Power calculations for multicenter imaging studies controlled by the false discovery rate

Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory

Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.

Volumetric differences in gray and white matter of cerebellar Crus I/II across the different clinical stages of schizophrenia

AIM

Schizophrenia is considered to be a disorder of progressive structural brain abnormalities. Previous studies have indicated that the cerebellar Crus I/II plays a critical role in schizophrenia. We aimed to investigate how specific morphological features in the Crus I/II at different critical stages of the schizophrenia spectrum contribute to the disease.

METHODS

The study involved 73 participants on the schizophrenia spectrum (28 with ultra-high risk for psychosis [UHR], 17 with first-episode schizophrenia [FES], and 28 with chronic schizophrenia) and 79 healthy controls. We undertook a detailed investigation into differences in Crus I/II volume using a semiautomated segmentation method optimized for the cerebellum. We analyzed the effects of group and sex, as well as their interaction, on Crus I/II volume in gray matter (GM) and white matter (WM).

RESULTS

Significant group × sex interactions were found in WM volumes of the bilateral Crus I/II; the males with UHR demonstrated significantly larger WM volumes compared with the other male groups, whereas no significant group differences were found in the female groups. Additionally, WM and GM volumes of the Crus I/II had positive associations with symptom severity in the UHR group, whereas, in contrast, GM volumes in the FES group were negatively associated with symptom severity.

CONCLUSIONS

The present findings provide evidence that the morphology of Crus I/II is involved in schizophrenia in a sex- and disease stage-dependent manner. Additionally, alterations of WM volumes of Crus I/II may have potential as a biological marker of early detection and treatment for individuals with UHR.

Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns

BACKGROUND

Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder.

METHODS

The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups.

RESULTS

Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables.

CONCLUSIONS

Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

The Status of the Acupuncture Mechanism Study Based on PET/PET-CT Technique: Design and Quality Control

PET/PET-CT is an important technique to investigate the central mechanism of acupuncture in vivo. This article collected original research papers with keywords of "Acupuncture," "PET," "PET/CT," and "Positron emission tomography" in PubMed and CNKI databases from January 2003 to December 2018. As a result, a total of 43 articles were included. Based on the literature analyses, we found that (1) reasonable arrangement of the operation process and the choice of appropriate acupuncture intervention time is conducive to a better interpretation of acupuncture-PET/PET-CT mechanism and (2) the selection of participants, sample size, acupuncture intervention, and experimental conditions would affect study results. Therefore, effective quality control is an important way to ensure the repeatability of research results.

Effects of tumor necrosis factor-α polymorphism on the brain structural changes of the patients with major depressive disorder

Single Nucleotide Polymorphic (SNP) variations of proinflammatory cytokines such as Tumor Necrosis Factor-α (TNF-α) have been reported to be closely associated with the major depressive disorder (MDD). However, it is unclear if proinflammatory genetic burden adversely affects the regional gray matter volume in patients with MDD. The aim of this study was to test whether rs1799724, an SNP of TNF-α, contributes to the neuroanatomical changes in MDD. In this cross-sectional study, a total of 144 MDD patients and 111 healthy controls (HC) well matched for age, sex and education were recruited from Shanghai Mental Health Center. Voxel-based morphometry (VBM) followed by graph theory based structural covariance analysis was applied to locate diagnosis x genotype interactions. Irrespective of diagnosis, individuals with the high-risk genotype (T-carriers) had reduced volume in left angular gyrus (main effect of genotype). Diagnosis x genotype interaction was exclusively localized to the visual cortex (right superior occipital gyrus). The same region also showed reduced volume in patients with MDD than HC (main effect of diagnosis), with this effect being most pronounced in patients carrying the high-risk genotype. However, neither global nor regional network of structural covariance was found to have group difference. In conclusion, a genetic variation which can increase TNF-α expression selectively affects the anatomy of the visual cortex among the depressed subjects, with no effect on the topographical organization of multiple cortical regions. This supports the notion that anatomical changes in depression are in part influenced by the genetic determinants of inflammatory activity.

Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data

Acquiring resting-state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi-site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi-site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi-site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI-derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi-site fMRI neuroimaging studies.

