Multimodal Magnetic Resonance Research Articles

Multimodal Transformer of Incomplete MRI Data for Brain Tumor Segmentation.

Accurate segmentation of brain tumors plays an important role for clinical diagnosis and treatment. Multimodal magnetic resonance imaging (MRI) can provide rich and complementary information for accurate brain tumor segmentation. However, some modalities may be absent in clinical practice. It is still challenging to integrate the incomplete multimodal MRI data for accurate segmentation of brain tumors. In this paper, we propose a brain tumor segmentation method based on multimodal transformer network with incomplete multimodal MRI data. The network is based on U-Net architecture consisting of modality specific encoders, multimodal transformer and multimodal shared-weight decoder. First, a convolutional encoder is built to extract the specific features of each modality. Then, a multimodal transformer is proposed to model the correlations of multimodal features and learn the features of missing modalities. Finally, a multimodal shared-weight decoder is proposed to progressively aggregate the multimodal and multi-level features with spatial and channel self-attention modules for brain tumor segmentation. A missing-full complementary learning strategy is used to explore the latent correlation between the missing and full modalities for feature compensation. For evaluation, our method is tested on the multimodal MRI data from BraTS 2018, BraTS 2019 and BraTS 2020 datasets. The extensive results demonstrate that our method outperforms the state-of-the-art methods for brain tumor segmentation on most subsets of missing modalities.

Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

AbstractAutomatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state‐of‐the‐art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi‐scale‐aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor‐based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature‐based method achieved the highest performance for survival days prediction over the state‐of‐the‐art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning‐based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days.

Whole-brain parcellation for macaque brain magnetic resonance images based on attention mechanism and multi-modality feature fusion

We propose a novel deep learning algorithm based on attention mechanism and multimodality feature fusion (DDAM) to achieve whole-brain parcellation of macaque brain magnetic resonance (MR) images. We collected multimodality brain MR images of 68 macaques (aged 13 to 36 months) with corresponding ground truth labels. To address the complexity and complementary nature of the multimodality data, we employed a multi- encoder structure to adapt to different modalities and performed feature extraction. In the decoder, an attention mechanism was introduced to construct the Attention-based Multimodality Feature Fusion module (AMFF). Leveraging the rich and complementary information between modalities, AMFF effectively integrated multimodality features of varying scales and complexities to enhance the performance of parcellation. We conducted ablation experiments and statistically analyzed the results. The incorporation of the multi- encoder structure and attention mechanism significantly improved the performance of the network for integrating the multimodality features, and achieved an average DSC of 0.904 and an ASD as low as 0.131 for macaque whole-brain parcellation (P < 0.05). The ablation experiments validated the effectiveness of each component of the DDAM method. The proposed DDAM method, with its enhanced ability to extract and integrate multimodality features, can significantly improve the accuracy of whole-brain MR image segmentation.

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

ObjectiveThis study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC). MethodsThis study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018–July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created. ResultsA total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555–0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy. ConclusionThe combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.

Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.

Investigated Parkinson's disease with multimodal magnetic resonance imaging

Investigated Parkinson's disease with multimodal magnetic resonance imaging

An Intra- and Inter-Modality Fusion Model with Invariant- and Specific-Constraints Using MR Images for Prediction of Glioma Isocitrate Dehydrogenase Mutation Status

In the 2021 World Health Organization classification of gliomas, it is proposed that Isocitrate Dehydrogenase (IDH) plays a key role. The prognosis of glioma is largely affected by IDH mutation status. Therefore, IDH mutation status needs to be predicted in advance before surgery. In the past decade, with the development of machine learning, more and more machine learning methods, especially deep learning methods, have been applied to the development of computer-aided diagnosis systems. At present, in this field, many deep learning and radiomics based methods have been proposed for IDH prediction using multimodal Magnetic Resonance Imaging (MRI). In this study, we proposed an intra- and inter-modality fusion model with invariant- and specific- constraints to improve the performance of IDH status prediction. First, MRI-based radiomics features were fused with deep learning features in each modality (intra-modality fusion) and then the features extracted from each modality of brain MRI were fused by using an inter-modality fusion model with invariant and specific constraints. We experimented our proposed method on the dataset provided by the Affiliated Hospital of Zhengzhou University in Zhengzhou, China and demonstrated the effectiveness of the proposed method. In our study, we propose two inter-modality fusion models, and our experimental results show that our best proposed method outperformed state-of-the-art methods with an accuracy of 0.79, precision of 0.80, recall of 0.75, and F1 score of 0.78. Thus, we predicted the IDH mutation status for glioma treatment with a 2% increase in accuracy and 4% increase in precision to predict the IDH mutation status for glioma treatment.

