Development, implementation, and validation of a new method for meta-analysis of voxel-based neuroimage studies

[spa] The objective of this thesis was to develop a method for the meta-analysis of neuroimaging studies that overcomes the drawbacks of the neuroimaging meta-analytic methods described above. To this end, we modified AES-SDM (Radua et al. (2014)) to incorporate several major changes that address each of the above limitations. This has resulted in a new method, called SDM-PSI, with profound differences from any previous neuroimaging meta-analytic method. I divided this overarching objective into the following individual objectives: 1. Accurately identify and circumscribe the drawbacks of the neuroimaging metaanalytic methods present at the time of the conception of this thesis. 2. Develop the statistical techniques of the new method. Optimize the usage of maximum likelihood estimation (MLE) to estimate the distribution of the missing information of the neuroimaging studies and combine it with the usage of multiple imputation techniques to recreate this non-published information. This combination shall intend to obtain better estimates of effect sizes in areas where CBM studies did not report results. 3. Develop an algorithm for the new method viable for being implemented efficiently, providing an implementation that can be successfully used in real-world scenarios. 4. Modify the SDM meta-analytic method to incorporate these new statistical techniques, as well as other novel changes in the field such as a standard subject-based permutation test instead of the spatial permutation approach used by previous methods. 5. Design and develop an algorithm for the usage of the standard permutation test as part of the SDM method. A computationally efficient implementation of such an algorithm shall also be feasible. 6. Adopt the scope not only of theoretical-methodological work but also of practical work with usable results and implementations ready to be used by the neuroimage meta-analysis community. This shall include the development of powerful software and a graphical user interface. 7. Disseminate the new method to ease the adoption of its application by the scientific community. This included the release of the new software in a user-friendly form, writing and recording a visual publication [117], and proactively seeking collaboration studies with scientific teams from other institutions and labs that would provide the first publications making use of the new method. The first objective is addressed in the first published article that comprises this thesis; the second and third objectives are addressed in the second published article; the fourth, fifth and sixth objectives are addressed in the third published article, and the seventh objective is addressed in the fourth published article

Amygdala where art thou?

The commentary of Morriss et al. on our recent meta-analysis of functional magnetic resonance imaging (fMRI) fear/threat extinction studies in humans (Fullana et al., 2018) raises some important issues for future research in the field. In essence, they argue that the lack of consistent evidence for amygdala and ventromedial prefrontal cortex (vmPFC) involvement in these studies, as summarized by meta-analysis, might be partly due to the fact that very few of these studies have provided appropriate analyses of time-varying neural responses, which Morriss et al. contend should be the gold standard

Meta-analysis of functional neuroimaging and cognitive control studies in schizophrenia: preliminary elucidation of a core dysfunctional timing network

Timing and other cognitive processes demanding cognitive control become interlinked when there is an increase in the level of difficulty or effort required. Both functions are interrelated and share neuroanatomical bases. A previous meta-analysis of neuroimaging studies found that people with schizophrenia had significantly lower activation, relative to normal controls, of most right hemisphere regions of the time circuit. This finding suggests that a pattern of disconnectivity of this circuit, particularly in the supplementary motor area, is a trait of this mental disease. We hypothesize that a dysfunctional temporal/cognitive control network underlies both cognitive and psychiatric symptoms of schizophrenia and that timing dysfunction is at the root of the cognitive deficits observed. The goal of our study was to look, in schizophrenia patients, for brain structures activated both by execution of cognitive tasks requiring increased effort and by performance of time perception tasks. We conducted a signed differential mapping (SDM) meta-analysis of functional neuroimaging studies in schizophrenia patients assessing the brain response to increasing levels of cognitive difficulty. Then, we performed a multimodal meta-analysis to identify common brain regions in the findings of that SDM meta-analysis and our previously-published activation likelihood estimate (ALE) meta-analysis of neuroimaging of time perception in schizophrenia patients. The current study supports the hypothesis that there exists an overlap between neural structures engaged by both timing tasks and non-temporal cognitive tasks of escalating difficulty in schizophrenia. The implication is that a deficit in timing can be considered as a trait marker of the schizophrenia cognitive profile

Cortical gray matter reduction precedes transition to psychosis in individuals at clinical high-risk for psychosis: A voxel-based meta-analysis

