Diffusion Magnetic Resonance Imaging Research Articles

Containerizing neuroimaging workflows for scalable and reproducible analyses

AbstractBackgroundLarge multi‐center, longitudinal neuroimaging projects aiming to understand neurodegenerative disorders, such as Alzheimer disease (AD), frequently generate large datasets for use by external researchers. However, maintaining a replicable data processing workflow in an on‐going study can be difficult due to changes in personnel, computer or software upgrades, and disparate computing resources across collaborating institutions. Replicating workflows from prior studies is also important for validating and building scientific knowledge but can present similar challenges. Containerization is an increasingly popular solution to these issues. This process allows users to package their code, software, and any dependencies together, ensuring stable and consistent output across computing platforms. The main goal of this project is to guide researchers through the steps of custom container development for their own needs.MethodWe demonstrate the process of containerization using a neuroimaging workflow that quantifies edge density (ED), an emerging metric of white matter integrity. To calculate ED, structural and diffusion magnetic resonance images were processed using standard FreeSurfer (5.3) and FSL (6.0) procedures. Whole‐brain probabilistic tractography, ED metrics, and diffusion tensor indices were computed. A template for building a container image for neuroimaging use was developed. Scripts used for analyses were incorporated into the template and compiled as reusable container images using Docker and Apptainer. Additional scripts and documentation were released to facilitate custom implementation.ResultContainerized tractography, ED metrics, and tensor indices were identical across multiple computing systems and in‐line with prior research showing white matter integrity begins to decline prior to outward cognitive symptoms. The provided code and documentation outlines the containerization process for our workflow (Figure 1) and describes how to run the resulting container image using data from the recently updated Open Access Series of Imaging Studies 3 dataset (OASIS‐3) as an example.ConclusionContainerizing a neuroimaging processing workflow ensures stable results across the lifespan of a study and facilitates replication of prior workflows in new datasets. The containerized ED pipeline provided enables other researchers to replicate or extend upon our analyses in this cohort. The provided documentation and templates can also be adapted for use with other datasets that use common neuroimaging file formats.

Containerizing neuroimaging workflows for scalable and reproducible analyses

AbstractBackgroundLarge multi‐center, longitudinal neuroimaging projects aiming to understand neurodegenerative disorders, such as Alzheimer disease (AD), frequently generate large datasets for use by external researchers. However, maintaining a replicable data processing workflow in an on‐going study can be difficult due to changes in personnel, computer or software upgrades, and disparate computing resources across collaborating institutions. Replicating workflows from prior studies is also important for validating and building scientific knowledge but can present similar challenges. Containerization is an increasingly popular solution to these issues. This process allows users to package their code, software, and any dependencies together, ensuring stable and consistent output across computing platforms. The main goal of this project is to guide researchers through the steps of custom container development for their own needs.MethodWe demonstrate the process of containerization using a neuroimaging workflow that quantifies edge density (ED), an emerging metric of white matter integrity. To calculate ED, structural and diffusion magnetic resonance images were processed using standard FreeSurfer (5.3) and FSL (6.0) procedures. Whole‐brain probabilistic tractography, ED metrics, and diffusion tensor indices were computed. A template for building a container image for neuroimaging use was developed. Scripts used for analyses were incorporated into the template and compiled as reusable container images using Docker and Apptainer. Additional scripts and documentation were released to facilitate custom implementation.ResultContainerized tractography, ED metrics, and tensor indices were identical across multiple computing systems and in‐line with prior research showing white matter integrity begins to decline prior to outward cognitive symptoms. The provided code and documentation outlines the containerization process for our workflow (Figure 1) and describes how to run the resulting container image using data from the recently updated Open Access Series of Imaging Studies 3 dataset (OASIS‐3) as an example.ConclusionContainerizing a neuroimaging processing workflow ensures stable results across the lifespan of a study and facilitates replication of prior workflows in new datasets. The containerized ED pipeline provided enables other researchers to replicate or extend upon our analyses in this cohort. The provided documentation and templates can also be adapted for use with other datasets that use common neuroimaging file formats.

