References of "Neuroimage"
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See detailFast and accurate modelling of longitudinal and repeated measures neuroimaging data.
Guillaume, Bryan ULiege; Hua, Xue; Thompson, Paul et al

in NeuroImage (2014), 94

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry--the state of all ... [more ▼]

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry--the state of all equal variances and equal correlations--or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the "so-called" Sandwich Estimator (SwE) which accounts for the within-subject correlation existing in longitudinal data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for neuroimaging data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http://warwick.ac.uk/tenichols/SwE. [less ▲]

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See detailTranscriptomics of cortical gray matter thickness decline during normal aging.
Kochunov, P.; Charlesworth, J.; Winkler, Anderson ULiege et al

in NeuroImage (2013), 82

INTRODUCTION: We performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathway analyses to evaluate the hypothesis that ... [more ▼]

INTRODUCTION: We performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathway analyses to evaluate the hypothesis that transcriptional activity can predict cortical gray matter thickness (GMT) variability during normal cerebral aging. METHODS: Transcriptome and GMT data were available for 379 individuals (age range=28-85) community-dwelling members of large extended Mexican American families. Collection of transcriptome data preceded that of neuroimaging data by 17 years. Genome-wide gene transcriptome data consisted of 20,413 heritable lymphocytes-based transcripts. GMT measurements were performed from high-resolution (isotropic 800 mum) T1-weighted MRI. Transcriptome-wide and pathway enrichment analysis was used to classify genes correlated with GMT. Transcripts for sixty genes from seven innate immune pathways were tested as specific predictors of GMT variability. RESULTS: Transcripts for eight genes (IGFBP3, LRRN3, CRIP2, SCD, IDS, TCF4, GATA3, and HN1) passed the transcriptome-wide significance threshold. Four orthogonal factors extracted from this set predicted 31.9% of the variability in the whole-brain and between 23.4 and 35% of regional GMT measurements. Pathway enrichment analysis identified six functional categories including cellular proliferation, aggregation, differentiation, viral infection, and metabolism. The integrin signaling pathway was significantly (p<10(-6)) enriched with GMT. Finally, three innate immune pathways (complement signaling, toll-receptors and scavenger and immunoglobulins) were significantly associated with GMT. CONCLUSION: Expression activity for the genes that regulate cellular proliferation, adhesion, differentiation and inflammation can explain a significant proportion of individual variability in cortical GMT. Our findings suggest that normal cerebral aging is the product of a progressive decline in regenerative capacity and increased neuroinflammation. [less ▲]

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See detailEEG acquisition in ultra-high static magnetic fields up to 9.4 T.
Neuner, Irene; Warbrick, Tracy; Arrubla Martinez, Jorge Andres ULiege et al

in NeuroImage (2013), 68

The simultaneous acquisition of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data has gained momentum in recent years due to the synergistic effects of the two modalities ... [more ▼]

The simultaneous acquisition of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data has gained momentum in recent years due to the synergistic effects of the two modalities with regard to temporal and spatial resolution. Currently, only EEG-data recorded in fields of up to 7 T have been reported. We investigated the feasibility of recording EEG inside a 9.4 T static magnetic field, specifically to determine whether meaningful EEG information could be recovered from the data after removal of the cardiac-related artefact. EEG-data were recorded reliably and reproducibly at 9.4 T and the cardiac-related artefact increased in amplitude with increasing B0, as expected. Furthermore, we were able to correct for the cardiac-related artefact and identify auditory event related responses at 9.4 T in 75% of subjects using independent component analysis (ICA). Also by means of ICA we detected event related spectral perturbations (ERSP) in subjects at 9.4 T in response to opening/closing the eyes comparable with the response at 0 T. Overall our results suggest that it is possible to record meaningful EEG data at ultra-high magnetic fields. The simultaneous EEG-fMRI approach at ultra-high-fields opens up the horizon for investigating brain dynamics at a superb spatial resolution and a temporal resolution in the millisecond domain. [less ▲]

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See detailNeural substrates of cognitive switching and inhibition in a face processing task.
Piguet, Camille; Sterpenich, Virginie; Desseilles, Martin ULiege et al

in NeuroImage (2013), 82

We frequently need to change our current occupation, an operation requiring additional effortful cognitive demands. Switching from one task to another may involve two distinct processes: inhibition of the ... [more ▼]

