Influence of Processing Pipeline on Cortical Thickness Measurement.

In recent years, replicability of neuroscientific findings, specifically those concerning correlates of morphological properties of gray matter (GM), have been subject of major scrutiny. Use of different processing pipelines and differences in their estimates of the macroscale GM may play an important role in this context. To address this issue, here, we investigated the cortical thickness estimates of three widely used pipelines. Based on analyses in two independent large-scale cohorts, we report high levels of within-pipeline reliability of the absolute cortical thickness-estimates and comparable spatial patterns of cortical thickness-estimates across all pipelines. Within each individual, absolute regional thickness differed between pipelines, indicating that in-vivo thickness measurements are only a proxy of actual thickness of the cortex, which shall only be compared within the same software package and thickness estimation technique. However, at group level, cortical thickness-estimates correlated strongly between pipelines, in most brain regions. The smallest between-pipeline correlations were observed in para-limbic areas and insula. These regions also demonstrated the highest interindividual variability and the lowest reliability of cortical thickness-estimates within each pipeline, suggesting that structural variations within these regions should be interpreted with caution.

Structural and Diffusion MRI Analyses With Histological Observations in Patients With Lissencephaly.

The development of cortical convolutions, gyri and sulci, is a complex process that takes place during prenatal development. Lissencephaly, a rare genetic condition characterized by the lack of cortical convolutions, offers a model to look into biological processes that lead to the development of convolutions. Retrospective, qualitative, and quantitative analyses of structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) were performed in patients with lissencephaly (N = 10) and age-/sex-matched controls (N = 10). In order to identify microstructural correlates of structural MRI and DTI findings, postmortem brains of patients with lissencephaly (N = 4) and age-matched controls (N = 4) were also examined with histology. Patients with lissencephaly had significantly smaller gyrification index and volumes of hemispheric white and gray matter, compared to the age-/sex-matched control group. However, there was no significant difference between groups in the subcortical gray matter volumes. Although the majority of patients with lissencephaly had a preserved normal-like appearance of major fissures and primary sulci, the spatial distribution of agyric cortical regions was different in patients with lissencephaly-1 (LIS1) and doublecortin (DCX) mutations. Lastly, in patients with lissencephaly, the spatiotemporal distribution of projection pathways was preserved while short- to medium-range cortico-cortical pathways were absent or fewer in number. Our results indicate that in the patients with lissencephaly cortical system is affected more than the subcortical one.

Exploring Individual Brain Variability during Development based on Patterns of Maturational Coupling of Cortical Thickness: A Longitudinal MRI Study.

Structural covariance has recently emerged as a tool to study brain connectivity in health and disease. The main assumption behind the phenomenon of structural covariance is that changes in brain structure during development occur in a coordinated fashion. However, no study has yet explored the correlation of structural brain changes within individuals across development. Here, we used longitudinal magnetic resonance imaging scans from 141 normally developing children and adolescents (scanned 3 times) to introduce a novel subject-based maturational coupling approach. For each subject, maturational coupling was defined as similarity in the trajectory of cortical thickness (across the time points) between any two cortical regions. Our approach largely captured features seen in population-based structural covariance, and confirmed strong maturational coupling between homologous and near-neighbor cortical regions. Stronger maturational coupling among several homologous regions was observed for females compared to males, possibly indicating greater interhemispheric connectivity in females. Developmental changes in maturational coupling within the default-mode network (DMN) aligned with developmental changes in structural and functional DMN connectivity. Our findings indicate that patterns of maturational coupling within individuals may provide mechanistic explanation for the phenomenon of structural covariance, and allow investigation of individual brain variability with respect to cognition and disease vulnerability.

Integration of "omics" Data and Phenotypic Data Within a Unified Extensible Multimodal Framework.

Analysis of "omics" data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data. In 2015, the Ludmer Centre for Neuroinformatics and Mental Health at McGill University brought together expertise from the Douglas Mental Health University Institute, the Lady Davis Institute and the Montreal Neurological Institute (MNI) to form a genetics/epigenetics working group. The objectives of this working group are to: (i) design an automated and seamless process for (epi)genetic data that consolidates heterogeneous datasets into the LORIS open-source data platform; (ii) streamline data analysis; (iii) integrate results with provenance information; and (iv) facilitate structured and versioned sharing of pipelines for optimized reproducibility using high-performance computing (HPC) environments via the CBRAIN processing portal. This article outlines the resulting generalizable "omics" framework and its benefits, specifically, the ability to: (i) integrate multiple types of biological and multi-modal datasets (imaging, clinical, demographics and behavioral); (ii) automate the process of launching analysis pipelines on HPC platforms; (iii) remove the bioinformatic barriers that are inherent to this process; (iv) ensure standardization and transparent sharing of processing pipelines to improve computational consistency; (v) store results in a queryable web interface; (vi) offer visualization tools to better view the data; and (vii) provide the mechanisms to ensure usability and reproducibility. This framework for workflows facilitates brain research discovery by reducing human error through automation of analysis pipelines and seamless linking of multimodal data, allowing investigators to focus on research instead of data handling.