Transferable individual brain regional atrophy progression prediction model using structural connectome

AbstractBackgroundBrain microstructure, as measured by diffusion MR imaging (dMRI), is an early biomarker of Alzheimer’s disease (AD) and has been previously demonstrated to predict whole brain atrophy and cognition. The network‐based neurodegeneration hypothesis posits that the onset of AD occurs within key vulnerable regions, whose network connectivity guides the spread of atrophy into new regions. Graph convolutional network (GCN) models can mimic this spread through its convolution process. Here we present a GCN‐based model to predict future individual nodal atrophy and disease progression across the AD spectrum using structural connectome.MethodWe analyzed 371 participants with baseline dMRI scans (154 cognitively normal (CN), 161 mild cognitive impairment (MCI), 56 AD) and longitudinal T1 scans (2.7±1.2 years) from the ADNI study. To predict annual nodal atrophy rates (126 nodes) from baseline structural connectivity (SC), we constructed a two‐layer GCN followed by multilayer perceptron (GCN‐MLP). Five‐fold nested cross‐validation and Optuna were used for hyperparameter tuning. We applied the trained GCN‐MLP model to predict future disease progression via transfer learning. GNNExplainer was used to identify the predictive SC features for atrophy or cognition. The GCN‐MLP model was validated in another independent dataset from Singapore (MACC) with 226 participants (61 CN, 112 MCI, 53 AD, follow‐up period of T1 scans (3.1±1.3 years)) using transfer learning.ResultThe proposed GCN‐MLP model predicted the annual rate of regional atrophy at the individual level in the main (R = 0.5407±0.0480; MAE = 0.0013±0.0001; EV = 0.2930±0.0516) and validation (R = 0.5323±0.0564; MAE = 0.0009±0.0001; EV = 0.2808±0.0559) datasets (Table 1; Figure 1). Graph edge SC of the target network contributed most to the atrophy prediction of its own network (Figure 2A&C). Nodal feature SC in hippocampus, striatum, and temporal network were major predictors of atrophy progression across all 10 networks (Figure 2B&D).Moreover, the trained atrophy prediction model predicted changes of CDR‐Sum‐of‐Boxes (main: R = 0.8035±0.0492; validation: R = 0.5555±0.0404), ADAS13 (main: R = 0.7475±0.0725), MMSE (main: R = 0.7124±0.0509; validation: R = 0.6377±0.0616), and MOCA (validation: R = 0.6764±0.0314).ConclusionWe developed an accurate and interpretable individualized longitudinal nodal atrophy and disease progression prediction model in the AD spectrum by leveraging the comprehensive properties of whole‐brain structural connectome. The approach was validated in both Caucasian and Asian datasets, highlighting its generalizability and interpretability.

A Machine Learning Approach to Identify Individualized Baseline MRI, Plasma, Cardiovascular and Lifestyle Predictors of a 5‐Year Cognitive Decline

AbstractBackgroundThe mapping between risk factors and biomarkers with cognitive performance and decline in the older adults is complex and varies significantly across different age and sex groups. Our goals were to (i) develop a machine learning (ML) model to predict baseline cognition and cognitive decline over a 5‐year follow‐up using only baseline assessments, and (ii) identify important modifiable and non‐modifiable predictors of cognitive decline across different age strata.MethodWe utilized longitudinal data from 2219 participants (mean age (SD): 70 (12) years, 49% male) from Mayo Clinic Study of Aging who had 43 features belonging to cardiovascular health, plasma, MRI (diffusion and structural), and lifestyle measures. Cognition was measured using a global cognition z‐score. We built ML models to predict baseline cognition and rate of cognitive decline over 5‐years (linear regression slopes over all individual followup measurements for each participant) using baseline assessments as inputs. We then built a model for each category separately and as a pooled model with all measures included, incorporating education and sex as demographic features in all models (Fig. 1). We applied a multi‐layer stack ensemble ML technique that utilized repeated k‐fold bagging to maximize accuracy. Importance of the top predictors were assessed in three age strata (55‐65 years (31%), 65‐80 years (46%), 80‐95 years (23%)).ResultThe pooled model explained the largest variance for prediction of baseline cognition and cognitive decline (Fig. 2). In individuals ≤80 years, lower NfL was most predictive of higher baseline cognition. A combination of imaging and plasma features predicted the 5‐year cognitive decline. Additionally, in individuals ≤65 years, diabetes and hypertension were important predictors of faster cognitive decline and in those >80, lower BMI and midlife physical/cognitive activity was an important predictor of faster cognitive decline (Fig. 3).ConclusionThe plasma model was best for baseline cognition prediction and imaging model was best for cognitive decline prediction. There was significant heterogeneity in important features across the age strata. In individuals >65 years, plasma GFAP was the most important predictor of cognitive decline. Our model allowed identification of key risk factors that predicted cognitive decline.

