Sex matters: The MouseX DW-ALLEN Atlas for mice diffusion-weighted MR imaging.

Overcoming sex bias in preclinical research requires not only including animals of both sexes in the experiments, but also developing proper tools to handle such data. Recent work revealed sensitivity of diffusion-weighted MRI to glia morphological changes in response to inflammatory stimuli, opening up exciting possibilities to characterize inflammation in a variety of preclinical models of pathologies, the great majority of them available in mice. However, there are limited resources dedicated to mouse imaging, like those required for the data processing and analysis. To fill this gap, we build a mouse MRI template of both structural and diffusion contrasts, with anatomical annotation according to the Allen Mouse Brain Atlas, the most detailed public resource for mouse brain investigation. To achieve a standardized resource, we use a large cohort of animals in vivo, and include animals of both sexes. To prove the utility of this resource to integrate imaging and molecular data, we demonstrate significant association between the mean diffusivity from MRI and gene expression-based glia density. To demonstrate the need of equitable sex representation, we compared across sexes the warp fields needed to match a male-based template, and our template built with both sexes. Then, we use both templates for analysing mice imaging data obtained in animals of different ages, demonstrating that using a male-based template creates spurious significant sex effects, not present otherwise. All in all, our MouseX DW-ALLEN Atlas will be a widely useful resource getting us one step closer to equitable healthcare.

Neurite density of the hippocampus is associated with trace eyeblink conditioning latency in 4- to 6-year-olds.

Limited options exist to evaluate the development of hippocampal function in young children. Research has established that trace eyeblink conditioning (EBC) relies on a functional hippocampus. Hence, we set out to investigate whether trace EBC is linked to hippocampal structure, potentially serving as a valuable indicator of hippocampal development. Our study explored potential associations between individual differences in hippocampal volume and neurite density with trace EBC performance in young children. We used onset latency of conditioned responses (CR) and percentage of conditioned responses (% CR) as measures of hippocampal-dependent associative learning. Using a sample of typically developing children aged 4 to 6 years (N = 30; 14 girls; M = 5.70 years), participants underwent T1- and diffusion-weighted MRI scans and completed a 15-min trace eyeblink conditioning task conducted outside the MRI. % CR and CR onset latency were calculated based on all trials involving tone-puff presentations and tone-alone trials. Findings revealed a connection between greater left hippocampal neurite density and delayed CR onset latency. Children with higher neurite density in the left hippocampus tended to blink closer to the onset of the unconditioned stimulus, indicating that structural variations in the hippocampus were associated with more precise timing of conditioned responses. No other relationships were observed between hippocampal volume, cerebellum volume or neurite density, hippocampal white matter connectivity and any EBC measures. Preliminary results suggest that trace EBC may serve as a straightforward yet innovative approach for studying hippocampal development in young children and populations with atypical development.

Fibre orientation atlas guided rapid segmentation of white matter tracts.

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

Distortion-free water-fat separated diffusion-weighted imaging using spatiotemporal joint reconstruction.

PURPOSE: Diffusion-weighted imaging (DWI) suffers from geometric distortion and chemical shift artifacts due to the commonly used Echo Planar Imaging (EPI) trajectory. Even with fat suppression in DWI, severe B0 and B1 variations can result in residual fat, which becomes both a source of image artifacts and a confounding factor in diffusion-weighted contrast in distinguishing benign and malignant tissues. This work presents a method for acquiring distortion-free diffusion-weighted images using spatiotemporal acquisition and joint reconstruction. Water-fat separation is performed by chemical-shift encoding. METHODS: Spatiotemporal acquisition is employed to obtain distortion-free images at a series of echo times. Chemical-shift encoding is used for water-fat separation. Reconstruction and separation are performed jointly in the spat-spectral domain. To address the shot-to-shot motion-induced phase in DWI, an Fast Spin Echo (FSE)-based phase navigator is incorporated into the sequence to obtain distortion-free phase information. The proposed method was validated in phantoms and in vivo for the brain, head and neck, and breast. RESULTS: The proposed method enables the acquisition of distortion-free diffusion-weighted images in the presence of B0 field inhomogenieties commonly observed in the body. Water and fat components are separated with no obvious spectral leakage artifacts. The estimated Apparent Diffusion Coefficient (ADC) is comparable to that of multishot DW-EPI. CONCLUSION: Distortion-free, water-fat separated diffusion-weighted images in body can be obtained through the utilization of spatiotemporal acquisition and joint reconstruction methods.

