Facilitating diffusion tensor imaging of the brain during continuous gross head motion with first and second order motion compensating diffusion gradients.

PURPOSE: To demonstrate the feasibility of motion compensating diffusion gradient schemes in the acquisition of quality diffusion tensor images (DTI) of the brain during continuous gross head motion. METHODS: Five healthy subjects were scanned using a clinical 3 T MRI with and without continuous head motion. For one volunteer, DTI data was acquired using standard (M0) diffusion-weighted (DW) gradients, and first (M1) and second (M2) order gradient schemes that were previously developed for use in cardiac DTI. In four additional volunteers, DTI data was acquired with M0 and M2 gradients. DTI parameters were calculated and compared with established retrospective motion corrections. RESULTS: In the absence of motion, DTI parameters calculated from M0, M1, and M2 data were consistent. In the presence of motion, up to 44% of DW images acquired with M0 gradients were corrupted by signal dropout, compared to 0% of the M2 images. In voxelwise comparisons, DTI parameters calculated using motion-M0 data were elevated compared to reference data. Retrospective corrections for extreme motion applied to motion-M0 data did not improve consistency with reference data in cases where motion corrupted >15% of DW images. In contrast, DTI parameters calculated with motion-M2 data were consistent with reference data. CONCLUSION: This proof-of-principle study demonstrates that motion compensating diffusion gradients can mitigate artifacts because of continuous motion in DTI of the brain and offers promise for improved DTI accessibility. Further study will be necessary to determine the robustness of the approach in patient populations with high susceptibility to head motion.

Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification.

PURPOSE: To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating. METHODS: The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments. RESULTS: The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved. CONCLUSION: The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency.

Combining magnetic resonance fingerprinting with voxel-based morphometric analysis to reduce false positives for focal cortical dysplasia detection.

OBJECTIVE: We aim to improve focal cortical dysplasia (FCD) detection by combining high-resolution, three-dimensional (3D) magnetic resonance fingerprinting (MRF) with voxel-based morphometric magnetic resonance imaging (MRI) analysis. METHODS: We included 37 patients with pharmacoresistant focal epilepsy and FCD (10 IIa, 15 IIb, 10 mild Malformation of Cortical Development [mMCD], and 2 mMCD with oligodendroglial hyperplasia and epilepsy [MOGHE]). Fifty-nine healthy controls (HCs) were also included. 3D lesion labels were manually created. Whole-brain MRF scans were obtained with 1 mm3 isotropic resolution, from which quantitative T1 and T2 maps were reconstructed. Voxel-based MRI postprocessing, implemented with the morphometric analysis program (MAP18), was performed for FCD detection using clinical T1w images, outputting clusters with voxel-wise lesion probabilities. Average MRF T1 and T2 were calculated in each cluster from MAP18 output for gray matter (GM) and white matter (WM) separately. Normalized MRF T1 and T2 were calculated by z-scores using HCs. Clusters that overlapped with the lesion labels were considered true positives (TPs); clusters with no overlap were considered false positives (FPs). Two-sample t-tests were performed to compare MRF measures between TP/FP clusters. A neural network model was trained using MRF values and cluster volume to distinguish TP/FP clusters. Ten-fold cross-validation was used to evaluate model performance at the cluster level. Leave-one-patient-out cross-validation was used to evaluate performance at the patient level. RESULTS: MRF metrics were significantly higher in TP than FP clusters, including GM T1, normalized WM T1, and normalized WM T2. The neural network model with normalized MRF measures and cluster volume as input achieved mean area under the curve (AUC) of .83, sensitivity of 82.1%, and specificity of 71.7%. This model showed superior performance over direct thresholding of MAP18 FCD probability map at both the cluster and patient levels, eliminating ≥75% FP clusters in 30% of patients and ≥50% of FP clusters in 91% of patients. SIGNIFICANCE: This pilot study suggests the efficacy of MRF for reducing FPs in FCD detection, due to its quantitative values reflecting in vivo pathological changes. © 2024 International League Against Epilepsy.

Normative quantitative relaxation atlases for characterization of cortical regions using magnetic resonance fingerprinting.

