The brain atlas concordance problem: quantitative comparison of anatomical parcellations

Mapping stroke outcomes: A review of brain connectivity atlases

The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.

Hemispheric functional organization, as revealed by naturalistic neuroimaging, in pediatric epilepsy patients with cortical resections

Functional changes in the pediatric brain following neural injuries attest to remarkable feats of plasticity. Investigations of the neurobiological mechanisms that underlie this plasticity have largely focused on activation in the penumbra of the lesion or in contralesional, homotopic regions. Here, we adopt a whole-brain approach to evaluate the plasticity of the cortex in patients with large unilateral cortical resections due to drug-resistant childhood epilepsy. We compared the functional connectivity (FC) in patients' preserved hemisphere with the corresponding hemisphere of matched controls as they viewed and listened to a movie excerpt in a functional magnetic resonance imaging (fMRI) scanner. The preserved hemisphere was segmented into 180 and 200 parcels using two different anatomical atlases. We calculated all pairwise multivariate statistical dependencies between parcels, or parcel edges, and between 22 and 7 larger-scale functional networks, or network edges, aggregated from the smaller parcel edges. Both the left and right hemisphere-preserved patient groups had widespread reductions in FC relative to matched controls, particularly for within-network edges. A case series analysis further uncovered subclusters of patients with distinctive edgewise changes relative to controls, illustrating individual postoperative connectivity profiles. The large-scale differences in networks of the preserved hemisphere potentially reflect plasticity in the service of maintained and/or retained cognitive function.

Establishing neuroanatomical correspondences across mouse and marmoset brain structures

Interest in the common marmoset is growing due to evolutionarily proximity to humans compared to laboratory mice, necessitating a comparison of mouse and marmoset brain architectures, including connectivity and cell type distributions. Creating an actionable comparative platform is challenging since these brains have distinct spatial organizations and expert neuroanatomists disagree. We propose a general theoretical framework to relate named atlas compartments across taxa and use it to establish a detailed correspondence between marmoset and mice brains. Contrary to conventional wisdom that brain structures may be easier to relate at higher levels of the atlas hierarchy, we find that finer parcellations at the leaf levels offer greater reconcilability despite naming discrepancies. Utilizing existing atlases and associated literature, we created a list of leaf-level structures for both species and establish five types of correspondence between them. One-to-one relations were found between 43% of the structures in mouse and 47% in marmoset, whereas 25% of mouse and 10% of marmoset structures were not relatable. The remaining structures show a set of more complex mappings which we quantify. Implementing this correspondence with volumetric atlases of the two species, we make available a computational tool for querying and visualizing relationships between the corresponding brains. Our findings provide a foundation for computational comparative analyses of mesoscale connectivity and cell type distributions in the laboratory mouse and the common marmoset.

Feature similarity gradients detect alterations in the neonatal cortex associated with preterm birth

The early life environment programmes cortical architecture and cognition across the life course. A measure of cortical organisation that integrates information from multimodal MRI and is unbound by arbitrary parcellations has proven elusive, which hampers efforts to uncover the perinatal origins of cortical health. Here, we use the Vogt-Bailey index to provide a fine-grained description of regional homogeneities and sharp variations in cortical microstructure based on feature gradients, and we investigate the impact of being born preterm on cortical development at term-equivalent age. Compared with term-born controls, preterm infants have a homogeneous microstructure in temporal and occipital lobes, and the medial parietal, cingulate, and frontal cortices, compared with term infants. These observations replicated across two independent datasets and were robust to differences that remain in the data after matching samples and alignment of processing and quality control strategies. We conclude that cortical microstructural architecture is altered in preterm infants in a spatially distributed rather than localised fashion.

Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark

The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.

Characterizing functional modules in the human thalamus: coactivation-based parcellation and systems-level functional decoding

The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.

Brain volumetric changes in menopausal women and its association with cognitive function: a structured review

The menopausal transition has been proposed to put women at risk for undesirable neurological symptoms, including cognitive decline. Previous studies suggest that alterations in the hormonal milieu modulate brain structures associated with cognitive function. This structured review provides an overview of the relevant studies that have utilized MRI to report volumetric differences in the brain following menopause, and its correlations with the evaluated cognitive functions. We performed an electronic literature search using Medline (Ovid) and Scopus to identify studies that assessed the influence of menopause on brain structure with MRI. Fourteen studies met the inclusion criteria. Brain volumetric differences have been reported most frequently in the frontal and temporal cortices as well as the hippocampus. These regions are important for higher cognitive tasks and memory. Additionally, the deficit in verbal and visuospatial memory in postmenopausal women has been associated with smaller regional brain volumes. Nevertheless, the limited number of eligible studies and cross-sectional study designs warrant further research to draw more robust conclusions.

AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures

Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardized description impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We discuss how AtOM can facilitate atlas interoperability and data integration, thereby increasing compliance with the FAIR guiding principles. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.

