Assessment of the impact of the scanner-related factors on brain morphometry analysis with Brainvisa.

BACKGROUND: Brain morphometry is extensively used in cross-sectional studies. However, the difference in the estimated values of the morphometric measures between patients and healthy subjects may be small and hence overshadowed by the scanner-related variability, especially with multicentre and longitudinal studies. It is important therefore to investigate the variability and reliability of morphometric measurements between different scanners and different sessions of the same scanner. METHODS: We assessed the variability and reliability for the grey matter, white matter, cerebrospinal fluid and cerebral hemisphere volumes as well as the global sulcal index, sulcal surface and mean geodesic depth using Brainvisa. We used datasets obtained across multiple MR scanners at 1.5 T and 3 T from the same groups of 13 and 11 healthy volunteers, respectively. For each morphometric measure, we conducted ANOVA analysis and verified whether the estimated values were significantly different across different scanners or different sessions of the same scanner. The between-centre and between-visit reliabilities were estimated from their contribution to the total variance, using a random-effects ANOVA model. To estimate the main processes responsible for low reliability, the results of brain segmentation were compared to those obtained using FAST within FSL. RESULTS: In a considerable number of cases, the main effects of both centre and visit factors were found to be significant. Moreover, both between-centre and between-visit reliabilities ranged from poor to excellent for most morphometric measures. A comparison between segmentation using Brainvisa and FAST revealed that FAST improved the reliabilities for most cases, suggesting that morphometry could benefit from improving the bias correction. However, the results were still significantly different across different scanners or different visits. CONCLUSIONS: Our results confirm that for morphometry analysis with the current version of Brainvisa using data from multicentre or longitudinal studies, the scanner-related variability must be taken into account and where possible should be corrected for. We also suggest providing some flexibility to Brainvisa for a step-by-step analysis of the robustness of this package in terms of reproducibility of the results by allowing the bias corrected images to be imported from other packages and bias correction step be skipped, for example.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Assessment of the potentials and limitations of cortical-based analysis for the integration of structure and function in normal and pathological brains using MRI

The software package Brainvisa (www.brainvisa.tnfo) offers a wide range of possibilities for cortical analysis using its automatic sulci recognition feature. Automated sulci identification is an attractive feature as the manual labelling of the cortical sulci is often challenging even for the experienced neuro-radiologists. This can also be of interest in fMRI studies of individual subjects where activated regions of the cortex can simply be identified using sulcal labels without the need for normalization to an atlas. As it will be explained later in this thesis, normalization to atlas can especially be problematic for pathologic brains. In addition, Brainvisa allows for sulcal morphometry from structural MR images by estimating a wide range of sulcal properties such as size, coordinates, direction, and pattern. Morphometry of abnormal brains has gained huge interest and has been widely used in finding the biomarkers of several neurological diseases or psychiatric disorders. However mainly because of its complexity, only a limited use of sulcal morphometry has been reported so far. With a wide range of possibilities for sulcal morphometry offered by Brainvisa, it is possible to thoroughly investigate the sulcal changes due to the abnormality. However, as any other automated method, Brainvisa can be susceptible to limitations associated with image quality. Factors such as noise, spatial resolution, and so on, can have an impact on the detection of the cortical folds and estimation of their attributes. Hence the robustness of Brainvisa needs to be assessed. This can be done by estimating the reliability and reproducibility of results as well as exploring the changes in results caused by other factors. This thesis is an attempt to investigate the possible benefits of sulci identification and sulcal morphometry for functional and structural MRI studies as well as the limitations of Brainvisa. In addition, the possibility of improvement of activation localization with functional MRI studies is further investigated. This investigation was motivated by a review of other cortical-based analysis methods, namely the cortical surface-based methods, which are discussed in the literature review chapter of this thesis. The application of these approaches in functional MRI data analysis and their potential benefits is used in this investigation

Are power calculations useful? A multicentre neuroimaging study

There are now many reports of imaging experiments with small cohorts of typical participants that precede large-scale, often multicentre studies of psychiatric and neurological disorders. Data from these calibration experiments are sufficient to make estimates of statistical power and predictions of sample size and minimum observable effect sizes. In this technical note, we suggest how previously reported voxel-based power calculations can support decision making in the design, execution and analysis of cross-sectional multicentre imaging studies. The choice of MRI acquisition sequence, distribution of recruitment across acquisition centres, and changes to the registration method applied during data analysis are considered as examples. The consequences of modification are explored in quantitative terms by assessing the impact on sample size for a fixed effect size and detectable effect size for a fixed sample size. The calibration experiment dataset used for illustration was a precursor to the now complete Medical Research Council Autism Imaging Multicentre Study (MRC-AIMS). Validation of the voxel-based power calculations is made by comparing the predicted values from the calibration experiment with those observed in MRC-AIMS. The effect of non-linear mappings during image registration to a standard stereotactic space on the prediction is explored with reference to the amount of local deformation. In summary, power calculations offer a validated, quantitative means of making informed choices on important factors that influence the outcome of studies that consume significant resources