Comparison of diffusion metrics in dementia

AbstractBackgroundThe recent FDA approval of a disease‐modifying drug for Alzheimer’s disease has stressed the need for a test which can be used at scale to detect neurodegeneration. MRI diffusion shows great promise (Douaud et al., 2013), and recently a multiplicity of analysis methods have been developed. We look at which diffusion analysis method might be most sensitive.MethodWe worked with data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Weber et al., 2021). Participants with Alzheimer’s disease (AD) and a group of age‐matched healthy controls (HC) were identified. T1 images were processed with FreeSurfer v7 (Fischl, 2012), providing tissue segmentations and cortical parcellations, as well as regional volumes. Diffusion images were preprocessed using the PreQual pipeline in native space (including FSL’s Topup and Eddy) (Cai et al, 2021), and analysed with Diffusion Tensor Imaging (DTI), Neurite Orientation Dispersion and Density Imaging (NODDI) (Zhang, Schneider, et al, 2012), Apparent Measures Using Reduced Acquisitions (AMURA) (Aja‐Fernández et al, 2022), Diffusion Orientation Complexity (DOC) (Haroon et al, 2009) and Probabilistic Index of Connectivity (PICo) (Parker et al, 2003). Regional values were extracted in FreeSurfer regions (grey and white matter).Pairwise Mann‐Whitney U‐tests were carried out between AD and HC regional measures. Regions with the most statistically significant volume differences were selected; diffusion‐based metrics for these regions were calculated using weighted values and compared. The 10 metrics with the smallest p‐values were used to find the most significant regions, and these were compared with volumes.ResultParticipants: 100 AD (38 female, age 74.5±7.9 years) and 207 HC (132 female, age 72.0±7.4 years).We selected the 25 most significant regions from the volumetric analyses. From these regions, the most effective diffusion metrics were DTI‐derived mean, axial and radial diffusivities, and 5 of the AMURA metrics.Given the 10 most significant metrics, of the 15 most significant volumes, 8 were also in the top diffusion regions.ConclusionThis study demonstrates that DTI metrics are most sensitive at differentiating HC from AD. If confirmed this has clinical significance, as these scans are widely available.

Role of MRI Diffusion Tensor Imaging in early diagnosis of Cervical Spondylotic Myelopathy (CSM)

Role of MRI Diffusion Tensor Imaging in early diagnosis of Cervical Spondylotic Myelopathy (CSM)

Role of Structural and Functional Whole-Brain Imaging in the Assessment of Preterm Infants

Approximately one in ten infants are born preterm, with each bearing some form of underdevelopment, ranging from behavioral problems to muscular disorders. These developmental issues can lead to increased healthcare costs, permanent injury, and even death. However, with the growth of whole-brain imaging, scientists have found new ways to study premature infants. Leading this overlapping of fields are magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion MRI (dMRI), electroencephalography (EEG), and magnetoencephalography (MEG). These technologies give insight into a premature infant’s disposition to lower brain mass, suboptimal learning, and neuropathological lesions. Although every technique has flaws, whether it be in its spatial or temporal resolution or just fundamental limits, each has its own unique benefits. In this paper, we will observe how different techniques are used to study the neurological health of premature infants.