Frontal networks in adults with autism spectrum disorder

It has been postulated that autism spectrum disorder is underpinned by an 'atypical connectivity' involving higher-order association brain regions. To test this hypothesis in a large cohort of adults with autism spectrum disorder we compared the white matter networks of 61 adult males with autism spectrum disorder and 61 neurotypical controls, using two complementary approaches to diffusion tensor magnetic resonance imaging. First, we applied tract-based spatial statistics, a 'whole brain' non-hypothesis driven method, to identify differences in white matter networks in adults with autism spectrum disorder. Following this we used a tract-specific analysis, based on tractography, to carry out a more detailed analysis of individual tracts identified by tract-based spatial statistics. Finally, within the autism spectrum disorder group, we studied the relationship between diffusion measures and autistic symptom severity. Tract-based spatial statistics revealed that autism spectrum disorder was associated with significantly reduced fractional anisotropy in regions that included frontal lobe pathways. Tractography analysis of these specific pathways showed increased mean and perpendicular diffusivity, and reduced number of streamlines in the anterior and long segments of the arcuate fasciculus, cingulum and uncinate--predominantly in the left hemisphere. Abnormalities were also evident in the anterior portions of the corpus callosum connecting left and right frontal lobes. The degree of microstructural alteration of the arcuate and uncinate fasciculi was associated with severity of symptoms in language and social reciprocity in childhood. Our results indicated that autism spectrum disorder is a developmental condition associated with abnormal connectivity of the frontal lobes. Furthermore our findings showed that male adults with autism spectrum disorder have regional differences in brain anatomy, which correlate with specific aspects of autistic symptoms. Overall these results suggest that autism spectrum disorder is a condition linked to aberrant developmental trajectories of the frontal networks that persist in adult life.

Reproducibility of Brain Morphometry from Short-Term Repeat Clinical MRI Examinations: A Retrospective Study

Purpose

To assess the inter session reproducibility of automatic segmented MRI-derived measures by FreeSurfer in a group of subjects with normal-appearing MR images.

Materials and Methods

After retrospectively reviewing a brain MRI database from our institute consisting of 14,758 adults, those subjects who had repeat scans and had no history of neurodegenerative disorders were selected for morphometry analysis using FreeSurfer. A total of 34 subjects were grouped by MRI scanner model. After automatic segmentation using FreeSurfer, label-wise comparison (involving area, thickness, and volume) was performed on all segmented results. An intraclass correlation coefficient was used to estimate the agreement between sessions. Wilcoxon signed rank test was used to assess the population mean rank differences across sessions. Mean-difference analysis was used to evaluate the difference intervals across scanners. Absolute percent difference was used to estimate the reproducibility errors across the MRI models. Kruskal-Wallis test was used to determine the across-scanner effect.

Results

The agreement in segmentation results for area, volume, and thickness measurements of all segmented anatomical labels was generally higher in Signa Excite and Verio models when compared with Sonata and TrioTim models. There were significant rank differences found across sessions in some labels of different measures. Smaller difference intervals in global volume measurements were noted on images acquired by Signa Excite and Verio models. For some brain regions, significant MRI model effects were observed on certain segmentation results.

Conclusions

Short-term scan-rescan reliability of automatic brain MRI morphometry is feasible in the clinical setting. However, since repeatability of software performance is contingent on the reproducibility of the scanner performance, the scanner performance must be calibrated before conducting such studies or before using such software for retrospective reviewing.

How Many Is Enough? Effect of Sample Size in Inter-Subject Correlation Analysis of fMRI

Inter-subject correlation (ISC) is a widely used method for analyzing functional magnetic resonance imaging (fMRI) data acquired during naturalistic stimuli. A challenge in ISC analysis is to define the required sample size in the way that the results are reliable. We studied the effect of the sample size on the reliability of ISC analysis and additionally addressed the following question: How many subjects are needed for the ISC statistics to converge to the ISC statistics obtained using a large sample? The study was realized using a large block design data set of 130 subjects. We performed a split-half resampling based analysis repeatedly sampling two nonoverlapping subsets of 10–65 subjects and comparing the ISC maps between the independent subject sets. Our findings suggested that with 20 subjects, on average, the ISC statistics had converged close to a large sample ISC statistic with 130 subjects. However, the split-half reliability of unthresholded and thresholded ISC maps improved notably when the number of subjects was increased from 20 to 30 or more.

Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI

Permutation testing has been widely implemented in voxel-based morphometry (VBM) tools. However, this type of non-parametric inference has yet to be thoroughly compared with traditional parametric inference in VBM studies of brain structure. Here we compare both types of inference and investigate what influence the number of permutations in permutation testing has on results in an exemplar study of how gray matter proportion changes with age in a group of working age adults. High resolution T1-weighted volume scans were acquired from 80 healthy adults aged 25-64years. Using a validated VBM procedure and voxel-based permutation testing for Pearson product-moment coefficient, the effect sizes of changes in gray matter proportion with age were assessed using traditional parametric and permutation testing inference with 100, 500, 1000, 5000, 10000 and 20000 permutations. The statistical significance was set at P<0.05 and false discovery rate (FDR) was used to correct for multiple comparisons. Clusters of voxels with statistically significant (PFDR<0.05) declines in gray matter proportion with age identified with permutation testing inference (N≈6000) were approximately twice the size of those identified with parametric inference (N=3221voxels). Permutation testing with 10000 (N=6251voxels) and 20000 (N=6233voxels) permutations produced clusters that were generally consistent with each other. However, with 1000 permutations there were approximately 20% more statistically significant voxels (N=7117voxels) than with ≥10000 permutations. Permutation testing inference may provide a more sensitive method than traditional parametric inference for identifying age-related differences in gray matter proportion. Based on the results reported here, at least 10000 permutations should be used in future univariate VBM studies investigating age related changes in gray matter to avoid potential false findings. Additional studies using permutation testing in large imaging databanks are required to address the impact of model complexity, multivariate analysis, number of observations, sampling bias and data quality on the accuracy with which subtle differences in brain structure associated with normal aging can be identified.

Magnetic resonance imaging and the prediction of outcome in first-episode schizophrenia: a review of current evidence and directions for future research

Magnetic Resonance Imaging (MRI) measures are promising outcome markers for schizophrenia, since regional frontal and temporal grey matter volumes reductions, and enlargement of the ventricles, have been associated with outcome in this disorder. However, a number of methodological issues have limited the potential clinical utility of these findings. This article reviewed studies that examined brain structure at illness onset as a predictor of outcome, discusses the limitations of the findings, and highlights the challenges that would need to be addressed if structural data are to inform the management of an individual patient.

METHODS

Using a set of a priori criteria, we systematically searched Medline and EMBASE databases for articles evaluating brain structure at the time of the first psychotic episode and assessed response to treatment, symptomatic outcome, or functional outcome at any point in the first 12 months of illness.

RESULTS

The 11 studies identified suggest that alterations in medial temporal and prefrontal cortical areas, and in the networks that connect them with subcortical structures, are promising neuroanatomical markers of poor symptomatic and functional outcomes.

CONCLUSION

Neuroimaging data, possibly in combination with other biomarkers of disease, could help stratifying patients with psychoses to generate patient clusters clinically meaningful, and useful to detect true therapeutic effects in clinical trials. Optimization of Treatment and Management of Schizophrenia in Europe (OPTiMiSE), a large multicenter study funded by the FP7 European Commission, could generate these much-needed findings.

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.

A multi-scanner study of subcortical brain volume abnormalities in schizophrenia

Schizophrenia patients show significant subcortical brain abnormalities. We examined these abnormalities using automated image analysis software and provide effect size estimates for prospective multi-scanner schizophrenia studies. Subcortical and intracranial volumes were obtained using FreeSurfer 5.0.0 from high-resolution structural imaging scans from 186 schizophrenia patients (mean age±S.D.=38.9±11.6, 78% males) and 176 demographically similar controls (mean age±S.D.=37.5±11.2, 72% males). Scans were acquired from seven 3-Tesla scanners. Univariate mixed model regression analyses compared between-group volume differences. Weighted mean effect sizes (and number of subjects needed for 80% power at α=0.05) were computed based on the individual single site studies as well as on the overall multi-site study. Schizophrenia patients have significantly smaller intracranial, amygdala, and hippocampus volumes and larger lateral ventricle, putamen and pallidum volumes compared with healthy volunteers. Weighted mean effect sizes based on single site studies were generally larger than effect sizes computed based on analysis of the overall multi-site sample. Prospectively collected structural imaging data can be combined across sites to increase statistical power for meaningful group comparisons. Even when using similar scan protocols at each scanner, some between-site variance remains. The multi-scanner effect sizes provided by this study should help in the design of future multi-scanner schizophrenia imaging studies.

