Structural Magnetic Resonance Imaging Images Research Articles

Sheep (Ovis aries) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions

Magnetic resonance imaging (MRI) is a non-invasive technique that requires the participant to be completely motionless. To date, MRI in awake and unrestrained animals has only been achieved with humans and dogs. For other species, alternative techniques such as anesthesia, restraint and/or sedation have been necessary. Anatomical and functional MRI studies with sheep have only been conducted under general anesthesia. This ensures the absence of movement and allows relatively long MRI experiments but it removes the non-invasive nature of the MRI technique (i.e., IV injections, intubation). Anesthesia can also be detrimental to health, disrupt neurovascular coupling, and does not permit the study of higher-level cognition. Here, we present a proof-of-concept that sheep can be trained to perform a series of tasks, enabling them to voluntarily participate in MRI sessions without anesthesia or restraint. We describe a step-by-step training protocol based on positive reinforcement (food and praise) that could be used as a basis for future neuroimaging research in sheep. This protocol details the two successive phases required for sheep to successfully achieve MRI acquisitions of their brain. By providing structural brain MRI images from six out of ten sheep, we demonstrate the feasibility of our training protocol. This innovative training protocol paves the way for the possibility of conducting animal welfare-friendly functional MRI studies with sheep to investigate ovine cognition.

Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection.

This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, modifications to the upscaler part of the generator, and experiments with various discriminators within a generative adversarial training setting. It empirically demonstrates the effectiveness of super-resolution in the MCI detection task, showcasing performance improvements across different state-of-the-art classification models. The paper also addresses the challenge of accurately capturing perceptual image quality, particularly when images contain checkerboard artifacts, and proposes a methodology that incorporates hyperparameter optimization through a Pareto optimal Markov blanket (POMB). This approach systematically explores the hyperparameter space, focusing on reducing overfitting and enhancing model generalizability. The research findings contribute to the field by demonstrating that super-resolution can significantly improve the quality of MRI images for MCI detection, highlighting the importance of choosing an adequate discriminator and the potential of super-resolution as a preprocessing step to boost classification model performance.

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline.

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .

Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning.

Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms. There is a pressing need for a quantitative, automated method for detecting TLE. This study employed MRI scans and clinical data from 51 retrospective epilepsy cases, dividing them into two groups: 34 patients in TLE group and 17 patients in non-TLE group. The criteria for defining the TLE group were successful surgical removal of the epileptogenic zone in the temporal lobe and a favorable postoperative prognosis. A standard procedure was used for normalization, brain extraction, tissue segmentation, regional brain partitioning, and cortical reconstruction of T1 structural MRI images. Morphometric features such as gray matter volume, cortical thickness, and surface area were extracted from a total of 20 temporal lobe regions in both hemispheres. Support vector machine (SVM), extreme learning machine (ELM), and cmcRVFL+ classifiers were employed for model training and validated using 10-fold cross-validation. The results demonstrated that employing ELM classifiers in conjunction with specific temporal lobe gray matter volume features led to a better identification of TLE. The classification accuracy was 92.79%, with an area under the curve (AUC) value of 0.8019. The method proposed in this study can significantly assist in the preoperative identification of TLE patients. By employing this method, TLE can be included in surgical criteria, which could alleviate patient symptoms and improve prognosis, thereby bearing substantial clinical significance.

Ensemble method employing quantitative biomarkers and deep learning approach from structural magnetic resonance imaging to diagnose Alzheimer’s disease

