Search

Background The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. Methods Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g. ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e. task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e. neurobiological group differences versus individual prediction. Results Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. Conclusions This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience. [ABSTRACT FROM AUTHOR]

This paper reports the development of a high-resolution 3-D MRI atlas of the Fischer 344 adult rat brain. The atlas is a 60 μm isotropic image volume composed of 256 coronal slices with 71 manually delineated structures and substructures. The atlas was developed using Pydpiper image registration pipeline to create an average brain image of 41 four-month-old male and female Fischer 344 rats. Slices in the average brain image were then manually segmented, individually and bilaterally, on the basis of image contrast in conjunction with Paxinos and Watson's (2007) stereotaxic rat brain atlas. Summary statistics (mean and standard deviation of regional volumes) are reported for each brain region across the sample used to generate the atlas, and a statistical comparison of a chosen subset of regional brain volumes between male and female rats is presented. On average, the coefficient of variation of regional brain volumes across all rats in our sample was 4%, with no individual brain region having a coefficient of variation greater than 13%. A full description of methods used, as well as the atlas, the template that the atlas was derived from, and a masking file, can be found on Zenodo at www.zenodo.org/record/3700210. To our knowledge, this is the first MRI atlas created using Fischer 344 rats and will thus provide an appropriate neuroanatomical model for researchers working with this strain. [ABSTRACT FROM AUTHOR]

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms. [ABSTRACT FROM AUTHOR]

Highlights • There is evidence that neurodevelopmental disorders overlap. Yet it is unclear how etiological pathways intersect. • SNPs identified in non-ADHD GWAS showed association with multiple dimensions related to ADHD. • Here we attempt to tease apart the heterogeneity in the phenotype, and understand its genetic underpinnings. • Results are presented as preliminary findings as they provide insight into cross-disorder associations. Abstract Several epidemiological and genetic studies have provided evidence of an overlap between neurodevelopmental disorders. However, the details of the etiological pathways remain to be elucidated. In this study, we garnered the findings of previous GWAS, conducted with schizophrenia and bipolar disorder. We conducted an exploratory study to examine the association between these SNPs and quantitative clinical/ behavioural/ cognitive/ structural brain parameters, as well as response to treatment with a fixed dose of methylphenidate, in a relatively large sample of children with ADHD. Family-based association tests were conducted with nine tag SNPs with 602 nuclear families. In addition, structural magnetic resonance imaging (sMRI) was conducted in a subset of children with ADHD (n = 76). Of the 9 tag SNPs examined, rs1602565 showed a significant association with ADHD, several dimensional measures and response to treatment. An association was also observed between rs1006737 (CACNA1C) and performance IQ. In addition, significant reductions in cortical thickness measurements were observed with the risk allele in rs1006737. These results provide preliminary evidence for putative shared genetic vulnerability between childhood ADHD and other neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]

Recently, much attention has been focused on the definition and structure of the hippocampus and its subfields, while the projections from the hippocampus have been relatively understudied. Here, we derive a reliable protocol for manual segmentation of hippocampal white matter regions (alveus, fimbria, and fornix) using high-resolution magnetic resonance images that are complementary to our previous definitions of the hippocampal subfields, both of which are freely available at https://github.com/cobralab/atlases . Our segmentation methods demonstrated high inter- and intra-rater reliability, were validated as inputs in automated segmentation, and were used to analyze the trajectory of these regions in both healthy aging (OASIS), and Alzheimer's disease (AD) and mild cognitive impairment (MCI; using ADNI). We observed significant bilateral decreases in the fornix in healthy aging while the alveus and cornu ammonis (CA) 1 were well preserved (all p's<0.006). MCI and AD demonstrated significant decreases in fimbriae and fornices. Many hippocampal subfields exhibited decreased volume in both MCI and AD, yet no significant differences were found between MCI and AD cohorts themselves. Our results suggest a neuroprotective or compensatory role for the alveus and CA1 in healthy aging and suggest that an improved understanding of the volumetric trajectories of these structures is required. [ABSTRACT FROM AUTHOR]

