Current Clinical Applications of Diffusion-Tensor Imaging in Neurological Disorders

Neuroimage analysis using artificial intelligence approaches: a systematic review

In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.

Insulin resistance and white matter microstructural abnormalities in nondiabetic adult: A population-based study

BACKGROUND

Insulin resistance (IR) is of growing concern yet its association with white matter integrity remains controversial. We aimed to investigate the association between IR and white matter integrity in nondiabetic adults.

METHODS

This cross-sectional analysis was conducted based on the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study. A total of 1709 nondiabetic community-dwelling adults with available diffusion-weighted imaging based on brain magnetic resonance imaging and completed oral glucose tolerance test were included. IR was measured noninvasively by insulin sensitivity indices (ISI), including ISIcomposite and ISI0,120, as well as homeostasis model assessment of insulin resistance (HOMA-IR). White matter microstructure abnormalities were identified by diffusion-weighted imaging along with tract-based spatial statistical analysis to compare diffusion metrics between groups. The multivariable linear regression models were applied to measure the association between white matter microstructure abnormalities and IR.

RESULTS

A total of 1709 nondiabetic individuals with a mean age of 60.8 ± 6.4 years and 54.1% female were included. We found that IR was associated with a significant increase in mean diffusivity, axial diffusivity, and radial diffusivity extensively in cerebral white matter in regions such as the anterior corona radiata, superior corona radiata, anterior limb of internal capsule, external capsule, and body of corpus callosum. The pattern of associations was more marked for ISIcomposite and ISI0,120. However, the effect of IR on white matter integrity was attenuated after, in addition, adjustment for history of hypertension and cardiovascular disease and antihypertensive medication use.

CONCLUSION

Our findings indicate a significant association between IR and white matter microstructural abnormalities in nondiabetic middle-aged community residents, while these associations were greatly influenced by the history of hypertension and cardiovascular disease, and antihypertensive medication use. Further investigation is needed to clarify the role of IR in white matter integrity, whereas prophylactic strategies of maintaining a low IR status may ameliorate disturbances in white matter integrity.

DATA ACCESSIBILITY STATEMENT

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

The role of the right hemispheric homologous language pathways in recovery from post-stroke aphasia: A systematic review

The involvement of the right hemisphere, mainly the activation of the right cerebral regions, in recovery from post-stroke aphasia has been widely recognized. In contrast, the role of the right white matter pathways in the recovery from post-stroke aphasia is rarely understood. In this study, we aimed to provide a primary overview of the correlation between the structural integrity of the right hemispheric neural tracts based on the dual-stream model of language organization and recovery from post-stroke aphasia by systematically reviewing prior longitudinal interventional studies. By searching electronic databases for relevant studies according to a standard protocol, a total of 10 records (seven group studies and three case studies) including 79 participants were finally included. After comprehensively analyzing these studies and reviewing the literature, although no definite correlation was found between the right hemispheric neural tracts and recovery from post-stroke aphasia, our review provideds a new perspective for investigating the linguistic role of the right hemispheric neural tracts. This suggests that the involvement of the right hemispheric neural tracts in recovery from post-stroke aphasia may be mediated by multiple factors; thus, this topic should be comprehensively investigated in the future.

Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas

OBJECTIVE

High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment.

METHODS

We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes.

RESULTS

The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes.

INTERPRETATION

The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

An MRI evaluation of white matter involvement in paradigmatic forms of spastic ataxia: results from the multi-center PROSPAX study

BACKGROUND

Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS) and Spastic Paraplegia Type 7 (SPG7) are paradigmatic spastic ataxias (SPAX) with suggested white matter (WM) involvement. Aim of this work was to thoroughly disentangle the degree of WM involvement in these conditions, evaluating both macrostructure and microstructure via the analysis of diffusion MRI (dMRI) data.

MATERIAL AND METHODS

In this multi-center prospective study, ARSACS and SPG7 patients and Healthy Controls (HC) were enrolled, all undergoing a standardized dMRI protocol and a clinimetrics evaluation including the Scale for the Assessment and Rating of Ataxia (SARA). Differences in terms of WM volume or global microstructural WM metrics were probed, as well as the possible occurrence of a spatially defined microstructural WM involvement via voxel-wise analyses, and its correlation with patients' clinical status.

RESULTS

Data of 37 ARSACS (M/F = 21/16; 33.4 ± 12.4 years), 37 SPG7 (M/F = 24/13; 55.7 ± 10.7 years), and 29 HC (M/F = 13/16; 42.1 ± 17.2 years) were analyzed. While in SPG7, only a mild mean microstructural damage was found compared to HC, ARSACS patients present a severe WM involvement, with a reduced global volume (p < 0.001), an alteration of all microstructural metrics (all with p < 0.001), without a spatially defined pattern of damage but with a prominent involvement of commissural fibers. Finally, in ARSACS, a correlation between microstructural damage and SARA scores was found (p = 0.004).

CONCLUSION

In ARSACS, but not SPG7 patients, we observed a complex and multi-faced involvement of brain WM, with a clinically meaningful widespread loss of axonal and dendritic integrity, secondary demyelination and, overall, a reduction in cellularity and volume.

Environmental enrichment reduces restricted repetitive behavior by altering gray matter microstructure

Restricted, repetitive behaviors are common symptoms in neurodevelopmental disorders including autism spectrum disorder. Despite being associated with poor developmental outcomes, repetitive behaviors remain poorly understood and have limited treatment options. Environmental enrichment attenuates the development of repetitive behaviors, but the exact mechanisms remain obscure. Using the C58 mouse model of repetitive behavior, we performed diffusion tensor imaging to examine microstructural alterations associated with the development of repetitive behavior and its attenuation by environmental enrichment. The C57BL/6 mouse strain, which displays little or no repetitive behavior, was used as a control group. We observed widespread differences in diffusion metrics between C58 mice and C57BL/6 mice. In juvenile C58 mice, repetitive motor behavior displayed strong negative correlations with fractional anisotropy in multiple gray matter regions, whereas in young adult C58 mice, high repetitive motor behavior was most strongly associated with lower fractional anisotropy and higher radial diffusivity in the striatum. Environmental enrichment increased fractional anisotropy and axial diffusivity throughout gray matter regions in the brains of juvenile C58 mice and overlapped predominantly with cerebellar and sensory regions associated with repetitive behavior. Our results suggest environmental enrichment reduces repetitive behavior development by altering gray matter microstructure in the cerebellum, medial entorhinal cortex, and sensory processing regions in juvenile C58 mice. Under standard laboratory conditions, early pathology in these regions appears to contribute to later striatal and white matter dysfunction in adult C58 mice. Future studies should examine the role these regions play in the development of repetitive behavior and the relationship between sensory processing and cerebellar deficits and repetitive behavior.

Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis

OBJECT

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).

MATERIALS AND METHODS

The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.

RESULTS

The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.

DISCUSSION

In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

The integrity of the corticospinal tract and corpus callosum, and the risk of ALS: univariable and multivariable Mendelian randomization

Studies suggest that amyotrophic lateral sclerosis (ALS) compromises the integrity of white matter fiber tracts, primarily affecting motor fibers. However, it remains uncertain whether the integrity of these fibers influences the risk of ALS. We performed bidirectional two-sample Mendelian randomization (MR) and multivariable MR analyses to evaluate the associative relationships between the integrity of fiber tracts [including the corticospinal tract (CST) and corpus callosum (CC)] and the risk of ALS. Genetic instrumental variables for specific fiber tracts were obtained from published genome-wide association studies (GWASs), including 33,292 European individuals from five diffusion magnetic resonance imaging (dMRI) datasets. Summary-level GWAS data for ALS were derived from 27,205 ALS patients and 110,881 controls. The MR results suggested that an increase in the first principal component (PC1) of fractional anisotropy (FA) in the genu of the CC (GCC) was correlated with an increased risk of ALS (PFDR = 0.001, odds ratio = 1.363, 95% confidence interval 1.178-1.577). Although other neuroimaging phenotypes [mean diffusivity in the CST, radial diffusivity (RD) in the CST, FA in the GCC, PC1 in the body of the CC (BCC), PC1 in the CST, and RD in the GCC] did not pass correction, they were also considered to have suggestive associations with the risk of ALS. No evidence revealed that ALS caused changes in the integrity of fiber tracts. In summary, the results of this study provide genetic support for the potential association between the integrity of specific fiber tracts and the risk of ALS. Greater fiber integrity in the GCC and BCC may be a risk factor for ALS, while greater fiber integrity in the CST may have a protective effect on ALS. This study provides insights into ALS development.

Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging

OBJECTIVE

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disorder with insidious onset. Accurate prediction of the disease progression has received increasing attention. Cognitive scores that reflect patients' cognitive status have become important criteria for predicting AD. Most existing methods consider the relationship between neuroimages and cognitive scores to improve prediction results. However, the inherent structure information in interrelated cognitive scores is rarely considered.

METHOD

In this article, we propose a relation-aware tensor completion multitask learning method (RATC-MTL), in which the cognitive scores are represented as a third-order tensor to preserve the global structure information in clinical scores. We combine both tensor completion and linear regression into a unified framework, which allows us to capture both inter and intra modes correlations in cognitive tensor with a low-rank constraint, as well as incorporate the relationship between biological features and cognitive status by imposing a regression model on multiple cognitive scores.

RESULT

Compared to the single-task and state-of-the-art multi-task algorithms, our proposed method obtains the best results for predicting cognitive scores in terms of four commonly used metrics. Furthermore, the overall performance of our method in classifying AD progress is also the best.

CONCLUSION

Our results demonstrate the effectiveness of the proposed framework in fully exploring the global structure information in cognitive scores.

SIGNIFICANCE

This study introduces a novel concept of leveraging tensor completion to assist in disease diagnoses, potentially offering a solution to the issue of data scarcity encountered in prolonged monitoring scenarios.

Tumefactive demyelinating lesions: A literature review of recent findings

Tumefactive demyelinating lesion is a variant of multiple sclerosis that is a diagnostic challenge. Tumefactive demyelinating lesion requires extensive work-up as its clinical and radiological features are often indistinguishable from other central nervous system lesions, such as tumors. Diagnosis is further complicated by the increasing recognition that tumefactive demyelinating lesions can occur alongside, evolve into, or develop from numerous conditions other than multiple sclerosis, pointing to a possible overlapping etiology. We review herein relevant studies from 2017 onwards to provide a current view on the pathogenesis, clinical and imaging findings, novel diagnostic techniques for differential diagnoses, and management of tumefactive demyelinating lesions.

Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI

PURPOSE

To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform.

METHODS

We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD).

RESULTS

Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences.

CONCLUSION

The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach

BACKGROUND

The prediction of post-stroke language function is essential for the development of individualized treatment plans based on the personal recovery potential of aphasic stroke patients.

OBJECTIVE

To establish a framework for integrating information on connectivity disruption of the language network based on routinely collected clinical magnetic resonance (MR) images into Random Forest modeling to predict post-stroke language function.

METHODS

Language function was assessed in 76 stroke patients from the Non-Invasive Repeated Therapeutic Stimulation for Aphasia Recovery trial, using the Token Test (TT), Boston Naming Test (BNT), and Semantic Verbal Fluency (sVF) Test as primary outcome measures. Individual infarct masks were superimposed onto a diffusion tensor imaging tractogram reference set to calculate Change in Connectivity scores of language-relevant gray matter regions as estimates of structural connectivity disruption. Multivariable Random Forest models were derived to predict language function.

RESULTS

Random Forest models explained moderate to high amount of variance at baseline and follow-up for the TT (62.7% and 76.2%), BNT (47.0% and 84.3%), and sVF (52.2% and 61.1%). Initial language function and non-verbal cognitive ability were the most important variables to predict language function. Connectivity disruption explained additional variance, resulting in a prediction error increase of up to 12.8% with variable omission. Left middle temporal gyrus (12.8%) and supramarginal gyrus (9.8%) were identified as among the most important network nodes.

CONCLUSION

Connectivity disruption of the language network adds predictive value beyond lesion volume, initial language function, and non-verbal cognitive ability. Obtaining information on connectivity disruption based on routine clinical MR images constitutes a significant advancement toward practical clinical application.

The mechanism of Sangdantongluo granule in treating post-stroke spasticity based on multimodal fMRI combined with TMS: Study protocol

Introduction

Post-stroke spasticity (PSS) is among the prevalent complications of stroke, greatly affecting motor function recovery and reducing patients' quality of life without timely treatment. Sangdantongluo granule, a modern traditional Chinese patent medicine, has significant clinical efficacy in treating PSS. However, the mechanism of Sangdantongluo granule in treating PSS is still unknown. We designed this study to explore the mechanism of Sangdantongluo granule in treating PSS through multimodal functional magnetic resonance imaging (fMRI) combined with transcranial magnetic stimulation (TMS).

Methods and analysis

In a single-center, randomized, double-blind, parallel placebo-controlled study, 60 PSS patients will be recruited in China and randomly assigned to either the experimental or control groups at a ratio of 1:1. For eight weeks, Sangdantongluo granule or placebo will be utilized for intervention. The main outcome is the Modified Ashworth Scale (MAS), the secondary outcome includes the Fugl-Meyer Assessment Scale-upper Extremity (FMA-UE), National Institute of Health Stroke Scale (NIHSS), and Modified Rankin Scale (mRS), the mechanism measure is the changes in cortical excitability and multimodal fMRI at baseline and after eight weeks.

Ethics and dissemination

This study was approved by the Ethics Committee of the Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine (approval number: [202364]).

Clinical trial registration

Chinese Clinical Trial Registry, identifier: ChiCTR2300074793. Registered on 16 August 2023.

Loss of white matter integrity mediates the association between cortical cerebral microinfarcts and cognitive dysfunction: A longitudinal study

Cortical cerebral microinfarcts (CMIs) are associated with loss of white matter (WM) integrity and cognitive impairment in cross-sectional studies, while further investigation using longitudinal datasets is required. This study aims to establish the association between cortical CMIs and WM integrity assessed by diffusion-tensor imaging (DTI) measures and to investigate whether DTI measures mediate the relationship between cortical CMIs and cognitive decline. Cortical CMIs were graded on 3T MRI. DTI measures were derived from histogram analysis of mean diffusivity (MD) and fractional anisotropy (FA). Cognitive function was assessed using a neuropsychological test battery. Linear mixed-effect models were employed to examine associations of cortical CMIs with longitudinal changes in DTI measures and cognitive function. Final analysis included 231 patients (71.14 ± 7.60 years). Presence of cortical CMIs at baseline was associated with longitudinal changes in MD median and peak height and FA median and peak height, as well as global cognition (β = -0.50, 95%CI: -0.91, -0.09) and executive function (β = -0.77, 95%CI: -1.25, -0.28). MD median mediated the cross-sectional association between cortical CMIs and global cognition. Further studies are required to investigate whether cortical CMIs and loss of WM integrity are causally related or if they are parallel mechanisms that contribute to cognitive decline.

