Structural brain atlases: design, rationale, and applications in normal and pathological cohorts

Brain color-coded diffusion imaging: Utility of ACPC reorientation of gradients in healthy subjects and patients

BACKGROUND AND OBJECTIVE

The common structural interpretation of diffusion color-encoded (DCE) maps assumes that the brain is aligned with the gradients of the MRI machine. This is seldom achieved in the field, leading to incorrect red (R), green (G) and blue (B) DCE values for the expected orientation of fiber bundles. We studied the virtual reorientation of gradients according to the anterior commissure - posterior commissure (ACPC) system on the RGB derivatives.

METHODS

We measured mean ± standard deviation of average, standard deviation, skewness and kurtosis of RGB derivatives, before (rO) and after (acpcO) gradient reorientation, in one healthy-subject group with head routinely positioned (HS-routine), and in two patient groups, one with essential tremor (ET-Opti), and one with Parkinson's disease (PD-Opti), with head position optimized according to ACPC before acquisition. We studied the pitch, roll and yaw angles of reorientation, and we compared rO and acpcO conditions, and groups (ad hoc statistics).

RESULTS

Pitch (maximum in the HS-routine group) was greater than roll and yaw. After reorientation of gradients, in the HS-routine group, DCE average increased, and Stddev, skewness and kurtosis decreased; R, G and B average increased, and R and B skewness and kurtosis decreased. By contrast, in the ET-Opti group and the PD-Opti group, R, G and B, average and Stddev increased, and skewness and kurtosis decreased. In both rO and acpcO conditions, in the ET-Opti and PD-Opti groups, average and standard deviation were higher, while skewness and kurtosis were lower.

CONCLUSIONS

DCE map interpretability depends on brain orientation. Reorientation realigns gradients with the anatomic and physiologic position of the head and brain, as exemplified.

Rest-fMRI-A Potential Substitute for Task-fMRI?

Objective  The aim of this study was to assess the reliability of resting-state functional magnetic resonance imaging (rest-fMRI) in mapping language areas for preoperative planning, versus standard task-based techniques, which are at times difficult to perform in clinical settings. Our study also aimed to evaluate the overlap between language areas identified through rest-fMRI and the standard task-fMRI, in neurosurgical cases. Materials and Methods  Using a seed-based analysis of rest-fMRI with multiple template seeds, we identified functionally connected language regions in patients undergoing preoperative language mapping. Four language task paradigms (word, verb, picture, and semantics) were evaluated. We quantified the degree of overlap between language areas identified on rest-fMRI and task-fMRI, categorizing the results as more than 50% or less than 50% overlap. Results  Seventy-seven percent of patients demonstrated an overlap exceeding 50% between rest- and task-fMRI maps, with the left Broca's area being the most frequently observed region of overlap. This finding was noted even in cases with lesions in Broca's or Wernicke's areas, highlighting the method's robustness. The verb task showed the best blood-oxygen-level dependent activity and overlap with rest-fMRI, highlighting its reliability. To identify a specific language area, the contralateral seed of the same area most commonly displayed connectivity with the area of interest. Conclusion  Our findings demonstrate the potential of using rest-fMRI in accurately mapping eloquent language areas, in clinical settings The strong concordance observed, especially in the left Broca's area, underscores the reliability of this method. Further research and larger studies are essential to validate these results, potentially establishing the use of routine rest-fMRI, in clinical preoperative workup.

Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-Based Machine Learning Approach

INTRODUCTION

Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models.

MATERIALS AND METHODS

Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components ${T_{xx}}$, ${T_{yy}}$, and ${T_{zz}}$ that capture the breadth, length, and height of the head, respectively, and shearing components (${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$) that capture the relative skewness of the head shape.

RESULTS

An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components ${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$ on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the ${T_{zz}}$ scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; ${T_{yy}}$ contributes approximately 20%, whereas ${T_{xx}}$ contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible.

CONCLUSIONS

Head shape has a considerable influence on the injury predictions of computational head injury models. Available "average" head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads.

A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control

This article seeks to investigate the impact of aging on functional connectivity across different cognitive control scenarios, particularly emphasizing the identification of brain regions significantly associated with early aging. By conceptualizing functional connectivity within each cognitive control scenario as a graph, with brain regions as nodes, the statistical challenge revolves around devising a regression framework to predict a binary scalar outcome (aging or normal) using multiple graph predictors. Popular regression methods utilizing multiplex graph predictors often face limitations in effectively harnessing information within and across graph layers, leading to potentially less accurate inference and predictive accuracy, especially for smaller sample sizes. To address this challenge, we propose the Bayesian Multiplex Graph Classifier (BMGC). Accounting for multiplex graph topology, our method models edge coefficients at each graph layer using bilinear interactions between the latent effects associated with the two nodes connected by the edge. This approach also employs a variable selection framework on node-specific latent effects from all graph layers to identify influential nodes linked to observed outcomes. Crucially, the proposed framework is computationally efficient and quantifies the uncertainty in node identification, coefficient estimation, and binary outcome prediction. BMGC outperforms alternative methods in terms of the aforementioned metrics in simulation studies. An additional BMGC validation was completed using an fMRI study of brain networks in adults. The proposed BMGC technique identified that sensory motor brain network obeys certain lateral symmetries, whereas the default mode network exhibits significant brain asymmetries associated with early aging.

Constructing the KOR152 Korean Young Adult Brain Atlas Utilizing the State-of-the-Art Method for the Age-Specific Population

OBJECTIVE

Spatial normalization is an essential process for comparative analyses that heavily depends on the standard brain template used. Brain morphological differences are observed in different populations due to genetic and environmental factors, causing mismatches in regions when the data are normalized to different population templates. Recent studies have indicated differences between Caucasian and East Asian populations as well as within East Asian populations, suggesting the necessity of population-specific brain templates. Thus, this study aimed to construct a Korean young adult age-specific brain template utilizing an advanced method of template construction to update the currently available Korean template.

METHODS

The KOR152 template was constructed via affine and nonlinear iterative procedures based on prior studies. We compared the morphological features of different population templates (MNI152, Indian_157, and CN200). The distance and volumetric changes before and after registering the data to these templates were calculated for registration accuracy.