Reduced parahippocampal cortical thickness in subjects at ultra-high risk for psychosis

BACKGROUND

Grey matter volume and cortical thickness represent two complementary aspects of brain structure. Several studies have described reductions in grey matter volume in people at ultra-high risk (UHR) of psychosis; however, little is known about cortical thickness in this group. The aim of the present study was to investigate cortical thickness alterations in UHR subjects and compare individuals who subsequently did and did not develop psychosis.

METHOD

We examined magnetic resonance imaging data collected at four different scanning sites. The UHR subjects were followed up for at least 2 years. Subsequent to scanning, 50 UHR subjects developed psychosis and 117 did not. Cortical thickness was examined in regions previously identified as sites of neuroanatomical alterations in UHR subjects, using voxel-based cortical thickness.

RESULTS

At baseline UHR subjects, compared with controls, showed reduced cortical thickness in the right parahippocampal gyrus (p < 0.05, familywise error corrected). There were no significant differences in cortical thickness between the UHR subjects who later developed psychosis and those who did not.

CONCLUSIONS

These data suggest that UHR symptomatology is characterized by alterations in the thickness of the medial temporal cortex. We did not find evidence that the later progression to psychosis was linked to additional alterations in cortical thickness, although we cannot exclude the possibility that the study lacked sufficient power to detect such differences.

A comparison of automated anatomical-behavioural mapping methods in a rodent model of stroke

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The first application of voxel lesion symptom mapping (VLSM) in rodents.

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A comparison of VLSM with tensor based morphometry (TBM) methods in a stroke model.

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Comparison of automated techniques with manual measurements and model power calculations.

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Analysis of both local and non-local lesion effects.

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Correlation of structural change with behaviour.

Neurological damage, due to conditions such as stroke, results in a complex pattern of structural changes and significant behavioural dysfunctions; the automated analysis of magnetic resonance imaging (MRI) and discovery of structural–behavioural correlates associated with these disorders remains challenging. Voxel lesion symptom mapping (VLSM) has been used to associate behaviour with lesion location in MRI, but this analysis requires the definition of lesion masks on each subject and does not exploit the rich structural information in the images. Tensor-based morphometry (TBM) has been used to perform voxel-wise structural analyses over the entire brain; however, a combination of lesion hyper-intensities and subtle structural remodelling away from the lesion might confound the interpretation of TBM. In this study, we compared and contrasted these techniques in a rodent model of stroke (n = 58) to assess the efficacy of these techniques in a challenging pre-clinical application. The results from the automated techniques were compared using manually derived region-of-interest measures of the lesion, cortex, striatum, ventricle and hippocampus, and considered against model power calculations. The automated TBM techniques successfully detect both lesion and non-lesion effects, consistent with manual measurements. These techniques do not require manual segmentation to the same extent as VLSM and should be considered part of the toolkit for the unbiased analysis of pre-clinical imaging-based studies.

Volumetric abnormalities predating the onset of schizophrenia and affective psychoses: an MRI study in subjects at ultrahigh risk of psychosis