Quantitative magnetic resonance (MR) has been used to study cyto- and myelo-architecture of the human brain non-invasively. However, analyzing brain cortex using high-resolution quantitative MR acquisition can be challenging to perform using 3T clinical scanners. MR fingerprinting (MRF) is a highly efficient and clinically feasible quantitative MR technique that simultaneously provides T1 and T2 relaxation maps. Using 3D MRF from 40 healthy subjects (mean age = 25.6 ± 4.3 years) scanned on 3T magnetic resonance imaging, we generated whole-brain gyral-based normative MR relaxation atlases and investigated cortical-region-based T1 and T2 variations. Gender and age dependency of T1 and T2 variations were additionally analyzed. The coefficient of variation of T1 and T2 for each cortical-region was 3.5% and 7.3%, respectively, supporting low variability of MRF measurements across subjects. Significant differences in T1 and T2 were identified among 34 brain regions (P < 0.001), lower in the precentral, postcentral, paracentral lobule, transverse temporal, lateral occipital, and cingulate areas, which contain sensorimotor, auditory, visual, and limbic functions. Significant correlations were identified between age and T1 and T2 values. This study established whole-brain MRF T1 and T2 atlases of healthy subjects using a clinical 3T scanner, which can provide a quantitative and region-specific baseline for future brain studies and pathology detection.

Evaluating whole-brain tissue-property changes in MRI-negative pharmacoresistant focal epilepsies using MR fingerprinting.

OBJECTIVE: We aim to quantify whole-brain tissue-property changes in patients with magnetic resonance imaging (MRI)-negative pharmacoresistant focal epilepsy by three-dimensional (3D) magnetic resonance fingerprinting (MRF). METHODS: We included 30 patients with pharmacoresistant focal epilepsy and negative MRI by official radiology report, as well as 40 age- and gender-matched healthy controls (HCs). MRF scans were obtained with 1 mm3 isotropic resolution. Quantitative T1 and T2 relaxometry maps were reconstructed from MRF and registered to the Montreal Neurological Institute (MNI) space. A two-sample t test was performed in Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) to evaluate significant abnormalities in patients comparing to HCs, with correction by the threshold-free cluster enhancement (TFCE) method. Subgroups analyses were performed for extra-temporal epilepsy/temporal epilepsy (ETLE/TLE), and for those with/without subtle abnormalities detected by morphometric analysis program (MAP), to investigate each subgroup's pattern of MRF changes. Correlation analyses were performed between the mean MRF values in each significant cluster and seizure-related clinical variables. RESULTS: Compared to HCs, patients exhibited significant group-level T1 increase ipsilateral to the epileptic origin, in the mesial temporal gray matter (GM) and white matter (WM), temporal pole GM, orbitofrontal GM, hippocampus, and amygdala, with scattered clusters in the neocortical temporal and insular GM. No significant T2 changes were detected. The ETLE subgroup showed a T1-increase pattern similar to the overall cohort, with additional involvement of the ipsilateral anterior cingulate GM. The subgroup of MAP+ patients also showed a T1-increase pattern similar to the overall cohort, with additional cluster in the ipsilateral lateral orbitofrontal GM. Higher T1 was associated with younger seizure-onset age, longer epilepsy duration, and higher seizure frequency. SIGNIFICANCE: MRF revealed group-level T1 increase in limbic/paralimbic structures ipsilateral to the epileptic origin, in patients with pharmacoresistant focal epilepsy and no apparent lesions on MRI, suggesting that these regions may be commonly affected by seizures in the epileptic brain. The significant association between T1 increase and higher seizure burden may reflect progressive tissue damage.

Leveraging redundancy in simultaneous multislice acquisitions to improve spike detection.

PURPOSE: To improve the performance of low-level spike noise artifact detection for daily quality assurance protocols by taking advantage of redundancy in simultaneous multislice (SMS) acquisitions. METHODS: Magnitude images were transformed into pseudo k-space images. Time series at each pseudo k-space point were detrended. A slice was determined to contain spiking artifact if it exceeded an intensity threshold and if all simultaneously acquired slices contained outliers. RESULTS: A total of 401 112 slices were inspected. Of these, 42 showed a spike artifact, based on visual inspection of image data and k-space data. With an intensity threshold of 4.6 SDs over time for each pseudo k-space point, all slices containing artifact were correctly flagged, and only 30 slices were incorrectly flagged when using the SMS criterion. Without the SMS criterion, 12 908 slices were incorrectly flagged as containing artifact. Without the SMS criterion, sensitivity to artifact would have to be sacrificed to substantially reduce the number of incorrectly flagged slices. CONCLUSION: This study demonstrates that the SMS criterion reduced the number of outliers reported to a manageable level while accurately identifying low-level spike artifacts. Successfully identifying low-level spikes allows early detection of hardware problems that can be fixed before the problem becomes debilitating and corrupts data. As part of a daily quality assurance protocol, the method prevents the need to retrospectively carry out time-intensive despiking and reanalysis of data.

MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan.

PURPOSE: Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time. METHODS: We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps. RESULTS: T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts. CONCLUSION: We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.

Characterizing thalamic and basal ganglia nuclei in medically intractable focal epilepsy by MR fingerprinting.

OBJECTIVES: Magnetic resonance fingerprinting (MRF) is a novel, quantitative, and noninvasive technique to measure brain tissue properties. We aim to use MRF for characterizing normal-appearing thalamic and basal ganglia nuclei in the epileptic brain. METHODS: A three-dimensional (3D) MRF protocol (1 mm3 isotropic resolution) was acquired from 48 patients with unilateral medically intractable focal epilepsy and 39 healthy controls (HCs). Whole-brain T1 and T2 maps (containing T1 and T2 relaxation times) were reconstructed for each subject. Ten subcortical nuclei in the thalamus and basal ganglia were segmented as regions of interest (ROIs), within which the mean T1 and T2 values, as well as their coefficient of variation (CV) were compared between the patients and HCs at the group level. Subgroup and correlation analyses were performed to examine the relationship between significant MRF measures and various clinical characteristics. Using significantly abnormal MRF measures from the group-level analyses, support vector machine (SVM) and logistic regression machine learning models were built and tested with 5-fold and 10-fold cross-validations, to separate patients from HCs, and to separate patients with left-sided and right-sided epilepsy, at the individual level. RESULTS: MRF revealed increased T1 mean value in the ipsilateral thalamus and nucleus accumbens; increased T1 CV in the bilateral thalamus, bilateral pallidum, and ipsilateral caudate; and increased T2 CV in the ipsilateral thalamus in patients compared to HCs (p < .05, false discovery rate [FDR] corrected). The SVM classifier produced 78.2% average accuracy to separate individual patients from HCs, with an area under the curve (AUC) of 0.83. The logistic regression classifier produced 67.4% average accuracy to separate patients with left-sided and right-sided epilepsy, with an AUC of 0.72. SIGNIFICANCE: MRF revealed bilateral tissue-property changes in the normal-appearing thalamus and basal ganglia, with ipsilateral predominance and thalamic preference, suggesting subcortical involvement/impairment in patients with medically intractable focal epilepsy. The individual-level performance of the MRF-based machine-learning models suggests potential opportunities for predicting lateralization.

Using magnetic resonance fingerprinting to characterize periventricular nodular heterotopias in pharmacoresistant epilepsy.

OBJECTIVE: We aimed to use a novel magnetic resonance fingerprinting (MRF) technique to examine in vivo tissue property characteristics of periventricular nodular heterotopia (PVNH). These characteristics were further correlated with stereotactic-electroencephalographic (SEEG) ictal onset findings. METHODS: We included five patients with PVNH who had SEEG-guided surgery and at least 1 year of seizure freedom or substantial seizure reduction. High-resolution MRF scans were acquired at 3 T, generating three-dimensional quantitative T1 and T2  maps. We assessed the differences between T1 and T2  values from the voxels in the nodules located in the SEEG-defined seizure onset zone (SOZ) and non-SOZ, on -individual and group levels. Receiver operating characteristic analyses were performed to obtain the optimal classification performance. Quantification of SEEG ictal onset signals from the nodules was performed by calculating power spectrum density (PSD). The association between PSD and T1 /T2  values was further assessed at different frequency bands. RESULTS: Individual-level analysis showed T1 was significantly higher in SOZ voxels than non-SOZ voxels (p < .05), with an average 73% classification accuracy. Group-level analysis also showed higher T1 was significantly associated with SOZ voxels (p < .001). At the optimal cutoff (normalized T1 of 1.1), a 76% accuracy for classifying SOZ nodules from non-SOZ nodules was achieved. T1  values were significantly associated with ictal onset PSD at the ultraslow, θ, ß, γ, and ripple bands (p < .05). T2  values were significantly associated with PSD only at the ultraslow band (p < .05). SIGNIFICANCE: Quantitative MRF measures, especially T1 , can provide additional noninvasive information to separate nodules in SOZ and non-SOZ. The T1 and T2 tissue property changes carry electrophysiological underpinnings relevant to the epilepsy, as shown by their significant positive associations with power changes during the SEEG seizure onset. The use of MRF as a supplementary noninvasive tool may improve presurgical evaluation for patients with PVNH and pharmacoresistant epilepsy.