Multifactorial causal model of brain (dis)organization and therapeutic intervention: Application to Alzheimer's disease.

Generative models focused on multifactorial causal mechanisms in brain disorders are scarce and generally based on limited data. Despite the biological importance of the multiple interacting processes, their effects remain poorly characterized from an integrative analytic perspective. Here, we propose a spatiotemporal multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention that accounts for local causal interactions, effects propagation via physical brain networks, cognitive alterations, and identification of optimum therapeutic interventions. In this article, we focus on describing the model and applying it at the population-based level for studying late onset Alzheimer's disease (LOAD). By interrelating six different neuroimaging modalities and cognitive measurements, this model accurately predicts spatiotemporal alterations in brain amyloid-ß (Aß) burden, glucose metabolism, vascular flow, resting state functional activity, structural properties, and cognitive integrity. The results suggest that a vascular dysregulation may be the most-likely initial pathologic event leading to LOAD. Nevertheless, they also suggest that LOAD it is not caused by a unique dominant biological factor (e.g. vascular or Aß) but by the complex interplay among multiple relevant direct interactions. Furthermore, using theoretical control analysis of the identified population-based multifactorial causal network, we show the crucial advantage of using combinatorial over single-target treatments, explain why one-target Aß based therapies might fail to improve clinical outcomes, and propose an efficiency ranking of possible LOAD interventions. Although still requiring further validation at the individual level, this work presents the first analytic framework for dynamic multifactorial brain (dis)organization that may explain both the pathologic evolution of progressive neurological disorders and operationalize the influence of multiple interventional strategies.

The Neuro Bureau ADHD-200 Preprocessed repository.

In 2011, the "ADHD-200 Global Competition" was held with the aim of identifying biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (s-MRI) data collected on 973 individuals. Statisticians and computer scientists were potentially the most qualified for the machine learning aspect of the competition, but generally lacked the specialized skills to implement the necessary steps of data preparation for rs-fMRI. Realizing this barrier to entry, the Neuro Bureau prospectively collaborated with all competitors by preprocessing the data and sharing these results at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (http://www.nitrc.org/frs/?group_id=383). This "ADHD-200 Preprocessed" release included multiple analytical pipelines to cater to different philosophies of data analysis. The processed derivatives included denoised and registered 4D fMRI volumes, regional time series extracted from brain parcellations, maps of 10 intrinsic connectivity networks, fractional amplitude of low frequency fluctuation, and regional homogeneity, along with grey matter density maps. The data was used by several teams who competed in the ADHD-200 Global Competition, including the winning entry by a group of biostaticians. To the best of our knowledge, the ADHD-200 Preprocessed release was the first large public resource of preprocessed resting-state fMRI and structural MRI data, and remains to this day the only resource featuring a battery of alternative processing paths.

Imaging structural covariance in the development of intelligence.

Verbal and non-verbal intelligence in children is highly correlated, and thus, it has been difficult to differentiate their neural substrates. Nevertheless, recent studies have shown that verbal and non-verbal intelligence can be dissociated and focal cortical regions corresponding to each have been demonstrated. However, the pattern of structural covariance corresponding to verbal and non-verbal intelligence remains unexplored. In this study, we used 586 longitudinal anatomical MRI scans of subjects aged 6-18 years, who had concurrent intelligence quotient (IQ) testing on the Wechsler Abbreviated Scale of Intelligence. Structural covariance networks (SCNs) were constructed using interregional correlations in cortical thickness for low-IQ (Performance IQ=100±8, Verbal IQ=100±7) and high-IQ (PIQ=121±8, VIQ=120±9) groups. From low- to high-VIQ group, we observed constrained patterns of anatomical coupling among cortical regions, complemented by observations of higher global efficiency and modularity, and lower local efficiency in high-VIQ group, suggesting a shift towards a more optimal topological organization. Analysis of nodal topological properties (regional efficiency and participation coefficient) revealed greater involvement of left-hemispheric language related regions including inferior frontal and superior temporal gyri for high-VIQ group. From low- to high-PIQ group, we did not observe significant differences in anatomical coupling patterns, global and nodal topological properties. Our findings indicate that people with higher verbal intelligence have structural brain differences from people with lower verbal intelligence - not only in localized cortical regions, but also in the patterns of anatomical coupling among widely distributed cortical regions, possibly resulting to a system-level reorganization that might lead to a more efficient organization in high-VIQ group.