Abstract Introduction: Alzheimer’s disease (AD) is a neurodegenerative condition that impairs activities of daily living and sharply declines gross cognitive ability. Over 152 million individuals worldwide will live with the dreaded consequence of a longer lifespan by the year 2050, making it a pressing public health issue. Magnetic resonance imaging (MRI) provides excellent soft tissue contrast and helps image the brain in vivo, non-invasively. Aims and Objectives: To summarize AD’s anatomical, physiological, and pathophysiological changes and derivation of quantifiable biomarkers from MRI to develop artificial intelligence (AI) based computer-aided detection (CAD) system to classify subjects among AD, mild cognitive impairment (MCI), and cognitively normal (CN). Materials and Methods: This retrospective study uses clinical and standardized, pre-processed, quality-controlled, and quality-checked—structural MRI imaging (diagnosed/labeled) data of 1069 subjects, age, gender, and class matched, taken from Alzheimer’s disease neuroimaging initiative. A pipeline is developed to get quantified biomarkers from the assessment of (1) cortical thickness, (2) volumetric segmentation for whole brain volumes, and (3) region of interest (ROI) areas most affected in AD. A gradient boosting method is used to predict class labels. The second approach implements a convolution neural network (CNN) model comprising 3D ROI. Results: Implemented CAD system using an ensemble gradient boosting approach has demonstrated good receiver operating characteristics characteristic and yielded balanced accuracy (BA) of 82.31%, 78.52%, and 72.73%, and the CNN approach has given better results 88.44%, 82.96%, and 74.34% for classification task AD versus CN, AD versus MCI, and MCI versus CN, respectively. Conclusion: This study has used a substantially large dataset of 1069 subjects. The deep learning-based efficient and optimal CNN model has used significantly large ROI-based 3-Dimentional volume, resulting in impressive performance improvements over comparable methods. The CNN model had given higher accuracy (6.13% for AD vs. CN, 4.44% for AD vs. MCI and 1.61% for MCI vs. CN) over gradient boosting, as the model uses significantly large ROI-based 3D brain volume and an inherent capability of it in learning most discriminative features automatically. However, quantitative biomarkers derived from brain morphometry, which accesses structural changes, yield reasonable estimates over pathophysiological alterations across the brain and augment a clinician with insightful and a holistic view, resulting in higher confidence over predicated class label by CNN and is a step closer to explainable AI. Accuracy for MCI versus CN drops as these classes share similar features and characteristics and can be improved by integrating biomarkers from other MRI modalities.

Graph Transformer Geometric Learning of Brain Networks Using Multimodal MR Images for Brain Age Estimation.

Brain age is considered as an important biomarker for detecting aging-related diseases such as Alzheimer's Disease (AD). Magnetic resonance imaging (MRI) have been widely investigated with deep neural networks for brain age estimation. However, most existing methods cannot make full use of multimodal MRIs due to the difference in data structure. In this paper, we propose a graph transformer geometric learning framework to model the multimodal brain network constructed by structural MRI (sMRI) and diffusion tensor imaging (DTI) for brain age estimation. First, we build a two-stream convolutional autoencoder to learn the latent representations for each imaging modality. The brain template with prior knowledge is utilized to calculate the features from the regions of interest (ROIs). Then, a multi-level construction of the brain network is proposed to establish the hybrid ROI connections in space, feature and modality. Next, a graph transformer network is proposed to model the cross-modal interaction and fusion by geometric learning for brain age estimation. Finally, the difference between the estimated age and the chronological age is used as an important biomarker for AD diagnosis. Our method is evaluated with the sMRI and DTI data from UK Biobank and Alzheimer's Disease Neuroimaging Initiative database. Experimental results demonstrate that our method has achieved promising performances for brain age estimation and AD diagnosis.

Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans

Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches.

Hyper-Adaptation in the Human Brain: Functional and Structural Changes in the Foot Section of the Primary Motor Cortex in a Top Wheelchair Racing Paralympian.