Repetitive Transcranial Magnetic Stimulation of the Contralesional Dorsal Premotor Cortex for Upper Extremity Motor Improvement in Severe Stroke: Study Protocol for a Pilot Randomized Clinical Trial.

Up to 50% of stroke survivors have persistent, severe upper extremity paresis even after receiving rehabilitation. Repetitive transcranial magnetic stimulation (rTMS) can augment the effects of rehabilitation by modulating corticomotor excitability, but the conventional approach of facilitating excitability of the ipsilesional primary motor cortex (iM1) fails to produce motor improvement in stroke survivors with severe loss of ipsilesional substrate. Instead, the undamaged, contralesional dorsal premotor cortex (cPMd) may be a more suitable target. CPMd can offer alternate, bi-hemispheric and ipsilateral connections in support of paretic limb movement. This pilot, randomized clinical trial seeks to investigate whether rTMS delivered to facilitate cPMd in conjunction with rehabilitation produces greater gains in motor function than conventional rTMS delivered to facilitate iM1 in conjunction with rehabilitation in severely impaired stroke survivors. Twenty-four chronic (≥6 months) stroke survivors with severe loss of ipsilesional substrate (defined by the absence of physiologic evidence of excitable residual pathways tested using TMS) will be included. Participants will be randomized to receive rTMS to facilitate cPMd or iM1 in conjunction with task-oriented upper limb rehabilitation given for 2 sessions/week for 6 weeks. Assessments of primary outcome related to motor impairment (upper extremity Fugl-Meyer [UEFM]), motor function, neurophysiology, and functional neuroimaging will be made at baseline and at 6-week end-of-treatment. An additional assessment of motor outcomes will be repeated at 3-month follow-up to evaluate retention. The primary endpoint is 6-week change in UEFM. This pilot trial will provide preliminary evidence on the effects and mechanisms associated with facilitating intact cPMd in chronic severe stroke survivors. The trial is registered on clinicaltrials.gov, NCT03868410.

Phase 2 Trial of Ibudilast in Progressive Multiple Sclerosis.

BACKGROUND: There are limited treatments for progressive multiple sclerosis. Ibudilast inhibits several cyclic nucleotide phosphodiesterases, macrophage migration inhibitory factor, and toll-like receptor 4 and can cross the blood-brain barrier, with potential salutary effects in progressive multiple sclerosis. METHODS: We enrolled patients with primary or secondary progressive multiple sclerosis in a phase 2 randomized trial of oral ibudilast (≤100 mg daily) or placebo for 96 weeks. The primary efficacy end point was the rate of brain atrophy, as measured by the brain parenchymal fraction (brain size relative to the volume of the outer surface contour of the brain). Major secondary end points included the change in the pyramidal tracts on diffusion tensor imaging, the magnetization transfer ratio in normal-appearing brain tissue, the thickness of the retinal nerve-fiber layer, and cortical atrophy, all measures of tissue damage in multiple sclerosis. RESULTS: Of 255 patients who underwent randomization, 129 were assigned to ibudilast and 126 to placebo. A total of 53% of the patients in the ibudilast group and 52% of those in the placebo group had primary progressive disease; the others had secondary progressive disease. The rate of change in the brain parenchymal fraction was -0.0010 per year with ibudilast and -0.0019 per year with placebo (difference, 0.0009; 95% confidence interval, 0.00004 to 0.0017; P=0.04), which represents approximately 2.5 ml less brain-tissue loss with ibudilast over a period of 96 weeks. Adverse events with ibudilast included gastrointestinal symptoms, headache, and depression. CONCLUSIONS: In a phase 2 trial involving patients with progressive multiple sclerosis, ibudilast was associated with slower progression of brain atrophy than placebo but was associated with higher rates of gastrointestinal side effects, headache, and depression. (Funded by the National Institute of Neurological Disorders and Stroke and others; NN102/SPRINT-MS ClinicalTrials.gov number, NCT01982942 .).

Importance of incorporating quantitative imaging biomarker technical performance characteristics when estimating treatment effects.

BACKGROUND/AIMS: Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. METHODS: Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. RESULTS: Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. CONCLUSION: Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

Evidence of Neural Microstructure Abnormalities in Type I Chiari Malformation: Associations Among Fiber Tract Integrity, Pain, and Cognitive Dysfunction.