Combining exercise with cognitive training and vitamin D3 to improve functional brain connectivity (FBC) in older adults with mild cognitive impairment (MCI). Results from the SYNERGIC trial

Changes in functional brain connectivity (FBC) may indicate how lifestyle modifications can prevent the progression to dementia; FBC identifies areas that are spatially separate but temporally synchronized in their activation and is altered in those with mild cognitive impairment (MCI), a prodromal state between healthy cognitive aging and dementia. Participants with MCI were randomly assigned to one of five study arms. Three times per week for 20-weeks, participants performed 30-min of (control) cognitive training, followed by 60-min of (control) physical exercise. Additionally, a vitamin D₃ (10,000 IU/pill) or a placebo capsule was ingested three times per week for 20-weeks. Using the CONN toolbox, we measured FBC change (Post-Pre) across four statistical models that collapsed for and/or included some or all study arms. We conducted Pearson correlations between FBC change and changes in physical and cognitive functioning. Our sample included 120 participants (mean age: 73.89 ± 6.50). Compared to the pure control, physical exercise (model one; p-False Discovery Rate (FDR) < 0.01 & < 0.05) with cognitive training (model two; p-FDR =  < 0.001), and all three interventions combined (model four; p-FDR =  < 0.01) demonstrated an increase in FBC between regions of the Default-Mode Network (i.e., hippocampus and angular gyrus). After controlling for false discovery rate, there were no significant correlations between change in connectivity and change in cognitive or physical function. Physical exercise alone appears to be as efficacious as combined interventional strategies in altering FBC, but implications for behavioral outcomes remain unclear.

A Proposed Human Structural Brain Connectivity Matrix in the Center for Morphometric Analysis Harvard-Oxford Atlas Framework: A Historical Perspective and Future Direction for Enhancing the Precision of Human Structural Connectivity with a Novel Neuroanatomical Typology

A complete structural definition of the human nervous system must include delineation of its wiring diagram (e.g., Swanson LW. Brain architecture: understanding the basic plan, 2012). The complete formulation of the human brain circuit diagram (BCD [Front Neuroanat. 2020;14:18]) has been hampered by an inability to determine connections in their entirety (i.e., not only pathway stems but also origins and terminations). From a structural point of view, a neuroanatomic formulation of the BCD should include the origins and terminations of each fiber tract as well as the topographic course of the fiber tract in three dimensions. Classic neuroanatomical studies have provided trajectory information for pathway stems and their speculative origins and terminations [Dejerine J and Dejerine-Klumpke A. Anatomie des Centres Nerveux, 1901; Dejerine J and Dejerine-Klumpke A. Anatomie des Centres Nerveux: Méthodes générales d'étude-embryologie-histogénèse et histologie. Anatomie du cerveau, 1895; Ludwig E and Klingler J. Atlas cerebri humani, 1956; Makris N. Delineation of human association fiber pathways using histologic and magnetic resonance methodologies; 1999; Neuroimage. 1999 Jan;9(1):18-45]. We have summarized these studies previously [Neuroimage. 1999 Jan;9(1):18-45] and present them here in a macroscale-level human cerebral structural connectivity matrix. A matrix in the present context is an organizational construct that embodies anatomical knowledge about cortical areas and their connections. This is represented in relation to parcellation units according to the Harvard-Oxford Atlas neuroanatomical framework established by the Center for Morphometric Analysis at Massachusetts General Hospital in the early 2000s, which is based on the MRI volumetrics paradigm of Dr. Verne Caviness and colleagues [Brain Dev. 1999 Jul;21(5):289-95]. This is a classic connectional matrix based mainly on data predating the advent of DTI tractography, which we refer to as the "pre-DTI era" human structural connectivity matrix. In addition, we present representative examples that incorporate validated structural connectivity information from nonhuman primates and more recent information on human structural connectivity emerging from DTI tractography studies. We refer to this as the "DTI era" human structural connectivity matrix. This newer matrix represents a work in progress and is necessarily incomplete due to the lack of validated human connectivity findings on origins and terminations as well as pathway stems. Importantly, we use a neuroanatomical typology to characterize different types of connections in the human brain, which is critical for organizing the matrices and the prospective database. Although substantial in detail, the present matrices may be assumed to be only partially complete because the sources of data relating to human fiber system organization are limited largely to inferences from gross dissections of anatomic specimens or extrapolations of pathway tracing information from nonhuman primate experiments [Front Neuroanat. 2020;14:18, Front Neuroanat. 2022;16:1035420, and Brain Imaging Behav. 2021;15(3):1589-1621]. These matrices, which embody a systematic description of cerebral connectivity, can be used in cognitive and clinical studies in neuroscience and, importantly, to guide research efforts for further elucidating, validating, and completing the human BCD [Front Neuroanat. 2020;14:18].

Topology of the lateral visual system: The fundus of the superior temporal sulcus and parietal area H connect nonvisual cerebrum to the lateral occipital lobe

BACKGROUND AND PURPOSE

Mapping the topology of the visual system is critical for understanding how complex cognitive processes like reading can occur. We aim to describe the connectivity of the visual system to understand how the cerebrum accesses visual information in the lateral occipital lobe.

METHODS

Using meta-analytic software focused on task-based functional MRI studies, an activation likelihood estimation (ALE) of the visual network was created. Regions of interest corresponding to the cortical parcellation scheme previously published under the Human Connectome Project were co-registered onto the ALE to identify the hub-like regions of the visual network. Diffusion Spectrum Imaging-based fiber tractography was performed to determine the structural connectivity of these regions with extraoccipital cortices.

RESULTS

The fundus of the superior temporal sulcus (FST) and parietal area H (PH) were identified as hub-like regions for the visual network. FST and PH demonstrated several areas of coactivation beyond the occipital lobe and visual network. Furthermore, these parcellations were highly interconnected with other cortical regions throughout extraoccipital cortices related to their nonvisual functional roles. A cortical model demonstrating connections to these hub-like areas was created.

CONCLUSIONS

FST and PH are two hub-like areas that demonstrate extensive functional coactivation and structural connections to nonvisual cerebrum. Their structural interconnectedness with language cortices along with the abnormal activation of areas commonly located in the temporo-occipital region in dyslexic individuals suggests possible important roles of FST and PH in the integration of information related to language and reading. Future studies should refine our model by examining the functional roles of these hub areas and their clinical significance.

