapping studies ever performed, we determined whether the genetic control over white matter architecture depends on age, sex, socioeconomic status (SES), and intelligence quotient (IQ). We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4-Tesla), in 705 twins and their siblings (age range 12-29; 290. M/415. F). White matter integrity was quantified using a widely accepted measure, fractional anisotropy (FA). We fitted gene-environment interaction models pointwise, to visualize brain regions where age, sex, SES and IQ modulate heritability of fiber integrity. We hypothesized that environmental factors would start to outweigh genetic factors during late childhood and adolescence. Genetic influences were greater in adolescence versus adulthood, and greater in males than in females. Socioeconomic status significantly interacted with genes that affect fiber integrity: heritability was higher in those with higher SES. In people with above-average IQ, genetic factors explained over 80% of the observed FA variability in the thalamus, genu, posterior internal capsule, and superior corona radiata. In those with below-average IQ, however, only around 40% FA variability in the same regions was attributable to genetic factors. Genes affect fiber integrity, but their effects vary with age, sex, SES and IQ. Gene-environment interactions are vital to consider in the search for specific genetic polymorphisms that affect brain integrity and connectivity.

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.

Prefrontal cortex involvement in selective letter generation: A functional magnetic resonance imaging study

Cerebral responses to alternating periods of a control task and a selective letter generation paradigm were investigated with functional Magnetic Resonance Imaging (fMRI). Subjects selectively generated letters from four designated sets of six letters from the English language alphabet, with the instruction that they were not to produce letters in alphabetical order either forward or backward, repeat or alternate letters. Performance during this condition was compared with that of a control condition in which subjects recited the same letters in alphabetical order. Analyses revealed significant and extensive foci of activation in a number of cerebral regions including mid-dorsolateral frontal cortex, inferior frontal gyrus, precuneus, supramarginal gyrus, and cerebellum during the selective letter generation condition. These findings are discussed with respect to recent positron emission tomography (PET) and fMRI studies of verbal working memory and encoding/retrieval in episodic memory.

Genetics of anisotropy asymmetry: Registration and sample size effects

Brain asymmetry has been a topic of interest for neuroscientists for many years. The advent of diffusion tensor imaging (DTI) allows researchers to extend the study of asymmetry to a microscopic scale by examining fiber integrity differences across hemispheres rather than the macroscopic differences in shape or structure volumes. Even so, the power to detect these microarchitectural differences depends on the sample size and how the brain images are registered and how many subjects are studied. We fluidly registered 4 Tesla DTI scans from 180 healthy adult twins (45 identical and fraternal pairs) to a geometrically-centered population mean template. We computed voxelwise maps of significant asymmetries (left/right hemisphere differences) for common fiber anisotropy indices (FA, GA). Quantitative genetic models revealed that 47-62% of the variance in asymmetry was due to genetic differences in the population. We studied how these heritability estimates varied with the type of registration target (T1- or T2-weighted) and with sample size. All methods consistently found that genetic factors strongly determined the lateralization of fiber anisotropy, facilitating the quest for specific genes that might influence brain asymmetry and fiber integrity.

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.