Evaluation of volume-based and surface-based brain image registration methods

A cortical surface template for human neuroscience

Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences across brains. Existing human cortical surface templates sample locations unevenly because of distortions introduced by inflation of the folded cortex into a standard shape. Here we present the onavg template, which affords uniform sampling of the cortex. We created the onavg template based on openly available high-quality structural scans of 1,031 brains-25 times more than existing cortical templates. We optimized the vertex locations based on cortical anatomy, achieving an even distribution. We observed consistently higher multivariate pattern classification accuracies and representational geometry inter-participant correlations based on onavg than on other templates, and onavg only needs three-quarters as much data to achieve the same performance compared with other templates. The optimized sampling also reduces CPU time across algorithms by 1.3-22.4% due to less variation in the number of vertices in each searchlight.

Electric field envelope focality in superficial brain areas with linear alignment montage in temporal interference stimulation

Temporal interference stimulation (TIS) uses two pairs of conventional transcranial alternating current stimulation (tACS) electrodes, each with a different frequency, to generate a time-varying electric field (EF) envelope (EFE). The EFE focality in primary somatosensory and motor cortex areas of a standard human brain was computed using newly defined linear alignment montages. Sixty head volume conductor models constructed from magnetic resonance images were considered to evaluate interindividual variability. Six TIS and two tACS electrode montages were considered, including linear and rectangular alignments. EFEs were computed using the scalar-potential finite-difference method. The computed EFE was projected onto the standard brain space for each montage. Computational results showed that TIS and tACS generated different EFE and EF distributions in postcentral and precentral gyri regions. For TIS, the EFE amplitude in the target areas had lower variability than the EF strength of tACS. However, bipolar tACS montages showed higher focality in the superficial postcentral and precentral gyri regions than in TIS. TIS generated greater EFE penetration than bipolar tACS at depths <5-10 mm below the brain surface. From group-level analysis, tACS with a bipolar montage was preferred for targets <5-10 mm in depth (gyral crowns) and TIS for deeper targets. TIS with a linear alignment montage could be an effective method for deep structures and sulcal walls. These findings provide valuable insights into the choice of TIS and tACS for stimulating specific brain regions.

Reducing individual differences in task fMRI with OGRE (One-step General Registration and Extraction) preprocessing

Volumetric analysis methods continue to enjoy great popularity in the analysis of task-related functional MRI (fMRI) data. Among these methods, the software package FSL (FMRIB, Oxford, UK) is omnipresent throughout the field. However, it remains unknown what advantages might be gained by integrating FSL with alternative preprocessing tools. Here we developed the One-step General Registration and Extraction (OGRE) pipeline to apply FreeSurfer brain extraction for simultaneous registration and motion correction ("one-step resampling"), for FSL volumetric analysis of fMRI data. We compared three preprocessing approaches (OGRE, FSL, and fMRIPrep) in a dataset wherein adult human volunteers (N=39) performed a precision drawing task during fMRI scanning. The three approaches produced grossly similar results, but OGRE's preprocessing led to lower inter-individual variability across the brain and greater detected activation in primary motor cortex contralateral to movement. This demonstrates that FreeSurfer tools and one-step resampling can improve FSL's volumetric analysis of fMRI data. The OGRE pipeline provides an off-the-shelf method to apply FreeSurfer-based brain extraction and one-step resampling of motion correction and registration for FSL analysis of task fMRI data.

Two common and distinct forms of variation in human functional brain networks

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.

Long-term follow-up of brain regional changes and the association with cognitive impairment in quarantined COVID-19 survivors

OBJECTIVE

This study aimed to evaluate the neuropsychiatric symptoms of quarantined COVID-19 survivors 15 months after discharge and explore its potential association with structural and functional brain changes and inflammation.

METHODS

A total of 51 quarantined COVID-19 survivors and 74 healthy controls were included in this study. Cognitive function was assessed using the THINC-integrated tool. Structural brain changes were examined through both surface- and volume-based analyses, and functional changes were assessed using resting-state amplitude low-frequency fluctuation (ALFF). Serum inflammatory markers were measured by a multiplexed flow cytometric assay.

RESULTS

COVID-19 survivors exhibited subjective cognitive decline compared to healthy controls, despite no significant differences in objective cognitive tasks. Structural analysis revealed significantly increased gray matter volume and cortical surface area in the left transverse temporal gyrus (Heschl's gyrus) in quarantined COVID-19 survivors. This enlargement was negatively correlated with cognitive impairment. The ALFF analysis showed decreased neural activity in multiple brain regions. Elevated levels of serum inflammatory markers were also found in COVID-19 survivors, including MIP-1a, MIP-1b, TNF-a, and IL-8, which correlated with functional abnormalities.

CONCLUSIONS

Our findings indicate a subjective cognitive decline in quarantined COVID-19 survivors 15 months after discharge, which is associated with brain structural alterations in the left Heschl's gyrus. The observed elevation of inflammatory markers suggests a potential mechanism involving inflammation-induced neurogenesis. These results contribute to our understanding of the possible mechanisms underlying long-term neuropsychiatric consequences of COVID-19 and highlight the need for further research to develop targeted interventions.

Evaluation of surface-based hippocampal registration using ground-truth subfield definitions

The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches.

Alcohol-induced damage to the fimbria/fornix reduces hippocampal-prefrontal cortex connection during early abstinence

INTRODUCTION

Alcohol dependence is characterized by a gradual reduction in cognitive control and inflexibility to contingency changes. The neuroadaptations underlying this aberrant behavior are poorly understood. Using an animal model of alcohol use disorders (AUD) and complementing diffusion-weighted (dw)-MRI with quantitative immunohistochemistry and electrophysiological recordings, we provide causal evidence that chronic intermittent alcohol exposure affects the microstructural integrity of the fimbria/fornix, decreasing myelin basic protein content, and reducing the effective communication from the hippocampus (HC) to the prefrontal cortex (PFC). Using a simple quantitative neural network model, we show how disturbed HC-PFC communication may impede the extinction of maladaptive memories, decreasing flexibility. Finally, combining dw-MRI and psychometric data in AUD patients, we discovered an association between the magnitude of microstructural alteration in the fimbria/fornix and the reduction in cognitive flexibility. Overall, these findings highlight the vulnerability of the fimbria/fornix microstructure in AUD and its potential contribution to alcohol pathophysiology. Fimbria vulnerability to alcohol underlies hippocampal-prefrontal cortex dysfunction and correlates with cognitive impairment.

Dysfunction of default mode network characterizes generalized anxiety disorder relative to social anxiety disorder and post-traumatic stress disorder

BACKGROUND

The perseverative cognition of generalized anxiety disorder (GAD) is distinctive compared to other anxiety disorders. However, the disease-specific and shared neuropathophysiological mechanisms of GAD remain unclear.

METHODS

We recruited medication-free patients of GAD (N = 33), social anxiety disorder (SAD; N = 36), post-traumatic stress disorder (PTSD; N = 59), and healthy controls (HC; N = 50). All subjects underwent clinical assessments and resting-state functional magnetic resonance imaging. We compared both the amplitude low-frequency fluctuation (ALFF) and seed-based functional connectivity across the whole brain, using the significantly different regions from the ALFF analyses as seed regions, followed by post-hoc tests.