The human brain has the capacity to drastically alter its somatotopic representations in response to congenital or acquired limb deficiencies and dysfunctions. The main purpose of the present study was to elucidate such extreme adaptability in the brain of an active top wheelchair racing Paralympian (participant P1) who has congenital paraplegia (dysfunction of bilateral lower limbs). Participant P1 has undergone long-term wheelchair racing training using bilateral upper limbs and has won a total of 19 medals in six consecutive summer Paralympic games as of 2021. We examined the functional and structural changes in the foot section of the primary motor cortex (M1) in participant P1 as compared to able-bodied control participants. We also examined the functional and structural changes in three other individuals (participants P2, P3, and P4) with acquired paraplegia, who also had long-term non-use period of the lower limbs and had undergone long-term training for wheelchair sports (but not top athletes at the level of participant P1). We measured brain activity in all the participants using functional magnetic resonance imaging (MRI) when bimanual wrist extension-flexion movement was performed, and the structural MRI images were collected. Compared to 37 control participants, participant P1 showed significantly greater activity in the M1 foot section during the bimanual task, and significant local GM expansion in this section. Significantly greater activity in the M1 foot section was also observed in participant P4, but not in P2 and P3, and the significant local GM expansion was observed in participant P2, but not in P3 and P4. Thus, functional or structural change was observed in an acquired paraplegic participant, but was not observed in all the paraplegic participants. The functional and structural changes typically observed in participant P1 may represent extreme adaptability of the human brain. We discuss the results in terms of a new idea of hyper-adaptation.

Current progress in neuroimaging research for the treatment of major depression with electroconvulsive therapy.

Electroconvulsive therapy (ECT) uses a certain amount of electric current to pass through the head of the patient, causing convulsions throughout the body, to relieve the symptoms of the disease and achieve the purpose of treatment. ECT can effectively improve the clinical symptoms of patients with major depression, but its therapeutic mechanism is still unclear. With the rapid development of neuroimaging technology, it is necessary to explore the neurobiological mechanism of major depression from the aspects of brain structure, brain function and brain metabolism, and to find that ECT can improve the brain function, metabolism and even brain structure of patients to a certain extent. Currently, an increasing number of neuroimaging studies adopt various neuroimaging techniques including functional magnetic resonance imaging (MRI), positron emission tomography, magnetic resonance spectroscopy, structural MRI, and diffusion tensor imaging to reveal the neural effects of ECT. This article reviews the recent progress in neuroimaging research on ECT for major depression. The results suggest that the neurobiological mechanism of ECT may be to modulate the functional activity and connectivity or neural structural plasticity in specific brain regions to the normal level, to achieve the therapeutic effect.

The correspondence between morphometric MRI and metabolic profile in Rasmussen’s encephalitis

Volumetric magnetic resonance imaging (MRI) atrophy is a hallmark of Rasmussen’s encephalitis (RE). Here, we aim to investigate voxel-wise gray matter (GM) atrophy in RE, and its associations with glucose hypometabolism and neurotransmitter distribution utilizing MRI and PET data. In this study, fifteen RE patients and fourteen MRI normal subjects were included in this study. Voxel-wise GM volume and glucose metabolic uptake were evaluated using structural MRI and FDG-PET images, respectively. Spatial Spearman’s correlation was performed between GM atrophy of RE with FDG uptake alterations, and neurotransmitter distributions provided in the JuSpace toolbox. Compared with the control group, RE patients displayed extensive GM volume loss not only in the ipsilateral hemisphere, but also in the frontal lobe, basal ganglia, and cerebellum in the contralateral hemisphere. Within the RE group, the insular and temporal cortices exhibited significantly more GM atrophy on the ipsilesional than the contralesional side. FDG-PET data revealed significant hypometabolism in areas surrounding the insular cortices in the ipsilesional hemisphere. RE-related GM volumetric atrophy was spatially correlated with hypomebolism in FDG uptake, and with spatial distribution of the dopaminergic and serotonergic neurotransmitter systems. The spatial concordance of morphological changes with metabolic abnormalities suggest FDG-PET offers potential value for RE diagnosis. The GM alterations associated with neurotransmitter distribution map could provide novel insight in understanding the neuropathological mechanisms and clinical feature of RE.

Association between long-term donepezil treatment and brain regional amyloid and tau burden among individuals with mild cognitive impairment assessed using 18 F-AV-45 and 18 F-AV-1451 PET.

