Probabilistic White Matter Atlases of Human Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Visual and Visuospatial Networks

MRI morphometry of the anterior and posterior cerebellar vermis and its relationship to sensorimotor and cognitive functions in children

INTRODUCTION

The human cerebellum emerges as a posterior brain structure integrating neural networks for sensorimotor, cognitive, and emotional processing across the lifespan. Developmental studies of the cerebellar anatomy and function are scant. We examine age-dependent MRI morphometry of the anterior cerebellar vermis, lobules I-V and posterior neocortical lobules VI-VII and their relationship to sensorimotor and cognitive functions.

METHODS

Typically developing children (TDC; n=38; age 9-15) and healthy adults (HAC; n=31; 18-40) participated in high-resolution MRI. Rigorous anatomically informed morphometry of the vermis lobules I-V and VI-VII and total brain volume (TBV) employed manual segmentation computer-assisted FreeSurfer Image Analysis Program [http://surfer.nmr.mgh.harvard.edu]. The neuropsychological scores (WASI-II) were normalized and related to volumes of anterior, posterior vermis, and TBV.

RESULTS

TBVs were age independent. Volumes of I-V and VI-VII were significantly reduced in TDC. The ratio of VI-VII to I-V (∼60%) was stable across age-groups; I-V correlated with visual-spatial-motor skills; VI-VII with verbal, visual-abstract and FSIQ.

CONCLUSIONS

In TDC neither anterior I-V nor posterior VI-VII vermis attained adult volumes. The "inverted U" developmental trajectory of gray matter peaking in adolescence does not explain this finding. The hypothesis of protracted development of oligodendrocyte/myelination is suggested as a contributor to TDC's lower cerebellar vermis volumes.

Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.

White matter fiber integrity and structural brain network topology: implications for balance function in postischemic stroke patients

Previous studies have suggested that ischemic stroke can result in white matter fiber injury and modifications in the structural brain network. However, the relationship with balance function scores remains insufficiently explored. Therefore, this study aims to explore the alterations in the microstructural properties of brain white matter and the topological characteristics of the structural brain network in postischemic stroke patients and their potential correlations with balance function. We enrolled 21 postischemic stroke patients and 21 age, sex, and education-matched healthy controls (HC). All participants underwent balance function assessment and brain diffusion tensor imaging. Tract-based spatial statistics (TBSS) were used to compare the fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity of white matter fibers between the two groups. The white matter structural brain network was constructed based on the automated anatomical labeling atlas, and we conducted a graph theory-based analysis of its topological properties, including global network properties and local node properties. Additionally, the correlation between the significant structural differences and balance function score was analyzed. The TBSS results showed that in comparison to the HC, postischemic stroke patients exhibited extensive damage to their whole-brain white matter fiber tracts (P < 0.05). Graph theory analysis showed that in comparison to the HC, postischemic stroke patients exhibited statistically significant reductions in the values of global efficiency, local efficiency, and clustering coefficient, as well as an increase in characteristic path length (P < 0.05). In addition, the degree centrality and nodal efficiency of some nodes in postischemic stroke patients were significantly reduced (P < 0.05). The white matter fibers of the entire brain in postischemic stroke patients are extensively damaged, and the topological properties of the structural brain network are altered, which are closely related to balance function. This study is helpful in further understanding the neural mechanism of balance function after ischemic stroke from the white matter fiber and structural brain network topological properties.

Microstructural differences in migraine: A diffusion-tensor imaging study

BACKGROUND

Diffusion-tensor imaging can be applied to describe the microstructural integrity of the whole brain. As findings about microstructural alterations in migraine are inconsistent, we aimed to replicate the most frequent results and assess a relationship between migraine parameters and changes in microstructure.

METHODS

Diffusion-weighted MRI data of 37 migraine patients and 40 controls were collected. Two indices of diffusion of water molecules, fractional anisotropy and mean diffusivity were used in a voxel-wise analysis. Group comparisons were carried out in SPM12 using age and sex as covariates. Statistically significant results survived family-wise error correction (pFWE < 0.05). Migraine intensity, frequency, and duration were self-reported and correlated with mean fractional anisotropy and mean diffusivity values across clusters.

RESULTS

Migraine patients showed significantly lower fractional anisotropy in occipital regions, and significantly higher fractional anisotropy in thirteen clusters across the brain. Mean diffusivity of migraine patients was significantly decreased in the cerebellum and pons, but it was not increased in any area. Correlation between migraine duration and fractional anisotropy was significantly positive in the frontal cortex and significantly negative in the superior parietal lobule.