Edge-wise analysis reveals white matter connectivity associated with focal to bilateral tonic-clonic seizures.

OBJECTIVE: Focal to bilateral tonic-clonic seizures (FBTCS) represent a challenging subtype of focal temporal lobe epilepsy (TLE) in terms of both severity and treatment response. Most studies have focused on regional brain analysis that is agnostic to the distribution of white matter (WM) pathways associated with a node. We implemented a more selective, edge-wise approach that allowed for identification of the individual connections unique to FBTCS. METHODS: T1-weighted and diffusion-weighted images were obtained from 22 patients with solely focal seizures (FS), 43 FBTCS patients, and 65 age/sex-matched healthy participants (HPs), yielding streamline (STR) connectome matrices. We used diffusion tensor-derived STRs in an edge-wise approach to determine specific structural connectivity changes associated with seizure generalization in FBTCS compared to matched FS and HPs. Graph theory metrics were computed on both node- and edge-based connectivity matrices. RESULTS: Edge-wise analyses demonstrated that all significantly abnormal cross-hemispheric connections belonged to the FBTCS group. Abnormal connections associated with FBTCS were mostly housed in the contralateral hemisphere, with graph metric values generally decreased compared to HPs. In FBTCS, the contralateral amygdala showed selective decreases in the structural connection pathways to the contralateral frontal lobe. Abnormal connections in TLE involved the amygdala, with the ipsilateral side showing increases and the contralateral decreases. All the FS findings indicated higher graph metrics for connections involving the ipsilateral amygdala. Data also showed that some FBTCS connectivity effects are moderated by aging, recent seizure frequency, and longer illness duration. SIGNIFICANCE: Data showed that not all STR pathways are equally affected by the seizure propagation of FBTCS. We demonstrated two key biases, one indicating a large role for the amygdala in the propagation of seizures, the other pointing to the prominent role of cross-hemispheric and contralateral hemisphere connections in FBTCS. We demonstrated topographic reorganization in FBTCS, pointing to the specific WM tracts involved.

Intravoxel incoherent motion (IVIM)-derived perfusion fraction mapping for the visual evaluation of MR-guided high intensity focused ultrasound (MR-HIFU) ablation of uterine fibroids.

BACKGROUND: A method for periprocedural contrast agent-free visualization of uterine fibroid perfusion could potentially shorten magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) treatment times and improve outcomes. Our goal was to test feasibility of perfusion fraction mapping by intravoxel incoherent motion (IVIM) modeling using diffusion-weighted MRI as method for visual evaluation of MR-HIFU treatment progression. METHODS: Conventional and T2-corrected IVIM-derived perfusion fraction maps were retrospectively calculated by applying two fitting methods to diffusion-weighted MRI data (b = 0, 50, 100, 200, 400, 600 and 800 s/mm2 at 1.5 T) from forty-four premenopausal women who underwent MR-HIFU ablation treatment of uterine fibroids. Contrast in perfusion fraction maps between areas with low perfusion fraction and surrounding tissue in the target uterine fibroid immediately following MR-HIFU treatment was evaluated. Additionally, the Dice similarity coefficient (DSC) was calculated between delineated areas with low IVIM-derived perfusion fraction and hypoperfusion based on CE-T1w. RESULTS: Average perfusion fraction ranged between 0.068 and 0.083 in areas with low perfusion fraction based on visual assessment, and between 0.256 and 0.335 in surrounding tissues (all p < 0.001). DSCs ranged from 0.714 to 0.734 between areas with low perfusion fraction and the CE-T1w derived non-perfused areas, with excellent intraobserver reliability of the delineated areas (ICC 0.97). CONCLUSION: The MR-HIFU treatment effect in uterine fibroids can be visualized using IVIM perfusion fraction mapping, in moderate concordance with contrast enhanced MRI. IVIM perfusion fraction mapping has therefore the potential to serve as a contrast agent-free imaging method to visualize the MR-HIFU treatment progression in uterine fibroids.