A Multi-Modality Magnetic Resonance Imaging Model for Predicting Traumatic Brain Injury Outcomes

Traumatic Brain Injury (TBI) is a heterogenous injury and a leading cause of long-term deficits and mortality in the United States. In order to improve TBI outcomes, an effective prognostication tool is necessary. Standard imaging modalities, computerized tomography (CT) and magnetic resonance imaging (MRI), have a limited ability to predict TBI outcomes. Currently, advanced MRI techniques are being studied for their efficacy. The aim of this study is to determine whether a multimodality MRI approach is superior to a single modality MRI approach in determining clinical outcomes of TBI. A secondary data analysis was conducted on TBI data obtained from 31 rat brains; 3-day MRI data in the Ipsilateral Perilesion Cortex and 28-day Behavioral Test data (Novel Object Recognition, Barnes Maze, and Open Field Test Total Distance and Total Act Time) were analyzed. A Best Subset Analysis was conducted for each of the behavioral tests. Three out of four behavioral tests show improved adjusted R2 values for models containing more than one imaging modality. A Multiple Linear Regression Analysis was then conducted on the MRIs from the highest predictive model determined by Best Subset Analysis. This analysis shows that a multimodality MRI approach can explain 25.2% of the variability in behavioral outcomes in the Novel Object Recognition Test with a P value of 0.012. Thus, the study demonstrates that a multi-modality MRI approach has a potential for effectively diagnosing and predicting TBI outcomes.

Characterizing Typhoon-related Posttraumatic Stress Disorder Based on Multimodal Fusion of Structural, Diffusion, and Functional Magnetic Resonance Imaging

Diagnosing posttraumatic stress disorder (PTSD) using only single-modality images is controversial. We aimed to use multimodal magnetic resonance imaging (MRI) combining structural, diffusion, and functional MRI to possibly provide a more comprehensive viewpoint on the decisive characteristics of PTSD patients. Typhoon-exposed individuals with (n = 26) and without PTSD (n = 32) and healthy volunteers (n = 30) were enrolled. Five MRI features from three modalities, including two resting-state functional MRI (rs-fMRI) features (amplitude of low-frequency fluctuation, ALFF; and regional homogeneity, ReHo), one structural MRI feature (gray matter density, GM), and two diffusion tensor imaging (DTI) features (fractional anisotropy, FA; and mean diffusivity, MD) were investigated simultaneously with a multimodal canonical correlation analysis + joint independent component analysis model to identify abnormalities in the PTSD brain. We identified statistical differences between PTSD patients and healthy controls in terms of 1 rs-fMRI (ALFF, ReHo) alterations in the superior frontal gyrus, precuneus, inferior parietal lobule (IPL), anterior cingulate cortex (ACC), and posterior cingulate cortex (PCC), 2 DTI (FA, MD) changes in the pons, genu, and splenium of the corpus callosum, and 3 Structural MRI abnormalities in the precuneus, IPL, ACC, and PCC. A novel ReHo component was found to distinguish PTSD and trauma-exposed controls, including the precuneus, IPL, middle frontal gyrus, middle occipital gyrus, and cerebellum. This study reveals that PTSD individuals exhibit intertwined functional and structural anomalies within the default mode network. Some alterations within this network may serve as a potential marker to distinguish between PTSD patients and trauma-exposed controls.

Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection

In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented. The method is based on an U-Net like convolutional neural network (CNN) for automated 2D slice-based-segmentation of brain MRI volumes. The individual modalities are encoded in separate downsampling branches without weight sharing, to leverage the specific features. Skip connections input feature maps to multi-scale feature fusion (MSFF) blocks at every decoder stage of the network. Those are followed by multi-scale feature upsampling (MSFU) blocks which use the information about lesion shape and location. The CNN is evaluated on two publicly available datasets: The ISBI 2015 longitudinal MS lesion segmentation challenge dataset containing 19 subjects and the MICCAI 2016 MSSEG challenge dataset containing 15 subjects from various scanners. The proposed multi-input 2D architecture is among the top performing approaches in the ISBI challenge, to which open-access papers are available, is able to outperform state-of-the-art 3D approaches without additional post-processing, can be adapted to other scanners quickly, is robust against scanner variability and can be deployed for inference even on a standard laptop without a dedicated GPU.

Periodontitis is associated with altered brain structure and function in normal cognition middle-aged and elderly individuals.