Advanced Diffusion Tensor Imaging in White Matter Injury After Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is recognized as an especially severe stroke variant, notorious for its high mortality and long-term disability rates, in addition to a range of both immediate and enduring neurologic impacts. Over half of the SAH survivors experience varying degrees of neurologic disorders, with many enduring chronic neuropsychiatric conditions. Due to the limitations of traditional imaging techniques in depicting subtle changes within brain tissues posthemorrhage, the accurate detection and diagnosis of white matter (WM) injuries are complicated. Against this backdrop, diffusion tensor imaging (DTI) has emerged as a promising biomarker for structural imaging, renowned for its enhanced sensitivity in identifying axonal damage. This capability positions DTI as an invaluable tool for forming precise and expedient prognoses for SAH survivors. This study synthesizes an assessment of DTI for the diagnosis and prognosis of neurologic dysfunctions in patients with SAH, emphasizing the notable changes observed in DTI metrics and their association with potential pathophysiological processes. Despite challenges associated with scanning technology differences and data processing, DTI demonstrates significant clinical potential for early diagnosis of cognitive impairments following SAH and monitoring therapeutic effects. Future research requires the development of highly standardized imaging paradigms to enhance diagnostic accuracy and devise targeted therapeutic strategies for SAH patients. In sum, DTI technology not only augments our understanding of the impact of SAH but also may offer new avenues for improving patient prognoses.

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning

Objective. Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme.Approach. FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared.Main results. Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error by about 50% on fractional anisotropy and 15% on mean diffusivity, compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme.Significance. FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network.

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

BACKGROUND

This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion-diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability.

METHODS

Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland-Altman analysis. Feature importance was determined for explainability.

RESULTS

In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6-157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1-144.9 mL). These PVs had a volume similarity of 0.88 (0.79-0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland-Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature.

CONCLUSIONS

Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings.

RELEVANCE STATEMENT

The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting.

KEY POINTS

• Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction.

Stepwise reduction of bony density in patients induces a higher risk of annular tears by deteriorating the local biomechanical environment

BACKGROUND CONTEXT

The relationship between osteoporosis and intervertebral disc degeneration (IDD) remains unclear. Considering that annular tear is the primary phenotype of IDD in the lumbar spine, the deteriorating local biomechanical environment may be the main trigger for annular tears.

PURPOSE

To investigate whether poor bone mineral density (BMD) in the vertebral bodies may increase the risk of annular tears via the degradation of the local biomechanical environment.

STUDY DESIGN

This study was a retrospective investigation with relevant numerical mechanical simulations.

PATIENT SAMPLE

A total of 64 patients with low back pain (LBP) and the most severe IDD in the L4-L5 motion segment were enrolled.

OUTCOME MEASURES

Annulus integration status was assessed using diffusion tensor fibre tractography (DTT). Hounsfield unit (HU) values of adjacent vertebral bodies were employed to determine BMD. Numerical simulations were conducted to compute stress values in the annulus of models with different BMDs and body positions.

METHODS

The clinical data of the 64 patients with low back pain were collected retrospectively. The BMD of the vertebral bodies was measured using the HU values, and the annulus integration status was determined according to DTT. The data of the patients with and without annular tears were compared, and regression analysis was used to identify the independent risk factors for annular tears. Furthermore, finite element models of the L4-L5 motion segment were constructed and validated, followed by estimating the maximum stress on the post and postlateral interfaces between the superior and inferior bony endplates (BEPs) and the annulus.

RESULTS

Patients with lower HU values in their vertebral bodies had significantly higher incidence rates of annular tears, with decreased HU values being an independent risk factor for annular tears. Moreover, increased stress on the BEP-annulus interfaces was associated with a stepwise reduction of bony density (ie, elastic modulus) in the numerical models.

CONCLUSIONS

The stepwise reduction of bony density in patients results in a higher risk of annular tears by deteriorating the local biomechanical environment. Thus, osteoporosis should be considered to be a potential risk factor for IDD biomechanically.

A review of the applications of generative adversarial networks to structural and functional MRI based diagnostic classification of brain disorders

Structural and functional MRI (magnetic resonance imaging) based diagnostic classification using machine learning has long held promise, but there are many roadblocks to achieving their potential. While traditional machine learning models suffered from their inability to capture the complex non-linear mapping, deep learning models tend to overfit the model. This is because there is data scarcity and imbalanced classes in neuroimaging; it is expensive to acquire data from human subjects and even more so in clinical populations. Due to their ability to augment data by learning underlying distributions, generative adversarial networks (GAN) provide a potential solution to this problem. Here, we provide a methodological primer on GANs and review the applications of GANs to classification of mental health disorders from neuroimaging data such as functional MRI and showcase the progress made thus far. We also highlight gaps in methodology as well as interpretability that are yet to be addressed. This provides directions about how the field can move forward. We suggest that since there are a range of methodological choices available to users, it is critical for users to interact with method developers so that the latter can tailor their development according to the users' needs. The field can be enriched by such synthesis between method developers and users in neuroimaging.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Function-guided differences of arcuate fascicle and inferior fronto-occipital fascicle tractography as diagnostic indicators for surgical risk stratification

BACKGROUND

Several patients with language-eloquent gliomas face language deterioration postoperatively. Persistent aphasia is frequently associated with damage to subcortical language pathways. Underlying mechanisms still need to be better understood, complicating preoperative risk assessment. This study compared qualitative and quantitative functionally relevant subcortical differences pre- and directly postoperatively in glioma patients with and without aphasia.

METHODS

Language-relevant cortical sites were defined using navigated transcranial magnetic stimulation (nTMS) language mapping in 74 patients between 07/2016 and 07/2019. Post-hoc nTMS-based diffusion tensor imaging tractography was used to compare a tract's pre- and postoperative visualization, volume and fractional anisotropy (FA), and the preoperative distance between tract and lesion and postoperative overlap with the resection cavity between the following groups: no aphasia (NoA), tumor- or previous resection induced aphasia persistent pre- and postoperatively (TIA_P), and surgery-induced transient or permanent aphasia (SIA_T or SIA_P).

RESULTS

Patients with NoA, TIA_P, SIA_T, and SIA_P showed distinct fasciculus arcuatus (AF) and inferior-fronto-occipital fasciculus (IFOF) properties. The AF was more frequently reconstructable, and the FA of IFOF was higher in NoA than TIA_P cases (all p ≤ 0.03). Simultaneously, SIA_T cases showed higher IFOF fractional anisotropy than TIA_P cases (p < 0.001) and the most considerable AF volume loss overall. While not statistically significant, the four SIA_P cases showed complete loss of ventral language streams postoperatively, the highest resection-cavity-AF-overlap, and the shortest AF to tumor distance.

CONCLUSION

Functionally relevant qualitative and quantitative differences in AF and IFOF provide a pre- and postoperative pathophysiological and clinically relevant diagnostic indicator that supports surgical risk stratification.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Diffusion tensor imaging in anisotropic tissues: application of reduced gradient vector schemes in peripheral nerves

BACKGROUND

In contrast to the brain, fibers within peripheral nerves have distinct monodirectional structure questioning the necessity of complex multidirectional gradient vector schemes for DTI. This proof-of-concept study investigated the diagnostic utility of reduced gradient vector schemes in peripheral nerve DTI.

METHODS

Three-Tesla magnetic resonance neurography of the tibial nerve using 20-vector DTI (DTI20) was performed in 10 healthy volunteers, 12 patients with type 2 diabetes, and 12 age-matched healthy controls. From the full DTI20 dataset, three reduced datasets including only two or three vectors along the x- and/or y- and z-axes were built to calculate major parameters. The influence of nerve angulation and intraneural connective tissue was assessed. The area under the receiver operating characteristics curve (ROC-AUC) was used for analysis.