Diagnostic utility of apparent diffusion coefficient in preoperative assessment of endometrial cancer: are we ready for the 2023 FIGO staging?

BACKGROUND: Although endometrial cancer (EC) is staged surgically, magnetic resonance imaging (MRI) plays a critical role in assessing and selecting the most appropriate treatment planning. We aimed to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging (DWI) in preoperative assessment of EC. METHODS: Prospective analysis was done for sixty-eight patients with pathology-proven endometrial cancer who underwent MRI and DWI. Apparent diffusion coefficient (ADC) values were measured by two independent radiologists and compared with the postoperative pathological results. RESULTS: There was excellent inter-observer reliability in measuring ADCmean values. There were statistically significant lower ADCmean values in patients with deep myometrial invasion (MI), cervical stromal invasion (CSI), type II EC, and lympho-vascular space involvement (LVSI) (AUC = 0.717, 0.816, 0.999, and 0.735 respectively) with optimal cut-off values of ≤ 0.84, ≤ 0.84, ≤ 0.78 and ≤ 0.82 mm2/s respectively. Also, there was a statistically significant negative correlation between ADC values and the updated 2023 FIGO stage and tumor grade (strong association), and the 2009 FIGO stage (medium association). CONCLUSIONS: The preoperative ADCmean values of EC were significantly correlated with main prognostic factors including depth of MI, CSI, EC type, grade, nodal involvement, and LVSI.

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion in differentiating invasive placentas.

PURPOSE: To investigate the diagnostic value of monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in distinguishing invasive placentas. METHODS: A total of 53 patients with invasive placentas and 47 patients with noninvasive placentas undergoing conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) were retrospectively enrolled. The mean, minimum, and maximum parameters including the apparent diffusion coefficient (ADC) and exponential ADC (eADC) from standard DWI, diffusion kurtosis (MK), and diffusion coefficient (MD) from DKI and pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM were measured and compared from the volumetric analysis. Receiver operating characteristics (ROC) curve and logistic regression analyses were conducted to evaluate the diagnostic efficiency of different diffusion parameters for distinguishing invasive placentas. RESULTS: Comparisons between accreta lesions in patients with invasive placentas (AL) and lower 1/3 part of the placenta in patients with noninvasive placentas (LP) demonstrated that MD mean, D mean, and D* mean were significantly lower while ADC max and D max were significantly higher in invasive placentas (all p < 0.05). Multivariate analysis demonstrated that D mean, D max and D* mean differed significantly among all the studied parameters for invasive placentas. A combined use of these three parameters yielded an AUC of 0.86 with sensitivity, specificity, and accuracy of 84.91%, 76.60%, and 80%, respectively. CONCLUSION: The combined use of different IVIM parameters is helpful in distinguishing invasive placentas.

Evaluation of multiparametric MRI differentiating pleomorphic adenoma from schwannoma in parapharyngeal space.