Numerous studies have proposed that periodontitis is a potential risk factor for Alzheimer's disease. However, the association between periodontitis and brain normal cognition in aged and elderly individuals (NCs) is unclear. Such a link could provide clues to Alzheimer's disease development and strategies for early prevention. To explore the associations between periodontal condition and metrics of both brain structure and function among NCs with the help of multimodal magnetic resonance imaging (MRI). High-resolution T1-weighted structural data, resting-state functional-MRI data, and measures of periodontal condition were collected from 40 NCs. Cortical volume, thickness, and area as well as regional homogeneity were calculated with the aid of DPABISurf software. Correlation analyses were then conducted between each imaging metric and periodontal index. Consistent negative correlations were observed between severity of periodontitis (mild, moderate, severe) and cortical volume, area, and thickness, not only in brain regions that took charge of primary function but also in brain regions associated with advanced cognition behavior. Among participants with mild attachment loss (AL) and a shallow periodontal pocket depth (PPD), periodontal index was positively correlated with most measures of brain structure and function, while among participants with severe AL and deep PPD, periodontal index was negatively correlated with measures of brain structure and function (all p < .005 for each hemisphere). Our results demonstrate that periodontitis is associated with widespread changes in brain structure and function among middle-aged and elderly adults without signs of cognitive decline, which might be a potential risk factor for brain damage.

Microstructural and functional alterations of the ventral pallidum are associated with levodopa-induced dyskinesia in Parkinson's disease.

The ventral pallidum (VP) regulates involuntary movements, but it is unclear whether the VP regulates the abnormal involuntary movements in Parkinson's disease (PD) patients who have levodopa-induced dyskinesia (LID). To further understand the role of the VP in PD patients with LID (PD-LID), we explored the structural and functional characteristics of the VP in such patients using multimodal magnetic resonance imaging (MRI). Thirty-one PD-LID patients, 39 PD patients without LID (PD-nLID), and 28 healthy controls (HCs) underwent T1-weighted MRI, quantitative susceptibility mapping, multi-shell diffusion MRI, and resting-state functional MRI (rs-fMRI). Different measures characterizing the VP were obtained using a region-of-interest-based approach. The left VP in the PD-LID group showed significantly higher intracellular volume fraction (ICVF) and isotropic volume fraction (IsoVF) compared with the PD-nLID and HC groups. Rs-MRI revealed that, compared with the PD-nLID group, the PD-LID group in the medication 'off' state had higher functional connectivity (FC) between the left VP and the left anterior caudate, left middle frontal gyrus and left precentral gyrus, as well as between the right VP and the right posterior ventral putamen and right mediodorsal thalamus. In addition, the ICVF values of the left VP, the FC between the left VP and the left anterior caudate and left middle frontal gyrus were positively correlated with Unified Dyskinesia Rating Scale scores. Our multimodal imaging findings show that the microstructural changes of the VP (i.e., the higher ICVF and IsoVF) and the functional change in the ventral striatum-VP-mediodorsal thalamus-cortex network may be associated with pathophysiological mechanisms of PD-LID.

Multimodal magnetic resonance imaging on brain structure and function changes in vascular cognitive impairment without dementia.

Vascular cognitive impairment not dementia (VCIND) is one of the three subtypes of vascular cognitive impairment (VCI), with cognitive dysfunction and symptoms ranging between normal cognitive function and vascular dementia. The specific mechanisms underlying VCIND are still not fully understood, and there is a lack of specific diagnostic markers in clinical practice. With the rapid development of magnetic resonance imaging (MRI) technology, structural MRI (sMRI) and functional MRI (fMRI) have become effective methods for exploring the neurobiological mechanisms of VCIND and have made continuous progress. This article provides a comprehensive overview of the research progress in VCIND using multimodal MRI, including sMRI, diffusion tensor imaging, resting-state fMRI, and magnetic resonance spectroscopy. By integrating findings from these multiple modalities, this study presents a novel perspective on the neuropathological mechanisms underlying VCIND. It not only highlights the importance of multimodal MRI in unraveling the complex nature of VCIND but also lays the foundation for future research examining the relationship between brain structure, function, and cognitive impairment in VCIND. These new perspectives and strategies ultimately hold the potential to contribute to the development of more effective diagnostic tools and therapeutic interventions for VCIND.

An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis

Objective. Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD. Approach. In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis. Main results. This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD. Significance. The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.