Environmental effects on brain functional networks in a juvenile twin population

The brain's intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10-30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored.

A neuroscientist's guide to using murine brain atlases for efficient analysis and transparent reporting

Brain atlases are widely used in neuroscience as resources for conducting experimental studies, and for integrating, analyzing, and reporting data from animal models. A variety of atlases are available, and it may be challenging to find the optimal atlas for a given purpose and to perform efficient atlas-based data analyses. Comparing findings reported using different atlases is also not trivial, and represents a barrier to reproducible science. With this perspective article, we provide a guide to how mouse and rat brain atlases can be used for analyzing and reporting data in accordance with the FAIR principles that advocate for data to be findable, accessible, interoperable, and re-usable. We first introduce how atlases can be interpreted and used for navigating to brain locations, before discussing how they can be used for different analytic purposes, including spatial registration and data visualization. We provide guidance on how neuroscientists can compare data mapped to different atlases and ensure transparent reporting of findings. Finally, we summarize key considerations when choosing an atlas and give an outlook on the relevance of increased uptake of atlas-based tools and workflows for FAIR data sharing.

A quantified comparison of cortical atlases on the basis of trait morphometricity

BACKGROUND

Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences.

METHODS

Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas.

RESULTS

Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated.

DISCUSSION

Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.

Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning

This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.

Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges

Ecological chemosensory stimuli almost always evoke responses in more than one sensory system. Moreover, any sensory processing takes place along a hierarchy of brain regions. So far, the field of chemosensory neuroimaging is dominated by studies that examine the role of brain regions in isolation. However, to completely understand neural processing of chemosensation, we must also examine interactions between regions. In general, the use of connectivity methods has increased in the neuroimaging field, providing important insights to physical sensory processing, such as vision, audition, and touch. A similar trend has been observed in chemosensory neuroimaging, however, these established techniques have largely not been rigorously applied to imaging studies on the chemical senses, leaving network insights overlooked. In this article, we first highlight some recent work in chemosensory connectomics and we summarize different connectomics techniques. Then, we outline specific challenges for chemosensory connectome neuroimaging studies. Finally, we review best practices from the general connectomics and neuroimaging fields. We recommend future studies to develop or use the following methods we perceive as key to improve chemosensory connectomics: (1) optimized study designs, (2) reporting guidelines, (3) consensus on brain parcellations, (4) consortium research, and (5) data sharing.

A framework For brain atlases: Lessons from seizure dynamics

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure–function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

Differential beta desynchronisation responses to dynamic emotional facial expressions are attenuated in higher trait anxiety and autism

Daily life demands that we differentiate between a multitude of emotional facial expressions (EFEs). The mirror neuron system (MNS) is becoming increasingly implicated as a neural network involved with understanding emotional body expressions. However, the specificity of the MNS's involvement in emotion recognition has remained largely unexplored. This study investigated whether six basic dynamic EFEs (anger, disgust, fear, happiness, sadness, and surprise) would be differentiated through event-related desynchronisation (ERD) of sensorimotor alpha and beta oscillatory activity, which indexes sensorimotor MNS activity. We found that beta ERD differentiated happy, fearful, and sad dynamic EFEs at the central region of interest, but not at occipital regions. Happy EFEs elicited significantly greater central beta ERD relative to fearful and sad EFEs within 800 - 2,000 ms after EFE onset. These differences were source-localised to the primary somatosensory cortex, which suggests they are likely to reflect differential sensorimotor simulation rather than differential attentional engagement. Furthermore, individuals with higher trait anxiety showed less beta ERD differentiation between happy and sad faces. Similarly, individuals with higher trait autism showed less beta ERD differentiation between happy and fearful faces. These findings suggest that the differential simulation of specific affective states is attenuated in individuals with higher trait anxiety and autism. In summary, the MNS appears to support the skills needed for emotion processing in daily life, which may be influenced by certain individual differences. This provides novel evidence for the notion that simulation-based emotional skills may underlie the emotional difficulties that accompany affective disorders, such as anxiety.

Blood Oxygen Level-Dependent Lag Patterns Differ Between Rest and Task Conditions, but Are Largely Typical in Autism

Background/Introduction: Autism spectrum disorder (ASD) is characterized by atypical functional connectivity (FC) within and between distributed brain networks. However, FC findings have often been inconsistent, possibly due to a focus on static FC rather than brain dynamics. Lagged connectivity analyses aim at evaluating temporal latency, and presumably neural propagation, between regions. This approach may, therefore, reveal a more detailed picture of network organization in ASD than traditional FC methods. Methods: The current study evaluated whole-brain lag patterns in adolescents with ASD (n = 28) and their typically developing peers (n = 22). Functional magnetic resonance imaging data were collected during rest and during a lexico-semantic decision task. Optimal lag was calculated for each pair of regions of interest by using cross-covariance, and mean latency projections were calculated for each region. Results: Latency projections did not regionally differ between groups, with the same regions emerging among the "earliest" and "latest." Although many of the longest absolute latencies were preserved across resting-state and task conditions, lag patterns overall were affected by condition, as many regions shifted toward zero-lag during task performance. Lag structure was also strongly associated with literature-derived estimates of arterial transit time. Discussion: Results suggest that lag patterns are broadly typical in ASD but undergo changes during task performance. Moreover, lag patterns appear to reflect a combination of neural and vascular sources, which should be carefully considered when interpreting lagged FC.