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

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

Differences in structural connectome organization across sleep quality

Sleep quality, which measures satisfaction with overall sleep, is an important factor affecting an individual's health. Although previous neuroimaging studies based on magnetic resonance imaging (MRI) have observed altered regional morphology and functional activation related to poor sleep quality, the impact of sleep quality on whole-brain structural connectome organization is relatively under-investigated. To address this gap, we utilized dimensionality reduction techniques to estimate low-dimensional eigenvectors of structural connectivity derived from diffusion MRI tractography, and assessed their associations with measures of sleep quality. We found significant effects in the unimodal association and limbic regions. Additionally, the meta-analytic cognitive decoding analysis revealed associations with negative terms, including nociceptive and pain. Our findings suggest a link between alterations in the whole-brain structural organization and sleep quality, providing insights into the relationship between sleep quality and brain structure.

Differentiating primary central nervous system lymphoma from glioblastoma by time-dependent diffusion using oscillating gradient

BackgroundThis study aimed to elucidate the impact of effective diffusion time setting on apparent diffusion coefficient (ADC)-based differentiation between primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBMs) and to investigate the usage of time-dependent diffusion magnetic resonance imaging (MRI) parameters.MethodsA retrospective study was conducted involving 21 patients with PCNSLs and 66 patients with GBMs using diffusion weighted imaging (DWI) sequences with oscillating gradient spin-echo (Δeff = 7.1 ms) and conventional pulsed gradient (Δeff = 44.5 ms). In addition to ADC maps at the two diffusion times (ADC7.1 ms and ADC44.5 ms), we generated maps of the ADC changes (cADC) and the relative ADC changes (rcADC) between the two diffusion times. Regions of interest were placed on enhancing regions and non-enhancing peritumoral regions. The mean and the fifth and 95th percentile values of each parameter were compared between PCNSLs and GBMs. The area under the receiver operating characteristic curve (AUC) values were used to compare the discriminating performances among the indices.ResultsIn enhancing regions, the mean and fifth and 95th percentile values of ADC44.5 ms and ADC7.1 ms in PCNSLs were significantly lower than those in GBMs (p = 0.02 for 95th percentile of ADC44.5 ms, p = 0.04 for ADC7.1 ms, and p < 0.01 for others). Furthermore, the mean and fifth and 95th percentile values of cADC and rcADC were significantly higher in PCNSLs than in GBMs (each p < 0.01). The AUC of the best-performing index for ADC7.1 ms was significantly lower than that for ADC44.5 ms (p < 0.001). The mean rcADC showed the highest discriminating performance (AUC = 0.920) among all indices. In peritumoral regions, no significant difference in any of the three indices of ADC44.5 ms, ADC7.1 ms, cADC, and rcADC was observed between PCNSLs and GBMs.ConclusionsEffective diffusion time setting can have a crucial impact on the performance of ADC in differentiating between PCNSLs and GBMs. The time-dependent diffusion MRI parameters may be useful in the differentiation of these lesions.

Investigation into white matter microstructure differences in visual training by using an automated fiber tract subclassification segmentation quantification method

Visual training has emerged as a useful framework for investigating training-related brain plasticity, a highly complex task involving the interaction of visual orientation, attention, reasoning, and cognitive functions. However, the effects of long-term visual training on microstructural changes within white matter (WM) is poorly understood. Therefore, a set of visual training programs was designed, and automated fiber tract subclassification segmentation quantification based on diffusion magnetic resonance imaging was performed to obtain the anatomical changes in the brains of visual trainees. First, 40 healthy matched participants were randomly assigned to the training group or the control group. The training group underwent 10 consecutive weeks of visual training. Then, the fiber tracts of the subjects were automatically identified and further classified into fiber clusters to determine the differences between the two groups on a detailed scale. Next, each fiber cluster was divided into segments that can analyze specific areas of a fiber cluster. Lastly, the diffusion metrics of the two groups were comparatively analyzed to delineate the effects of visual training on WM microstructure. Our results showed that there were significant differences in the fiber clusters of the cingulate bundle, thalamus frontal, uncinate fasciculus, and corpus callosum between the training group compared and the control group. In addition, the training group exhibited lower mean fractional anisotropy, higher mean diffusivity and radial diffusivity than the control group. Therefore, the long-term cognitive activities, such as visual training, may systematically influence the WM properties of cognition, attention, memory, and processing speed.