PU-NET DEEP LEARNING ARCHITECTURE FOR GLIOMAS BRAIN TUMOUR SEGMENTATION IN MAGNETIC RESONANCE IMAGES

Automatic medical image segmentation is one of the main tasks for many organs and pathology structure delineation. It is also a crucial technique in the posterior clinical examination of brain tumours, like applying radiotherapy or tumour restrictions. Various image segmentation techniques have been proposed and applied to different image types of images. Recently, it has been shown that the deep learning approach accurately segments images, and its implementation is usually straightforward. In this paper, we proposed a novel approach, called PU-net, for automatic brain tumour segmentation in multi-modal magnetic resonance images (MRI) based on deep learning. We introduced an input processing block to a customised fully convolutional network derived from the U-Net network to handle the multi-modal inputs. We performed experiments over the Brats brain tumour dataset collected in 2018 and achieved dice scores of 0.905,0.827, and 0.803 for the whole tumour (WT), tumour core (TC), and enhancing tumour (ET) classes, respectively. This study also provides promising results compared to the traditional machine learning methods, such as support vector machines (SVM), random forest (RF) and other deep learning methods used in this context.

Neurobiological mechanisms of ECT and TMS treatment in depression: study protocol of a multimodal magnetic resonance investigation

BackgroundNoninvasive neurostimulation treatments are increasingly being used to treat major depression, which is a common cause of disability worldwide. While electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) are both effective in treating depressive episodes, their mechanisms of action are, however, not completely understood. ECT is given under general anesthesia, where an electrical pulse is administered through electrodes placed on the patient’s head to trigger a seizure. ECT is used for the most severe cases of depression and is usually not prescribed before other options have failed. With TMS, brain stimulation is achieved through rapidly changing magnetic fields that induce electric currents underneath a ferromagnetic coil. Its efficacy in depressive episodes has been well documented. This project aims to identify the neurobiological underpinnings of both the effects and side effects of the neurostimulation techniques ECT and TMS.MethodsThe study will utilize a pre-post case control longitudinal design. The sample will consist of 150 subjects: 100 patients (bipolar and major depressive disorder) who are treated with either ECT (N = 50) or TMS (N = 50) and matched healthy controls (N = 50) not receiving any treatment. All participants will undergo multimodal magnetic resonance imaging (MRI) as well as neuropsychological and clinical assessments at multiple time points before, during and after treatment. Arterial spin labeling MRI at baseline will be used to test whether brain perfusion can predict outcomes. Signs of brain disruption, potentiation and rewiring will be explored with resting-state functional MRI, magnetic resonance spectroscopy and multishell diffusion weighted imaging (DWI). Clinical outcome will be measured by clinician assessed and patient reported outcome measures. Memory-related side effects will be investigated, and specific tests of spatial navigation to test hippocampal function will be administered both before and after treatment. Blood samples will be stored in a biobank for future analyses. The observation time is 6 months. Data will be explored in light of the recently proposed disrupt, potentiate and rewire (DPR) hypothesis.DiscussionThe study will contribute data and novel analyses important for our understanding of neurostimulation as well as for the development of enhanced and more personalized treatment.Trial registrationClinicalTrials.gov Identifier: NCT05135897.

Aerobic exercise and metformin on intermuscular adipose tissue (IMAT): insights from multimodal MRI and histological changes in prediabetic rats

BackgroundPhysical exercise is the first-line intervention for prediabetes, and metformin is the most widely used oral insulin-sensitizing agent. Moreover, intermuscular adipose tissue (IMAT) directly affects insulin resistance by helping maintain glucose homeostasis. Here, we evaluated the effects of moderate aerobic exercise and/or metformin on histological IMAT parameters in non-streptozotocin-induced prediabetes.MethodsMale Wistar rats with prediabetes fed a high-fat diet and high-sugar drinks were randomly assigned to high-fat diet (PRE), metformin (MET), moderate aerobic exercise (EXE), combined therapy (EMC), or EMC + compound-c (EMA) groups for 4 weeks. Multimodal magnetic resonance imaging (MRI) was then performed, and tissue-specific inflammation and energy and lipid metabolism were evaluated in IMAT.ResultsThe EXE group had lower inflammatory factor levels, lipid metabolism, and mitochondrial oxidative stress, and shorter IMAT adipocyte diameters than the MET group. The MET group exhibited lower IL-1β and Plin5 expression than the PRE group. Furthermore, the IMAT of the EMC group had lower TNF-α and phosphorylated NF-κB levels and higher GLUT1 and GLUT4 expression than the PRE group. Multimodal MRI revealed significant changes in transverse-relaxation time 2, apparent diffusion coefficient, and fractional anisotropy values in the IMAT and muscles, as well as lower IMAT% values in the EXE and EMC groups than in the MET and PRE groups.ConclusionModerate aerobic exercise training can effectively improve IMAT function and structure via the AMP-activated protein kinase pathway in prediabetes. Combining metformin with moderate aerobic exercise might elicit modest synergy, and metformin does not counterbalance the beneficial effects of exercise.