Susceptibility-Weighted Magnetic Resonance Imaging (MRI) of Microbleeds in Pediatric Concussion

OBJECTIVE

The long-term consequences of pediatric concussion on brain structure are poorly understood. This study aimed to evaluate the presence and clinical significance of cerebral microbleeds several years after pediatric concussion.

METHODS

Children and adolescents 8-19 years of age with either a history of concussion (n = 35), or orthopedic injury (n = 20) participated. Mean time since injury for the sample was 30.4 months (SD = 19.6). Participants underwent susceptibility-weighted imaging, rated their depression and postconcussion symptoms, and completed cognitive testing. Parents of participants also completed symptom ratings for their child. Hypointensities in susceptibility-weighted images indicative of cerebral microbleeds were calculated as a measure of hypointensity burden.

RESULTS

Hypointensity burden did not differ significantly between participants with a history of concussion and those with a history of orthopedic injury. Depression ratings (self and parent report), postconcussion symptom ratings (self and parent report), and cognitive performance did not significantly correlate with hypointensity burden in the concussion group.

CONCLUSIONS

These findings suggest that at approximately 2.5 years postinjury, children and adolescents with prior concussion do not have a greater amount of cerebral microbleeds compared to those with orthopedic injury. Future research should use longitudinal study designs and investigate children with persistent postconcussive symptoms to gain better insight into the long-term effects of concussion on cerebral microbleeds.

Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability

There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K-nearest neighbour graphs and graph partitioning with Markov stability to determine optimal clustering of participants. Resultant clusters were (1) checked whether they were replicated in an independent cohort and (2) tested for associations with depression status and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case-control status in either cohort (GS subsample: p_FDR  = .2239-.6585; UKB: p_FDR  = .2003-.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets, for CSA, CV and subCV (GS subsample: d = 0.2529-.3490, p_FDR  < .005; UKB: d = 0.0868-0.1070, p_FDR  < .005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case-control status.

Automatic linear measurements of the fetal brain on MRI with deep neural networks

PURPOSE

Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are cerebral biparietal diameter (CBD), bone biparietal diameter (BBD), and trans-cerebellum diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI.

METHODS

The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: (1) computation of a region of interest that includes the fetal brain with an anisotropic 3D U-Net classifier; (2) reference slice selection with a convolutional neural network; (3) slice-wise fetal brain structures segmentation with a multi-class U-Net classifier; (4) computation of the fetal brain midsagittal line and fetal brain orientation, and; (5) computation of the measurements.

RESULTS

Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean [Formula: see text] difference of 1.55 mm, 1.45 mm and 1.23 mm, respectively, and a Bland-Altman 95% confidence interval ([Formula: see text] of 3.92 mm, 3.98 mm and 2.25 mm, respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions.

CONCLUSIONS

The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human-level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

Using connectomics for predictive assessment of brain parcellations

The organization of the human brain remains elusive, yet is of great importance to the mechanisms of integrative brain function. At the macroscale, its structural and functional interpretation is conventionally assessed at the level of cortical units. However, the definition and validation of such cortical parcellations are problematic due to the absence of a true gold standard. We propose a framework for quantitative evaluation of brain parcellations via statistical prediction of connectomics data. Specifically, we evaluate the extent in which the network representation at the level of cortical units (defined as parcels) accounts for high-resolution brain connectivity. Herein, we assess the pertinence and comparative ranking of ten existing parcellation atlases to account for functional (FC) and structural connectivity (SC) data based on data from the Human Connectome Project (HCP), and compare them to data-driven as well as spatially-homogeneous geometric parcellations including geodesic parcellations with similar size distributions as the atlases. We find substantial discrepancy in parcellation structures that well characterize FC and SC and differences in what well represents an individual's functional connectome when compared against the FC structure that is preserved across individuals. Surprisingly, simple spatial homogenous parcellations generally provide good representations of both FC and SC, but are inferior when their within-parcellation distribution of individual parcel sizes is matched to that of a valid atlas. This suggests that the choice of fine grained and coarse representations used by existing atlases are important. However, we find that resolution is more critical than the exact border location of parcels.

Music Intervention Reduces Persistent Fibromyalgia Pain and Alters Functional Connectivity Between the Insula and Default Mode Network

OBJECTIVE

The aims of the present study were to examine the effects of short-term music interventions among patients with fibromyalgia (FM) and to clarify the alterations in functional connectivity and persistent pain.

DESIGN

Pilot study.

SETTING

All participants were evaluated at Juntendo University from November 2017 to January 2019.

SUBJECTS

We enrolled female patients who had been clinically diagnosed with FM (N = 23).

METHODS

All participants listened to Mozart's Duo for Violin and Viola No. 1, K. 423, in a quiet room for 17 minutes. We compared the degree of pain using resting-state functional magnetic resonance imaging and the numeric rating scale before and after listening to music.

RESULTS

Pain scores were significantly reduced after listening to music. Further, we observed there was a significant difference in connectivity between the right insular cortex (IC) and posterior cingulate cortex (PCC)/precuneus (PCu) before and after listening to music. We also found that the difference between the right IC-PCu connectivity and the difference in pain scores were significantly correlated.

CONCLUSIONS

We found that a short period of music intervention reduced chronic pain and altered functional IC-default mode network connectivity. Furthermore, music potentially normalized the neural network via IC-default mode network connectivity, yielding temporary pain relief in patients with FM. Further longitudinal studies with larger sample sizes are required to confirm these results.

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel-reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs-fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered-SWC (T-SWC) with different window lengths) based on both simulated and real rs-fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T-SWC (p-value = .001) and SWC (p-value <.001), respectively. Results of these different methods applied on real rs-fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T-SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC-based method was unable to detect these dynamic connections.