We frequently need to change our current occupation, an operation requiring additional effortful cognitive demands. Switching from one task to another may involve two distinct processes: inhibition of the previously relevant task-set, and initiation of a new one. Here we tested whether these two processes are underpinned by separate neural substrates, and whether they differ depending on the nature of the task and the emotional content of stimuli. We used functional magnetic resonance imaging in healthy human volunteers who categorize emotional faces according to three different judgment rules (color, gender, or emotional expression). Our paradigm allowed us to separate neural activity associated with inhibition and switching based on the sequence of the tasks required on successive trials. We found that the bilateral medial superior parietal lobule and left intraparietal sulcus showed consistent activation during switching regardless of the task. On the other hand, no common region was activated (or suppressed) as a consequence of inhibition across all tasks. Rather, task-specific effects were observed in brain regions that were more activated when switching to a particular task but less activated after inhibition of the same task. In addition, compared to other conditions, the emotional task elicited a similar switching cost but lower inhibition cost, accompanied by selective decrease in the anterior cingulate cortex when returning to this task shortly after inhibiting it. These results demonstrate that switching relies on domain-general processes mediated by postero-medial parietal areas, engaged across all tasks, but also provide novel evidence that task inhibition produces domain-specific decreases as a function of particular task demands, with only the latter inhibition component being modulated by emotional information. [less ▲]

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See detailBinary classification of (1)(8)F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI
Vandenberghe, R.; Nelissen, N.; Salmon, Eric ULiege et al

in NeuroImage (2013), 64

(18)F-flutemetamol is a positron emission tomography (PET) tracer for in vivo amyloid imaging. The ability to classify amyloid scans in a binary manner as 'normal' versus 'Alzheimer-like', is of high ... [more ▼]

(18)F-flutemetamol is a positron emission tomography (PET) tracer for in vivo amyloid imaging. The ability to classify amyloid scans in a binary manner as 'normal' versus 'Alzheimer-like', is of high clinical relevance. We evaluated whether a supervised machine learning technique, support vector machines (SVM), can replicate the assignments made by visual readers blind to the clinical diagnosis, which image components have highest diagnostic value according to SVM and how (18)F-flutemetamol-based classification using SVM relates to structural MRI-based classification using SVM within the same subjects. By means of SVM with a linear kernel, we analyzed (18)F-flutemetamol scans and volumetric MRI scans from 72 cases from the (18)F-flutemetamol phase 2 study (27 clinically probable Alzheimer's disease (AD), 20 amnestic mild cognitive impairment (MCI), 25 controls). In a leave-one-out approach, we trained the (18)F-flutemetamol based classifier by means of the visual reads and tested whether the classifier was able to reproduce the assignment based on visual reads and which voxels had the highest feature weights. The (18)F-flutemetamol based classifier was able to replicate the assignments obtained by visual reads with 100% accuracy. The voxels with highest feature weights were in the striatum, precuneus, cingulate and middle frontal gyrus. Second, to determine concordance between the gray matter volume- and the (18)F-flutemetamol-based classification, we trained the classifier with the clinical diagnosis as gold standard. Overall sensitivity of the (18)F-flutemetamol- and the gray matter volume-based classifiers were identical (85.2%), albeit with discordant classification in three cases. Specificity of the (18)F-flutemetamol based classifier was 92% compared to 68% for MRI. In the MCI group, the (18)F-flutemetamol based classifier distinguished more reliably between converters and non-converters than the gray matter-based classifier. The visual read-based binary classification of (18)F-flutemetamol scans can be replicated using SVM. In this sample the specificity of (18)F-flutemetamol based SVM for distinguishing AD from controls is higher than that of gray matter volume-based SVM. [less ▲]

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See detailMetabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults
Yakushev, Igor; Chételat, Gael; Fischer F.U. et al

in NeuroImage (2013), 79

Studies of functional connectivity suggest that the default mode network (DMN) might be relevant for cognitive functions. Here, we examined metabolic and structural connectivity between major DMN nodes ... [more ▼]