RESULTS

Simplified datasets achieved excellent diagnostic accuracy equal to DTI20 (ROC-AUC 0.847-0.868, p ≤ 0.005), but compared to DTI20, the reduced models yielded mostly lower absolute values of DTI scalars: median fractional anisotropy (FA) ≤ 0.12; apparent diffusion coefficient (ADC) ≤ 0.25; axial diffusivity ≤ 0.96, radial diffusivity ≤ 0.07). The precision of FA and ADC with the three-vector model was closest to DTI20. Intraneural connective tissue was negatively correlated with FA and ADC (r ≥ -0.49, p < 0.001). Small deviations of nerve angulation had little effect on FA accuracy.

CONCLUSIONS

In peripheral nerves, bulk tissue DTI metrics can be approximated with only three predefined gradient vectors along the scanner's main axes, yielding similar diagnostic accuracy as a 20-vector DTI, resulting in substantial scan time reduction.

RELEVANCE STATEMENT

DTI bulk tissue parameters of peripheral nerves can be calculated with only three predefined gradient vectors at similar diagnostic performance as a standard DTI but providing a substantial scan time reduction.

KEY POINTS

• In peripheral nerves, DTI parameters can be approximated using only three gradient vectors. • The simplified model achieves a similar diagnostic performance as a standard DTI. • The simplified model allows for a significant acceleration of image acquisition. • This can help to introduce multi-b-value DTI techniques into clinical practice.

Fornix fractional anisotropy mediates the association between Mediterranean diet adherence and memory four years later in older adults without dementia

Here, we investigated whether fractional anisotropy (FA) of hippocampus-relevant white-matter tracts mediates the association between baseline Mediterranean diet adherence (MeDiAd) and verbal episodic memory over four years. Participants were healthy older adults with and without subjective cognitive decline and patients with amnestic mild cognitive impairment from the DELCODE cohort study (n = 376; age: 71.47 ± 6.09 years; 48.7 % female). MeDiAd and diffusion data were obtained at baseline. Verbal episodic memory was assessed at baseline and four yearly follow-ups. The associations between baseline MeDiAd and white matter, and verbal episodic memory's mean and rate of change over four years were tested with latent growth curve modeling. Baseline MeDiAd was associated with verbal episodic memory four years later (95 % confidence interval, CI [0.01, 0.32]) but not with its rate of change over this period. Baseline Fornix FA mediated - and, thus, explained - that association (95 % CI [0.002, 0.09]). Fornix FA may be an appropriate response biomarker of Mediterranean diet interventions on verbal memory in older adults.

Based on white matter microstructure to early identify bipolar disorder from patients with depressive episode

OBJECTIVE

Because of similar clinical manifestations, bipolar disorder (BD) patients are often misdiagnosed as major depressive disorder (MDD). This study aimed to compare the difference between depressed patients later converting to BD and unipolar depression (UD) according to diffusion tensor imaging (DTI).

METHOD

Patients with MDD (562 participants) in depressive episode states and healthy controls (HCs) (145 participants) were recruited over 10 years. Demographic and magnetic resonance imaging (MRI) data were collected at the time of recruitment. All patients with MDD were followed up for 5 years and classified into the transfer to BD (tBD) group (83 participants) and UD group (160 participants) according to the follow-up results. DTI and functional magnetic resonance imaging at baseline were compared.

RESULTS

Common abnormalities were found in both tBD and UD groups, including left superior cerebellar peduncle (SCP.L), right anterior limb of the internal capsule (ALIC.R), right superior fronto-occipital fasciculus (SFOF.R), and right inferior fronto-occipital fasciculus (IFOF.R). The tBD showed more extensive abnormalities than the UD in the body of corpus callosum, fornix, left superior corona radiata, left posterior corona radiata, left superior longitudinal fasciculus, and left superior fronto-occipital fasciculus.

CONCLUSION

The study demonstrated the common and distinct abnormalities of tBD and UD when compared to HC. The tBD group showed more extensive disruptions of white matter integrity, which could be a potential biomarker for the early identification of BD.

IVD fibrosis and disc collapse comprehensively aggravate vertebral body disuse osteoporosis and zygapophyseal joint osteoarthritis by posteriorly shifting the load transmission pattern

BACKGROUND

Disuse is a typical phenotype of osteoporosis, but the underlying mechanism has yet to be identified in elderly patients. Disc collapse and intervertebral disc (IVD) fibrosis are two main pathological changes in IVD degeneration (IDD) progression, given that these changes affect load transmission patterns, which may lead to disuse osteoporosis of vertebral bodies and zygapophyseal joint (ZJ) osteoarthritis (ZJOA) biomechanically.

METHODS

Clinical data from 59 patients were collected retrospectively. Patient vertebral bony density, ZJOA grade, and disc collapse status were judged via CT. The IVD fibrosis grade was determined based on the FA measurements. Regression analyses identified potential independent risk factors for osteoporosis and ZJOA. L4-L5 numerical models with and without disc collapse and IVD fibrosis were constructed; stress distributions on the bony endplate (BEP) and zygapophyseal joint (ZJ) cartilages were computed in models with and without disc collapse and IVD fibrosis.

RESULTS

A significantly lower disc height ratio and significantly greater FA were recorded in patients with ZJOA. A significant correlation was observed between lower HU values and two parameters related to IDD progression. These factors were also proven to be independent risk factors for both osteoporosis and ZJOA. Correspondingly, compared to the intact model without IDD. Lower stress on vertebral bodies and greater stress on ZJOA can be simultaneously recorded in models of disc collapse and IVD fibrosis.

CONCLUSIONS

IVD fibrosis and disc collapse simultaneously aggravate vertebral body disuse osteoporosis and ZJOA by posteriorly shifting the load transmission pattern.

The application of functional imaging in visual field defects: a brief review

Visual field defects (VFDs) represent a prevalent complication stemming from neurological and ophthalmic conditions. A range of factors, including tumors, brain surgery, glaucoma, and other disorders, can induce varying degrees of VFDs, significantly impacting patients' quality of life. Over recent decades, functional imaging has emerged as a pivotal field, employing imaging technology to illustrate functional changes within tissues and organs. As functional imaging continues to advance, its integration into various clinical aspects of VFDs has substantially enhanced the diagnostic, therapeutic, and management capabilities of healthcare professionals. Notably, prominent imaging techniques such as DTI, OCT, and MRI have garnered widespread adoption, yet they possess unique applications and considerations. This comprehensive review aims to meticulously examine the application and evolution of functional imaging in the context of VFDs. Our objective is to furnish neurologists and ophthalmologists with a systematic and comprehensive comprehension of this critical subject matter.

Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) has been regarded as a disconnection syndrome with functional and structural disturbances. However, as the anatomic determinants, the structural disconnections in PD have yet to be fully elucidated.

PURPOSE

To non-invasively construct structural networks based on microstructural complexity and to further investigate their potential topological abnormalities in PD given the technical superiority of diffusion kurtosis imaging (DKI) to the quantification of microstructure.

MATERIAL AND METHODS

The microstructural data of gray matter in both the PD group and the healthy control (HC) group were acquired using DKI. The structural networks were constructed at the group level by a covariation approach, followed by the calculation of topological properties based on graph theory and statistical comparisons between groups.

RESULTS

A total of 51 patients with PD and 50 HCs were enrolled. Individuals were matched between groups with respect to demographic characteristics (P >0.05). The constructed structural networks in both the PD and HC groups featured small-world properties. In comparison with the HC group, the PD group exhibited significantly altered global properties, with higher normalized characteristic path lengths, clustering coefficients, local efficiency values, and characteristic path lengths and lower global efficiency values (P <0.05). In terms of nodal centralities, extensive nodal disruptions were observed in patients with PD (P <0.05); these disruptions were mainly distributed in the sensorimotor network, default mode network, frontal-parietal network, visual network, and subcortical network.

CONCLUSION

These findings contribute to the technical application of DKI and the elucidation of disconnection syndrome in PD.