Cortical reorganization following auditory deprivation predicts cochlear implant performance in postlingually deaf adults

Long‐term hearing loss in postlingually deaf (PD) adults may lead to brain structural changes that affect the outcomes of cochlear implantation. We studied 94 PD patients who underwent cochlear implantation and 37 patients who were MRI‐scanned within 2 weeks after the onset of sudden hearing loss and expected with minimal brain structural changes in relation to deafness. Compared with those with sudden hearing loss, we found lower gray matter (GM) probabilities in bilateral thalami, superior, middle, inferior temporal cortices as well as the central cortical regions corresponding to the movement and sensation of the lips, tongue, and larynx in the PD group. Among these brain areas, the GM in the middle temporal cortex showed negative correlation with disease duration, whereas the other areas displayed positive correlations. Left superior, middle temporal cortical, and bilateral thalamic GMs were the most accurate predictors of post‐cochlear implantation word recognition scores (mean absolute error [MAE] = 10.1, r = .82), which was superior to clinical variables used (MAE: 12.1, p < .05). Using the combined brain morphological and clinical features, we achieved the best prediction of the outcome (MAE: 8.51, r = .90). Our findings suggest that the cross‐modal plasticity allowing the superior temporal cortex and thalamus to process other modal sensory inputs reverses the initially lower volume when deafness becomes persistent. The middle temporal cortex processing higher‐level language comprehension shows persistent negative correlations with disease duration, suggesting this area's association with degraded speech comprehensions due to long‐term deafness. Morphological features combined with clinical variables might play a key role in predicting outcomes of cochlear implantation.

Exercise alters brain activation in Gulf War Illness and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Gulf War Illness affects 25-30% of American veterans deployed to the 1990-91 Persian Gulf War and is characterized by cognitive post-exertional malaise following physical effort. Gulf War Illness remains controversial since cognitive post-exertional malaise is also present in the more common Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. An objective dissociation between neural substrates for cognitive post-exertional malaise in Gulf War Illness and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome would represent a biological basis for diagnostically distinguishing these two illnesses. Here, we used functional magnetic resonance imaging to measure neural activity in healthy controls and patients with Gulf War Illness and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome during an N-back working memory task both before and after exercise. Whole brain activation during working memory (2-Back > 0-Back) was equal between groups prior to exercise. Exercise had no effect on neural activity in healthy controls yet caused deactivation within dorsal midbrain and cerebellar vermis in Gulf War Illness relative to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients. Further, exercise caused increased activation among Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients within the dorsal midbrain, left operculo-insular cortex (Rolandic operculum) and right middle insula. These regions-of-interest underlie threat assessment, pain, interoception, negative emotion and vigilant attention. As they only emerge post-exercise, these regional differences likely represent neural substrates of cognitive post-exertional malaise useful for developing distinct diagnostic criteria for Gulf War Illness and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

Cholinergic nucleus 4 atrophy and gait impairment in Parkinson's disease

BACKGROUND

There is evidence that cortical cholinergic denervation contributes to gait and balance impairment in Parkinson's Disease (PD), especially reduced gait speed.

OBJECTIVES

The objective of this study was to determine the relationship between cholinergic basal forebrain gray matter density (GMD) and gait in PD patients.

METHODS

We investigated 66 PD patients who underwent a pre-surgical evaluation for a neurosurgical procedure to treat motor symptoms of PD. As part of this evaluation patients had a brain MRI and formal gait assessments. By applying probabilistic maps of the cholinergic basal forebrain to voxel-based morphometry of brain MRI, we calculated gray matter density (GMD) for cholinergic nucleus 4 (Ch4), cholinergic nucleus 1, 2, and 3 (Ch123), and the entire cortex.

RESULTS

Reduced Ch4 GMD was associated with reduced Fast Walking Speed in the "on" medication state (FWSON, p = 0.004). Bilateral cortical GMD was also associated with FWSON (p = 0.009), but Ch123 GMD was not (p = 0.1). Bilateral cortical GMD was not associated with FWSON after adjusting for Ch4 GMD (p = 0.44). While Ch4 GMD was not associated with improvement in Timed Up and Go (TUG) or Cognitive TUG in the "on" medication state, reduced Ch4 GMD was associated with greater percent worsening based on dual tasks (p = 0.021).

CONCLUSIONS

Reduced Ch4 GMD is associated with slower gait speed in PD and greater percent worsening in TUG during dual tasks in patients with PD. These findings have implications for planning of future clinical trials investigating cholinergic therapies to improve gait impairment in PD.

Assessment of cortical reorganization and preserved function in phantom limb pain: a methodological perspective

Phantom limb pain (PLP) has been associated with reorganization in primary somatosensory cortex (S1) and preserved S1 function. Here we examined if methodological differences in the assessment of cortical representations might explain these findings. We used functional magnetic resonance imaging during a virtual reality movement task, analogous to the classical mirror box task, in twenty amputees with and without PLP and twenty matched healthy controls. We assessed the relationship between task-related activation maxima and PLP intensity in S1 and motor cortex (M1) in individually-defined or group-conjoint regions of interest (ROI) (overlap of task-related activation between the groups). We also measured cortical distances between both locations and correlated them with PLP intensity. Amputees compared to controls showed significantly increased activation in M1, S1 and S1M1 unrelated to PLP. Neural activity in M1 was positively related to PLP intensity in amputees with PLP when a group-conjoint ROI was chosen. The location of activation maxima differed between groups in S1 and M1. Cortical distance measures were unrelated to PLP. These findings suggest that sensory and motor maps differentially relate to PLP and that methodological differences might explain discrepant findings in the literature.