It remains unclear whether brain structural abnormalities observed before the onset of psychosis are specific to schizophrenia or are common to all psychotic disorders. This study aimed to measure regional gray matter volume prior to the onset of schizophreniform and of affective psychoses. We investigated 102 subjects at ultrahigh risk (UHR) of developing psychosis recruited from the Personal Assessment and Crisis Evaluation Clinic in Melbourne, Australia. Twenty-eight of these subjects developed psychosis subsequent to scanning: 19 schizophrenia, 7 affective psychoses, and 2 other psychoses. We examined regional gray matter volume using 1.5 mm thick, coronal, 1.5 Tesla magnetic resonance imaging and voxel-based morphometry methods of image analysis. Subjects were scanned at presentation and were followed up clinically for a minimum of 12 months, to detect later transition to psychosis. We found that both groups of subjects who subsequently developed psychosis (schizophrenia and affective psychosis) showed reductions in the frontal cortex relative to UHR subjects who did not develop psychosis. The subgroup that subsequently developed schizophrenia also showed smaller volumes in the parietal cortex and, at trend level, in the temporal cortex, whereas those who developed an affective psychosis had significantly smaller subgenual cingulate volumes. These preliminary findings suggest that volumetric abnormalities in UHR individuals developing schizophrenia vs affective psychoses comprise a combination of features that predate both disorders and others that may be specific to the nature of the subsequent disorder.

Individual differences in brain structure underpin empathizing-systemizing cognitive styles in male adults

Individual differences in cognitive style can be characterized along two dimensions: 'systemizing' (S, the drive to analyze or build 'rule-based' systems) and 'empathizing' (E, the drive to identify another's mental state and respond to this with an appropriate emotion). Discrepancies between these two dimensions in one direction (S>E) or the other (E>S) are associated with sex differences in cognition: on average more males show an S>E cognitive style, while on average more females show an E>S profile. The neurobiological basis of these different profiles remains unknown. Since individuals may be typical or atypical for their sex, it is important to move away from the study of sex differences and towards the study of differences in cognitive style. Using structural magnetic resonance imaging we examined how neuroanatomy varies as a function of the discrepancy between E and S in 88 adult males from the general population. Selecting just males allows us to study discrepant E-S profiles in a pure way, unconfounded by other factors related to sex and gender. An increasing S>E profile was associated with increased gray matter volume in cingulate and dorsal medial prefrontal areas which have been implicated in processes related to cognitive control, monitoring, error detection, and probabilistic inference. An increasing E>S profile was associated with larger hypothalamic and ventral basal ganglia regions which have been implicated in neuroendocrine control, motivation and reward. These results suggest an underlying neuroanatomical basis linked to the discrepancy between these two important dimensions of individual differences in cognitive style.

An iterative jackknife approach for assessing reliability and power of FMRI group analyses

For functional magnetic resonance imaging (fMRI) group activation maps, so-called second-level random effect approaches are commonly used, which are intended to be generalizable to the population as a whole. However, reliability of a certain activation focus as a function of group composition or group size cannot directly be deduced from such maps. This question is of particular relevance when examining smaller groups (<20–27 subjects). The approach presented here tries to address this issue by iteratively excluding each subject from a group study and presenting the overlap of the resulting (reduced) second-level maps in a group percent overlap map. This allows to judge where activation is reliable even upon excluding one, two, or three (or more) subjects, thereby also demonstrating the inherent variability that is still present in second-level analyses. Moreover, when progressively decreasing group size, foci of activation will become smaller and/or disappear; hence, the group size at which a given activation disappears can be considered to reflect the power necessary to detect this particular activation. Systematically exploiting this effect allows to rank clusters according to their observable effect size. The approach is tested using different scenarios from a recent fMRI study (children performing a “dual-use” fMRI task, n = 39), and the implications of this approach are discussed.

No association of COMT (Val158Met) genotype with brain structure differences between men and women

We examined the effect of the catechol-O-methyltransferase (COMT) Val158Met polymorphism (rs4680), on brain structure in a subset (N = 82) of general population members of the Northern Finland 1966 Birth Cohort, selected through a randomization procedure, aged 33–35. Optimised voxel-based morphometry was used to produce grey matter maps from each subject's high resolution T1 weighted brain magnetic resonance images, which were subsequently entered into a general linear model with COMT genotype as defined by Met allele loading, gender and genotype by gender interaction as independent variables. Additional analyses were carried out on grey matter volumes within the dorsal lateral pre-frontal cortex (DLPFC) to examine effects on overall DLPFC volume and also using the DLPFC as a mask for voxelwise analyses, as this is an area previously reported as associated with Met allele loading. We failed to find any statistically significant association with grey matter volume and Met allele loading in the COMT gene or interaction affects between COMT and gender in either the whole brain voxel-wise analysis or in the area of the DLPFC.