Studies of functional connectivity suggest that the default mode network (DMN) might be relevant for cognitive functions. Here, we examined metabolic and structural connectivity between major DMN nodes, the posterior cingulate (PCC) and medial prefrontal cortex (MPFC), in relation to normal working memory (WM). DMN was captured using independent component analysis of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) data from 35 young healthy adults (27.1±5.1 years). Metabolic connectivity, a correlation between FDG uptake in PCC and MPFC, was examined in groups of subjects with (relative to median) low (n=18) and high (n=17) performance on digit span backward test as an index of verbal WM. In addition, fiber tractography based on PCC and MPFC nodes as way points was performed in a subset of subjects. FDG uptake in the DMN nodes did not differ between high and low performers. However, significantly (p=0.01) lower metabolic connectivity was found in the group of low performers. Furthermore, as compared to high performers, low performers showed lower density of the left superior cingulate bundle. Verbal WM performance is related to metabolic and structural connectivity within the DMN in young healthy adults. Metabolic connectivity as quantified with FDG-PET might be a sensitive marker of the normal variability in some cognitive functions. [less ▲]

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See detailRelationships between brain metabolism decrease in normal aging and changes in structural and functional connectivity
Chételat, Gael; Landeau, Brigitte; Salmon, Eric ULiege et al

in NeuroImage (2013), 76

Normal aging is characterized by brain glucose metabolism decline predominantly in the prefrontal cortex. The goal of the present study was to assess whether this change was associated with age-related ... [more ▼]

Normal aging is characterized by brain glucose metabolism decline predominantly in the prefrontal cortex. The goal of the present study was to assess whether this change was associated with age-related alteration of white matter (WM) structural integrity and/or functional connectivity. FDG-PET data from 40 young and 57 elderly healthy participants from two research centres (n=49/48 in Centre 1/2) were analyzed. WM volume from T1-weighted MRI (Centre 1), fractional anisotropy from diffusion-tensor imaging (Centre 2), and resting-state fMRI data (Centre 1) were also obtained. Group comparisons were performed within each imaging modality. Then, positive correlations were assessed, within the elderly, between metabolism in the most affected region and the other neuroimaging modalities. Metabolism decline in the elderly predominated in the left inferior frontal junction (LIFJ). LIFJ hypometabolism was significantly associated with macrostructural and microstructural WM disturbances in long association fronto-temporo-occipital fibers, while no relationship was found with functional connectivity. The findings offer new perspectives to understand normal aging processes and open avenues for future studies to explore causality between age-related metabolism and connectivity changes. [less ▲]

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See detailMeasuring and comparing brain cortical surface area and other areal quantities.
Winkler, Anderson ULiege; Sabuncu, Mert R.; Yeo, B. T. Thomas et al

in NeuroImage (2012), 61(4), 1428-43

Structural analysis of MRI data on the cortical surface usually focuses on cortical thickness. Cortical surface area, when considered, has been measured only over gross regions or approached indirectly ... [more ▼]

Structural analysis of MRI data on the cortical surface usually focuses on cortical thickness. Cortical surface area, when considered, has been measured only over gross regions or approached indirectly via comparisons with a standard brain. Here we demonstrate that direct measurement and comparison of the surface area of the cerebral cortex at a fine scale is possible using mass conservative interpolation methods. We present a framework for analyses of the cortical surface area, as well as for any other measurement distributed across the cortex that is areal by nature. The method consists of the construction of a mesh representation of the cortex, registration to a common coordinate system and, crucially, interpolation using a pycnophylactic method. Statistical analysis of surface area is done with power-transformed data to address lognormality, and inference is done with permutation methods. We introduce the concept of facewise analysis, discuss its interpretation and potential applications. [less ▲]

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See detailNeural Correlates of Performance Variabilty during Motor Sequence Acquisition
Albouy, Geneviève ULiege; Sterpenich, V.; Vandewalle, Gilles ULiege et al

in NeuroImage (2012), 60(1), 324-331

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See detailComa and consciousness: Paradigms (re)framed by neuroimaging.
Laureys, Steven ULiege; Schiff, N.

in NeuroImage (2012)

The past 15years has provided an unprecedented collection of discoveries that bear upon our scientific understanding of recovery of consciousness in the human brain following severe brain damage ... [more ▼]