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages

We report improved automated open-source methodology for head-fixed mesoscale cortical imaging and/or behavioral training of home cage mice using Raspberry Pi-based hardware. Staged partial and probabilistic restraint allows mice to adjust to self-initiated headfixation over 3 weeks' time with ~50% participation rate. We support a cue-based behavioral licking task monitored by a capacitive touch-sensor water spout. While automatically head-fixed, we acquire spontaneous, movement-triggered, or licking task-evoked GCaMP6 cortical signals. An analysis pipeline marked both behavioral events, as well as analyzed brain fluorescence signals as they relate to spontaneous and/or task-evoked behavioral activity. Mice were trained to suppress licking and wait for cues that marked the delivery of water. Correct rewarded go-trials were associated with widespread activation of midline and lateral barrel cortex areas following a vibration cue and delayed frontal and lateral motor cortex activation. Cortical GCaMP signals predicted trial success and correlated strongly with trial-outcome dependent body movements.

Psychotic-like experiences, polygenic risk scores for schizophrenia, and structural properties of the salience, default mode, and central-executive networks in healthy participants from UK Biobank

Schizophrenia is a highly heritable disorder with considerable phenotypic heterogeneity. Hallmark psychotic symptoms can be considered as existing on a continuum from non-clinical to clinical populations. Assessing genetic risk and psychotic-like experiences (PLEs) in non-clinical populations and their associated neurobiological underpinnings can offer valuable insights into symptom-associated brain mechanisms without the potential confounds of the effects of schizophrenia and its treatment. We leveraged a large population-based cohort (UKBiobank, N = 3875) including information on PLEs (obtained from the Mental Health Questionnaire (MHQ); UKBiobank Category: 144; N auditory hallucinations = 55, N visual hallucinations = 79, N persecutory delusions = 16, N delusions of reference = 13), polygenic risk scores for schizophrenia (PRSSZ) and multi-modal brain imaging in combination with network neuroscience. Morphometric (cortical thickness, volume) and water diffusion (fractional anisotropy) properties of the regions and pathways belonging to the salience, default-mode, and central-executive networks were computed. We hypothesized that these anatomical concomitants of functional dysconnectivity would be negatively associated with PRSSZ and PLEs. PRSSZ was significantly associated with a latent measure of cortical thickness across the salience network (r = -0.069, p = 0.010) and PLEs showed a number of significant associations, both negative and positive, with properties of the salience and default mode networks (involving the insular cortex, supramarginal gyrus, and pars orbitalis, pFDR < 0.050); with the cortical thickness of the insula largely mediating the relationship between PRSSZ and auditory hallucinations. Generally, these results are consistent with the hypothesis that higher genetic liability for schizophrenia is related to subtle disruptions in brain structure and may predispose to PLEs even among healthy participants. In addition, our study suggests that networks engaged during auditory hallucinations show structural associations with PLEs in the general population.

On brain atlas choice and automatic segmentation methods: a comparison of MAPER & FreeSurfer using three atlas databases

Several automatic image segmentation methods and few atlas databases exist for analysing structural T1-weighted magnetic resonance brain images. The impact of choosing a combination has not hitherto been described but may bias comparisons across studies. We evaluated two segmentation methods (MAPER and FreeSurfer), using three publicly available atlas databases (Hammers_mith, Desikan-Killiany-Tourville, and MICCAI 2012 Grand Challenge). For each combination of atlas and method, we conducted a leave-one-out cross-comparison to estimate the segmentation accuracy of FreeSurfer and MAPER. We also used each possible combination to segment two datasets of patients with known structural abnormalities (Alzheimer's disease (AD) and mesial temporal lobe epilepsy with hippocampal sclerosis (HS)) and their matched healthy controls. MAPER was better than FreeSurfer at modelling manual segmentations in the healthy control leave-one-out analyses in two of the three atlas databases, and the Hammers_mith atlas database transferred to new datasets best regardless of segmentation method. Both segmentation methods reliably identified known abnormalities in each patient group. Better separation was seen for FreeSurfer in the AD and left-HS datasets, and for MAPER in the right-HS dataset. We provide detailed quantitative comparisons for multiple anatomical regions, thus enabling researchers to make evidence-based decisions on their choice of atlas and segmentation method.

Atypical Relationships Between Spontaneous EEG and fMRI Activity in Autism

Autism spectrum disorders (ASDs) have been linked to atypical communication among distributed brain networks. However, despite decades of research, the exact nature of these differences between typically developing (TD) individuals and those with ASDs remains unclear. ASDs have been widely studied using resting-state neuroimaging methods, including both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). However, little is known about how fMRI and EEG measures of spontaneous brain activity are related in ASDs. In the present study, two cohorts of children and adolescents underwent resting-state EEG (n = 38 per group) or fMRI (n = 66 ASD, 57 TD), with a subset of individuals in both the EEG and fMRI cohorts (n = 17 per group). In the EEG cohort, parieto-occipital EEG alpha power was found to be reduced in ASDs. In the fMRI cohort, blood oxygen level-dependent (BOLD) power was regionally increased in right temporal regions and there was widespread overconnectivity between the thalamus and cortical regions in the ASD group relative to the TD group. Finally, multimodal analyses indicated that while TD children showed consistently positive relationships between EEG alpha power and regional BOLD power, these associations were weak or negative in ASDs. These findings suggest atypical links between alpha rhythms and regional BOLD activity in ASDs, possibly implicating neural substrates and processes that coordinate thalamocortical regulation of the alpha rhythm.