This study aims to investigate the association between long-term donepezil treatment and brain neuropathological burden and cognitive function in mild cognitive impairment (MCI) patients. Preprocessed 18 F-AV-45 amyloid and 18 F-AV-1451 tau positron emission tomography (PET) images, magnetic resonance imaging images (MRIs), demographic information, and donepezil use status were downloaded from 255 MCI participants enrolled in the Alzheimer's Disease Neuroimaging Initiative database. Partial volume correction was applied to all PET images. Structural MRIs were used for PET spatial normalization. Regions of interest (ROIs) were defined in standard space, and standardized uptake value ratio (SUVR) images relative to the cerebellum were computed. Multiple linear regression with the least absolute shrinkage selector operator was performed to analyze the effect of long-term donepezil treatment on (a) the SUVR of each 18 F-AV-45 or 18 F-AV-1451 brain PET ROI after adjusting for age, sex, education, ApoE ε4 status, and AD-associated disease risk factors; and (b) cognitive performance after adjusting for age, sex education, ApoE ε4 status, AD-associated disease risk factors, and regional amyloid or tau burden. In adjusted models, long-term donepezil treatment was associated with greater amyloid load in the orbital frontal, superior frontal, parietal, posterior precuneus, posterior cingulate, lateral temporal, inferior temporal and fusiform regions, and tau burden in the posterior cingulate, entorhinal and parahippocampal gyrus. Long-term donepezil treatment was also associated with worse performance on the 13-item Alzheimer's Disease Assessment Scale-Cognitive subscale after adjusting for AD-related risk factors and regional brain amyloid or tau load. These results indicate that long-term donepezil treatment is associated with increased regional amyloid and tau burden and worse cognitive performance among individuals with MCI. Our study highlights the importance of using noninvasive and quantitative 18 F-AV-45 and 18 F-AV-1451 PET to elucidate the consequences of drug administration in AD studies.

Sex-Specific Differences in Life Span Brain Volumes in Multiple Sclerosis.

Numerous sex-specific differences in multiple sclerosis (MS) susceptibility, disease manifestation, disability progression, inflammation, and neurodegeneration have been previously reported. Previous magnetic resonance imaging (MRI) studies have shown structural differences between female and male MS brain volumes. To determine sex-specific global and tissue-specific brain volume throughout the MS life span in a real-world large MRI database. A total of 2,199 MS patients (female/male ratio of 1,651/548) underwent structural MRI imaging on either a 1.5-T or 3-T scanner. Global and tissue-specific volumes of whole brain (WBV), white matter, and gray matter (GMV) were determined by utilizing Structural Image Evaluation using Normalisation of Atrophy Cross-sectional (SIENAX). Lateral ventricular volume (LVV) was determined with the Neurological Software Tool for REliable Atrophy Measurement (NeuroSTREAM). General linear models investigated sex and age interactions, and post hoc comparative sex analyses were performed. Despite being age-matched with female MS patents, a greater proportion of male MS patients were diagnosed with progressive MS and had lower normalized WBV (P<.001), GMV (P <.001), and greater LVV (P <.001). In addition to significant stand-alone main effects, an interaction between sex and age had an additional effect on the LVV (F-statistics = 4.53, P = .033) and GMV (F-statistics = 4.59, P = .032). The sex and age interaction was retained in both models of LVV (F-statistics = 3.31, P = .069) and GMV (F-statistics = 6.1, P = .003) when disease subtype and disease-modifying treatment (DMT) were also included. Although male MS patients presented with significantly greater LVV and lower GMV during the early and midlife period when compared to their female counterparts (P <.001 for LVV and P <.019 for GMV), these differences were nullified in 60+ years old patients. Similar findings were seen within a subanalysis of MS patients that were not on any DMT at the time of enrollment. There are sex-specific differences in the LVV and GMV over the MS life span.