BACKGROUND: Previous case-control investigations of type I Chiari malformation (CMI) have reported cognitive deficits and microstructural white matter abnormalities, as measured by diffusion tensor imaging (DTI). CMI is also typically associated with pain, including occipital headache, but the relationship between pain symptoms and microstructure is not known. METHODS: Eighteen CMI patients and 18 adult age- and education-matched control participants underwent DTI, were tested using digit symbol coding and digit span tasks, and completed a self-report measure of chronic pain. Tissue microstructure indices were used to examine microstructural abnormalities in CMI as compared with healthy controls. Group differences in DTI parameters were then reassessed after controlling for self-reported pain. Finally, DTI parameters were correlated with performance on the digit symbol coding and digit span tasks within each group. RESULTS: CMI patients exhibited greater fractional anisotropy (FA), lower radial diffusivity, and lower mean diffusivity in multiple brain regions compared with controls in diffuse white matter regions. Group differences no longer existed after controlling for self-reported pain. A significant correlation between FA and the Repeatable Battery for the Assessment of Neuropsychological Status coding performance was observed for controls but not for the CMI group. CONCLUSIONS: Diffuse microstructural abnormalities appear to be a feature of CMI, manifesting predominantly as greater FA and less diffusivity on DTI sequences. These white matter changes are associated with the subjective pain experience of CMI patients and may reflect reactivity to neuroinflammatory responses. However, this hypothesis will require further deliberate testing in future studies.

Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions.

BACKGROUND: Conventional MRI can be limited in detecting subtle epileptic lesions or identifying active/epileptic lesions among widespread, multifocal lesions. PURPOSE: We developed a high-resolution 3D MR fingerprinting (MRF) protocol to simultaneously provide quantitative T1 , T2 , proton density, and tissue fraction maps for detection and characterization of epileptic lesions. STUDY TYPE: Prospective. POPULATION: National Institute of Standards and Technology (NIST) / International Society for Magnetic Resonance in Medicine (ISMRM) phantom, five healthy volunteers and 15 patients with medically intractable epilepsy undergoing presurgical evaluation with noninvasive or invasive electroclinical data. FIELD STRENGTH/SEQUENCE: 3D MRF scans and routine clinical epilepsy MR protocols were acquired at 3 T. ASSESSMENT: The accuracy of the T1 and T2 values were first evaluated using the NIST/ISMRM phantom. The repeatability was then estimated with both phantom and volunteers based on the coefficient of variance (CV). For epilepsy patients, all the maps were qualitatively reviewed for lesion detection by three independent reviewers (S.E.J., M.L., I.N.) blinded to clinical data. Region of interest (ROI) analysis was performed on T1 and T2 maps to quantify the multiparametric signal differences between lesion and normal tissues. Findings from qualitative review and quantitative ROI analysis were compared with patients' electroclinical data to assess concordance. STATISTICAL TESTS: Phantom results were compared using R-squared, and patient results were compared using linear regression models. RESULTS: The phantom study showed high accuracy with the standard values, with an R2 of 0.99. The volunteer study showed high repeatability, with an average CV of 4.3% for T1 and T2 in various tissue regions. For the 15 patients, MRF showed additional findings in four patients, with the remaining 11 patients showing findings consistent with conventional MRI. The additional MRF findings were highly concordant with patients' electroclinical presentation. DATA CONCLUSION: The 3D MRF protocol showed potential to identify otherwise inconspicuous epileptogenic lesions from the patients with negative conventional MRI diagnosis, as well as to correlate with different levels of epileptogenicity when widespread lesions were present. LEVEL OF EVIDENCE: 3. Technical Efficacy Stage: 3. J. Magn. Reson. Imaging 2019;49:1333-1346.

The role of the thalamus and hippocampus in episodic memory performance in patients with multiple sclerosis.

BACKGROUND: Episodic memory loss is one of the most common cognitive symptoms in patients with multiple sclerosis (MS), but the pathophysiology of this symptom remains unclear. Both the hippocampus and thalamus have been implicated in episodic memory and show regional atrophy in patients with MS. OBJECTIVE: In this work, we used functional magnetic resonance imaging (fMRI) during a verbal episodic memory task, lesion load, and volumetric measures of the hippocampus and thalamus to assess the relative contributions to verbal and visual-spatial episodic memory. METHODS: Functional activation, lesion load, and volumetric measures from 32 patients with MS and 16 healthy controls were used in a predictive analysis of episodic memory function. RESULTS: After adjusting for disease duration, immediate recall performance on a visual-spatial episodic memory task was significantly predicted by hippocampal volume ( p < 0.003). Delayed recall on the same task was significantly predicted by volume of the left thalamus ( p < 0.003). For both memory measures, functional activation of the thalamus during encoding was more predictive than that of volume measures ( p < 0.002). CONCLUSION: Our results suggest that functional activation may be useful as a predictive measure of episodic memory loss in patients with MS.

Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison.

A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the 'White Matter Modeling Challenge' during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion-based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non-Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal-predicting strategies, such as bootstrapping or cross-validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.

Interactive effects of physical activity and APOE-ε4 on white matter tract diffusivity in healthy elders.

Older adult apolipoprotein-E epsilon 4 (APOE-ε4) allele carriers vary considerably in the expression of clinical symptoms of Alzheimer's disease (AD), suggesting that lifestyle or other factors may offer protection from AD-related neurodegeneration. We recently reported that physically active APOE-ε4 allele carriers exhibit a stable cognitive trajectory and protection from hippocampal atrophy over 18months compared to sedentary ε4 allele carriers. The aim of this study was to examine the interactions between genetic risk for AD and physical activity (PA) on white matter (WM) tract integrity, using diffusion tensor imaging (DTI) MRI, in this cohort of healthy older adults (ages of 65 to 89). Four groups were compared based on the presence or absence of an APOE-ε4 allele (High Risk; Low Risk) and self-reported frequency and intensity of leisure time physical activity (PA) (High PA; Low PA). As predicted, greater levels of PA were associated with greater fractional anisotropy (FA) and lower radial diffusivity in healthy older adults who did not possess the APOE-ε4 allele. However, the effects of PA were reversed in older adults who were at increased genetic risk for AD, resulting in significant interactions between PA and genetic risk in several WM tracts. In the High Risk-Low PA participants, who had exhibited episodic memory decline over the previous 18-months, radial diffusivity was lower and fractional anisotropy was higher, compared to the High Risk-High PA participants. In WM tracts that subserve learning and memory processes, radial diffusivity (DR) was negatively correlated with episodic memory performance in physically inactive APOE-ε4 carriers, whereas DR was positively correlated with episodic memory performance in physically active APOE-ε4 carriers and the two Low Risk groups. The common model of demyelination-induced increase in radial diffusivity cannot directly explain these results. Rather, we hypothesize that PA may protect APOE-ε4 allele carriers from selective neurodegeneration of individual fiber populations at locations of crossing fibers within projection and association WM fiber tracts.

Modern Methods for Interrogating the Human Connectome.

OBJECTIVES: Connectionist theories of brain function took hold with the seminal contributions of Norman Geschwind a half century ago. Modern neuroimaging techniques have expanded the scientific interest in the study of brain connectivity to include the intact as well as disordered brain. METHODS: In this review, we describe the most common techniques used to measure functional and structural connectivity, including resting state functional MRI, diffusion MRI, and electroencephalography and magnetoencephalography coherence. We also review the most common analytical approaches used for examining brain interconnectivity associated with these various imaging methods. RESULTS: This review presents a critical analysis of the assumptions, as well as methodological limitations, of each imaging and analysis approach. CONCLUSIONS: The overall goal of this review is to provide the reader with an introduction to evaluating the scientific methods underlying investigations that probe the human connectome.

The relationship between cognitive function and high-resolution diffusion tensor MRI of the cingulum bundle in multiple sclerosis.

BACKGROUND: Imaging can provide noninvasive neural markers of disease progression in multiple sclerosis (MS) that are related to behavioral and cognitive symptoms. Past work suggests that diffusion tensor imaging (DTI) provides a measure of white matter pathology, including demyelination and axonal counts. OBJECTIVES: In the current study, the authors investigate the relationship of DTI measures in the cingulum bundle to common deficits in MS, including episodic memory, working memory, and information processing speed. METHODS: Fifty-seven patients with MS and 17 age- and education-matched controls underwent high-spatial resolution diffusion scans and cognitive testing. Probabilistic tracking was used to generate tracks from the posterior cingulate cortex to the entorhinal cortex. RESULTS: Radial and axial diffusivity values were significantly different between patients and controls (p < 0.031), and in patients bilateral diffusion measures were significantly related to measures of episodic memory and speed of processing (p < 0.033). CONCLUSIONS: The tractography-based measures of posterior cingulum integrity reported here support further development of DTI as a viable measure of axonal integrity and cognitive function in patients with MS.