Fluctuating asymmetry in brain structure and general intelligence in 73-year-olds

Fluctuating body asymmetry is theorized to indicate developmental instability, and to have small positive associations with low socioeconomic status (SES). Previous studies have reported small negative associations between fluctuating body asymmetry and cognitive functioning, but relationships between fluctuating brain asymmetry and cognitive functioning remain unclear. The present study investigated the association between general intelligence (a latent factor derived from a factor analysis on 13 cognitive tests) and the fluctuating asymmetry of four structural measures of brain hemispheric asymmetry: cortical surface area, cortical volume, cortical thickness, and white matter fractional anisotropy. The sample comprised members of the Lothian Birth Cohort 1936 (LBC1936, N = 636, mean age = 72.9 years). Two methods were used to calculate structural hemispheric asymmetry: in the first method, regions contributed equally to the overall asymmetry score; in the second method, regions contributed proportionally to their size. When regions contributed equally, cortical thickness asymmetry was negatively associated with general intelligence (β = −0.18,p < .001). There was no association between cortical thickness asymmetry and childhood SES, suggesting that other mechanisms are involved in the thickness asymmetry-intelligence association. Across all cortical metrics, asymmetry of regions identified by the parieto-frontal integration theory (P-FIT) was not more strongly associated with general intelligence than non-P-FIT asymmetry. When regions contributed proportionally, there were no associations between general intelligence and any of the asymmetry measures. The implications of these findings, and of different methods of calculating structural hemispheric asymmetry, are discussed.

•

Previous work has shown links between cortical asymmetry and intelligence.

•

In the current study, two methods of calculating global cortical hemispheric asymmetry were compared.

•

Equal region contribution: the association between cortical thickness asymmetry and general intelligence was β = −0.18.

•

Proportional region contribution: no associations between three measures of cortical asymmetry and general intelligence.

Lower volume, more impairment: reduced cholinergic basal forebrain grey matter density is associated with impaired cognition in Parkinson disease

OBJECTIVE

A major contributor to dementia in Parkinson disease (PD) is degeneration of the cholinergic basal forebrain. This study determined whether cholinergic nucleus 4 (Ch4) density is associated with cognition in early and more advanced PD.

METHODS

We analysed brain MRIs and neuropsychological test scores for 228 newly diagnosed PD participants from the Parkinson's Progression Markers Initiative (PPMI), 101 healthy controls from the PPMI and 125 more advanced PD patients from a local retrospective cohort. Cholinergic basal forebrain nuclei densities were determined by applying probabilistic maps to MPRAGE T1 sequences processed using voxel-based morphometry methods. Relationships between grey matter densities and cognitive scores were analysed using correlations and linear regression models.

RESULTS

In more advanced PD, greater Ch4 density was associated with Montreal Cognitive Assessment (MoCA) score (β=14.2; 95% CI=1.5 to 27.0; p=0.03), attention domain z-score (β=3.2; 95% CI=0.8 to 5.5; p=0.008) and visuospatial domain z-score (β=7.9; 95% CI=2.0 to 13.8; p=0.009). In the PPMI PD cohort, higher Ch4 was associated with higher scores on MoCA (β=9.2; 95% CI=1.9 to 16.5; p=0.01), Judgement of Line Orientation (β=20.4; 95% CI=13.8 to 27.0; p<0.001), Letter Number Sequencing (β=16.5; 95% CI=9.5 to 23.4; p<0.001) and Symbol Digit Modalities Test (β=41.8; 95% CI=18.7 to 65.0; p<0.001). These same relationships were observed in 97 PPMI PD participants at 4 years. There were no significant associations between Ch4 density and cognitive outcomes in healthy controls.

CONCLUSION

In de novo and more advanced PD, lower Ch4 density is associated with impaired global cognition, attention and visuospatial function.

Structural brain imaging correlates of general intelligence in UK Biobank

The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants' age range was 44-81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age- and sex- corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macro- and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5. 4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction.

Impact of non-uniform attenuation correction in a dynamic [18F]-FDOPA brain PET/MRI study

Background

PET (positron emission tomography) biokinetic modelling relies on accurate quantitative data. One of the main corrections required in PET imaging to obtain high quantitative accuracy is tissue attenuation correction (AC). Incorrect non-uniform PET-AC may result in local bias in the emission images, and thus in relative activity distributions and time activity curves for different regions. MRI (magnetic resonance imaging)-based AC is an active area of research in PET/MRI neuroimaging, where several groups developed in the last few years different methods to calculate accurate attenuation (μ-)maps. Some AC methods have been evaluated for different PET radioisotopes and pathologies. However, AC in PET/MRI has scantly been investigated in dynamic PET studies where the aim is to get quantitative kinetic parameters, rather than semi-quantitative parameters from static PET studies. In this work, we investigated the impact of AC accuracy in PET image absolute quantification and, more importantly, in the slope of the Patlak analysis based on the simplified reference tissue model, from a dynamic [¹⁸F]-fluorodopa (FDOPA) PET/MRI study. In the study, we considered the two AC methods provided by the vendor and an in-house AC method based on the dual ultrashort time echo MRI sequence, using as reference a multi-atlas-based AC method based on a T1-weighted MRI sequence.