RESULTS

The KOR152 global brain features revealed a shorter overall length than the other population templates. The registration accuracy by distance and volumetric change was significantly lower than that of the other population templates, implying that the KOR152 was more accurate than other templates for the young adult Korean population.

CONCLUSION

This study provided evidence for the need for a population-specific template that may be more appropriate for structural and functional studies in Korean populations.

Mahmoud L, Sadek I. AI-based tool for early detection of Alzheimer's disease

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.

Subject-specific information enhances spatial accuracy of high-density diffuse optical tomography

Functional near-infrared spectroscopy (fNIRS) is a widely used imaging method for mapping brain activation based on cerebral hemodynamics. The accurate quantification of cortical activation using fNIRS data is highly dependent on the ability to correctly localize the positions of light sources and photodetectors on the scalp surface. Variations in head size and shape across participants greatly impact the precise locations of these optodes and consequently, the regions of the cortical surface being reached. Such variations can therefore influence the conclusions drawn in NIRS studies that attempt to explore specific cortical regions. In order to preserve the spatial identity of each NIRS channel, subject-specific differences in NIRS array registration must be considered. Using high-density diffuse optical tomography (HD-DOT), we have demonstrated the inter-subject variability of the same HD-DOT array applied to ten participants recorded in the resting state. We have also compared three-dimensional image reconstruction results obtained using subject-specific positioning information to those obtained using generic optode locations. To mitigate the error introduced by using generic information for all participants, photogrammetry was used to identify specific optode locations per-participant. The present work demonstrates the large variation between subjects in terms of which cortical parcels are sampled by equivalent channels in the HD-DOT array. In particular, motor cortex recordings suffered from the largest optode localization errors, with a median localization error of 27.4 mm between generic and subject-specific optodes, leading to large differences in parcel sensitivity. These results illustrate the importance of collecting subject-specific optode locations for all wearable NIRS experiments, in order to perform accurate group-level analysis using cortical parcellation.

Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics

OBJECTIVES

To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics.

METHODS

One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models.

RESULTS

The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model.

CONCLUSIONS

Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics.

CLINICAL RELEVANCE STATEMENT

This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance.

KEY POINTS

•Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.

Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images

This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous 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.

Volumetric registration framework for multimodal functional magnetic resonance and optoacoustic tomography of the rodent brain

Optoacoustic tomography (OAT) provides a non-invasive means to characterize cerebral hemodynamics across an entire murine brain while attaining multi-parametric readouts not available with other modalities. This unique capability can massively impact our understanding of brain function. However, OAT largely lacks the soft tissue contrast required for unambiguous identification of brain regions. Hence, its accurate registration to a reference brain atlas is paramount for attaining meaningful functional readings. Herein, we capitalized on the simultaneously acquired bi-modal data from the recently-developed hybrid magnetic resonance optoacoustic tomography (MROT) scanner in order to devise an image coregistration paradigm that facilitates brain parcellation and anatomical referencing. We evaluated the performance of the proposed methodology by coregistering OAT data acquired with a standalone system using different registration methods. The enhanced performance is further demonstrated for functional OAT data analysis and characterization of stimulus-evoked brain responses. The suggested approach enables better consolidation of the research findings thus facilitating wider acceptance of OAT as a powerful neuroimaging tool to study brain functions and diseases.

Topographic Mapping of Isolated Thalamic Infarcts Using Vascular and Novel Probabilistic Functional Thalamic Landmarks

Purpose

We aimed to re-evaluate the relationship between thalamic infarct (TI) localization and clinical symptoms using a vascular (VTM) and a novel functional territorial thalamic map (FTM).

Methods

Magnetic resonance imaging (MRI) and clinical data of 65 patients with isolated TI were evaluated (female n = 23, male n = 42, right n = 23, left n = 42). A VTM depicted the known seven thalamic vascular territories (VT: inferolateral, anterolateral, inferomedial, posterior, central, anteromedian, posterolateral). An FTM was generated from a probabilistic thalamic nuclei atlas to determine six functionally defined territories (FT: anterior: memory/emotions; ventral: motor/somatosensory/language; medial: behavior/emotions/nociception, oculomotor; intralaminar: arousal/pain; lateral: visuospatial/somatosensory/conceptual and analytic thinking; posterior: audiovisual/somatosensory). Four neuroradiologists independently assigned diffusion-weighted imaging (DWI) lesions to the territories mapped by the VTM and FTM. Findings were correlated with clinical features.

Results

The most frequent symptom was a hemisensory syndrome (58%), which was not specific for any territory. A co-occurrence of hemisensory syndrome and hemiparesis had positive predictive values (PPV) of 76% and 82% for the involvement of the inferolateral VT and ventral FT, respectively. Thalamic aphasia had a PPV of 63% each for involvement of the anterolateral VT and ventral FT. Neglect was associated with involvement of the inferolateral VT/ventral FT. Interrater reliability for the assignment of DWI lesions to the VTM was fair (κ = 0.36), but good (κ = 0.73) for the FTM.

Conclusion

The FTM revealed a greater reproducibility for the topographical assignment of TI than the VTM. Sensorimotor hemiparesis and neglect are predictive for a TI in the inferolateral VT/ventral FT. The hemisensory syndrome alone does not allow any topographical assignment.

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images

It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.

Machine learning based analysis of stroke lesions on mouse tissue sections

An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named "StrokeAnalyst", which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2-2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models.

Neuroplasticity after upper-extremity rehabilitation therapy with sensory stimulation in chronic stroke survivors

This study investigated the effect of using subthreshold vibration as a peripheral sensory stimulation during therapy on cortical activity. Secondary analysis of a pilot triple-blinded randomized controlled trial. Twelve chronic stroke survivors underwent 2-week upper-extremity task-practice therapy. Half received subthreshold vibratory stimulation on their paretic wrist (treatment group) and the other half did not (control). EEG connectivity and event-related de-/resynchronization for the sensorimotor network during hand grip were examined at pre-intervention, post-intervention and follow-up. Statistically significant group by time interactions were observed for both connectivity and event-related spectral perturbation. For the treatment group, connectivity increased at post-intervention and decreased at follow-up. Event-related desynchronization decreased and event-related resynchronization increased at post-intervention, which was maintained at follow-up. The control group had the opposite trend for connectivity and no change in event-related spectral perturbation. The stimulation altered cortical sensorimotor activity. The findings complement the clinical results of the trial in which the treatment group significantly improved gross manual dexterity while the control group did not. Increased connectivity in the treatment group may indicate neuroplasticity for motor learning, while reduced event-related desynchronization and increased event-related resynchronization may indicate lessened effort for grip and improved inhibitory control. EEG may improve understanding of neural processes underlying motor recovery.