Brain connectivity in normally developing children and adolescents.

The developing human brain undergoes an astonishing sequence of events that continuously shape the structural and functional brain connectivity. Distinct regional variations in the timelines of maturational events (synaptogenesis and synaptic pruning) occurring at the synaptic level are reflected in brain measures at macroscopic resolution (cortical thickness and gray matter density). Interestingly, the observed brain changes coincide with cognitive milestones suggesting that the changing scaffold of brain circuits may subserve cognitive development. Recent advances in connectivity analysis propelled by graph theory have allowed, on one hand, the investigation of maturational changes in global organization of structural and functional brain networks; and on the other hand, the exploration of specific networks within the context of global brain networks. An emerging picture from several connectivity studies is a system-level rewiring that constantly refines the connectivity of the developing brain.

Heritable changes in regional cortical thickness with age.

It is now well established that regional indices of brain structure such as cortical thickness, surface area or grey matter volume exhibit spatially variable patterns of heritability. However, a recent study found these patterns to change with age during development, a result supported by gene expression studies. Changes in heritability have not been investigated in adulthood so far and could have important implications in the study of heritability and genetic correlations in the brain as well as in the discovery of specific genes explaining them. Herein, we tested for genotype by age (G ×A) interactions, an extension of genotype by environment interactions, through adulthood and healthy aging in 902 subjects from the Genetics of Brain Structure (GOBS) study. A "jackknife" based method for the analysis of stable cortical thickness clusters (JASC) and scale selection is also introduced. Although additive genetic variance remained constant throughout adulthood, we found evidence for incomplete pleiotropy across age in the cortical thickness of paralimbic and parieto-temporal areas. This suggests that different genetic factors account for cortical thickness heritability at different ages in these regions.

Decreased caudate response to milkshake is associated with higher body mass index and greater impulsivity.

Previous investigations consistently report a negative association between body mass index (BMI) and response in the caudate nucleus during the consumption of palatable and energy dense food. Since this response has also been linked to weight gain, we sought to replicate this finding and determine if the reduced response is associated with measures of impulsivity or food reward. Two studies were conducted in which fMRI was used to measure brain response to milkshake and a tasteless control solution. In Study 1 (n=25) we also assessed self-reported impulsivity, willingness to work for food, and subjective experiences of the pleasantness of milkshake taste and aroma. Replicating prior work, we report a negative association between BMI and brain response to milkshake vs. tasteless in the caudate nucleus. The opposite pattern was observed in the ventral putamen, with greater response observed in the 13 overweight compared to the 12 healthy weight subjects. Regression of brain response against impulsivity and food reward measures revealed one significant association: in the overweight but not healthy weight group self-reported impulsivity was negatively associated with caudate response to milkshake. In Study 2 (n=14), in addition to assessing brain response to milkshake and tasteless solutions subjects completed a go/no-go task outside the scanner. As predicted, we identified an inverse relationship between caudate response to milkshake vs. tasteless and failure to inhibit responses on the no-go trials. We conclude that the inverse correlation between BMI and caudate response to milkshake is associated with impulsivity but not food reward. These findings suggest that response to milkshake in the dorsal striatum may be related to weight gain by promoting impulsive eating behavior.

Genetically determined differences in brain response to a primary food reward.

Combining genetic and neuroimaging techniques may elucidate the biological underpinnings of individual differences in neurophysiology and potential vulnerabilities to disease. The TaqIA A1 variant is associated with diminished dopamine D(2) receptor density, higher body mass, and food reinforcement. It also moderates the relationship between brain response to food and future weight gain. This suggests that the polymorphism is associated with a fundamental difference in the neurophysiology of food that may predispose toward overeating. An alternative possibility is that factors, such as impulsivity, eating style, reward drive, and perception, which may covary with the polymorphism, influence reward coding and eating behavior. To distinguish between these alternatives, we used functional magnetic resonance imaging to measure neural response to the ingestion of palatable and caloric milkshakes in healthy subjects with (A1+; n = 13) and without (A1-; n = 13) the TaqIA A1 allele. The groups were selected from a larger group to be matched for linked individual factors such as age, gender, education, body mass index, impulsivity, eating style, and perceptual responses to the milkshake. We demonstrate an interaction between genotype (A1+ vs A1-) and stimulus (milkshake vs a tasteless/odorless baseline) in the midbrain, thalamus, and orbital frontal cortex; whereas A1- shows increased responses to milkshake, A1+ shows decreased responses to milkshake relative to baseline. This interaction occurs despite similar ratings of milkshake pleasantness, intensity, and familiarity. We therefore conclude that there is a specific association between the TaqIA A1 polymorphism and brain response during ingestion of a palatable food.