Results

Non-uniform bias in absolute PET quantification across the brain, from − 20% near the skull to − 10% in the central region, was observed using the two vendor’s μ-maps. The AC method developed in-house showed a − 5% and 1% bias, respectively. Our study resulted in a 5–9% overestimation of the PET kinetic parameters with the vendor-provided μ-maps, while our in-house-developed AC method showed < 2% overestimation compared to the atlas-based AC method, using the cerebellar cortex as reference region. The overestimation obtained using the occipital pole as reference region resulted in a 7–10% with the vendor-provided μ-maps, while our in-house-developed AC method showed < 6% overestimation.

Conclusions

PET kinetic analyses based on a reference region are especially sensitive to the non-uniform bias in PET quantification from AC inaccuracies in brain PET/MRI. Depending on the position of the reference region and the bias with respect to the analysed region, kinetic analyses suffer different levels of bias. Considering bone in the μ-map can potentially result in larger errors, compared to the absence of bone, when non-uniformities in PET quantification are introduced.

Genetic Single Neuron Anatomy Reveals Fine Granularity of Cortical Axo-Axonic Cells

Parsing diverse nerve cells into biological types is necessary for understanding neural circuit organization. Morphology is an intuitive criterion for neuronal classification and a proxy of connectivity, but morphological diversity and variability often preclude resolving the granularity of neuron types. Combining genetic labeling with high-resolution, large-volume light microscopy, we established a single neuron anatomy platform that resolves, registers, and quantifies complete neuron morphologies in the mouse brain. We discovered that cortical axo-axonic cells (AACs), a cardinal GABAergic interneuron type that controls pyramidal neuron (PyN) spiking at axon initial segments, consist of multiple subtypes distinguished by highly laminar-specific soma position and dendritic and axonal arborization patterns. Whereas the laminar arrangements of AAC dendrites reflect differential recruitment by input streams, the laminar distribution and local geometry of AAC axons enable differential innervation of PyN ensembles. This platform will facilitate genetically targeted, high-resolution, and scalable single neuron anatomy in the mouse brain.

Manually-parcellated gyral data accounting for all known anatomical variability

Morphometric brain changes occur throughout the lifetime and are often investigated to understand healthy ageing and disease, to identify novel biomarkers, and to classify patient groups. Yet, to accurately characterise such changes, an accurate parcellation of the brain must be achieved. Here, we present a manually-parcellated dataset of the superior frontal, the supramarginal, and the cingulate gyri of 10 healthy middle-aged subjects along with a fully detailed protocol based on two anatomical atlases. Gyral parcels were hand-drawn then reviewed by specialists blinded from the protocol to ensure consistency. Importantly, we follow a procedure that allows accounting for anatomical variability beyond what is usually achieved by standard analysis packages and avoids mutually referring to neighbouring gyri when defining gyral edges. We also provide grey matter thickness, grey matter volume, and white matter surface area information for each parcel. This dataset and corresponding measurements are useful in assessing the accuracy of equivalent parcels and metrics generated by image analysis tools and their impact on morphometric studies.

Toward a Common Terminology for the Thalamus

The wealth of competing parcellations with limited cross-correspondence between atlases of the human thalamus raises problems in a time when the usefulness of neuroanatomical methods is increasingly appreciated for modern computational analyses of the brain. An unequivocal nomenclature is, however, compulsory for the understanding of the organization of the thalamus. This situation cannot be improved by renewed discussion but with implementation of neuroinformatics tools. We adopted a new volumetric approach to characterize the significant subdivisions and determined the relationships between the parcellation schemes of nine most influential atlases of the human thalamus. The volumes of each atlas were 3d-reconstructed and spatially registered to the standard MNI/ICBM2009b reference volume of the Human Brain Atlas in the MNI (Montreal Neurological Institute) space (Mai and Majtanik, 2017). This normalization of the individual thalamus shapes allowed for the comparison of the nuclear regions delineated by the different authors. Quantitative cross-comparisons revealed the extent of predictability of territorial borders for 11 area clusters. In case of discordant parcellations we re-analyzed the underlying histological features and the original descriptions. The final scheme of the spatial organization provided the frame for the selected terms for the subdivisions of the human thalamus using on the (modified) terminology of the Federative International Programme for Anatomical Terminology (FIPAT). Waiving of exact individual definition of regional boundaries in favor of the statistical representation within the open MNI platform provides the common and objective (standardized) ground to achieve concordance between results from different sources (microscopy, imaging etc.).

Multimodal Brain Parcellation Based on Functional and Anatomical Connectivity

Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.

Decoding sound categories based on whole-brain functional connectivity patterns

2Sound decoding is important for patients with sensory loss, such as the blind. Previous studies on sound categorization were conducted by estimating brain activity using univariate analysis or voxel-wise multivariate decoding methods and suggested some regions were sensitive to auditory categories. It is proposed that feedback connections between brain areas may facilitate auditory object selection. Therefore, it is important to explore whether functional connectivity among regions can be used to decode sound category. In this study, we constructed whole-brain functional connectivity patterns when subjects perceived four different sound categories and combined them with multivariate pattern classification analysis for sound decoding. The categorical discriminative networks and regions were determined based on the weight maps. Results showed that a high accuracy in multi-category classification was obtained based on the whole-brain functional connectivity patterns and the results were verified by different preprocessing parameters. Insight into the category discriminative functional networks showed that contributive connections crossed the left and right brain, and ranged from primary regions to high-level cognitive regions, which provide new evidence for the distributed representation of auditory object. Further analysis of brain regions in the discriminative networks showed that superior temporal gyrus and Heschl's gyrus significantly contributed to discriminating sound categories. Together, the findings reveal that functional connectivity based multivariate classification method provides rich information for auditory category decoding. The successful decoding results implicate the interactive properties of the distributed brain areas in auditory sound representation.

Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods

The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.

A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.