Abstract Background Attention-deficit/hyperactivity disorder (ADHD) is a major sequela of traumatic brain injury (TBI) in youths. The objective of this study was to examine whether ADHD symptoms are differentially associated with genetic risk and brain structure in youths with and without a history of TBI. Methods Medical history, ADHD symptoms, genetic data, and neuroimaging data were obtained from a community sample of youths. ADHD symptom severity was compared between those with and without TBI (TBI n = 418, no TBI n = 3193). The relationship of TBI history, genetic vulnerability, brain structure, and ADHD symptoms was examined by assessing 1) ADHD polygenic score (discovery sample ADHD n = 19,099, control sample n = 34,194), 2) basal ganglia volumes, and 3) fractional anisotropy in the corpus callosum and corona radiata. Results Youths with TBI reported greater ADHD symptom severity compared with those without TBI. Polygenic score was positively associated with ADHD symptoms in youths without TBI but not in youths with TBI. The negative association between the caudate volume and ADHD symptoms was not moderated by a history of TBI. However, the relationship between ADHD symptoms and structure of the genu of the corpus callosum was negative in youths with TBI and positive in youths without TBI. Conclusions The identification of distinct ADHD etiology in youths with TBI provides neurobiological insight into the clinical heterogeneity in the disorder. Results indicate that genetic predisposition to ADHD does not increase the risk for ADHD symptoms associated with TBI. ADHD symptoms associated with TBI may be a result of a mechanical insult rather than neurodevelopmental factors. [ABSTRACT FROM AUTHOR]

Accurate automated quantification of subcortical structures is a greatly pursued endeavour in neuroimaging. In an effort to establish the validity and reliability of these methods in defining the striatum, globus pallidus, and thalamus, we investigated differences in volumetry between manual delineation and automated segmentations derived by widely used FreeSurfer and FSL packages, and a more recent segmentation method, the MAGeT-Brain algorithm. In a first set of experiments, the basal ganglia and thalamus of thirty subjects (15 first episode psychosis [FEP], 15 controls) were manually defined and compared to the labels generated by the three automated methods. Our results suggest that all methods overestimate volumes compared to the manually derived “gold standard”, with the least pronounced differences produced using MAGeT. The least between-method variability was noted for the striatum, whereas marked differences between manual segmentation and MAGeT compared to FreeSurfer and FSL emerged for the globus pallidus and thalamus. Correlations between manual segmentation and automated methods were strongest for MAGeT (range: 0.51 to 0.92; p<0.01, corrected), whereas FreeSurfer and FSL showed moderate to strong Pearson correlations (range 0.44–0.86; p<0.05, corrected), with the exception of FreeSurfer pallidal (r=0.31, p=0.10) and FSL thalamic segmentations (r=0.37, p=0.051). Bland-Altman plots highlighted a tendency for greater volumetric differences between manual labels and automated methods at the lower end of the distribution (i.e. smaller structures), which was most prominent for bilateral thalamus across automated pipelines, and left globus pallidus for FSL. We then went on to examine volume and shape of the basal ganglia structures using automated techniques in 135 FEP patients and 88 controls. The striatum and globus pallidus were significantly larger in FEP patients compared to controls bilaterally, irrespective of the method used. MAGeT-Brain was more sensitive to shape-based group differences, and uncovered widespread surface expansions in the striatum and globus pallidus bilaterally in FEP patients compared to controls, and surface contractions in bilateral thalamus (FDR-corrected). By contrast, after using a recommended cluster-wise thresholding method, FSL only detected differences in the right ventral striatum (FEP>Control) and one cluster of the left thalamus (Control>FEP). These results suggest that different automated pipelines segment subcortical structures with varying degrees of variability compared to manual methods, with particularly pronounced differences found with FreeSurfer and FSL for the globus pallidus and thalamus. [ABSTRACT FROM AUTHOR]