Prediction of cognitive outcome after mild traumatic brain injury from acute measures of communication within brain networks

A considerable but ill-defined proportion of patients with mild traumatic brain injury (mTBI) experience persistent cognitive sequelae; the ability to identify such individuals early can help their neurorehabilitation. Here we tested the hypothesis that acute measures of efficient communication within brain networks are associated with patients’ risk for unfavorable cognitive outcome six months after mTBI. Diffusion and T1-weighted magnetic resonance imaging, alongside cognitive measures, were obtained to map connectomes both one week and six months post injury in 113 adult patients with mTBI (71 males). For task-related brain networks, communication measures (characteristic path length, global efficiency, navigation efficiency) were moderately correlated with changes in cognition. Taking into account the covariance of age and sex, more unfavorable communication within networks were associated with worse outcomes within cognitive domains frequently impacted by mTBI (episodic and working memory, verbal fluency, inductive reasoning, and processing speed). Individuals with more unfavorable outcomes had significantly longer and less efficient pathways within networks supporting verbal fluency (all t > 2.786, p < .006), highlighting the vulnerability of language to mTBI. Participants in whom a task-related network was relatively inefficient one week post injury were up to eight times more likely to have unfavorable cognitive outcome pertaining to that task. Our findings suggest that communication measures within task-related networks identify mTBI patients who are unlikely to develop persistent cognitive deficits after mTBI. Our approach and findings can help to stratify mTBI patients according to their expected need for follow-up and/or neurorehabilitation.

Comparison of three magnetic resonance imaging measures of brain iron in healthy and cocaine use disorder participants.

Several magnetic resonance imaging (MRI) measures for quantifying endogenous nonheme brain iron have been proposed. These correspond to distinct physical properties with varying sensitivities and specificities to iron. Moreover, they may depend not only on tissue iron concentration, but also on the intravoxel spatial pattern of iron deposition, which is complex in many brain regions. Here, the three MRI brain iron measures of , magnetic field correlation (MFC), and magnetic susceptibility are compared in several deep gray matter regions for both healthy participants (HPs) and individuals with cocaine use disorder (CUD). Their concordance is assessed from their correlations with each other and their relative dependencies on age. In addition, associations between the iron measures and microstructure in adjacent white matter regions are investigated by calculating their correlations with diffusion MRI measures from the internal capsule, and associations with cognition are determined by using results from a battery of standardized tests relevant to CUD. It is found that all three iron measures are strongly correlated with each other for the considered gray matter regions, but with correlation coefficients substantially less than one indicating important differences. The age dependencies of all three measures are qualitatively similar in most regions, except for the red nucleus, where the susceptibility has a significantly stronger correlation with age than . Weak to moderate correlations are seen for the iron measures with several of the diffusion and cognitive measures, with the strongest correlations being obtained for . The iron measures differ little between the HP and CUD groups, although susceptibility is significantly lower in the red nucleus for the CUD group. For the comparisons made, the iron measures behave similarly in most respects, but with notable quantitative differences. It is suggested that these differences may be, in part, attributable to a higher sensitivity to the spatial pattern of iron deposition for and MFC than for susceptibility. This is supported most strongly by a sharp contrast between the values of the iron measures in the globus pallidus relative to those in the red nucleus. The observed correlations of the iron measures with diffusion and cognitive scores point to possible connections between gray matter iron, white matter microstructure, and cognition.

The correlation of conventional and advanced MRI findings with cognitive function in multiple sclerosis: A systematic review and meta-analysis