RESULTS

We found that ALFF of the left angular gyrus (AG), left inferior parietal lobule (IPL), left precentral gyrus, left middle temporal gyrus, and left cerebellum were higher in GAD compared with SAD, PTSD and HC. This trend was further corroborated by the higher functional connectivity between left AG and bilateral IPL, left inferior temporal gyrus, and left medial prefrontal cortex (mPFC) in GAD. In addition, GAD and SAD both showed abnormally higher left AG-right insula connectivity. Significant correlations were found between anxiety symptom severity and the left AG regional activity and left AG-left mPFC connectivity.

LIMITATIONS

We did not compare the differences in neuroimaging between GAD and other anxiety disorders, such as panic disorder.

CONCLUSIONS

The default mode network dysfunction may underlie the distinctive perseverative thoughts of GAD relative to other anxiety disorders, and left AG-right insula connectivity may reflect somatic anxiety of anxiety disorder spectrum.

It is the locus coeruleus! Or… is it?: a proposition for analyses and reporting standards for structural and functional magnetic resonance imaging of the noradrenergic locus coeruleus

The noradrenergic locus coeruleus (LC) is one of the protein pathology epicenters in neurodegenerative diseases. In contrast to PET (positron emission tomography), MRI (magnetic resonance imaging) offers the spatial resolution necessary to investigate the 3-4 mm wide and 1.5 cm long LC. However, standard data postprocessing is often too spatially imprecise to allow investigating the structure and function of the LC at the group level. Our analysis pipeline uses a combination of existing toolboxes (SPM12, ANTs, FSL, FreeSurfer), and is tailored towards achieving suitable spatial precision in the brainstem area. Its effectiveness is demonstrated using 2 datasets comprising both younger and older adults. We also suggest quality assessment procedures which allow to quantify the spatial precision obtained. Spatial deviations below 2.5 mm in the LC area are achieved, which is superior to current standard approaches. Relevant for ageing and clinical researchers interested in brainstem imaging, we provide a tool for more reliable analyses of structural and functional LC imaging data which can be also adapted for investigating other nuclei of the brainstem.

Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models

We describe CounterSynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesised counterfactuals with voxel-based morphometry, classification and regression of the conditioning attributes, and the Fréchet inception distance. Examining downstream discriminative performance in the context of engineered demographic imbalance and confounding, we use UK Biobank and OASIS magnetic resonance imaging data to benchmark CounterSynth augmentation against current solutions to these problems. We achieve state-of-the-art improvements, both in overall fidelity and equity. The source code for CounterSynth is available at https://github.com/guilherme-pombo/CounterSynth.

Asymmetric signaling across the hierarchy of cytoarchitecture within the human connectome

Cortical variations in cytoarchitecture form a sensory-fugal axis that shapes regional profiles of extrinsic connectivity and is thought to guide signal propagation and integration across the cortical hierarchy. While neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy, indicating that bottom-up transitions were easier to complete compared to top-down. Supporting analyses demonstrated that asymmetries were underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Lastly, we found that asymmetries correlated with differences in communicability and intrinsic neuronal time scales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.

Connectome-wide Functional Connectivity Abnormalities in Youth With Obsessive-Compulsive Symptoms

BACKGROUND

Obsessive-compulsive symptomatology (OCS) is common in adolescence but usually does not meet the diagnostic threshold for obsessive-compulsive disorder. Nevertheless, both obsessive-compulsive disorder and subthreshold OCS are associated with increased likelihood of experiencing other serious psychiatric conditions, including depression and suicidal ideation. Unfortunately, there is limited information on the neurobiology of OCS.

METHODS

Here, we undertook one of the first brain imaging studies of OCS in a large adolescent sample (analyzed n = 832) from the Philadelphia Neurodevelopmental Cohort. We investigated resting-state functional magnetic resonance imaging functional connectivity using complementary analytic approaches that focus on different neuroanatomical scales, from known functional systems to connectome-wide tests.

RESULTS

We found a robust pattern of connectome-wide, OCS-related differences, as well as evidence of specific abnormalities involving known functional systems, including dorsal and ventral attention, frontoparietal, and default mode systems. Analysis of cerebral perfusion imaging and high-resolution structural imaging did not show OCS-related differences, consistent with domain specificity to functional connectivity.

CONCLUSIONS

The brain connectomic associations with OCS reported here, together with early studies of its clinical relevance, support the potential for OCS as an early marker of psychiatric risk that may enhance our understanding of mechanisms underlying the onset of adolescent psychopathology.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Improved correspondence of fMRI visual field localizer data after cortex-based macroanatomical alignment

Studying the visual system with fMRI often requires using localizer paradigms to define regions of interest (ROIs). However, the considerable interindividual variability of the cerebral cortex represents a crucial confound for group-level analyses. Cortex-based alignment (CBA) techniques reliably reduce interindividual macroanatomical variability. Yet, their utility has not been assessed for visual field localizer paradigms, which map specific parts of the visual field within retinotopically organized visual areas. We evaluated CBA for an attention-enhanced visual field localizer, mapping homologous parts of each visual quadrant in 50 participants. We compared CBA with volume-based alignment and a surface-based analysis, which did not include macroanatomical alignment. CBA led to the strongest increase in the probability of activation overlap (up to 86%). At the group level, CBA led to the most consistent increase in ROI size while preserving vertical ROI symmetry. Overall, our results indicate that in addition to the increased signal-to-noise ratio of a surface-based analysis, macroanatomical alignment considerably improves statistical power. These findings confirm and extend the utility of CBA for the study of the visual system in the context of group analyses. CBA should be particularly relevant when studying neuropsychiatric disorders with abnormally increased interindividual macroanatomical variability.

An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery

Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI images using an improved deep learning-based skull-stripping and data augmentation strategy, which enables segmentation of large brain regions by atlas or template registration, and 2) address segmentation of small brain structures, such as the paraflocculus, a small lobule of the cerebellum, for which DeepBrainIPP performs direct segmentation with a dedicated model, producing results superior to the skull-stripping/atlas-registration paradigm. We demonstrate our approach on data from both in vivo and ex vivo samples, using an in-house dataset of 172 images, expanded to 4,040 samples through data augmentation. Our skull stripping model produced an average Dice score of 0.96 and residual volume of 2.18%. This facilitated automatic registration of the skull-stripped brain to an atlas yielding an average cross-correlation of 0.98. For small brain structures, direct segmentation yielded an average Dice score of 0.89 and 5.32% residual volume error, well below the tolerance threshold for phenotype detection. Full pipeline execution is provided to non-expert users via a Web-based interface, which exposes analysis parameters, and is powered by a service that manages job submission, monitors job status and provides job history. Usability, reliability, and user experience of DeepBrainIPP was measured using the Customer Satisfaction Score (CSAT) and a modified PYTHEIA Scale, with a rating of excellent. DeepBrainIPP code, documentation and network weights are freely available to the research community.

Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare

Brain development and atrophy accompany people's life. Brain development diseases, such as autism and Alzheimer's disease, affect a large part of the population. Analyzing brain development is very important in public healthcare, and image registration is essential in medical brain image analysis. Many previous studies investigate registration accuracy by the “ground truth” dataset, marker-based similarity calculation, and expert check to find the best registration algorithms. But the evaluation of image registration technology only at the accuracy level is not comprehensive. Here, we compare the performance of three publicly available registration techniques in brain magnetic resonance imaging (MRI) analysis based on some key features widely used in previous MRI studies for classification and detection tasks. According to the analysis results, SPM12 has a stable speed and success rate, and it always works as a guiding tool for newcomers to medical image analysis. It can preserve maximum contrast information, which will facilitate studies such as tumor diagnosis. FSL is a mature and widely applicable toolkit for users, with a relatively stable success rate and good performance. It has complete functions and its function-based integrated toolbox can meet the requirements of different researchers. AFNI is a flexible and complex tool that is more suitable for professional researchers. It retains most details in medical image analysis, which makes it useful in fine-grained analysis such as volume estimation. Our study provides a new idea for comparing registration tools, where tool selection strategy mainly depends on the research task in which the selected tool can leverage its unique advantages.

BEAN: Brain Extraction and Alignment Network for 3D Fetal Neurosonography

Brain extraction (masking of extra-cerebral tissues) and alignment are fundamental first steps of most neuroimage analysis pipelines. The lack of automated solutions for 3D ultrasound (US) has therefore limited its potential as a neuroimaging modality for studying fetal brain development using routinely acquired scans. In this work, we propose a convolutional neural network (CNN) that accurately and consistently aligns and extracts the fetal brain from minimally pre-processed 3D US scans. Our multi-task CNN, Brain Extraction and Alignment Network (BEAN), consists of two independent branches: 1) a fully-convolutional encoder-decoder branch for brain extraction of unaligned scans, and 2) a two-step regression-based branch for similarity alignment of the brain to a common coordinate space. BEAN was tested on 356 fetal head 3D scans spanning the gestational range of 14 to 30 weeks, significantly outperforming all current alternatives for fetal brain extraction and alignment. BEAN achieved state-of-the-art performance for both tasks, with a mean Dice Similarity Coefficient (DSC) of 0.94 for the brain extraction masks, and a mean DSC of 0.93 for the alignment of the target brain masks. The presented experimental results show that brain structures such as the thalamus, choroid plexus, cavum septum pellucidum, and Sylvian fissure, are consistently aligned throughout the dataset and remain clearly visible when the scans are averaged together. The BEAN implementation and related code can be found under www.github.com/felipemoser/kelluwen.

Distinct patterns of altered quantitative T1ρ and functional BOLD response associated with history of suicide attempts in bipolar disorder

Despite the high risk for suicide, relatively few studies have explored the relationship between suicide and brain imaging measures in bipolar disorder. In addition, fewer studies have explored the possibility that altered brain metabolism may be associated with suicide attempt. To begin to fill in these gaps, we evaluated functional (task based fMRI) and metabolic (quantitative T1ρ) differences associated with suicide attempt in participants with bipolar disorder. Thirty-nine participants with bipolar disorder underwent fMRI during a flashing checkerboard task and 27 also underwent quantitative T1ρ. The relationship between neuroimaging and history of suicide attempt was tested using multiple regression while adjusting for age, sex, and current mood state. Differences between two measures of suicide attempt (binary: yes/no and continuous: number of attempts) were quantified using the corrected Akaike Information Criterion. Participants who had attempted suicide had greater fMRI task-related activation in visual areas and the cerebellum. The number of suicide attempts was associated with a difference in BOLD response in the amygdala, prefrontal cortex, and cerebellum. Increased quantitative T1ρ was associated with number of suicide attempts in limbic, basal ganglia, and prefrontal cortex regions. This study is a secondary analysis with a modest sample size. Differences between measures of suicide history may be due to differences in statistical power. History of suicide was associated with limbic, prefrontal, and cerebellar alterations. Results comparing those with and without suicide attempts differed from results using number of suicide attempts, suggesting that these variables have different neurobiological underpinnings.

Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.

Improving Imaging of the Brainstem and Cerebellum in Autistic Children: Transformation-Based High-Resolution Diffusion MRI (TiDi-Fused) in the Human Brainstem

Diffusion-weighted magnetic resonance imaging (dMRI) of the brainstem is technically challenging, especially in young autistic children as nearby tissue-air interfaces and motion (voluntary and physiological) can lead to artifacts. This limits the availability of high-resolution images, which are desirable for improving the ability to study brainstem structures. Furthermore, inherently low signal-to-noise ratios, geometric distortions, and sensitivity to motion not related to molecular diffusion have resulted in limited techniques for high-resolution data acquisition compared to other modalities such as T1-weighted imaging. Here, we implement a method for achieving increased apparent spatial resolution in pediatric dMRI that hinges on accurate geometric distortion correction and on high fidelity within subject image registration between dMRI and magnetization prepared rapid acquisition gradient echo (MPnRAGE) images. We call this post-processing pipeline T1 weighted-diffusion fused, or "TiDi-Fused". Data used in this work consists of dMRI data (2.4 mm resolution, corrected using FSL's Topup) and T1-weighted (T1w) MPnRAGE anatomical data (1 mm resolution) acquired from 128 autistic and non-autistic children (ages 6-10 years old). Accurate correction of geometric distortion permitted for a further increase in apparent resolution of the dMRI scan via boundary-based registration to the MPnRAGE T1w. Estimation of fiber orientation distributions and further analyses were carried out in the T1w space. Data processed with the TiDi-Fused method were qualitatively and quantitatively compared to data processed with conventional dMRI processing methods. Results show the advantages of the TiDi-Fused pipeline including sharper brainstem gray-white matter tissue contrast, improved inter-subject spatial alignment for group analyses of dMRI based measures, accurate spatial alignment with histology-based imaging of the brainstem, reduced variability in brainstem-cerebellar white matter tracts, and more robust biologically plausible relationships between age and brainstem-cerebellar white matter tracts. Overall, this work identifies a promising pipeline for achieving high-resolution imaging of brainstem structures in pediatric and clinical populations who may not be able to endure long scan times. This pipeline may serve as a gateway for feasibly elucidating brainstem contributions to autism and other conditions.