Diffusion tensor imaging in photosensitive and nonphotosensitive juvenile myoclonic epilepsy

INTRODUCTION/BACKGROUND

Juvenile myoclonic epilepsy (JME) syndrome is known to cause alterations in brain structure and white matter integrity. The study aimed to determine structural white matter changes in patients with JME and to reveal the differences between the photosensitive (PS) and nonphotosensitive (NPS) subgroups by diffusion tensor imaging (DTI) using the tract-based spatial statistics (TBSS) method.

METHODS

This study included data from 16 PS, 15 NPS patients with JME, and 41 healthy participants. The mean fractional anisotropy (FA) values of these groups were calculated, and comparisons were made via the TBSS method over FA values in the whole-brain and 81 regions of interest (ROI) obtained from the John Hopkins University White Matter Atlas.

RESULTS

In the whole-brain TBSS analysis, no significant differences in FA values were observed in pairwise comparisons of JME patient group and subgroups with healthy controls (HCs) and in comparison between JME subgroups. In ROI-based TBSS analysis, an increase in FA values of right anterior corona radiata and left corticospinal pathways was found in JME patient group compared with HC group. When comparing JME-PS patients with HCs, an FA increase was observed in the bilateral anterior corona radiata region, whereas when comparing JME-NPS patients with HCs, an FA increase was observed in bilateral corticospinal pathway. Moreover, in subgroup comparison, an increase in FA values was noted in corpus callosum genu region in JME-PS compared with JME-NPS.

CONCLUSIONS

Our results support the disruption in thalamofrontal white matter integrity in JME, and subgroups and highlight the importance of using different analysis methods to show the underlying microstructural changes.

Diffusion Tensor Imaging in Alzheimer's Studies

In this chapter, we describe the use of quantitative metrics of white matter obtained from the diffusion tensor model based on diffusion-weighted imaging in Alzheimer's disease (AD). Our description synthesizes insights not only from patient populations with AD dementia but also from participants at risk for AD dementia (e.g., amnestic mild cognitive impairment, subjective cognitive decline, or familial AD mutation carriers). A reference to studies examining correlations with behavioral variables is also included. Our main message is to caution against the overinterpretation of diffusion metrics and to favor analyses that focus on regions of interest or major white matter tracts for biomarker studies in AD.

Diffusion tensor imaging changes of the cortico-thalamic-striatal tracts correlate with fatigue and disability in people with relapsing-remitting MS

PURPOSE

To investigate how the microstructural neural integrity of cortico-thalamic-striatal (CTS) tracts correlate with fatigue and disability over time. The primary outcome was diffusion tensor imaging (DTI) metrics change over time, and the secondary outcome was correlations with fatigue and disability in people with RRMS (pw-RRMS).

METHODS

76 clinically stable pw-RRMS and 43 matched healthy controls (HCs). The pw-RRMS cohort consisted of three different treatment subgroups. All participants underwent disability, cognitive, fatigue and mental health assessments. Structural and diffusion scans were performed at baseline (BL) and 2-year follow-up (2-YFU) for all participants. Fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD, AD) of normal-appearing white matter (NAWM) and white matter lesion (WML) in nine tracts-of-interests (TOIs) were estimated using our MRtrix3 in-house pipeline.

RESULTS

We found significant BL and 2-YFU differences in most diffusion metrics in TOIs in pw-RRMS compared to HCs (pFDR ≤ 0.001; false-detection-rate (FDR)-corrected). There was a significant decrease in WML diffusivities and an increase in FA over the follow-up period in most TOIs (pFDR ≤ 0.001). Additionally, there were no differences in DTI parameters across treatment groups. AD and MD were positively correlated with fatigue scores (r ≤ 0.33, p ≤ 0.01) in NAWM-TOIs, while disability (EDSS) was negatively correlated with FA in most NAWM-TOIs (|r|≤0.31, p ≤ 0.01) at both time points. Disability scores correlated with all diffusivity parameters (r ≤ 0.29, p ≤ 0.01) in most WML-TOIs at both time points.

CONCLUSION

Statistically significant changes in diffusion metrics in WML might be indicative of integrity improvement over two years in CTS tracts in clinically stable pw-RRMS. This finding represents structural changes within lesioned tracts. Measuring diffusivity in pw-RRMS affected tracts might be a relevant measure for future remyelination clinical trials.

What clinical parameter strongly associates white matter tract alterations in a Multiple Sclerosis population with voiding dysfunction? A prospective exploratory study

Purpose

To correlate clinical and urodynamics parameters in Multiple Sclerosis patients (MS) presenting Lower Urinary Tract Symptoms (LUTS) with both Expanded Disability Status Scale (EDSS) and changes in white matter integrity as seen on Diffusion Tensor Images (DTI). LUTS worsen throughout MS, as does lesion burden. We investigated which symptoms correlated best with structural changes in white matter structure.

Materials and Methods

Ten adult women >18 years were recruited with stable MS for ≥3 months and voiding dysfunction defined as %PVR/BV > 20%. Patients participated in a clinical Urodynamic Study (UDS) and completed several questionnaires (i.e., HAM, AUASS, NBS-QoL). DTI images were acquired using a 7-Tesla Siemens MAGNETOM Terra MRI scanner. DTI maps were constructed, and individual patients were co-registered with the ICBM-DTI-81 white matter atlas to extract fractional anisotropy (FA) and mean diffusivity (MD). Pearson's correlation test was performed between each WMT and clinical parameters and between clinical parameters and the EDSS score as well. P-values < 0.05 were considered significant.

Results

Of the clinical parameters, %PVR/BV obtained from the average of multiple un-instrumented uroflow assessments had significant correlations to the greatest number of WMTs. Furthermore, we observed that in all recorded clinical parameters, %PVR/BV was the only significant parameter correlated to the EDSS score.

Conclusion

This study demonstrates that %PVR/BV can be used as an objective parameter to gauge WMT changes and disease progression in MS patients. Future studies are needed to refine this model.

Relationship between disproportionately enlarged subarachnoid-space hydrocephalus and white matter tract integrity in normal pressure hydrocephalus

Normal pressure hydrocephalus (NPH) patients had altered white matter tract integrities on diffusion tensor imaging (DTI). Previous studies suggested disproportionately enlarged subarachnoid space hydrocephalus (DESH) as a prognostic sign of NPH. We examined DTI indices in NPH subgroups by DESH severity and clinical symptoms. This retrospective case-control study included 33 NPH patients and 33 age-, sex-, and education-matched controls. The NPH grading scales (0-12) were used to rate neurological symptoms. Patients with NPH were categorized into two subgroups, high-DESH and low-DESH groups, by the average value of the DESH scale. DTI indices, including fractional anisotropy, were compared across 14 regions of interest (ROIs). The high-DESH group had increased axial diffusivity in the lateral side of corona radiata (1.43 ± 0.25 vs. 1.72 ± 0.25, p = 0.04), and showed decreased fractional anisotropy and increased mean, and radial diffusivity in the anterior and lateral sides of corona radiata and the periventricular white matter surrounding the anterior horn of lateral ventricle. In patients with a high NPH grading scale, fractional anisotropy in the white matter surrounding the anterior horn of the lateral ventricle was significantly reduced (0.36 ± 0.08 vs. 0.26 ± 0.06, p = 0.03). These data show that DESH may be a biomarker for DTI-detected microstructural alterations and clinical symptom severity.

Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes

Introduction

Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan.

Methods

We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds.

Results

In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults.

Discussion

We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

Application study of DTI combined with ASL in the crossed cerebellar diaschisis after subacute cerebral hemorrhage

PURPOSE

To study the value of 3.0T magnetic resonance imaging with diffusion tensor imaging (DTI) and 3D-arterial spin labeling (ASL) perfusion imaging in the diagnosis of the crossed cerebellar diaschisis (CCD) after the unilateral supratentorial subacute cerebral hemorrhage.