Using sulcal and gyral measures of brain structure to investigate benefits of an active lifestyle

Background: Physical activity is associated with brain and cognitive health in ageing. Higher levels of physical activity are linked to larger cerebral volumes, lower rates of atrophy, better cognitive function and lesser risk of cognitive decline and dementia. Neuroimaging studies have traditionally focused on volumetric brain tissue 17 measures to test associations between factors of interest (e.g. physical activity) and brain structure. However, cortical sulci may provide additional information to these more standard measures. Method: Associations between physical activity, brain structure, and cognition were investigated in a large, community-based sample of cognitively healthy individuals (N = 317) using both sulcal and volumetric measures. Results: Physical activity was associated with narrower width of the Left Superior Frontal Sulcus and the Right Central Sulcus,while volumetric measures showed no association with physical activity. In addition, Left Superior Frontal Sulcal width was associated with processing speed and executive function. Discussion: These findings suggest sulcalmeasuresmay be a sensitive index of physical activity related to cerebral health and cognitive function in healthy older individuals. Further research is required to confirm these findings and to examine how sulcal measures may be most effectively used in neuroimaging

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA\u27s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way

Biomarkers of brain function in psychosis and their genetic basis

Psychotic disorders, including schizophrenia and bipolar disorder, are amongst the most severe and enduring mental illnesses. Recent research has identified several genetic variants associated with an increased risk of developing psychosis; however, it remains largely unknown how these lead to the illness. This is where endophenotypes – heritable traits associated with the illness and observed in unaffected family members of patients – could be valuable. Endophenotypes are linked to the genetic underpinnings of disorders, and can help elucidate the functional effects of genetic risk variants. This thesis investigates endophenotypes for psychosis, with the overall aim of identify such biological markers, as well as to examine the relationships between different endophenotypes and their associations with genetic risk for psychosis. A family design has been used throughout, including patients with psychosis, their unaffected first-degree relatives, as well as healthy controls. In chapter 1, I review the endophenotype approach and those markers proposed for psychosis genetic research. Chapters 2 and 3 investigate whether different neurophysiological measures are potential endophenotypes for psychosis. In chapter 2, resting state EEG was studied and it was shown that risk groups, including unaffected relatives and people with an at-risk mental state, presented no abnormalities. This suggests that – rather than endophenotypes – the low frequency electrophysiological abnormalities seen in chronic patients in this study might be related to illness progression or long-term medication effects, and be more useful as biomarkers in non-genetic research. In chapter 3, I used dynamic causal modelling to investigate effective connectivity – the influence that one neuronal system exerts over another – underlying the mismatch negativity evoked potential, a marker of pre-attentive auditory perception. Results indicate that, compared to controls, both patients and their relatives show abnormalities of the excitability of superficial pyramidal cells in prefrontal cortex. Hence, this appears to be linked to the genetic aetiology of psychosis, and constitutes a potential endophenotype. Chapters 4 and 5 investigate several pre-identified endophenotypes for psychosis: Electrophysiological (the P300 event related potential), cognitive (working memory, spatial visualisation, and verbal memory), and neuroanatomical (lateral ventricular volume). In chapter 4, the associations between these endophenotypes were examined. Results showed that the P300 amplitude and latency are independent measures; the former indexing attention and working memory and the latter possibly a correlate of basic speed of processing. Importantly, individuals with psychosis, their unaffected relatives, and healthy controls all showed similar patterns of associations between all pairs of endophenotypes, supporting the notion of a continuum of psychosis across the population. Lastly, in chapter 5, polygenic risk scores – a measure of the combined effect of a large number of common genetic risk variants – were used to investigate the relationships between genetic risk for schizophrenia and bipolar disorder, and the endophenotypes studied in the previous chapter. Results showed that higher polygenic score for schizophrenia nominally predicts poorer performance on a spatial visualisation task; providing some evidence that the two traits share genetic risk variants as hypothesised. No other associations approached significance, possibly due to insufficient statistical power. However, as discovery samples grow, the use of polygenic scores is promising. This thesis has thus contributed to the field of mental health research by investigating key electrophysiological, cognitive and imaging endophenotypes for psychosis, as well as their genetic influences. Well defined and reliably measured endophenotypes are valuable in mental health research by clarifying the functional effects of identified genetic risk factors, and by providing ways of identifying groups of people with similar abnormalities, both within and between current diagnostic categories