There are limited data on the possible association between conventional and advanced magnetic resonance imaging (MRI) findings and cognitive function in patients with multiple sclerosis (MS). Therefore, this systematic review and meta-analysis aimed to explore the correlation between MRI-derived metrics and cognitive tests in patients with MS. An electronic literature search of the PubMed, Web of Sciences, Embase, and Scopus databases was performed to identify related studies. The correlation coefficients of the MRI indices and cognitive tests were pooled. Thirteen studies were selected for inclusion of 824 patients diagnosed with MS. Most evaluated patients (60.44%) had relapsing-remitting MS (RRMS). The Paced Auditory Serial Addition Test (PASAT-3), Brief Visuospatial Memory Test (BVMT), and Symbol Digit Modalities Test (SDMT) were inversely correlated with the mean diffusivity (MD) of the brain with pooled correlation coefficient of -0.225, -0.361, and -0.438, respectively (P&lt;0.0001). The SDMT test positively correlated with fractional anisotropy (FA) with a correlation coefficient of 0.351 (P&lt;0.0001) and inversely correlated with T2 lesion volume with a correlation coefficient of -0.367 (P&lt;0.0001). In the case of other tests, there was low number of studies with significant correlations being reported. We found significant correlations between some neuropsychological tests and MRI findings in patients with MS. Brain atrophy might disrupt the process of correct registration between anatomical and MRI diffusion scans. However, we did not have enough studies with exactly matched anatomical areas to evaluate correlations and we recommend that histological validation of diffusion tensor imaging (DTI) findings for brain atrophy is needed as a basis for picture processing procedures and correlation with cognition status.

Early Detection of hyperemia with Magnetic Resonance Fluid Attenuation Inversion Recovery Imaging after Superficial Temporal Artery to Middle Cerebral Artery Anastomosis.

Cerebral hyperperfusion syndrome(CHS) manifests as a collection of symptoms brought on by heightened focal cerebral blood flow, afflicting nearly 30% of patients who have undergone superficial temporal artery(STA)-middle cerebral artery(MCA) anastomosis. The aim of this study was to investigate whether the amalgamation of magnetic resonance imaging(MRI) fluid-attenuated inversion recovery(FLAIR) and apparent diffusion coefficient(ADC) imaging via MRI can discern cerebral hyperemia after STA-MCA anastomosis surgery. A retrospective study was performed of patients who underwent STA-MCA anastomosis due to Moyamoya disease or atherosclerotic steno-occlusive disease. A protocol aimed at preventing CHS was instituted, leveraging the use of MRI FLAIR. Patients underwent MRI diffusion with FLAIR imaging 24 h after STA-MCA anastomosis. A high signal on FLAIR images signified the presence of hyperemia at the bypass site, triggering a protocol of hyperemia care. All patients underwent hemodynamic evaluations, including perfusion MRI, single-photon emission computed tomography(SPECT), and digital subtraction angiography, both before and after the surgery. If a high signal intensity is observed on MRI FLAIR within 24 hours of the surgery, a repeat MRI is performed to confirm the presence of hyperemia. Patients with confirmed hyperemia are managed according to a protocol aimed at preventing further progression. Out of a total of 162 patients, 24 individuals(comprising 16 women and 8 men) exhibited hyperemia on their MRI FLAIR scans following the procedure. SPECT was conducted on 23 patients, and 11 of them yielded positive results. All 24 patients underwent perfusion MRI, but 9 of them showed no significant findings. Among the patients, 10 displayed elevations in both cerebral blood flow(CBF) and cerebral blood volume(CBV), 3 only showed elevation in CBF, and 2 only showed elevation in CBV. Follow-up MRI FLAIR scans conducted 6 months later on these patients revealed complete normalization of the previously observed high signal intensity, with no evidence of ischemic injury. The study determined that the use of MRI FLAIR and ADC mapping is a competent means of early detection of hyperemia after STA-MCA anastomosis surgery. The protocol established can be adopted by other neurosurgical institutions to enhance patient outcomes and mitigate the hazard of permanent cerebral injury caused by cerebral hyperemia.