METHODS

Fifty-eight patients with the unilateral supratentorial subacute cerebral hemorrhage who underwent diffusion tensor imaging (DTI), 3D-arterial spin labeling (ASL), and conventional magnetic resonance imaging (MRI) scanning were enrolled. Cerebral blood flow (CBF) values of the perihematomal edema (PHE) and bilateral cerebellar hemisphere were measured on ASL mapping, and the fractional anisotropy (FA) and mean diffusivity (MD) values of the bilateral cortical, pontine, and middle cerebellar peduncle (MCP) were measured on DTI mapping.

RESULTS

In the CCD(+) group, FA values of the cerebral cortex and pontine ipsilateral to the lesion were statistically reduced compared to the contralateral side (P < 0.05), and the FA and MD values of the middle cerebellar peduncle (MCP) contralateral to the lesion were statistically reduced compared to the ipsilateral side (P < 0.05). Positive correlation was detected between the CBF values of the perihematomal edema (PHE) and the CBF values of cerebellar hemispheres (r = 0.642, P < 0.05), whereas the CBF values of PHE had a significantly high positive correlation with the FA in the contralateral MCP (r = 0.854, P < 0.05). CBF values in the contralateral cerebellar hemisphere correlated with FA (r = 0.466, P < 0.05) and MD values (r = 0.718, P < 0.05) in the contralateral MCP.

CONCLUSION

Hemodynamic alterations of PHE and cortical-ponts-cerebellum (CPC) fibrous pathway damage are associated with the development of CCD; DTI technique can assess the degree of CPC fiber pathway injury at an early stage.

Diffusion in the corpus callosum predicts persistence of clinical symptoms after mild traumatic brain injury, a multi-scanner study

Background

Mild traumatic brain injuries (mTBIs) comprise 80% of all TBI, but conventional MRI techniques are often insensitive to the subtle changes and injuries produced in a concussion. Diffusion tensor imaging (DTI) is one of the most sensitive MRI techniques for mTBI studies with outcome and symptom associations described. The corpus callosum (CC) is one of the most studied fiber tracts in TBI and mTBI, but the comprehensive post-mTBI symptom relationship has not fully been explored.

Methods

This is a retrospective observational study of how quantitative DTI data of the CC and its sub-regions may relate to clinical presentation of symptoms and timing of resolution of symptoms in patients diagnosed with uncomplicated mTBI. DTI and clinical data were obtained retrospectively from 446 (mean age 42 years, range 13-82) civilian patients. From patient medical charts, presentation of the following common post-concussive symptoms was noted: headache, balance issues, cognitive deficits, fatigue, anxiety, depression, and emotional lability. Also recorded was the time between injury and a visit to the physician when improvement or resolution of a particular symptom was reported. FA values from the total CC and 3 subregions of the CC (genu or anterior, mid body, and splenium or posterior) were obtained from hand tracing on the Olea Sphere v3.0 SP12 free-standing workstation. DTI data was obtained from 8 different 3T MRI scanners and harmonized via ComBat harmonization. The statistical models used to explore the association between regional Fractional Anisotropy (FA) values and symptom presentation and time to symptom resolution were logistic regression and interval-censored semi-parametric Cox proportional hazard models, respectively. Subgroups related to age and timing of first scan were also analyzed.

Results

Patients with the highest FA in the total CC (p = 0.01), anterior CC (p < 0.01), and mid-body CC (p = 0.03), but not the posterior CC (p = 0.91) recovered faster from post-concussive cognitive deficits. Patients with the highest FA in the posterior CC recovered faster from depression (p = 0.04) and emotional lability (p = 0.01). There was no evidence that FA in the CC or any of its sub-regions was associated with symptom presentation or with time to resolution of headache, balance issues, fatigue, or anxiety. Patients with mTBI under 40 had higher FA in the CC and the anterior and mid-body subregions (but not the posterior subregion: p = 1.00) compared to patients 40 or over (p ≤ 0.01). There was no evidence for differences in symptom presentation based on loss of consciousness (LOC) or sex (p ≥ 0.18).

Conclusion

This study suggests that FA of the CC has diagnostic and prognostic value for clinical assessment of mTBI in a large diverse civilian population, particularly in patients with cognitive symptoms.

Multimodal Investigation of Deep Gray Matter Nucleus in Patients with Multiple Sclerosis and Their Clinical Correlations: A Multivariate Pattern Analysis Study

Deep gray matter (DGM) nucleus are involved in patients with multiple sclerosis (MS) and are strongly associated with clinical symptoms. We used machine learning approach to further explore microstructural alterations in DGM of MS patients. One hundred and fifteen MS patients and seventy-one healthy controls (HC) underwent brain MRI. The fractional anisotropy (FA), mean diffusivity (MD), quantitative susceptibility value (QSV) and volumes of the caudate nucleus (CN), putamen (PT), globus pallidus (GP), and thalamus (TH) were measured. Multivariate pattern analysis, based on a machine-learning algorithm, was applied to investigate the most damaged regions. Partial correlation analysis was used to investigate the correlation between MRI quantitative metrics and clinical neurological scores. The area under the curve of FA-based classification model was 0.83, while they were 0.93 for MD and 0.81 for QSV. The Montreal cognitive assessment scores were correlated with the volume of the DGM and the expanded disability status scale scores were correlated with the MD of the GP and PT. The study results indicated that MS patients had involvement of DGM with the CN being the most affected. The atrophy of DGM in MS patients mainly affected cognitive function and the microstructural damage of DGM was mainly correlated with clinical disability.

MCC950 ameliorates cognitive function by reducing white matter microstructure damage in rats after SAH

Neuroinflammation and white matter microstructure damage are important causes of cognitive impairment after subarachnoid hemorrhage (SAH). Nod-like receptor protein 3 (NLRP3) plays an important role in neuroinflammation after SAH and may be a potential therapeutic target for treatment of white matter microstructure injury. In this study, we observed whether MCC950, a specific inhibitor of the NLRP3 inflammasome, exerted a therapeutic effect after SAH. The SAH model was induced by endovascular perforation in SpragueDawley rats. MCC950 was injected intraperitoneally 1 h after SAH at a dose of 10 mg/kg. The results showed that MCC950 significantly attenuated white matter microstructure damage in some brain regions, and behavioral experiments confirmed that MCC950 ameliorated cognitive function in rats after SAH, which may provide a new method for the treatment of cognitive dysfunction in SAH patients.

Epileptogenicity of white matter lesions in cerebral small vessel disease: a systematic review and meta-analysis

BACKGROUND

The epileptogenic properties of white matter lesions (WML) in cerebral small vessel disease (CSVD) are not yet understood. The aim of our systematic review and meta-analysis was to estimate the association between the extent of WML in CSVD and epilepsy, analyze whether these WML are associated with an increased risk of seizure recurrence, and evaluate if treatment with anti-seizure medication (ASM) is justified in first-seizure patients with WML and no cortical lesions.

METHODS

Following a pre-registered study protocol (PROSPERO-ID CRD42023390665), we systematically searched Pubmed and Embase for relevant literature comparing WML load between patients with epilepsy and controls as well as studies on seizure recurrence risk and ASM therapy in the presence vs. absence of WML. We calculated pooled estimates using a random effects model.

RESULTS

Eleven studies comprising 2983 patients were included in our study. Presence of WML (OR 2.14, 95% CI 1.38-3.33) and presence of relevant WML as assessed by visual rating scales (OR 3.96, 95% CI 2.55-6.16) but not WML volume (OR 1.30, 95% CI 0.91-1.85) were significantly associated with seizures. These results stayed robust in sensitivity analyses restricted to studies on patients with late-onset seizures/epilepsy. Only two studies assessed the association between WML and risk of seizure recurrence with conflicting results. Currently, there are no studies on the efficacy of ASM therapy in the presence of WML in CSVD.