PURPOSE: This study aimed to investigate whether multiparametric magnetic resonance imaging (MRI) including dynamic contrast-enhanced (DCE) and diffusion weighted (DW) MRI can differentiate pleomorphic adenoma (PA) from schwannoma in the parapharyngeal space. METHODS: Forty-six patients with pathologically proven PAs and 47 schwannomas in the parapharyngeal space were enrolled. All patients underwent conventional MRI, and DW-MRI and DCE-MRI were performed in 30 and 33 patients, respectively. Fisher's exact, Mann-Whitney-U tests and Independent samples t-test were used to compare variables between PAs and schwannomas. Multivariate logistic regression analysis was used to examine the diagnostic performance of MRI parameters. RESULTS: The PAs usually show lobulation sign, posterior displacement of ICA and attached to the parotid gland deep leaf, while bird beak configuration, anterior displacement of ICA and involvement of foramen jugular were more commonly seen in the schwannomas(all p < 0.001). The washout rate of PAs was found to be higher than that of schwannomas (p = 0.035), whereas no significance was found in the other DCE-MRI parameters and in ADCs(p > 0.05). Using a combination of conventional MRI features including lobulation sign, bird beak configuration, direction of internal carotid artery(ICA) displacement and attached to the parotid gland in multivariate logistic regression analysis, sensitivity, specificity, and accuracy in differential diagnosis of PAs and schwannomas were 97.8%, 91.5% and 94.6%, respectively. CONCLUSION: Conventional MRI can effectively differentiate PAs from schwannomas in the parapharyngeal space with a high diagnostic accuracy. The DCE-MRI and DWI have limited added diagnostic value to conventional MRI in the differential diagnosis.

dtiRIM: A generalisable deep learning method for diffusion tensor imaging.

Diffusion weighted MRI is an indispensable tool for routine patient screening and diagnostics of pathology. Recently, several deep learning methods have been proposed to quantify diffusion parameters, but poor generalisation to new data prevents broader use of these methods, as they require retraining of the neural network for each new scan protocol. In this work, we present the dtiRIM, a new deep learning method for Diffusion Tensor Imaging (DTI) based on the Recurrent Inference Machines. Thanks to its ability to learn how to solve inverse problems and to use the diffusion tensor model to promote data consistency, the dtiRIM can generalise to variations in the acquisition settings. This enables a single trained network to produce high quality tensor estimates for a variety of cases. We performed extensive validation of our method using simulation and in vivo data, and compared it to the Iterated Weighted Linear Least Squares (IWLLS), the approach of the state-of-the-art MRTrix3 software, and to an implementation of the Maximum Likelihood Estimator (MLE). Our results show that dtiRIM predictions present low dependency on tissue properties, anatomy and scanning parameters, with results comparable to or better than both IWLLS and MLE. Further, we demonstrate that a single dtiRIM model can be used for a diversity of data sets without significant loss in quality, representing, to our knowledge, the first generalisable deep learning based solver for DTI.

Outliers in diffusion-weighted MRI: Exploring detection models and mitigation strategies.

Diffusion-weighted MRI (dMRI) is a medical imaging method that can be used to investigate the brain microstructure and structural connections between different brain regions. The method, however, requires relatively complex data processing frameworks and analysis pipelines. Many of these approaches are vulnerable to signal dropout artefacts that can originate from subjects moving their head during the scan. To combat these artefacts and eliminate such outliers, researchers have proposed two approaches: to replace outliers or to downweight outliers during modelling and analysis. With the rising interest in dMRI for clinical research, these types of corrections are increasingly important. Therefore, we set out to investigate the differences between outlier replacement and weighting approaches to help the dMRI community to select the best tool for their data processing pipelines. We evaluated dMRI motion correction registration and single tensor model fit pipelines using Gaussian Process and Spherical Harmonic based replacement approaches and outlier downweighting using highly realistic whole-brain simulations. As a proof of concept, we applied these approaches to dMRI infant data sets that contained varying numbers of dropout artefacts. Based on our results, we concluded that the Gaussian Process based outlier replacement provided similar tensor fit results to Gaussian Process based outlier detection and downweighting. Therefore, if only the least-squares estimate of the single tensor model is of interest, our recommendation is to use outlier replacement. However, outlier downweighting can potentially provide a more accurate estimate of the model precision which could be relevant for applications such as probabilistic tractoraphy.