A Comparison of Cranial Cavity Extraction Tools for Non-contrast Enhanced CT Scans in Acute Stroke Patients

Cranial cavity extraction is often the first step in quantitative neuroimaging analyses. However, few automated, validated extraction tools have been developed for non-contrast enhanced CT scans (NECT). The purpose of this study was to compare and contrast freely available tools in an unseen dataset of real-world clinical NECT head scans in order to assess the performance and generalisability of these tools. This study included data from a demographically representative sample of 428 patients who had completed NECT scans following hospitalisation for stroke. In a subset of the scans (n = 20), the intracranial spaces were segmented using automated tools and compared to the gold standard of manual delineation to calculate accuracy, precision, recall, and dice similarity coefficient (DSC) values. Further, three readers independently performed regional visual comparisons of the quality of the results in a larger dataset (n = 428). Three tools were found; one of these had unreliable performance so subsequent evaluation was discontinued. The remaining tools included one that was adapted from the FMRIB software library (fBET) and a convolutional neural network- based tool (rBET). Quantitative comparison showed comparable accuracy, precision, recall and DSC values (fBET: 0.984 ± 0.002; rBET: 0.984 ± 0.003; p = 0.99) between the tools; however, intracranial volume was overestimated. Visual comparisons identified characteristic regional differences in the resulting cranial cavity segmentations. Overall fBET had highest visual quality ratings and was preferred by the readers in the majority of subject results (84%). However, both tools produced high quality extractions of the intracranial space and our findings should improve confidence in these automated CT tools. Pre- and post-processing techniques may further improve these results.

Advantages of Using Both Voxel- and Surface-based Morphometry in Cortical Morphology Analysis: A Review of Various Applications

Surface-based morphometry (SBM) is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification, whereas voxel-based morphometry (VBM) is a typical method of gray matter (GM) volumetry that includes cortex measurement. In cases where SBM is used to estimate cortical morphology, it remains controversial as to whether VBM should be used in addition to estimate GM volume. Therefore, this review has two main goals. First, we summarize the differences between the two methods regarding preprocessing, statistical analysis, and reliability. Second, we review studies that estimate cortical morphological changes using VBM and/or SBM and discuss whether using VBM in conjunction with SBM produces additional values. We found cases in which detection of morphological change in either VBM or SBM was superior, and others that showed equivalent performance between the two methods. Therefore, we concluded that using VBM and SBM together can help researchers and clinicians obtain a better understanding of normal neurobiological processes of the brain. Moreover, the use of both methods may improve the accuracy of the detection of morphological changes when comparing the data of patients and controls.

In addition, we introduce two other recent methods as future directions for estimating cortical morphological changes: a multi-modal parcellation method using structural and functional images, and a synthetic segmentation method using multi-contrast images (such as T1- and proton density-weighted images).

Deep learning improves utility of tau PET in the study of Alzheimer's disease

INTRODUCTION

Positron emission tomography (PET) imaging targeting neurofibrillary tau tangles is increasingly used in the study of Alzheimer's disease (AD), but its utility may be limited by conventional quantitative or qualitative evaluation techniques in earlier disease states. Convolutional neural networks (CNNs) are effective in learning spatial patterns for image classification.

METHODS

18F-MK6240 (n = 320) and AV-1451 (n = 446) PET images were pooled from multiple studies. We performed iterations with differing permutations of radioligands, heuristics, and architectures. Performance was compared to a standard region of interest (ROI)-based approach on prediction of memory impairment. We visualized attention of the network to illustrate decision making.

RESULTS

Overall, models had high accuracy (> 80%) with good average sensitivity and specificity (75% and 82%, respectively), and had comparable or higher accuracy to the ROI standard. Visualizations of model attention highlight known characteristics of tau radioligand binding.

DISCUSSION

CNNs could improve tau PET's role in early disease and extend the utility of tau PET across generations of radioligands.

Predicting Individual Task Contrasts From Resting-state Functional Connectivity using a Surface-based Convolutional Network

Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.

A sub+cortical fMRI-based surface parcellation

Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface‐based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute—Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life‐span (6–85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface‐based measures. We provide our extended functional parcellation for the use of the neuroimaging community.

Probabilistic Atlas of the Mesencephalic Reticular Formation, Isthmic Reticular Formation, Microcellular Tegmental Nucleus, Ventral Tegmental Area Nucleus Complex, and Caudal-Rostral Linear Raphe Nucleus Complex in Living Humans from 7 Tesla Magnetic Resonance Imaging

Introduction: The mesencephalic reticular formation, isthmic reticular formation, microcellular tegmental nucleus, ventral tegmental area-parabrachial pigmented nucleus complex, and caudal-rostral linear nucleus of the raphe are small brainstem regions crucially involved in arousal, sleep, and reward. Yet, these nuclei are difficult to identify with magnetic resonance imaging (MRI) of living humans. In the current work, we developed a probabilistic atlas of these brainstem nuclei in living humans, using noninvasive ultra-high-field MRI. Methods: We acquired single-subject, multicontrast (diffusion and T₂-weighted), 1.1-mm isotropic resolution, 7 Tesla MRI images of 12 healthy subjects. After preprocessing and alignment to the stereotactic space, these images were used to delineate (in each subject) the nuclei of interest based on the image contrast as well as on neighboring nuclei and landmarks. Nucleus labels were averaged across subjects to yield probabilistic labels. The latter were further validated by assessment of the label inter-rater agreement, internal consistency, and volume. Results: Labels were delineated for each nucleus with good overlap across subjects. The inter-rater agreement and internal consistency were below (p < 10^-8) the linear spatial imaging resolution (1.1 mm), thus validating the generated probabilistic atlas labels. The volumes of our labels did not differ from literature volumes (p < 0.05), further validating our atlas. Discussion and Conclusion: The probabilistic atlas of these five mesopontine nuclei expands current in vivo brainstem nuclei atlases and can be used as a tool to identify the location of these areas in conventional (e.g., 3 Tesla) images. This might serve to unravel the brainstem structure-to-function link and thus improve clinical outcomes. Impact statement The mesencephalic reticular formation, isthmic reticular formation, microcellular tegmental nucleus, ventral tegmental area-parabrachial pigmented nucleus complex, and caudal-rostral linear nucleus of the raphe are small brainstem regions crucially involved in arousal, sleep, and reward. In the current work, we developed a probabilistic atlas of these brainstem nuclei in living humans, using noninvasive, ultra-high-field magnetic resonance imaging. The probabilistic atlas of these five mesopontine nuclei expands current in vivo brainstem nuclei atlases and can be used as a tool to identify the location of these areas in conventional (e.g., 3 Tesla) images. This might serve to unravel the brainstem structure-to-function link and thus improve clinical outcomes.

Strategic Infarct Locations for Poststroke Depressive Symptoms: A Lesion- and Disconnection-Symptom Mapping Study

BACKGROUND

Depression is the most common neuropsychiatric complication after stroke. Infarct location is associated with poststroke depressive symptoms (PSDS), but it remains debated which brain structures are critically involved. We performed a large-scale lesion-symptom mapping study to identify infarct locations and white matter disconnections associated with PSDS.

METHODS

We included 553 patients (mean [SD] age = 69 [11] years, 42% female) with acute ischemic stroke. PSDS were measured using the 30-item Geriatric Depression Scale. Multivariable support vector regression (SVR)-based analyses were performed both at the level of individual voxels (voxel-based lesion-symptom mapping) and at predefined regions of interest to relate infarct location to PSDS. We externally validated our findings in an independent stroke cohort (N = 459). Finally, disconnectome-based analyses were performed using SVR voxel-based lesion-symptom mapping, in which white matter fibers disconnected by the infarct were analyzed instead of the infarct itself.