The past 15years has provided an unprecedented collection of discoveries that bear upon our scientific understanding of recovery of consciousness in the human brain following severe brain damage. Highlighted among these discoveries are unique demonstrations that patients with little or no behavioral evidence of conscious awareness may retain critical cognitive capacities and the first scientific demonstrations that some patients, with severely injured brains and very longstanding conditions of limited behavioral responsiveness, may nonetheless harbor latent capacities for significant recovery. Included among such capacities are particularly human functions of language and higher-level cognition that either spontaneously or through direct interventions may reemerge even at long time intervals or remain unrecognized. Collectively, these observations have reframed scientific inquiry and further led to important new insights into mechanisms underlying consciousness in the human brain. These studies support a model of consciousness as the emergent property of the collective behavior of widespread frontoparietal network connectivity modulated by specific forebrain circuit mechanisms. We here review these advances in measurement and the scientific and broader implications of this rapidly progressing field of research. [less ▲]

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See detailCognitive reserve impacts on inter-individual variability in resting-state cerebral metabolism in normal aging
Bastin, Christine ULiege; Yakushev, Igor; Bahri, Mohamed Ali ULiege et al

in NeuroImage (2012), 63

There is a great deal of heterogeneity in the impact of aging on cognition and cerebral functioning. One potential factor contributing to individual differences among the elders is the cognitive reserve ... [more ▼]

There is a great deal of heterogeneity in the impact of aging on cognition and cerebral functioning. One potential factor contributing to individual differences among the elders is the cognitive reserve, which designates the partial protection from the deleterious effects of aging that lifetime experience provides. Neuroimaging studies examining task-related activation in elderly people suggested that cognitive reserve takes the form of more efficient use of brain networks and/or greater ability to recruit alternative networks to compensate for age-related cerebral changes. In this multi-centre study, we examined the relationships between cognitive reserve, as measured by education and verbal intelligence, and cerebral metabolism at rest (FDG-PET) in a sample of 74 healthy older participants. Higher degree of education and verbal intelligence was associated with less metabolic activity in the right posterior temporoparietal cortex and the left anterior intraparietal sulcus. Functional connectivity analyses of resting-state fMRI images in a subset of 41 participants indicated that these regions belong to the default mode network and the dorsal attention network respectively. Lower metabolism in the temporoparietal cortex was also associated with better memory abilities. The findings provide evidence for an inverse relationship between cognitive reserve and resting-state activity in key regions of two functional networks respectively involved in internal mentation and goal-directed attention. [less ▲]

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See detailBrain functional integration decreases during propofol-induced loss of consciousness.
Schrouff, Jessica ULiege; Perlbarg, Vincent; Boly, Mélanie ULiege et al

in NeuroImage (2011), 57(1), 198-205

Consciousness has been related to the amount of integrated information that the brain is able to generate. In this paper, we tested the hypothesis that the loss of consciousness caused by propofol ... [more ▼]

Consciousness has been related to the amount of integrated information that the brain is able to generate. In this paper, we tested the hypothesis that the loss of consciousness caused by propofol anesthesia is associated with a significant reduction in the capacity of the brain to integrate information. To assess the functional structure of the whole brain, functional integration and partial correlations were computed from fMRI data acquired from 18 healthy volunteers during resting wakefulness and propofol-induced deep sedation. Total integration was significantly reduced from wakefulness to deep sedation in the whole brain as well as within and between its constituent networks (or systems). Integration was systematically reduced within each system (i.e., brain or networks), as well as between networks. However, the ventral attentional network maintained interactions with most other networks during deep sedation. Partial correlations further suggested that functional connectivity was particularly affected between parietal areas and frontal or temporal regions during deep sedation. Our findings suggest that the breakdown in brain integration is the neural correlate of the loss of consciousness induced by propofol. They stress the important role played by parietal and frontal areas in the generation of consciousness. [less ▲]

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See detailThe impact of blue light on non-visual brain functions changes with age
Daneault*; Vandewalle*, Gilles ULiege; Hébert, M et al

in NeuroImage (2011), 56(Suppl. 1),

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See detailOptimal design of multi-subject blocked fMRI experiments.
Maus, Bärbel ULiege; van Breukelen, Gerard J P; Goebel, Rainer et al

in NeuroImage (2011), 56(3), 1338-1352

The design of a multi-subject fMRI experiment needs specification of the number of subjects and scanning time per subject. For example, for a blocked design with conditions A or B, fixed block length and ... [more ▼]