Diagnosis of Alzheimer's disease using universum support vector machine based recursive feature elimination (USVM-RFE)

Alzheimer's disease is one of the most common causes of death in today's world. Magnetic resonance imaging (MRI) provides an efficient and non-invasive approach for diagnosis of Alzheimer's disease. Efficient feature extraction techniques are needed for accurate classification of MRI images. Motivated by the work on support vector machine based recursive feature elimination (SVM-RFE) [16], we propose a novel feature selection technique to incorporate prior information about data distribution in the recursive feature elimination process. Our method is termed as universum support vector machine based recursive feature elimination (USVM-RFE). The proposed method provides global information about data in the RFE process as compared to the local approach of feature selection in SVM-RFE. We also present the application of feature selection and classification algorithms on both voxel based as well as volume based morphometry analysis of structural MRI images (ADNI database). Feature selection is performed using MRI data of brain tissues such as gray matter, white matter, and cerebrospinal fluid. USVM-RFE provides improvement over SVM-RFE in classification of control normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) subjects. Moreover, better accuracy is obtained by USVM-RFE with lesser number of features in comparison to SVM-RFE. This leads to identification of prominent brain regions for feature selection and classification of MRI images. The highest accuracies obtained by our method for classification of CN vs AD, CN vs MCI, and MCI vs AD are 100%, 90%, and 73.68%, respectively.

Diagnosis of Alzheimer’s Disease using Structural MRI and Convolution Neural Network

Background: Alzheimer’s disease (AD) is a prevalent, neurological disease without effective treatment. However, if diagnosed early, the progression of the disease could be delayed through medication. Currently, one method to effectively diagnose AD early is to use Alternate Covering Neural Network (ACNN) network to discern various non-invasive Magnetic Resonance Imaging (MRI) images. This research aims to create an approach better than the current one and thus increase the accuracy of classifying MRI images, thereby diagnosing AD earlier and more perfectly. Methods: Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U19 AG024904) database provided 3013 different sets of 3D MRI images labeled as cognitively normal (CN), mild cognitive impairment (MCI), and AD. A newly-proposed, modified Residual Network (ResNet) and an ACNN network were then constructed. Their common goal was to learn how to classify these labeled MRI images. After training, the two models got unique parameters for using the updated network to diagnose new images. Finally, inference, or testing the diagnostic accuracy of the two models, were performed based on another 469 different 3D MRI image sets. The accuracy of classification for two separate models were compared. Results: Compared with the ACNN network with a weighted classification accuracy of 80.17%, the newly proposed ResNet network enhances the weighted accuracy to 85.07% and showed statistical significance (p&lt;0.001). Through analyzing the occurrence of falsepositive cases by two models, a Receiver Operating Characteristic (ROC) curve was drawn. The area under the curve of the ROC confirms this enhancement as the area under the curve of ROC is greater than that of the ACNN model in two of the three cases (MCI 0.9293&gt;0.9196; AD 0.9389&gt;0.9146). Conclusions: The research proposed a new deep learning convolutional network to classify 3D structural MRI images. The new ResNet is better in that it showed increased accuracy with statistical significance and had fewer false-positive results compare with the traditional ACNN network, thereby promising to help doctors diagnose AD more quickly and more accurately.

Volumetric magnetic resonance and diffusion tensor imaging of C58/J mice: neural correlates of repetitive behavior.

Restricted, repetitive behavior (RRB) involves sequences of responding with little variability and no obvious function. RRB is diagnostic for autism spectrum disorder (ASD) and a significant feature in several neurodevelopmental disorders. Despite its clinical importance, relatively little is known about how RRB is mediated by broader neural circuits. In this study, we employed ultra-high field (17.6 Tesla) magnetic resonance imaging (MRI) to study the C58/J mouse model of RRB. We determined alterations in brain morphology and connectivity of C58/J mice and their relationship to repetitive motor behavior using structural MRI and diffusion tensor imaging (DTI). Compared to the genetically similar C57BL/6 control mouse strain, C58/J mice showed evidence of structural alterations in basal ganglia and cerebellar networks. In particular, C58/J mice exhibited reduced volumes of key cortical and basal ganglia regions that have been implicated in repetitive behavior, including motor cortex, striatum, globus pallidus, and subthalamic nucleus, as well as volume differences in the cerebellum. Moreover, DTI revealed differences in fractional anisotropy and axial diffusivity in cerebellar white matter of C58/J mice. Importantly, we found that RRB exhibited by C58/J mice was correlated with volume of the striatum, subthalamic nucleus, and crus II of the cerebellum. These regions are key nodes in circuits connecting the basal ganglia and cerebellum and our findings implicate their role in RRB, particularly the indirect pathway.