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.

Personalized tDCS for Focal Epilepsy—A Narrative Review: A Data-Driven Workflow Based on Imaging and EEG Data

Conventional transcranial electric stimulation(tES) using standard anatomical positions for the electrodes and standard stimulation currents is frequently not sufficiently selective in targeting and reaching specific brain locations, leading to suboptimal application of electric fields. Recent advancements in in vivo electric field characterization may enable clinical researchers to derive better relationships between the electric field strength and the clinical results. Subject-specific electric field simulations could lead to improved electrode placement and more efficient treatments. Through this narrative review, we present a processing workflow to personalize tES for focal epilepsy, for which there is a clear cortical target to stimulate. The workflow utilizes clinical imaging and electroencephalography data and enables us to relate the simulated fields to clinical outcomes. We review and analyze the relevant literature for the processing steps in the workflow, which are the following: tissue segmentation, source localization, and stimulation optimization. In addition, we identify shortcomings and ongoing trends with regard to, for example, segmentation quality and tissue conductivity measurements. The presented processing steps result in personalized tES based on metrics like focality and field strength, which allow for correlation with clinical outcomes.

Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data

Many modern techniques for analyzing time-varying longitudinal data rely on parametric models to interrogate the time-courses of univariate or multivariate processes. Typical analytic objectives include utilizing retrospective observations to model current trends, predict prospective trajectories, derive categorical traits, or characterize various relations. Among the many mathematical, statistical, and computational strategies for analyzing longitudinal data, tensor-based linear modeling offers a unique algebraic approach that encodes different characterizations of the observed measurements in terms of state indices. This paper introduces a new method of representing, modeling, and analyzing repeated-measurement longitudinal data using a generalization of event order from the positive reals to the complex plane. Using complex time (kime), we transform classical time-varying signals as 2D manifolds called kimesurfaces. This kime characterization extends the classical protocols for analyzing time-series data and offers unique opportunities to design novel inference, prediction, classification, and regression techniques based on the corresponding kimesurface manifolds. We define complex time and illustrate alternative time-series to kimesurface transformations. Using the Laplace transform and its inverse, we demonstrate the bijective mapping between time-series and kimesurfaces. A proposed general tensor regression based linear model is validated using functional Magnetic Resonance Imaging (fMRI) data. This kimesurface representation method can be used with a wide range of machine learning algorithms, artificial intelligence tools, analytical approaches, and inferential techniques to interrogate multivariate, complex-domain, and complex-range longitudinal processes.

A Novel MRI-Based Quantitative Water Content Atlas of the Human Brain

The measurement of quantitative, tissue-specific MR properties, e.g., water content, longitudinal relaxation time (T₁) and effective transverse relaxation time (T₂*), using quantitative MRI at a clinical field strength (1.5 T to 3T) is a well-explored topic. However, none of the commonly used standard brain atlases, such as MNI or JHU, provide quantitative information. Within the framework of quantitative MRI of the brain, this work reports on the development of the first quantitative brain atlas for tissue water content at 3T. A methodology to create this quantitative atlas of in vivo brain water content based on healthy volunteers is presented, and preliminary, practical examples of its potential applications are also shown. Established methods for the fast and reliable measurement of the absolute water content were used to achieve high precision and accuracy. Water content and T₂* were mapped based on two different methods: an intermediate-TR, two-point method and a long-TR, single-scan method. Twenty healthy subjects (age 25.3 ± 2.5 years) were examined with these quantitative imaging protocols. The images were normalised to MNI stereotactic coordinates, and water content atlases of healthy volunteers were created for each method and compared. Regions-of-interest were generated with the help of a standard MNI template, and water content values averaged across the ROIs were compared to water content values from the literature. Finally, in order to demonstrate the strength of quantitative MRI, water content maps from patients with pathological changes in the brain due to stroke, tumour (glioblastoma) and multiple sclerosis were voxel-wise compared to the healthy brain. The water content atlases were largely independent of the method used to acquire the individual water maps. Global grey matter and white matter water content values between the methods agreed with each other to within 0.5 %. The feasibility of detecting abnormal water content in the brains of patients based on comparison to a healthy brain water content atlas was demonstrated. In summary, the first quantitative water content brain atlas in vivo has been developed and a voxel-wise assessment of pathology-related changes in the brain water content has been performed. These results suggest that qMRI, in combination with a water content atlas, allows for a quantitative interpretation of changes due to disease and could be used for disease monitoring.

Computational anatomy and geometric shape analysis enables analysis of complex craniofacial phenotypes in zebrafish

Due to the complexity of fish skulls, previous attempts to classify craniofacial phenotypes have relied on qualitative features or sparce 2D landmarks. In this work we aim to identify previously unknown 3D craniofacial phenotypes with a semiautomated pipeline in adult zebrafish mutants. We first estimate a synthetic 'normative' zebrafish template using MicroCT scans from a sample pool of wild-type animals using the Advanced Normalization Tools (ANTs). We apply a computational anatomy (CA) approach to quantify the phenotype of zebrafish with disruptions in bmp1a, a gene implicated in later skeletal development and whose human ortholog when disrupted is associated with Osteogenesis Imperfecta. Compared to controls, the bmp1a fish have larger otoliths, larger normalized centroid sizes, and exhibit shape differences concentrated around the operculum, anterior frontal, and posterior parietal bones. Moreover, bmp1a fish differ in the degree of asymmetry. Our CA approach offers a potential pipeline for high-throughput screening of complex fish craniofacial shape to discover novel phenotypes for which traditional landmarks are too sparce to detect. The current pipeline successfully identifies areas of variation in zebrafish mutants, which are an important model system for testing genome to phenome relationships in the study of development, evolution, and human diseases. This article has an associated First Person interview with the first author of the paper.

Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation

Background: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies. Methods and procedures: This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), to many thousands of synthetic cases. Results: Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model’s capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities. Conclusion: We can confirm the ability of deep learning methods and the proposed Znet framework to detect and segment tumors in MR images. Furthermore, pixel accuracy evaluation may not be a suitable evaluation measure for semantic segmentation in case of class imbalance in MR images segmentation. This is because the dominant class in ground truth images is the background. Therefore, a high value of pixel accuracy can be misleading in some computer vision applications. On the other hand, alternative evaluation metrics, such as dice and IoU (Intersection over Union), are more factual for semantic segmentation. Clinical impact: Artificial intelligence (AI) applications in medicine are advancing swiftly, however, there is a lack of deployed techniques in clinical practice. This research demonstrates a practical example of AI applications in medical imaging, which can be deployed as a tool for auto-segmentation of tumors in MR images.

Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas

Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.

A Probabilistic Atlas of the Pineal Gland in the Standard Space

Pineal gland (PG) is a structure located in the midline of the brain, and is considered as a main part of the epithalamus. There are numerous reports on the facilitatory role of this area for brain function; hormone secretion and its role in sleep cycle are the major reports. However, reports are rarely available on the direct role of this structure in brain cognition and in information processing. A suggestion for the limited number of such studies is the lack of a standard atlas for the PG; none of the available MRI templates and atlases has provided parcellations for this structure. In this study, we used the three-dimensional (3D) T1-weighted MRI data of 152 healthy young volunteers, and provided a probabilistic map of the PG in the standard Montreal Neurologic Institute (MNI) space. The methods included collecting the data using a 64-channel head coil on a 3-Tesla Prisma MRI Scanner, manual delineation of the PG by two experts, and robust template and atlas construction algorithms. This atlas is freely accessible, and we hope importing this atlas in the well-known neuroimaging software packages would help to identify other probable roles of the PG in brain function. It could also be used to study pineal cysts, for volumetric analyses, and to test any associations between the cognitive abilities of the human and the structure of the PG.

Multi-modal neuroimaging feature fusion via 3D Convolutional Neural Network architecture for schizophrenia diagnosis

Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the quality of a patient’s life and future treatments. Structural and functional neuroimaging provides robust biomarkers for understanding the anatomical and functional changes associated with SZ. Each of the neuroimaging techniques shows only a different perspective on the functional or structural of the brain, while multi-modal fusion can reveal latent connections in the brain. In this paper, we propose an approach for the fusion of structural and functional brain data with a deep learning-based model to take advantage of data fusion and increase the accuracy of schizophrenia disorder diagnosis. The proposed method consists of an architecture of 3D convolutional neural networks (CNNs) that applied to magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) extracted features. We use 3D MRI patches, fMRI spatial independent component analysis (ICA) map, and DTI fractional anisotropy (FA) as model inputs. Our method is validated on the COBRE dataset, and an average accuracy of 99.35% is obtained. The proposed method demonstrates promising classification performance and can be applied to real data.

Variability in atlas registration of optical intrinsic signal imaging and its effect on functional connectivity analysis

To compare neuroimaging data between subjects, images from individual sessions need to be aligned to a common reference or "atlas." Atlas registration of optical intrinsic signal imaging of mice, for example, is commonly performed using affine transforms with parameters determined by manual selection of canonical skull landmarks. Errors introduced by such procedures have not previously been investigated. We quantify the variability that arises from this process and consequent errors from misalignment that affect interpretation of functional neuroimaging data. We propose an improved method, using separately acquired high-resolution images and demonstrate improvements in variability and alignment using this method.

Virtual brain grafting: Enabling whole brain parcellation in the presence of large lesions

Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate magnetic resonance imaging (MRI) datasets in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted image, which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n = 100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic-patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic-patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labeling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations using methods such as FreeSurfer, CAT12, SPM, Connectome Workbench, as well as structural and functional connectomics. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license (https://github.com/KUL-Radneuron/KUL_VBG).

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.

Resting-state functional connectivity and quantitation of glutamate and GABA of the PCC/precuneus by magnetic resonance spectroscopy at 7T in healthy individuals

The default mode network (DMN) is the main large-scale network of the resting brain and the PCC/precuneus is a major hub of this network. Glutamate and GABA (γ-amino butyric acid) are the main excitatory and inhibitory neurotransmitters in the CNS, respectively. We studied glutamate and GABA concentrations in the PCC/precuneus via magnetic resonance spectroscopy (MRS) at 7T in relation to age and correlated them with functional connectivity between this region and other DMN nodes in ten healthy right-handed volunteers ranging in age between 23–68 years. Mean functional connectivity of the PCC/precuneus to the other DMN nodes and the glutamate/GABA ratio significantly correlated with age (r = 0.802, p = 0.005 and r = 0.793, p = 0.006, respectively) but not with each other. Glutamate and GABA alone did not significantly correlate with age nor with functional connectivity within the DMN. The glutamate/GABA ratio and functional connectivity of the PCC/precuneus are, therefore, independent age-related biomarkers of the DMN and may be combined in a multimodal pipeline to study DMN alterations in various disease states.

Depth relationships and measures of tissue thickness in dorsal midbrain

Dorsal human midbrain contains two nuclei with clear laminar organization, the superior and inferior colliculi. These nuclei extend in depth between the superficial dorsal surface of midbrain and a deep midbrain nucleus, the periaqueductal gray matter (PAG). The PAG, in turn, surrounds the cerebral aqueduct (CA). This study examined the use of two depth metrics to characterize depth and thickness relationships within dorsal midbrain using the superficial surface of midbrain and CA as references. The first utilized nearest-neighbor Euclidean distance from one reference surface, while the second used a level-set approach that combines signed distances from both reference surfaces. Both depth methods provided similar functional depth profiles generated by saccadic eye movements in a functional MRI task, confirming their efficacy for delineating depth for superficial functional activity. Next, the boundaries of the PAG were estimated using Euclidean distance together with elliptical fitting, indicating that the PAG can be readily characterized by a smooth surface surrounding PAG. Finally, we used the level-set approach to measure tissue depth between the superficial surface and the PAG, thus characterizing the variable thickness of the colliculi. Overall, this study demonstrates depth-mapping schemes for human midbrain that enables accurate segmentation of the PAG and consistent depth and thickness estimates of the superior and inferior colliculi.