RESULTS

Infarcts in the right amygdala, right hippocampus, and right pallidum were consistently associated with PSDS (permutation-based p < .05) in SVR voxel-based lesion-symptom mapping and SVR region-of-interest analyses. External validation confirmed the association between infarcts in the right amygdala and pallidum, but not the right hippocampus, and PSDS. Disconnectome-based analyses revealed that disconnections in the right parahippocampal white matter, right thalamus and pallidum, and right anterior thalamic radiation were significantly associated (permutation-based p < .05) with PSDS.

CONCLUSIONS

Infarcts in the right amygdala and pallidum and disconnections of right limbic and frontal cortico-basal ganglia-thalamic circuits are associated with PSDS. Our findings provide a comprehensive and integrative picture of strategic infarct locations for PSDS and shed new light on pathophysiological mechanisms of depression after stroke.

Anatomy of phonemic and semantic fluency: A lesion and disconnectome study in 1231 stroke patients

Disturbances of semantic and phonemic fluency are common after brain damage, as a manifestation of language, executive, or memory dysfunction. Lesion-symptom mapping (LSM) studies can provide fundamental insights in shared and distinct anatomical correlates of these cognitive functions and help to understand which patients suffer from these deficits. We performed a multivariate support vector regression-based lesion-symptom mapping and structural disconnection study on semantic and phonemic fluency in 1231 patients with acute ischemic stroke. With the largest-ever LSM study on verbal fluency we achieved almost complete brain lesion coverage. Lower performance on both fluency types was related to left hemispheric frontotemporal and parietal cortical regions, and subcortical regions centering on the left thalamus. Distinct correlates for phonemic fluency were the anterior divisions of middle and inferior frontal gyri. Distinct correlates for semantic fluency were the posterior regions of the middle and inferior temporal gyri, parahippocampal and fusiform gyri and triangular part of the inferior frontal gyrus. The disconnectome-based analyses additionally revealed phonemic fluency was associated with a more extensive frontoparietal white matter network, whereas semantic fluency was associated with disconnection of the fornix, mesiotemporal white matter, splenium of the corpus callosum. These results provide the most detailed outline of the anatomical correlates of phonemic and semantic fluency to date, stress the crucial role of subcortical regions and reveal a novel dissociation in the left temporal lobe.

Improved normalization of lesioned brains via cohort-specific templates

In MRI studies, spatial normalization is required to infer results at the group level. In the presence of a brain lesion, such as in stroke patients, the normalization process can be affected by tissue loss, spatial deformations, signal intensity changes, and other stroke sequelae that introduce confounds into the group analysis results. Previously, most neuroimaging studies with lesioned brains have used normalization methods optimized for intact brains, raising potential concerns about the accuracy of the resulting transformations and, in turn, their reported group level results. In this study, we demonstrate the benefits of creating an intermediate, cohort‐specific template in conjunction with diffeomorphism‐based methods to normalize structural MRI images in stroke patients. We show that including this cohort‐specific template improves accuracy compared to standard methods for normalizing lesioned brains. Critically, this method reduces overall differences in normalization accuracy between stroke patients and healthy controls, and may improve the localization and connectivity of BOLD signal in functional neuroimaging data.

The Haskins pediatric atlas: a magnetic-resonance-imaging-based pediatric template and atlas

BACKGROUND

Spatial normalization plays an essential role in multi-subject MRI and functional MRI (fMRI) experiments by facilitating a common space in which group analyses are performed. Although many prominent adult templates are available, their use for pediatric data is problematic. Generalized templates for pediatric populations are limited or constructed using older methods that result in less ideal normalization.

OBJECTIVE

The Haskins pediatric templates and atlases aim to provide superior registration and more precise accuracy in labeling of anatomical and functional regions essential for all fMRI studies involving pediatric populations.

MATERIALS AND METHODS

The Haskins pediatric templates and atlases were generated with nonlinear methods using structural MRI from 72 children (age range 7-14 years, median 10 years), allowing for a detailed template with corresponding parcellations of labeled atlas regions. The accuracy of these templates and atlases was assessed using multiple metrics of deformation distance and overlap.

RESULTS

When comparing the deformation distances from normalizing pediatric data between this template and both the adult templates and other pediatric templates, we found significantly less deformation distance for the Haskins pediatric template (P<0.0001). Further, the correct atlas classification was higher using the Haskins pediatric template in 74% of regions (P<0.0001).

CONCLUSION

The Haskins pediatric template results in more accurate correspondence across subjects because of lower deformation distances. This correspondence also provides better accuracy in atlas locations to benefit structural and functional imaging analyses of pediatric populations.

Surgery requiring general anesthesia in preterm infants is associated with altered brain volumes at term equivalent age and neurodevelopmental impairment

BACKGROUND

The aim of the study was to describe and contrast the brain development and outcome among very preterm infants that were and were not exposed to surgery requiring general anesthesia prior to term equivalent age (TEA).

METHODS

Preterm infants born ≤30 weeks' gestation who did (n = 25) and did not (n = 59) have surgery requiring general anesthesia during the preterm period were studied. At TEA, infants had MRI scans performed with measures of brain tissue volumes, cortical surface area, Gyrification Index, and white matter microstructure. Neurodevelopmental follow-up with the Bayley Scales of Infant and Toddler Development, Third Edition was undertaken at 2 years of corrected age. Multivariate models, adjusted for clinical and social risk factors, were used to compare the groups.

RESULTS

After controlling for clinical and social variables, preterm infants exposed to surgical anesthesia demonstrated decreased relative white matter volumes at TEA and lower cognitive and motor composite scores at 2-year follow-up. Those with longer surgical exposure demonstrated the greatest decrease in white matter volumes and lower cognitive and motor outcomes at age 2 years.

CONCLUSIONS

Very preterm infants who required surgery during the preterm period had lower white mater volumes at TEA and worse neurodevelopmental outcome at age 2 years.

IMPACT

In very preterm infants, there is an association between surgery requiring general anesthesia during the preterm period and reduced white mater volume on MRI at TEA and lower cognitive and motor composite scores at age 2 years. It is known that the very preterm infant's brain undergoes rapid growth during the period corresponding to the third trimester. The current study suggests an association between surgery requiring general anesthesia during this period and worse outcomes.