Gray matter and cortical thickness reductions have been documented in individuals at clinical high-risk for psychosis and may be more pronounced in those who transition to psychosis. However, these findings rely on small samples and are inconsistent across studies. In this review and meta-analysis we aimed to investigate neuroanatomical correlates of clinical high-risk for psychosis and potential predictors of transition, using a novel metaanalytic method (Seed-based d Mapping with Permutation of Subject Images) and cortical mask, combining data from surface-based and voxel-based morphometry studies. Individuals at clinical high-risk for psychosis who later transitioned to psychosis were compared to those who did not and to controls, and included three statistical maps. Overall, individuals at clinical high-risk for psychosis did not differ from controls, however, within the clinical high-risk for psychosis group, transition to psychosis was associated with less cortical gray matter in the right temporal lobe (Hedges' g = −0.377), anterior cingulate and paracingulate (Hedges' g = −0.391). These findings have the potential to help refine prognostic and etiopathological research in early psychos

Focusing on Comorbidity A Novel Meta-Analytic Approach and Protocol to Disentangle the Specific Neuroanatomy of Co-occurring Mental Disorders

Background: In mental health, comorbidities are the norm rather than the exception. However, current meta-analytic methods for summarizing the neural correlates of mental disorders do not consider comorbidities, reducing them to a source of noise and bias rather than benefitting from their valuable information. Objectives: We describe and validate a novel neuroimaging meta-analytic approach that focuses on comorbidities. In addition, we present the protocol for a meta-analysis of all major mental disorders and their comorbidities. Methods: The novel approach consists of a modification of Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) in which the linear models have no intercept. As in previous SDM meta-analyses, the dependent variable is the brain anatomical difference between patients and controls in a voxel. However, there is no primary disorder, and the independent variables are the percentages of patients with each disorder and each pair of potentially comorbid disorders. We use simulations to validate and provide an example of this novel approach, which correctly disentangled the abnormalities associated with each disorder and comorbidity. We then describe a protocol for conducting the new meta-analysis of all major mental disorders and their comorbidities. Specifically, we will include all voxel-based morphometry (VBM) studies of mental disorders for which a meta-analysis has already been published, including at least 10 studies. We will use the novel approach to analyze all included studies in two separate single linear models, one for children/adolescents and one for adults. Discussion: The novel approach is a valid method to focus on comorbidities. The meta-analysis will yield a comprehensive atlas of the neuroanatomy of all major mental disorders and their comorbidities, which we hope might help develop potential diagnostic and therapeutic tools

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to examine age‐related trajectories inferred from cross‐sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter‐individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age‐related morphometric patterns

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large‐scale studies. In response, we used cross‐sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to infer age‐related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta‐analysis and one‐way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes

Development, implementation, and validation of a new method for meta-analysis of voxel-based neuroimage studies

Programa de Doctorat en Medicina i Recerca Translacional[eng] The objective of this thesis was to develop a method for the meta-analysis of neuroimaging studies that overcomes the drawbacks of the neuroimaging meta-analytic methods described above. To this end, we modified AES-SDM (Radua et al. (2014)) to incorporate several major changes that address each of the above limitations. This has resulted in a new method, called SDM-PSI, with profound differences from any previous neuroimaging meta-analytic method. I divided this overarching objective into the following individual objectives: 1. Accurately identify and circumscribe the drawbacks of the neuroimaging metaanalytic methods present at the time of the conception of this thesis. 2. Develop the statistical techniques of the new method. Optimize the usage of maximum likelihood estimation (MLE) to estimate the distribution of the missing information of the neuroimaging studies and combine it with the usage of multiple imputation techniques to recreate this non-published information. This combination shall intend to obtain better estimates of effect sizes in areas where CBM studies did not report results. 3. Develop an algorithm for the new method viable for being implemented efficiently, providing an implementation that can be successfully used in real-world scenarios. 4. Modify the SDM meta-analytic method to incorporate these new statistical techniques, as well as other novel changes in the field such as a standard subject-based permutation test instead of the spatial permutation approach used by previous methods. 5. Design and develop an algorithm for the usage of the standard permutation test as part of the SDM method. A computationally efficient implementation of such an algorithm shall also be feasible. 6. Adopt the scope not only of theoretical-methodological work but also of practical work with usable results and implementations ready to be used by the neuroimage meta-analysis community. This shall include the development of powerful software and a graphical user interface. 7. Disseminate the new method to ease the adoption of its application by the scientific community. This included the release of the new software in a user-friendly form, writing and recording a visual publication [117], and proactively seeking collaboration studies with scientific teams from other institutions and labs that would provide the first publications making use of the new method. The first objective is addressed in the first published article that comprises this thesis; the second and third objectives are addressed in the second published article; the fourth, fifth and sixth objectives are addressed in the third published article, and the seventh objective is addressed in the fourth published article