An equal-TE ultrafast 3D gradient-echo imaging method with high tolerance to magnetic susceptibility artifacts: Application to BOLD functional MRI

PURPOSE

To develop an ultrafast 3D gradient echo-based MRI method with constant TE and high tolerance to B₀ inhomogeneity, dubbed ERASE (equal-TE rapid acquisition with sequential excitation), and to introduce its use in BOLD functional MRI (fMRI).

THEORY AND METHODS

Essential features of ERASE, including spin behavior, were characterized, and a comparison study was conducted with conventional EPI. To demonstrate high tolerance to B₀ inhomogeneity, in vivo imaging of the mouse brain with a fiber-optic implant was performed at 9.4 T, and human brain imaging (including the orbitofrontal cortex) was performed at 3 T and 7 T. To evaluate the performance of ERASE in BOLD-fMRI, the characteristics of SNR and temporal SNR were analyzed for in vivo rat brains at 9.4 T in comparison with multislice gradient-echo EPI. Percent signal changes and t-scores are also presented.

RESULTS

For both mouse brain and human brain imaging, ERASE exhibited a high tolerance to magnetic susceptibility artifacts, showing much lower distortion and signal dropout, especially in the regions involving large magnetic susceptibility effects. For BOLD-fMRI, ERASE provided higher temporal SNR and t-scores than EPI, but exhibited similar percent signal changes in in vivo rat brains at 9.4 T.

CONCLUSION

When compared with conventional EPI, ERASE is much less sensitive, not only to EPI-related artifacts such as Nyquist ghosting, but also to B₀ inhomogeneity including magnetic susceptibility effects. It is promising for use in BOLD-fMRI, providing higher temporal SNR and t-scores with constant TE when compared with EPI, although further optimization is needed for human fMRI.

Whole Brain and Cranial Size Adjustments in Volumetric Brain Analyses of Sex- and Age-Related Trends

Our goal was to determine the influence of sex, age and the head/brain size on the compartmental brain volumes in the radiologically verified healthy population (96 subjects; 54 women and 42 men) from the Upper Silesia region in Poland. The MRI examinations were done using 3T Philips Achieva with the same T1-weighted and T2-weighted protocols. The image segmentation procedures were performed with SPM (Statistical Parameter Mapping) and FSL-FIRST software. The volumes of 14 subcortical structures for the left and right hemispheres and 4 overall volumes were calculated. The General Linear Models (GLM) analysis was used with and without the Total Brain Volume (TBV) and Intracranial Volume (ICV) parameters as the covariates to study the regional vs. global brain atrophy. After the ICV/TBV adjustments, the majority of sex differences in the specific volumes of interest (VOIs) revealed to be linked to the difference in the head/brain size parameters. The analysis also confirmed the significant effect of the aging process on the brain loss. After the TBV adjustment, the age- and sex-related volumetric trends for the gray and white matter volumes were observed: the negative age dependence of the gray matter volume is more pronounced in the males, while in case of the white matter the positive age-related trend in the female group is weaker. The local losses of the left caudate nucleus and the right thalamus are more advanced than the global brain atrophy. Different head-size correction strategies are not interchangeable and may yield various volumetric results, but when used together, facilitate studies on the regional dependencies inherent to a healthy, but aging, brain.

Human Cochlear Nucleus on 7 Tesla Diffusion Tensor Imaging: Insights Into Micro-anatomy and Function for Auditory Brainstem Implant Surgery

OBJECTIVE

The cochlear nucleus (CN) is the target of the auditory brainstem implant (ABI). Most ABI candidates have Neurofibromatosis Type 2 (NF2) and distorted brainstem anatomy from bilateral vestibular schwannomas. The CN is difficult to characterize as routine structural MRI does not resolve detailed anatomy. We hypothesize that diffusion tensor imaging (DTI) enables both in vivo localization and quantitative measurements of CN morphology.

STUDY DESIGN

We analyzed 7 Tesla (T) DTI images of 100 subjects (200 CN) and relevant anatomic structures using an MRI brainstem atlas with submillimetric (50 μm) resolution.

SETTING

Tertiary referral center.

PATIENTS

Young healthy normal hearing adults.

INTERVENTION

Diagnostic.

MAIN OUTCOME MEASURES

Diffusion scalar measures such as fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (Mode), principal eigenvectors of the CN, and the adjacent inferior cerebellar peduncle (ICP).

RESULTS

The CN had a lamellar structure and ventral-dorsal fiber orientation and could be localized lateral to the inferior cerebellar peduncle (ICP). This fiber orientation was orthogonal to tracts of the adjacent ICP where the fibers run mainly caudal-rostrally. The CN had lower FA compared to the medial aspect of the ICP (0.44 ± 0.09 vs. 0.64 ± 0.08, p < 0.001).

CONCLUSIONS

7T DTI enables characterization of human CN morphology and neuronal substructure. An ABI array insertion vector directed more caudally would better correspond to the main fiber axis of CN. State-of-the-art DTI has implications for ABI preoperative planning and future image guidance-assisted placement of the electrode array.

Stereotactic Cortical Atlas of the Domestic Canine Brain

The domestic canine (canis familiaris) is a growing novel model for human neuroscientific research. Unlike rodents and primates, they demonstrate unique convergent sociocognitive skills with humans, are highly trainable and able to undergo non-invasive experimental procedures without restraint, including fMRI. In addition, the gyrencephalic structure of the canine brain is more similar to that of human than rodent models. The increasing use of dogs for non-invasive neuroscience studies has generating a need for a standard canine cortical atlas that provides common spatial referencing and cortical segmentation for advanced neuroimaging data processing and analysis. In this manuscript we create and make available a detailed MRI-based cortical atlas for the canine brain. This atlas includes a population template generated from 30 neurologically and clinically normal non-brachycephalic dogs, tissue segmentation maps and a cortical atlas generated from Jerzy Kreiner's myeloarchitectonic-based histology atlas. The provided cortical parcellation includes 234 priors from frontal, sensorimotor, parietal, temporal, occipital, cingular and subcortical regions. The atlas was validated using an additional canine cohort with variable cranial conformations. This comprehensive cortical atlas provides a reference standard for canine brain research and will improve and standardize processing and data analysis and interpretation in functional and structural MRI research.