Structural brain abnormalities in borderline personality disorder correlate with clinical severity and predict psychotherapy response

Although previous imaging studies in borderline personality disorder (BPD) have found brain abnormalities, the results have been inconsistent. This study aimed to investigate structural brain abnormalities using voxel-based morphometry (VBM) and cortical thickness (Cth) analyses in a large sample of patients with BPD. Additionally, we aimed to determine the correlation between structural abnormalities and clinical severity and to assess its potential value at predicting psychotherapeutic response. Sixty-one individuals with BPD and 19 healthy controls underwent magnetic resonance imaging. Participants with BPD completed several self-report clinical scales, received dialectical-behavioral therapy skills training and post-therapy changes in clinical scores were also recorded. Gray matter volume (GMV) and Cth differences between groups were compared. Within the BPD group, we further characterized the structural brain correlates of clinical severity and investigated the relationship between pre-therapy structural abnormalities and therapeutic response. As potential confounders we included age, sex, educational level, and total intracranial volume (the latter only in VBM analyses). Compared to controls, the BPD group showed a reduced GMV/Cth in prefrontal areas but increased GMV in the limbic structures (amygdala and parahippocampal regions). Prefrontal abnormalities correlated with higher baseline scores on impulsivity and general BPD severity. Increased GMV in the parahippocampal area correlated with a greater emotion dysregulation. Importantly, several baseline structural abnormalities correlated with worse response to psychotherapy. Patients with BPD showed a reduced GMV in the prefrontal areas but a greater GMV in the limbic structures. Several structural abnormalities (i.e. middle and inferior prefrontal areas, anterior insula, or parahippocampal area) correlated with clinical severity and could potentially be used as imaging biological correlates biomarkers to predict psychotherapy response.

Prefrontal and hippocampal atrophy using 7-tesla magnetic resonance imaging in patients with Parkinson's disease

Background

The pathology of Parkinson's disease leads to morphological changes in brain structure. Currently, the progressive changes in gray matter volume that occur with time and are specific to patients with Parkinson's disease, compared to healthy controls, remain unclear. High-tesla magnetic resonance imaging might be useful in differentiating neurological disorders by brain cortical changes.

Purpose

We aimed to investigate patterns in gray matter changes in patients with Parkinson's disease by using an automated segmentation method with 7-tesla magnetic resonance imaging.

Material and Methods

High-resolution T1-weighted 7 tesla magnetic resonance imaging volumes of 24 hemispheres were acquired from 12 Parkinson's disease patients and 12 age- and sex-matched healthy controls with median ages of 64.5 (range, 41-82) years and 60.5 (range, 25-74) years, respectively. Subgroup analysis was performed according to whether axial motor symptoms were present in the Parkinson's disease patients. Cortical volume, cortical thickness, and subcortical volume were measured using a high-resolution image processing technique based on the Desikan-Killiany-Tourville atlas and an automated segmentation method (FreeSurfer version 6.0).

Results

After cortical reconstruction, in 7 tesla magnetic resonance imaging volume segmental analysis, compared with the healthy controls, the Parkinson's disease patients showed global cortical atrophy, mostly in the prefrontal area (rostral middle frontal, superior frontal, inferior parietal lobule, medial orbitofrontal, rostral anterior cingulate area), and subcortical volume atrophy in limbic/paralimbic areas (fusiform, hippocampus, amygdala).

Conclusion

We first demonstrated that 7 tesla magnetic resonance imaging detects structural abnormalities in Parkinson's disease patients compared to healthy controls using an automated segmentation method. Compared with the healthy controls, the Parkinson's disease patients showed global prefrontal cortical atrophy and hippocampal area atrophy.

Neural Correlates of Long-Term Memory Enhancement Following Retrieval Practice

Retrieval practice, relative to further study, leads to long-term memory enhancement known as the “testing effect.” The neurobiological correlates of the testing effect at retrieval, when the learning benefits of testing are expressed, have not been fully characterized. Participants learned Swahili-English word-pairs and were assigned randomly to either the Study-Group or the Test-Group. After a week delay, all participants completed a cued-recall test while undergoing functional magnetic resonance imaging (fMRI). The Test-Group had superior memory for the word-pairs compared to the Study-Group. While both groups exhibited largely overlapping activations for remembered word-pairs, following an interaction analysis the Test-Group exhibited differential performance-related effects in the left putamen and left inferior parietal cortex near the supramarginal gyrus. The same analysis showed the Study-Group exhibited greater activations in the dorsal MPFC/pre-SMA and bilateral frontal operculum for remembered vs. forgotten word-pairs, whereas the Test-Group showed the opposite pattern of activation in the same regions. Thus, retrieval practice during training establishes a unique striatal-supramarginal network at retrieval that promotes enhanced memory performance. In contrast, study alone yields poorer memory but greater activations in frontal regions.

Sex differences in medial prefrontal and parietal cortex structure in children with disruptive behavior

Sex differences in brain structure in children with disruptive behavior disorders (DBD) remain poorly understood. This study examined sex differences in gray matter volume in children with DBD in a priori regions-of-interest implicated in the pathophysiology of disruptive behavior. We then conducted a whole-brain analysis of cortical thickness to examine sex differences in regions not included in our hypothesis. Exploratory analyses investigated unique associations between structure, and dimensional measures of severity of disruptive behavior and callous-unemotional traits. This cross-sectional study included 88 children with DBD (30 females) aged 8-16 years and 50 healthy controls (20 females). Structural MRI data were analyzed using surface-based morphometry to test for interactions between sex and group. Multiple-regression analyses tested for sex-specific associations between structure, callous-unemotional traits, and disruptive behavior severity. Boys with DBD showed reduced gray matter volume in the left ventromedial prefrontal cortex (vmPFC) and reduced cortical thickness in the supramarginal gyrus, but not girls compared to respective controls. Dimensional analyses revealed associations between sex, callous-unemotional traits, and disruptive behavior for amygdala and vmPFC volume, and ventrolateral prefrontal cortex cortical thickness. Sex-specific differences in prefrontal structures involved in emotion regulation may support identification of neural biomarkers of disruptive behavior to inform target-based treatments.

More Than Words: Extra-Sylvian Neuroanatomic Networks Support Indirect Speech Act Comprehension and Discourse in Behavioral Variant Frontotemporal Dementia

Indirect speech acts—responding “I forgot to wear my watch today” to someone who asked for the time—are ubiquitous in daily conversation, but are understudied in current neurobiological models of language. To comprehend an indirect speech act like this one, listeners must not only decode the lexical-semantic content of the utterance, but also make a pragmatic, bridging inference. This inference allows listeners to derive the speaker’s true, intended meaning—in the above dialog, for example, that the speaker cannot provide the time. In the present work, we address this major gap by asking non-aphasic patients with behavioral variant frontotemporal dementia (bvFTD, n = 21) and brain-damaged controls with amnestic mild cognitive impairment (MCI, n = 17) to judge simple question-answer dialogs of the form: “Do you want some cake for dessert?” “I’m on a very strict diet right now,” and relate the results to structural and diffusion MRI. Accuracy and reaction time results demonstrate that subjects with bvFTD, but not MCI, are selectively impaired in indirect relative to direct speech act comprehension, due in part to their social and executive limitations, and performance is related to caregivers’ judgment of communication efficacy. MRI imaging associates the observed impairment in bvFTD to cortical thinning not only in traditional language-associated regions, but also in fronto-parietal regions implicated in social and executive cerebral networks. Finally, diffusion tensor imaging analyses implicate white matter tracts in both dorsal and ventral projection streams, including superior longitudinal fasciculus, frontal aslant, and uncinate fasciculus. These results have strong implications for updated neurobiological models of language, and emphasize a core, language-mediated social disorder in patients with bvFTD.