The design of a multi-subject fMRI experiment needs specification of the number of subjects and scanning time per subject. For example, for a blocked design with conditions A or B, fixed block length and block order ABN, where N denotes a null block, the optimal number of cycles of ABN and the optimal number of subjects have to be determined. This paper presents a method to determine the optimal number of subjects and optimal number of cycles for a blocked design based on the A-optimality criterion and a linear cost function by which the number of cycles and the number of subjects are restricted. Estimation of individual stimulus effects and estimation of contrasts between stimulus effects are both considered. The mixed-effects model is applied and analytical results for the A-optimal number of subjects and A-optimal number of cycles are obtained under the assumption of uncorrelated errors. For correlated errors with a first-order autoregressive (AR1) error structure, numerical results are presented. Our results show how the optimal number of cycles and subjects depend on the within- to between-subject variance ratio. Our method is a new approach to determine the optimal scanning time and optimal number of subjects for a multi-subject fMRI experiment. In contrast to previous results based on power analyses, the optimal number of cycles and subjects can be described analytically and costs are considered. [less ▲]

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See detail"Relevance vector machine" consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients.
Phillips, Christophe ULiege; Bruno, Marie-Aurélie ULiege; Maquet, Pierre ULiege et al

in NeuroImage (2011), 56(2), 797808

The vegetative state is a devastating condition where patients awaken from their coma (i.e., open their eyes) but fail to show any behavioural sign of conscious awareness. Locked-in syndrome patients also ... [more ▼]

The vegetative state is a devastating condition where patients awaken from their coma (i.e., open their eyes) but fail to show any behavioural sign of conscious awareness. Locked-in syndrome patients also awaken from their coma and are unable to show any motor response to command (except for small eye movements or blinks) but recover full conscious awareness of self and environment. Bedside evaluation of residual cognitive function in coma survivors often is difficult because motor responses may be very limited or inconsistent. We here aimed to disentangle vegetative from "locked-in" patients by an automatic procedure based on machine learning using fluorodeoxyglucose PET data obtained in 37 healthy controls and in 13 patients in a vegetative state. Next, the trained machine was tested on brain scans obtained in 8 patients with locked-in syndrome. We used a sparse probabilistic Bayesian learning framework called "relevance vector machine" (RVM) to classify the scans. The trained RVM classifier, applied on an input scan, returns a probability value (p-value) of being in one class or the other, here being "conscious" or not. Training on the control and vegetative state groups was assessed with a leave-one-out cross-validation procedure, leading to 100% classification accuracy. When applied on the locked-in patients, all scans were classified as "conscious" with a mean p-value of .95 (min .85). In conclusion, even with this relatively limited data set, we could train a classifier distinguishing between normal consciousness (i.e., wakeful conscious awareness) and the vegetative state (i.e., wakeful unawareness). Cross-validation also indicated that the clinical classification and the one predicted by the automatic RVM classifier were in accordance. Moreover, when applied on a third group of "locked-in" consciously aware patients, they all had a strong probability of being similar to the normal controls, as expected. Therefore, RVM classification of cerebral metabolic images obtained in coma survivors could become a useful tool for the automated PET-based diagnosis of altered states of consciousness. [less ▲]

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See detailDepression alters "top-down" visual attention: a dynamic causal modeling comparison between depressed and healthy subjects.
Desseilles, Martin ULiege; Schwartz, Sophie; Dang Vu, Thien Thanh ULiege et al

in NeuroImage (2011), 54(2), 1662-8

Using functional magnetic resonance imaging (fMRI), we recently demonstrated that nonmedicated patients with a first episode of unipolar major depression (MDD) compared to matched controls exhibited an ... [more ▼]

Using functional magnetic resonance imaging (fMRI), we recently demonstrated that nonmedicated patients with a first episode of unipolar major depression (MDD) compared to matched controls exhibited an abnormal neural filtering of irrelevant visual information (Desseilles et al., 2009). During scanning, subjects performed a visual attention task imposing two different levels of attentional load at fixation (low or high), while task-irrelevant colored stimuli were presented in the periphery. In the present study, we focused on the visuo-attentional system and used "Dynamic Causal Modeling" (DCM) on the same dataset to assess how attention influences a network of three dynamically-interconnected brain regions (visual areas V1 and V4, and intraparietal sulcus (P), differentially in MDD patients and healthy controls. Bayesian model selection (BMS) and model space partitioning (MSP) were used to determine the best model in each population. The best model for the controls revealed that the increase of parietal activity by high attention load was selectively associated with a negative modulation of P on V4, consistent with high attention reducing the processing of irrelevant colored peripheral stimuli. The best model accounting for the data from the MDD patients showed that both low and high attention levels exerted modulatory effects on P. The present results document abnormal effective connectivity across visuo-attentional networks in MDD, which likely contributes to deficient attentional filtering of information. [less ▲]