Multimodal prediction of neoadjuvant treatment outcome by serial FDG PET and MRI in women with locally advanced breast cancer.

PURPOSE: To investigate combined MRI and 18F-FDG PET for assessing breast tumor metabolism/perfusion mismatch and predicting pathological response and recurrence-free survival (RFS) in women treated for breast cancer. METHODS: Patients undergoing neoadjuvant chemotherapy (NAC) for locally-advanced breast cancer were imaged at three timepoints (pre, mid, and post-NAC), prior to surgery. Imaging included diffusion-weighted and dynamic contrast-enhanced (DCE-) MRI and quantitative 18F-FDG PET. Tumor imaging measures included apparent diffusion coefficient, peak percent enhancement (PE), peak signal enhancement ratio (SER), functional tumor volume, and washout volume on MRI and standardized uptake value (SUVmax), glucose delivery (K1) and FDG metabolic rate (MRFDG) on PET, with percentage changes from baseline calculated at mid- and post-NAC. Associations of imaging measures with pathological response (residual cancer burden [RCB] 0/I vs. II/III) and RFS were evaluated. RESULTS: Thirty-five patients with stage II/III invasive breast cancer were enrolled in the prospective study (median age: 43, range: 31-66 years, RCB 0/I: N = 11/35, 31%). Baseline imaging metrics were not significantly associated with pathologic response or RFS (p > 0.05). Greater mid-treatment decreases in peak PE, along with greater post-treatment decreases in several DCE-MRI and 18F-FDG PET measures were associated with RCB 0/I after NAC (p < 0.05). Additionally, greater mid- and post-treatment decreases in DCE-MRI (peak SER, washout volume) and 18F-FDG PET (K1) were predictive of prolonged RFS. Mid-treatment decreases in metabolism/perfusion ratios (MRFDG/peak PE, MRFDG/peak SER) were associated with improved RFS. CONCLUSION: Mid-treatment changes in both PET and MRI measures were predictive of RCB status and RFS following NAC. Specifically, our results indicate a complementary relationship between DCE-MRI and 18F-FDG PET metrics and potential value of metabolism/perfusion mismatch as a marker of patient outcome.

White matter microstructure shows sex differences in late childhood: Evidence from 6797 children.

Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.

What lies beneath: White matter microstructure in pediatric myalgic encephalomyelitis/chronic fatigue syndrome using diffusion MRI.

Recent studies in adults with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) suggest that changes in brain white matter microstructural organization may correlate with core ME/CFS symptoms, and represent a potential biomarker of disease. However, this has yet to be investigated in the pediatric ME/CFS population. We examined group differences in macrostructural and microstructural white matter properties, and their relationship with clinical measures, between adolescents recently diagnosed with ME/CFS and healthy controls. Forty-eight adolescents (25 ME/CFS, 23 controls, mean age 16 years) underwent brain diffusion MRI, and a robust multi-analytic approach was used to evaluate white and gray matter volume, regional brain volume, cortical thickness, fractional anisotropy, mean/axial/radial diffusivity, neurite dispersion and density, fiber density, and fiber cross section. From a clinical perspective, adolescents with ME/CFS showed greater fatigue and pain, poorer sleep quality, and poorer performance on cognitive measures of processing speed and sustained attention compared with controls. However, no significant group differences in white matter properties were observed, with the exception of greater white matter fiber cross section of the left inferior longitudinal fasciculus in the ME/CFS group compared with controls, which did not survive correction for intracranial volume. Overall, our findings suggest that white matter abnormalities may not be predominant in pediatric ME/CFS in the early stages following diagnosis. The discrepancy between our null findings and white matter abnormalities identified in the adult ME/CFS literature could suggest that older age and/or longer illness duration influence changes in brain structure and brain-behavior relationships that are not yet established in adolescence.

Measuring cardiomyocyte cellular characteristics in cardiac hypertrophy using diffusion-weighted MRI.