Behavioural performance improvement in visuomotor learning correlates with functional and microstructural brain changes

A better understanding of practice-induced functional and structural changes in our brains can help us design more effective learning environments that provide better outcomes. Although there is growing evidence from human neuroimaging that experience-dependent brain plasticity is expressed in measurable brain changes that are correlated with behavioural performance, the relationship between behavioural performance and structural or functional brain changes, and particularly the time course of these changes, is not well characterised. To understand the link between neuroplastic changes and behavioural performance, 15 healthy participants in this study followed a systematic eye movement training programme for 30 min daily at home, 5 days a week and for 6 consecutive weeks. Behavioural performance statistics and eye tracking data were captured throughout the training period to evaluate learning outcomes. Imaging data (DTI and fMRI) were collected at baseline, after two and six weeks of continuous training, and four weeks after training ended. Participants showed significant improvements in behavioural performance (faster task completion time, lower fixation number and fixation duration). Spatially overlapping reductions in microstructural diffusivity measures (MD, AD and RD) and functional activation increases (BOLD signal) were observed in two main areas: extrastriate visual cortex (V3d) and the frontal part of the cerebellum/Fastigial Oculomotor Region (FOR), which are both involved in visual processing. An increase of functional activity was also recorded in the right frontal eye field. Behavioural, structural and functional changes were correlated. Microstructural change is a better predictor for long-term behavioural change than functional activation is, whereas the latter is superior in predicting instantaneous performance. Structural and functional measures at week 2 of the training programme also predict performance at week 6 and 10, which suggests that imaging data at an early stage of training may be useful in optimising practice environments or rehabilitative training programmes.

Morphological and functional variability in central and subcentral motor cortex of the human brain

There is a long-established link between anatomy and function in the somatomotor system in the mammalian cerebral cortex. The morphology of the central sulcus is predictive of the location of functional activation peaks relating to movement of different effectors in individuals. By contrast, morphological variation in the subcentral region and its relationship to function is, as yet, unknown. Investigating the subcentral region is particularly important in the context of speech, since control of the larynx during human speech production is related to activity in this region. Here, we examined the relationship between morphology in the central and subcentral region and the location of functional activity during movement of the hand, lips, tongue, and larynx at the individual participant level. We provide a systematic description of the sulcal patterns of the subcentral and adjacent opercular cortex, including the inter-individual variability in sulcal morphology. We show that, in the majority of participants, the anterior subcentral sulcus is not continuous, but consists of two distinct segments. A robust relationship between morphology of the central and subcentral sulcal segments and movement of different effectors is demonstrated. Inter-individual variability of underlying anatomy might thus explain previous inconsistent findings, in particular regarding the ventral larynx area in subcentral cortex. A surface registration based on sulcal labels indicated that such anatomical information can improve the alignment of functional data for group studies.

Charting brain growth in tandem with brain templates at school age

Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental trajectories. The present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging protocol. Two accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and China. Anatomical magnetic resonance imaging (MRI) of typically developing school-age children (TDC) was obtained up to three times at nominal intervals of 1.25 years. The protocol generated and compared population- and age-specific brain templates and growth charts, respectively. A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American TDC. Population- and age-specific brain templates were used to quantify warp cost, the differences between individual brains and brain templates. Volumetric growth charts for labeled brain network areas were generated. Shape analyses of cost functions supported the necessity of age-specific and ethnicity-matched brain templates, which was confirmed by growth chart analyses. These analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas, regions which are most variable across individuals in regard to their structure and function. Age- and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts, indicating the necessity of the brain charts and brain templates generated in tandem. These templates and growth charts as well as related codes have been made freely available to the public for open neuroscience (https://github.com/zuoxinian/CCS/tree/master/H3/GrowthCharts).

Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample

The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study

Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.

Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Connectivity hyperalignment can be used to estimate a single shared response space across disjoint datasets. We develop a connectivity-based shared response model that factorizes aggregated fMRI datasets into a single reduced-dimension shared connectivity space and subject-specific topographic transformations. These transformations resolve idiosyncratic functional topographies and can be used to project response time series into shared space. We evaluate this algorithm on a large collection of heterogeneous, naturalistic fMRI datasets acquired while subjects listened to spoken stories. Projecting subject data into shared space dramatically improves between-subject story time-segment classification and increases the dimensionality of shared information across subjects. This improvement generalizes to subjects and stories excluded when estimating the shared space. We demonstrate that estimating a simple semantic encoding model in shared space improves between-subject forward encoding and inverted encoding model performance. The shared space estimated across all datasets is distinct from the shared space derived from any particular constituent dataset; the algorithm leverages shared connectivity to yield a consensus shared space conjoining diverse story stimuli.

Gestational Age is Dimensionally Associated with Structural Brain Network Abnormalities Across Development

Prematurity is associated with diverse developmental abnormalities, yet few studies relate cognitive and neurostructural deficits to a dimensional measure of prematurity. Leveraging a large sample of children, adolescents, and young adults (age 8-22 years) studied as part of the Philadelphia Neurodevelopmental Cohort, we examined how variation in gestational age impacted cognition and brain structure later in development. Participants included 72 preterm youth born before 37 weeks' gestation and 206 youth who were born at term (37 weeks or later). Using a previously-validated factor analysis, cognitive performance was assessed in three domains: (1) executive function and complex reasoning, (2) social cognition, and (3) episodic memory. All participants completed T1-weighted neuroimaging at 3 T to measure brain volume. Structural covariance networks were delineated using non-negative matrix factorization, an advanced multivariate analysis technique. Lower gestational age was associated with both deficits in executive function and reduced volume within 11 of 26 structural covariance networks, which included orbitofrontal, temporal, and parietal cortices as well as subcortical regions including the hippocampus. Notably, the relationship between lower gestational age and executive dysfunction was accounted for in part by structural network deficits. Together, these findings emphasize the durable impact of prematurity on cognition and brain structure, which persists across development.

Automated analysis of natural speech in amyotrophic lateral sclerosis spectrum disorders

OBJECTIVE

We implemented automated methods to analyze speech and evaluate the hypothesis that cognitive and motor factors impair prosody in partially distinct ways in patients with amyotrophic lateral sclerosis (ALS).

METHODS

We recruited 213 participants, including 67 with ALS (44 with motor ALS, 23 with ALS and frontotemporal degeneration [FTD]), 33 healthy controls, and neurodegenerative reference groups with behavioral variant FTD (n = 90) and nonfluent/agrammatic primary progressive aphasia (n = 23). Digitized, semistructured speech samples obtained from picture descriptions were automatically segmented with a Speech Activity Detector; continuous speech segments were pitch-tracked; and duration measures for speech and silent pause segments were extracted. Acoustic measures were calculated, including fundamental frequency (f0) range, mean speech and pause segment durations, total speech duration, and pause rate (pause count per minute of speech). Group comparisons related performance on acoustic measures to clinical scales of cognitive and motor impairments and explored MRI cortical thinning in ALS and ALS-FTD.