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See detailCOMT Val158Met polymorphism, verbalizing of emotion and activation of affective brain systems
Swart, M.; Bruggeman, R.; Laroi, Frank ULiege et al

in NeuroImage (2011), 55

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See detailGenetics of microstructure of cerebral white matter using diffusion tensor imaging.
Kochunov, P.; Glahn, D. C.; Lancaster, J. L. et al

in NeuroImage (2010), 53(3), 1109-16

We analyzed the degree of genetic control over intersubject variability in the microstructure of cerebral white matter (WM) using diffusion tensor imaging (DTI). We performed heritability, genetic ... [more ▼]

We analyzed the degree of genetic control over intersubject variability in the microstructure of cerebral white matter (WM) using diffusion tensor imaging (DTI). We performed heritability, genetic correlation and quantitative trait loci (QTL) analyses for the whole-brain and 10 major cerebral WM tracts. Average measurements for fractional anisotropy (FA), radial (L( perpendicular)) and axial (L( vertical line)) diffusivities served as quantitative traits. These analyses were done in 467 healthy individuals (182 males/285 females; average age 47.9+/-13.5 years; age range: 19-85 years), recruited from randomly-ascertained pedigrees of extended families. Significant heritability was observed for FA (h(2)=0.52+/-0.11; p=10(-7)) and L( perpendicular) (h(2)=0.37+/-0.14; p=0.001), while L( vertical line) measurements were not significantly heritable (h(2)=0.09+/-0.12; p=0.20). Genetic correlation analysis indicated that the FA and L( perpendicular) shared 46% of the genetic variance. Tract-wise analysis revealed a regionally diverse pattern of genetic control, which was unrelated to ontogenic factors, such as tract-wise age-of-peak FA values and rates of age-related change in FA. QTL analysis indicated linkages for whole-brain average FA (LOD=2.36) at the marker D15S816 on chromosome 15q25, and for L( perpendicular) (LOD=2.24) near the marker D3S1754 on the chromosome 3q27. These sites have been reported to have significant co-inheritance with two psychiatric disorders (major depression and obsessive-compulsive disorder) in which patients show characteristic alterations in cerebral WM. Our findings suggest that the microstructure of cerebral white matter is under a strong genetic control and further studies in healthy as well as patients with brain-related illnesses are imperative to identify the genes that may influence cerebral white matter. [less ▲]

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See detailCortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies.
Winkler, Anderson ULiege; Kochunov, Peter; Blangero, John et al

in NeuroImage (2010), 53(3), 1135-46

Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes ... [more ▼]

Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies. [less ▲]

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See detailRobustness of optimal design of fMRI experiments with application of a genetic algorithm.
Maus, Bärbel ULiege; van Breukelen, Gerard J P; Goebel, Rainer et al

in NeuroImage (2010), 49(3), 2433-2443

In this paper we apply the genetic algorithm developed by Kao et al. (2009) to find designs which are robust against misspecification of the error autocorrelation. Two common optimality criteria, the A ... [more ▼]

In this paper we apply the genetic algorithm developed by Kao et al. (2009) to find designs which are robust against misspecification of the error autocorrelation. Two common optimality criteria, the A-optimality criterion and the D-optimality criterion, based upon a general linear model are employed to obtain locally optimal designs for a given value of the autocorrelation. The maximin criterion is then used to obtain designs which are robust against misspecification of the autocorrelation. Furthermore, robustness depending on the choice of optimality criterion is evaluated. We show analytically and empirically that the A- and D-optimality criterion will result in different optimal designs, e.g. with different stimulus frequencies. Optimal stimulus frequency for the A-optimality criterion has been derived by Liu et al. (2004) whereas we derive here the optimal stimulus frequency for the D-optimality criterion. Conclusions about the robustness of an optimal design against misspecification of model parameters and choice of optimality criterion are drawn based upon our results. [less ▲]

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