Neuroanatomical Correlates of Cognitive Tests in Young-onset MCI.

Mild Cognitive Impairment (MCI) is a heterogeneous condition characterised by cognitive changes that do not affect everyday functioning and may represent a predementia phase. Research on the neuroanatomical correlates of cognitive tests used to diagnose MCI is heterogeneous and has mainly focused on elderly populations of patients with MCI, usually well above the age of 65. However, the effect of ageing on brain structure is known to be substantial and to affect brain-behaviour associations in older people. We explored the brain correlates of different cognitive tests in a group of young-onset MCI (i.e., with symptoms onset before the age of 65) to minimise the effect of ageing on brain-behaviour associations. Patients with a clinical diagnosis of young-onset MCI underwent extensive cognitive assessment and multimodal Magnetic Resonance Imaging (MRI) including high-resolution T1-weighted and Diffusion Tensor Imaging (DTI) sequences. Their scores on cognitive tests were related to measures of grey matter (GM) density and white matter (WM) integrity using, respectively, Voxel Based Morphometry (VBM) and Tract-Based Spatial Statistics (TBSS). 104 young-onset MCI were recruited. VBM and TBSS whole-brain correlational analyses showed that between-subject variability in cognitive performance was significantly associated with regional variability in GM density and WM integrity. While associations between cognitive scores and focal GM density in our young-onset MCI group reflected the well-known lateralization of verbal and visuo-spatial abilities on the left and right hemispheres respectively, the associations between cognitive scores and WM microstructural integrity were widespread and diffusely involved most of the WM tracts in both hemispheres. We investigated the structural neuroanatomical correlates of cognitive tests in young-onset MCI in order to minimise the effect of ageing on brain-behaviour associations.

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

BackgroundNeuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer’s disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response.MethodsSixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline.ResultsWe found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54).ConclusionOur findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response.Relevance statementDynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease.Key points• rTMS at the left angular gyrus could induce cognitive improvement.• rTMS could suppress primary network connections to compensate for higher-order networks.• Dynamic reorganization of brain networks could predict individual treatment response to rTMS.Graphical

Assessment of white matter hyperintensity severity using multimodal magnetic resonance imaging.

White matter hyperintensities are radiological abnormalities reflecting cerebrovascular dysfunction detectable using MRI. White matter hyperintensities are often present in individuals at the later stages of the lifespan and in prodromal stages in the Alzheimer's disease spectrum. Tissue alterations underlying white matter hyperintensities may include demyelination, inflammation and oedema, but these are highly variable by neuroanatomical location and between individuals. There is a crucial need to characterize these white matter hyperintensity tissue alterations in vivo to improve prognosis and, potentially, treatment outcomes. How different MRI measure(s) of tissue microstructure capture clinically-relevant white matter hyperintensity tissue damage is currently unknown. Here, we compared six MRI signal measures sampled within white matter hyperintensities and their associations with multiple clinically-relevant outcomes, consisting of global and cortical brain morphometry, cognitive function, diagnostic and demographic differences and cardiovascular risk factors. We used cross-sectional data from 118 participants: healthy controls (n = 30), individuals at high risk for Alzheimer's disease due to familial history (n = 47), mild cognitive impairment (n = 32) and clinical Alzheimer's disease dementia (n = 9). We sampled the median signal within white matter hyperintensities on weighted MRI images [T1-weighted (T1w), T2-weighted (T2w), T1w/T2w ratio, fluid-attenuated inversion recovery (FLAIR)] as well as the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. qT2* and fluid-attenuated inversion recovery signals within white matter hyperintensities displayed different age- and disease-related trends compared to normal-appearing white matter signals, suggesting sensitivity to white matter hyperintensity-specific tissue deterioration. Further, white matter hyperintensity qT2*, particularly in periventricular and occipital white matter regions, was consistently associated with all types of clinically-relevant outcomes in both univariate and multivariate analyses and across two parcellation schemes. qT1 and fluid-attenuated inversion recovery measures showed consistent clinical relationships in multivariate but not univariate analyses, while T1w, T2w and T1w/T2w ratio measures were not consistently associated with clinical variables. We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to white matter hyperintensities. Our results suggest that combining volumetric and signal measures of white matter hyperintensity should be considered to fully characterize the severity of white matter hyperintensities in vivo. These findings may have implications in determining the reversibility of white matter hyperintensities and the potential efficacy of cardio- and cerebrovascular treatments.