RESULTS

The f0 range was significantly impaired in ALS spectrum disorders and was related to bulbar motor disease, and regression analyses related this to cortical thickness in primary motor cortex and perisylvian regions. Impaired speech and pause duration measures were related to the degree of cognitive impairment in ALS spectrum disorders, and regressions related duration measures to bilateral frontal opercula and left anterior insula.

CONCLUSION

Automated analyses of acoustic speech properties dissociate motor and cognitive components of speech deficits in ALS spectrum disorders.

Precentral and cerebellar atrophic changes in moyamoya disease using 7-T magnetic resonance imaging

Background

Chronic repeated transient ischemic changes are one of the common symptoms of moyamoya disease that could affect cortical and subcortical atrophy.

Purpose

We aimed to assess the cortical gray matter volume and thickness, white matter subcortical volume, and clinical characteristics using 7-T magnetic resonance imaging (MRI) and MR angiography (MRA).

Material and Methods

In this case-control study, whole-brain parcellation of gray matter and subcortical volumes were manually assessed in nine patients with moyamoya disease (18 hemispheres; median age = 34 years; age range = 10–60 years) and nine healthy controls (18 hemispheres; median age = 29 years; age range = 20–62 years) matched for age and sex, who underwent both 7-T MRI and MRA. The volumes were measured using high-resolution image (<1 mm) processing based on the Desikan-Killiany-Tourville (DKT) atlas, via an automated segmentation method (FreeSurfer version 6.0).

Results

The gray matter volume of the left precentral cortex and the white matter volume of the subcortical cerebellum were lower in both hemispheres in the patients with moyamoya disease compared to the healthy controls.

Conclusion

Gray matter atrophy in the precentral cortex and cerebellar white matter were detected in this 7-T MRI volumetric analysis study of patients with moyamoya disease who experienced repeated transient ischemic changes. Cortical atrophy in precentral cortex and cerebellum could explain the transient motor weakness in patients with moyamoya disease, as one of the early findings was that patients with moyamoya disease do not have detectable infarction changes on conventional MRI images.

Surface-based analysis increases the specificity of cortical activation patterns and connectivity results

Spatial smoothing of functional magnetic resonance imaging (fMRI) data can be performed on volumetric images and on the extracted surface of the brain. Smoothing on the unfolded cortex should theoretically improve the ability to separate signals between brain areas that are near together in the folded cortex but are more distant in the unfolded cortex. However, surface-based method approaches (SBA) are currently not utilized as standard procedure in the preprocessing of neuroimaging data. Recent improvements in the quality of cortical surface modeling and improvements in its usability nevertheless advocate this method. In the current study, we evaluated the benefits of an up-to-date surface-based smoothing in comparison to volume-based smoothing. We focused on the effect of signal contamination between different functional systems using the primary motor and primary somatosensory cortex as an example. We were particularly interested in how this signal contamination influences the results of activity and connectivity analyses for these brain regions. We addressed this question by performing fMRI on 19 subjects during a tactile stimulation paradigm and by using simulated BOLD responses. We demonstrated that volume-based smoothing causes contamination of the primary motor cortex by somatosensory cortical responses, leading to false positive motor activation. These false positive motor activations were not found by using surface-based smoothing for reasonable kernel sizes. Accordingly, volume-based smoothing caused an exaggeration of connectivity estimates between these regions. In conclusion, this study showed that surface-based smoothing decreases signal contamination considerably between neighboring functional brain regions and improves the validity of activity and connectivity results.

Improving Spatial Normalization of Brain Diffusion MRI to Measure Longitudinal Changes of Tissue Microstructure in the Cortex and White Matter

BACKGROUND

Fractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter (WM) microstructure. Recently, there has been a growing interest in identifying experience-dependent plasticity in gray matter using MD. Improving registration has thus become a major goal to enhance the detection of subtle longitudinal changes in cortical microstructure.

PURPOSE

To optimize normalization of diffusion tensor images (DTI) to improve registration in gray matter and reduce variability associated with multisession registrations.

STUDY TYPE

Prospective longitudinal study.

SUBJECTS

Twenty-one healthy subjects (18-31 years old) underwent nine MRI scanning sessions each.

FIELD STRENGTH/SEQUENCE

3.0T, diffusion-weighted multiband-accelerated sequence, MP2RAGE sequence.

ASSESSMENT

Diffusion-weighted images were registered to standard space using different pipelines that varied in the features used for normalization, namely, the nonlinear registration algorithm (FSL vs. ANTs), the registration target (FA-based vs. T₁ -based templates), and the use of intermediate individual (FA-based or T₁ -based) targets. We compared the across-session test-retest reproducibility error of these normalization approaches for FA and MD in white and gray matter.

STATISTICAL TESTS

Reproducibility errors were compared using a repeated-measures analysis of variance with pipeline as the within-subject factor.

RESULTS

The registration of FA data to the FMRIB58 FA atlas using ANTs yielded lower reproducibility errors in white matter (P < 0.0001) with respect to FSL. Moreover, using the MNI152 T₁ template as the target of registration resulted in lower reproducibility errors for MD (P < 0.0001), whereas the FMRIB58 FA template performed better for FA (P < 0.0001). Finally, the use of an intermediate individual template improved reproducibility when registration of the FA images to the MNI152 T₁ was carried out within modality (FA-FA) (P < 0.05), but not via a T₁ -based individual template.

DATA CONCLUSION

A normalization approach using ANTs to register FA images to the MNI152 T₁ template via an individual FA template minimized test-retest reproducibility errors both for gray and white matter.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:766-775.

Probabilistic Template of the Lateral Parabrachial Nucleus, Medial Parabrachial Nucleus, Vestibular Nuclei Complex, and Medullary Viscero-Sensory-Motor Nuclei Complex in Living Humans From 7 Tesla MRI

The lateral parabrachial nucleus, medial parabrachial nucleus, vestibular nuclei complex, and medullary viscero-sensory-motor (VSM) nuclei complex (the latter including among others the solitary nucleus, vagus nerve nucleus, and hypoglossal nucleus) are anatomically and functionally connected brainstem gray matter structures that convey signals across multiple modalities between the brain and the spinal cord to regulate vital bodily functions. It is remarkably difficult to precisely extrapolate the location of these nuclei from ex vivo atlases to conventional 3 Tesla in vivo images; thus, a probabilistic brainstem template in stereotaxic neuroimaging space in living humans is needed. We delineated these nuclei using single-subject high contrast 1.1 mm isotropic resolution 7 Tesla MRI images. After precise coregistration of nuclei labels to stereotaxic space, we generated a probabilistic template of their anatomical locations. Finally, we validated the nuclei labels in the template by assessing their inter-rater agreement, consistency across subjects and volumes. We also performed a preliminary comparison of their location and microstructural properties to histologic sections of a postmortem human brainstem specimen. In future, the resulting probabilistic template of these brainstem nuclei in stereotaxic space may assist researchers and clinicians in evaluating autonomic, vestibular and VSM nuclei structure, function and connectivity in living humans using conventional 3 Tesla MRI scanners.