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to prevent or slow the progression of AD, we need to develop objective measures that are able to discriminate the MCI patients who are at risk of AD from those MCI patients who have less risk to develop AD. Here, we present a novel deep learning architecture, based on dual learning and an ad hoc layer for 3D separable convolutions, which aims at identifying MCI patients who have a high likelihood of developing AD within 3 years.Our deep learning procedures combine structural magnetic resonance imaging (MRI), demographic, neuropsychological, and APOe4 genetic data as input measures. The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data. All analyses were performed on a subset of the database made publicly available via the Alzheimer's Disease Neuroimaging Initiative (ADNI), (n = 785 participants, n = 192 AD patients, n = 409 MCI patients (including both MCI patients who convert to AD and MCI patients who do not covert to AD), and n = 184 healthy controls).The most predictive combination of inputs were the structural MRI images and the demographic, neuropsychological, and APOe4 data. In contrast, the warp field metrics were of little added predictive value. The algorithm was able to distinguish the MCI patients developing AD within 3 years from those patients with stable MCI over the same time-period with an area under the curve (AUC) of 0.925 and a 10-fold cross-validated accuracy of 86%, a sensitivity of 87.5%, and specificity of 85%. To our knowledge, this is the highest performance achieved so far using similar datasets. The same network provided an AUC of 1 and 100% accuracy, sensitivity, and specificity when classifying patients with AD from healthy controls. Our classification framework was also robust to the use of different co-registration templates and potentially irrelevant features/image portions.Our approach is flexible and can in principle integrate other imaging modalities, such as PET, and diverse other sets of clinical data. The convolutional framework is potentially applicable to any 3D image dataset and gives the flexibility to design a computer-aided diagnosis system targeting the prediction of several medical conditions and neuropsychiatric disorders via multi-modal imaging and tabular clinical data.

Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder

ObjectivesNeuroimaging studies have independently demonstrated brain anatomical and functional impairments in participants with ADHD. The aim of the current study was to explore the relationship between structural and functional brain alterations in ADHD through an integrated analysis of multimodal neuroimaging data.MethodsWe performed a multimodal analysis to integrate resting-state functional magnetic resonance imaging (MRI), structural MRI, and diffusion-weighted imaging data in a large, single-site sample of children with and without diagnosis for ADHD. The inferred subject contributions were fed into regression models to investigate the relationships between diagnosis, symptom severity, gender, and age.ResultsCompared with controls, children with ADHD diagnosis showed altered white matter microstructure in widespread white matter fiber tracts as well as greater gray matter volume (GMV) in bilateral frontal regions, smaller GMV in posterior regions, and altered functional connectivity (FC) in default mode and fronto-parietal networks. Age-related growth of GMV of bilateral occipital lobe, FC in frontal regions as well as age-related decline of GMV in medial regions seen in controls appeared reversed in children with ADHD. In the whole group, higher symptom severity was related to smaller GMV in widespread regions in bilateral frontal, parietal, and temporal lobes, as well as greater GMV in intracalcarine and temporal cortices.ConclusionsThrough a multimodal analysis approach we show that structural and functional alterations in brain regions known to be altered in subjects with ADHD from unimodal studies are linked across modalities. The brain alterations were related to clinical features of ADHD, including disorder status, age, and symptom severity.