CONCLUSIONS

This meta-analysis suggests an association between presence of WML in CSVD and seizures. More research is needed addressing the association between WML and risk of seizure recurrence and ASM therapy focusing on a population of patients with a first unprovoked seizure.

Microstructural Differences of the Cerebellum-Thalamus-Basal Ganglia-Limbic Cortex in Patients with Somatic Symptom Disorders: a Diffusion Kurtosis Imaging Study

Somatic symp tom disorders (SSDs) are a group of psychiatric disorders characterized by persistent disproportionate concern and obsessive behaviors regarding physical conditions. Currently, SSDs lack effective treatments and their pathophysiology is unclear. In this paper, we aimed to examine microstructural abnormalities in the brains of patients with SSD using diffusion kurtosis imaging (DKI) and to investigate the correlation between these abnormalities and clinical indicators. Diffusion kurtosis images were acquired from 30 patients with SSD and 30 healthy controls (HCs). Whole-brain maps of multiple diffusion measures, including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), mean kurtosis (MK), radial kurtosis (RK), and axial kurtosis (AK), were calculated. To analyze differences between the two groups, nonparametric permutation testing with 10,000 randomized permutations and threshold-free cluster enhancement was used with family-wise error-corrected p values < 0.05 as the threshold for statistical significance. Then, the correlations between significant changes in these diffusion measures and clinical factors were examined. Compared to HCs, patients with SSD had significantly higher FA, MK, and RK and significantly lower MD and RD in the cerebellum, thalamus, basal ganglia, and limbic cortex. The FA in the left caudate and the pontine crossing tract were negatively correlated with disease duration; the MD and the RD in the genu of the corpus callosum were positively correlated with disease duration. Our findings highlight the role of the cerebellum-thalamus-basal ganglia-limbic cortex pathway, especially the cerebellum, in SSDs and enhance our understanding of the pathogenesis of SSDs.

Application of diffusion tensor imaging in the diagnosis of post-stroke aphasia: a meta-analysis and systematic review

Introduction

Diffusion Tensor Imaging (DTI) indicators of different white matter (WM) fibers and brain region lesions for post-stroke aphasia (PSA) are inconsistent in existing studies. Our study examines the consistency and differences between PSA tests performed with DTI. In addition, obtaining consistent and independent conclusions between studies was made possible by utilizing DTI in PSA assessment.

Methods

In order to gather relevant studies using DTI for diagnosing PSA, we searched the Web of Science, PubMed, Embase, and CNKI databases. Based on the screening and evaluation of the included studies, the meta-analysis was used to conduct a quantitative analysis. Narrative descriptions were provided for studies that met the inclusion criteria but lacked data.

Results

First, we reported on the left hemisphere. The meta-analysis showed that fractional anisotropy (FA) of the arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF), inferior frontal-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), and uncinate fasciculus (UF) were decreased in the PSA group in comparison with the healthy controls (p < 0.00001). However, in the comparison of axial diffusivity (AD), there was no statistically significant difference in white matter fiber tracts in the dual-stream language model of the PSA group. Elevated radial diffusivity (RD) was seen only in the IFOF and ILF (PIFOF = 0.01; PILF = 0.05). In the classic Broca's area, the FA of the PSA group was decreased (p < 0.00001) while the apparent diffusion coefficient was elevated (p = 0.03). Secondly, we evaluated the white matter fiber tracts in the dual-stream language model of the right hemisphere. The FA of the PSA group was decreased only in the IFOF (p = 0.001). AD was elevated in the AF and UF (PAF < 0.00001; PUF = 0.009). RD was elevated in the AF and UF (PAF = 0.01; PUF = 0.003). The other fiber tracts did not undergo similar alterations.

Conclusion

In conclusion, DTI is vital for diagnosing PSA because it detects WM changes effectively, but it still has some limitations. Due to a lack of relevant language scales and clinical manifestations, diagnosing and differentiating PSA independently remain challenging.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=365897.

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/.

Reduction of restricted repetitive behavior by environmental enrichment: Potential neurobiological mechanisms

Restricted repetitive behaviors (RRB) are one of two diagnostic criteria for autism spectrum disorder and common in other neurodevelopmental and psychiatric disorders. The term restricted repetitive behavior refers to a wide variety of inflexible patterns of behavior including stereotypy, self-injury, restricted interests, insistence on sameness, and ritualistic and compulsive behavior. However, despite their prevalence in clinical populations, their underlying causes remain poorly understood hampering the development of effective treatments. Intriguingly, numerous animal studies have demonstrated that these behaviors are reduced by rearing in enriched environments (EE). Understanding the processes responsible for the attenuation of repetitive behaviors by EE should offer insights into potential therapeutic approaches, as well as shed light on the underlying neurobiology of repetitive behaviors. This review summarizes the current knowledge of the relationship between EE and RRB and discusses potential mechanisms for EE's attenuation of RRB based on the broader EE literature. Existing gaps in the literature and future directions are also discussed.

Symptom aggravation after withdrawal of metal chelating agent therapy in patients with Wilson's disease

OBJECTIVE

To study the aggravation of clinical symptoms after discontinuation of metal chelating agent therapy in Wilson's disease (WD) patients, analyze the causes of aggravation, and observe the prognosis.

METHODS

40 WD patients (cerebral type 30 cases and hepatic type 10 cases) who stopped using metal chelating agent were selected, 40 WD patients with normal therapy, and 10 normal control cases were selected. All patients underwent neurological symptom evaluation using modified Young scale, Child-Pugh liver function grading, metal metabolism, and disease typing. Magnetic sensitivity imaging (SWI), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy imaging (MRS) were performed. According to the imaging results, WD patients were divided into metal deposition stage, fiber damage stage, and neuron necrosis stage. All patients were treated with metal chelating agent for 6 months.

RESULTS

The score of modified Young scale in drug withdrawal group was lower than that in normal treatment group before drug withdrawal (p = .032). The score of modified Young scale was higher after drug withdrawal than before (p = .011). The number of Child-Pugh B-grade patients after drug withdrawal was more than that before drug withdrawal and in normal treatment group. The proportion of patients in the stage of neuronal necrosis after drug withdrawal (25%) was higher than that before drug withdrawal (10%) (p = .025). After drug withdrawal, urine copper was significantly higher than that before drug withdrawal and in the normal treatment group (p = .032, .039). After the withdrawal group resumed metal chelating agent treatment, 34.2% of neurological symptoms worsened.

CONCLUSIONS

WD patients showed neurological symptoms aggravation and increased liver injury after metal chelating agent withdrawal. Increased metal deposition and new nerve injury occurred in the brain. After re-treatment, the aggravated neurological symptoms of WD patients are difficult to reverse.

Diffusion tensor imaging in the courtroom: Distinction between scientific specificity and legally admissible evidence

Interest and uptake of science and medicine peer-reviewed literature by readers outside of a paper’s topical subject, field or even discipline is ever-expanding. While the application of knowledge from one field or discipline to others can stimulate innovative solutions to problems facing modern society, it is also fraught with danger for misuse. In the practice of law in the United States, academic papers are submitted to the courts as evidence in personal injury litigation from both the plaintiff (complainant) and defendant. Such transcendence of an academic publication over disciplinary boundaries is immediately met with the challenge of application by a group that inherently lacks in-depth knowledge on the scientific method, the practice of evidence-based medicine, or the publication process as a structured and internationally synthesized process involving peer review and guided by ethical standards and norms. A modern-day example of this is the ongoing conflict between the sensitivity of diffusion tensor imaging (DTI) and the legal standards for admissibility of evidence in litigation cases of mild traumatic brain injury (mTBI). In this review, we amalgamate the peer-reviewed research on DTI in mTBI with the court’s rationale underlying decisions to admit or exclude evidence of DTI abnormalities to support claims of brain injury. We found that the papers which are critical of the use of DTI in the courtroom reflect a primary misunderstanding about how diagnostic biomarkers differ legally from relevant and admissible evidence. The clinical use of DTI to identify white matter abnormalities in the brain at the chronic stage is a valid methodology both clinically as well as forensically, contributes data that may or may not corroborate the existence of white matter damage, and should be admitted into evidence in personal injury trials if supported by a clinician. We also delve into an aspect of science publication and peer review that can be manipulated by scientists and clinicians to publish an opinion piece and misrepresent it as an unbiased, evidence-based, systematic research article in court cases, the decisions of which establish precedence for future cases and have implications on future legislation that will impact the lives of every citizen and erode the integrity of science and medicine practitioners.