Customized FreeSurfer-based brain atlas for diffeomorphic anatomical registration through exponentiated lie algebra tool

OBJECTIVE

Digital brain template and atlas designed for a specific group provide advantages for the analysis and interpretation of neuroimaging data, but require a significant workload for development. We developed a simple method to create customized brain atlas for diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) tool using FreeSurfer-generated volume-of-interest (FSVOI) images and validated.

METHODS

¹⁸F-florbetaben positron emission tomography (PET) and magnetic resonance (MR) imaging data were obtained from 248 participants of Alzheimer's disease spectrum (from cognitively normal to Alzheimer's disease dementia). To create a customized atlas, MR images of 84 amyloid-negative controls were first processed with FreeSurfer to obtain individual FSVOI and with DARTEL tool to create DARTEL template. Individual FSVOI images were spatially normalized, and each voxel was then labelled with a VOI label with maximum probability. Using these template and atlas, all images were normalized, and the regional standardized uptake value ratios (SUVR) were measured.

RESULTS

¹⁸F-florbetaben SUVR values measured with customized atlas showed excellent one-to-one correlation with SUVR measured with individual FSVOI in all regions, and thereby showed almost identical between-group comparison results and outperformed the classic methods.

CONCLUSIONS

Customized FreeSurfer-based brain atlas for DARTEL tool is easy to create and useful for the analysis of PET and MR images with high adaptability and reliability for broad research purposes.

BRAHMA: Population specific T1, T2, and FLAIR weighted brain templates and their impact in structural and functional imaging studies

Differences in brain morphology across population groups necessitate creation of population-specific Magnetic Resonance Imaging (MRI) brain templates for interpretation of neuroimaging data. Variations in the neuroanatomy in a genetically heterogeneous population make the development of a population-specific brain template for the Indian subcontinent imperative. A dataset of high-resolution 3D T1, T2-weighted, and FLAIR images acquired from a group of 113 volunteers (M/F - 56/57, mean age-28.96 ± 7.80 years) are used to construct T1, T2-weighted, and FLAIR templates, collectively referred to as Indian Brain Template, "BRAHMA". A processing pipeline is developed and implemented in a MATLAB based toolbox for template construction and generation of tissue probability maps and segmentation atlases, with additional labels for deep brain regions such as the Substantia Nigra generated from the T2-weighted and FLAIR templates. The use of BRAHMA template for analysis of structural and functional neuroimaging data obtained from Indian participants, provides improved accuracy with statistically significant results over that obtained using the ICBM-152 (International Consortium for Brain Mapping) template. Our results indicate that segmentations generated on structural images are closer in volume to those obtained from registration to the BRAHMA template than to the ICBM-152. Furthermore, functional MRI data obtained for Working Memory and Finger Tapping paradigms processed using the BRAHMA template show a significantly higher percentage of the activation area than ICBM-152 in relevant brain regions, i.e. the left middle frontal gyrus, and the left and right precentral gyri, respectively. The availability of different image contrasts, tissue maps, and segmentation atlases makes the BRAHMA template a comprehensive tool for multi-modal image analysis in laboratory and clinical settings.

Human brain function and brain-computer interfaces

Human brain function research has evolved dramatically in the last decades. In this chapter the role of modern methods of recording brain activity in understanding human brain function is explained. Current knowledge of brain function relevant to brain-computer interface (BCI) research is detailed, with an emphasis on the motor system which provides an exceptional level of detail to decoding of intended or attempted movements in paralyzed beneficiaries of BCI technology and translation to computer-mediated actions. BCI technologies that stand to benefit the most of the detailed organization of the human cortex are, and for the foreseeable future are likely to be, reliant on intracranial electrodes. These evolving technologies are expected to enable severely paralyzed people to regain the faculty of movement and speech in the coming decades.

The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization

Preclinical imaging studies offer a unique access to the rat brain, allowing investigations that go beyond what is possible in human studies. Unfortunately, these techniques still suffer from a lack of dedicated and standardized neuroimaging tools, namely brain templates and descriptive atlases. Here, we present two rat brain MRI templates and their associated gray matter, white matter and cerebrospinal fluid probability maps, generated from ex vivo [Formula: see text]-weighted images (90 µm isotropic resolution) and in vivo T2-weighted images (150 µm isotropic resolution). In association with these templates, we also provide both anatomical and functional 3D brain atlases, respectively derived from the merging of the Waxholm and Tohoku atlases, and analysis of resting-state functional MRI data. Finally, we propose a complete set of preclinical MRI reference resources, compatible with common neuroimaging software, for the investigation of rat brain structures and functions.

Evaluation of PiB visual interpretation with CSF Aβ and longitudinal SUVR in J-ADNI study

Objective

The objectives of the present study were to investigate (1) whether trinary visual interpretation of amyloid positron emission tomography (PET) imaging (negative/equivocal/positive) reflects quantitative amyloid measurements and the time course of ¹¹C-Pittsburgh compound B (PiB) amyloid accumulation, and (2) whether visually equivocal scans represent an early stage of the Alzheimer’s disease (AD) continuum in terms of an intermediate state of quantitative amyloid measurements and the changes in amyloid accumulation over time.

Methods

From the National Bioscience Database Center Human Database of the Japanese Alzheimer’s Disease Neuroimaging Initiative, we selected 133 individuals for this study including 33 with Alzheimer’s disease dementia (ADD), 52 with late mild cognitive impairment (LMCI), and 48 cognitively normal (CN) subjects who underwent clinical assessment, PiB PET, and structural magnetic resonance imaging (MRI) with 2 or 3-years of follow-up. Sixty-eight of the 133 individuals underwent cerebrospinal fluid amyloid-β_1-42 (CSF-Ab₄₂) analysis at baseline. The standard uptake value ratio (SUVR) of PiB PET was calculated with a method using MRI at each visit. The cross-sectional values, longitudinal changes in SUVR, and baseline CSF-Ab₄₂ were compared among groups, which were categorized based on trinary visual reads of amyloid PET (negative/equivocal/positive).