Study of the Potential of MRI (Magnetic Resonance Imaging) in Detecting Epileptogenic Zones in the Brain

The epileptogenic zone is an area of the brain that is a source of uncontrolled epileptic activity. MRI can produce images of the brain with high spatial resolution, allowing detailed visualization of brain structure. This allows identification of pathological changes, such as lesions, hemorrhages, or vascular malformations, that may be a cause or indicator of an epileptogenic zone. MRI offers a variety of scanning techniques, including structural MRI (T1-weighted and T2-weighted), diffusion MRI, functional MRI (fMRI), and proton spectroscopy MRI. The literature search process was carried out on various databases (PubMed, Web of Sciences, EMBASE, Cochrane Libraries, and Google Scholar) regarding the potential of MRI (Magnetic Resonance Imaging) in detecting epileptogenic zones in the brain. This study follows the preferred reporting items for systematic reviews and meta-analysis (PRISMA) recommendations. MRI can produce images of the brain with high spatial resolution, allowing detailed identification of brain structures that may be associated with the epileptogenic zone. Lesions such as tumors, blood clots, or scar tissue that are the focus of epileptic activity can be clearly seen on MRI. Various MRI techniques, such as structural MRI, diffusion MRI, functional MRI, and proton spectroscopy MRI, can be used together to provide comprehensive information about the epileptogenic zone. MRI allows monitoring of brain structural changes over time, which is useful in determining the evolution of the epileptogenic zone. fMRI techniques help understand brain activity during epileptic attacks or as part of the epileptogenic zone.

Whole tumor based texture analysis of magnetic resonance diffusion imaging for colorectal liver metastases: A prospective study for diffusion model comparison and early response biomarker

PurposeTo evaluate and compare the diagnostic value of diffusion-related texture analysis parameters obtained from various magnetic resonance diffusion models as early predictors of the clinical response to chemotherapy in patients with colorectal liver metastases (CRLM). MethodsPatients (n = 145) with CRLM were prospectively and consecutively enrolled and scanned using diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI)/intravoxel incoherent motion (IVIM)/diffusion kurtosis imaging (DKI) before (baseline) and two–three weeks after (follow-up) commencing chemotherapy. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1). The histogram and texture parameters of each diffusion-related parametric map were analysed between the responding and non-responding groups, screened using LASSO, and fitted with binary logistic regression models. The diagnostic efficacy of each model in the early prediction of CRLM was analysed, and the corresponding receiver operating characteristic (ROC) curve was drawn. The area under the curve (AUC) and 95% confidence intervals (CI) were calculated. ResultsOf the 145 analysed patients, 69 were in the responding group and 76 were in the non-responding group. Among all models, the difference value based on the histogram and texture features of the DKI-derived parameters performed best for the early prediction of CRLM treatment efficacy. The AUC of the DKI model in the validation set reached 0.795 (95% CI 0.652–0.938). Among the IVIM-derived parameters, the difference model based on D and D* performed best, and the AUC in the validation set reached 0.737 (95% CI 0.586–0.889). Finally, in the DWI sequence, the model comprising baseline features performed the best, with an AUC of 0.699 (95% CI 0.537–0.86) in the validation set. ConclusionsBaseline DWI parameters and follow-up changes in IVIM and DKI parameters predicted the chemotherapeutic response in patients with CRLM. In addition, as very early predictors, DKI-derived parameters were more effective than DWI- and IVIM-related parameters, in which changes in D-parameters performed best.

Extended Window Thrombolytics for Ischemic Stroke: A Telestroke versus In-person Experience.

Telestroke evaluation of patients with acute ischemic stroke is supported by American Heart and Stroke Association Guidelines. However, there is no data on outcomes or safety of administering IV thrombolytic stroke therapy using extended window criteria (>4.5h since onset of symptoms with a hyperacute MRI diffusion T2/FLAIR mismatch) via telestroke. Here, we report adverse events and outcomes of extended-window thrombolysis by telestroke vs in-person care. We performed a retrospective cohort review from 2020 to 2022 of prospectively collected multinstitutional databases from a large, not-for-profit health system with both in-person stroke and telestroke care. The primary outcome was frequency of symptomatic intracranial hemorrhage (sICH). Secondary outcomes were favorable functional outcome at hospital discharge (modified Rankin Scale, mRS, 0-3) and discharge disposition. A total of 33 patients were treated with extended-window thrombolysis (n = 20 in-person, n = 13 telestroke). The median NIH stroke scale was 6, and time since last known normal was similar (median [95% CI]: in-person 13h [11-15h] vs telestroke 12h [9-16h], P = .33). The sICH frequency was low and occurred in one patient (4.8% in-person vs 0% by telestroke). Favorable outcome at discharge was not different between in-person and telestroke care (median mRS [95% CI]: 2 [1-3] vs 1 [0-2], OR .0 [.0-1.8], P = .27), and discharge deposition was also similar. In patients eligible for extended window acute stroke treatment with thrombolytics, there was no difference in adverse events between telestroke and in-person care.