CONCLUSION

We suggest that microstructural integrity of the migraine brain is impaired in visual areas and shows duration-related alterations in regions of the default mode network.

Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain's white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks.

Periventricular and juxtacortical characterization of UManitoba-JHU functionally defined human white matter atlas networks

Background

The open-access UManitoba-JHU functionally defined human white matter (WM) atlas contains specific WM pathways and general WM regions underlying 12 functional brain networks in ICBM152 template space. However, it is not known whether any of these WM networks are disproportionately co-localized with periventricular and/or juxtacortical WM (PVWM and JCWM), which could potentially impact their ability to infer network-specific effects in future studies-particularly in patient populations expected to have disproportionate PVWM and/or JCWM damage.

Methods

The current study therefore identified intersecting regions of PVWM and JCWM (defined as WM within 5 mm of the ventricular and cortical boundaries) and: (1) the ICBM152 global WM mask, and (2) all 12 UManitoba-JHU WM networks. Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), and proportion of volume (POV) values between PVWM (and JCWM) and each functionally defined WM network were then compared to corresponding values between PVWM (and JCWM) and global WM.

Results

Between the 12 WM networks and PVWM, 8 had lower DSC, JSC, and POV; 1 had lower DSC and JSC, but higher POV; and 3 had higher DSC, JSC, and POV compared to global WM. For JCWM, all 12 WM networks had lower DSC, JSC, and POV compared to global WM.

Conclusion

The majority of UManitoba-JHU functionally defined WM networks exhibited lower than average spatial similarity with PVWM, and all exhibited lower than average spatial similarity with JCWM. This suggests that they can be used to explore network-specific WM changes, even in patient populations with known predispositions toward PVWM and/or JCWM damage.

OPRM1 A118G polymorphism modulating motor pathway for pain adaptability in women with primary dysmenorrhea

Introduction

Primary dysmenorrhea (PDM) is a common condition among women of reproductive age, characterized by menstrual pain in the absence of any organic causes. Previous research has established a link between the A118G polymorphism in the mu-opioid receptor (OPRM1) gene and pain experience in PDM. Specifically, carriers of the G allele have been found to exhibit maladaptive functional connectivity between the descending pain modulatory system and the motor system in young women with PDM. This study aims to explore the potential relationship between the OPRM1 A118G polymorphism and changes in white matter in young women with PDM.

Methods

The study enrolled 43 individuals with PDM, including 13 AA homozygotes and 30 G allele carriers. Diffusion tensor imaging (DTI) scans were performed during both the menstrual and peri-ovulatory phases, and tract-based spatial statistics (TBSS) and probabilistic tractography were used to explore variations in white matter microstructure related to the OPRM1 A118G polymorphism. The short-form McGill Pain Questionnaire (MPQ) was used to access participants' pain experience during the MEN phase.

Results

Two-way ANOVA on TBSS analysis revealed a significant main effect of genotype, with no phase effect or phase-gene interaction detected. Planned contrast analysis showed that during the menstrual phase, G allele carriers had higher fractional anisotropy (FA) and lower radial diffusivity in the corpus callosum and the left corona radiata compared to AA homozygotes. Tractographic analysis indicated the involvement of the left internal capsule, left corticospinal tract, and bilateral medial motor cortex. Additionally, the mean FA of the corpus callosum and the corona radiata was negatively correlated with MPQ scales in AA homozygotes, but this correlation was not observed in G allele carriers. No significant genotype difference was found during the pain-free peri-ovulary phase.

Discussion

OPRM1 A118G polymorphism may influence the connection between structural integrity and dysmenorrheic pain, where the G allele could impede the pain-regulating effects of the A allele. These novel findings shed light on the underlying mechanisms of both adaptive and maladaptive structural neuroplasticity in PDM, depending on the specific OPRM1 polymorphism.

Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia. Low-frequency repetitive transcranial magnetic stimulation (rTMS) has shown potential in the treatment of AVH. However, the underlying neural mechanismof rTMS in the treatment of AVH remains largely unknown. In this study, we used a static and dynamic functional network connectivity approach to investigate the connectivity changes among the brain functional networks in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The static functional network connectivity (sFNC) analysis revealed that patients at baseline had significantly decreased connectivity between the default mode network (DMN) and language network (LAN), and within the executive control network (ECN) as well as within the auditory network (AUD) compared to controls. However, the abnormal network connectivity patterns were normalized or restored after rTMS treatment in patients, instead of increased connectivity between the ECN and LAN, as well as within the AUD. Moreover, the dynamic functional network connectivity (dFNC) analysis showed that the patients at baseline spent more time in this state that was characterized by strongly negative connectivity between the ENC and AUD, as well as within the AUD relative to controls. While after rTMS treatment, the patients showed a higher occurrence rate in this state that was characterized by strongly positive connectivity among the LAN, DMN, and ENC, as well as within the ECN. In addition, the altered static and dynamic connectivity properties were associated with reduced severity of clinical symptoms. Both sFNC and dFNC analyses provided complementary information and suggested that low-frequency rTMS treatment could induce intrinsic functional network alternations and contribute to improvements in clinical symptoms in patients with AVH.

A multi-scale probabilistic atlas of the human connectome

The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.

The Effect of Long-Term Menstrual Pain on Large-Scale Brain Network in Primary Dysmenorrhea Patients

Purpose

Primary dysmenorrhea (PD) is a common gynecological disease, characterized by crampy and suprapubic pain occurring with menses. Growing evidences demonstrated that PD patients were associated with abnormalities in brain function and structure. However, little is known regarding whether the large-scale brain network changes in PD patients. The purpose of this study was to investigate the effect of long-term menstrual pain on large-scale brain network in PD patients using independent component analysis (ICA) method.

Methods

Twenty-eight PD patients (female, mean age, 24.25±1.00 years) and twenty-eight healthy controls (HCs) (mean age, 24.46±1.31 years), closely matched for age, sex, and education, underwent resting-state magnetic resonance imaging scans. ICA was applied to extract the resting-state networks (RSNs) in two groups. Then, two-sample t-tests were conducted to investigate different intranetwork FCs within RSNs and interactions among RSNs between two groups.

Results

Compared to the HC group, PD patients showed significant increased intra-network FCs within the auditory network (AN), sensorimotor network (SMN), right executive control network (RECN). However, PD patients showed significant decreased intra-network FCs within ventral default mode network (vDMN) and salience network (SN). Moreover, FNC analysis showed increased VN-AN and decreased VN-SMN functional connectivity between two groups.

Conclusion

Our study highlighted that PD patients had abnormal brain networks related to auditory, sensorimotor and higher cognitive network. Our results offer important insights into the altered large-scale brain network neural mechanisms of pain in PD patients.

White matter substrates of functional connectivity dynamics in the human brain

The contribution of structural connectivity to functional connectivity dynamics is still far from being elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.

Aberrant Structural Connectivity of the Triple Network System in Borderline Personality Disorder Is Associated with Behavioral Dysregulation

Background: Core symptoms of Borderline Personality Disorder (BPD) are associated to aberrant connectivity of the triple network system (salience network [SN], default mode network [DMN], executive control network [ECN]). While functional abnormalities are widely reported, structural connectivity (SC) and anatomical changes have not yet been investigated. Here, we explored the triple network’s SC, structure, and its association with BPD clinical features. Methods: A total of 60 BPD and 26 healthy controls (HC) underwent a multidomain neuropsychological and multimodal MRI (diffusion- and T1-weighted imaging) assessment. Metrics (fractional anisotropy [FA], mean diffusivity [MD], cortical thickness) were extracted from SN, DMN, ECN (triple network), and visual network (control network) using established atlases. Multivariate general linear models were conducted to assess group differences in metrics and associations with clinical features. Results: Patients showed increased MD in the anterior SN, dorsal DMN, and right ECN compared to HC. Diffusivity increases were more pronounced in patients with higher behavioral dysregulation, i.e., suicidal attempting, self-harm, and aggressiveness. No differences were detected in network structure. Conclusions: These results indicate that the triple network system is impaired in BPD at the microstructural level. The preferential involvement of anterior and right-lateralized subsystems and their clinical association suggests that these abnormalities could contribute to behavioral dysregulation.

MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI

PURPOSE

Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge.

METHODS

To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length.

RESULTS

We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability.

CONCLUSIONS

This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.

Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography

Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate "regions" rather than as white matter "bundles" or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different automated state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

Apathy in small vessel cerebrovascular disease is associated with deficits in effort-based decision making

Patients with small vessel cerebrovascular disease frequently suffer from apathy, a debilitating neuropsychiatric syndrome, the underlying mechanisms of which remain to be established. Here we investigated the hypothesis that apathy is associated with disrupted decision making in effort-based decision making, and that these alterations are associated with abnormalities in the white matter network connecting brain regions that underpin such decisions. Eighty-two patients with MRI evidence of small vessel disease were assessed using a behavioural paradigm as well as diffusion weighted MRI. The decision-making task involved accepting or rejecting monetary rewards in return for performing different levels of physical effort (hand grip force). Choice data and reaction times were integrated into a drift diffusion model that framed decisions to accept or reject offers as stochastic processes approaching a decision boundary with a particular drift rate. Tract-based spatial statistics were used to assess the relationship between white matter tract integrity and apathy, while accounting for depression. Overall, patients with apathy accepted significantly fewer offers on this decision-making task. Notably, while apathetic patients were less responsive to low rewards, they were also significantly averse to investing in high effort. Significant reductions in white matter integrity were observed to be specifically related to apathy, but not to depression. These included pathways connecting brain regions previously implicated in effort-based decision making in healthy people. The drift rate to decision parameter was significantly associated with both apathy and altered white matter tracts, suggesting that both brain and behavioural changes in apathy are associated with this single parameter. On the other hand, depression was associated with an increase in the decision boundary, consistent with an increase in the amount of evidence required prior to making a decision. These findings demonstrate altered effort-based decision making for reward in apathy, and also highlight dissociable mechanisms underlying apathy and depression in small vessel disease. They provide clear potential brain and behavioural targets for future therapeutic interventions, as well as modelling parameters that can be used to measure the effects of treatment at the behavioural level.

Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.

Alterations of local functional connectivity in lifespan: A resting-state fMRI study

INTRODUCTION

As aging attracted attention globally, revealing changes in brain function across the lifespan was largely concerned. In this study, we aimed to reveal the changes of functional networks of the brain (via local functional connectivity, local FC) in lifespan and explore the mechanism underlying them.

MATERIALS AND METHODS

A total of 523 healthy participants (258 males and 265 females) aged 18-88 years from part of the Cambridge Center for Ageing and Neuroscience (CamCAN) were involved in this study. Next, two data-driven measures of local FC, local functional connectivity density (lFCD) and four-dimensional spatial-temporal consistency of local neural activity (FOCA), were calculated, and then, general linear models were used to assess the changes of them in lifespan.

RESULTS

Local functional connectivity (lFCD and FOCA) within visual networks (VN), sensorimotor network (SMN), and default mode network (DMN) decreased across the lifespan, while within basal ganglia network (BGN), local connectivity was increased across the lifespan. And, the fluid intelligence decreased within BGN while increased within VN, SMN, and DMN.

CONCLUSION

These results might suggest that the decline of executive control and intrinsic cognitive ability in the aging population was related to the decline of functional connectivity in VN, SMN, and DMN. Meanwhile, BGN might play a regulatory role in the aging process to compensate for the dysfunction of other functional systems. Our findings may provide important neuroimaging evidence for exploring the brain functional mechanism in lifespan.

Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package

BACKGROUND

Spatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI - including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms - to evaluate their performance in the presence of MS-related pathologies.

METHODS

3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests.

RESULTS

All four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms.

CONCLUSIONS

SPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.

Making Sense of Connectivity

In addition to the assessment of local alterations of specific brain regions, the investigation of entire networks with in vivo neuroimaging techniques has gained increasing attention. In general, connectivity analysis refers to the investigation of links between brain regions, with the aim to characterize their interactions and information transfer. These may represent or relate to different physiological characteristics (structural, functional, or metabolic information) and can be calculated across different levels of granularity (2 regions vs whole brain). In this article, we provide an overview of different connectivity analysis approaches with interpretations and limitations as well as examples in pharmacological imaging and clinical applications. Structural connectivity obtained from diffusion MRI enables the reconstruction of neuronal fiber tracts. These physical links represent major constraints of functional connections, which are in turn defined as correlations between signal time courses. In addition, molecular connectivity approaches based on PET imaging enable the assessment of interregional associations of metabolic demands and neurotransmitter systems. Application of these approaches in clinical investigations has demonstrated novel alterations in various neurological and psychiatric disorders on a network level. Future work should aim for the combined assessment of multiple imaging modalities and to establish robust biomarkers for clinical use. These advancements will further improve the biological interpretation of connectivity metrics and networks of the human brain.

Baby brain atlases

The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.