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.

Effects of combining field strengths on auditory functional MRI group analysis: 1.5T and 3T

PURPOSE

To evaluate effects of combining functional magnetic resonance imaging (fMRI) data acquired from different field strengths on group analysis as a function of the number of subjects at each field strength.

MATERIALS AND METHODS

In all, 28 subjects (18 at 3T) participated in an auditory task of passively listening to a 0.75s segment of jazz music in an event-related design. Results of single-subject analysis were combined to create all possible subject combinations for a group size of eight subjects from each of the 3T and 1.5T pools, comprising subject mixtures of (3T/1.5T) 0/8, 2/6, 4/4, 6/2, and 8/0. Group analysis performance of each subject permutation was measured by receiver operating characteristic (ROC) curves and activation overlap maps.

RESULTS

While area under ROC curves, extent of activation in the gold standard region, and reliability of activation increased with the number of 3T subjects, marginal gain decreased. ROC performance overlap across mixtures was observed, indicating that some combinations of subjects markedly outperformed others. For detection of activation, 4/4 was arguably the minimum mixture level that was comparable to 3T-only group results.

CONCLUSION

Inclusion of 1.5T data does not necessarily reduce the validity of group analysis. Lower field strength data was found only to limit detection power, but did not affect specificity. Within the limits of realignment error, these results should also extend to group longitudinal analyses of subject mixtures from different field strengths.

Using brain imaging measures in studies of procognitive pharmacologic agents in schizophrenia: psychometric and quality assurance considerations

The first phase of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICs) initiative focused on the identification of cognitive constructs from human and animal neuroscience that were relevant to understanding cognitive deficits in schizophrenia, as well as promising task paradigms that could be used to assess these constructs behaviorally. The current phase of CNTRICs has the goal of expanding this initial work by including measures of brain function that can augment these behavioral tasks as biomarkers to be used in drug development processing. Here we review many of the psychometric issues that need to be addressed regarding the development and inclusion of such methods in the drug development process. In addition, we review quality assurance concerns, issues associated with multicenter trials, concerns associated with potential pharmacologic confounds on imaging measures, as well as power and analysis considerations. Although review is couched in the context of the use of biomarkers for treatment studies in schizophrenia, we believe the issues and suggestions included are relevant to the entire range of neuropsychiatric disorders as well as to a wide range of imaging modalities (i.e., functional magnetic resonance imaging, positron emission tomography, event-related potentials, electroencephalography, transcranial magnetic stimulation, near infrared spectroscopy, etc.) and are relevant to both pharmacologic and psychological intervention approaches.

The Neuro/PsyGRID calibration experiment: identifying sources of variance and bias in multicenter MRI studies

Calibration experiments precede multicenter trials to identify potential sources of variance and bias. In support of future imaging studies of mental health disorders and their treatment, the Neuro/PsyGRID consortium commissioned a calibration experiment to acquire functional and structural MRI from twelve healthy volunteers attending five centers on two occasions. Measures were derived of task activation from a working memory paradigm, fractal scaling (Hurst exponent) from resting fMRI, and grey matter distributions from T(1) -weighted sequences. At each intracerebral voxel a fixed-effects analysis of variance estimated components of variance corresponding to factors of center, subject, occasion, and within-occasion order, and interactions of center-by-occasion, subject-by-occasion, and center-by-subject, the latter (since there is no intervention) a surrogate of the expected variance of the treatment effect standard error across centers. A rank order test of between-center differences was indicative of crossover or noncrossover subject-by-center interactions. In general, factors of center, subject and error variance constituted >90% of the total variance, whereas occasion, order, and all interactions were generally <5%. Subject was the primary source of variance (70%-80%) for grey-matter, with error variance the dominant component for fMRI-derived measures. Spatially, variance was broadly homogenous with the exception of fractal scaling measures which delineated white matter, related to the flip angle of the EPI sequence. Maps of P values for the associated F-tests were also derived. Rank tests were highly significant indicating the order of measures across centers was preserved. In summary, center effects should be modeled at the voxel-level using existing and long-standing statistical recommendations.