Brains of endurance athletes differ in the association areas but not in the primary areas.

Regular participation in sports results in a series of physiological adaptations. However, little is known about the brain adaptations to physical activity. Here we aimed to investigate whether young endurance athletes and non-athletes differ in the gray and white matter of the brain and whether cardiorespiratory fitness (CRF) is associated with these differences. We assessed the CRF, volumes of the gray and white matter of the brain using structural magnetic resonance imaging (sMRI), and brain white matter connections using diffusion magnetic resonance imaging (dMRI) in 20 young male endurance athletes and 21 healthy non-athletes. While total brain volume was similar in both groups, the white matter volume was larger and the gray matter volume was smaller in the athletes compared to non-athletes. The reduction of gray matter was located in the association areas of the brain that are specialized in processing of sensory stimuli. In the microstructure analysis, significant group differences were found only in the association tracts, for example, the inferior occipito-frontal fascicle (IOFF) showing higher fractional anisotropy and lower radial diffusivity, indicating stronger myelination in this tract. Additionally, gray and white matter brain volumes, as well as association tracts correlated with CRF. No changes were observed in other brain areas or tracts. In summary, the brain signature of the endurance athlete is characterized by changes in the integration of sensory and motor information in the association areas.

Value of radiomics models based on MRI diffusion weighted imaging and apparent diffusion coefficient in differentiating benign and malignant thyroid nodules

Objective: To investigate the value of radiomics models based on magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps in distinguishing benign and malignant thyroid nodules. Methods: A cross-sectional study. Clinical data of 148 thyroid nodules (50 benign, 98 malignant) from 140 patients who underwent thyroid MRI examination in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences between January 2019 and December 2022 were retrospectively analyzed. The nodules were used as the study units, and a leave-one-out method was used to randomly divide the nodules into a training set and a test set at a 7∶3 ratio. Region of interest was segmented and radiomics features were extracted from the DWI and ADC images. In the training set, feature selection was performed using inter-observer agreement analysis, U-test, least absolute shrinkage and selection operator algorithm, and correlation analysis. Four classifiers, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR) were used to build models with the selected features, including the DWI models, ADC models, and combined models. The models were independently tested in the test set. The performance of the radiomics models in distinguishing benign and malignant thyroid nodules was evaluated using the receiver operating characteristic (ROC) curve, with pathological results as the gold standard. Results: Of the 140 patients, there were 40 males and 100 females, with a mean age of (38.4±12.2) years. After feature selection, 11 DWI features and 11 ADC features were used to build the models. In the training set, the AUC values of the combined models were higher than those of the corresponding DWI and ADC models. In the test set, the SVM combined model showed the best predictive performance, with an AUC of 0.873 (95%CI:0.740-0.954), accuracy of 75.6%, sensitivity of 46.7%, specificity of 90.0%, positive predictive value (PPV) of 70.0% and negative predictive value (NPV) of 77.1%, while the RF combined model had an AUC of 0.836 (95%CI:0.695-0.929), accuracy of 77.8%, sensitivity of 40.0%, specificity of 96.7%, PPV of 85.7% and NPV of 76.3%, the KNN combined model had an AUC of 0.832 (95%CI:0.691-0.927), accuracy of 77.8%, sensitivity of 33.3%, specificity of 100%, PPV of 100% and NPV of 75.0%, the LR combined model had an AUC of 0.813 (95%CI:0.669-0.914), accuracy of 77.8%, sensitivity of 60.0%, specificity of 86.7%, PPV of 69.2% and NPV of 81.3%. Conclusions: Radiomics models based on DWI and ADC image features can effectively distinguish benign and malignant thyroid nodules. The SVM combined model had the best prediction performance.