Results

From the trinary visual interpretation of the PiB PET images, 55 subjects were negative, 8 were equivocal, and 70 were positive. Negative interpretation was most frequent in the CN group (70.8/10.4/18.8%: negative/equivocal/positive), and positive was most frequent in the LMCI group (34.6/1.9/63.5%) and in the ADD group (9.1/6.1/84.8%). The baseline SUVRs were 1.08 ± 0.06 in the negative group, 1.23 ± 0.15 in the equivocal group, and 1.86 ± 0.31 in the positive group (F = 174.9, p < 0.001). The baseline CSF-Ab₄₂ level was 463 ± 112 pg/mL in the negative group, 383 ± 125 pg/mL in the equivocal group, and 264 ± 69 pg/mL in the positive group (F = 37, p < 0.001). Over the 3-year follow-up, annual changes in SUVR were − 0.00 ± 0.02 in the negative group, 0.02 ± 0.02 in the equivocal group, and 0.04 ± 0.07 in the positive group (F = 8.4, p < 0.001).

Conclusions

Trinary visual interpretation (negative/equivocal/positive) of amyloid PET imaging reflects quantitative amyloid measurements evaluated with PET and the CSF amyloid test as well as the amyloid accumulation over time evaluated with PET over 3 years. Subjects in the early stage of the AD continuum could be identified with an equivocal scan, because they showed intermediate quantitative amyloid PET, CSF measurements, and the amyloid accumulation over time.

Electronic supplementary material

The online version of this article (10.1007/s12149-019-01420-2) contains supplementary material, which is available to authorized users.

Neuroimaging of pain in animal models: a review of recent literature

Neuroimaging of pain in animals allows us to better understand mechanisms of pain processing and modulation. In this review, we discuss recently published brain imaging studies in rats, mice, and monkeys, including functional magnetic resonance imaging (MRI), manganese-enhanced MRI, positron emission tomography, and electroencephalography. We provide an overview of innovations and limitations in neuroimaging techniques, as well as results of functional brain imaging studies of pain from January 1, 2016, to October 10, 2018. We then discuss how future investigations can address some bias and gaps in the field. Despite the limitations of neuroimaging techniques, the 28 studies reinforced that transition from acute to chronic pain entails considerable changes in brain function. Brain activations in acute pain were in areas more related to the sensory aspect of noxious stimulation, including primary somatosensory cortex, insula, cingulate cortex, thalamus, retrosplenial cortex, and periaqueductal gray. Pharmacological and nonpharmacological treatments modulated these brain regions in several pain models. On the other hand, in chronic pain models, brain activity was observed in regions commonly associated with emotion and motivation, including prefrontal cortex, anterior cingulate cortex, hippocampus, amygdala, basal ganglia, and nucleus accumbens. Neuroimaging of pain in animals holds great promise for advancing our knowledge of brain function and allowing us to expand human subject research. Additional research is needed to address effects of anesthesia, analysis approaches, sex bias and omission, and potential effects of development and aging.

Spatial Signature of White Matter Hyperintensities in Stroke Patients

Purpose: White matter hyperintensity (WMH) is a common phenotype across a variety of neurological diseases, particularly prevalent in stroke patients; however, vascular territory dependent variation in WMH burden has not yet been identified. Here, we sought to investigate the spatial specificity of WMH burden in patients with acute ischemic stroke (AIS). Materials and Methods: We created a novel age-appropriate high-resolution brain template and anatomically delineated the cerebral vascular territories. We used WMH masks derived from the clinical T2 Fluid Attenuated Inverse Recovery (FLAIR) MRI scans and spatial normalization of the template to discriminate between WMH volume within each subject's anterior cerebral artery (ACA), middle cerebral artery (MCA), and posterior cerebral artery (PCA) territories. Linear regression modeling including age, sex, common vascular risk factors, and TOAST stroke subtypes was used to assess for spatial specificity of WMH volume (WMHv) in a cohort of 882 AIS patients. Results: Mean age of this cohort was 65.23 ± 14.79 years, 61.7% were male, 63.6% were hypertensive, 35.8% never smoked. Mean WMHv was 11.58c ± 13.49 cc. There were significant differences in territory-specific, relative to global, WMH burden. In contrast to PCA territory, age (0.018 ± 0.002, p < 0.001) and small-vessel stroke subtype (0.212 ± 0.098, p < 0.001) were associated with relative increase of WMH burden within the anterior (ACA and MCA) territories, whereas male sex (-0.275 ± 0.067, p < 0.001) was associated with a relative decrease in WMHv. Conclusions: Our data establish the spatial specificity of WMH distribution in relation to vascular territory and risk factor exposure in AIS patients and offer new insights into the underlying pathology.

Construction of population-specific Indian MRI brain template: Morphometric comparison with Chinese and Caucasian templates

OBJECTIVE

Spatial normalization of brain MR images is highly dependent on the choice of target brain template. Morphological differences caused by factors like genetic and environmental exposures, generates a necessity to construct population specific brain templates. Brain image analysis performed using brain templates from Caucasian population may not be appropriate for non-Caucasian population. In this study, our objective was to construct an Indian brain template from a large population (N = 157 subjects) and compare the morphometric parameters of this template with that of Chinese-56 and MNI-152 templates. In addition, using an independent MRI data of 15 Indian subjects, we also evaluated the potential registration accuracy differences using these three templates.

METHODS

Indian brain template was constructed using iterative routines as per established procedures. We compared our Indian template with standard MNI-152 template and Chinese template by measuring global brain features. We also examined accuracy of registration by aligning 15 new Indian brains to Indian, Chinese and MNI templates. Furthermore, we supported our measurement protocol with inter-rater and intra-rater reliability analysis.

RESULTS

Our results showed that there were significant differences in global brain features of Indian template in comparison with Chinese and MNI brain templates. The results of registration accuracy analysis revealed that fewer deformations are required when Indian brains are registered to Indian template as compared to Chinese and MNI templates.

CONCLUSION

This study concludes that population specific Indian template is likely to be more appropriate for structural and functional image analysis of Indian population.