PURPOSE: This paper presents a hierarchical modeling approach for estimating cardiomyocyte major and minor diameters and intracellular volume fraction (ICV) using diffusion-weighted MRI (DWI) data in ex vivo mouse hearts. METHODS: DWI data were acquired on two healthy controls and two hearts 3 weeks post transverse aortic constriction (TAC) using a bespoke diffusion scheme with multiple diffusion times ( Δ $$ \Delta $$ ), q-shells and diffusion encoding directions. Firstly, a bi-exponential tensor model was fitted separately at each diffusion time to disentangle the dependence on diffusion times from diffusion weightings, that is, b-values. The slow-diffusing component was attributed to the restricted diffusion inside cardiomyocytes. ICV was then extrapolated at Δ = 0 $$ \Delta =0 $$ using linear regression. Secondly, given the secondary and the tertiary diffusion eigenvalue measurements for the slow-diffusing component obtained at different diffusion times, major and minor diameters were estimated assuming a cylinder model with an elliptical cross-section (ECS). High-resolution three-dimensional synchrotron X-ray imaging (SRI) data from the same specimen was utilized to evaluate the biophysical parameters. RESULTS: Estimated parameters using DWI data were (control 1/control 2 vs. TAC 1/TAC 2): major diameter-17.4 µ $$ \mu $$ m/18.0 µ $$ \mu $$ m versus 19.2 µ $$ \mu $$ m/19.0 µ $$ \mu $$ m; minor diameter-10.2 µ $$ \mu $$ m/9.4 µ $$ \mu $$ m versus 12.8 µ $$ \mu $$ m/13.4 µ $$ \mu $$ m; and ICV-62%/62% versus 68%/47%. These findings were consistent with SRI measurements. CONCLUSION: The proposed method allowed for accurate estimation of biophysical parameters suggesting cardiomyocyte diameters as sensitive biomarkers of hypertrophy in the heart.

Association of neighborhood deprivation with white matter connectome abnormalities in temporal lobe epilepsy.

OBJECTIVE: Social determinants of health, including the effects of neighborhood disadvantage, impact epilepsy prevalence, treatment, and outcomes. This study characterized the association between aberrant white matter connectivity in temporal lobe epilepsy (TLE) and disadvantage using a US census-based neighborhood disadvantage metric, the Area Deprivation Index (ADI), derived from measures of income, education, employment, and housing quality. METHODS: Participants including 74 TLE patients (47 male, mean age = 39.2 years) and 45 healthy controls (27 male, mean age = 31.9 years) from the Epilepsy Connectome Project were classified into ADI-defined low and high disadvantage groups. Graph theoretic metrics were applied to multishell connectome diffusion-weighted imaging (DWI) measurements to derive 162 × 162 structural connectivity matrices (SCMs). The SCMs were harmonized using neuroCombat to account for interscanner differences. Threshold-free network-based statistics were used for analysis, and findings were correlated with ADI quintile metrics. A decrease in cross-sectional area (CSA) indicates reduced white matter integrity. RESULTS: Sex- and age-adjusted CSA in TLE groups was significantly reduced compared to controls regardless of disadvantage status, revealing discrete aberrant white matter tract connectivity abnormalities in addition to apparent differences in graph measures of connectivity and network-based statistics. When comparing broadly defined disadvantaged TLE groups, differences were at trend level. Sensitivity analyses of ADI quintile extremes revealed significantly lower CSA in the most compared to least disadvantaged TLE group. SIGNIFICANCE: Our findings demonstrate (1) the general impact of TLE on DWI connectome status is larger than the association with neighborhood disadvantage; however, (2) neighborhood disadvantage, indexed by ADI, revealed modest relationships with white matter structure and integrity on sensitivity analysis in TLE. Further studies are needed to explore this relationship and determine whether the white matter relationship with ADI is driven by social drift or environmental influences on brain development. Understanding the etiology and course of the disadvantage-brain integrity relationship may serve to inform care, management, and policy for patients.