Dilated 3D Convolutional Neural Networks for Brain MRI Data Classification

Benefiting from the research of machine learning (ML) and deep learning(DL), multivariate methods based on ML and DL have been the mainstream and successful analysis methods in Neural Engineering or Neuroimaging research, for example, assisting diagnosis based on brain Magnetic Resonance Imaging (MRI). However, many existing methods based on traditional ML methods cannot sufficiently extract discriminative features, especially feature patterns across long-distance brain areas, resulting in unsatisfactory classification performance. Designing an effective and robust classifier for different MRI images remains a challenge. In this paper, we introduced dilated 3D CNN method for classifying 3D MRI images combining CNN structure and dilated convolution with a small number of feature maps. We also presented a methodology framework based on dilated 3D CNN method, which can classify both single MRI images and image sequences. Our method and framework were evaluated on the structural MRI images of ADHD-200 dataset and fMRI images of a Schizophrenia dataset, demonstrating better performances than some other state-of-the-art methods.

Interconnected Anatomical and Functional Brain Alterations in Children with Attention-Deficit/Hyperactivity Disorder

Neuroimaging studies have independently demonstrated brain anatomical and functional impairments in participants with ADHD. The aim of the current study was to explore the relationship between structural and functional brain alterations in ADHD through an integrated analysis of multimodal neuroimaging data. Methods: We performed a multimodal analysis to integrate resting-state functional magnetic resonance imaging (MRI), structural MRI, and diffusion-weighted imaging data in a large, single-site sample of children with and without diagnosis for ADHD. The inferred subject contributions were fed into a regression models to investigate the relationships between diagnosis, symptom severity, gender, and age. Results: Compared with controls, children with ADHD diagnosis showed altered white matter microstructure in widespread white matter fiber tracts as well as greater gray matter volume (GMV) in bilateral frontal regions, smaller GMV in posterior regions, and altered functional connectivity (FC) in default mode and fronto-parietal networks. Agerelated growth of GMV of bilateral occipital lobe, FC in frontal regions as well as age-related decline of GMV in medial regions seen in controls appeared reversed in children with ADHD. In the whole group, higher symptom severity was related to smaller GMV in widespread regions in bilateral frontal, parietal, and temporal lobes, as well as greater GMV in intracalcarine and temporal cortices. Conclusions: Through a multimodel analysis approach we show that structural and functional alterations in brain regions known to be altered in subjects with ADHD from unimodal studies are linked across modalities. The brain alterations were related to clinical features of ADHD, including disorder status, age, and symptom severity.

The Japan Monkey Centre Primates Brain Imaging Repository for comparative neuroscience: an archive of digital records including records for endangered species

Advances in magnetic resonance imaging (MRI) and computational analysis technology have enabled comparisons among various primate brains in a three-dimensional electronic format. Results from comparative studies provide information about common features across primates and species-specific features of neuroanatomy. Investigation of various species of non-human primates is important for understanding such features, but the majority of comparative MRI studies have been based on experimental primates, such as common marmoset, macaques, and chimpanzee. A major obstacle has been the lack of a database that includes non-experimental primates’ brain MRIs. To facilitate scientific discoveries in the field of comparative neuroanatomy and brain evolution, we launched a collaborative project to develop an open-resource repository of non-human primate brain images obtained using ex vivo MRI. As an initial open resource, here we release a collection of structural MRI and diffusion tensor images obtained from 12 species: pygmy marmoset, owl monkey, white-fronted capuchin, crab-eating macaque, Japanese macaque, bonnet macaque, toque macaque, Sykes’ monkey, red-tailed monkey, Schmidt’s guenon, de Brazza’s guenon, and lar gibbon. Sixteen postmortem brain samples from the 12 species, stored in the Japan Monkey Centre (JMC), were scanned using a 9.4-T MRI scanner and made available through the JMC collaborative research program (http://www.j-monkey.jp/BIR/index_e.html). The expected significant contributions of the JMC Primates Brain Imaging Repository include (1) resources for comparative neuroscience research, (2) preservation of various primate brains, including those of endangered species, in a permanent digital form, (3) resources with higher resolution for identifying neuroanatomical features, compared to previous MRI atlases, (4) resources for optimizing methods of scanning large fixed brains, and (5) references for veterinary neuroradiology. User-initiated research projects beyond these contributions are also anticipated.