Micro-structural white matter abnormalities in new daily persistent headache: a DTI study using TBSS analysis

BACKGROUND

New daily persistent headache (NDPH) is a rare primary headache disorder characterized by daily and persistent sudden onset headaches. The pathogenesis of NDPH remains unclear, and there are few white matter imaging studies related to NDPH. The purpose of this study was to investigate the micro-structural abnormalities of white matter in NDPH and provided insights into the pathogenesis of this disease based on tract-based spatial statistics (TBSS).

METHODS

Twenty-one patients with NDPH and 25 healthy controls (HCs) were included in this study. T1 structural and diffusion magnetic resonance imaging (MRI) were acquired from all participants. Differences in the fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) between patients with NDPH and HCs were investigated using TBSS analysis.

RESULTS

Significantly decreased FA, increased MD and RD were found in patients with NDPH compared to HCs. White matter regions overlaid with decreased FA, increased MD and RD were found in 16 white matter tracts from the Johns Hopkins University ICBM-DTI-81 White-Matter Atlas and Johns Hopkins University White-Matter Tractography Atlas. Specifically, these white matter regions included the right anterior thalamic radiation (ATR), body of the corpus callosum (BCC), bilateral cingulum, left hippocampal cingulum (CGH), left corticospinal tract (CST), forceps major, fornix, left inferior fronto-occipital fasciculus (IFOF), bilateral inferior longitudinal fasciculus (ILF), left posterior limb of the internal capsule (PLIC), right retrolenticular part of the internal capsule (RPIC), splenium of the corpus callosum (SCC), right superior longitudinal fasciculus (SLF) and left uncinate fasciculus (UF). After Bonferroni correction, there were no correlations between the FA, MD, AD and RD values and the clinical characteristics of patients with NDPH (p > 0.05/96).

CONCLUSION

The results of our research indicated that patients with NDPH might have widespread abnormalities in the white matter of the brain.

Structural Connectomes of COVID-Survivors Show Disruption in Global Integration and Small-Worldness

Even after recovery from the COVID-19 infection, there have been a multitude of cases reporting post-COVID neurological symptoms including memory loss, brain fog, and attention deficit. Many studies have observed localized microstructural damages in the white matter regions of COVID survivors, indicating potential damage to the axonal pathways in the brain. Therefore, in this study, we have investigated the global impact of localized damage to white matter tracts using graph theoretical analysis of the structural connectome of 45 COVID-recovered subjects and 30 Healthy Controls (HCs). We have implemented Diffusion Tensor Imaging based reconstruction followed by deterministic tractography to extract structural connections among different regions of the brain. Interpreting this structural connectivity as weighted undirected graphs, we have used graph theoretical measures like global efficiency, characteristic path length (CPL), clustering coefficient (CC), modularity, Fiedler value, and assortativity coefficient to quantify the global integration, segregation, and robustness of the brain networks. We statistically compare the cohorts based on these graph measures by employing permutation testing for 100,000 permutations. Post multiple comparisons error correction, we find that the COVID-recovered cohort shows a reduction in global efficiency and CC while they exhibit higher modularity and CPL. This disruption of the balance between global integration and segregation indicates the loss of small-world property in COVID survivors' connectomes which has been linked with other disorders such as cognitive impairment and Alzheimer's. Overall, our study sheds light on the alterations in structural connectivity and its role in post-COVID symptoms.

Evidence of An Association Between Cerebral Blood Flow and Microstructural Integrity in Normative Aging Using a Holistic MRI Approach

BACKGROUND

Cerebral tissue integrity decline and cerebral blood flow (CBF) alteration are major aspects of motor and cognitive dysfunctions and neurodegeneration. However, little is known about the association between blood flow and brain microstructural integrity, especially in normal aging.

PURPOSE

To assess the association between CBF and cerebral microstructural integrity.

STUDY TYPE

Cross sectional.

POPULATION

A total of 94 cognitively unimpaired adults (mean age 50.7 years, age range between 22 and 88 years, 56 Men).

FIELD STRENGTH/SEQUENCE

A 3 T; pseudo-continuous arterial spin labeling (pCASL), diffusion tensor imaging (DTI), Bayesian Monte Carlo analysis of multicomponent driven equilibrium steady-state observation of T1 and T2 (BMC-mcDESPOT).

ASSESSMENT

Lobar associations between CBF derived from pCASL, and longitudinal relaxation rate (R1 ), transverse relaxation rate (R2 ) and myelin water fraction (MWF) derived from BMC-mcDESPOT, or radial diffusivity (RD), axial diffusivity (AxD), mean diffusivity (MD) and fractional anisotropy (FA) derived from DTI were assessed.

STATISTICAL TESTS

Multiple linear regression models were used using the mean region of interest (ROI) values for MWF, R1 , R2 , FA, MD, RD, or AxD as the dependent variable and CBF, age, age2 , and sex as the independent variables. A two-sided P value of <0.05 defined statistical significance.

RESULTS

R1 , R2 , MWF, FA, MD, RD, and AxD parameters were associated with CBF in most of the cerebral regions evaluated. Specifically, higher CBF values were significantly associated with higher FA, MWF, R1 and R2 , or lower MD, RD and AxD values.

DATA CONCLUSION

These findings suggest that cerebral tissue microstructure may be impacted by global brain perfusion, adding further evidence to the intimate relationship between cerebral blood supply and cerebral tissue integrity.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 4.

Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders

Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.

Improved prediction of glioma-related aphasia by diffusion MRI metrics, machine learning, and automated fiber bundle segmentation

White matter impairments caused by gliomas can lead to functional disorders. In this study, we predicted aphasia in patients with gliomas infiltrating the language network using machine learning methods. We included 78 patients with left-hemispheric perisylvian gliomas. Aphasia was graded preoperatively using the Aachen aphasia test (AAT). Subsequently, we created bundle segmentations based on automatically generated tract orientation mappings using TractSeg. To prepare the input for the support vector machine (SVM), we first preselected aphasia-related fiber bundles based on the associations between relative tract volumes and AAT subtests. In addition, diffusion magnetic resonance imaging (dMRI)-based metrics [axial diffusivity (AD), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and radial diffusivity (RD)] were extracted within the fiber bundles' masks with their mean, standard deviation, kurtosis, and skewness values. Our model consisted of random forest-based feature selection followed by an SVM. The best model performance achieved 81% accuracy (specificity = 85%, sensitivity = 73%, and AUC = 85%) using dMRI-based features, demographics, tumor WHO grade, tumor location, and relative tract volumes. The most effective features resulted from the arcuate fasciculus (AF), middle longitudinal fasciculus (MLF), and inferior fronto-occipital fasciculus (IFOF). The most effective dMRI-based metrics were FA, ADC, and AD. We achieved a prediction of aphasia using dMRI-based features and demonstrated that AF, IFOF, and MLF were the most important fiber bundles for predicting aphasia in this cohort.