Diffusion basis spectrum imaging detects pathological alterations in substantia nigra and white matter tracts with early-stage Parkinson's disease.

OBJECTIVES: Using diffusion basis spectrum imaging (DBSI) to examine the microstructural changes in the substantia nigra (SN) and global white matter (WM) tracts of patients with early-stage PD. METHODS: Thirty-seven age- and sex-matched patients with early-stage PD and 22 healthy controls (HCs) were enrolled in this study. All participants underwent clinical assessments and diffusion-weighted MRI scans, analyzed by diffusion tensor imaging (DTI) and DBSI to assess the pathologies of PD in SN and global WM tracts. RESULTS: The lower DTI fraction anisotropy (FA) was seen in SN of PD patients (PD: 0.316 ± 0.034 vs HCs: 0.331 ± 0.019, p = 0.015). The putative cells marker-DBSI-restricted fraction (PD: 0.132 ± 0.051 vs HCs: 0.105 ± 0.039, p = 0.031) and the edema/extracellular space marker-DBSI non-restricted-fraction (PD: 0.150 ± 0.052 vs HCs: 0.122 ± 0.052, p = 0.020) were both significantly higher and the density of axons/dendrites marker-DBSI fiber-fraction (PD: 0.718 ± 0.073 vs HCs: 0.773 ± 0.071, p = 0.003) was significantly lower in SN of PD patients. DBSI-restricted fraction in SN was negatively correlated with HAMA scores (r = - 0.501, p = 0.005), whereas DTI-FA was not correlated with any clinical scales. In WM tracts, only higher DTI axial diffusivity (AD) among DTI metrics was found in multiple WM regions in PD, while lower DBSI fiber-fraction and higher DBSI non-restricted-fraction were detected in multiple WM regions. DBSI non-restricted-fraction in both left fornix (cres)/stria terminalis (r = -0.472, p = 0.004) and right posterior thalamic radiation (r = - 0.467, p = 0.005) was negatively correlated with MMSE scores. CONCLUSION: DBSI could potentially detect and quantify the extent of inflammatory cell infiltration, fiber/dendrite loss, and edema in both SN and WM tracts in patients with early-stage PD, a finding remains to be further investigated through more extensive longitudinal DBSI analysis. CLINICAL RELEVANCE STATEMENT: Our study shows that DBSI indexes can potentially detect early-stage PD's pathological changes, with a notable ability to distinguish between inflammation and edema. This implies that DBSI has the potential to be an imaging biomarker for early PD diagnosis. KEY POINTS: • Diffusion basis spectrum imaging detected higher restricted-fraction in Parkinson's disease, potentially reflecting inflammatory cell infiltration. • Diffusion basis spectrum imaging detected higher non-restricted-fraction and lower fiber-fraction in Parkinson's disease, indicating the presence of edema and/or dopaminergic neuronal/dendritic loss. • Diffusion basis spectrum imaging metrics correlated with non-motor symptoms, suggesting its potential diagnostic role to detect early-stage PD dysfunctions.

Diagnostic accuracy of state-of-the-art rectal MRI sequences for the diagnosis of extramural vascular invasion in locally advanced rectal cancer after preoperative chemoradiotherapy: dos or maybes?