Modeling the Effects of Age and Sex on Normal Pediatric Brain Metabolism Using 18F-FDG PET/CT

Reference databases of pediatric brain metabolism are uncommon, because local brain metabolism evolves significantly with age throughout childhood, limiting their clinical applicability. The aim of this study was to develop mathematic models of regional relative brain metabolism using pediatric ¹⁸F-FDG PET with CT data of normal pediatric brains, accounting for sex and age. Methods: PET/CT brain acquisitions were obtained from 88 neurologically normal subjects, aged 6 mo to 18 y. Subjects were assigned to either a development group (n = 59) or a validation group (n = 29). For each subject, commercially available software was used to quantify the relative metabolism of 47 separate brain regions using whole-brain-normalized (WBN) and pons-normalized (PN) activity. The effects of age on regional relative brain metabolism were modeled using multiple linear and nonlinear mathematic equations, and the significance of sex was assessed using the Student t test. Optimal models were selected using the Akaike information criterion. Mean predicted values and 95% prediction intervals were derived for all regions. Model predictions were compared with the validation dataset, and mean predicted error was calculated for all regions using both WBN and PN models. Results: As a function of age, optimal models of regional relative brain metabolism were linear for 9 regions, quadratic for 13, cubic for 6, logarithmic for 12, power law for 7, and modified power law for 2 using WBN data and were linear for 9, quadratic for 25, cubic for 2, logarithmic for 6, and power law for 4 using PN data. Sex differences were found to be statistically significant only in the posterior cingulate cortex for the WBN data. Comparing our models with the validation group resulted in 94.3% of regions falling within the 95% prediction interval for WBN and 94.1% for PN. For all brain regions in the validation group, the error in prediction was 3% ± 0.96% using WBN data and 4.72% ± 1.25% when compared with the PN data (P < 0.0001). Conclusion: Pediatric brain metabolism is a complex function of age and sex. We have developed mathematic models of brain activity that allow for accurate prediction of regional pediatric brain metabolism.

ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks

Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of 'engagement' of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations.

A population level atlas of Mus musculus craniofacial skeleton and automated image-based shape analysis

Laboratory mice are staples for evo/devo and genetics studies. Inbred strains provide a uniform genetic background to manipulate and understand gene-environment interactions, while their crosses have been instrumental in studies of genetic architecture, integration and modularity, and mapping of complex biological traits. Recently, there have been multiple large-scale studies of laboratory mice to further our understanding of the developmental basis, evolution, and genetic control of shape variation in the craniofacial skeleton (i.e. skull and mandible). These experiments typically use micro-computed tomography (micro-CT) to capture the craniofacial phenotype in 3D and rely on manually annotated anatomical landmarks to conduct statistical shape analysis. Although the common choice for imaging modality and phenotyping provides the potential for collaborative research for even larger studies with more statistical power, the investigator (or lab-specific) nature of the data collection hampers these efforts. Investigators are rightly concerned that subtle differences in how anatomical landmarks were recorded will create systematic bias between studies that will eventually influence scientific findings. Even if researchers are willing to repeat landmark annotation on a combined dataset, different lab practices and software choices may create obstacles for standardization beyond the underlying imaging data. Here, we propose a freely available analysis system that could assist in the standardization of micro-CT studies in the mouse. Our proposal uses best practices developed in biomedical imaging and takes advantage of existing open-source software and imaging formats. Our first contribution is the creation of a synthetic template for the adult mouse craniofacial skeleton from 25 inbred strains and five F1 crosses that are widely used in biological research. The template contains a fully segmented cranium, left and right hemi-mandibles, endocranial space, and the first few cervical vertebrae. We have been using this template in our lab to segment and isolate cranial structures in an automated fashion from a mixed population of mice, including craniofacial mutants, aged 4-12.5 weeks. As a secondary contribution, we demonstrate an application of nearly automated shape analysis, using symmetric diffeomorphic image registration. This approach, which we call diGPA, closely approximates the popular generalized Procrustes analysis (GPA) but negates the collection of anatomical landmarks. We achieve our goals by using the open-source advanced normalization tools (ANT) image quantification library, as well as its associated R library (ANTsR) for statistical image analysis. Finally, we make a plea to investigators to commit to using open imaging standards and software in their labs to the extent possible to increase the potential for data exchange and improve the reproducibility of findings. Future work will incorporate more anatomical detail (such as individual cranial bones, turbinals, dentition, middle ear ossicles) and more diversity into the template.

Population differences in brain morphology: Need for population specific brain template

Brain templates provide a standard anatomical platform for population based morphometric assessments. Typically, standard brain templates for such assessments are created using Caucasian brains, which may not be ideal to analyze brains from other ethnicities. To effectively demonstrate this, we compared brain morphometric differences between T1 weighted structural MRI images of 27 healthy Indian and Caucasian subjects of similar age and same sex ratio. Furthermore, a population specific brain template was created from MRI images of healthy Indian subjects and compared with standard Montreal Neurological Institute (MNI-152) template. We also examined the accuracy of registration of by acquiring a different T1 weighted MRI data set and registering them to newly created Indian template and MNI-152 template. The statistical analysis indicates significant difference in global brain measures and regional brain structures of Indian and Caucasian subjects. Specifically, the global brain measurements of the Indian brain template were smaller than that of the MNI template. Also, Indian brain images were better realigned to the newly created template than to the MNI-152 template. The notable variations in Indian and Caucasian brains convey the need to build a population specific Indian brain template and atlas.

Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development

Functional Magnetic Resonance Imaging (fMRI) represents a powerful tool with which to examine brain functioning and development in typically developing pediatric groups as well as children and adolescents with clinical disorders. However, fMRI data can be highly susceptible to misinterpretation due to the effects of excessive levels of noise, often related to head motion. Imaging children, especially with developmental disorders, requires extra considerations related to hyperactivity, anxiety and the ability to perform and maintain attention to the fMRI paradigm. We discuss a number of methods that can be employed to minimize noise, in particular movement-related noise. To this end we focus on strategies prior to, during and following the data acquisition phase employed primarily within our own laboratory. We discuss the impact of factors such as experimental design, screening of potential participants and pre-scan training on head motion in our adolescents with developmental disorders and typical development. We make some suggestions that may minimize noise during data acquisition itself and finally we briefly discuss some current processing techniques that may help to identify and remove noise in the data. Many advances have been made in the field of pediatric imaging, particularly with regard to research involving children with developmental disorders. Mindfulness of issues such as those discussed here will ensure continued progress and greater consistency across studies.

Mobile Device Applications for the Visualization of Functional Connectivity Networks and EEG Electrodes: iBraiN and iBraiNEEG

Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10-20 EEG electrodes with Yeo's seven functional connectivity networks.