OBJECTIVES: The aim of this study was to determine the accuracy of three state-of-the-art MRI sequences for the detection of extramural venous invasion (EMVI) in locally advanced rectal cancer (LARC) patients after preoperative chemoradiotherapy (pCRT). METHODS: This retrospective study included 103 patients (median age 66 years old [43-84]) surgically treated with pCRT for LARC and submitted to preoperative contrast-enhanced pelvic MRI after pCRT. T2-weighted, DWI, and contrast-enhanced sequences were evaluated by two radiologists with expertise in abdominal imaging, blinded to clinical and histopathological data. Patients were scored according to the probability of EMVI presence on each sequence using a grading score ranging from 0 (no evidence of EMVI) to 4 (strong evidence of EMVI). Results from 0 to 2 were ranked as EMVI negative and from 3 to 4 as EMVI positive. ROC curves were drawn for each technique, using histopathological results as reference standard. RESULTS: T2-weighted, DWI, and contrast-enhanced sequences demonstrated an area under the ROC curve (AUC) respectively of 0.610 (95% CI: 0.509-0.704), 0.729 (95% CI: 0.633-0.812), and 0.624 (95% CI: 0.523-0.718). The AUC of DWI sequence was significantly higher than that of T2-weighted (p = 0.0494) and contrast-enhanced (p = 0.0315) sequences. CONCLUSIONS: DWI is more accurate than T2-weighted and contrast-enhanced sequences for the identification of EMVI following pCRT in LARC patients. CLINICAL RELEVANCE STATEMENT: MRI protocol for restaging locally advanced rectal cancer after preoperative chemoradiotherapy should routinely include DWI due to its higher accuracy for the diagnosis of extramural venous invasion compared to high-resolution T2-weighted and contrast-enhanced T1-weighted sequences. KEY POINTS: • MRI has a moderately high accuracy for the diagnosis of extramural venous invasion in locally advanced rectal cancer after preoperative chemoradiotherapy. • DWI is more accurate than T2-weighted and contrast-enhanced T1-weighted sequences in the detection of extramural venous invasion after preoperative chemoradiotherapy of locally advanced rectal cancer. • DWI should be routinely included in the MRI protocol for restaging locally advanced rectal cancer after preoperative chemoradiotherapy.

Structural and functional connectivity abnormalities of the default mode network in patients with Alzheimer's disease and mild cognitive impairment within two independent datasets.

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia, and amnestic mild cognitive impairment (aMCI) has been defined as a transitional stage between normal aging and AD. Accumulating evidence has shown that altered functional connectivity (FC) and structural connectivity (SC) in the default mode network (DMN) is the prominent hallmarks of AD. However, the relationship between the changes in SC and FC of the DMN is not yet clear. In the present study, we derived the FC and SC matrices of the DMN with functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI) data and further assessed FC and SC abnormalities within a discovery dataset of 120 participants (39 normal controls, 34 patients with aMCI and 47 patients with AD), as well as a replication dataset of 122 participants (43 normal controls, 37 patients with aMCI and 42 patients with AD). Disrupted SC and FC were found among DMN components (e.g., the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), and hippocampus) in patients in the aMCI and AD groups in the discovery dataset; most of the disrupted connections were also identified in the replication dataset. More importantly, some SC and FC elements were significantly correlated with the cognitive ability of patients with aMCI and AD. In addition, we found structural-functional decoupling between the PCC and the right hippocampus in patients in the aMCI and AD groups. These findings of the alteration of DMN connectivity in neurodegenerative cohorts deepen our understanding of the pathophysiological mechanisms of AD.

Aberrant Subnetwork and Hub Dysconnectivity in Adult Bipolar Disorder: A Multicenter Graph Theory Analysis.

Neuroimaging evidence implicates structural network-level abnormalities in bipolar disorder (BD); however, there remain conflicting results in the current literature hampered by sample size limitations and clinical heterogeneity. Here, we set out to perform a multisite graph theory analysis to assess the extent of neuroanatomical dysconnectivity in a large representative study of individuals with BD. This cross-sectional multicenter international study assessed structural and diffusion-weighted magnetic resonance imaging data obtained from 109 subjects with BD type 1 and 103 psychiatrically healthy volunteers. Whole-brain metrics, permutation-based statistics, and connectivity of highly connected nodes were used to compare network-level connectivity patterns in individuals with BD compared with controls. The BD group displayed longer characteristic path length, a weakly connected left frontotemporal network, and increased rich-club dysconnectivity compared with healthy controls. Our multisite findings implicate emotion and reward networks dysconnectivity in bipolar illness and may guide larger scale global efforts in understanding how human brain architecture impacts mood regulation in BD.